JournalTOCs

IEEE Transactions on Sustainable Energy
Journal Prestige (SJR): 2.318

Citation Impact (citeScore): 7
Number of Followers: 14

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1949-3029
Published by IEEE

[228 journals]

IEEE Transactions on Sustainable Energy Publication Information
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  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
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  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
IEEE Industry Applications Society Information
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  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
IEEE Transactions on Sustainable Energy Information for Authors
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  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Hierarchical Intelligent Operation of Energy Storage Systems in Power
Distribution Grids
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  Authors: Mohammad Mehdi Hosseini;Masood Parvania;
  Pages: 741 - 750
  Abstract: High penetration of distributed energy storage systems (ESS) offers an unparalleled opportunity to reinforce the distribution grid at the local level against upstream disruptions; however, their mass operation under uncertainty of load and renewable generation is computationally expensive. While deep reinforcement learning (DRL) has been suggested to train operator agents capable of handling uncertainty and high dimensionality of the problem, it falls short when safety and feasibility assurances are required in critical operations. This paper proposes a model for hierarchical coupling of DRL and mathematical optimization for operation of ESS in distribution grids, in order to take advantage of DRL fast response while keeping network constraints in check. In the proposed method, strategic scheduling of distributed ESS units are performed locally by fast DRL-trained agents, while critical grid-wide operations such as fault management and voltage control are performed by an optimization-based central controller. The local controller is trained by Twin Delayed Deep Deterministic Policy Gradient (TD3), whose response time is three orders of magnitude faster than stochastic optimization, while the optimality of solutions are similar in both cases.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Coordinated Power Oscillation Damping From a VSC-HVDC Grid Integrated With
Offshore Wind Farms: Using Capacitors Energy
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  Authors: Zuan Zhang;Xiaowei Zhao;
  Pages: 751 - 762
  Abstract: This paper proposes a novel coordinated control strategy for a voltage source converter (VSC) based high-voltage direct current (HVDC) grid integrated offshore wind farms (OWFs) to damp the power system oscillations. A feature of this strategy is aiming to use the DC-link capacitor energy of offshore wind turbines (WTs) to reduce interactions between power oscillations and HVDC grid voltage when onshore grid-side VSCs (GSVSCs) modulate the active and reactive power injections. Unlike the previous communication-based method, the coordination from offshore WTs in this strategy depends on the local measurements of the HVDC grid voltage instead of the remote communication data from the onshore AC grid. A modified IEEE 39-bus power system with a 5-terminal VSC-HVDC grid connected to two OWFs has been developed to validate the effectiveness of this proposed strategy. Both the eigenvalue analysis and time-domain simulation results indicate that this strategy can significantly improve the power oscillation damping (POD). Comparative simulation studies also conclude that the proposed strategy has similar POD improvements to the previous communication-based method without the negative impact of communication delay from onshore to offshore.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Frequency Constrained Scheduling Under Multiple Uncertainties via
Data-Driven Distributionally Robust Chance-Constrained Approach
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  Authors: Lun Yang;Zhihao Li;Yinliang Xu;Jianguo Zhou;Hongbin Sun;
  Pages: 763 - 776
  Abstract: The declining system inertia in renewable-rich power systems raises a concern about the frequency stability problem. The wind farm equipped with the power electronic controller is capable of providing frequency support after a disturbance. However, both virtual inertia provision and wind power from wind farms are time-varying and uncertain. To account for this issue, we propose a data-driven distributionally robust (DR) chance-constrained approach for the frequency constrained scheduling problem, which simultaneously optimizes the unit commitment, generation dispatch, regulation reserves, and frequency responses. This approach explicitly considers frequency constraints and formulates virtual inertia uncertainty- and wind power uncertainty-related operational/frequency constraints as DR chance constraints under Wasserstein-metric ambiguity sets, which can limit the risk of constraint violations. Case studies demonstrate the effectiveness of the proposed approach and show that the proposed approach can achieve a desirable trade-off between operational cost and constraint violations.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Wind Power Scenario Synthesis With Smoothing Effect Through Spectral
Decomposition and Its Application to Flexible Resource Adequacy
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  Authors: Byoungryul Oh;Sooyeon Kim;Duehee Lee;
  Pages: 777 - 789
  Abstract: We synthesize future scenarios of total wind power generation (WPG) of future wind farms by considering the smoothing effect, in which mid-term variability fades out, but periodicity and short-term variability remain as more geographically distributed wind farms are aggregated. First, we decompose the WPG into a periodic component (PC) and non-periodic component (NC). We design the future PC by modifying the standard deviation and mean of the current PC. Then, we assume that the NC can be decomposed into a variable component (VC), which is a source of mid-term variability, and an uncertain component (UC), which is a source of short-term variability. We separate the VC and UC in the frequency domain. We design the VC by modifying the slope of power spectral density (PSD) to represent the smoothing effect and the UC by synthesizing the PSD distribution to represent randomness. Based on our scenarios, we develop a new flexibility index and a novel recursive algorithm to calculate the required amount of future flexible resources and the value of the smoothing effect to maintain the flexible resource adequacy. Finally, we verify the smoothing effect and the amount of flexible resources by simulating the IEEE test network based on actual WPG in Texas and Australia.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Carbon-Oriented Planning of Distributed Generation and Energy Storage
Assets in Power Distribution Network With Hydrogen-Based Microgrids
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  Authors: Chenjia Gu;Yikui Liu;Jianxue Wang;Qingtao Li;Lei Wu;
  Pages: 790 - 802
  Abstract: The pressure of climate change has been driving the transition of power distribution networks (PDNs) to low-carbon energy systems. Hydrogen-based microgrids (HMGs), as emerging urban energy subsystems in PDNs with significant carbon emissions reduction potentials, are valuable assets in smoothing the economic transition to low-carbon energy systems. However, it remains a challenging issue to make the HMGs perceive their carbon emissions in the planning and operation process, so that they can effectively change energy consumption patterns to reduce carbon emissions. To achieve the overall carbon emission reduction target, a carbon-oriented planning method for PDN and HMGs is proposed. Firstly, an integrated planning model of distributed generation and energy storage assets is formulated with embedded carbon emission constraints. Secondly, a chronological carbon emission flow model for electrical storage systems is introduced to accurately capture its impact on the chronological carbon emission flow distribution, which is incorporated into the planning problem to quantify carbon emissions of different HMGs. Finally, the proposed planning problem is formulated as a mixed-integer nonconvex quadratically constrained programming (MINCQCP) problem, and solved by the tailored penalty-based proximal distance algorithm to derive the local optimum. Numerical results indicate that integrated planning of PDN and HMGs could avoid overinvestment and meet the given carbon emission target in a cost-effective way.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Deep Reinforcement Learning Based Unit Commitment Scheduling under Load
and Wind Power Uncertainty
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  Authors: Akshay Ajagekar;Fengqi You;
  Pages: 803 - 812
  Abstract: The intermittent nature of renewable energy sources and fluctuating electricity demand induce significant uncertainty that needs to be tackled with computationally efficient solution techniques to provide reliable and cost-effective generation schedules of power systems. In this work, we present a deep reinforcement learning (DRL) based approach for the day-ahead scheduling of generation resources under demand and wind power uncertainties. The proposed approach yields a causal policy relying only on historical uncertainty realizations and forecast data that is trained with an actor-critic-based reinforcement learning algorithm. Through safe exploration, the DRL-based approach guarantees a feasible commitment schedule without any operational constraint violations. We conduct computational experiments on the IEEE 39-bus and 118-bus test cases to demonstrate the effectiveness of the proposed solution strategy and improvement over existing approaches, including a deterministic approach with point forecasts and the stochastic dual dynamic integer programming method. The results show that the proposed approach enjoys superior performance in terms of computational efficiency and incurred operational costs, with significant reduction in penalty costs caused by insufficient net load supply than the deterministic approach.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Determination and Cost Allocation for Regulation Reserve With Renewables:
A Data-Driven Assisted Approach
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  Authors: Mingxu Xiang;Zhifang Yang;Juan Yu;Gang Wang;
  Pages: 813 - 825
  Abstract: Proper setting of regulation reserve (RR) requirements is essential for the secure and economic operation of power grids. Currently, the RR requirement is normally determined based on ad-hoc experience or numerical methods that cannot comprehensively consider uncertainties and fluctuation characters. Regarding the increasing penetration of renewables, it becomes an obvious challenge for determining the proper RR requirement and reasonably allocating the RR costs to market participants. To address these issues, a data-driven assisted determination and cost allocation method for RR is proposed. A data-driven method considering fluctuation features is used to predict the RR requirement. To avoid the insufficiency of the RR resulting from prediction error, a compensation strategy based on error decomposition is proposed. Ensemble learning is used to handle the model error. The data error caused by the multiperiod forecast uncertainty of load and renewables is compensated by a computationally tractable chance-constrained optimization problem. To encourage grid-friendly behaviors, a hierarchical method for RR cost allocation is proposed to account for the comprehensive contributions of market participants. Case studies show that compared with existing methods, the proposed RR requirement determination method can achieve a more than 24% improvement in frequency performance without excessive costs. The reasonability of the proposed cost allocation method is also illustrated.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
A Day-Ahead Scheduling Model of Power Systems Incorporating Multiple Tidal
Range Power Stations
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  Authors: Tong Zhang;Nicolas Hanousek;Meysam Qadrdan;Reza Ahmadian;
  Pages: 826 - 836
  Abstract: With the global trend to exploit more renewable energy sources, tidal range energy has been gaining more attention recently. This power generation technology is expected to reduce the share of fossil fuels and provide flexibility to the power system. With a pioneering tidal range generation project just granted in Wales and more proposals planned in Great Britain, it is important to study how the incorporation of multiple tidal range power stations will affect power system operation. In this paper, the role of tidal range energy generation in the future Great Britain power system is investigated based on a day-ahead scheduling model of power system incorporating multiple tidal range power stations. In the proposed model, tidal range power stations situated at different sites operate flexibly and in coordination, supporting the power system to reach the minimum operating cost. A case study based on the Great Britain electricity transmission system in 2030 with one tidal barrage and one tidal lagoon was investigated. The results showed that the coordination of flexible tidal range power stations can reduce the power system's operating cost. Furthermore, the energy-storage feature of tidal range power stations can act as a stable source of flexibility in the power system.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Machine Learning-Based Sizing of a Renewable-Battery System for
Grid-Connected Homes With Fast-Charging Electric Vehicle
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  Authors: Rahmat Khezri;Peyman Razmi;Amin Mahmoudi;Ali Bidram;Mohammad Hassan Khooban;
  Pages: 837 - 848
  Abstract: This paper develops a sizing model of solar photovoltaic (SPV), small wind turbine (SWT) and battery storage system (BSS) for a grid-connected home with a fast-charging plug-in electric vehicle (PEV). The home trades energy with the main grid under time-of-use tariffs for selling and purchasing electricity that affects the energy management. In this paper, a practical rule-based operation strategy is developed for the grid-connected home with fast-charging PEV that enables efficient and cheap energy management. The sizing problem is solved using a supervised machine learning algorithm, which is a feed forward neural network, by minimizing the cost of electricity. While the developed renewable-battery sizing model is general, it is examined using actual data of insolation, wind speed, temperature, load, grid constraints, as well as technical and economic data of BSS, SPV, SWT, and PEV in Australia. Uncertainty analysis is investigated based on ten scenarios of data for wind speed, temperature, load, insolation, and PEV. The effectiveness of the proposed model with fast-charging PEV is verified by comparing to slow charging and uncontrolled fast-charging models, as well as two other machine learning methods and a metaheuristic algorithm. It is found that the proposed model decreases the cost of electricity by 10.1% and 19.6% compared to slow charging and uncontrolled fast-charging models for the grid-connected home with PEV.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Resident-Centric Distributed Community Energy Management System
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  Authors: Leehter Yao;J. C. Teo;Che-Hao Wang;
  Pages: 849 - 863
  Abstract: This study presents a resident-centric distributed community energy management system (CEMS). More specifically, the proposed resident-centric distributed CEMS allows residents to schedule their appliances autonomously, without the need to collaborate with the community and to consider whether their appliance scheduling is optimal from the perspective of the entire community. The central controller in the proposed CEMS will then determine a solution that is optimal for the entire community by dispatching the community's distributed energy sources according to the appliance scheduling of residents. In other words, the proposed distributed resident-centric CEMS allows residents to act autonomously while securing the collective goals of the community to a certain extent. In this paper, the collective goals of the community include participating in incentive-based demand response (IBDR) events at a specific time interval, and decreasing the total electricity cost of the community in response to time-varying electricity prices. The proposed distributed resident-centric CEMS is developed using the concept of distributed optimization and mixed-integer linear programming. Different types of public loads are incorporated into the proposed framework including stoppable and deferrable public loads. The simulation results show that the proposed framework dispatches power optimally.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Non-synchronous Inertia Estimation in a Renewable Energy Integrated Power
System With Reduced Number of Monitoring Nodes
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  Authors: Pijush Kanti Dhara;Zakir Hussain Rather;
  Pages: 864 - 875
  Abstract: Accurate estimation of non-synchronous inertia in renewable energy (RE)-integrated power systems is challenging through conventional approaches, as emulated inertia from inverter-based resources (IBRs) is fundamentally different from the inherent inertial response of synchronous generators. In this context, this paper presents a novel realistic approach for estimating the non-synchronous inertial response in a large-scale RE-integrated power system. The method incorporates an optimal number of frequency and voltage monitoring nodes while ensuring an accurate estimation of non-synchronous inertia. In the proposed method, the synchronous inertial response for a frequency disturbance is estimated first using the conventional approach, followed by an estimate of the loads’ inertial contribution. The latter is calculated using center-of-inertia frequency and voltage measurement across the power system with a voltage-controlled zone approach. Finally, the non-synchronous inertial response is estimated by segregating the aggregate inertial response of synchronous generators and the loads from the overall estimated inertia of the system. The proposed approach can help the system operator determine inertial contributions from the loads and IBRs depending on loading conditions and the availability of virtual inertia from RE sources, estimate the minimum required synchronous inertia, and select the appropriate proportional gains and time constants of virtual inertial controllers. The proposed method is validated by implementing it on a modified IEEE 39-bus system and a real-life Gujarat State grid model (in India).
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Hypothesis Testing for Mitigation of Operational Infeasibility on
Distribution System Under Rising Renewable Penetration
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  Authors: Yu Weng;Sel Ly;Peng Wang;Hung Dinh Nguyen;
  Pages: 876 - 891
  Abstract: High penetration of renewables and unconventional loads expose power systems to more operational infringements, which can be the bottleneck for deploying more renewables. Understanding system infeasibility characterization, in terms of the types and probability of the infeasibility occurrence at different renewable penetration levels towards 100%, is indispensable to develop suitable solutions for the seamless operation and expansion of power systems. Meanwhile, the privacy constraints, enormous scenarios, and high dimensions of variables make it impractical to access full data even through simulations. Hence, this work presents a systematic hypothesis testing framework to analyze the infeasibility characterization following the rising level of renewables with making the most of available data. Instead of making conjectures based on empirical observations. The proposed framework through p-values, Rank-biserial and Kendall's $tau _{b}$ coefficients, logistic and linear regressions, and Bayes factors provides sufficient statistical evidences to confirm the associations between the penetration levels and operational infringements, together with power losses. Besides, an optimal control scheme is designed to mitigate the odds ratio of violations. This logit-based control leverages logistic regression models to provide a convenient form of violation probability for convex optimization. Moreover, a nonlinear feature selection method called Feature-Wise Kernelized Lasso is the first time incorporated to select the optimal control sets to overcome the multi-collinearity and overfitting problems in Big Data. Simulation results under various IEEE test feeders with different means of renewable levels illustrate the effectiveness of the proposed methods.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Multiple Sources Restoration for Soft Open Points in Distribution Networks
With a Two-Stage Accelerated Algorithm
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  Authors: Tao Zhang;Xiaodan Yu;Yunfei Mu;Hongjie Jia;Kai Hou;Xiaolong Jin;
  Pages: 892 - 905
  Abstract: Soft open points (SOPs) connected to distribution networks are instrumental in improving operational performance during outage periods, such as power supply reliability and power quality, and they have not been sufficiently studied. To fully harness the potential benefits of SOPs, a multiple-source distribution service restoration (DSR) strategy considering SOPs is proposed in this paper. The power outputs of distributed energy resources (DERs) and SOP flexibility are exploited to enhance the load restoration level and mitigate voltage deviations over a time horizon. Additionally, to cope with uncertainties, the chance-constrained method is adopted by the proposed strategy to allow intermittent renewable resources to participate in DSR. DSR is formulated as a mixed-integer linear programming (MILP) problem with many integer variables, making it very challenging to solve. To reduce the computational complexity, a two-stage accelerated algorithm (TSAA) is proposed. This allows us to solve a relaxed MILP problem in the first stage to decide the radial topology. An accelerated MILP problem is solved in the second stage to determine the load status as well as the outputs of the DERs and SOPs. Case studies on an IEEE 33-bus distribution network illustrate the effectiveness of the proposed DSR strategy and the feasibility of TSAA in reducing the computation time.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Distribution Network Expansion Planning Approach for Large Scale Electric
Vehicles Accommodation
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  Authors: Xiaowen Wang;Xiaoying Shi;Yinliang Xu;Xinwei Shen;
  Pages: 906 - 919
  Abstract: The increased power demand incurred by electric vehicles (EVs) has imposed new challenges on the distribution network. One significant responsibility of the distribution network operator (DNO) is to design and expand the distribution network periodically to accommodate the ever-increasing demand. The previous researches mainly use relaxation techniques to enhance the problem tractability, which would involve additional binary variables and incur computation burden to the optimization problem, or even worse, may lead to an intractable problem. This paper develops a mixed-integer non-linear programming (MINLP) model considering various network constraints for the distribution network expansion planning under a high penetration level of EVs. The stochastic characteristics of demands are addressed through set of scenarios. A distributed biased min-consensus algorithm based approach is proposed to solve the MINLP model. Finally, comparison tests are conducted on three different scales of distribution networks to validate the effectiveness of the proposed approach. Simulation results demonstrate that the computation time of the proposed approach is reduced by 42.46% on average for different systems compared with that of the traditional shortest path algorithm based approach.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Reliability Evaluation for Integrated Electricity-Gas Systems Considering
Hydrogen
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  Authors: Tao Wu;Jianhui Wang;
  Pages: 920 - 934
  Abstract: Regarded as the cleanest and a versatile energy carrier, green hydrogen generated with renewable energy is receiving increased attention in the transition to a carbon-neutral society. On the other hand, the integration of hydrogen tightens the coupling between power systems and natural gas systems, which highlights the importance of reliability evaluation of the integrated electricity-gas systems (IEGSs) with hydrogen. This paper proposes a reliability evaluation model for the IEGSs considering the effects of hydrogen. The power to hydrogen and methane process is proposed to convert the surplus renewable energy to hydrogen and methane, which are then mixed into natural gas systems. A novel optimal energy shedding model is proposed to explicitly account for the impact of hydrogen on the energy flow. To consider the temporal feature of renewable energy, a sequential Monte Carlo simulation approach is applied to evaluate the reliability of the IEGSs. The numerical simulations are performed on the integrated IEEE 24-bus and 20-node energy systems and the integrated IEEE 72-bus and 40-node energy systems to verify the effectiveness of the proposed model.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Modeling and Impact Analysis for Solar Road Integration in Distribution
Networks
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  Authors: Bochen Shi;Wei Dai;Ceheng Luo;Hui Hwang Goh;Jinghua Li;
  Pages: 935 - 947
  Abstract: Solar road (SR), as an emerging generation technic with increasing potential, could save valuable land resources and promote the low-carbon development of both the transport and energy sectors. However, the strong uncertainties of SR may make it challenging for the secure operation of power systems. Thus, this paper aims to investigate the influences of uncertainties of SR on the safe operation of distribution networks. To this end, a novel analytical SR generation model considering the dynamic shadow of vehicles (DSoV) and mixed traffic flow (MTF) is proposed for the first time based on solar geometry and shoelace theorem. The proposed SR generation model can directly reflect the influence of shading effects composed of different types of vehicles on SR generation. The uncertainty of MTF is modeled by improved kernel density estimation (IKDE) and Gaussian mixture model (GMM). Finally, a learning-based probabilistic power flow (PPF) is introduced for the impact assessment of SR integration, while reducing the computational complexity of PPF. Case studies performed on practical power-traffic networks with real historical data corroborate the effectiveness and scalability of the proposed method.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Three-Stage Flexibility Provision Framework for Radial Distribution
Systems Considering Uncertainties
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  Authors: Xuehan Zhang;Dongyob Shin;Yongju Son;Hyeon Woo;Sung-Yul Kim;Sungyun Choi;
  Pages: 948 - 961
  Abstract: Transmission system operators (TSOs) are procuring increasing services through distributed energy resources (DERs), many of which are distribution-level renewable energy sources (RESs). However, RES inherent uncertainties have negative impacts on distribution system operations, which is a critical problem when distribution system operators (DSOs) respond to the TSO's flexibility requirement. This raises the question of how the DSO can arrange flexible resources in each different time stage so that the DSO can offer the preset flexibility at as little economic loss as possible. In this context, a novel three-stage flexibility provision framework for radial distribution systems considering uncertainties is proposed. In the first stage, the DSO responds to the TSO's specific flexibility requirement by scheduling priced-based demand response (PBDR), battery energy storage systems (BESSs), and electric vehicles (EVs) on a day-ahead (DA) horizon. In the second stage, the BESS and EV scheduling are re-optimized to respond to the most recent intraday short-term predictions of RESs and loads. In the third stage, real-time frequency control is provided by BESSs and EVs to alleviate frequency variations and power imbalance at the TSO–DSO interface. The simulation results on a modified IEEE 33-bus system demonstrate the effectiveness of the proposed flexibility provision framework.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Interacting Multiple Model Strategy Based Adaptive Wide-Area Damping
Controller Design for Wind Farm Embedded Power System
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  Authors: Abhineet Prakash;Ranjeet Kumar Tiwari;Kundan Kumar;S. K. Parida;
  Pages: 962 - 973
  Abstract: The intermittent nature of Wind Turbine Systems (WTSs) can severely affect the Low-Frequency Oscillations (LFOs) in the power system. Hence, a Wide-Area Damping Controller (WADC) is designed in this paper to provide adequate damping to critical LFO modes. This WADC utilizes a modal-based prescribed degree approach, ensuring the specified shift of concerned modes from their existing position. However, the design of WADC at a fixed operating point and time delay in feedback signals does not provide robust performance as these uncertainties may vary for different time intervals. Specifically, time-varying delays should be tackled appropriately, or the system's damping performance will be severely hampered. Keeping this in view, an Interacting Multiple Model (IMM) infrastructure is employed to provide robust damping performance for such uncertainties. The IMM strategy utilizes the deviation of output error between actual and probable plants, through which weights are assigned to corresponding probable WADCs via the Bayesian framework. The simulations are performed on the nonlinear 4-machine, 11-bus system and the complex 16-machine, 68-bus system using MATLAB/Simulink platform. The results demonstrate that the proposed IMM-based WADC furnishes adequate damping to critical LFO modes for operating point and time delay uncertainties.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Adaptive Dynamic Voltage Support Scheme for Fault Ride-Through Operation
of a Microgrid
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  Authors: Bonu Ramesh Naidu;Prabodh Bajpai;Chandan Chakraborty;Muli Malakondaiah;Boddeti Kalyan Kumar;
  Pages: 974 - 986
  Abstract: This paper proposes a control strategy for enabling voltage fault ride-through (VFRT) operation with adaptive dynamic voltage support (DVS) scheme using the energy storage in a microgrid. Firstly, the condition for maximum DVS from the microgrid is presented and its dependency on the parameters of the equivalent network seen by the microgrid is elucidated. Considering the impediments associated with the real-time parameter estimation during the short-term voltage disturbances, reference current signals based on the extreme voltage tracking algorithm are proposed in this paper to achieve an adaptive DVS irrespective of the varying system conditions. Secondly, a control strategy is proposed to enable the energy storage-based VFRT operation of a microgrid. Thirdly, a unique methodology of sizing the energy storage for VFRT operation is presented considering the grid code requirements, equivalent network characteristics, and the maximum current rating of the power electronic interface with the grid. The proposed control strategy is validated through real-time digital simulation case studies for low-voltage, high-voltage, and multiple-fault scenarios. The scalability of the proposed approach is illustrated using the CIGRE distribution test network.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
F-LMS Adaptive Filter Based Control Algorithm With Power Management
Strategy for Grid Integrated Rooftop SPV-BES System
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  Authors: Gaurav Modi;Bhim Singh;
  Pages: 987 - 999
  Abstract: The power quality performance of a grid-integrated rooftop solar photovoltaic (SPV) system is significantly affected by the local nonlinear loads. Further, during peak solar insolation and load demand, the SPV system experiences overvoltage and undervoltage at its point of interconnection. Both these issues infringe the IEEE std. 519 and 1547. Therefore, this paper presents a fractional least mean square (F-LMS) based control with the power management strategy (PMS) for the SPV-battery energy storage (BES) system. This presented control uses the SPV system's power converter to compensate load currents locally. By this means, the grid voltages and currents remain distortion-free. The introduced F-LMS algorithm is used as a pre-filter for load current. It estimates the load current fundamental component more accurately and swiftly than the existing LMS method. The designed PMS supervises the SPV array generation, BES power, and grid power to cater to both over and undervoltage issues and keeps the PIC voltage amplitude within the IEEE std. 1547 limits. In addition, this work enables the autonomous operation of SPV system during grid disruption, enhancing power resilience. The efficacy of the introduced work is experimentally verified.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
A MILP-Based Battery Degradation Model for Economic Scheduling of Power
System
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  Authors: Chunyang Liu;Houzhen Ma;Hengxu Zhang;Xiaohan Shi;Fang Shi;
  Pages: 1000 - 1009
  Abstract: Battery energy storage (BES) systems play an increasingly important part in power system operation because of their high efficiency and decreasing cost. This article proposes a mixed integer linear programming (MILP) model of battery degradation considering both depth of discharge (DoD) and state of charge (SoC), which can reflect the battery degradation mechanism. To improve computational efficiency for the economic scheduling of power system, a novel one-dimensional linearization is proposed to linearize this two-variable function, which significantly reduces computation burdens without loss of accuracy. Considering the battery degradation cost, a day-ahead economic scheduling model is built and a MILP algorithm is applied to solve it. The effectiveness of the MILP-based battery degradation model for economic scheduling is validated using the modified IEEE 30-bus system. For comparison, other existing battery degradation models are also implemented in the same system.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Coordinated Planning of Electricity and Hydrogen Networks with Hydrogen
Supply Chain for Fuel Cell Electric Vehicles
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  Authors: Yuechuan Tao;Jing Qiu;Shuying Lai;Xianzhuo Sun;
  Pages: 1010 - 1023
  Abstract: Hydrogen production has become an emerging technology and offered a pathway to sustainable energy utilization in transportation systems. In this paper, we propose a coordinated planning of the electricity network and hydrogen refueling and production station planning strategy for hydrogen fuel cell electric vehicles (FCEVs). In the proposed system structure, a hydrogen supply chain for FCEVs mainly composes hydrogen refueling stations (HRSs), hydrogen production stations (HPSs), hydrogen storage, and hydrogen delivery network. To enhance the system's flexibility, hydrogen delivery combines both gas pipeline delivery and truck logistics delivery. In the mathematical models, the synergistic effect and coupling relationship of the electricity network, gas network, and transportation network are presented. To ensure the efficiency and effectiveness of the hydrogen refueling system, a quality of service (QoS) and reliability assessment method is presented. The proposed methodology is verified in case studies. It is found out that compared with the single delivery methods, the proposed multi-network coordinated planning has the lowest total cost with less unserved energy. Besides, through QoS and reliability assessment, enough hydrogen reserves can be maintained to cope with contingencies.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Optimal Energy Management for Battery Swapping Based Electric Bus Fleets
With Consideration of Grid Ancillary Services Provision
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  Authors: Nader A. El-Taweel;Abdelrahman Ayad;Hany E. Z. Farag;Moataz Mohamed;
  Pages: 1024 - 1036
  Abstract: In this paper, a novel optimization model is formulated to optimize the scheduling of battery swapping stations (BSS) operating electrified public bus transit fleets. The BSS consists of a number of battery modules that are combined together to create a MW-scale battery storage system. As such, the formulated optimization problem aims at minimizing the running costs of the BSS via i) exploiting the low electricity prices in the market (i.e., charging the battery modules at low prices), and ii) utilizing the BSS in the provision of grid ancillary services. The proposed model considers the operational requirements of the bus transit fleet to satisfy the energy needs of the battery electric buses (BEBs) and maintain their defined timetable. Also, the model integrates the power distribution network constraints such as bus voltages and line capacity limits to ensure its reliable operation. The impact of the BSS participation in the provision of ancillary grid services on the operation, degradation, and lifetime of the battery modules is investigated in this work by using a proposed saving cost index (SCI). It is demonstrated that the proposed model propels the economic viability of the BSS operation concept.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Corrections to “A Reliable Medium-Voltage High-Power Conversion System
for MWs Wind Turbines”
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  Authors: Yonglei Zhang;Xibo Yuan;Mo Al-Akayshee;
  Pages: 1037 - 1037
  Abstract: This addresses errors in [1]. The first author's affiliation information in the footnote is mistyped. The corrected sentence should read:
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Guest Editorial Special Section on Sustainable Energy for Enhancing Grid
Resiliency
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  Authors: Zhaoyu Wang;Alberto Borghetti;Chen Chen;Fei Ding;Chen-Ching Liu;Jay Liu;Mathaios Panteli;Feng Qiu;Mohammad Shahidehpour;
  Pages: 1041 - 1042
  Abstract: The twenty-two papers in this special section focus on innovative research advances tackling fundamental challenges of achieving more resilient grids to real-world demonstrations of leveraging sustainable energy resources to enhance grid resiliency.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Utilizing Aggregated Distributed Renewable Energy Sources With Control
Coordination for Resilient Distribution System Restoration
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  Authors: Fei Liu;Chen Chen;Chaofan Lin;Gengfeng Li;Haipeng Xie;Zhaohong Bie;
  Pages: 1043 - 1056
  Abstract: Due to the energy transition process, distribution systems will feature a high penetration of distributed renewable energy sources (RESs). The multiple distributed generation can provide emergency power supply to critical loads against blackouts caused by natural disasters and malicious attacks. However, the uncertainty of RESs, the control mode variation of RESs together with energy storage systems (ESSs), and the interaction among distribution system operator (DSO) and RESs add increasing difficulties to load restoration decisions. This paper applies the coordination control strategy to enable RESs to regulate the frequency and voltage during restoration process. Then, distributed energy resource management systems (DERMSs) and its aggregation characteristic are studied in load restoration. Finally, a two-step scenario-based stochastic optimization with the DSO-DERMS interaction framework is formulated to utilize RESs for resilient distribution systems. At last, the proposed critical load restoration optimization method is validated on a modified IEEE 37 and 123 node test feeder to verify the effectiveness.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
A Secure and Adaptive Hierarchical Multi-Timescale Framework for Resilient
Load Restoration Using a Community Microgrid
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  Authors: Ashwin Shirsat;Valliappan Muthukaruppan;Rongxing Hu;Victor Daldegan Paduani;Bei Xu;Lidong Song;Yiyan Li;Ning Lu;Mesut Baran;David Lubkeman;Wenyuan Tang;
  Pages: 1057 - 1075
  Abstract: Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. This paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using new obtained forecasts, followed by the RT dispatch stage. To make the decisions more secure and robust against forecast errors, a novel concept called delayed recourse is designed. The approach is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS and hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure distribution network operation under different operating scenarios.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Autonomous Restoration of Networked Microgrids Using Communication-Free
Smart Sensing and Protection Units
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  Authors: Abhishek Banerjee;Vaibhav Uttam Pawaskar;Gab-Su Seo;Amit Pandey;Uttam Reddy Pailla;Xiaofan Wu;Ulrich Muenz;
  Pages: 1076 - 1087
  Abstract: This paper presents autonomous restoration of networked microgrids using distributed energy resources (DERs), inverter-based resources (IBRs) in general, and controllable assets of intelligence without communication to achieve a extremely resilient power system restoration. The main innovation of this work lies in use of real-time smart sensing and decision making that enable autonomous recombination of islanded microgrids, which are individually black-started by local grid-forming (GFM) inverter units. The smart circuit breakers (SCBs) designed and demonstrated here play critical roles in the restoration process to achieve autonomy; the autonomous restoration scheme is not pre-engineered and may be subject to encounter overloading, live or downed segments as the boundary of the microgrids dynamically expands without prior knowledge and only with local measurement. We demonstrate autonomous microgrid restoration both in simulation of a modified IEEE 13-bus system, and in a hardware testbed comprising 24 GFM-IBRs and 6-SCBs, depicting several scenarios to evaluate their functionalities and interoperability. This work validates high potential of extremely resilient microgrid using novel approaches for inverter-based bottom-up system restoration in a communication-free paradigm.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Hierarchical Combination of Deep Reinforcement Learning and Quadratic
Programming for Distribution System Restoration
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  Authors: Mohammad Mehdi Hosseini;Luis Rodriguez-Garcia;Masood Parvania;
  Pages: 1088 - 1098
  Abstract: This paper proposes a model for hierarchical combination of deep reinforcement learning (DRL) with quadratic programming for distribution system restoration after major outages. In the proposed model, optimal power dispatch of a collection of distributed energy resources, called integrated hybrid resources (IHRs), is determined by a DRL-trained controller, while a grid-level quadratic programming problem checks grid constraints and performs critical restoration operation. DRL is implemented using Soft Actor-Critic (SAC) algorithm, which is shown to outperform the common Deep Deterministic Policy Gradient in continuous action spaces. The numerical studies, performed on the 123-bus test distribution system, demonstrates that the hierarchical combination of DRL and quadratic programming not only speeds up the local operation of multiple IHRs, but also ensures that the network constraints are satisfied during the restoration operation.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
An Integrated Situational Awareness Tool for Resilience-Driven Restoration
With Sustainable Energy Resources
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  Authors: Chuan Qin;Linli Jia;Surendra Bajagain;Sanjeev Pannala;Anurag K. Srivastava;Anamika Dubey;
  Pages: 1099 - 1111
  Abstract: Integrating sustainable energy resources transforms the distribution grid into an active system with higher variations observed in load and generation. Estimating distributed generation, gross load, and cold load pick-up (CLPU) become more challenging with behind-the-meter (BTM) distributed energy resources (DERs), especially in case of outages caused by extreme events. This work proposes a resilience-driven restoration scheme using the most updated information from an integrated and enhanced situational awareness tool (ESAT) using kernelized Bayesian state-space inference (KBSI) with Markov Chain Monte Carlo (MCMC) and multiple optimization algorithms. ESAT consists of the BTM load/ DER estimation and disaggregation, CLPU estimation, and network topology estimation with de-energized islands. The proposed work provides solutions to establish informed restoration schemes considering resilience criteria for a quick recovery of high-priority loads. A resilience metric is utilized after outages to measure the effectiveness of ESAT-driven restoration for the supposed threats. The performance of developed ESAT is demonstrated using actual field datasets and validated using the emulated real scenarios on a benchmark model.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Power System Restoration With Renewable Participation
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  Authors: Feng Qiu;Yichen Zhang;Rui Yao;Pengwei Du;
  Pages: 1112 - 1121
  Abstract: Power system restoration requires initial power sources to restart generators, energizing transmissions, and pick up critical loads. Sufficient initial power capacity is critical for restoration success and reducing outage duration. Due to the reliability requirement and high standby costs, black-start resources are scarce and expensive in areas where hydro power is not available. Variable renewable energy (VRE), whose fast-ramping capability and economical standby costs are suitable for black-start, is traditionally excluded from participation in system restoration because its uncertainty and variability could cause severe disturbance to the system that is already fragile during restoration. Recently, VRE is becoming a potential participant in system restoration as auxiliary black-start resources. This work will explore the potential benefits of renewable energy in restoration and potential ways for VRE to participate in system restoration. We propose two dispatch models for wind participation. The uncertainty of wind generation is handled by probabilistic constraints to guarantee a feasible restoration plan with a high probability. We perform computational experiments to demonstrate the effectiveness of proposed models and benefits of renewable participation in restoration with the two approaches.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Model Predictive Control Based Voltage Regulation Strategy Using Wind Farm
as Black-Start Source
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  Authors: Weipeng Liu;Yutian Liu;Lei Wu;
  Pages: 1122 - 1134
  Abstract: A coordinated voltage regulation method based on model predictive control (MPC) is proposed in this paper for utilizing wind farms (WF) as black-start (BS) source to start up a thermal generating unit. The reactive power regulation devices with different dynamic response characteristics including wind turbine generators (WTGs), energy storage system (ESS), and static var generator (SVG) are coordinated by the proposed MPC to handle disturbances caused by ancillary machine start during the BS process. The reactive power sharing between WTGs is optimized to maximize the dynamic reactive power reserve. The capabilities of ESS and SVG in providing sufficient dynamic reactive power against disturbances are also fully exploited, which helps accelerate voltage recovery for low voltage ride through to avoid the tripping incidents of WTGs. The impact of active power on bus voltage variation due to low X/R ratio is also considered. The reactive power and active power of WTGs and ESS are coordinately controlled for handling voltage disturbance without harming frequency control. A WF with 33 WTGs rated 1.5 MW each is used in case studies to demonstrate the enhanced disturbance handling capability of the proposed voltage regulation strategy during BS progress.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
A Post-Event Generator Start-Up Strategy for Renewable Penetrated
Transmission System Considering Dynamic Frequency Regulation
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  Authors: Lizhou Jiang;Zhaohong Bie;Tao Long;Xu Wang;Haipeng Xie;Gengfeng Li;
  Pages: 1135 - 1150
  Abstract: Restoring transmission systems (TSs) with a high penetration of renewable generation is a troublesome task. To enhance the resilience of these TSs, a deterministic post-event generator start-up strategy (GSUS) is proposed in this paper, where the conventional and renewable units are co-operated. However, the utilization of renewable generation yields a concern that the recovering TS may be exposed to the risk of frequency instability since the renewable units have the characteristics of low inertia and high uncertainty. Consequently, dynamic frequency regulation (DFR) is considered while a novel approximate method is proposed to linearize the non-convex DFR constraints. Besides, a novel uncertainty set associated with restoring states is established to model the nodal power fluctuations. On the basic of the above works, a robust GSUS with DFR is developed. To solve this GSUS efficiently, an adaptive robust-based methodology is proposed to explore the “worst” realization of the uncertainty and then the non-convex bi-level robust constraints are reformulated. Finally, the effectiveness of our works is verified on a real-world 129-bus TS.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Distribution Service Restoration With Renewable Energy Sources: A Review
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  Authors: Abdulraheem H. Alobaidi;Seyed Saeed Fazlhashemi;Mahdi Khodayar;Jianhui Wang;Mohammad E. Khodayar;
  Pages: 1151 - 1168
  Abstract: Distribution service restoration plays a vital role in mitigating the adverse impacts of power outages stemming from extreme weather conditions. With incentives toward reducing the carbon footprint of power generation on the environment and the reliance on fossil fuel resources, the future distribution networks are equipped with distributed renewable energy sources. This paper reviews the state-of-the-art frameworks proposed for service restoration in the distribution networks with renewable generation technologies. First, the objectives of the distribution service restoration and the challenges to accomplishing this task are discussed. Later, the existing approaches to restoring the distribution networks and the control architectures are presented. The contribution of microgrid technology and the models used to incorporate renewable energy resources in distribution service restoration are discussed. In this context, the application of artificial intelligence to distribution service restoration is highlighted, and potential research opportunities in distribution service restoration are presented.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Grid Resilience With High Renewable Penetration: A PJM Approach
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  Authors: Hong Chen;Chris Pilong;Patricio Rocha-Garrido;Darrell Frogg;Marilyn Jayachandran;Dean Manno;Jason Sexauer;Chris Callaghan;Rob Dropkin;Joe Mulhern;Elizabeth Davis;Ray Lee;Lagy Mathew;Ilyana Dropkin;Elizabeth Anastasio;
  Pages: 1169 - 1177
  Abstract: Grid resilience is critical to modern society. With the global energy transition, renewable resources are fast growing in the energy mix. Renewable resources are highly volatile and intermittent and also closely correlated with weather conditions. Ensuring grid resilience under high renewable penetration is becoming more challenging and pressing. PJM has been working on creating a more resilient system from the perspectives of operations, infrastructure planning, market, cyber and physical security. This paper describes a PJM approach to evaluate and analyze the impact of high renewable penetration on grid resilience, formulate plans to be prepared for such impact, and use market mechanisms to improve grid resilience.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Risk-Based Active Distribution System Planning for Resilience Against
Extreme Weather Events
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  Authors: Abodh Poudyal;Shiva Poudel;Anamika Dubey;
  Pages: 1178 - 1192
  Abstract: Enhancing the resilience of power distribution systems to extreme weather events is of critical concern. Upgrading the distribution system infrastructure by system hardening and investing in smart grid technologies effectively enhances grid resilience. Existing distribution system planning methods primarily consider the persistent cost of the expected events (such as faults and outages likely to occur) and aim at improving system reliability. The resilience to extreme weather events requires reducing the impacts of the high impact low probability (HILP) events that are characterized by the tail probability of the event impact distribution. Thus, the resilience-oriented system upgrades solutions need to be driven by the risks imposed by extreme weather events on the power grid infrastructure rather than persistent costs. This paper aims to develop a risk-based approach for the long-term resilience planning of active power distribution systems against extreme weather events. The proposed approach explicitly models (1) the impacts of HILP events using a two-stage risk-averse stochastic optimization framework, thus, explicitly incorporating the risks of HILP events in long-term planning decisions, and (2) the advanced distribution grid operations (in the aftermath of the event) such as intentional islanding into infrastructure planning problem. The inclusion of risk in the planning objective provides additional flexibility to the grid planners to analyze the trade-off between risk-neutral and risk-averse planning strategies.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
A Resilience-Oriented Multi-Stage Adaptive Distribution System Planning
Considering Multiple Extreme Weather Events
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  Authors: Siyuan Wang;Rui Bo;
  Pages: 1193 - 1204
  Abstract: Climate change may increase the risk of an area being hit by multiple extreme weather events, which brings significant challenges for distribution system planners in an increasing renewable penetration era. There is an urgent need for planning approaches to be more flexible and allow for adaptive adjustments in the future to hedge against high uncertainties in extreme weather event scenarios. In this work, we propose a resilience-oriented distribution system planning approach that considers multiple extreme weather events. A multi-stage hybrid-stochastic-and-robust formulation is developed to model decisions not only for initial investments, but also for adaptive investments and emergent operations in response to particular extreme events, meanwhile considering both long-term and short-term uncertainties. Our model is solved by a novel progressive hedging algorithm that is embedded with a nested column-and-constraint generation method. Case studies demonstrate the benefits of the proposed approach in making flexible and affordable planning decisions to protect distribution systems against multiple extreme weather events.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Optimal Energy Storage System and Smart Switch Placement in Dynamic
Microgrids With Applications to Marine Energy Integration
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  Authors: Xuan Wu;Jianzhe Liu;Yuxi Men;Bo Chen;Xiaonan Lu;
  Pages: 1205 - 1216
  Abstract: This paper studies a dynamic microgrid (DMG) planning problem that places energy storage systems (ESSs) and smart switches (SSWs) optimally in the system. We apply the proposed methodology to applications concerning marine renewable energy (MRE). MRE is an emerging clean energy resource with enormous capacity but volatile and intermittent energy output profiles. Innovative grid-integration technologies designed to enhance the reliability of an MRE-integrated system are needed. However, there are still limited studies in this regard. Existing works have shown the promising prospect of using a dynamic microgrid (DMG) operational concept to accommodate renewable resources in distribution systems, but they usually assume fixed ESS and SSW installations. To further improve the operational flexibility of DMGs, we propose a DMG planning methodology that optimally places ESSs and SSWs so that a DMG with MRE is warranted with proper resource adequacy and topological flexibility in both the contingency and normal operations. We use realistic case studies based on a real-world distribution network and the U.S. Department of Energy's MRE dataset to verify the value and validity of the proposed work.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Coordinated Planning Strategies of Power Systems and Energy Transportation
Networks for Resilience Enhancement
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  Authors: Tianyuan Xu;Chengcheng Shao;Mohammad Shahidehpour;Xifan Wang;
  Pages: 1217 - 1229
  Abstract: Steady and continuous supply of primary energy sources under extreme events is crucial to maintaining the stable operation of power systems, especially for those with highly penetrated renewable generation which is usually underestimated or ignored in existing works. This paper proposes a coordinated planning method of power systems and energy transportation networks (ETNs) for resilience enhancement. Considering both haulage roads and gas pipes, the ETN model is first formulated in the range of primary energy sources and expanded to a more generalized conception subsequently. Then a series of novel uncertainty sets are proposed to simulate the road capacity loss and renewable power variations under extreme events as the extensions of conventional state-attack ones. Based on them, a resilience constrained coordinated planning model is established in which the expansion decisions of both power systems and ETNs are considered as well as the potential influence of extreme events on them. Correspondingly, a solution method is developed to solve the proposed tri-level model efficiently. The case studies on the modified IEEE-RTS have verified the validity of the proposed resilient planning method. The significance of ETNs to power system economical operation and resilience enhancement has also been revealed.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
On Machine Learning-Based Techniques for Future Sustainable and Resilient
Energy Systems
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  Authors: Jiawei Wang;Pierre Pinson;Spyros Chatzivasileiadis;Mathaios Panteli;Goran Strbac;Vladimir Terzija;
  Pages: 1230 - 1243
  Abstract: Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Quantitative Metrics for Grid Resilience Evaluation and Optimization
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  Authors: Yiyun Yao;Weijia Liu;Rishabh Jain;Badrul Chowdhury;Jianhui Wang;Robert Cox;
  Pages: 1244 - 1258
  Abstract: Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. This paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Inertia and Frequency Support From Britain's AC Powered Trains
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  Authors: Callum Henderson;Agusti Egea-Alvarez;Joan Rull-Duran;Marcel Nedd;Panagiotis N. Papadopoulos;Lie Xu;
  Pages: 1259 - 1268
  Abstract: The penetration of converter connected generation is increasing globally, bringing with it valid concerns over the stability of the modern electricity network. In terms of frequency stability, the provision of inertia and frequency support from converter interfaced generation has been the topic of significant research with a wide range of systems considered. One resource that has avoided significant attention is the GB rail electrical rolling stock. Everyday thousands of trains run on a strict schedule, travelling at high speeds with considerable mass all acting as one large energy store. The AC connected trains possess regenerative braking systems allowing for this energy to be harvested. With simple software modifications this energy can be extracted during large frequency events. This article investigates the power available for inertia and frequency response throughout a working day. A sensitivity analysis of parameters is conducted and the work looks to the future by considering increasing penetration of AC trains. A response between 300–850 MW is estimated for a one-minute frequency response. The calculated energy and response profile was then used to investigate the effect that the trains would have had on the 9th of August power cut that occurred in the U.K. in 2019.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Resilience Constrained Scheduling of Mobile Emergency Resources in
Electricity-Hydrogen Distribution Network
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  Authors: Xiaoyu Cao;Tianxiang Cao;Zhanbo Xu;Bo Zeng;Feng Gao;Xiaohong Guan;
  Pages: 1269 - 1284
  Abstract: This paper proposes a trilevel mixed-integer formulation to coordinately schedule the electricity-hydrogen distribution network (EHDN) with resilience consideration. The rescue hydrogen distribution system is first studied and modeled based on the roadway transportation and vehicle-to-grid power supply of mobile hydrogen energy resources (MHERs). Various practical constraints on traffic capacity, travel distances and time delaying of hydrogen transportation are incorporated. Then, to improve the economic viability of EHDN to fulfill the load survivability requirement in disastrous situations, we propose a risk-constrained trilevel optimization formulation. In particular, the resilience constraint with a $min -max$ mixed-integer program structure is introduced, which ensures the post-event service preservation by co-optimizing the re-routing of MHERs and network reconfiguration under worst-case damage scenario. Then, by making use of its structural properties, this complex formulation is solved by a customized nested column-and-constraint generation (NC&CG) algorithm with effective enhancements. Numerical results on an exemplary EHDN indicate that the proactive deployment and adaptive relocation of MHERs provide an economically feasible solution to achieve grid resiliency target. Also, comparing to the standard NC&CG, our improved customization demonstrates a superior performance that drastically reduces the computation time, and thus enables efficient emergency response and disaster relief with synergistic network operations.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Multi-Segment Decentralized Control Strategies for Renewables-Rich
Microgrids in Extreme Conditions
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  Authors: Lei Yan;Mehrdad Sheikholeslami;Xing Hao;Zuyi Li;Wei Tian;
  Pages: 1285 - 1298
  Abstract: Microgrids provide a promising approach to accommodating various distributed energy resources (DERs), while requiring significant communication infrastructures that may be affected by extreme conditions such as natural disasters and cyber-attacks. In this paper, a fully decentralized control strategy without the need for communication is proposed for islanded microgrids with high renewables penetration. First, special multi-segment power/frequency characteristic curves are designed, so that different DERs can be automatically coordinated in a prioritized manner such as renewables first to maintain power balance while DER frequencies are regulated at their reference values. Second, piecewise linear served load versus frequency models are designed to prioritize loads according to their significance, so that only noncritical loads will be curtailed as needed while critical loads are supplied without any interruptions during power deficiency. The proposed strategy can effectively deal with various normal and extreme system conditions including 100% renewables penetration, loads and renewables variations, power deficiencies requiring load curtailments, disconnection of existing DERs, connection of new DERs as well as network sectionalization and reconfiguration. The proposed control strategy is validated in the real-time digital simulator (RTDS) model of the IIT Campus Microgrid to demonstrate its effectiveness in enhancing the resiliency of renewables-rich microgrids in extreme conditions.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Resilience-Based Coordinated Scheduling of Cascaded Hydro Power With
Sequential Heavy Precipitation
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  Authors: Chong Wang;Ping Ju;Can Wan;Feng Wu;Shunbo Lei;Xueping Pan;Tianguang Lu;
  Pages: 1299 - 1311
  Abstract: Cascaded hydro units and thermal units play important roles in power systems, and some external weather-related events, e.g., heavy precipitation, have great impacts on hydro power generation, especially in consideration of the cascaded characteristics between different upstream-downstream hydro units, and the coordinated dispatch between hydro units and thermal units constrained by the system network. This article presents resilience-based of thermal units and cascaded hydro units in consideration of sequential heavy precipitation to improve power source resilience. Reduced generated scenarios are used to describe uncertainty of heavy precipitation along the hydropower stations sequentially located on the river and wind power. For the cascaded hydro units, the piecewise linearization technique for two-dimensional and three-dimensional functions are used to address the nonlinear relations among reservoir volume, reservoir forebay water level, reservoir tailrace water, net water head, unit turbined outflow, power outputs, etc. Cascaded hydro unit commitment and thermal unit commitment are conducted with the network constraints. The whole problem is established as a mixed integer linear programming model with an expected operational cost as the optimization objective. Two cases are used to validate the model, and the resilience-based coordinated strategies are demonstrated to improve the power source resilience against sequential heavy precipitation.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Controlled Islanding Resilience With High Penetration of Renewable Energy
Resources
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  Authors: Rui Ma;Sagnik Basumallik;Sara Eftekharnejad;
  Pages: 1312 - 1323
  Abstract: Controlled islanding is a last-resort recovery scheme for preventing cascading failures. Although effective, high levels of uncertainty from renewable energy resources challenge the promise of controlled islanding to prevent additional failures. Such uncertainties can lead to subsequent failures in individual islands, render the overall recovery approach ineffective, and reduce the grid resilience. This paper develops a stochastic cascading failure model to assess the resilience of islands against cascading failures under high penetration of renewables. The stochastic model incorporates physical system constraints and system data to predict the failures. Hence, the most probable cascades in islands with high uncertainties are efficiently identified, which enables evaluating the islands' resilience to cascades. The developed model significantly improves the efficiency and accuracy of the resilience study in the presence of uncertainties. Case studies indicate that the increased penetration of intermittent renewables can improve or degrade the system resilience depending on the level of renewable generation. The case studies also reveal that an islanding strategy to minimize the imbalance of load and generation can lead to less resilient islands. These findings substantially enhance the understanding of controlled islanding under high generation uncertainties and improve resilience-oriented grid planning.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)
Photovoltaic System Control for Power System Frequency Support in Case of
Cascading Events
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  Authors: Tomislav Baškarad;Ninoslav Holjevac;Igor Kuzle;
  Pages: 1324 - 1334
  Abstract: In a power system with a high-share of photovoltaic systems, the frequency response can be improved by the participation of photovoltaics in frequency control. If the photovoltaic system operates at reduced power, in a so-called de-loaded mode, and maintains a specific amount of power reserve, then participation in system frequency control is usually realized by a droop control method. The implementation of this method is very simple, but it does not provide the recovery of PV power reserve before the secondary frequency control reacts. This means the PV system cannot provide additional support to the grid if a new disturbance occurs in a short time following the first one. This paper presents a novel approach for PV system control in providing support to system frequency. The novel control algorithm aims to enable rapid recovery of PV power reserve which makes the PV system capable of providing support to the cascading disturbance events. The development of such PV system control model is based on a detailed mathematical analysis of the dynamic frequency response. The effectiveness of the proposed method was tested on a two-area multi-machine power system model and through 4 different study cases.
  PubDate: April 2023
  Issue No: Vol. 14, No. 2 (2023)