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)

NUCLEAR ENERGY (40 journals)

Showing 1 - 37 of 37 Journals sorted alphabetically
Atom Indonesia     Open Access  
Bulletin of the Atomic Scientists     Hybrid Journal   (Followers: 8)
CNL Nuclear Review     Partially Free  
Eksplorium : Buletin Pusat Pengembangan Bahan Galian Nuklir     Open Access  
EPJ Nuclear Sciences & Technologies     Open Access   (Followers: 3)
Fusion Science and Technology     Hybrid Journal   (Followers: 4)
Ganendra : Majalah IPTEK Nuklir     Open Access  
Hyperfine Interactions     Hybrid Journal   (Followers: 1)
IEEE Transactions on Sustainable Energy     Hybrid Journal   (Followers: 13)
International Journal of Advanced Nuclear Reactor Design and Technology     Open Access  
International Journal of Critical Infrastructure Protection     Hybrid Journal   (Followers: 4)
International Journal of Nuclear Energy Science and Engineering     Open Access   (Followers: 5)
International Journal of Nuclear Law     Hybrid Journal   (Followers: 3)
International Journal of Nuclear Safety and Security     Hybrid Journal   (Followers: 1)
International Journal of Nuclear Security     Open Access   (Followers: 1)
Journal of Nuclear Energy Science & Power Generation Technology     Hybrid Journal   (Followers: 2)
Journal of Nuclear Engineering & Technology     Full-text available via subscription   (Followers: 3)
Journal of Nuclear Science and Technology     Hybrid Journal   (Followers: 2)
Journal of Power Technologies     Open Access   (Followers: 6)
Journal of Radiation Research     Open Access   (Followers: 3)
Journal of the Physical Society of Japan     Hybrid Journal   (Followers: 2)
Kerntechnik     Full-text available via subscription  
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
Nano Energy     Open Access   (Followers: 11)
Nanomaterials and Energy     Hybrid Journal   (Followers: 1)
Nuclear Energy and Technology     Open Access   (Followers: 3)
Nuclear Engineering and Technology     Open Access   (Followers: 5)
Nuclear Materials and Energy     Open Access   (Followers: 1)
Nuclear Science and Engineering     Hybrid Journal   (Followers: 7)
Nuclear Science and Techniques     Full-text available via subscription  
Nuclear Technology     Hybrid Journal   (Followers: 5)
Nucleus     Open Access  
Nukleonika     Open Access  
Radiation Detection Technology and Methods     Hybrid Journal   (Followers: 1)
Tri Dasa Mega : Jurnal Teknologi Reaktor Nuklir     Open Access  
Urania Jurnal Ilmiah Daur Bahan Bakar Nuklir     Open Access  
World Journal of Nuclear Science and Technology     Open Access   (Followers: 4)
Similar Journals
Journal Cover
IEEE Transactions on Sustainable Energy
Journal Prestige (SJR): 2.318
Citation Impact (citeScore): 7
Number of Followers: 13  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1949-3029
Published by IEEE Homepage  [228 journals]
  • IEEE Transactions on Sustainable Energy Publication Information

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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: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • IEEE Industry Applications Society Information

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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: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • IEEE Transactions on Sustainable Energy 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: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Bayesian Actor-Critic Wave Energy Converter Control With Modeling Errors

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      Authors: Leila Ghorban Zadeh;Ali Shahbaz Haider;Ted K. A. Brekken;
      Pages: 3 - 11
      Abstract: This paper presents a comparison of a Reinforcement-Learning (RL) based wave energy conversion controller against standard reactive damping and model predictive control (MPC) approaches, in the presence of modeling errors. Wave energy converters (WECs) are under the influence of many non-linear hydrodynamic forces, yet for ease and expediency, it is common to formulate linear WEC models and control laws. Therefore it is expected that significant modeling errors may be present, which may degrade model-based control performance. Model-free RL approaches to control may offer a significant advantage in robustness to modeling errors, in that the model is learned by the controller by experience. It is shown that, for an annual average sea state, RL-based controllers can outperform model-based control – reactive control and MPC – by 19% and 16%, respectively, when significant modeling error is present. Furthermore, compared to similar studies of RL-based control, the proposed model can reduce the training time from 8.4 hr to 1.5 hr.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Assigning Shadow Prices to Synthetic Inertia and Frequency Response
           Reserves From Renewable Energy Sources

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      Authors: Luis Badesa;Carlos Matamala;Yujing Zhou;Goran Strbac;
      Pages: 12 - 26
      Abstract: Modern electricity grids throughout the world, particularly in islands such as Great Britain, face a major problem on the road to decarbonisation: the significantly reduced level of system inertia due to integration of Renewable Energy Sources (RES). Given that most RES such as wind and solar are decoupled from the grid through power electronics converters, they do not naturally contribute to system inertia. However, RES could support grid stability through appropriately controlling the converters, but currently no market incentives exist for RES to provide this support. In this paper we develop a methodology to optimally clear a market of ancillary services for frequency control, while explicitly considering the participation of grid-forming and grid-following inverter-based technologies. We propose a mathematical framework that allows to compute shadow prices for ancillary services offered by a pool of diverse providers: synchronous and synthetic inertia, enhanced frequency response (e.g. from curtailed RES) and traditional primary frequency response (e.g. by thermal generators). Several case studies are run on a simplified Great Britain system, to illustrate the applicability and benefits of this pricing scheme.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Determining the Required Frequency Control Reserve and Capacity and
           Location of Synchronous and Virtual Inertial Resources

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      Authors: Masoud Hajiakbari Fini;Mohamad Esmail Hamedani Golshan;José R. Martí;Abbas Ketabi;
      Pages: 27 - 38
      Abstract: Replacing conventional generating units with inverter-based generation has lowered the natural inertia of power grids and the primary frequency control reserve provided by synchronous generators. Some of the most important consequences include large frequency deviations and large rates of change of frequency (RoCoF) during sudden load and generation loss. Increasing the inertia of the grid through virtual inertia and synchronous machines, and fast primary frequency response are possible solutions for these problems. This paper proposes an optimization problem to determine the capacity of ultracapacitors and synchronous condensers installed at each bus for inertial response and also the optimal amount of fast primary frequency response while considering technical and economic metrics. A combinational model is proposed to calculate the values of these technical metrics, which results in acceptable accuracy and computational cost. Finally, a modified low-inertia version of the IEEE 39-bus test system is used to verify the effectiveness of the proposed method. The results show that the method is successful in keeping the RoCoF and frequency deviations within acceptable limits while reaching a compromise between technical and economic aspects.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • A Spatiotemporal Directed Graph Convolution Network for Ultra-Short-Term
           Wind Power Prediction

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      Authors: Zhuo Li;Lin Ye;Yongning Zhao;Ming Pei;Peng Lu;Yilin Li;Binhua Dai;
      Pages: 39 - 54
      Abstract: The expansion of wind generation and the advance in deep learning have provided feasibility for multisite wind power prediction motivated by spatiotemporal dependencies. This paper introduces a novel spatiotemporal directed graph convolution neural network to sufficiently represent spatiotemporal prior knowledge and simultaneously generate ultra-short-term multisite wind power prediction. At first, a spatial dependency-based directed graph is established to learn the intrinsic topology structure of wind farms taking sites as graph nodes and Granger causality-defined spatial relation as directed edges. Subsequently, a unified spatiotemporal directed graph learning model is presented by embedding the multi-scale temporal convolution network as a sub-layer into the improved graph convolution operator, where the temporal features of each node are extracted by the above sub-layer to capture time patterns with different lengths, and the improved graph convolution layer is introduced by redefining K-order adjacent nodes to further share and integrate the deep spatiotemporal knowledge on the graph containing temporal features. Finally, under a comprehensive training loss function, this method is capable of improving the accuracy of each site for 4h-ahead prediction along with decent robustness and generalization. Experiment results verify the superiority of the proposed model in spatiotemporal correlation representation compared with classic and advanced benchmarks.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • A Model-Adaptive Clustering-Based Time Aggregation Method for Low-Carbon
           Energy System Optimization

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      Authors: Yuheng Zhang;Vivian Cheng;Dharik S. Mallapragada;Jie Song;Guannan He;
      Pages: 55 - 64
      Abstract: Intermittent renewable energy resources like wind and solar introduce uncertainty across multiple time scales, from minutes to years, on the design and operation of power systems. Energy system optimization models have been developed to find the least-cost solution that manages the multi-timescale variability using an optimal portfolio of flexible resources. However, input data that capture such multi-timescale uncertainty are characterized with a long time horizon and high resolution, which brings great difficulty to solving the optimization model. Here we propose a model-adaptive time aggregation method based on clustering to alleviate the computational complexity, in which the energy system is solved over selected representative time periods instead of the full time horizon. The proposed clustering method is adaptive to various energy system optimization models or settings, because it extracts features from the optimization models to inform the clustering process. Results show that the proposed adaptive method can significantly lower the error in approximating the solution of the optimization model with the full time horizon, compared to traditional time aggregation methods.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Robust Tube-Based Model Predictive Control for Wave Energy Converters

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      Authors: Yujia Zhang;Guang Li;
      Pages: 65 - 74
      Abstract: This paper proposes an efficient robust tube-based model predictive control (RTMPC) strategy for energy-maximization control of wave energy converters (WECs) subject to constraints due to safety considerations. Compared with the existing MPC strategies developed for the WEC control problem, the RTMPC method provides an effective approach to explicitly handle plant-model mismatches with guaranteed constraint satisfaction, contributing to improved energy capture efficiency. The fundamental idea is to integrate disturbance invariant sets into the MPC scheme for energy-maximization control to form a tube-based predictive controller, which enhances the robustness of MPC for a WEC without increasing online computational complexity. The resulting RTMPC controller can bound the WEC plant trajectories in a tube centered around a nominal WEC model trajectory, and uncertainties from un-modeled WEC dynamics and unmeasured disturbances can be mitigated by an error feedback portion. Numerical simulations demonstrate the effectiveness of the proposed control strategy.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Optimal Semi-Active Control for a Hybrid Wind-Wave Energy System on Motion
           Reduction

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      Authors: Hongzhong Zhu;
      Pages: 75 - 82
      Abstract: Integration of wave energy converters and floating wind turbine can potentially reduce the levelized cost of energy. The coupling effects between the wave energy converters and the foundation, however, could amplify the foundation motion so that both the efficiency and the fatigue life of wind turbine would be degraded. In this study, wave energy converters are designed as a controlled suspension system for a novel hybrid wind-wave energy system, and an optimal semi-active control method having predictive ability is proposed for reducing the foundation motion. In the controller design, the physical constraints and the passivity of the wave energy converters are also taken into consideration. A full numerical model taking account of aero-hydro-servo-mooring coupled dynamics is proposed for a comprehensive analysis of the controller under various environmental conditions. Numerical results show that the foundation motion can be reduced by 16% even in very rough sea states with the help of the controller.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Hierarchical Software-Defined Control Architecture With MPC-Based Power
           Module to Interface Renewable Sources and Motor Drives

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      Authors: Liwei Zhou;Matthias Preindl;
      Pages: 83 - 96
      Abstract: A hierarchical control architecture is designed with model predictive control (MPC)-based power module for generalized renewable applications including single/three-phase grid, solar, battery, electric motor, etc. The hierarchical control architecture is composed of three layers: (1) Central control layer for mode recognition of different types of interfaced load/source, reconstruction of power converter topologies, high level current/voltage/power control, generating references for local power module control and grid services for utility support; (2) Local module control layer for implementing MPC algorithm to track references from central controller with improved dynamic performance, stabilizing common-mode voltage, collecting ADC samplings and generate PWM signals for local power switches; (3) Application layer for the interface with different types of renewable loads/sources including single/three-phase grid, solar, battery, electric motor and so on. The merits of the designed control architecture include: (1) the reconfigurability to be suitable for different types of applications; (2) all non-isolated topologies with common-mode noise attenuation capability for renewable energy interfaces; (3) improved dynamic performance by local MPC power module; (4) high accuracy and robustness of the multi-layer MPC-based control without being influenced by the parametric modeling error from various interfaced applications; (5) grid services for abnormal condition utility support. The experimental results verified the proposed hierarchical control architecture.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Decentralized Dispatch of Distributed Multi-Energy Systems With
           Comprehensive Regulation of Heat Transport in District Heating Networks

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      Authors: Qinghan Sun;Tian Zhao;Qun Chen;Kelun He;Huan Ma;
      Pages: 97 - 110
      Abstract: Distributed Energy Systems(DES) interconnected with Electric Power Network(EPN) and District Heating Network(DHN) have drawn great attention recently as they promote user-side coordination of multi-energy flows. However, the difference in physical nature between electric power transmission and heat transport has brought difficulties to the modelling and decentralized optimization. In this article, a new DHN model considering delay and storage features of pipeline heat migration and heat transfer between fluids is proposed through trigonometric expansion of the decision series and the heat current method. The model comprehensively characterizes the heat transport in the system and a dispatch problem considering hybrid regulation of fluid flow rates and temperatures in DHN is then established. A primal-decomposition-based decentralized gradient descent method in accompany with Alternating Direction Method of Multipliers(ADMM) is proposed to optimize the DESs in a fully decentralized manner. Case study on two test systems validates the effectiveness of the proposed model and method to further harness the potential of DHN, which reduce renewable energy curtailment by 17.3% and 27.0% respectively.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Robust Coordinated Optimization With Adaptive Uncertainty Set for a
           Multi-Energy Microgrid

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      Authors: Junjie Zhong;Yong Li;Yijia Cao;Yi Tan;Yanjian Peng;Yicheng Zhou;Yosuke Nakanishi;Zhengmao Li;
      Pages: 111 - 124
      Abstract: With the increasing integration of the multi-energy microgrid (MEM) with the distribution network (DN), the distributed coordination between MEM and DN becomes critical. This paper proposes a distributed scheduling method for the coupled MEM-DN under operational uncertainties. First, a worst-expectation min-max-max-min robust optimization model is formulated for MEM considering the uncertainties of renewable distributed generation (wind and photovoltaic) as well as its class probability. Second, the column and constraint generation algorithm with an alternating iteration strategy (C&CG-AIS) is proposed to accelerate the solution by decoupling the subproblems. Third, the multi-interval convex hull uncertainty set (MCHUS) is proposed to reduce the conservatism of robust optimization by decreasing the low-probability scenarios. Furthermore, the Bregman alternating direction method with multipliers (BADMM) is combined with the alternating optimization procedure (AOP) to overcome the convergence difficulty in the nonconvex distributed MEM-DN model. Finally, the effectiveness of the proposed model and method is verified by simulation tests based on IEEE-33 node DN and a park-level MEM.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • SSR Stable Wind Speed Range Quantification for DFIG-Based Wind Power
           Conversion System Considering Frequency Coupling

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      Authors: Chengmao Du;Xiong Du;Chenghui Tong;
      Pages: 125 - 139
      Abstract: The subsynchronous resonance (SSR) characteristics of doubly-fed induction generation-based wind power conversion system (DFIG-WPCS) varies with the wind speed. Obtaining the small signal SSR stable wind speed range is urgently important for wind farm dispatch planning. However, there are few methods to quantify the small signal SSR stable wind speed range of DFIG-WPCS. Moreover, the inherent frequency coupling when DFIG-WPCS is integrated to modular multilevel converter-high voltage direct current (MMC-HVDC) may lead to inaccurate stability assessment results. In this article, the frequency coupling mechanism between WPCS and MMC is revealed by analyzing the law of harmonic interaction in the interconnected system. The wind speed-frequency two-variable equivalent admittance model of DFIG-WPCS is established by taking the frequency coupling account. Relying on the two-variable open-loop transfer function, the multiple phase margin contours plot approach is proposed to quantify the small signal SSR stable wind speed range and damping properties. The effectiveness of two-variable admittance and analysis method are validated against electromagnetic transient (EMT) testbed with MATLAB /SimPowerSystems. The analysis results can provide a reference for the early planning and stable operation of DFIG-WPCS.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Distributionally Robust Chance-Constrained Optimal Transmission Switching
           for Renewable Integration

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      Authors: Yuqi Zhou;Hao Zhu;Grani A. Hanasusanto;
      Pages: 140 - 151
      Abstract: Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims to develop a robust optimal transmission switching (OTS) framework that can effectively relieve grid congestion and mitigate renewable curtailment. We formulate a two-stage distributionally robust chance-constrained (DRCC) problem that assures limited constraint violations for any uncertainty distribution within an ambiguity set. Here, the second-stage recourse variables are represented as linear functions of uncertainty, yielding an equivalent reformulation involving linear constraints only. We utilize moment-based (mean-mean absolute deviation) and distance-based ($infty$-Wasserstein distance) ambiguity sets that lead to scalable mixed-integer linear program (MILP) formulations. Numerical experiments on the IEEE 14-bus and 118-bus systems have demonstrated the performance improvements of the proposed DRCC-OTS approaches in terms of guaranteed constraint violations and reduced renewable curtailment. In particular, the computational efficiency of the moment-based MILP approach, which is scenario-free with fixed problem dimensions, has been confirmed, making it suitable for real-time grid operations.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Power Decoupling Strategy for Voltage Modulated Direct Power Control of
           Voltage Source Inverters Connected to Weak Grids

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      Authors: Zhen Gong;Chengxi Liu;Lei Shang;Qiupin Lai;Yacine Terriche;
      Pages: 152 - 167
      Abstract: The grid voltage modulated direct power control (GVM-DPC)-based inverter is an attractive solution to regulate the instantaneous real and reactive powers injected into power grids. However, the power coupling in GVM-DPC, i.e., interactions between the real and reactive power loops, may deteriorate its transient performance. In this paper, a power decoupling strategy for GVM-DPC is proposed based on a dynamic feedforward power compensation (DFPC) algorithm. It is proved that the proposed strategy has better decoupling performance than that of the traditional virtual impedance method (VIM). Firstly, power coupling characteristics in GVM-DPC are analyzed, indicating that the ignorance of voltage angle variation at the point of common coupling (PCC) will result in severe power coupling. Then, power coupling magnitudes are derived according to relationships among the real power, reactive power and PCC voltage. Next, the coupling magnitudes are compensated into the power control loop of the GVM-DPC for the better decoupling performance. The transient performance of inverter using DFPC is studied based on the impedance analysis. Also, the relationship between coupling magnitude and grid side impedance is studied, which indicates the limited power decoupling capability of VIM. Finally, the proposed method is validated by the simulations and a hardware-in-loop system.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Accounting for Environmental Conditions in Data-Driven Wind Turbine Power
           Models

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      Authors: Ravi Pandit;David Infield;Matilde Santos;
      Pages: 168 - 177
      Abstract: Continuous assessment of wind turbine performance is a key to maximising power generation at a very low cost. A wind turbine power curve is a non-linear function between power output and wind speed and is widely used to approach numerous problems linked to turbine operation. According to the current IEC standard, power curves are determined by a data reduction method, called binning, where hub height, wind speed and air density are considered as appropriate input parameters. However, as turbine rotors have grown in size over recent years, the impact of variations in wind speed, and thus of power output, can no longer be overlooked. Two environmental variables, namely wind shear and turbulence intensity, have the greatest impact on power output. Therefore, taking account of these factors may improve the accuracy as well as reduce the uncertainty of data-driven power curve models, which could be helpful in performance monitoring applications. This paper aims to quantify and analyse the impact of these two environmental factors on wind turbine power curves. Gaussian process (GP) is a data-driven, nonparametric based approach to power curve modelling that can incorporate these two additional environmental factors. The proposed technique's effectiveness is trained and validated using historical 10-minute average supervisory control and data acquisition (SCADA) datasets from variable speed, pitch control, and wind turbines rated at 2.5 MW. The results suggest that (i) the inclusion of the additional environmental parameters increases GP model accuracy and reduces uncertainty in estimating the power curve; (ii) a comparative study reveals that turbulence intensity has a relatively greater impact on GP model accuracy, together with uncertainty as compared to blade pitch angle. These conclusions are confirmed using performance error metrics and uncertainty calculations. The results have practical beneficial consequences for O&M related activit-es such as early failure detection.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Three Solution Approaches to Stochastic Multi-Period AC Optimal Power Flow
           in Active Distribution Systems

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      Authors: Muhammad Usman;Florin Capitanescu;
      Pages: 178 - 192
      Abstract: This paper proposes three novel solution approaches (A1, A2, A3) to solve stochastic multi-period AC optimal power flow (S-MP-OPF) for day-ahead flexibility procurement from distributed energy resources (DER) in active distribution systems (ADSs). The S-MP-OPF is a mixed-integer nonlinear programming (MINLP) problem due to the binary variables modeling the operation of storage devices and flexible loads. The proposed three approaches have a shared common first step, which resorts to a new mixed-integer linear programming (MILP) model approximation of the S-MP-OPF problem. The MILP model employs second-order Taylor series expansion of trigonometric terms and formulates the linear approximations relying on variables such as square of voltage magnitude and voltage angle difference. This first step serves also the purpose of fixing the binary variables to the values computed by the MILP problem. Then, the approach A1 only checks the AC feasibility of MILP solution while approaches A2 and A3 further optimize continuous variables. Specifically, the sophisticated heuristic approach A2 employs sequential linear programming and AC power flow while the approach A3 models and solves directly the remaining nonlinear programming (NLP) problem. The performances of these approaches, a benchmark MINLP solver, and a state-of-the-art method are thoroughly compared in three radial or weakly meshed ADSs of 34, 31, and 191 nodes, respectively. The numerical results indicate that, albeit the approach A2 performs best overall, these approaches present distinct accuracy vs speed trade-offs, which make them suitable to problems of different sizes and diverse accuracy or speed requirements.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Two-Stage Hybrid Deep Learning With Strong Adaptability for Detailed
           Day-Ahead Photovoltaic Power Forecasting

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      Authors: Jianjing Li;Chenghui Zhang;Bo Sun;
      Pages: 193 - 205
      Abstract: Deep learning (DL) has been widely used in photovoltaic (PV) power forecasting due to its advantages in nonlinear processing and feature extraction. However, it faces an overfitting challenge when daily weather change, resulting in poor adaptability for practical applications. Conversely, the mapping strategy primarily applied in plant sacrifices the PV power fluctuation details for forecasting under complex weather conditions to improve adaptation. To keep long-term adaptations and accurately forecast fluctuation detail, this study proposes a novel two-stage hybrid DL (HDL) framework for day-ahead PV power forecasting with 15-min intervals. In the first numerical weather prediction (NWP) information mapping stage, motivated by forecasting strategies in plants, a mapping model is developed based on long short-term memory (LSTM) networks to forecast the general power trends with strong adaptation. Then, in the second stage of historical features (adjacency and similarity features) extraction, wavelet decomposition, LSTM, and a convolutional neural network are used to forecast more fluctuation details accurately. Moreover, a hyperparameter optimization method based on grid search and Bayesian optimization is proposed, which facilitates an unbiased development of DL. The developed framework is implemented on 10 MW and 100 kW plants and the results show that the proposed method can achieve correctness and adaptation among the rivals.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • A Distributed Control Strategy for State-of-Charge Balance of Energy
           Storage Without Continuous Communication in AC Microgrids

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      Authors: Jinghang Lu;Xiaojie Liu;Xiaochao Hou;Peng Wang;
      Pages: 206 - 216
      Abstract: With the high penetration of renewable energy sources (RES), the energy storage system (ESS) units have been employed as critical components to compensate for the power fluctuation generated by RESs in an ac microgrid. However, it's a major challenge to achieve the state-of-charge (SoC) balance of ESS units due to the difference of initial SoC values and varied capacities. To deal with this issue, a distributed event-triggered control strategy has been proposed to realize the SoC balance among ESS units by regulating the virtual resistances. Compared with existing methods, only the local SoC of each energy storage is exchanged with the neighboring ESS units. Furthermore, by employing the proposed event-triggered mechanism, the data sampling and signal transmission are only conducted when the predefined triggering condition is satisfied, which effectively saves the usage of communication resources. Moreover, the stability and inter-event interval with the proposed control strategy is analyzed in this paper. Finally, several case studies are presented in the experiment where the presented approach shows its robustness against dynamic load and unexpected single-point communication failure.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Analytical Modeling and Control of Grid-Scale Alkaline Electrolyzer Plant
           for Frequency Support in Wind-Dominated Electricity-Hydrogen Systems

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      Authors: Chunjun Huang;Yi Zong;Shi You;Chresten Træholt;
      Pages: 217 - 232
      Abstract: Integration of the hydrogen sector into power systems via electrolyzers could greatly facilitate energy sustainability by producing green hydrogen, moreover, assist in reinforcing frequency stability based on optimal control of electrolyzers. In order to improve the frequency performance of the wind-dominated electricity-hydrogen systems (WEHS), this paper investigates how to analytically evaluate and implement the dynamic frequency regulation (DFR) supported by a grid-scale alkaline electrolyzer (AEL) plant. The DFR's potential of the AEL plant is released by an emulated power-frequency characteristic, and its contributions to improving system frequency response are also analytically evaluated from the aspects of transient, steady-state, and stability indicators. Furthermore, the DFR dependency on the frequency containment reserve provision and pre-contingency operating points of AELs are analytically clarified. A DFR-driven controller to coordinate multiple modules in the AEL plant is also designed in detail. In addition, the economic analysis is presented to evaluate the profitability of AELs providing frequency support. Cases of a simple WEHS and a modified IEEE 9-bus system integrated with grid-scale AEL plants during frequency contingencies are studied to validate the proposed DFR method, which can improve the system frequency response with smoother transients and lower steady-state deviation.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • A Three-Stage Multi-Energy Trading Strategy Based on P2P Trading Mode

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      Authors: Jie Yang;Wenya Xu;Kai Ma;Conghui Li;
      Pages: 233 - 241
      Abstract: This paper proposes a three-stage multi-energy sharing strategy for a gas-electricity integrated energy system (IES). It aims to solve the multi-energy imbalance problem among energy hubs (EHs) based on the peer-to-peer (P2P) trading mode. First, considering the characteristics of multi-energy coupling and conversion, the quantity of shareable energy is determined for EHs that participate in the P2P trading. Furthermore, EHs conduct multi-bilateral negotiations based on the Raiffa-Kalai-Smorodinsky bargaining solution (RBS) to determine the optimal energy trading price. Finally, the buyer agent and the seller agent will design the optimal energy sharing trading strategy for all EHs. Moreover, the results show that the pricing mechanism improves the fairness of satisfaction obtained by the EHs from the utility distribution, and the social welfare of the system is improved, which proves that the three-stage multi-energy sharing trading strategy is effective.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Mitigating Subsynchronous Oscillation Using Model-Free Adaptive Control of
           DFIGs

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      Authors: Xi Wu;Shanshan Xu;Xingyu Shi;Mohammad Shahidehpour;Mengting Wang;Zhiyi Li;
      Pages: 242 - 253
      Abstract: Incorporating subsynchronous damping controllers (SSDCs) into the control loops of doubly-fed induction generators (DFIGs) is one of the most cost-effective methods to suppress subsynchronous oscillation (SSO). However, it is difficult to achieve satisfactory control performance of SSDCs due to the time-varying structure and operating conditions of wind integrated power systems. In this paper, an improved model-free adaptive control (MFAC) method is developed for DFIGs to overcome the limitations of conventional methods for SSO mitigation. First, the structure of the MFAC-based SSDC (M-SSDC) for DFIGs is designed. Then, an improved MFAC predictive control algorithm is proposed to achieve SSO mitigation. Moreover, the stability of the closed-loop system with the proposed M-SSDC is analyzed, which provides some guidelines for determining controller parameters. Additionally, the input-output signal pair of M-SSDC is selected by using geometric measure for the optimum damping performance. Case studies under various operating conditions of the test power system validate the effectiveness of the proposed M-SSDC as well as its superior damping performance over conventional approaches.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Multistage Mixed-Integer Robust Optimization for Power Grid Scheduling: An
           Efficient Reformulation Algorithm

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      Authors: Haifeng Qiu;Wei Gu;Chao Ning;Xi Lu;Pengxiang Liu;Zhi Wu;
      Pages: 254 - 271
      Abstract: The penetration of renewables into power systems is gradually increasing, but its inherent uncertainty poses tremendous challenges to the coordinated operation of power grids. To overcome the demerits of canonical two-stage mixed-integer robust optimization (RO) method that neglects the nonanticipativity requirement and the computational burden in engineering applications, this paper offers a reformulated multistage mixed-integer RO method for regional power grid scheduling. Firstly, a multistage mixed-integer RO scheduling model is established considering the scheduling requests in real-world projects. The scheduling plans under the nominal scenario are determined ahead of uncertainty in the first-stage optimization, and the multistage max-min optimization with binary recourse variables is then executed for feasibility-checking with respect to the nonanticipative realization of uncertainty. Secondly, a dedicated reformulation algorithm is proposed for this intractable multistage mixed-integer RO model. Based on the implicit affine strategy, the multistage max-min optimization is equivalently encapsulated to a single-stage max-min problem, and the dual Fourier-Motzkin elimination is put forward to eliminate both continuous and binary recourse variables in the resulting feasibility-checking optimization. Therefore, the original multistage mixed-integer RO is finally recast as a mixed-integer linear programming that can be solved directly. Numerical tests on an actual 16-bus grid, the IEEE 118-bus system and the 319-bus provincial grid verify the superiority and applicability of the proposed reformulated mixed-integer RO scheduling method, which is of great significance in guiding system operations.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Discrete-Time Distributed Secondary Control for DC Microgrids via Virtual
           Voltage Drop Averaging

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      Authors: Lantao Xing;Yang Qi;Xiao-Kang Liu;Changyun Wen;Meiqin Liu;Yu-Chu Tian;
      Pages: 272 - 282
      Abstract: DC microgrids have been widely applied in industrial applications recently due to their robust reliability, high efficiency, and flexible accessibility to sustainable energy resources. Various distributed secondary control methods have been developed for DC microgrids to achieve current sharing and voltage restoration. However, the majority of these methods are presented with continuous-time controllers while digital controllers in real systems are in discrete time. Considering the inherent discrete-time property in practical implementations, this paper proposes a discrete-time distributed secondary control strategy. Using the concept of “virtual voltage drop,” the proposed controller guarantees flexible current sharing and ensures accurate voltage regulation. Moreover, the proposed strategy allows for both resistive loads (linear loads) and constant power loads (CPLs, nonlinear loads). Both simulation and experiments are conducted to demonstrate the presented strategy.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • V-Iq Based Control Scheme for Mitigation of Transient Overvoltage in
           Distribution Feeders With High PV Penetration

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      Authors: Amin Amanipoor;Mohammad Sadegh Golsorkhi;Navid Bayati;Mehdi Savaghebi;
      Pages: 283 - 296
      Abstract: High penetration of rooftop photovoltaic (PV) systems in distribution feeders can give rise to overvoltage during peak generation hours. A well-known solution for this problem is Volt-VAR control method, in which PV inverters mitigate the overvoltage by reactive power absorption. The conventional Volt-VAR scheme calculates the reactive power reference based on a limited bandwidth V-Q droop controller. Although the conventional method is capable of steady-state voltage regulation, it is not effective against voltage overshoots caused by fast solar irradiance variations. In this paper, a new V-Iq based Volt-VAR control scheme is presented to suppress the transient voltage overshoots during cloud passing. In this method, the bandwidth of the Volt-VAR controller is increased to enhance its responsiveness to voltage variations. To prevent instability caused by unwanted interaction between the high bandwidth Volt-VAR controller and the voltage feedforward section of the inner control loop, a low pass filter is added to the feedforward path. Small signal analysis of the proposed method provides a guideline for designing the bandwidths of Volt-VAR controller and feedforward filter. The stability of the proposed method is assessed based on Lyapunov method. Hardware in the Loop (HIL) experimental results are presented to validate the proposed scheme.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Unrolled Spatiotemporal Graph Convolutional Network for Distribution
           System State Estimation and Forecasting

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      Authors: Huayi Wu;Zhao Xu;Minghao Wang;
      Pages: 297 - 308
      Abstract: Timely perception of distribution system states is critical for the control and operation of power grids. Recently, it has been seriously challenged by the dramatic voltage fluctuations induced by high renewables. To address this issue, an Unrolled Spatiotemporal Graph Convolutional Network (USGCN) is proposed for distribution system state estimation (DSSE) and forecasting with augmented consideration of the underlying complex spatiotemporal correlations of renewable energy sources (RES). Specifically, the interconnection among individual spatial graphs of adjacent time steps will lead to an unrolled spatiotemporal graph and benefit the synchronous capture of spatial and temporal correlations to achieve enhanced accuracy. On top of this, the node-embedding technique is employed in the unrolled spatiotemporal convolutional layer to reveal the hidden nonlinear spatiotemporal correlations of RES outputs without relying on full prior knowledge. Moreover, the proposed USGCN stacks the unrolled spatiotemporal convolutional layers, leading to the perception of longtime correlations to obtain effective ahead-of-time state forecasting results robustly. The simulation results have been provided to verify the accuracy and efficiency of the proposed model in 118-node and 1746-node distribution systems.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Identifying Differential Scheduling Plans for Microgrid Operations Under
           Diverse Uncertainties

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      Authors: Haifeng Qiu;Hoay Beng Gooi;
      Pages: 309 - 324
      Abstract: To satisfy the differential operation requirements of microgrids under the nominal and uncertain scenarios, a novel three-stage close-looped robust optimization (TSCL-RO) method is proposed to obtain more practical scheduling plans. In the first stage, the fixed startup and shutdown plans are identified considering both the cutting planes from the nominal and uncertain scenarios. According to the startup/shutdown schemes, the decision-making of the basic flexible plans under the nominal scenario is performed to minimize the operation cost in the second stage considering the second-order cone relaxed distflow model. To confront the disturbances from the power and N-k uncertainties, the basic flexible variables are revised to capture the worst-case scenario via a max-min bi-level optimization in the third stage, and the derived results are returned as feasibility cuts to preserve the robustness of the fixed plans. To solve this intractable TSCL-RO model proficiently, a tailored bi-layer chaining decomposition algorithm is further devised to handle the resulting multi-level mixed-integer second-order cone programming (MISOCP) via alternate iterations. Finally, numerical simulations verify the applicability and superiority of the investigated TSCL-RO model and the decomposition algorithm.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Coordination of Neighboring Active Distribution Networks Under Electricity
           Price Uncertainty Using Distributed Robust Bi-Level Programming

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      Authors: Omid Homaee;Arsalan Najafi;Michal Jasinski;Georgios Tsaousoglou;Zbigniew Leonowicz;
      Pages: 325 - 338
      Abstract: Distributed energy resources transform the passive distribution networks into active distribution networks (ADNs). Coordinating the dispatch actions of distributed resources has been studies in the literature, both within an ADN and between an ADN and the transmission system. However, the direct coordination between ADNs interconnected via a physical tie line is a topic boldly under-discussed, despite its practical relevance. In this paper, we consider the problem of coordinating the dispatch actions, including the energy exchange, of two interconnected ADNs, each one integrated with flexible loads (managed by demand response aggregators), energy storage systems, and microturbines (MTs). The bilateral energy trading enables the neighboring ADNs to benefit from the difference in locational marginal prices. The coordination problem is formulated as a robust bi-level program under price uncertainty. At the upper level, the total cost of the ADNs is minimized subject to the uncertainty of electricity market prices and technical constraints of the networks and the resources. At the lower level, the DR aggregators present at each ADN selfishly minimize their own cost. Moreover, the worst case realization of wholesale electricity market prices is considered. The problem is linearized using the Karush–Kuhn–Tucker (KKT) conditions and decomposed using the alternating direction method of multipliers (ADMM). Simulation results verify the convergence behavior of the proposed method and quantify the value of DSO-DSO coordination in the presence of an interconnecting line between the ADNs.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Tri-Level Multi-Energy System Planning Method for Zero Energy Buildings
           Considering Long- and Short-Term Uncertainties

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      Authors: Qirun Sun;Zhi Wu;Wei Gu;Xiao-Ping Zhang;Pengxiang Liu;Guangsheng Pan;Haifeng Qiu;
      Pages: 339 - 355
      Abstract: To effectively supply the multi-energy loads and achieve the annual zero energy targets of zero energy buildings (ZEBs) throughout the planning horizon, this paper proposes a tri-level multi-energy system planning method for ZEBs considering both long- and short-term uncertainties. In the upper level, the optimal goal is to obtain an optimal device sizing scheme within the electric-thermal-hydrogen integrated multi-energy system (EHT-MES). The middle and lower levels tackle the long-term temperature change and short-term source-load uncertainties separately. For the former, a set of representative scenarios that include typical and extreme weather conditions are generated from the future temperature forecast dataset, and an ambiguous set is utilized to model the uncertain probability distributions of the scenario set. For the latter, to guarantee the reliable and economic operation of ZEBs under different seasonal-daily patterns, a hybrid stochastic and robust optimization (HSRO) method is applied to deal with short-term uncertainties from solar radiation, wind output, outdoor temperature, electric loads, and hot water loads. A reformulation method is proposed to transform the multi-level coupling planning model into an equivalent and tractable form, and an improved column-and-constraint generation (C&CG) algorithm is developed to solve the recast model. Simulation results verify the effectiveness of the proposed planning method in deploying ZEBs’ multi-energy devices and immunizing against multiple timescale uncertainties.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Distributed Coordination of Charging Stations Considering Aggregate EV
           Power Flexibility

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      Authors: Dongxiang Yan;Chengbin Ma;Yue Chen;
      Pages: 356 - 370
      Abstract: In recent years, electric vehicle (EV) charging stations have witnessed rapid growth. However, effective management of charging stations is challenging due to individual EV owners' privacy concerns, competing interests of different stations, and the coupling distribution network constraints. To cope with this challenge, this paper proposes a two-stage scheme. In the first stage, the aggregate EV power flexibility region is derived by solving an optimization problem. We prove that any trajectory within the obtained region corresponds to at least one feasible EV dispatch strategy. By submitting this flexibility region instead of the detailed EV data to the charging station operator, EV owners' privacy can be preserved and the computational burden can be reduced. In the second stage, a distributed coordination mechanism with a clear physical interpretation is developed considering the AC power flow based network constraints. We prove that the proposed mechanism converges to the centralized optimum. Case studies validate the theoretical results. Comprehensive performance comparisons are carried out to demonstrate the advantages of the proposed scheme.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Optimized Active Power Dispatching of Wind Farms Considering Data-Driven
           Fatigue Load Suppression

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      Authors: Qi Yao;Bo Ma;Tianyang Zhao;Yang Hu;Fang Fang;
      Pages: 371 - 380
      Abstract: The active power fluctuation of wind turbines is not only related to their friendliness to the grid, but also to their fatigue damage. In this paper, the active power of wind turbines in wind farms is optimally scheduled to achieve the suppression of fatigue load of wind turbines. Considering the complexity of fatigue load calculation, it is difficult to apply to real-time active scheduling using metrics that directly characterize fatigue load. To address this problem, a data-driven modeling method for wind turbine fatigue based on deep neural network (DNN) is proposed in this paper, and the relationship between wind speed, power and other easily measurable parameters and fatigue load is established. Further, an improved multi-objective grey wolf optimizer (MOGWO) is designed to achieve the wind farm active scheduling process with the data-driven fatigue calculation results as the optimization objective. The results show that: The fatigue load prediction model of data-driven fatigue calculation proposed in this paper has a satisfactory effect, and the fatigue load of wind turbines can be effectively reduced by adjusting the active power.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Hydropower Aggregation by Spatial Decomposition—An SDDP Approach

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      Authors: Arild Helseth;Birger Mo;
      Pages: 381 - 392
      Abstract: The balance between detailed technical description, representation of uncertainty and computational complexity is central in long-term scheduling models applied to hydro-dominated power system. The aggregation of complex hydropower systems into equivalent energy representations (EER) is a commonly used technique to reduce dimensionality and computation time in scheduling models. This work presents a method for coordinating the EERs with their detailed hydropower system representation within a model based on stochastic dual dynamic programming (SDDP). SDDP is applied to an EER representation of the hydropower system, where feasibility cuts derived from optimization of the detailed hydropower are used to constrain the flexibility of the EERs. These cuts can be computed either before or during the execution of the SDDP algorithm and allow system details to be captured within the SDDP strategies without compromising the convergence rate and state-space dimensionality. Results in terms of computational performance and system operation are reported from a test system comprising realistic hydropower watercourses.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • An Adaptive Optimal Scheduling Strategy for Islanded Micro-Energy Grid
           Considering the Multiple System Operating States

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      Authors: Yi Yang;Ping Yang;Zhuoli Zhao;Yufeng Tang;Loi Lei Lai;
      Pages: 393 - 408
      Abstract: Various disturbances can lead to frequency deviations and multiple operating states of the islanded micro-energy grid (MEG) system. However, the existing research on optimal scheduling of MEG does not perform optimal scheduling based on the frequency deviation division, which is difficult to adapt to the variable operating states of MEG. Therefore, this paper proposes an adaptive optimal scheduling strategy for islanded MEG based on the classification of the frequency deviation. Firstly, the MEG is divided into four operating states according to the frequency deviation: frequency normal, frequency alert, frequency emergency, and frequency collapse. Secondly, a multi-objective optimization model of the MEG is established considering the frequency deviation, operating cost, and user satisfaction. Then a method of adaptively adjusting the weight coefficients of each sub-objective is proposed using adaptive scheduling rules which are defined according to the frequency deviation. By classifying the system state using the frequency, the proposed strategy can avoid load shedding during normal operation and quickly restore stable operation during an emergency state, realizing the stable operation and economic dispatch of the MEG under different frequency deviations. In addition, the stability of the system is analyzed using zero-poles. Finally, the simulation results verify the feasibility of the proposed strategy.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Increasing PV Hosting Capacity With an Adjustable Hybrid Power Flow Model

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      Authors: Luomeng Zhang;Hongxing Ye;Fei Ding;Zuyi Li;Mohammad Shahidehpour;
      Pages: 409 - 422
      Abstract: Physical constraints must be enforced when distributed energy resources, such as PV, are integrated into distribution network. Hosting capacity (HC) is thus introduced to define the maximum renewable generation that distribution system can accommodate. When the grid is further pushed towards decarbonization, improving HC becomes even more important. This work presents a hybrid adjustable power flow model to increase the uncertainty-proof HC, aiming to securely and cost-effectively utilize flexible resources. We propose a novel hybrid relaxation approach to convexifying a two-stage AC model with variable uncertainty set. An iterative algorithm is designed to solve the problem. We perform the case study in a single-phase 141-node system and a three-phase 33-bus system. The proposed approach shows promising performance in accuracy, convergence, and robustness.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Optimized Voltage Search Algorithm for Fast Global Maximum Power Point
           Tracking in Photovoltaic Systems

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      Authors: Zhuoya Sun;Yohan Jang;Sungwoo Bae;
      Pages: 423 - 441
      Abstract: This paper introduces an improved maximum power point tracking (MPPT) algorithm, the optimized voltage search algorithm (OVS). The OVS is used to search for the global maximum power point (GMPP) under partial shading conditions (PSCs) for photovoltaic (PV) systems. The proposed algorithm aims to optimize the search areas of the power–voltage curve and can be implemented by determining different search areas during the GMPP tracking. After determining these search areas, the proposed algorithm optimally searches these areas with multiple forward and optional backward processes as required. These multiple forward processes can skip more unnecessary voltage intervals than other MPPT methods, thereby reducing voltage search intervals and GMPP tracking time. The performance of the proposed algorithm was evaluated against seven other MPPT algorithms: the search-skip-judge, section-dividing point (SDP), skipping adaptive perturbation and observation, modified maximum power trapezium (M-MPT), high-performance, voltage track optimizer, and plain hybrid M-MPT and SDP algorithms. These MPPT algorithms were validated on the basis of simulation and experimental results, which demonstrated that the proposed algorithm exhibited a shorter tracking time, fewer computational iterations, shorter voltage scanning intervals, and longer voltage skipping intervals than those of the other algorithms under PSCs.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Clustering Wind Turbines for SCADA Data-Based Fault Detection

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      Authors: Bojian Du;Yoshiaki Narusue;Yoko Furusawa;Nozomu Nishihara;Kentaro Indo;Hiroyuki Morikawa;Makoto Iida;
      Pages: 442 - 452
      Abstract: Condition monitoring systems are commonly employed for incipient fault detection of wind turbines (WTs) to reduce downtime and increase availability. Data from supervisory control and data acquisition (SCADA) systems offer a potential monitoring solution. Multi-turbine approaches, which merge variables recorded from different WTs on the same wind farm, have been developed to improve fault detection performance by reducing the influence of variations in environmental conditions. However, in complex terrain, environmental conditions vary among WTs. Moreover, manual control (e.g., maintenance and curtailment) can also raise false alarms in some WTs. Here, the false alarm characteristics of a wind farm in complex terrain are investigated. A clustering-based multi-turbine fault detection approach is proposed, consisting of three steps: WT clustering, single-turbine modeling, and fault indicator calculation. First, k-medoids clustering with dependent multivariate dynamic time warping is applied for WT clustering. Then, an autoregressive neural network is used to construct a single-turbine model. Finally, residuals between median values of the model output of all WTs in the same cluster and the target WT are used to calculate the anomaly level. Evaluation results for real large-scale SCADA data confirm that the proposed approach raises fewer false alarms without degrading detection performance.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Hierarchical Distributed Energy Management Framework for Multiple
           Greenhouses Considering Demand Response

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      Authors: Ehsan Rezaei;Hanane Dagdougui;Kianoosh Ojand;
      Pages: 453 - 464
      Abstract: Greenhouses are a key component of modernised agriculture, aiming for producing high-quality crops and plants. Furthermore, a network of greenhouses has enormous potential as part of demand response programs. Saving energy during off-peak time, reducing power consumption and delaying the start time of subsystems during on-peak time are some strategies that can be used to limit power exchanged with the main grid. In this work, a hierarchical distributed alternating direction method of multipliers-based model predictive control framework is proposed that has two main objectives: 1) providing appropriate conditions for greenhouses' crops and plants to grow, and 2) limiting the total power exchanged with the main grid. At each time step in the framework, an aggregator coordinates the greenhouses to reach a consensus and limit the total electric power exchanged while managing shared resources, e.g., reservoir water. The proposed framework's performance is investigated through a case study.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Storage Right-Based Hybrid Discrete-Time and Continuous-Time Flexibility
           Trading Between Energy Storage Station and Renewable Power Plants

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      Authors: Bo Zhou;Jiakun Fang;Xiaomeng Ai;Shichang Cui;Wei Yao;Zhe Chen;Jinyu Wen;
      Pages: 465 - 481
      Abstract: Renewables, widely regarded as the predominant energy in the future, have primary responsibility for future power supply adequacy and thus are becoming the main flexibility demander considering their self-induced uncertainties. This paper proposes a novel storage right-based hybrid discrete-time and continuous-time (HT) flexibility trading between energy storage station (ESS) and renewable power plants (RPP), where ESS sells flexibility for profits and RPPs buy flexibility to hedge power supply shortage risks. The flexibility package (FP), composed of energy/power/ramping capacity rights and stored energy sell/recycle, is proposed as the trading product to reach a higher profit for ESS and to provide a more customized selection for RPPs. The HT bilevel optimization is proposed for flexibility trading, where the upper-level discrete-time (DT) optimization decides the arbitrage schedule and FP price of ESS and the lower-level continuous-time (CT) optimization decides the bidding strategy and FP order of each RPP. The enhanced solution space transformation and the KKT condition method are utilized to reduce the HT bilevel optimization into common DT optimization which is then linearized into mixed-integer linear programming for tractable calculation. Case studies validate the higher profits of ESS and the lower power supply shortage risks of RPPs under the proposed method.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Probabilistic Load Flow for Wind Integrated Power System Considering Node
           Power Uncertainties and Random Branch Outages

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      Authors: Vikas Singh;Tukaram Moger;Debashisha Jena;
      Pages: 482 - 489
      Abstract: This paper proposes an analytical probabilistic load flow (PLF) approach that considers conventional generator outages, load variability, and random branch outages. The branch outages are modeled as 0-1 distributions of fictitious power injections at the appropriate nodes. The distribution of state variables and line power flows is then obtained using a combined Cumulant and Gram-Charlier series expansion approach. The proposed PLF performs contingency sequencing with fuzzy logic to eliminate random line checking and avoid masking mistakes faced by performance index-based algorithms. The Jacobian inverse calculation in the traditional Cumulant method is eliminated to conserve storage space and speed up the computation using the Gauss-Jordan method. The correlations among loads and wind power generations has been modeled using the Nataf transformation process. Results of 24-bus and 259-bus equivalent systems of the Indian southern and western power grids are analyzed and validated with those obtained using the Monte Carlo simulation method. The suggested method's efficacy is justified by its accuracy and low computational burden.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Two-Timescale Dynamic Energy and Reserve Dispatch With Wind Power and
           Energy Storage

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      Authors: Zhongjie Guo;Wei Wei;Mohammad Shahidehpour;Laijun Chen;Shengwei Mei;
      Pages: 490 - 503
      Abstract: The integration of volatile renewable resources and energy storage entails making dispatch decisions for conventional coal-fired units and fast-response devices in different timescales. This paper studies intraday dynamic energy-reserve dispatch following a two-timescale setting. The coarse timescale determines the hourly reference output and reserve allocation, which offers a sufficient backup for the fine-timescale operation; the fine timescale determines the adjustment of gas-fired units and energy storage system every 15 minutes in response to the actual wind power. A stochastic dynamic programming method is proposed to make decisions at the coarse timescale while guaranteeing the robust feasibility of the fast process via vertex scenarios of uncertainty set and bounding the state-of-charge intervals for energy storage; the fast-response actions at the fine timescale are updated using a truncated rolling-horizon optimization, which incorporates the cost-to-go functions calculated at the coarse timescale to prevent the fast decisions from being myopic. Case studies on the modified IEEE 5-bus system and 118-bus system validate the proposed framework.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Hierarchical Frequency Control of Hybrid Power Plants Using Frequency
           Response Observer

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      Authors: Qian Long;Kaushik Das;Poul Sørensen;
      Pages: 504 - 515
      Abstract: With frequency stability being challenged in modern power systems, transmission system operators have been designing new mitigation measures, such as fast frequency response (FFR), to maintain operation security of power systems. To accommodate technical requirements of new frequency control services (FCSs), the corresponding control should be implemented at asset controllers to enable fast responses. However, control counteraction can arise between plant controllers and asset controllers during the provision of FCSs. In this paper, a novel hierarchical frequency control approach is proposed to allow hybrid power plants (HPPs) to provide three types of FCSs, namely FFR, frequency containment response (FCR) and frequency restoration response (FRR). To solve control counteraction issue, an innovative frequency response observer (FROB) is proposed. The FROB at plant controllers and the hybrid power plant controller (HPPC) accurately estimates frequency response initiated by asset controllers, and the obtained estimation is used for control compensation at plant controllers and the HPPC to avoid control counteraction. Design guidelines and robustness analysis of the FROB are then discussed. Case studies are implemented in a power system dynamic model in MATLAB/Simulink, and the results show that the proposed frequency control approach enables coordinated operation of multiple technology power plants, with robust performance achieved when there are system uncertainties in HPPs.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Maximum Power Extraction for PMVG-Based WECS Using Q-Learning MPPT
           Algorithm With Finite-Time Control Scheme

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      Authors: Raghul Venkateswaran;Balasubramani Natesan;Seong Ryong Lee;Young Hoon Joo;
      Pages: 516 - 524
      Abstract: This study is concerned with the problem of maximum power extraction for a permanent magnet vernier generator (PMVG)-based wind energy conversion system (WECS) using the Q-learning maximum power point tracking (MPPT) algorithm with the finite-time control (FTC) scheme. To do this, the model-free sensorless reinforcement Q-learning algorithm-based MPPT method is firstly proposed. At this time, the reward and learning rate are updated by the Q-values for each state-action pair. Next, the proposed learning algorithm is used to construct an optimal speed–power curve for achieving the fast and steady MPPT operation for the WECS using the learned action values. Besides, the wind-speed change detection algorithm is added to the proposed method so that the PMVG-based WECS can work in various wind speed conditions. And then, the FTC method is proposed to track the reference speed of PMVG which support to achieving the maximum power extraction, and Lyapunov stability theory is derived to ensure the overall system's stability. Finally, the simulation and experimental results from the 5 kW PMVG-based WECS are presented to demonstrate the applicability and superiority of the proposed control method.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Heterogeneous Aggregation and Control Modeling for Electric Vehicles With
           Random Charging Behaviors

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      Authors: Xin Wu;Lijuan Yao;Yawen Yu;Xinyu Jiang;Ruofan Wu;Gangjun Gong;
      Pages: 525 - 536
      Abstract: Electric vehicles (EVs) with both power consumption and energy storage are important load resources involved in the adjustment of supply and demand. The aggregation and control of a large number of EVs is a way to make the energy structure upgrade. This paper plans to explore the cluster response mechanism, group effect and coordination ability of distributed EVs in the regional power grid on the basis of solving the following problems. Firstly, the EV cluster presents heterogeneous characteristics due to differences of physical parameters. Secondly, EVs participating in the aggregation dynamically change in real time because of the stochastic charging behaviors. In this paper, a heterogeneous aggregation model for EVs with random charging behaviors is proposed based on the changing relation of charging power to remaining electric quantity. And a variable sliding mode control model is constructed to address the randomness of the charging process and realize stable responses in a short-time scale. Besides, the battery module and the user strategy are proposed to coordinate with the heterogeneous cluster to improve the adjustment capacity and flexibility. Finally, A clean energy output tracking simulation is provided to verify the effectiveness of the heterogeneous aggregation and control model.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Optimal Solar and Energy Storage System Sizing for Behind the Meter
           Applications

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      Authors: Juan Arteaga;Mostafa Farrokhabadi;Nima Amjady;Hamidreza Zareipour;
      Pages: 537 - 549
      Abstract: In this paper, we propose an optimal sizing model for a solar plus energy storage (PV-ESS) system for behind the meter applications. A dynamic optimization algorithm is proposed that maximizes the net worth of a project; the method can account for decreasing technology costs in the future and defer some of the investment costs. Two kinds of uncertainties are considered and mitigated according to their frequency of occurrence and forecast accuracy. The proposed optimization model is decomposed and structured in such a way that it can be efficiently solved using parallel computation. The simulation results provide evidence of the algorithm's ability to optimally size and time the investment in a PV-ESS system so that the total project cost is minimized.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Optimal Coordinated Operation for a Distribution Network With Virtual
           Power Plants Considering Load Shaping

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      Authors: Mingyang Zhang;Yinliang Xu;Hongbin Sun;
      Pages: 550 - 562
      Abstract: Increasing load demand and renewable energy resource integration will exacerbate the net load profile by enlarging the peak-to-valley difference. Virtual power plant (VPP) technology presents a promising pathway for the efficient energy management of active distribution networks (ADNs) with large-scale distributed energy resources. This paper proposes a bilevel model for the collaborative operation of an ADN with multiple VPPs in a joint energy-reserve market that is organized by a distribution system operator (DSO). In the upper-level problem, the DSO minimizes the total operational cost of the ADN to set the energy and reserve prices for trading with VPPs while considering the network technical constraints and the load shaping performance with reserve participation. In the lower-level problem, VPPs aim to maximize their profits by adjusting bidding quantities according to the price issued by the DSO. Then, the proposed bilevel model is transformed into a tractable single-level optimization problem via the Karush-Kuhn–Tucker optimality conditions and a sequence of reformulation techniques, and an analytical method is designed to calculate the potential losses. Simulation results demonstrate the effectiveness and superiority of the proposed approach in shaping the load profile and improving the system operational economy.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • A Central Limit Theorem-Based Method for DC and AC Power Flow Analysis
           Under Interval Uncertainty of Renewable Power Generation

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      Authors: Cong Zhang;Qian Liu;Bin Zhou;Chi Yung Chung;Jiayong Li;Lipeng Zhu;Zhikang Shuai;
      Pages: 563 - 575
      Abstract: This paper proposes a central limit theorem-based method (CLTM) to overcome the conservatism of interval DC and AC power flow analysis under uncertainty of renewable power generation. Interval DC power flow (IDCPF) models are solved by expressing the bus angle and active transmission power as linear combinations of interval nodal power injections, and then the central limit theorem is applied to obtain high-confidence intervals of DC power flow variables. Interval AC power flow (IACPF) models are solved by first applying the optimizing-scenarios method to acquire more accurate affine arithmetic forms of the power flow variables defined according to linear combinations of nodal power injections, and then high-confidence intervals of AC power flow variables are obtained via the central limit theorem. In addition, a criterion formulation is established to evaluate the errors of interval power flow methods. The results of simulations validate the effectiveness and superiority of the proposed method relative to the performances of previously established methods, including the Monte Carlo simulation, affine arithmetic-based method and optimizing-scenarios method.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Low-Carbon Economic Dispatch Method for Integrated Energy System
           Considering Seasonal Carbon Flow Dynamic Balance

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      Authors: Ning Yan;Guangchao Ma;Xiangjun Li;Josep M. Guerrero;
      Pages: 576 - 586
      Abstract: In order to solve the problems of excessive carbon emissions and environmental pollution caused by the current carbon trading policy, this paper breaks the traditional annual carbon trading mechanism and proposes an integrated energy system (IES) optimal dispatching method considering the seasonal carbon trading mechanism. Firstly, in order to ensure the dynamic balance of the multi-energy flow in the IES, the carbon flow equivalent to the electricity-gas-heat energy flow is introduced into the energy hub model, and the multi-energy flow energy hub model is established. Secondly, an optimized-stepped carbon trading mechanism is formulated to ensure internal carbon balance and restrain carbon emissions to prevent the annual carbon emissions settlement of the IES from exceeding the standard. Finally, according to the energy supply demand of the IES in different seasons, a seasonal carbon trading mechanism is formulated, which comprehensively considers carbon emissions and economics to optimize dispatch. The impact of the optimized-stepped carbon price and seasonal dispatch on carbon emissions and economics is compared. The cost of using the optimized-stepped carbon price is reduced by at least 14.50%, and carbon emissions are reduced by at least 2.54%. Under guaranteeing the same carbon emission quota, the IES net-benefit is increased by 6.8%.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Eliminating the Need for a Less Strict Requirement for the
           Negative-Sequence LVRT Current of Type-III Wind Turbine Generators in the
           IEEE 2800 Standard

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      Authors: Hassan Mohammadpour;Amin Banaiemoqadam;Ali Hooshyar;Ahmed Al-Durra;
      Pages: 587 - 601
      Abstract: Similar to emerging regional grid codes, the recently approved IEEE 2800 Standard mandates that inverter-based resources (IBRs) generate negative-sequence current during low-voltage ride-through (LVRT) conditions. The 2800 Standard requires that the IBRs' negative-sequence current lead the negative-sequence voltage by 90$^circ$–100$^circ$ to emulate synchronous generators and reduce the likelihood of protection malfunction. However, the limitations of existing doubly-fed induction generators (DFIGs) led the Standard to exempt the DFIGs from this requirement and allow a wider range for their negative-sequence current angle. Meanwhile, the 2800 Standard also acknowledged that this exemption had unidentified and potentially negative impacts on protective relays. This paper (i) sheds light on several so-far-unknown DFIG characteristics that impact the angle of the negative-sequence current during LVRT, (ii) reveals the impacts of the above DFIG exemption on relays, and (iii) develops a solution to prevent the need for this exemption in the future revisions of the IEEE 2800 Standard.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Secure Multi-Party Household Load Scheduling Framework for Real-Time
           Demand-Side Management

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      Authors: Lilin Cheng;Haixiang Zang;Zhinong Wei;Guoqiang Sun;
      Pages: 602 - 612
      Abstract: Renewable power sources are being increasingly incorporated into distribution networks. Therefore, demand-side management (DSM) has become more critical for improving system reliability. Currently, decentralized real-time DSM is practicable based on home energy management system (HEMS). However, coordinating these HEMSs is difficult because DSM customers may not wish to communicate with each other due to competition and privacy contents. A new peak may even emerge in the aggregator if HEMSs shift their loads without proper coordination. Hence, a secure multi-party household load scheduling framework was proposed in this study to ensure encrypted data sharing between HEMSs based on homomorphic encryption (HE) technology. In order to solve decentralized real-time DSM by directly using additive HE data in this proposed framework, a reinforcement learning (RL) method, namely boosting tree-based deep Q-network, was developed to be trained on a distributed algorithm. The results of case studies revealed that the proposed data-sharing framework outperformed the conventional DSM in shaving peak loads of the aggregator, whereas the electricity cost of each customer did not increase. Moreover, the proposed RL method protected the privacy of users and obtained a similar result compared with no-privacy-preserving RL methods.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Optimal Scheduling of Distribution Network With Autonomous Microgrids:
           Frequency Security Constraints and Uncertainties

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      Authors: Shunwei Zheng;Kai Liao;Jianwei Yang;Zhengyou He;
      Pages: 613 - 629
      Abstract: Widespread integration of autonomous microgrids (MGs) into the distribution network (DN) makes the scheduling of distribution systems challenging. Especially, the uncertainties of renewable energy sources (RESs) and load demand put the MG frequency at the risk of violation for its low inertia and the high penetration of RESs. To address the schedule problems associated with uncertainties and frequency security, a day-ahead scheduling optimization model is proposed based on the chance-constrained programming (CCP) theory. The frequency security constraints and uncertainties of RESs/load demand associated with MGs are considered during the development of the proposed model. The total operation cost of the distribution system consisting of DN and MGs is minimized, while the MG frequency deviations caused by uncertainties are restricted in the predefined safe range by the proposed CCP-based model. Besides, a linearization method is presented and used to transform the proposed model into a mixed-integer linear program to determine the efficiency of the CCP-based model. Finally, numerous case studies are conducted to illustrate the effectiveness and good scalability of the proposed model and understand the advantages of using the model. The RT-LAB hardware-in-loop tests are carried out to study the real-time performance under the scheduling solved by the proposed model. Simulation and experimental results validate that the proposed model can be used to minimize the total operation cost of the DN system integrated with MGs while ensuring that the MG frequency deviations are operated within an acceptable range.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Universal Maximum Power Extraction Controller for Wind Energy Conversion
           Systems Using Deep Belief Neural Network

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      Authors: Mohamad Alzayed;Hicham Chaoui;Emad Elhaji;Caizhi Zhang;
      Pages: 630 - 641
      Abstract: Over the previous years, there has been a surge in need for wind turbines. Consequently, this paper aims to propose a Deep Belief Neural Network (DBNN) that preserves the Maximum Power Extraction's (MPE's) benefits while allowing the output power to be limited with substantially less complexity in the control loop. The suggested technique uses a deep belief neural network to learn the wind turbine's nonlinear aerodynamics. It is generalized to cover a wide range of wind turbine sizes and operating conditions to precisely track their maximum power trajectories. Also, the proposed method rewrites the machine model in order to run Wind Energy Conversion Systems (WECSs) under the MPE technique; which takes into account the radius and pitch angle of wind turbine blades, wind speed, air temperature, and power demand for any permanent magnet synchronous generators types (IPMSG or SPMSG). Moreover, the proposed methodology is considered a plug-and-play technique while it does not need any tuning measures. The suggested DBNN MPE controller's power tracking achievement is examined in various operating conditions using a set of experimental and simulation tests for different generator types and sizes. Finally, the findings are compared to the well-known verified technique to validate its generalized productiveness.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Collaborative Optimization of PV Greenhouses and Clean Energy Systems in
           Rural Areas

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      Authors: Xueqian Fu;Yazhong Zhou;
      Pages: 642 - 656
      Abstract: As an important infrastructure supporting rural development, an integrated energy system plays an irreplaceable role in China's rural revitalization strategy. The deployment of rural energy projects is an effective way for rural areas to achieve double carbon goals and accelerate agricultural modernization. Based on the actual rural energy systems in northern China, this paper takes the rural energy system with photovoltaic greenhouses as the research object. Both the agrometeorological and energy meteorological models are established considering the meteorological sensitivity of agricultural production and photovoltaic generation. We propose a novel method for optimizing the collaboration between photovoltaic greenhouse load control and rural energy systems. The combined coordination model of agriculture and energy networks is established, and the combined model involves carbon, electrical energy, and thermal energy. Supplemental greenhouse lighting and greenhouse heating consume most of the energy and are finely modeled with focused attention on photosynthesis. Finally, a real-world 47-bus distribution network and three photovoltaic greenhouses in northern China are simulated as an analytical example. The simulation results showed that by using the proposed optimization method, a 3996 m2 greenhouse with a 25% photovoltaic coverage ratio can save 15% on energy costs.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Deep-Quantile-Regression-Based Surrogate Model for Joint
           Chance-Constrained Optimal Power Flow With Renewable Generation

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      Authors: Ge Chen;Hongcai Zhang;Hongxun Hui;Yonghua Song;
      Pages: 657 - 672
      Abstract: Joint chance-constrained optimal power flow (JCC-OPF) is a promising tool for managing distributed renewable generation uncertainties. However, existing works are usually based on power flow equations, which require accurate network parameters that may be unobservable in many distribution systems. To address this issue, this paper proposes a learning-based surrogate model for JCC-OPF with renewable generation. This model equivalently converts joint chance constraints into quantile-based forms. Two multi-layer perceptrons are trained based on special loss functions to predict the quantile of constraint violations and expected power loss. By reformulating these two MLPs into mixed-integer linear constraints, we can replicate the JCC-OPF without network parameters. Two pre-processing steps, i.e., data augmentation and calibration, are further developed to improve its performance. The former trains a simulator to generate more training samples for enhancing the prediction accuracy of MLPs. The latter designs a positive parameter based on empirical prediction errors to calibrate the outputs of MLPs so that feasibility can be guaranteed. Numerical experiments based on the IEEE 33- and 123-bus systems validate that the proposed model can achieve desirable feasibility and optimality simultaneously with no need for network parameters.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Online Grid Impedance Estimation-Based Adaptive Control of Virtual
           Synchronous Generators Considering Strong and Weak Grid Conditions

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      Authors: Nabil Mohammed;Mohammad Hasan Ravanji;Weihua Zhou;Behrooz Bahrani;
      Pages: 673 - 687
      Abstract: The conventional virtual synchronous generator (VSG) is typically designed to meet certain operational and control requirements in the islanded mode. However, once the VSG is switched to grid-connected mode (GCM), the robust operation cannot be guaranteed under different grid conditions. It can lead to poor dynamic performance, especially in strong grids, such as significant oscillation, long settling time, and large overshoot. To improve the VSG performance in the GCM, this article first analyzes in depth the inherent coupling between the active and reactive power and its dependence on grid conditions, such as the short circuit ratio and the grid impedance ratio. Subsequently, an adaptive VSG (AVSG) control strategy based on online grid impedance estimation is proposed to ensure robust operation of the VSG considering both strong and weak grid conditions. This technique allows the operator to specify the desired settling time of the output power and damping ratio. To estimate the grid impedance in real time without additional hardware and reduce the associated impacts on power quality, an online event-based grid impedance estimation algorithm is embedded in the control loop of the AVSG. The simulation and experimental results indicate that, compared with the conventional fixed-parameters-based controller design method, the AVSG exhibits desired performance such as no oscillation, specified time duration for the settling time, and minimal overshoot regardless of the grid conditions.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • A Novel Integral Reinforcement Learning-Based Control Method Assisted by
           Twin Delayed Deep Deterministic Policy Gradient for Solid Oxide Fuel Cell
           in DC Microgrid

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      Authors: Yulin Liu;Tianhao Qie;Yang Yu;Yuxuan Wang;Tat Kei Chau;Xinan Zhang;Ujjal Manandhar;Sinan Li;Herbert H. C. Iu;Tyrone Fernando;
      Pages: 688 - 703
      Abstract: This paper proposes a new online integral reinforcement learning (IRL)-based control algorithm for the solid oxide fuel cell (SOFC) to overcome the long-lasting problems of model dependency and sensitivity to offline training dataset in the existing SOFC control approaches. The proposed method automatically updates the optimal control gains through the online neural network training. Unlike the other online learning-based control methods that rely on the assumption of initial stabilizing control or trial-and-error based initial control policy search, the proposed method employs the offline twin delayed deep deterministic policy gradient (TD3) algorithm to systematically determine the initial stabilizing control policy. Compared to the conventional IRL-based control, the proposed method contributes to greatly reduce the computational burden without compromising the control performance. The excellent performance of the proposed method is verified by hardware-in-the-loop experiments.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Multi-Rate Sampling Control Design and Stability Analysis for Frequency
           and Voltage Regulation in Islanded Microgrids

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      Authors: Kuo Feng;Chunhua Liu;
      Pages: 704 - 716
      Abstract: The wide use of inverters in microgrids motivates the development of digital controllers with sampling systems. This paper proposes a multi-rate sampling frequency and voltage regulation method for inverters-based distributed generators (DGs). The fast-sampling signals are adopted to achieve the primary control, which is composed of the VSG-based frequency control and the voltage droop control with a low-pass filter (LPF). Based on the unified primary control model, the low-sampling distributed model predictive control (DMPC) is proposed as secondary frequency and voltage control. Considering the microgrid network, the inverter-based DGs supply power for both the local load and loads on other buses. The proposed DMPC method can overcome the disproportionate impedances of interconnecting lines among buses. Then the frequency and voltage of islanded microgrid can be restored to rated values with accurate power sharing. The DMPC-based secondary control and the primary control have different sampling rates. The stability and dynamic performance of the proposed multi-rate sampling control system are investigated for the first time. Finally, both simulation and experiment tests are carried out to validate the effectiveness of the proposed control strategy.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Improved Active Current Control Scheme of Wind Energy Conversion Systems
           With PLL Synchronization During Grid Faults

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      Authors: Zhihao Wang;Lei Ding;Xuesong Gao;Guofang Zhu;Xiaohui Wang;Vladimir Terzija;
      Pages: 717 - 729
      Abstract: Wind energy conversion systems (WECSs) depend upon phase-locked loops (PLLs) for synchronization with the power grid. As the voltage dip during grid faults lowers the transfer capacity between WECSs and the grid, PLLs may suffer loss of synchronization (LOS) if the active current reference of WECSs exceeds the transfer capacity. To analyze the PLL synchronization stability, a U-Id plane analysis method is presented. The transfer capacity constraint and the WECS active current control scheme are mapped to the U(Id) curve and control curve in a U-Id plane, respectively, describing the relationship between the terminal voltage and the WECS active current. Hence, the influence of grid parameters and the WECS control scheme can be analyzed separately. Accordingly, to prevent LOS, an improved active current control scheme of WECSs is proposed based on the short circuit ratio. The scheme corresponds to a control curve in the U-Id plane that ensures the existence of stable equilibrium points and is quite easy to apply in practice. Finally, both the simulations using DIgSILENT PowerFactory and the experiments based on RT-lab and RTDS validate the performance of the proposed control scheme.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Two-Stage Optimal Re/Active Power Control of Large-Scale Wind Farm to
           Mitigate Voltage Dip Induced Frequency Excursion

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      Authors: Chen Zhao;Dan Sun;Long Zhang;Heng Nian;Bin Hu;
      Pages: 730 - 733
      Abstract: A two-stage optimal active and reactive power coordinated control strategy of large-scale wind farm (WF) to mitigate voltage dip induced frequency excursion (VDIFE) is proposed in this letter. Before the voltage fault occurs, the power reference look-up table of each wind turbine (WT) are calculated. When voltage fault occurs, the power reference values of each WT are optimized based on model predictive control. Distinguished from existing works, the proposed optimal power control can better mitigate VDIFE and balance the variation of rotor speed of each WT. Case studies on a doubly-fed-induction-generator based WF built in MATLAB/Simulink are carried out to demonstrate the effectiveness of the proposed control scheme.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • Analysis of 0.1-Hz Var Oscillations in Solar Photovoltaic Power Plants

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      Authors: Lingling Fan;Zhixin Miao;David Piper;Deepak Ramasubramanian;Lin Zhu;Parag Mitra;
      Pages: 734 - 737
      Abstract: Oscillations with very low frequency at 0.1 Hz, have been observed in voltage and var in practical solar photovoltaic (PV) systems when power exporting ramps up to a certain level. This letter provides an explanation on the formation of 0.1-Hz oscillations and identifies three critical factors that lead to the oscillations: communication delay between the plant-level control and the inverter-level control, high volt/var sensitivity at a high power exporting level, and the volt-var feedback system consisting of the plant control, inverter control and the grid impact. Furthermore, a critical feature of the 0.1-Hz oscillation is also explained: why oscillations appear only in voltage and var, but not in real power.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
  • TechRxiv: Share Your Preprint Research with the World!

    • Free pre-print version: Loading...

      Pages: 738 - 738
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: Jan. 2023
      Issue No: Vol. 14, No. 1 (2023)
       
 
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