Pub Date : 2025-01-15DOI: 10.1109/TSTE.2025.3529199
Weiye Song;Jie Yan;Shuang Han;Ning Zhang;Shihua Liu;Chang Ge;Yongqian Liu
As wind power is becoming a major energy source of power systems, the risk of power shortages due to its intermittent low power output is growing. Accurate forecasting of low wind power is crucial for mitigating these impacts. However, conventional methods struggle with few-sample issues due to the infrequent occurrence of low wind power, limiting accuracy improvements. To address this, a self-supervised pre-learning method is proposed to forecast low wind power occurrence period and output, leveraging the similarities and differences among low output samples to enhance forecasting accuracy. Low wind power output is decomposed into low wind power events (LWPE), which represent the occurrence timeframe, and low wind power processes (LWPP), which represent the power sequences. For LWPE forecasting, a siamese residual shrinkage network based on contrastive learning is introduced. This network pre-learns LWPE features from sample pairs to mitigate the impact of imbalanced sample distribution. For LWPP forecasting, a pattern recognition-based embedded forecasting framework is proposed, embedding typical LWPP fluctuations into the prediction network to improve fit under limited sample conditions. A case study on 3 wind farm clusters shows that this method improves LWPP forecasting accuracy from 84.99%-86.6% to 89.97%, outperforming traditional methods without pre-learning.
{"title":"A Self-Supervised Pre-Learning Method for Low Wind Power Forecasting","authors":"Weiye Song;Jie Yan;Shuang Han;Ning Zhang;Shihua Liu;Chang Ge;Yongqian Liu","doi":"10.1109/TSTE.2025.3529199","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529199","url":null,"abstract":"As wind power is becoming a major energy source of power systems, the risk of power shortages due to its intermittent low power output is growing. Accurate forecasting of low wind power is crucial for mitigating these impacts. However, conventional methods struggle with few-sample issues due to the infrequent occurrence of low wind power, limiting accuracy improvements. To address this, a self-supervised pre-learning method is proposed to forecast low wind power occurrence period and output, leveraging the similarities and differences among low output samples to enhance forecasting accuracy. Low wind power output is decomposed into low wind power events (LWPE), which represent the occurrence timeframe, and low wind power processes (LWPP), which represent the power sequences. For LWPE forecasting, a siamese residual shrinkage network based on contrastive learning is introduced. This network pre-learns LWPE features from sample pairs to mitigate the impact of imbalanced sample distribution. For LWPP forecasting, a pattern recognition-based embedded forecasting framework is proposed, embedding typical LWPP fluctuations into the prediction network to improve fit under limited sample conditions. A case study on 3 wind farm clusters shows that this method improves LWPP forecasting accuracy from 84.99%-86.6% to 89.97%, outperforming traditional methods without pre-learning.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1723-1736"},"PeriodicalIF":8.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article proposes an optimal scheduling algorithm for an integrated PV-ESS system to maximize the overall revenue from both system marginal price (SMP) and renewable energy certificate (REC), considering detailed settlement rules in South Korea. Furthermore, to prevent revenue losses caused by forecasting errors, robust optimization (RO) and receding horizon rescheduling (RHR) approaches, are exploited. The academic contributions of this work are: 1) the formulation of complex settlement rules as an optimization problem, and 2) the implementation of a mixed integer linear programming (MILP)-based RO that can be solved by non-commercial solvers. To verify the effectiveness of the proposed method, simulations and experiments were conducted using a commercial testbed. Compared to the rule-based algorithm which had been adopted in the testbed, the proposed algorithm achieved a 9.3% increase in revenue.
{"title":"Optimal Scheduling and Commercial Testbed-Based Verification of Integrated PV-ESS Systems Considering Settlement Rules in South Korea","authors":"Rae-Kyun Kim;Gyu-Sub Lee;Jae-Gyun Park;Hyoseop Lee;Seung-Il Moon;Jae-Won Chang","doi":"10.1109/TSTE.2025.3529693","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529693","url":null,"abstract":"This article proposes an optimal scheduling algorithm for an integrated PV-ESS system to maximize the overall revenue from both system marginal price (SMP) and renewable energy certificate (REC), considering detailed settlement rules in South Korea. Furthermore, to prevent revenue losses caused by forecasting errors, robust optimization (RO) and receding horizon rescheduling (RHR) approaches, are exploited. The academic contributions of this work are: 1) the formulation of complex settlement rules as an optimization problem, and 2) the implementation of a mixed integer linear programming (MILP)-based RO that can be solved by non-commercial solvers. To verify the effectiveness of the proposed method, simulations and experiments were conducted using a commercial testbed. Compared to the rule-based algorithm which had been adopted in the testbed, the proposed algorithm achieved a 9.3% increase in revenue.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1372-1387"},"PeriodicalIF":8.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To fully utilize the potential of massive small-scale distributed photovoltaics (DPVs) for secondary frequency regulation (SFR), this article introduces a hierarchical coordination framework that incorporates the dynamic response characteristic (DRC) of DPV to automatic generation control (AGC) signals, thereby reflecting the dynamic flexibility of the aggregated DPVs (ADPVs). First, a reserved power feasible range is derived for scheduling the power reserve control (PRC) scheme considering the uncertainty in PV generation and the de-loaded margin base constraint. Second, a two-stage multi-cluster DRC aggregation method that considers the impact of the PRC scheme is developed to describe the equivalent DRC of the ADPVs. Last, the article constructs an integrated cost function (ICF) that reveals the interdependencies between SFR capacity, equivalent DRC and regulation cost, which enables the decoupled scheduling of the SFR indices and the PRC scheme. An event-triggered duty factor reassignment mechanism is further proposed to improve the reliability of SFR service deployment in case of unexpected events. Simulation results indicate that the framework is an efficient approach for quantifying, trading and realizing the dynamic flexibility of the ADPVs.
{"title":"Secondary Frequency Regulation From Aggregated Distributed Photovoltaics: A Dynamic Flexibility Aggregation Approach","authors":"Songyan Zhang;Peixuan Wu;Chao Lu;Huanhuan Yang;Tuo Jiang","doi":"10.1109/TSTE.2025.3529512","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529512","url":null,"abstract":"To fully utilize the potential of massive small-scale distributed photovoltaics (DPVs) for secondary frequency regulation (SFR), this article introduces a hierarchical coordination framework that incorporates the dynamic response characteristic (DRC) of DPV to automatic generation control (AGC) signals, thereby reflecting the dynamic flexibility of the aggregated DPVs (ADPVs). First, a reserved power feasible range is derived for scheduling the power reserve control (PRC) scheme considering the uncertainty in PV generation and the de-loaded margin base constraint. Second, a two-stage multi-cluster DRC aggregation method that considers the impact of the PRC scheme is developed to describe the equivalent DRC of the ADPVs. Last, the article constructs an integrated cost function (ICF) that reveals the interdependencies between SFR capacity, equivalent DRC and regulation cost, which enables the decoupled scheduling of the SFR indices and the PRC scheme. An event-triggered duty factor reassignment mechanism is further proposed to improve the reliability of SFR service deployment in case of unexpected events. Simulation results indicate that the framework is an efficient approach for quantifying, trading and realizing the dynamic flexibility of the ADPVs.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1356-1371"},"PeriodicalIF":8.6,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1109/TSTE.2025.3528948
Boyou Jiang;Chuangxin Guo;Zhe Chen
With the increasing penetration of wind power and gradual retirement of conventional generating units (CGUs), wind turbines (WTs) become promising resources to provide steady-state energy reserve (ER) and frequency support for the grid to facilitate supply-demand balance and frequency security. In this regard, a novel frequency constrained dispatch framework with ER and virtual inertia from WTs is proposed. Firstly, this paper establishes the WT model with both ER and virtual inertia, whose energy sources are WT's deloading and rotor kinetic energy, respectively. Secondly, the system frequency response and CGUs' power response are derived while considering WTs exiting inertia response at frequency nadir. Then, this paper develops a stochastic-optimization-based frequency constrained dispatch model, where both WTs' frequency regulation parameters and rotor speeds are decision variables, so that the coupling between WT's mechanical and electrical parts and the coupling between system's transient dynamics and steady-state operation can be fully reflected. Finally, convex hull relaxation, convex hull approximation and deep neural networks are used to transform the original nonlinear model into a mixed-integer second-order cone programming model. Case studies on the 118-bus system verify the effectiveness of the proposed models and methods.
{"title":"Frequency Constrained Dispatch With Energy Reserve and Virtual Inertia From Wind Turbines","authors":"Boyou Jiang;Chuangxin Guo;Zhe Chen","doi":"10.1109/TSTE.2025.3528948","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3528948","url":null,"abstract":"With the increasing penetration of wind power and gradual retirement of conventional generating units (CGUs), wind turbines (WTs) become promising resources to provide steady-state energy reserve (ER) and frequency support for the grid to facilitate supply-demand balance and frequency security. In this regard, a novel frequency constrained dispatch framework with ER and virtual inertia from WTs is proposed. Firstly, this paper establishes the WT model with both ER and virtual inertia, whose energy sources are WT's deloading and rotor kinetic energy, respectively. Secondly, the system frequency response and CGUs' power response are derived while considering WTs exiting inertia response at frequency nadir. Then, this paper develops a stochastic-optimization-based frequency constrained dispatch model, where both WTs' frequency regulation parameters and rotor speeds are decision variables, so that the coupling between WT's mechanical and electrical parts and the coupling between system's transient dynamics and steady-state operation can be fully reflected. Finally, convex hull relaxation, convex hull approximation and deep neural networks are used to transform the original nonlinear model into a mixed-integer second-order cone programming model. Case studies on the 118-bus system verify the effectiveness of the proposed models and methods.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1340-1355"},"PeriodicalIF":8.6,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/TSTE.2025.3528952
Chanjuan Zhao;Yunlong Li;Qian Zhang;Lina Ren
In this paper, an enhanced dueling double deep Q network algorithm with mixed penalty function (EN-D3QN-MPF) for microgrid energy management control is developed. First, a novel microgrid model including PV, wind turbine generator, electric storage system, electric vehicle charging station, thermostatically controlled loads, and residential price-responsive loads are proposed. Then, by combining the mixed penalty function method with D3QN reinforcement learning together, a mixed penalty function method is implemented to balance the reward weightings. Accordingly, an EN-D3QN-MPF algorithm is presented to achieve low-carbon economic and EV users' charging satisfaction operation of the microgrid. The effectiveness of the proposed method is verified by the dataset collected from eastern China in 2019. Simulation results validate that our proposed method has superior energy management performance over the genetic algorithm (GA), Particle Swarm Optimization (PSO), dueling deep Q network (dueling DQN), double DQN (DDQN), and D3QN algorithms.
本文提出了一种用于微电网能量管理控制的带有混合罚函数的增强决斗双深Q网络算法（EN-D3QN-MPF）。首先，提出了包括光伏发电、风力发电、储能系统、电动汽车充电站、恒温控制负荷和住宅价格响应负荷在内的新型微电网模型。然后，将混合惩罚函数法与D3QN强化学习相结合，实现混合惩罚函数法来平衡奖励权重。为此，提出EN-D3QN-MPF算法，实现微电网低碳经济和电动汽车用户充电满意运行。通过2019年中国东部地区的数据验证了该方法的有效性。仿真结果表明，该方法比遗传算法（GA）、粒子群算法（PSO）、dueling deep Q network （dueling DQN）、双DQN （DDQN）和D3QN算法具有更好的能量管理性能。
{"title":"Low Carbon Economic Energy Management Method in a Microgrid Based on Enhanced D3QN Algorithm With Mixed Penalty Function","authors":"Chanjuan Zhao;Yunlong Li;Qian Zhang;Lina Ren","doi":"10.1109/TSTE.2025.3528952","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3528952","url":null,"abstract":"In this paper, an enhanced dueling double deep Q network algorithm with mixed penalty function (EN-D3QN-MPF) for microgrid energy management control is developed. First, a novel microgrid model including PV, wind turbine generator, electric storage system, electric vehicle charging station, thermostatically controlled loads, and residential price-responsive loads are proposed. Then, by combining the mixed penalty function method with D3QN reinforcement learning together, a mixed penalty function method is implemented to balance the reward weightings. Accordingly, an EN-D3QN-MPF algorithm is presented to achieve low-carbon economic and EV users' charging satisfaction operation of the microgrid. The effectiveness of the proposed method is verified by the dataset collected from eastern China in 2019. Simulation results validate that our proposed method has superior energy management performance over the genetic algorithm (GA), Particle Swarm Optimization (PSO), dueling deep Q network (dueling DQN), double DQN (DDQN), and D3QN algorithms.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1686-1696"},"PeriodicalIF":8.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/TSTE.2025.3529254
Jianshu Yu;Pei Yong;Zhifang Yang;Juan Yu
The diversification of power system operation modes raises the necessity of incorporating dynamic characteristics into steady-state operation. Small-signal stability is a representative issue. Though, existing frameworks either ignore the uncertainties of renewables, or only focus on the worst case. In this regard, this paper establishes a small-signal stability constrained stochastic-robust optimal power flow (OPF) model, which aims to optimize the expected cost of scheduling results with respect to the probability distributions of uncertainties while ensuring the small-signal stability requirement even in extreme scenarios. However, the synergy of uncertainties and the complicated small-signal stability mechanism significantly increase the solving difficulty. This paper proposes a comprehensive framework to overcome this challenge. First, we solve the stochastic OPF without small-signal stability constraints. For those results that do not meet the stability requirements, we use them as initial points to locate the effective boundary of the OPF feasible region where the robust small-signal stability requirement is satisfied. The effective boundary location is realized in an iterative manner. Then, in the neighborhood of this effective boundary, we construct a linear surrogate expression to represent the original robust small-signal stability constraint with Markov-chain Monte Carlo (MCMC) sampling and sample weighted support vector machine (swSVM). Finally, we solve the OPF model with the surrogate constraint. Moreover, we further propose a constraint correction strategy to guarantee the stability requirement. Case studies verify that the proposed method can acquire economical operation schemes and meet the robust small-signal stability requirement at the same time.
{"title":"Stochastic-Robust Optimal Power Flow With Small-Signal Stability Guarantee Under Renewable Uncertainties","authors":"Jianshu Yu;Pei Yong;Zhifang Yang;Juan Yu","doi":"10.1109/TSTE.2025.3529254","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529254","url":null,"abstract":"The diversification of power system operation modes raises the necessity of incorporating dynamic characteristics into steady-state operation. Small-signal stability is a representative issue. Though, existing frameworks either ignore the uncertainties of renewables, or only focus on the worst case. In this regard, this paper establishes a small-signal stability constrained stochastic-robust optimal power flow (OPF) model, which aims to optimize the expected cost of scheduling results with respect to the probability distributions of uncertainties while ensuring the small-signal stability requirement even in extreme scenarios. However, the synergy of uncertainties and the complicated small-signal stability mechanism significantly increase the solving difficulty. This paper proposes a comprehensive framework to overcome this challenge. First, we solve the stochastic OPF without small-signal stability constraints. For those results that do not meet the stability requirements, we use them as initial points to locate the effective boundary of the OPF feasible region where the robust small-signal stability requirement is satisfied. The effective boundary location is realized in an iterative manner. Then, in the neighborhood of this effective boundary, we construct a linear surrogate expression to represent the original robust small-signal stability constraint with Markov-chain Monte Carlo (MCMC) sampling and sample weighted support vector machine (swSVM). Finally, we solve the OPF model with the surrogate constraint. Moreover, we further propose a constraint correction strategy to guarantee the stability requirement. Case studies verify that the proposed method can acquire economical operation schemes and meet the robust small-signal stability requirement at the same time.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1711-1722"},"PeriodicalIF":8.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1109/TSTE.2025.3529215
Huaiyuan Zhang;Kai Liao;Jianwei Yang;Zhe Yin;Zhengyou He
For wind-photovoltaic-hydro-storage hybrid energy systems (WPHS-HES) grappling with the complexities of multiple scheduling cycles, traditional long-term strategies often impair short-term regulation capabilities, leading to extensive resource waste and critical power shortages. Thus, this paper introduces a novel framework that intricately nests short-term operational characteristics within long-term operating rules to synchronize multi-timescale scheduling for WPHS-HES. The cornerstone of our approach is the novel formulation of the long-term scheduling as a Markov Decision Process (MDP). It is integrated seamlessly with short-term generation schedules developed through an optimal model embedded at each MDP step. To achieve computational effectiveness and reliability, we propose a hybrid data-model-driven solution that harnesses the synergistic benefits of both data-driven and model-driven methodologies. By leveraging deep reinforcement learning our approach significantly streamlines long-term decision variables, while ensuring strict adherence to short-term operational constraints through mixed integer linear programming. Empirical simulations on an operational WPHS-HES validate the superior efficacy of our method over traditional scenario reduction and robust optimization techniques. The results are striking that it achieves a reduction in sustainable energy curtailment from 11.67% to 0.63% and slashes the load shedding rate from 3.3% to 0.69%, thereby setting a new benchmark for intelligent energy management in complex hybrid systems.
{"title":"Long-Term and Short-Term Coordinated Scheduling for Wind-PV-Hydro-Storage Hybrid Energy System Based on Deep Reinforcement Learning","authors":"Huaiyuan Zhang;Kai Liao;Jianwei Yang;Zhe Yin;Zhengyou He","doi":"10.1109/TSTE.2025.3529215","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3529215","url":null,"abstract":"For wind-photovoltaic-hydro-storage hybrid energy systems (WPHS-HES) grappling with the complexities of multiple scheduling cycles, traditional long-term strategies often impair short-term regulation capabilities, leading to extensive resource waste and critical power shortages. Thus, this paper introduces a novel framework that intricately nests short-term operational characteristics within long-term operating rules to synchronize multi-timescale scheduling for WPHS-HES. The cornerstone of our approach is the novel formulation of the long-term scheduling as a Markov Decision Process (MDP). It is integrated seamlessly with short-term generation schedules developed through an optimal model embedded at each MDP step. To achieve computational effectiveness and reliability, we propose a hybrid data-model-driven solution that harnesses the synergistic benefits of both data-driven and model-driven methodologies. By leveraging deep reinforcement learning our approach significantly streamlines long-term decision variables, while ensuring strict adherence to short-term operational constraints through mixed integer linear programming. Empirical simulations on an operational WPHS-HES validate the superior efficacy of our method over traditional scenario reduction and robust optimization techniques. The results are striking that it achieves a reduction in sustainable energy curtailment from 11.67% to 0.63% and slashes the load shedding rate from 3.3% to 0.69%, thereby setting a new benchmark for intelligent energy management in complex hybrid systems.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1697-1710"},"PeriodicalIF":8.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-10DOI: 10.1109/TSTE.2025.3528027
Jinning Wang;Fangxing Li;Xin Fang;Hantao Cui;Buxin She;Hang Shuai;Qiwei Zhang;Kevin L. Tomsovic
In this paper, a modularized modeling framework is designed to enable a dynamics-incorporated power system scheduling under high-penetration of renewable energy. This unique framework incorporates an adapted hybrid symbolic-numeric approach to scheduling models, effectively bridging the gap between device- and system-level optimization models and streamlining the scheduling modeling effort. The adaptability of the proposed framework stems from four key aspects: extensible scheduling formulations through modeling blocks, scalable performance via effective vectorization and sparsity-aware techniques, compatible data structure aligned with dynamic simulators by common power flow data, and interoperable dynamic interface for bi-direction data exchange between steady-state generation scheduling and time-domain dynamic simulation. Through extensive benchmarks with various usage scenarios, the framework's accuracy and scalability are validated. The case studies also demonstrate the efficient interoperation of generation scheduling and dynamics, significantly reducing the modeling conversion work in stability-constrained grid operation towards high-penetration of renewable energy.
{"title":"Dynamics-Incorporated Modeling Framework for Stability Constrained Scheduling Under High-Penetration of Renewable Energy","authors":"Jinning Wang;Fangxing Li;Xin Fang;Hantao Cui;Buxin She;Hang Shuai;Qiwei Zhang;Kevin L. Tomsovic","doi":"10.1109/TSTE.2025.3528027","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3528027","url":null,"abstract":"In this paper, a modularized modeling framework is designed to enable a dynamics-incorporated power system scheduling under high-penetration of renewable energy. This unique framework incorporates an adapted hybrid symbolic-numeric approach to scheduling models, effectively bridging the gap between device- and system-level optimization models and streamlining the scheduling modeling effort. The adaptability of the proposed framework stems from four key aspects: extensible scheduling formulations through modeling blocks, scalable performance via effective vectorization and sparsity-aware techniques, compatible data structure aligned with dynamic simulators by common power flow data, and interoperable dynamic interface for bi-direction data exchange between steady-state generation scheduling and time-domain dynamic simulation. Through extensive benchmarks with various usage scenarios, the framework's accuracy and scalability are validated. The case studies also demonstrate the efficient interoperation of generation scheduling and dynamics, significantly reducing the modeling conversion work in stability-constrained grid operation towards high-penetration of renewable energy.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1673-1685"},"PeriodicalIF":8.6,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/TSTE.2025.3525478
Ya Zhao;Xiyun Yang;Yanfeng Zhang;Qiliang Zhang
The complex wind and wave environment can lead to increased external disturbances and power fluctuations of floating offshore wind turbines, posing a significant challenge to their stable operation. To cope with this issue, this paper formulates an event-triggered H-infinity pitch control strategy for floating offshore wind turbines. Firstly, a linear parameter varying model of floating offshore wind turbines is proposed, utilizing the dynamic characteristics of subsystems while considering the combined external disturbances from wind and wave. Then, the event-triggered control strategy is introduced into the H-infinity pitch control of floating offshore wind turbines. Based on this, a criterion for the asymptotic stability and H-infinity norm boundedness of floating offshore wind turbines is derived. Furthermore, an algorithm is presented for designing feedback gain matrices of the event-triggered H-infinity pitch control, which can effectively reduce the update frequency of the controller. Finally, a simulation is conducted on the IEA 15 MW Reference Wind Turbine by integrating OpenFAST with MATLAB/Simulink. The simulation results provide a comparative analysis of the event-triggered H-infinity pitch control strategy and the continuous-time pitch control strategy, demonstrating the superiority of the method proposed in this paper.
{"title":"Event-Triggered H-Infinity Pitch Control for Floating Offshore Wind Turbines","authors":"Ya Zhao;Xiyun Yang;Yanfeng Zhang;Qiliang Zhang","doi":"10.1109/TSTE.2025.3525478","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3525478","url":null,"abstract":"The complex wind and wave environment can lead to increased external disturbances and power fluctuations of floating offshore wind turbines, posing a significant challenge to their stable operation. To cope with this issue, this paper formulates an event-triggered H-infinity pitch control strategy for floating offshore wind turbines. Firstly, a linear parameter varying model of floating offshore wind turbines is proposed, utilizing the dynamic characteristics of subsystems while considering the combined external disturbances from wind and wave. Then, the event-triggered control strategy is introduced into the H-infinity pitch control of floating offshore wind turbines. Based on this, a criterion for the asymptotic stability and H-infinity norm boundedness of floating offshore wind turbines is derived. Furthermore, an algorithm is presented for designing feedback gain matrices of the event-triggered H-infinity pitch control, which can effectively reduce the update frequency of the controller. Finally, a simulation is conducted on the IEA 15 MW Reference Wind Turbine by integrating OpenFAST with MATLAB/Simulink. The simulation results provide a comparative analysis of the event-triggered H-infinity pitch control strategy and the continuous-time pitch control strategy, demonstrating the superiority of the method proposed in this paper.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1329-1339"},"PeriodicalIF":8.6,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/TSTE.2025.3525498
Mao Yang;Yutong Huang;Zhao Wang;Bo Wang;Xin Su
Wind power forecasting (WPF) systems are essential to maintain the safe and stable operation of the power system in case of large-scale grid-connected wind farms. However, the current forecasting has the problem of disunity between statistical value and application value, that is, it only pays attention to its forecasting accuracy and ignores the risks caused by it in the power system. In order to solve the above problems, this study proposes a framework of wind supply power forecasting (WSPF) for wind farm cluster, which takes into account the risk scenario perception. First of all, aiming at the predicted risk phenomenon in WPF, TimesNet combined with the fluctuation information of Numerical Weather Prediction (NWP) wind speed is used to identify the corresponding risk scenarios. Secondly, the effective consumption area and power supply risk area evaluation index, as well as the accuracy of WSPF are defined, and the optimal forecasting curve correction scheme is fitted according to the index. Thirdly, taking into account the correction scheme and identification results, a variety of predictors are used to verify the WSPF according to the above framework. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia Autonomous region of China, the average accuracy of WSPF has increased by 37%, which verifies the effectiveness and universality of this method.
{"title":"A Framework of Day-Ahead Wind Supply Power Forecasting by Risk Scenario Perception","authors":"Mao Yang;Yutong Huang;Zhao Wang;Bo Wang;Xin Su","doi":"10.1109/TSTE.2025.3525498","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3525498","url":null,"abstract":"Wind power forecasting (WPF) systems are essential to maintain the safe and stable operation of the power system in case of large-scale grid-connected wind farms. However, the current forecasting has the problem of disunity between statistical value and application value, that is, it only pays attention to its forecasting accuracy and ignores the risks caused by it in the power system. In order to solve the above problems, this study proposes a framework of wind supply power forecasting (WSPF) for wind farm cluster, which takes into account the risk scenario perception. First of all, aiming at the predicted risk phenomenon in WPF, TimesNet combined with the fluctuation information of Numerical Weather Prediction (NWP) wind speed is used to identify the corresponding risk scenarios. Secondly, the effective consumption area and power supply risk area evaluation index, as well as the accuracy of WSPF are defined, and the optimal forecasting curve correction scheme is fitted according to the index. Thirdly, taking into account the correction scheme and identification results, a variety of predictors are used to verify the WSPF according to the above framework. Finally, the proposed method is applied to a wind farm cluster in Inner Mongolia Autonomous region of China, the average accuracy of WSPF has increased by 37%, which verifies the effectiveness and universality of this method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"1659-1672"},"PeriodicalIF":8.6,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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