The significant increase in the proportion of renewable energy sources (RESs) has elevated risks of extreme ramp events and frequency instability in power systems. In recent years, frequency stability events have occurred in several countries/regions worldwide due to flexibility deficiencies. Generation flexibility has emerged as a critical factor influencing the frequency stability of power systems. This paper proposes a domain of attraction (DOA)-based quantitative method to assess the frequency stability region of power systems with a high proportion of RESs, considering generation flexibility constraints. First, ramp rate is adopted as the core indicator to characterize generation flexibility within automatic generation control (AGC) timescale, through which a nonlinear AGC model with rate saturation constraints is established. Second, the concept of DOA is introduced to define the stability region of the nonlinear AGC. Third, a quadratic Lyapunov-based estimation method is employed to quantitatively analyze the DOA of the nonlinear AGC at different generation flexibility levels. Simulation results demonstrate that increased generation flexibility expands the estimated DOA of the nonlinear AGC, whereas generation flexibility deficiency induces AGC instability. Moreover, state trajectory and time-domain simulation verify that the proposed estimation method accurately represents the stability region of the nonlinear AGC.
{"title":"Frequency Stability Analysis Based on Generation Flexibility and Domain of Attraction for Power Systems with High Proportion of Renewable Energy Sources","authors":"Xiaohui Zhang;Changhong Deng;Qiang Xu;Peng Cao;Wei Li;Li Feng","doi":"10.35833/MPCE.2024.000714","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000714","url":null,"abstract":"The significant increase in the proportion of renewable energy sources (RESs) has elevated risks of extreme ramp events and frequency instability in power systems. In recent years, frequency stability events have occurred in several countries/regions worldwide due to flexibility deficiencies. Generation flexibility has emerged as a critical factor influencing the frequency stability of power systems. This paper proposes a domain of attraction (DOA)-based quantitative method to assess the frequency stability region of power systems with a high proportion of RESs, considering generation flexibility constraints. First, ramp rate is adopted as the core indicator to characterize generation flexibility within automatic generation control (AGC) timescale, through which a nonlinear AGC model with rate saturation constraints is established. Second, the concept of DOA is introduced to define the stability region of the nonlinear AGC. Third, a quadratic Lyapunov-based estimation method is employed to quantitatively analyze the DOA of the nonlinear AGC at different generation flexibility levels. Simulation results demonstrate that increased generation flexibility expands the estimated DOA of the nonlinear AGC, whereas generation flexibility deficiency induces AGC instability. Moreover, state trajectory and time-domain simulation verify that the proposed estimation method accurately represents the stability region of the nonlinear AGC.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 4","pages":"1139-1150"},"PeriodicalIF":5.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A mathematical programming approach rooted in distributionally robust optimization (DRO) provides an effective data-driven strategy for battery energy storage system (BESS) planning. Nevertheless, the DRO paradigm often lacks interpretability in its results, obscuring the causal relationships between data distribution characteristics and the outcomes. Furthermore, the current approach to battery type selection is not included in traditional BESS planning, hindering comprehensive optimization. To tackle these BESS planning problems, this paper presents a universal method for BESS planning, which is designed to enhance the interpretability of DRO. First, mathematical definitions of interpretable DRO (IDRO) are introduced. Next, the uncertainties in wind power, photovoltaic power, and loads are modeled by using second-order cone ambiguity sets (SOCASs). In addition, the proposed method integrates selection, sizing, and siting. Moreover, a second-order cone bidirectional-orthogonal strategy is proposed to solve the BESS planning problems. Finally, the effectiveness of the proposed method is demonstrated through case studies, offering planners richer decision-making insights.
{"title":"Interpretable Distributionally Robust Optimization for Battery Energy Storage System Planning","authors":"Qian Wang;Xueguang Zhang;Ying Xu;Zhongkai Yi;Dianguo Xu","doi":"10.35833/MPCE.2024.000974","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000974","url":null,"abstract":"A mathematical programming approach rooted in distributionally robust optimization (DRO) provides an effective data-driven strategy for battery energy storage system (BESS) planning. Nevertheless, the DRO paradigm often lacks interpretability in its results, obscuring the causal relationships between data distribution characteristics and the outcomes. Furthermore, the current approach to battery type selection is not included in traditional BESS planning, hindering comprehensive optimization. To tackle these BESS planning problems, this paper presents a universal method for BESS planning, which is designed to enhance the interpretability of DRO. First, mathematical definitions of interpretable DRO (IDRO) are introduced. Next, the uncertainties in wind power, photovoltaic power, and loads are modeled by using second-order cone ambiguity sets (SOCASs). In addition, the proposed method integrates selection, sizing, and siting. Moreover, a second-order cone bidirectional-orthogonal strategy is proposed to solve the BESS planning problems. Finally, the effectiveness of the proposed method is demonstrated through case studies, offering planners richer decision-making insights.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1664-1676"},"PeriodicalIF":6.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10988702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-02DOI: 10.35833/MPCE.2024.001135
Mingyu Yang;Yusheng Xue;Bin Cai;Feng Xue
Planning the low-carbon transition pathway of the power sector to meet the carbon neutrality goal poses a significant challenge due to the complex interplay of temporal, spatial, and cross-domain factors. A novel framework is proposed, grounded in the cyber-physical-social system in energy (CPSSE) and whole-reductionism thinking (WRT), incorporating a tailored mathematical model and optimization method to formalize the co-optimization of carbon reduction and carbon sequestration in the power sector. Using the carbon peaking and carbon neutrality transition of China as a case study, clustering method is employed to construct a diverse set of strategically distinct carbon trajectories. For each trajectory, the evolution of the generation mix and the deployment pathways of carbon capture and storage (CCS) technologies are analyzed, identifying the optimal transition pathway based on the criterion of minimizing cumulative economic costs. Further, by comparing non-fossil energy substitution and CCS retrofitting in thermal power, the analysis high-lights the potential for co-optimization of carbon reduction and carbon sequestration. The results demonstrate that leveraging the spatiotemporal complementarities between the two can substantially lower the economic cost of achieving carbon neutrality, providing insights for integrated decarbonization strategies in power system planning.
{"title":"Co-Optimization of Carbon Reduction and Carbon Sequestration in Power Sector Toward Carbon Neutrality","authors":"Mingyu Yang;Yusheng Xue;Bin Cai;Feng Xue","doi":"10.35833/MPCE.2024.001135","DOIUrl":"https://doi.org/10.35833/MPCE.2024.001135","url":null,"abstract":"Planning the low-carbon transition pathway of the power sector to meet the carbon neutrality goal poses a significant challenge due to the complex interplay of temporal, spatial, and cross-domain factors. A novel framework is proposed, grounded in the cyber-physical-social system in energy (CPSSE) and whole-reductionism thinking (WRT), incorporating a tailored mathematical model and optimization method to formalize the co-optimization of carbon reduction and carbon sequestration in the power sector. Using the carbon peaking and carbon neutrality transition of China as a case study, clustering method is employed to construct a diverse set of strategically distinct carbon trajectories. For each trajectory, the evolution of the generation mix and the deployment pathways of carbon capture and storage (CCS) technologies are analyzed, identifying the optimal transition pathway based on the criterion of minimizing cumulative economic costs. Further, by comparing non-fossil energy substitution and CCS retrofitting in thermal power, the analysis high-lights the potential for co-optimization of carbon reduction and carbon sequestration. The results demonstrate that leveraging the spatiotemporal complementarities between the two can substantially lower the economic cost of achieving carbon neutrality, providing insights for integrated decarbonization strategies in power system planning.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1481-1494"},"PeriodicalIF":6.1,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-02DOI: 10.35833/MPCE.2024.001044
Bin Zou;Ge Chen;Hongcai Zhang;Yonghua Song
Data centers are promising demand-side flexible resources that can provide frequency regulation services to power grids. While most existing studies focus on individual data centers, coordinating multiple geo-distributed data centers can significantly enhance operational flexibility and market participation. However, the inherent uncertainty in both data center workloads and regulation signals pose significant challenges to maintaining effective operations, let alone determining regulation capacity offerings. To address these challenges, this paper proposes a coordinated bidding strategy for electricity purchases and regulation capacity offerings for multiple geo-distributed data centers in electricity markets. This strategy expands the feasible region of operational decisions, including workload dispatch, server activation, and cooling behaviors. To enhance the participation of data centers in frequency regulation services under uncertainty, chance-constrained programming is adopted. This paper presents explicit models for these uncertainties involved, starting with the Poisson-distributed workloads and then addressing the unpredictable regulation signals. Numerical experiments based on real-world datasets validate the effectiveness of the proposed strategy compared with state-of-the-art strategies.
{"title":"Coordinating Multiple Geo-Distributed Data Centers for Enhanced Participation in Frequency Regulation Services Under Uncertainty","authors":"Bin Zou;Ge Chen;Hongcai Zhang;Yonghua Song","doi":"10.35833/MPCE.2024.001044","DOIUrl":"https://doi.org/10.35833/MPCE.2024.001044","url":null,"abstract":"Data centers are promising demand-side flexible resources that can provide frequency regulation services to power grids. While most existing studies focus on individual data centers, coordinating multiple geo-distributed data centers can significantly enhance operational flexibility and market participation. However, the inherent uncertainty in both data center workloads and regulation signals pose significant challenges to maintaining effective operations, let alone determining regulation capacity offerings. To address these challenges, this paper proposes a coordinated bidding strategy for electricity purchases and regulation capacity offerings for multiple geo-distributed data centers in electricity markets. This strategy expands the feasible region of operational decisions, including workload dispatch, server activation, and cooling behaviors. To enhance the participation of data centers in frequency regulation services under uncertainty, chance-constrained programming is adopted. This paper presents explicit models for these uncertainties involved, starting with the Poisson-distributed workloads and then addressing the unpredictable regulation signals. Numerical experiments based on real-world datasets validate the effectiveness of the proposed strategy compared with state-of-the-art strategies.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1677-1688"},"PeriodicalIF":6.1,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.35833/MPCE.2024.000548
Xingyu Liu;Yunting Yao;Tianran Li;Yening Lai;Qi Wang;Zhenya Ji
Peer-to-peer (P2P) energy trading enables an efficient regulation of distributed renewable energy among prosumers, implicitly promoting low-carbon operation. This study proposes a novel P2P energy trading scheme with coupled electricity-carbon (E/C) market that co-optimizes both power and carbon emission flows. To facilitate the low-carbon operations in the market, we introduce a prosumer-driven carbon-aware distribution locational marginal price (PDC-DLMP) to serve as a pricing signal for the distribution system operator (DSO). To efficiently determine the optimal trading solutions, we adopt a two-layer data-driven approach. The first layer employs a rein-forcement learning algorithm named multi-agent twin-delayed deep deterministic policy gradient (MATD3); the second layer uses a deep neural network (DNN) driven surrogate model, which is designed to map the PDC-DLMP signals, thereby eliminating the need for direct DSO intervention during market operation. This approach protects the physical model parameters of the distribution network and ensures multilevel privacy protection. Simulation results validate the effectiveness of the proposed P2P energy trading scheme with coupled E/C market, demonstrating its ability to achieve both reduced carbon emissions and lower operational costs for microgrid prosumers.
{"title":"Data-Driven Peer-to-Peer Energy Trading Based on Prosumer-Driven Carbon-Aware Distribution Locational Marginal Price","authors":"Xingyu Liu;Yunting Yao;Tianran Li;Yening Lai;Qi Wang;Zhenya Ji","doi":"10.35833/MPCE.2024.000548","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000548","url":null,"abstract":"Peer-to-peer (P2P) energy trading enables an efficient regulation of distributed renewable energy among prosumers, implicitly promoting low-carbon operation. This study proposes a novel P2P energy trading scheme with coupled electricity-carbon (E/C) market that co-optimizes both power and carbon emission flows. To facilitate the low-carbon operations in the market, we introduce a prosumer-driven carbon-aware distribution locational marginal price (PDC-DLMP) to serve as a pricing signal for the distribution system operator (DSO). To efficiently determine the optimal trading solutions, we adopt a two-layer data-driven approach. The first layer employs a rein-forcement learning algorithm named multi-agent twin-delayed deep deterministic policy gradient (MATD3); the second layer uses a deep neural network (DNN) driven surrogate model, which is designed to map the PDC-DLMP signals, thereby eliminating the need for direct DSO intervention during market operation. This approach protects the physical model parameters of the distribution network and ensures multilevel privacy protection. Simulation results validate the effectiveness of the proposed P2P energy trading scheme with coupled E/C market, demonstrating its ability to achieve both reduced carbon emissions and lower operational costs for microgrid prosumers.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1836-1848"},"PeriodicalIF":6.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-28DOI: 10.35833/MPCE.2024.000164
Jinghan Zhao;Keting Wan;Yongpan Chen;Miao Yu
In DC power systems dominated by power electronic devices, constant power loads (CPLs) and saturation components significantly impact large-signal stability. During the large-signal stability analysis process, the presence of multiple state variables and high-order system poses substantial challenges. To address this, considering the complete control dynamics, this paper proposes an equivalent single-machine (ESM) model of the droop-based DC power systems to reduce the complexity of the large-signal analysis. Building on the proposed ESM model, considering the dynamics of CPL and saturation constraints, a region of attraction (ROA) estimation algorithm based on sum of squares (SOS) programming is proposed, which significantly reduces the conservativeness compared with other existing methods. Furthermore, a control parameter optimization algorithm based on SOS programming is proposed with the aim of expanding the ROA. Furthermgre, with the aim of expanding the ROA, controller sythesis is conducted with proposed control parameter optimization algorithm based on SOS programming. Ultimately, simulation experiments validate the accuracy of the proposed ESM model and the proposed ROA estimation algorithm, as well as the effectiveness of the control parameter optimization algorithm.
{"title":"Large-Signal Analysis and Controller Synthesis of Droop-Based DC Power System with Saturation Constraints","authors":"Jinghan Zhao;Keting Wan;Yongpan Chen;Miao Yu","doi":"10.35833/MPCE.2024.000164","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000164","url":null,"abstract":"In DC power systems dominated by power electronic devices, constant power loads (CPLs) and saturation components significantly impact large-signal stability. During the large-signal stability analysis process, the presence of multiple state variables and high-order system poses substantial challenges. To address this, considering the complete control dynamics, this paper proposes an equivalent single-machine (ESM) model of the droop-based DC power systems to reduce the complexity of the large-signal analysis. Building on the proposed ESM model, considering the dynamics of CPL and saturation constraints, a region of attraction (ROA) estimation algorithm based on sum of squares (SOS) programming is proposed, which significantly reduces the conservativeness compared with other existing methods. Furthermore, a control parameter optimization algorithm based on SOS programming is proposed with the aim of expanding the ROA. Furthermgre, with the aim of expanding the ROA, controller sythesis is conducted with proposed control parameter optimization algorithm based on SOS programming. Ultimately, simulation experiments validate the accuracy of the proposed ESM model and the proposed ROA estimation algorithm, as well as the effectiveness of the control parameter optimization algorithm.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 3","pages":"791-801"},"PeriodicalIF":5.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Driven by increasing penetration of intermittent renewable energy generation, modern power systems are promoting the integration of energy storage (ES) and advocating high-resolution dynamic security constrained optimal power flow (DSCOPF) models to exploit ES time-shifting flexibility against contingencies and respond promptly to more frequent variations in the system operating status. While pioneering research works explore different methods to solve security constrained optimal power flow (SCOPF) problems at individual time steps, real-time implementation of DSCOPF still faces challenges associated with uncertainty adaptation, complex constraint satisfaction, and computational efficiency. This paper proposes a physics-guided safe policy learning method, featuring an analytical evaluation model to provide both accurate safety and cost-efficiency evaluations. A primal-dual-based learning procedure is developed to guide policy learning, fostering prompt convergence. A spatial-temporal graph neural network is constructed to enhance perception on the spatial-temporal uncertainties and leverage policy generalization. Case studies validate the effectiveness and scalability of the proposed method in safety, cost-efficiency, and computational performance and highlight the value of enhanced perception on IEEE 39-bus and 118-bus test systems.
{"title":"Physics-Guided Safe Policy Learning with Enhanced Perception for Real-Time Dynamic Security Constrained Optimal Power Flow","authors":"Yujian Ye;Yizhi Wu;Jianxiong Hu;Hao Hu;Siqi Qian;Xi Zhang;Qiong Wang;Goran Strbac","doi":"10.35833/MPCE.2024.001219","DOIUrl":"https://doi.org/10.35833/MPCE.2024.001219","url":null,"abstract":"Driven by increasing penetration of intermittent renewable energy generation, modern power systems are promoting the integration of energy storage (ES) and advocating high-resolution dynamic security constrained optimal power flow (DSCOPF) models to exploit ES time-shifting flexibility against contingencies and respond promptly to more frequent variations in the system operating status. While pioneering research works explore different methods to solve security constrained optimal power flow (SCOPF) problems at individual time steps, real-time implementation of DSCOPF still faces challenges associated with uncertainty adaptation, complex constraint satisfaction, and computational efficiency. This paper proposes a physics-guided safe policy learning method, featuring an analytical evaluation model to provide both accurate safety and cost-efficiency evaluations. A primal-dual-based learning procedure is developed to guide policy learning, fostering prompt convergence. A spatial-temporal graph neural network is constructed to enhance perception on the spatial-temporal uncertainties and leverage policy generalization. Case studies validate the effectiveness and scalability of the proposed method in safety, cost-efficiency, and computational performance and highlight the value of enhanced perception on IEEE 39-bus and 118-bus test systems.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1507-1519"},"PeriodicalIF":6.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10906317","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-26DOI: 10.35833/MPCE.2024.000925
Jiani Lu;Chao Qin;Yuan Zeng;Guilian Wu;Hao Chen
In cyber-physical distribution systems (CPDSs), the complex coupling between cyber and physical components poses significant challenges to system resilience. When extreme weather disasters occur, these coupling relationships greatly increase the complexity of recovery decisions, which prolongs recovery time and increases recovery costs. In this paper, a collaborative recovery method for CPDS considering multiple coupling constraints is proposed to avoid large-scale outages. First, a fictitious flow based model is established to describe the functional availability of cyber nodes. Second, three typical components are analytically modeled to describe the energy-control coupling relationships. Then, a collaborative recovery method is proposed for post-disaster crew dispatch, network reconfiguration, and fault repair to restore critical loads, considering both the cyber availability constraints and cyber-physical coupling constraints. Finally, the effectiveness of the proposed recovery method is verified by the DCPS-160 test system.
{"title":"Collaborative Recovery Method for Cyber-Physical Distribution System Considering Multiple Coupling Constraints","authors":"Jiani Lu;Chao Qin;Yuan Zeng;Guilian Wu;Hao Chen","doi":"10.35833/MPCE.2024.000925","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000925","url":null,"abstract":"In cyber-physical distribution systems (CPDSs), the complex coupling between cyber and physical components poses significant challenges to system resilience. When extreme weather disasters occur, these coupling relationships greatly increase the complexity of recovery decisions, which prolongs recovery time and increases recovery costs. In this paper, a collaborative recovery method for CPDS considering multiple coupling constraints is proposed to avoid large-scale outages. First, a fictitious flow based model is established to describe the functional availability of cyber nodes. Second, three typical components are analytically modeled to describe the energy-control coupling relationships. Then, a collaborative recovery method is proposed for post-disaster crew dispatch, network reconfiguration, and fault repair to restore critical loads, considering both the cyber availability constraints and cyber-physical coupling constraints. Finally, the effectiveness of the proposed recovery method is verified by the DCPS-160 test system.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 5","pages":"1752-1762"},"PeriodicalIF":6.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10906399","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21DOI: 10.35833/MPCE.2024.000244
Tianhui Meng;Jilai Yu
The increasing penetration of wind power poses challenges to the power grid operation and scheduling. Yet, if the uncertainty of wind power can be economically and effectively managed on the source side, it can drive the power grids towards renewable-dominant future. In this paper, an enhanced scheduling strategy for wind farm-flexible load joint operation system (WF-FLJOS) is proposed. The proposed strategy is designed to manage the uncertainty of wind power on the generation side when integrated into a large-scale power grid. Moreover, it can contribute to saving energy costs on the load side. Compared with the current wind farm operation rules, more stringent assessment requirements are put forward for wind power output accuracy, and the internal organization framework of WF-FLJOS is designed. For potential power violations of wind farms and flexible loads, the violation penalty mechanisms are developed to regulate the behavior of the participants. The joint operation model of the WF-FLJOS is proposed and the submission and tracking approach of the generation schedule for the wind farm is investigated. Numerical results indicate that the proposed strategy can not only improve the ability of the wind farm to track the generation schedule, but also consider the benefits of both the farm side and the load side. Meanwhile, the proposed strategy effectively reduces the schedule adjustment pressure on the main grid caused by the rolling correction mode of the intraday schedule for wind farms.
{"title":"Enhanced Scheduling Strategy for Wind Farm-Flexible Load Joint Operation System","authors":"Tianhui Meng;Jilai Yu","doi":"10.35833/MPCE.2024.000244","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000244","url":null,"abstract":"The increasing penetration of wind power poses challenges to the power grid operation and scheduling. Yet, if the uncertainty of wind power can be economically and effectively managed on the source side, it can drive the power grids towards renewable-dominant future. In this paper, an enhanced scheduling strategy for wind farm-flexible load joint operation system (WF-FLJOS) is proposed. The proposed strategy is designed to manage the uncertainty of wind power on the generation side when integrated into a large-scale power grid. Moreover, it can contribute to saving energy costs on the load side. Compared with the current wind farm operation rules, more stringent assessment requirements are put forward for wind power output accuracy, and the internal organization framework of WF-FLJOS is designed. For potential power violations of wind farms and flexible loads, the violation penalty mechanisms are developed to regulate the behavior of the participants. The joint operation model of the WF-FLJOS is proposed and the submission and tracking approach of the generation schedule for the wind farm is investigated. Numerical results indicate that the proposed strategy can not only improve the ability of the wind farm to track the generation schedule, but also consider the benefits of both the farm side and the load side. Meanwhile, the proposed strategy effectively reduces the schedule adjustment pressure on the main grid caused by the rolling correction mode of the intraday schedule for wind farms.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 4","pages":"1211-1223"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21DOI: 10.35833/MPCE.2024.000152
Jingwen Huang;Guannan Lou;Wei Gu;Chao Shen
In extreme events, microgrid (MG) formation has drawn attention due to its potential to assist in load restoration in the distribution network by utilizing the distributed generations (DGs). However, most of the state-of-the-art studies pay attention to the steady constraints without considering the transient performance during MG formation process. Power fluctuations caused by line switch operations can lead to frequency overruns in low-inertia DG-based systems, thus tripping protective relays. This paper proposes an MG formation method for load restoration in the distribution network with dynamic frequency constraints during the load restoration process. Firstly, considering the frequency constraints, a frequency nadir formula is derived based on the aggregated model. The proposed MG formation method offers two solutions to ensure the frequency safety. One solution is to incorporate the dynamic frequency constraints into the MG formation optimization model to satisfy the frequency requirements if the load restoration amount is preferred. Another alternative solution is to introduce an inertia-adjustable control strategy using virtual synchronous generators (VSGs), which is aimed to improve the frequency nadir during MG formation process. This solution is implemented without changing the MG formation result that is subject to only steady constraints when the load restoration speed is privileged. Theoretical validity is verified through the simulation results. Case study results prove the effectiveness of proposed solutions under various demands in the aspect of frequency improvement.
{"title":"Microgrid Formation Method for Load Restoration in Distribution Network with Dynamic Frequency Constraints","authors":"Jingwen Huang;Guannan Lou;Wei Gu;Chao Shen","doi":"10.35833/MPCE.2024.000152","DOIUrl":"https://doi.org/10.35833/MPCE.2024.000152","url":null,"abstract":"In extreme events, microgrid (MG) formation has drawn attention due to its potential to assist in load restoration in the distribution network by utilizing the distributed generations (DGs). However, most of the state-of-the-art studies pay attention to the steady constraints without considering the transient performance during MG formation process. Power fluctuations caused by line switch operations can lead to frequency overruns in low-inertia DG-based systems, thus tripping protective relays. This paper proposes an MG formation method for load restoration in the distribution network with dynamic frequency constraints during the load restoration process. Firstly, considering the frequency constraints, a frequency nadir formula is derived based on the aggregated model. The proposed MG formation method offers two solutions to ensure the frequency safety. One solution is to incorporate the dynamic frequency constraints into the MG formation optimization model to satisfy the frequency requirements if the load restoration amount is preferred. Another alternative solution is to introduce an inertia-adjustable control strategy using virtual synchronous generators (VSGs), which is aimed to improve the frequency nadir during MG formation process. This solution is implemented without changing the MG formation result that is subject to only steady constraints when the load restoration speed is privileged. Theoretical validity is verified through the simulation results. Case study results prove the effectiveness of proposed solutions under various demands in the aspect of frequency improvement.","PeriodicalId":51326,"journal":{"name":"Journal of Modern Power Systems and Clean Energy","volume":"13 4","pages":"1323-1334"},"PeriodicalIF":5.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10899799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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