Subjects -> ENERGY (Total: 414 journals)
    - ELECTRICAL ENERGY (12 journals)
    - ENERGY (252 journals)
    - ENERGY: GENERAL (7 journals)
    - NUCLEAR ENERGY (40 journals)
    - PETROLEUM AND GAS (58 journals)
    - RENEWABLE ENERGY (45 journals)

ENERGY (252 journals)                  1 2 | Last

Showing 1 - 200 of 406 Journals sorted by number of followers
IET Power Electronics     Open Access   (Followers: 76)
Energy Policy     Partially Free   (Followers: 73)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 50)
IEEE Transactions on Power Systems     Hybrid Journal   (Followers: 45)
Energy     Partially Free   (Followers: 40)
IEEE Power and Energy     Full-text available via subscription   (Followers: 36)
Journal of Physical Chemistry C     Hybrid Journal   (Followers: 36)
Solar Energy Materials and Solar Cells     Hybrid Journal   (Followers: 29)
Advances in Natural Sciences : Nanoscience and Nanotechnology     Open Access   (Followers: 28)
Energy & Fuels     Hybrid Journal   (Followers: 27)
Nature Energy     Hybrid Journal   (Followers: 27)
Applied Energy     Partially Free   (Followers: 26)
Energy & Environment     Hybrid Journal   (Followers: 24)
International Journal of Alternative Propulsion     Hybrid Journal   (Followers: 23)
Energy and Power Engineering     Open Access   (Followers: 23)
International Journal of Turbomachinery, Propulsion and Power     Open Access   (Followers: 23)
Advances in High Energy Physics     Open Access   (Followers: 23)
Applied Solar Energy     Hybrid Journal   (Followers: 20)
Water International     Hybrid Journal   (Followers: 19)
Energy Materials : Materials Science and Engineering for Energy Systems     Hybrid Journal   (Followers: 19)
Solar Energy     Hybrid Journal   (Followers: 19)
International Journal of Hydrogen Energy     Partially Free   (Followers: 19)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 19)
Energy and Power     Open Access   (Followers: 18)
Journal of Solar Energy Engineering     Full-text available via subscription   (Followers: 18)
Canadian Water Resources Journal     Hybrid Journal   (Followers: 18)
Advances in Energy and Power     Open Access   (Followers: 16)
Nuclear Engineering and Design     Hybrid Journal   (Followers: 16)
IEEE Transactions on Energy Conversion     Hybrid Journal   (Followers: 16)
Progress in Energy and Combustion Science     Hybrid Journal   (Followers: 15)
Energy Conversion and Management     Hybrid Journal   (Followers: 15)
International Journal of Thermodynamics     Open Access   (Followers: 15)
Energy, Sustainability and Society     Open Access   (Followers: 15)
Energy Journal The     Hybrid Journal   (Followers: 14)
Energy and Environment Research     Open Access   (Followers: 13)
Surface Science Reports     Full-text available via subscription   (Followers: 13)
Economics and Policy of Energy and the Environment     Full-text available via subscription   (Followers: 13)
Waste Management     Hybrid Journal   (Followers: 13)
International Journal of Sustainable Energy     Hybrid Journal   (Followers: 12)
Energy and Buildings     Hybrid Journal   (Followers: 12)
Energy Systems     Hybrid Journal   (Followers: 11)
Energy Research & Social Science     Full-text available via subscription   (Followers: 11)
Oil and Gas Journal     Full-text available via subscription   (Followers: 11)
Advances in Building Energy Research     Hybrid Journal   (Followers: 11)
Journal of Solar Energy     Open Access   (Followers: 11)
Biofuels     Hybrid Journal   (Followers: 11)
Energy Science and Technology     Open Access   (Followers: 10)
Annual Review of Resource Economics     Full-text available via subscription   (Followers: 10)
Innovations : Technology, Governance, Globalization     Hybrid Journal   (Followers: 10)
Archives of Thermodynamics     Open Access   (Followers: 10)
IEEE Transactions on Nuclear Science     Hybrid Journal   (Followers: 10)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 10)
Journal of Ocean and Climate     Open Access   (Followers: 9)
Energy Efficiency     Hybrid Journal   (Followers: 9)
Journal of Modern Power Systems and Clean Energy     Open Access   (Followers: 9)
International Journal of Green Energy     Hybrid Journal   (Followers: 8)
Energy Strategy Reviews     Open Access   (Followers: 8)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 8)
International Journal of Energy Research     Hybrid Journal   (Followers: 8)
International Journal of Global Energy Issues     Hybrid Journal   (Followers: 8)
Batteries     Open Access   (Followers: 8)
Wiley Interdisciplinary Reviews : Energy and Environment     Hybrid Journal   (Followers: 8)
Journal of Renewable Energy     Open Access   (Followers: 8)
International Journal of Energy and Power     Open Access   (Followers: 8)
CERN courier. International journal of high energy physics     Free   (Followers: 8)
Environmental Progress & Sustainable Energy     Hybrid Journal   (Followers: 7)
Applied Nanoscience     Open Access   (Followers: 7)
Energy and Environment Focus     Free   (Followers: 7)
American Journal of Energy Research     Open Access   (Followers: 7)
Annals of Nuclear Energy     Hybrid Journal   (Followers: 7)
Fuel and Energy Abstracts     Full-text available via subscription   (Followers: 7)
Structural Control and Health Monitoring     Hybrid Journal   (Followers: 6)
Energy Science & Engineering     Open Access   (Followers: 6)
Energy Prices and Taxes     Full-text available via subscription   (Followers: 6)
Materials for Renewable and Sustainable Energy     Open Access   (Followers: 6)
Energy Reports     Open Access   (Followers: 5)
Joule     Hybrid Journal   (Followers: 5)
Computational Water, Energy, and Environmental Engineering     Open Access   (Followers: 5)
Energy Technology     Partially Free   (Followers: 5)
Journal of Building Performance Simulation     Hybrid Journal   (Followers: 5)
Journal of Energy & Natural Resources Law     Hybrid Journal   (Followers: 5)
Frontiers in Energy Research     Open Access   (Followers: 5)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
International Journal of Sustainable Energy Planning and Management     Open Access   (Followers: 5)
Atomic Energy     Hybrid Journal   (Followers: 5)
Energy and Environmental Engineering     Open Access   (Followers: 5)
Geothermal Energy     Open Access   (Followers: 5)
Energy Studies Review     Open Access   (Followers: 5)
Journal of Energy Technologies and Policy     Open Access   (Followers: 5)
Energy Storage Materials     Full-text available via subscription   (Followers: 5)
Carbon Management     Hybrid Journal   (Followers: 5)
Journal of Nano Energy and Power Research     Full-text available via subscription   (Followers: 4)
Journal of Energy, Environment & Carbon Credits     Full-text available via subscription   (Followers: 4)
American Journal of Energy and Environment     Open Access   (Followers: 4)
International Journal of Applied Power Engineering     Open Access   (Followers: 4)
Journal of International Energy Policy     Open Access   (Followers: 4)
International Journal of Nuclear Energy Science and Technology     Hybrid Journal   (Followers: 4)
Journal of Photochemistry and Photobiology B: Biology     Hybrid Journal   (Followers: 4)
ACS Energy Letters     Hybrid Journal   (Followers: 4)
International Journal of Sustainable Engineering     Hybrid Journal   (Followers: 4)
Annual Reports on NMR Spectroscopy     Full-text available via subscription   (Followers: 4)
Development of Energy Science     Open Access   (Followers: 4)
ACS Applied Energy Materials     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Foundations and Trends® in Renewable Energy     Full-text available via subscription   (Followers: 4)
Strategic Planning for Energy and the Environment     Hybrid Journal   (Followers: 4)
Sustainable Energy, Grids and Networks     Hybrid Journal   (Followers: 4)
Frontiers in Energy     Hybrid Journal   (Followers: 4)
Protection and Control of Modern Power Systems     Open Access   (Followers: 3)
Functional Materials Letters     Hybrid Journal   (Followers: 3)
Power Technology and Engineering     Hybrid Journal   (Followers: 3)
Dams and Reservoirs     Hybrid Journal   (Followers: 3)
Journal of Energy, Mechanical, Material and Manufacturing Engineering     Open Access   (Followers: 3)
Journal of Energy Chemistry     Full-text available via subscription   (Followers: 3)
Journal of Photochemistry and Photobiology C: Photochemistry Reviews     Full-text available via subscription   (Followers: 3)
Energy and Emission Control Technologies     Open Access   (Followers: 3)
International Journal of Energy and Environmental Engineering     Open Access   (Followers: 3)
Wind Energy     Hybrid Journal   (Followers: 3)
International Journal of Energy and Statistics     Hybrid Journal   (Followers: 3)
Distributed Generation & Alternative Energy Journal     Hybrid Journal   (Followers: 3)
Science and Technology of Nuclear Installations     Open Access   (Followers: 3)
Journal of Photochemistry and Photobiology A: Chemistry     Hybrid Journal   (Followers: 3)
Journal of Power and Energy Engineering     Open Access   (Followers: 2)
Sustainable Energy     Open Access   (Followers: 2)
Clean Energy     Open Access   (Followers: 2)
Energy Technology & Policy     Open Access   (Followers: 2)
Green Energy & Environment     Open Access   (Followers: 2)
Materials Today Energy     Hybrid Journal   (Followers: 2)
Energy Storage     Hybrid Journal   (Followers: 2)
Journal of Technology Innovations in Renewable Energy     Hybrid Journal   (Followers: 2)
Journal of Ocean Engineering and Marine Energy     Hybrid Journal   (Followers: 2)
International Journal of Smart Grid and Green Communications     Hybrid Journal   (Followers: 2)
IET Smart Grid     Open Access   (Followers: 2)
Carbon Resources Conversion     Open Access   (Followers: 2)
Advanced Materials Technologies     Hybrid Journal   (Followers: 2)
Journal of Energy     Open Access   (Followers: 2)
International Journal of Nuclear Knowledge Management     Hybrid Journal   (Followers: 2)
Proceedings of the Institution of Civil Engineers - Energy     Hybrid Journal   (Followers: 2)
EPJ Photovoltaics     Open Access   (Followers: 2)
International Journal of Clean Coal and Energy     Open Access   (Followers: 2)
Progress in Nuclear Energy     Hybrid Journal   (Followers: 2)
Journal of Fusion Energy     Hybrid Journal   (Followers: 2)
Gcb Bioenergy     Open Access   (Followers: 2)
Science and Technology for Energy Transition     Open Access   (Followers: 2)
Journal of Energy in Southern Africa     Open Access   (Followers: 2)
Journal of Alternate Energy Sources & Technologies     Full-text available via subscription   (Followers: 2)
Journal of Nuclear Energy Science & Power Generation Technology     Hybrid Journal   (Followers: 2)
Sustainable Energy & Fuels     Hybrid Journal   (Followers: 1)
International Journal of Energy and Smart Grid     Open Access   (Followers: 1)
International Journal of Nuclear Hydrogen Production and Applications     Hybrid Journal   (Followers: 1)
Clean Technologies     Open Access   (Followers: 1)
Energy Conversion and Management : X     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
Radioprotection     Hybrid Journal   (Followers: 1)
Electricity Journal     Hybrid Journal   (Followers: 1)
International Journal of Nuclear Desalination     Hybrid Journal   (Followers: 1)
Michigan Journal of Sustainability     Open Access   (Followers: 1)
Clefs CEA     Full-text available via subscription   (Followers: 1)
Journal of Computational Multiphase Flows     Open Access   (Followers: 1)
International Journal of Power and Energy Conversion     Hybrid Journal   (Followers: 1)
Technology Audit and Production Reserves     Open Access   (Followers: 1)
International Journal of Coal Science & Technology     Open Access   (Followers: 1)
Nigerian Journal of Technological Research     Full-text available via subscription   (Followers: 1)
Open Journal of Energy Efficiency     Open Access   (Followers: 1)
Asian Bulletin of Energy Economics and Technology     Open Access   (Followers: 1)
Biofuel Research Journal     Open Access   (Followers: 1)
International Journal of Energy and Water Resources     Hybrid Journal  
BioPhysical Economics and Resource Quality     Hybrid Journal  
Geomechanics and Geophysics for Geo-Energy and Geo-Resources     Hybrid Journal  
Energy, Ecology and Environment     Hybrid Journal  
Technology and Economics of Smart Grids and Sustainable Energy     Hybrid Journal  
BMC Energy     Open Access  
Journal of Energy and Environment Technology of Graduate School Siam Technology College     Open Access  
Global Energy Interconnection     Open Access  
Journal of Energy Systems     Open Access  
International Journal of Energy & Engineering Sciences     Open Access  
Turkish Journal of Energy Policy     Open Access  
Energy Informatics     Open Access  
Global Challenges     Open Access  
High Voltage     Open Access  
Geomechanics for Energy and the Environment     Full-text available via subscription  
ENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations     Open Access  
Sustainable Energy Technologies and Assessments     Full-text available via subscription  
Journal of China Coal Society     Open Access  
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Universal Journal of Applied Science     Open Access  
Ingeniería Energética     Open Access  
E3S Web of Conferences     Open Access  
Washington and Lee Journal of Energy, Climate, and the Environment     Open Access  
Journal of Sustainable Bioenergy Systems     Full-text available via subscription  
International Journal of Ambient Energy     Hybrid Journal  
CT&F - Ciencia, Tecnología y Futuro     Open Access  
Multequina     Open Access  
Natural Resources     Open Access  
South Pacific Journal of Natural and Applied Sciences     Hybrid Journal  
Chain Reaction     Full-text available via subscription  
Wind Engineering     Hybrid Journal  
Nuclear Data Sheets     Full-text available via subscription  
Global Energy Law and Sustainability     Hybrid Journal  
International Journal of Nuclear Governance, Economy and Ecology     Hybrid Journal  

        1 2 | Last

Similar Journals
Journal Cover
IEEE Transactions on Power Systems
Journal Prestige (SJR): 2.742
Citation Impact (citeScore): 7
Number of Followers: 45  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0885-8950
Published by IEEE Homepage  [228 journals]
  • IEEE Power & Energy Society

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      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Information for Authors

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      Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
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      Abstract: This page or pages intentionally left blank.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Learning-Aided Asynchronous ADMM for Optimal Power Flow

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      Authors: Ali Mohammadi;Amin Kargarian;
      Pages: 1671 - 1681
      Abstract: The synchronization requirement is a bottleneck of many distributed optimization algorithms, particularly for solving problems with computationally heterogeneous subproblems and during the occurrence of communication failure/delay. This paper presents a double-loop learning-aided asynchronous alternating direction method of multipliers (LA-ADMM) that has information prediction capability and handles a considerable level of asynchrony between subproblems. A momentum-extrapolation prediction-correction technique is developed to enable subproblems to predict their neighbors missing shared variable information instead of using the latest received values. An online streaming-based anomaly classification is designed to observe the performance of predicted data and control Lagrange multipliers update over the course of iterations. The proposed LA-ADMM reduces under-utilization of computation resources, especially if subproblems are computationally heterogeneous. This algorithm also enhances distributed optimization robustness against communication failure/delay that may result in a considerable level of asynchrony between subproblems. LA-ADMM is applied to solve the optimal power flow problem for several test systems. Promising results are obtained as compared to the classical synchronous ADMM and asynchronous ADMM without the anomaly switch control.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Multi-Agent Deep Reinforcement Learning Method for EV Charging Station
           Game

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      Authors: Tao Qian;Chengcheng Shao;Xuliang Li;Xiuli Wang;Zhiping Chen;Mohammad Shahidehpour;
      Pages: 1682 - 1694
      Abstract: The ongoing quest for transportation electrification with the massive proliferation of EV charging stations (EVCSs) will deepen the interaction and require the further coordination of coupled power and transportation networks (PTN). The individually-owned EVCSs located in an urban transportation network (UTN) will compete using price signals to maximize their respective payoffs. In this paper, a multi-agent deep reinforcement learning (MA-DRL) method is proposed to model the pricing game in UTN and determine the optimal charging prices for a single EVCS. The EVCS charging demand is first analyzed using a modified user equilibrium traffic assignment problem (UE-TAP) with elastic traveling demands and different charging prices. The price competition problem is then formulated as a game with incomplete information in which the market environment is complex due to nonlinear traffic assignments. Thus, the MA-DRL approach is proposed to learn the charging pricing strategies of multiple EVCSs and approximate the Nash Equilibrium (NE) of the pricing game using the incomplete information. The proposed solution will determine the optimal pricing strategies for an EVCS in UTN. The case studies on a 24-node Sioux-Falls network, and the real-world Xi'an and Hangzhou cities are conducted to verify the effectiveness and scalability of the proposed approach.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Fast Newton-Raphson Power Flow Analysis Based on Sparse Techniques and
           Parallel Processing

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      Authors: Afshin Ahmadi;Melissa C. Smith;Edward R. Collins;Vahid Dargahi;Shuangshuang Jin;
      Pages: 1695 - 1705
      Abstract: Power flow (PF) calculation provides the basis for the steady-state power system analysis and is the backbone of many power system applications ranging from operations to planning. The calculated voltage and power values by PF are essential to determining the system condition and ensuring the security and stability of the grid. The emergence of multicore processors provides an opportunity to accelerate the speed of PF computation and, consequently, improve the performance of applications that run PF within their processes. This paper introduces a fast Newton-Raphson power flow implementation on multicore CPUs by combining sparse matrix techniques, mathematical methods, and parallel processing. Experimental results validate the effectiveness of our approach by finding the power flow solution of a synthetic U.S. grid test case with 82,000 buses in just 1.8 seconds.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Grid-Aware Aggregation and Realtime Disaggregation of Distributed Energy
           Resources in Radial Networks

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      Authors: Nawaf Nazir;Mads Almassalkhi;
      Pages: 1706 - 1717
      Abstract: Dispatching a fleet of distributed energy resources (DERs) in response to wholesale energy market or regional grid signals requires solving a challenging disaggregation problem when the DERs are coupled within a distribution network. This manuscript presents a computationally tractable convex inner approximation for the optimal power flow (OPF) problem that quantifies a feeder’s aggregate DERs hosting capacity and enables a realtime, grid-aware control policy for DERs in radial distribution networks. The inner approximation is derived by considering convex envelopes on the nonlinear terms in the AC power flow equations. The resulting convex formulation is then used to derive provable nodal injection limits, such that any combination of DER dispatches within their respective nodal limits is guaranteed to be AC admissible. These nodal injection limits are then used to construct a realtime, open-loop control policy for dispatching DERs at each location in the network to deliver fast grid services in aggregate. The IEEE-37 distribution network is used to validate the technical results and illustrate use cases.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Novel High-Performance Deep Learning Framework for Load Recognition:
           Deep-Shallow Model Based on Fast Backpropagation

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      Authors: Chen Li;Guo Chen;Gaoqi Liang;Zhao Yang Dong;
      Pages: 1718 - 1729
      Abstract: This paper proposes a novel high-performance deep learning framework for load recognition. The framework consists of a deep-shallow model and a fast backpropagation (FBP) algorithm. In the deep-shallow model, power-related wave patterns are perceived by a convolutional neural network (CNN), and feature statistics of the power consumption data are analyzed by a sparse feed-forward neural network. The new architecture improves model interpretability and prevents information loss problems in CNNs. The architecture also greatly boosts the convergence speed and significantly enhances the test set accuracy of a neural network. Compared with conventional CNN models utilized by many load recognition applications, the FBP algorithm consisting of four sub-algorithms converges faster at the start of the training process and reduces at least 87.5% of the filter gradient computations on average. The deep-shallow-fast model that combines the deep-shallow model and the FBP algorithm attains 97.62% accuracy on the test set in the load recognition task. To fully utilize the training data, a data augmentation technique is invented that transforms the voltage and current time series into an image-like 4-D tensor. Experiments illustrate that the proposed framework is much more accurate and converges considerably faster than the conventional CNN model that many deep-learning-based load recognition applications are based upon.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Design and Experimentation Guidelines for DER’s Emulation Testbed

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      Authors: Hasnae Bilil;Anfal Hathah;Abdella Battou;
      Pages: 1730 - 1738
      Abstract: Due to the increasing integration of distributed energy resources (DERs) into the national grid, research is needed to understand high DER penetration in relation to grid reliability and stability. Emulation-based research is a viable option to consider hardware intensive investigations. Indeed, DER emulation-based research has advantages over DER software-based simulation and real DER implementation research. Some of these advantages are more realistic performance and boundaries and considerably less expense, higher safety, and modularity. While the downside of DER emulation-based research is the complexity of modeling large scale grid systems. This paper provides a rationalization for the selection of an emulation-based DER laboratory and description of the DER laboratory’s capabilities. Additionally, guidelines for running experiments are discussed and two approaches, power scaling using rescaling coefficients and system sizing using Multi-Port Thevenin Equivalence (MPTE), are proposed to emulate realistic complex interconnected DER grid connected systems.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Linear Power Flow Algorithm With Losses for Multi-Terminal VSC
           AC/DC Power Systems

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      Authors: José-Carlos Fernández-Pérez;Francisco M. Echavarren Cerezo;Luis Rouco Rodríguez;
      Pages: 1739 - 1749
      Abstract: Power flow analysis is a key tool for planning, operation and control of power systems. With future hybrid Supergrids in mind, a wide variety of power flow algorithms for multi-terminal VSC AC/DC systems have been proposed. However, the nonlinear nature of these formulations represents a challenge for its use in real power systems. Whereas in the study of AC networks linear approximations are frequently utilized to speed up multi-scenario steady-state analysis, an equivalent tool has not been proposed for hybrid AC/DC grids. This paper fills that gap by formulating a linear MW-only power flow algorithm with losses for multi-terminal VSC AC/DC systems. The proposed algorithm provides a consistent estimation of converters' losses and allows modelling of all branches within the converter stations. Moreover, two novel schemes for the inclusion of DC network losses are proposed and a circuit-equivalent model of the linear power flow problem in multi-terminal VSC AC/DC systems is suggested.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Large Scale Multi-Period Optimal Power Flow With Energy Storage Systems
           Using Differential Dynamic Programming

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      Authors: Aayushya Agarwal;Larry Pileggi;
      Pages: 1750 - 1759
      Abstract: With theincreased penetration of renewable sources, power gridsare becoming stressed due to fluctuating generation. To alleviate stress from inconsistent sources, utilities employ energy storage systems alongside renewable sources and rely on dispatching synchronous generators. However, optimal dispatch of such devices is limited by traditional AC optimal power flow methods that do not account for time-dependent constraints, such as the state of charge of energy storage systems and generator ramping constraints. Multi-period optimal power flow is proposed as a large non-convex non-linear problem to optimally dispatch and control generators and energy storage elements across multiple time periods. In this paper, we introduce a scalable, robust framework to solve multi-period optimal power flow using a differential dynamic programming scheme that makes it capable of scaling to large systems containing energy storage devices. We demonstrate the efficacy of this solution by optimizing the SyntheticUSA testcase over a set of time periods. A robust homotopy method is applied to achieve fast simulation times that can be parallelized for further improvements.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • The Value of Increased HVDC Capacity Between Eastern and Western U.S.
           Grids: The Interconnections Seam Study

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      Authors: Aaron Bloom;Josh Novacheck;Greg Brinkman;James McCalley;Armando Figueroa-Acevedo;Ali Jahanbani-Ardakani;Hussam Nosair;Abhinav Venkatraman;Jay Caspary;Dale Osborn;Jessica Lau;
      Pages: 1760 - 1769
      Abstract: The Interconnections Seam Study examines the potential economic value of increasing electricity transfer between the Eastern and Western Interconnections using high-voltage direct-current (HVDC) transmission and cost-optimizing both generation and transmission resources across the United States, proposing, assessing, justifying, and illustrating a major infrastructure change involving two of the world's largest power grids. The study conducted a multi-model analysis that used co-optimized generation and transmission expansion planning and production cost modeling. Four transmission designs under eight scenarios were developed and studied to estimate costs and potential benefits. The results show benefit-to-cost ratios that reach as high as 2.5, indicating significant value to increasing the transmission capacity between the interconnections under the cases considered, realized through sharing generation resources and flexibility across regions.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Adaptive Tuning of PV Generator Control to Improve Stability Constrained
           Power Transfer Capability Limit

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      Authors: Indla Rajitha Sai Priyamvada;Sarasij Das;
      Pages: 1770 - 1781
      Abstract: The stability of power systems can largely limit the power transfer capability limit of transmission lines. The PV generator control is different from synchronous generator control. The impact of PV generator dynamics on the power transfer limit constrained by stability is not well explored in the literature. This paper focuses on improving the stability constrained power transfer capability limit of transmission lines emanating from PV generators. The PV generator is provided with dc link voltage and reactive power control and is equipped with low voltage ride through capability, voltage and frequency support functionalities. In this paper, Lyapunov function analysis based adaptive tuning laws are proposed for PV control parameters to improve the power transfer capability limit (constrained by stability) of transmission lines connecting PV generators to grid. The tuning laws are proposed for Phase Locked Loop (PLL), outer and inner control loop parameters. The proposed tuning laws have inherent robustness towards faults, changes in solar irradiation, changes in grid topology, network impedances, generation and loads in the system. The effectiveness of the proposed method is validated on a Single PV-Synchronous Machine system, modified IEEE-39 and IEEE-118 bus system. The performance of the proposed method is investigated considering various disturbances such as symmetrical and asymmetrical faults on transmission line, switching of a transmission line and change in solar irradiation of PV. Comparison with existing method shows that the proposed tuning method can achieve higher power transfer capability limit considering stability.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Network Topology Invariant Stability Certificates for DC Microgrids With
           Arbitrary Load Dynamics

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      Authors: Samuel Chevalier;Federico Martin Ibanez;Kathleen Cavanagh;Konstantin Turitsyn;Luca Daniel;Petr Vorobev;
      Pages: 1782 - 1797
      Abstract: DC microgrids are prone to small-signal instabilities due to the presence of tightly regulated loads. This paper develops a decentralized stability certificate which is capable of certifying the small-signal stability of an islanded DC network containing such loads. Utilizing a novel homotopy approach, the proposed standards ensure that no system eigenmodes are able to cross into the unstable right half plane for a continuous range of controller gain levels. The resulting “standards” can be applied to variety of grid components which meet the specified, but non-unique, criteria. These standards thus take a step towards offering plug-and-play operability of DC microgrids. The proposed theorems are explicitly illustrated and numerically validated on multiple DC microgrid test-cases containing both buck and boost converter dynamics.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Data-Driven Power Flow Calculation Method: A Lifting Dimension Linear
           Regression Approach

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      Authors: Li Guo;Yuxuan Zhang;Xialin Li;Zhongguan Wang;Yixin Liu;Linquan Bai;Chengshan Wang;
      Pages: 1798 - 1808
      Abstract: The high-precision parameters in distribution networks are difficult to obtain, which brings difficulties to the model-based methods and analysis. With the widespread deployment of high-precision measurement units, data-driven methods have greater advantages in practice. In addition, with massive integration of distributed renewable generation and fast electric vehicle chargers, the fluctuations of net load increase significantly. The data-driven power flow calculation method based on the linear model becomes difficult to obtain accurate results for the low nonlinear adaptability. To improve the data-driven power flow calculation accuracy under high penetration of renewable distribution generation, this paper proposed an approach with high adaptability to the nonlinearity of power flow. Based on the thought of Koopman operator theory, the nonlinear relationship in power flow calculation is converted into a linear mapping in a higher dimension state space, which can significantly improve the calculation accuracy. Case studies on different IEEE cases have demonstrated that the proposed method can realize higher accuracy in power flow calculation with large-scale power fluctuations, compared to the existing data-driven method, in both mesh and radial networks. Finally, measurement data of a practical 10kV distribution network has been further used to verify the effectiveness of the proposed method in practical applications.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Stochastic-Dynamic-Optimization Approach to Estimating the Capacity
           Value of Energy Storage

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      Authors: Hyeong Kim;Ramteen Sioshansi;Eamonn Lannoye;Erik Ela;
      Pages: 1809 - 1819
      Abstract: Energy storage can contribute to the resource-adequacy needs of power systems. However, the energy-limited nature of energy storage complicates estimating its resource-adequacy contribution. Energy storage that discharges to mitigate a loss-of-load event may have less energy available to mitigate a subsequent loss-of-load event. We present a stochastic-dynamic-optimization approach to capture such impacts endogenously. We demonstrate our approach using an example and two case studies, which show that energy storage's capacity value is sensitive to the load patterns of the system in which it is deployed.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Offshore Windfarm Design Optimization Using Dynamic Rating for
           Transmission Components

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      Authors: Syed Hamza H. Kazmi;Nicola Viafora;Troels S. Sørensen;Thomas H. Olesen;Bikash C. Pal;Joachim Holbøll;
      Pages: 1820 - 1830
      Abstract: The foreseen development of large-scale Offshore Windfarms (OWFs) further from the shore dictates that the OWF transmission system must be optimally designed based on Dynamic Thermal Rating (DTR) in order to fully utilize the intermittent nature of the wind and to keep the offshore wind cost-competitive. In this paper, a comprehensive, DTR-based, two-stage stochastic model is presented, which has been developed for investment decision support for OWF size and HVAC transmission systems. Complex DTR models for all the critical HV components are made fit for the mixed-integer linear programming problem, while accounting for the stochasticity in wind generation and component availability. The main decisions incorporate the discrete size of OWFs, HV subsea export cable cross-sections and ratings for transformers and shunt reactors. For validation, an actual testcase OWF off the east coast of U.K. has been used. Results indicate that DTR-based iterative design of OWF and its transmission components can significantly improve the business case, even though transmission efficiency and energy delivered are not maximum for the optimal design
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Parametric Distribution Optimal Power Flow With Variable Renewable
           Generation

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      Authors: Zhongjie Guo;Wei Wei;Laijun Chen;ZhaoYang Dong;Shengwei Mei;
      Pages: 1831 - 1841
      Abstract: The output of renewable generation depends on the real-time weather conditions and changes rapidly; so the economic operating point of the power system varies over time. This paper aims to find the explicit mapping from variable renewable power to optimal power flow solutions. To this end, we propose a parametric distribution optimal power flow (P-DOPF) method, which gives the optimal dispatch strategy and power flow status as analytical functions of the renewable output. With the established distribution optimal power flow problem based on the relaxed Distflow model, the first step is to perform a global polyhedral approximation on the second-order cone constraints to develop a linearized formulation. The second step is to obtain the P-DOPF model by treating renewable power output as parameters; then, the P-DOPF problem gives rise to a multi-parametric linear program (mp-LP). Third, we prove that the optimal solution and optimal value of the P-DOPF are piecewise linear functions of the parameters and we design an adaptive-sampling algorithm to construct the optimal value and optimal solution functions, as well as the partition of the parameter set, subject to a given error tolerance; this algorithm is not influenced by model degeneracy, a common difficulty of existing mp-LP algorithms. The P-DOPF framework provides an explicit real-time control policy of generators in response to the renewable output. Case studies on the IEEE 33 and 69-bus systems verify the effectiveness and performance of the proposed method; by comparison, the proposed method outperforms the established affine policy method in computational efficiency and optimality by 24.5% and 4.62%, respectively.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Day-Ahead Operational Scheduling With Enhanced Flexible Ramping Product:
           Design and Analysis

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      Authors: Mohammad Ghaljehei;Mojdeh Khorsand;
      Pages: 1842 - 1856
      Abstract: New resource mix, e.g., renewable resources, are imposing operational complexities to modern power systems by intensifying uncertainty and variability in the system net load. This issue has motivated independent system operators (ISOs), e.g., California ISO (CAISO), to add flexible ramping products (FRPs) to their day-ahead (DA) market models. Such structural changes in the DA market formulation require further analyses and detailed design to ensure adequate operational flexibility, market efficiency, and reliability. This paper conducts a comprehensive study to: (a) augment existing DA market models with enhanced FRP design to schedule ramp capabilities that are more adaptive with respect to the real-time (RT) 15-min net load variability and uncertainty, and (b) design corresponding market payment policies that accurately reflect the value of the added flexibility through enhanced FRP design. The proposed FRP design can be implemented in present-day system operations with minimal disruption to existing DA market models. Performance of the proposed DA market model, which includes the enhanced FRP design, is compared against the DA market model with existing FRP design through a validation methodology based on RT unit commitment model. This validation methodology mimics fifteen-minute markets of CAISO. The proposed method is tested on an IEEE 118-bus test system.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Robust Exciter Controller Design for Synchronous Condensers in Weak
           Grids

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      Authors: Sajjad Hadavi;Dayan B. Rathnayake;Gamini Jayasinghe;Ali Mehrizi-Sani;Behrooz Bahrani;
      Pages: 1857 - 1867
      Abstract: Weak grid scenarios and low-inertia systems are emerging issues in power systems, which could lead to voltage and frequency instabilities. Synchronous Condensers (SynCons) have recently drawn renewed attention as a promising solution to provide system strength and inertia support. Even though the exciter control of SynCons is a well-established technology, further developments are required to guarantee the stability of post-fault operations, in particular, in weak grids. This paper proposes a data-driven approach for designing higher-order optimized exciter controllers to meet this requirement. A pseudo-random binary sequence (PRBS)-based system identification method is used to obtain frequency response data of the power system, from the exciter point of view, which is then fed into the proposed optimal control design procedure. The proposed exciter controller is tested for voltage ride-through and fault scenarios in a single machine infinite bus (SMIB) case and the IEEE 39-bus test system to assess its performance compared to the conventional AC1A exciter controller. The results obtained through simulation tests carried out using the PSCAD/EMTDC software verify that the proposed exciter controller guarantees the post-fault stability in both strong and weak grids.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • An Asynchronous Online Negotiation Mechanism for Real-Time Peer-to-Peer
           Electricity Markets

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      Authors: Zhenwei Guo;Pierre Pinson;Qianhong Wu;Shibo Chen;Qinmin Yang;Zaiyue Yang;
      Pages: 1868 - 1880
      Abstract: Participants in electricity markets are becoming more proactive owing to the fast deployment of distributed energy resources (DERs) and the further development of demand-side management (DSM), which also boosts the emergence of Peer-to-Peer (P2P) market mechanisms. Moreover, the market is also required to operate in a real-time scheme in response to changes in generation and load to maintain power balance. It is highly desirable to propose and analyse novel approaches suitable for real-time P2P market mechanisms. These are challenging since most often involving a heavy computational burden, while the time available for negotiation in real-time is very short. Our core contribution is to design and analyse a novel asynchronous online optimization framework and related real-time P2P market negotiation mechanism, which can greatly reduce the computation and communication burden from two aspects. First, a novel online consensus alternating direction method of multipliers (ADMM) algorithm is proposed. It significantly reduces computation since only one iteration is performed for each agent at every time period. Second, the market operates in an asynchronous mode so that all agents can freely trade without waiting for idle or inactive neighboring agents. The sublinear regret upper bound is proved for our asynchronous online algorithm, which indicates that social welfare in the market can be maximized in the long run on average (over time). Simulations show that our algorithm enjoys good convergence performance, robustness, and fairness.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Impact Analysis of Fast Dynamics on Stability of Grid-Tied Inverter Based
           on Oscillator Model and Damping Torque Analysis

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      Authors: Pan Yu;Zhen Tian;Xiaoming Zha;Jianjun Sun;Peijun Zhong;Meng Huang;
      Pages: 1881 - 1892
      Abstract: As the crucial interface of renewable energy integration, the grid-tied inverter is a complicated system with a multi-timescale feature, containing both fast dynamics and slow dynamics that determine the system stability. However, previous researches on stability analysis mainly focus on the slow dynamics without considering the impact of fast dynamics, which may bring to inaccurate stability prediction. In this paper, the impact of fast dynamics on stability is investigated for grid-tied inverter from a physical perspective. Considering the dynamics of the current control loop and network, a two degrees-of-freedom oscillator model is firstly established to extract the fast dynamics separately. The damping torque analysis is then applied to analyze the impact of the extracted fast dynamics on the damping and spring coefficients of slow dynamics. Moreover, based on the fast dynamic analysis, a novel reduced-order model is proposed with the consideration of the dominant characteristics of fast dynamics, which improves the model accuracy without increasing the complexity. Eventually, both numerical evaluations and experiments are performed to validate the proposed analysis method.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Stability Analysis of DC Distribution System Considering Stochastic State
           of Electric Vehicle Charging Stations

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      Authors: Qiang Fu;Wenjuan Du;Haifeng Wang;Xianyong Xiao;
      Pages: 1893 - 1903
      Abstract: A DC distribution system integrated with electric vehicle charging stations (EVCSs) is typically constructed to satisfy the large power exchange demand of electric vehicles (EVs). However, the stochastic operating state of the EVCSs results in difficulties in the stability analysis of the DC distribution system. In this study, a linearized model of the DC distribution system connected with multiple EVCSs is established, which considers the charging and the discharging states of the EVCSs. Based on the similarity transformation, this study theoretically verifies the generalized conclusion that a DC distribution system has a higher possibility of instability if the EVCSs are in the charging rather than the discharging state, and that the system stability is the worst if all the EVCSs operate at the maximum charging state. Moreover, a method to quickly evaluate the stability region of the DC distribution system is proposed. It can provide a numerical instability risk estimation of a complex DC distribution system considering all the possible states of the EVCSs. Lastly, the conclusions are validated by two SIMULINK cases, and the application of the numerical instability risk estimation to the DC distribution system is demonstrated.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Data-Driven Joint Voltage Stability Assessment Considering Load
           Uncertainty: A Variational Bayes Inference Integrated With Multi-CNNs

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      Authors: Mingjian Cui;Fangxing Li;Hantao Cui;Siqi Bu;Di Shi;
      Pages: 1904 - 1915
      Abstract: Few studies have focused on assessing the transient and steady-state voltage stability status of dynamic systems simultaneously. This motivated us to propose a new concept referred to as joint voltage stability assessment (JVSA). Towards this end, this paper proposes a novel data-driven JVSA method considering load uncertainty. It combines multiple convolutional neural networks (multi-CNNs) and a novel variational Bayes (VB) inference for better JVSA accuracy. First, the multi-CNN model is utilized to fast estimate the maximum voltage deviations during the transient and steady-state process. Uncertain load scenarios and system topology under $N$-1 contingency with are chosen as inputs of each CNN model. Second, estimated voltage deviations are put into the VB inference to automatically infer the transient and steady-state voltage stability status. To validate its effectiveness, numerical simulations are performed on the modified WECC 179-bus system by comparing with benchmark algorithms. It is demonstrated that the proposed data-driven JVSA method is more accurate and faster than the conventional VSA method.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Transmission Expansion Planning Considering Resistance Variations of
           Overhead Lines Using Minimum-Volume Covering Ellipsoid

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      Authors: Mohammed A. El-Meligy;Ahmed M. El-Sherbeeny;Amjad Anvari-Moghaddam;
      Pages: 1916 - 1926
      Abstract: Reliable planning of electricity networks is a crucial challenge to maintain the continuous power supply. This can be ensured through careful planning which requires a number of parameters as inputs such as electricity demand, generation capacity, fuel price, renewable intermittent generation, among others. The uncertain behaviors of such parameters have been well studied in literature. Another important parameter which is a vital requirement for reliable planning is the resistance of overhead transmission lines. Traditionally, the current models ignore to consider the resistance variations due to Joule heating and ambient temperature changes in transmission expansion planning (TEP) problem. In this sense, this paper presents an adaptive robust optimization (ARO) framework for TEP to model the uncertain resistance through ellipsoidal uncertainty set. Moreover, a data-driven selection of the ellipsoidal uncertainty set is proposed. In this regard, Khachiyan's algorithm (KA) is used to identify the minimum-volume covering ellipsoid (MVCE) that contains all uncertain parameters. A case study based on IEEE 118-bus power system is presented to demonstrate the effectiveness of the proposed method. Simulation results show that resistance uncertainty is of serious concern in the TEP problem since the solutions are highly sensitive to fluctuations in the line resistances.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Tractable Convex Approximations for Distributionally Robust Joint
           Chance-Constrained Optimal Power Flow Under Uncertainty

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      Authors: Lun Yang;Yinliang Xu;Hongbin Sun;Wenchuan Wu;
      Pages: 1927 - 1941
      Abstract: Uncertainty arising from renewable energy results in considerable challenges in optimal power flow (OPF) analysis. Various chance-constrained approaches are proposed to address the uncertainty within OPF models. However, most existing approaches either assume that the uncertainty distributions are known a priori or consider individual chance constraint modeling. This paper proposes a distributionally robust (DR) joint chance-constrained OPF model, which ensures that the operational constraints are simultaneously satisfied with a given probability and does not require an assumption of specific probability distributions. An ambiguity set built on the first two moments is used to model the uncertainty. An optimized Bonferroni approximation (OBA) is first introduced to decompose the DR joint chance constraint into DR individual chance constraints. The resulting OBA formulation is strongly nonconvex. Different convex approximations are then proposed to formulate the OBA formulation as tractable forms. The proposed convex approximations can be easily extended to incorporate the structural information associated with uncertainty, and correlations among reserve chance constraints. Case studies demonstrate the effectiveness of the proposed convex approximation methods and their extensions.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Encoding Frequency Constraints in Preventive Unit Commitment Using Deep
           Learning With Region-of-Interest Active Sampling

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      Authors: Yichen Zhang;Hantao Cui;Jianzhe Liu;Feng Qiu;Tianqi Hong;Rui Yao;Fangxing Li;
      Pages: 1942 - 1955
      Abstract: With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Energy Management and Control of Photovoltaic and Storage Systems in
           Active Distribution Grids

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      Authors: Lysandros Tziovani;Lenos Hadjidemetriou;Panayiotis Kolios;Alessandro Astolfi;Elias Kyriakides;Stelios Timotheou;
      Pages: 1956 - 1968
      Abstract: The evolution of power distribution grids from passive to active systems creates reliability and efficiency challenges to the distribution system operators. In this paper, an energy management and control scheme for managing the operation of an active distribution grid with prosumers is proposed. A multi-objective optimization model to minimize (i) the prosumers electricity cost and (ii) the cost of the grid energy losses, while guaranteeing safe and reliable grid operation is formulated. This is done by determining the active and reactive power set-points of the photovoltaic and storage systems integrated in the grid buildings. The resulting optimization model is non-convex, thus a convex second-order cone program is developed by appropriately relaxing the non-convex constraints which yields optimal results in most operating conditions. The convexified model is further utilized to develop an algorithm that yields feasible solutions to the non-convex problem under any operating conditions. Moreover, a second novel algorithm to find the operating point that provides fairness between the prosumers and the grid costs is proposed. Simulation results demonstrate the effectiveness and superiority of the proposed scheme in managing an industrial distribution grid compared to a self-consumption approach.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Deep Learning Based Model-Free Robust Load Restoration to Enhance Bulk
           System Resilience With Wind Power Penetration

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      Authors: Jin Zhao;Fangxing Li;Xi Chen;Qiuwei Wu;
      Pages: 1969 - 1978
      Abstract: This paper proposes a new deep learning (DL) based model-free robust method for bulk system on-line load restoration with high penetration of wind power. Inspired by the iterative calculation of the two-stage robust load restoration model, the deep neural network (DNN) and deep convolutional neural network (CNN) are respectively designed to find the worst-case system condition of a load pickup decision and evaluate the corresponding security. In order to find the optimal result within a limited number of checks, a load pickup checklist generation (LPCG) algorithm is developed to ensure the optimality. Then, the fast robust load restoration strategy acquisition is achieved based on the designed one-line strategy generation (OSG) algorithm. The proposed method finds the optimal result in a model-free way, holds the robustness to handle uncertainties, and provides real-time computation. It can completely replace conventional robust optimization and supports on-line robust load restoration which better satisfies the changeable restoration process. The effectiveness of the proposed method is validated using the IEEE 30-bus system and the IEEE 118-bus system, showing high computational efficiency and considerable accuracy.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Distributed Finite-Time Event-Triggered Frequency and Voltage Control of
           AC Microgrids

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      Authors: Jeewon Choi;Seyed Iman Habibi;Ali Bidram;
      Pages: 1979 - 1994
      Abstract: This paper proposes a finite-time event-triggered secondary frequency and voltage control for islanded AC microgrids (MGs) in a distributed fashion. The proposed control strategy can effectively perform frequency restoration and voltage regulations, while sharing the active and reactive power among the distributed generators (DGs) based on their power ratings. The finite-time control enables a system to reach consensus in a finite period of time enhanced from the asymptotic convergence. The event-triggered communication is utilized to reduce the communication burden among the DG controllers by transmitting data among DGs if an event-triggering condition is satisfied. The performance of the proposed finite-time event-triggered frequency control is verified utilizing a hardware-in-the-loop experimental testbed which simulates an AC MG in Opal-RT.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Distribution Grid Modeling Using Smart Meter Data

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      Authors: Yifei Guo;Yuxuan Yuan;Zhaoyu Wang;
      Pages: 1995 - 2004
      Abstract: The knowledge of distribution grid models, including topologies and line impedances, is essential for grid monitoring, control and protection. However, such information is often unavailable, incomplete or outdated. The increasing deployment of smart meters (SMs) provides a unique opportunity to tackle this issue. This paper proposes a two-stage framework for distribution grid modeling using SM data. In the first stage, the network topology is identified by reconstructing a weighted Laplacian matrix of distribution networks. In the second stage, a least absolute deviations (LAD) regression model is developed for estimating line impedance of a single branch based on the nonlinear (inverse) power flow model, wherein a conductor library is leveraged to narrow down the solution space. The LAD regression model is originally a mixed-integer nonlinear program whose continuous relaxation is still non-convex. Thus, we specially address its convex relaxation and discuss the exactness. The modified regression model is then embedded within a bottom-up sweep algorithm to achieve the identification across the network in a branch-wise manner. Numerical results on the IEEE 13-bus, 37-bus and 69-bus test feeders validate the effectiveness of the proposed methods.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Adaptive Coalition Formation-Based Coordinated Voltage Regulation in
           Distribution Networks

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      Authors: Yao Long;Ryan T. Elliott;Daniel S. Kirschen;
      Pages: 2005 - 2018
      Abstract: High penetrations of photovoltaic (PV) systems can cause severe voltage quality problems in distribution networks. This paper proposes a distributed control strategy based on the dynamic formation of coalitions to coordinate a large number of PV inverters for voltage regulation. In this strategy, a rule-based coalition formation scheme deals with the zonal voltage difference caused by the uneven integration of PV capacity. Under this scheme, PV inverters form into separate voltage regulation coalitions autonomously according to local, neighbor as well as coalition voltage magnitude and regulation capacity information. To coordinate control within each coalition, we develop a feedback-based leader-follower consensus algorithm which eliminates the voltage violations caused by the fast fluctuations of load and PV generation. This algorithm allocates the required reactive power contribution among the PV inverters according to their maximum available capacity to promote an effective and fair use of the overall voltage regulation capacity. Case studies based on realistic distribution networks and field-recorded data validate the effectiveness of the proposed control strategy. Moreover, comparison with a centralized network decomposition-based scheme shows the flexibility of coalition formation in organizing the distributed PV inverters. The robustness and generalizability of the proposed strategy are also demonstrated.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Topology Identification of Distribution Networks Using a Split-EM Based
           Data-Driven Approach

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      Authors: Li Ma;Lingfeng Wang;Zhaoxi Liu;
      Pages: 2019 - 2031
      Abstract: To improve the topology observability in power distribution networks (PDNs), a two-stage topology identification framework is proposed to recognize the mixed topologies in a large set of historical data and predict the real-time topology based on the nodal measurements. A split expectation-maximization (split-EM) method is proposed considering the measurement errors to deal with the topology identification problem on the historical batch data, in which the number of topology categories does not need to be given in advance. Based on the topology identification results of historical data, the number of topology categories is reduced. Then, feasible classifiers are trained using machine learning methods to predict the real-time topology efficiently. An error-correcting mechanism is proposed for the real-time identification involving the credibility analysis and the reidentification based on the Bayesian recursion model. Finally, via a practical example, the effectiveness of the proposed models is verified by efficiently identifying the PDN's topologies in both the historical batch data with mixed topologies and real-time measurements. In addition, the partition-based extension application solution of the topology identification models for large-scale PDNs is proposed without extra measurements to relieve the calculation burden and reduce the identification time notably while maintaining the accuracy as the non-partitioned scheme.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Large-Scale Preventive Security-Constrained Unit Commitment Considering
           N-k Line Outages and Transmission Losses

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      Authors: Guillermo Gutiérrez-Alcaraz;Berenice Díaz-López;José M. Arroyo;Víctor H. Hinojosa;
      Pages: 2032 - 2041
      Abstract: This paper presents a new formulation for the preventive security-constrained unit commitment problem modeling N-k line outages and transmission losses. The pre- and post-contingency transmission constraints, representing N-k line outages, are explicitly included by using generalized generation distribution factors. To account for security, a contingency selection procedure based on line outage distribution factors finds a list of worst-case contingencies. Transmission losses are incorporated using piecewise linear expressions. The proposed model is formulated as an instance of mixed-integer linear programming. The effectiveness of the proposed approach is illustrated with the IEEE 57-bus system and the 1,354-bus portion of the European transmission system. As empirically evidenced, the explicit consideration of N-k line outages and transmission losses leads to different decisions in the generation scheduling and dispatch, ensuring secure power system operation.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Privacy-Preserving Distributed Control of Optimal Power Flow

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      Authors: Minseok Ryu;Kibaek Kim;
      Pages: 2042 - 2051
      Abstract: We consider a distributed optimal power flow formulated as an optimization problem that maximizes a nondifferentiable concave function. Solving such a problem by the existing distributed algorithms can lead to data privacy issues because the solution information exchanged within the algorithms can be utilized by an adversary to infer the data. To preserve data privacy, in this paper we propose a differentially private projected subgradient (DP-PS) algorithm that includes a solution encryption step. We show that a sequence generated by DP-PS converges in expectation, in probability, and with probability 1. Moreover, we show that the rate of convergence in expectation is affected by a target privacy level of DP-PS chosen by the user. We conduct numerical experiments that demonstrate the convergence and data privacy preservation of DP-PS.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Zero-Inertia Offshore Grids: N-1 Security and Active Power Sharing

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      Authors: Georgios S. Misyris;Andrea Tosatto;Spyros Chatzivasileiadis;Tilman Weckesser;
      Pages: 2052 - 2062
      Abstract: With Denmark dedicated to maintaining its leading position in the integration of massive shares of wind energy, the construction of new offshore energy islands has been recently approved by the Danish government. These new islands will be zero-inertia systems, meaning that no synchronous generation will be installed in the island and that power imbalances will be shared only among converters. To this end, this paper proposes a methodology to calculate and update the frequency droops gains of the offshore converters in compliance with the N-1 security criterion in case of converter outage. The frequency droop gains are calculated solving an optimization problem which takes into consideration the power limitations of the converters as well as the stability of the system. As a consequence, the proposed controller ensures safe operation of off-shore systems in the event of any power imbalance and allows for greater loadability at pre-fault state, as confirmed by the simulation results.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Co-optimization of Energy and Reserve With Incentives to Wind Generation

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      Authors: Yves Smeers;Sebastian Martin;José Antonio Aguado;
      Pages: 2063 - 2074
      Abstract: Transitioning from fossil fuel dominated power systems to high penetrations of intermittent renewable generation is affecting classical electricity market designs. Here, a method is proposed to model and assess the absence of co-optimization of energy and reserve that prevails in the European system. Co-optimization is formulated through an optimization problem (COM), and its absence as an equilibrium problem (EQM) built on Karush-Kuhn-Tucker conditions of agents’ optimization and market clearing equations. EQM cannot be reformulated as a single optimization problem. Market distortions are identified by comparing the complementarity conditions of both models. These are then discussed on system with Feed in Premium to wind generation. Parameters in the models allow to represent different market configurations regarding: available wind generation, Feed in Premium to wind, generators’ risk aversion, and required reserve from wind generation.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Framework for Constrained Static State Estimation in Unbalanced
           Distribution Networks

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      Authors: Marta Vanin;Tom Van Acker;Reinhilde D’hulst;Dirk Van Hertem;
      Pages: 2075 - 2085
      Abstract: State estimation plays a key role in the transition from the passive to the active operation of distribution systems, as it allows to monitor the networks and provides the necessary information to perform control actions. However, designing state estimators for distribution systems is challenging, due to the characteristics of the networks, such as limited measurement availability. Furthermore, the features of the distribution system present significant local variations, e.g., voltage level and number and type of customers, which makes it hard to design a “one-size-fits-all” state estimator. This paper introduces a unifying framework that allows to easily implement and compare diverse unbalanced static state estimation models. This is achieved by formulating state estimation as a general constrained optimization problem. The advantages of this approach are described and supported by numerical illustration on a large set of real distribution feeders. The framework is also implemented in software and made available open-source.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Mitigating High-Frequency Resonance in MMC-HVDC Systems Using Adaptive
           Notch Filters

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      Authors: Jiufang Man;Lei Chen;Vladimir Terzija;Xiaorong Xie;
      Pages: 2086 - 2096
      Abstract: Recently, high-frequency resonance (HFR) incidents have occurred in several MMC-HVDC projects. The resonance frequency highly depends on system conditions, making mitigation methods designed for specific scenarios ineffective once the grid condition changes. To address the issue, this paper proposes a novel HFR mitigation method based on adaptive notch filters (ANFs). The embedding positions of ANFs in the MMC control are optimally determined through impedance-based analysis. With the HFR frequency estimated by the interpolated discrete Fourier transform, the parameters of ANFs are adaptively tuned. The ANFs improve the damping performance of the captured HFR mode without affecting the dynamic characteristics at other frequencies. As a result, the HFR under varying grid conditions can be mitigated effectively. The effectiveness of the proposed method is verified through electromagnetic transient (EMT) simulations of an actual MMC-HVDC system.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Optimal Allocation of Energy Storage System in DFIG Wind Farms for
           Frequency Support Considering Wake Effect

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      Authors: Linyun Xiong;Shaobo Yang;Sunhua Huang;Donglin He;Penghan Li;Muhammad Waseem Khan;Jie Wang;
      Pages: 2097 - 2112
      Abstract: Energy storage systems (ESSs) are being utilized to improve wind farms’ (WF) frequency support capability due to their high reliability, fast response and the dual role of energy users and suppliers. Nevertheless, the problem of how much capacity should each ESS possesses in order to better serve the WFs has never been investigated. With this perspective, this paper proposes an optimal ESS allocation (OEA) scheme for doubly fed induction generator (DFIG) based WFs to mitigate the impact of wake effect in frequency support. Firstly, the synchronous stability of wind turbines under frequency dips is analyzed with the concept of frequency support margin (FSM), and the detrimental impact of the wake effect is also investigated. Subsequently, the role of ESSs to improve wind turbines’ synchronous stability is demonstrated. To make the OEA scheme practical, the wind turbines in a WF are segmented into different clusters based on the received wind speed. Afterwards, the OEA problem is formulated, where the objective is to optimize the coherency of the wind turbine clusters’ FSM level. The simulation results show that ESS can provide secondary frequency support under major grid frequency drops, and the proposed OEA scheme can reduce the risk of loss of synchronous stability.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Greedy Algorithm for Optimizing Offshore Wind Transmission Topologies

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      Authors: Stephen Hardy;Hakan Ergun;Dirk Van Hertem;
      Pages: 2113 - 2121
      Abstract: This work develops a mathematical formulation to determine the combinatorial search space of the Offshore Wind Transmission Optimization Problem (OWTOP). The model accounts for Capital Expenditures (CAPEX), Corrective Maintenance (CM), losses and Expected Energy Not Transmitted (EENT) and determines the optimal radial transmission system topology to connect a number of Offshore Wind Power Plants (OWPPs) to the shore. The model also considers the stochastic nature of wind. In this context a greedy search algorithm is developed capable of finding the globally optimal solution for the location of offshore substations (OSSs), sizing of the transmission infrastructure and topological layout. Rather than a single optimal solution, the algorithm finds a solution space of feasible topologies, bounded from below by the optimal radial solution. The algorithm is applied to 4 test cases including one based on the Belgian North Sea area. It is shown to outperform a traditional Transmission Network Expansion Problem (TNEP) formulation both in computational speed and solution quality.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of
           Market Participants’ Performance: A Case of California ISO

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      Authors: Ehsan Samani;Mahdi Kohansal;Hamed Mohsenian-Rad;
      Pages: 2122 - 2136
      Abstract: Convergence bidding, a.k.a., virtual bidding, has been widely adopted in wholesale electricity markets in recent years. It provides opportunities for market participants to arbitrage on the difference between the day-ahead market locational marginal prices and the real-time market locational marginal prices. Given the fact that convergence bids (CBs) have a significant impact on the operation of electricity markets, it is important to understand how market participants strategically select their CBs in real-world electricity markets. We address this open problem with focus on the electricity market that is operated by the California Independent System Operator (ISO). In this regard, we use the publicly available electricity market data to learn, characterize, and evaluate different types of convergence bidding strategies that are currently used by market participants. Our analysis includes developing a data-driven reverse engineering method that we apply to three years of real-world California ISO market data. Our analysis involves feature selection and density-based data clustering. It results in identifying three main clusters of CB strategies in the California ISO market. Different characteristics and the performance of each cluster of strategies are analyzed. Interestingly, we unmask a common real-world strategy that does not match any of the existing strategic convergence bidding methods in the literature. Next, we build upon the lessons learned from the advantages and disadvantages of the existing real-world strategies in order to propose a new CB strategy that can significantly outperform them. Our analysis includes developing a new strategy for convergence bidding. The new strategy has three steps: net profit maximization by capturing price spikes, dynamic node labeling, and strategy select-on algorithm. We show through case studies that the annual net profit for the most lucrative market participants can increase by over 40% if the proposed convergence bidding strategy is used.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Two-Stage Simultaneous Control Scheme for the Transient Angle Stability
           of VSG Considering Current Limitation and Voltage Support

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      Authors: Kun Sun;Wei Yao;Jinyu Wen;Lin Jiang;
      Pages: 2137 - 2150
      Abstract: Since the wide application of virtual synchronous generators (VSGs), the power grid faces great challenges in the safe and stable operation due to their limited thermal capacity and weak anti-disturbance ability. During transient period, for example, a fault occurs in the transmission line, the VSG may lose the transient angle stability and provoke the current hard limit. Even if the fault is cleared by tripping of line, it still faces the problem of instability and voltage dips. To address this problem, in this paper, the post-fault large-signal model of VSG is derived first via the travelling waves based fault information acquisition. Subsequently, with the effect of both active and reactive power loops taken into account, a two-stage simultaneous control scheme is proposed for improving the transient stability of VSG, while considering the current limitation during fault state and voltage support after fault clearance. This method is fulfilled by mode switching and an additional feedback control based on the fault signal. Finally, the effectiveness of the proposed method under both symmetrical and asymmetrical faults is verified. Moreover, the application of the proposed method in a multiple VSGs system is also verified. Besides, the robustness to parameter mismatch and the feasible operating region for the method are discussed.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Accelerated Probabilistic Power Flow in Electrical Distribution Networks
           via Model Order Reduction and Neumann Series Expansion

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      Authors: Samuel Chevalier;Luca Schenato;Luca Daniel;
      Pages: 2151 - 2163
      Abstract: This paper develops a computationally efficient algorithm which speeds up the probabilistic power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage profile in electrical power distribution networks. The algorithm is accordingly termed the Accelerated-PPF (APPF), since it can accelerate “any” sampling-based PPF solver. As the APPF runs, it concurrently generates a low-dimensional subspace of orthonormalized solution vectors. This subspace is used to construct and update a reduced order model (ROM) of the full nonlinear system, resulting in a highly efficient simulation for future voltage profiles. When constructing and updating the subspace, the power flow problem must still be solved on the full nonlinear system. In order to accelerate the computation of these solutions, a Neumann expansion of a modified power flow Jacobian is implemented. Applicable when load bus injections are small, this Neumann expansion allows for a considerable speed up of Jacobian system solves during the standard Newton iterations. APPF test results, from experiments run on the full IEEE 8500-node test feeder, are finally presented.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Multi-Period AC/DC Transmission Expansion Planning Including Shunt
           Compensation

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      Authors: Prudhvi Anand Bhattiprolu;Antonio J. Conejo;
      Pages: 2164 - 2176
      Abstract: Due to the increasing penetration of large-scale renewable energy generation at transmission level, hybrid AC/DC transmission expansion is becoming more relevant by the day, especially if the considered renewable generation is far away from existing AC grid infrastructure. This paper presents a multi-period hybrid AC/DC network expansion planning model that incorporates node voltages, reactive power and network losses. Since the operation of a high voltage DC link is influenced considerably by the converter stations, converter transformer, filter, and phase reactor are modeled in each converter station. Since the AC power flow equations render a non-convex problem whose solution algorithms do not always guarantee convergence or optimality, a second-order conic relaxation is utilized. The resulting model is a mixed-integer convex problem with a convergence guarantee that embodies a better approximation of system operation than its DC linear counterpart. Three case studies are considered to illustrate the proposed model.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Dynamic Valuation of Battery Lifetime

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      Authors: Bolun Xu;
      Pages: 2177 - 2186
      Abstract: This paper proposes a dynamic valuation framework to determine the opportunity value of battery capacity degradation in grid applications based on the internal degradation mechanism and utilization scenarios. The proposed framework follows a dynamic programming approach and includes a piecewise linear value function approximation solution that solves the optimization problem over a long planning horizon. The paper provides two case studies on price arbitrage and frequency regulation using real market and system data to demonstrate the broad applicability of the proposed framework. Results show that the battery lifetime value is critically dependent on both the external market environment and its internal state of health. On the grid service side, results show that second-life batteries can provide more than 50% of the value compared to new batteries, and frequency regulation provides two times more revenue than price arbitrage throughout the battery lifetime.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • AC Network-Constrained Unit Commitment via Relaxation and Decomposition

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      Authors: Gonzalo E. Constante-Flores;Antonio J. Conejo;Feng Qiu;
      Pages: 2187 - 2196
      Abstract: We propose an approach to solving the AC network-constrained unit commitment problem (AC-NCUC) of large-scale power systems. This approach relies on solving convex (continuous and mixed-integer) optimization problems. First, we formulate a second-order conic relaxation of the original optimization problem, which renders a mixed-integer second-order conic optimization problem. To solve this problem, we use Benders’ decomposition, which decomposes it into a master problem, which is mixed-integer linear, and a subproblem, which is continuous and second-order conic. To improve the convergence of the Benders’ algorithm, we add several linear feasibility constraints to the master problem. Since the second-order relaxation of the original problem cannot guarantee AC feasibility of the solution found, we ensure such feasibility by solving a sequence of continuous convex optimization problems. We numerically validate the proposed approach using a Texas 2000-bus system.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Scale-Free Cooperative Control of Inverter-Based Microgrids With General
           Time-Varying Communication Graphs

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      Authors: Donya Nojavanzadeh;Saeed Lotfifard;Zhenwei Liu;Ali Saberi;Anton A. Stoorvogel;
      Pages: 2197 - 2207
      Abstract: This paper presents a method for controlling the voltage of inverter-based Microgrids by proposing a new scale-free distributed cooperative controller. The main contribution of this paper is that the proposed distributed cooperative controller is scale-free where is independent of any information about the communication system and the number of distributed generators, as such it works for any Microgrids with any size. Moreover, the communication network is modeled by a general time-varying graph which enhances the resilience of the proposed protocol against communication link failure, data packet loss, and arbitrarily fast plug and play operation in the presence of arbitrarily finite communication delays as the protocol does not require the knowledge of the upper bound on the delay. The stability analysis of the proposed protocol is provided. The proposed method is simulated on the CIGRE medium voltage Microgrid test system. The simulation results demonstrate the feasibility of the proposed scale-free distributed nonlinear protocol for regulating voltage of Microgrids in the presence of communication failures, data packet loss, noise, and degradation.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Stochastic Hybrid Approximation for Uncertainty Management in Gas-Electric
           Systems

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      Authors: Conor O’ Malley;Gabriela Hug;Line Roald;
      Pages: 2208 - 2219
      Abstract: Gas-fired generators, with their ability to quickly ramp up and down their electricity production, play an important role in managing renewable energy variability. However, these changes in electricity production translate into variability in the consumption of natural gas, and propagation of uncertainty from the electric grid to the natural gas system. To ensure that both systems are operating safely, there is an increasing need for coordination and uncertainty management among the electricity and gas networks. A challenging aspect of this coordination is the consideration of natural gas dynamics, which play an important role in intra-day operation, but give rise to a set of non-linear and non-convex equations that are hard to optimize even in the deterministic case. Ideally, the problem is formulated as a stochastic problem but many conventional methods for stochastic optimization are numerically intractable because they either incorporate a large number of scenarios directly or require the underlying problem to be convex. To address these challenges, we propose using a Stochastic Hybrid Approximation algorithm to more efficiently solve these problems and investigate the efficacy of several different variants of this algorithm. Our case study demonstrates that the proposed technique is able to quickly obtain high quality solutions and outperforms existing benchmarks such as Generalized Benders Decomposition. We demonstrate that coordinated uncertainty management that accounts for the gas system can significantly reduce both electric and gas system load shed in stressed conditions.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology
           Estimation in an Unbalanced Power Distribution System

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      Authors: Yijun Xu;Jaber Valinejad;Mert Korkali;Lamine Mili;Yajun Wang;Xiao Chen;Zongsheng Zheng;
      Pages: 2220 - 2232
      Abstract: The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)-sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Distributed Predefined-Time Fractional-Order Sliding Mode Control for
           Power System With Prescribed Tracking Performance

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      Authors: Sunhua Huang;Jie Wang;Linyun Xiong;Jiayan Liu;Penghan Li;Ziqiang Wang;
      Pages: 2233 - 2246
      Abstract: In this paper, a distributed predefined-time fractional-order sliding mode controller (DPTFOSMC) is proposed for performance improvement of power system. The proposed DPTFOSMC is designed based on the multi-agent framework and distributed control theory, which can reduce the pressure of central controller in communication and calculation, thus obtaining fast and coordinated response. The DPTFOSMC can achieve the advantages of sliding mode control (SMC) with fast response and strong robustness for the power system. In addition, the power system under the proposed DPTFOSMC can be stabilized in stable range within a predefined time which is directly equal to the tunable parameter, thus meeting the high requirement for convergence time of the power system. The prescribed performance function (PPF) is carried out to design the DPTFOSMC, which can ensure that the power system can achieve the expected transient and steady responses. Meanwhile, fractional calculus is implemented to devise the DPTFOSMC to eliminate the chattering phenomenon of conventional SMC. The Lyapunov function is adopted to analysis the predefined-time stability of the power system under the proposed DPTFOSMC. Simulations are taken to validate the effectiveness and superior performance of the proposed DPTFOSMC for the power system. The results show that the DPTFOSMC can ensure the power system to achieve superior performances than the existing control methods.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A DSO Framework for Market Participation of DER Aggregators in Unbalanced
           Distribution Networks

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      Authors: Mohammad Mousavi;Meng Wu;
      Pages: 2247 - 2258
      Abstract: This paper presents a distribution system operator (DSO) framework for wholesale and retail market participation of distributed energy resources (DERs) aggregators. The DSO coordinates aggregators’ energy and regulation offers as well as end-users’ energy consumption through the unbalanced retail market and submits balanced energy and regulation offers to the wholesale market on behalf of all the aggregators and end-users within its territory. Various kinds of DER aggregators including demand response aggregators (DRAGs), energy storage aggregators (ESAGs), electric vehicle (EV) charging stations (EVCSs), dispatchable distributed generation aggregators (DDGAGs), and renewable energy aggregators (REAGs) are modeled. To handle unbalanced distribution grids with single-phase aggregators, a linearized unbalanced power flow is tailored to model operating constraints of the distribution grid with various aggregators. A market settlement approach is proposed for the DSO, which coordinates with wholesale market clearing process and ensures the DSO’s non-profit characteristic. It is proved that at the wholesale-DSO coupling substation, the total payment received/compensated by the DSO under the wholesale price is identical to that under three single-phase retail prices for each phase at the substation. Case studies are performed on the modified IEEE 33-node and 240-node distribution test systems to investigate the market outcomes of the proposed DSO.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Machine Learning-Based Vulnerability Analysis for Cascading Failures of
           Integrated Power-Gas Systems

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      Authors: Shuai Li;Tao Ding;Wenhao Jia;Can Huang;João P. S. Catalão;Fangxing Li;
      Pages: 2259 - 2270
      Abstract: This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Transmission Line Parameter Error Identification and Estimation in
           Three-Phase Networks

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      Authors: Ramtin Khalili;Ali Abur;
      Pages: 2271 - 2282
      Abstract: This paper concerns the detection and identification of errors in the parameters of three-phase transposed as well as untransposed transmission line (TL) models used by various network applications. Such detailed models are increasingly needed in particular by power system applications where loads may not be balanced and/or TLs may not be symmetrical, and a full detailed three-phase solution may have to be obtained. To address this problem, the paper proposes an efficient algorithm for detection, identification, and estimation of parameter errors using synchronized phasor measurements. The suspect TL is detected using the modal domain networks in the first stage. The developed algorithm extends the previously developed largest normalized Lagrange multiplier (NLM) test for positive sequence parameters to the full coupled three-phase lines. An estimation method is also proposed for estimating the erroneous parameters, which takes into account the correlation of the parameters. To illustrate the effectiveness of the method, several tests are performed on the IEEE 118-bus system and a large 3474-bus utility system.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Bibliometric Analysis of Power System Planning Research During
           1971–2020

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      Authors: Jie Xu;Tao Lv;Xiaoran Hou;Xu Deng;Feng Liu;
      Pages: 2283 - 2296
      Abstract: Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 1971–2020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identified and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central nodes as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Proliferation of Small Data Networks for Aggregated Demand Response in
           Electricity Markets

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      Authors: Min Chen;Ciwei Gao;Mohammad Shahidehpour;Zuyi Li;
      Pages: 2297 - 2311
      Abstract: This paper proposes an aggregation method for proliferated small-size data networks to apply data networks’ spatial load regulation potentials for demand response (DR). First, each data network is modeled as a virtual power network, where the Internet data center load is modeled as a set of linear constraints. Second, an aggregated virtual power network (AVPN) is proposed to demonstrate the aggregation method potentials in power systems and to emulate DR applications for multiple virtual power networks in wholesale markets. The coupling of AVPN and power network would develop a linear load model for an aggregated Internet data center. Furthermore, the supply curves representing AVPN DR are formulated to capture heterogeneous regulation costs of virtual power networks, which guarantees limited welfare losses in AVPN DR compared with the individual DR of virtual power networks. Last, an AVPN DR implementation mechanism is deduced to reveal the potentials of the aggregation method in power system applications. Simulation results verify the efficiency of the proposed aggregation method for spatially-coupled DR resources, where, the computing time of the AVPN-based OPF without welfare loss is reduced by 99.94% when there are 3×104 data networks. The proposed aggregation strategy implies that the proliferation of small-size data networks will offer a reasonable DR for enhancing the power system operation in wholesale electricity markets.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Financial Resilience and Financial Reliability for Systemic Risk
           Assessment of Electricity Markets With High-Penetration Renewables

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      Authors: Qiwei Zhang;Fangxing Li;
      Pages: 2312 - 2321
      Abstract: Over the past 20 years, the rapid integration of renewable energy has resulted in electricity markets that are increasingly complex, interconnected, and uncertain. Similar to the banking system's financial crisis in 2008 due to chained reactions, severe financial losses due to uncertainty at a large renewable farm could induce significant financial losses at other market participants. The spread of such financial shocks can be worsened by centralized market clearing, and systemic risks consequently germinate in electricity market operations. Therefore, our previous work has compared systemic risk with systematic risk in the electricity market with uncertainties. However, the scope of our previous work has been limited to risk indices. This paper aims to broaden the study by proposing the theoretical foundation of systemic risk analysis in electricity markets with uncertainties. First, an electricity market financial network is defined to describe the cash inflow/outflow of all participants, and the financial contagion is employed to model the interlinks between different entities. Then, two financial properties, financial resilience and financial reliability, are proposed to evaluate the systemic risk in market settlements with uncertainties. Finally, the proposed theoretical foundation of systemic risk is demonstrated on the Texas synthetic 2000-bus system with 70% renewable penetration.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Discrete-Time State-Space Construction Method for SSO Analysis of
           Renewable Power Generation Integrated AC/DC Hybrid System

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      Authors: Yingsheng Han;Haishun Sun;Biyue Huang;Shiyao Qin;Qing Mu;Yongjun Yu;
      Pages: 2322 - 2334
      Abstract: Construction of the linearized state-space model is the key step of eigenvalue analysis for SSO study of power systems with large-scale renewable power generation (RPG) integrated. This paper proposes a novel method to construct the state matrix of the AC/DC hybrid power system with RPG within the discrete-time domain. The continuous-time state-space model of each AC/DC component as well as RPG units in the power system, is discretized together with their corresponding control. The discretized equation of each component is then represented by an equivalent circuit consisting of parallel historical current and conductance. According to the power system configuration, the discrete-time equivalent circuit of the whole AC/DC hybrid system is established, and the nodal analysis method is applied for the construction of the discrete-time state-space model of the power system. In this way, the state matrix can be acquired much more conveniently than that of continuous-time state-space method, which needs topology analysis to find independent state variables of the network from the proper tree. The proposed method has been applied in the eigenvalue analysis for the SSO study of a case system adapted from the AC/DC hybrid transmission system in northwest China. Eigenvalue results are in high accordance with time-domain simulation results in PSCAD/EMTDC.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Solar Power Prediction Based on Satellite Measurements – A Graphical
           Learning Method for Tracking Cloud Motion

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      Authors: Lilin Cheng;Haixiang Zang;Zhinong Wei;Tao Ding;Guoqiang Sun;
      Pages: 2335 - 2345
      Abstract: The stochastic cloud cover on photovoltaic (PV) panels affects the solar power outputs, producing high instability in the integrated power systems. It is an effective approach to track the cloud motion during short-term PV power forecasting based on data sources of satellite images. However, since temporal variations of these images are noisy and non-stationary, pixel-sensitive prediction methods are critically needed in order to seek a balance between the forecast precision and the huge computation burden due to a large image size. Hence, a graphical learning framework is proposed in this study for intra-hour PV power prediction. By simulating the cloud motion using bi-directional extrapolation, a directed graph is generated representing the pixel values from multiple frames of historical images. The nodes and edges in the graph denote the shapes and motion directions of the regions of interest (ROIs) in satellite images. A spatial-temporal graph neural network (GNN) is then proposed to deal with the graph. Comparing with conventional deep-learning-based models, GNN is more flexible for varying sizes of input, in order to be able to handle dynamic ROIs. Referring to the comparative studies, the proposed method greatly reduces the redundancy of image inputs without sacrificing the visual scope, and slightly improves the prediction accuracy.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Two-Step Electricity Theft Detection Strategy Considering Economic Return
           Based on Convolutional Autoencoder and Improved Regression Algorithm

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      Authors: Xueyuan Cui;Shengyuan Liu;Zhenzhi Lin;Jien Ma;Fushuan Wen;Yi Ding;Li Yang;Wenchong Guo;Xiaofeng Feng;
      Pages: 2346 - 2359
      Abstract: Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Estimating Demand Flexibility Using Siamese LSTM Neural Networks

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      Authors: Guangchun Ruan;Daniel S. Kirschen;Haiwang Zhong;Qing Xia;Chongqing Kang;
      Pages: 2360 - 2370
      Abstract: There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Scenario Partitioning Methods for Two-Stage Stochastic Generation
           Expansion Under Multi-Scale Uncertainty

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      Authors: Bining Zhao;Jesse Bukenberger;Mort Webster;
      Pages: 2371 - 2383
      Abstract: Generation Expansion Planning (GEP) can inform regulation, electricity market design, and regional system planning by identifying adaptive investment strategies. Relevant uncertainties include hourly variability in load and renewable generation and decadal-scale uncertainty in technology, markets, and regulation. A multi-stage and multi-scale stochastic GEP model that represents these uncertainties at sufficient resolution becomes intractable. We present an approach for representing this multi-scale uncertainty, and compare it to existing methods, applied to a two-stage stochastic GEP model with a cumulative carbon emission target. For long-term uncertainty, we compare partitioning methods, which reduce the number of decision variables but retain all scenarios, to representative scenario methods, which retain only a subset of the original scenarios. For short-term uncertainty, we compare methods that select representative weeks based on distance metrics in the parameter space to methods that use the covariance of outcomes across feasible decisions to select weeks. We find that scenario reduction methods struggle to find the appropriate investment levels for variable renewable generation and consequently produce more costly plans than scenario partitioning methods. While simple approximating methods perform well with larger models, covariance-based approximations have the best performance overall.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Multi-Channel Recovery for Distributed Quality Management of Synchrophasor
           Data

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      Authors: Gyul Lee;Seon Hyeog Kim;Do-In Kim;Austin White;Yong-June Shin;
      Pages: 2384 - 2396
      Abstract: This paper presents a distributed synchrophasor data management (DSDM) technique for wide-area monitoring systems. The proposed technique focuses on managing voltage/current phasor data measured from a specific target bus by defining neighboring phasor measurement units (PMUs). We developed an algorithm for searching neighboring PMUs by interpreting the power network topology as a graph structure. By exploiting the data of neighboring PMUs, we constructed a multi-channel structure to recover low-quality entries by using the parallel implementation of long short-term memory networks. A successive output decision-making rule automatically determines the final output, considering the occurrence of defective input, data quality of the measured data, and operational conditions. The proposed DSDM can complement existing centralized management techniques by preserving information on local dynamics. In particular, because of the multi-channel structure, DSDM can provide robust responses to defective data in neighboring PMUs, such as an unintended change in a network topology. We verified the effectiveness of the DSDM with real-world synchrophasor data as well as simulated data from the IEEE 68-bus test system.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Forecast Competition in Energy Imbalance Market

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      Authors: Jingshi Cui;Nan Gu;Tianyu Zhao;Chenye Wu;Minghua Chen;
      Pages: 2397 - 2413
      Abstract: Uncertainties in renewable generation make accurate load forecast essential for reliable power system operation. This paper considers energy imbalance market (EIM), where market players are allowed to procure energy ahead of time and trade the mismatch due to forecast error and strategic behaviors. The ISO sets the trading prices according to the market conditions, and pursues various system-level objectives. We first identify the power-law relationship between data volume and forecast accuracy, which enables the formulation of forecast cost model. Then, we cast the interactions in the EIM in the Stackelberg game framework with the ISO acting as the leader. We offer explicit subgame perfect equilibrium among the players in EIM, and derive the sufficient condition for the existence of unique equilibrium. Then, we show that this equilibrium, if exists, supports the maximal social welfare and under certain conditions, minimizes the total mismatch. We further examine the local and global impacts of the forecast errors under mild conditions, together with robustness analysis. Such analysis provides mechanism design guidelines for the ISO to enable the data sharing and forecast method sharing among market players in the EIM. Numerical studies further examine the effectiveness, robustness and sensitivity of the subgame.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Frequency Regulation From Electrified Railway

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      Authors: Zihan He;Can Wan;Yonghua Song;
      Pages: 2414 - 2431
      Abstract: With the improvement of traffic electrification, the coupling between power and transportation systems becomes more and more intensified. In this paper, electrified railway is firstly introduced into power system to support frequency regulation under high penetration of renewable energy sources. A novel frequency regulation framework consisting of day-ahead capacity estimation, frequency control parameters dispatch, and real-time response is constructed for electrified railway to provide primary frequency control support. Motion equations of electric trains under regulation are developed in detail. To fulfill the frequency regulation requirement and ensure punctual arrival, a multi-train joint response strategy is proposed to coordinate different electric trains for primary frequency control. The frequency regulation capacity estimation problem and frequency control power dispatch problem are established based on the multi-train joint response strategy. A novel sequential cutting plane algorithm is developed to linearly relax the nonlinear capacity estimation problem and power dispatch problem with ordinary differential equation constraints and iteratively tighten the relaxation area to improve solution optimality with desirable efficiency. Comprehensive numerical studies verify the effectiveness of the proposed primary frequency control framework, indicating the high potential of electrified railway to support frequency regulation of modern power systems.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Penalty-Based Volt/VAr Optimization in Complex Coordinates

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      Authors: Rabih A. Jabr;Izudin Džafić;
      Pages: 2432 - 2440
      Abstract: Volt/VAr Optimization (VVO) is becoming increasingly crucial in distribution management systems with renewable sources that require setting their reactive power in coordination with legacy voltage and reactive power control devices. This paper presents a VVO method that operates in complex variables based on the Wirtinger calculus; it employs a penalty method that keeps voltage magnitudes and controller parameters within limits together with a probabilistic rounding technique for handling switched device variables. The complex variable implementation permits using the compensation technique when solving the Karush-Kuhn Tucker (KKT) system, thus achieving a significant speedup due to limiting the number of linear system factorizations. The proposed method is contrasted with a classical VVO implementation employing discrete coordinate search from the current operating point and an enhanced version that uses sensitivity information. Numerical results on distribution networks having up to 3147 nodes show that the proposed method is significantly faster than classical methods and gives operating points free of voltage magnitude violations and lower power loss.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Semi-Supervised Ensemble Learning Framework for Accelerating Power System
           Transient Stability Knowledge Base Generation

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      Authors: Lipeng Zhu;David J. Hill;Chao Lu;
      Pages: 2441 - 2454
      Abstract: Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Internally Induced Branch-and-Cut Acceleration for Unit Commitment Based
           on Improvement of Upper Bound

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      Authors: Qian Gao;Zhifang Yang;Wotao Yin;Wenyuan Li;Juan Yu;
      Pages: 2455 - 2458
      Abstract: In this letter, we present an internally induced acceleration method for the branch-and-cut (B&C) process of unit commitment (UC) problem on the basis of general mixed-integer programming (MIP) solvers. The B&C process is internally guided by solving a small-scale induced MIP model formulated by combining the congestion management information with global upper and lower bounds. Based on the test cases of RTS-GMLC and practical utility data, the UC calculation can be accelerated by 2.29 and 2.06 times on average, respectively, with no influence on the solution optimality.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Can Center-of-Inertia Model be Identified From Ambient Frequency
           Measurements'

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      Authors: Andrey Gorbunov;Jimmy Chih-Hsien Peng;Janusz W. Bialek;Petr Vorobev;
      Pages: 2459 - 2462
      Abstract: This letter analyzes the difficulty of estimating power system inertia under ambient conditions using the Center-of-Inertia (CoI) system model. We show that the main obstacle to doing this is a difficulty in detecting a peak in the Power Spectral Density (PSD) of the frequency trace. This is due to a combination of two factors: (i) the Ornstein-Uhlenbeck (OU) process with a high mean-reversion time, which models the load disturbance and is mathematically equivalent to a low-pass filter with a high time constant; (ii) the CoI dominant mode is highly damped. This observation also explains why it is possible to estimate system inertia under ambient conditions using wide-area PMU measurements by exploiting information about inter-area oscillations which have lower damping than the dominant mode of the CoI model. We validated those findings by using the PSD of the actual 2-hour frequency trace for Great Britain from 00:00 to 02:00 of January 01, 2019.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Nonequilibrium Initialization: Seamless Connection Between Dynamic State
           Estimation and Dynamic Security Assessment

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      Authors: Shaobu Wang;Henry Huang;
      Pages: 2463 - 2466
      Abstract: With the integration of renewable energy and demand response, the behavior of power systems has become more fluctuating than ever before. This challenges traditional dynamic security assessment based on the “steady-state” assumption of current operating points. To address these challenges, we formulate here a dynamic state estimation (DSE)-based dynamic security assessment (DSA1) framework. In the new framework, we propose to use nonequilibrium initialization techniques to bridge DSE and DSA. Case studies demonstrate how the proposed nonequilibrium initialization can capture the dynamic that cannot be reflected in traditional static state-estimation–based security assessment.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • A Stochastic Response Surface Method Based Probabilistic Energy Flow
           Analysis Method for Integrated Electricity and Gas Systems

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      Authors: Yunpeng Jiang;Zhouyang Ren;Zhiyuan Sun;Wenyuan Li;Xin Yang;
      Pages: 2467 - 2470
      Abstract: An efficient probabilistic energy flow (PEF) analysis method is proposed for integrated electricity and gas systems (IEGSs) in this letter. A network decoupling based deterministic energy flow (DEF) calculation method is developed. A PEF analysis method is presented based on stochastic response surface method (SRSM). The computational efficiency and convergence performance of DEF and PEF are largely improved. The correctness and effectiveness of the proposed method are verified by using three test systems.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Sustained Oscillation Analysis of VSC Considering High-Order Oscillation
           Components

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      Authors: Yuzhi Wang;Liang Wang;Qirong Jiang;
      Pages: 2471 - 2474
      Abstract: Oscillations of grid-connected voltage source converter (VSC) usually reaches sustained state because of the limiters in controller. Describing function has been adopted to analyze the amplitude and frequency of the sustained oscillations, but high-order oscillation components caused by the limiter are ignored in previous literatures. This letter proposes an analysis method for sustained oscillations which considers high-order oscillation components. This method could obtain a more precise oscillation frequency and the amplitude of 1st, 2nd, and 3rd order components. The proposed method is verified by case studies and simulations in PSCAD/EMTDC.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • PMU-Based State Estimation for Networks Containing LCC-HVDC Links

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      Authors: Orestis Darmis;Georgios Karvelis;George N. Korres;
      Pages: 2475 - 2478
      Abstract: This letter proposes an equality-constrained weighted least squares (WLS) state estimation model for networks containing LCC-HVDC links. Voltage and current phasor measurements for the AC network are provided by phasor measurement units (PMUs) and DC voltage and current measurements for the DC transmission lines are acquired by appropriate DC sensors. AC/DC coupling equations and zero injections are treated as equality constraints. Simulation results for a 6-bus hybrid AC/DC network are shown to validate the proposed model and algorithm.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • An Improved Electromechanical Oscillation-Based Inertia Estimation Method

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      Authors: Bo Wang;Deyou Yang;Guowei Cai;Zhe Chen;Jin Ma;
      Pages: 2479 - 2482
      Abstract: The existing inertia estimation based on electromechanical oscillation relies on a single mode, which is limited in complex systems. In this letter, the inertia is estimated without worrying about the number of modes in the oscillation. The inertia expression is derived based on the swing equation in the frequency domain, which considers the mode coupling in the electromechanical bandwidth using a summation calculation. The effectiveness of the proposed inertia estimation method is validated through simulation data and real measurements.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Practical Charts for Sizing Neutral Grounding Elements for Machine-Based
           Distributed Energy Source Step-Up Transformers

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      Authors: Alexandre B. Nassif;Julio Romero Aguero;Ming Dong;
      Pages: 2483 - 2486
      Abstract: One of the challenges resulting from the adoption of machine-based distributed energy resources lies within the intersection of protection and performance grounding. These are strongly interrelated and conflicting subjects that dictate the occurrence of ground fault overvoltage and the effectiveness of ground overcurrent protection. Currently, there remains a gap in the distribution planning of most jurisdictions in formulating these aspects and adopting simple and practical measures to balance these subjects. This letter introduces a simulation-based method to develop a set of practical charts intended to be used as a sizing guideline for neutral grounding elements, which are to be employed in the associated step-up transformers. This solution became pivotal to the electrical utility conducting the research and has allowed planning engineers to simplify design and streamline cooperation between the utility and large-scale DER developers.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Impact of PLL Frequency Limiter on Synchronization Stability of Grid
           Feeding Converter

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      Authors: Junru Chen;Muyang Liu;Hua Geng;Terence O'Donnell;Federico Milano;
      Pages: 2487 - 2490
      Abstract: It is well known that grid-feeding converters that synchronize to the grid through a Phase-Locked Loop (PLL) can become unstable after a fault. An often-neglected element that plays an important role in the converter synchronization stability is the PLL frequency limiter. While it slows down the phase change during the fault, the frequency limiter also constrains the error of the PLL input, thus leading to a longer settling time. This letter investigates the mechanism of the converter synchronization stability caused by the frequency limiter and provides a taxonomy to evaluate its impact on the overall system dynamic response.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Estimation of Dynamic Grid Flexibility Using Matrix Perturbation Theory

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      Authors: Debargha Brahma;Nilanjan Senroy;
      Pages: 2491 - 2494
      Abstract: In this letter, a numerically-based method is proposed to estimate the metrics of dynamic grid flexibility, namely the inertial index, and the flexibility index. The novelty of the proposed method lies in using matrix perturbation theory, through which the sensitivity of a system to any perturbation is determined quantitatively. This notion is applied to the power system scenario under small disturbances, through which the inertial and the flexibility indices are calculated directly from the system matrix. This makes the proposed method immune to modeling complexities, and enables to implicitly understand the impact of any used generator or controller model. The applicability of the method is tested for the IEEE 39-bus system.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Erratum to “Theoretical Parameter Design Method of SFCL for Concurrent
           Commutation Failure Inhibition in SFCL-Segmented Multi-Infeed LCC-HVDC
           Systems”

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      Authors: Hao Xiao;Yinhong Li;Xianzhong Duan;
      Pages: 2495 - 2495
      Abstract: In the paper [1] below published in IEEE Trans. Power Syst., vol. 35, no. 3, pp. 1741–1757, May 2020.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
  • Introducing the IEEE PES Resource Center

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      Pages: 2496 - 2496
      Abstract: Advertisement, IEEE.
      PubDate: May 2022
      Issue No: Vol. 37, No. 3 (2022)
       
 
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