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Based on the Scenario Set Generation technology, the Shared energy Storage Site Selection and Capacity Determination Problem Considering the Grid Constraints

Based on the Scenario Set Generation Technology, the Shared ...

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power Science and technology (ICPST), IEEE International Conference on

作者： Xiaobiao Fu Jinyu Guo Baoju Li Cuiping Li Yao Wang Junhui Li Guanqun Zhang Xingxu Zhu Dispatching and Control Center State Grid Jilin Electric Power Co. Ltd Chang Chun China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education(Northeast Electric Power University) Ji Lin China Power Science Research Institute State Grid Jilin Electric Power Co. Ltd Chang Chun China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

This paper addresses the shared energy storage siting and sizing problem, considering grid constraints based on scenario generation techniques. In the context of high penetration of renewable energy, the power system faces practical challenges such as increased uncertainty and scarcity of flexible resources. To tackle these issues, a two-layer optimization model is proposed in this study. It utilizes scenario generation techniques to obtain possible scenarios for renewable energy and load outputs, and incorporates grid constraints for economic dispatch. Through iterative optimization, an optimal shared energy storage siting and sizing solution that meets the requirements of the power grid is obtained. Additionally, the paper discusses the impact on the accommodation capacity of renewable energy and proposes countermeasures in system economic dispatch. However, there are still limitations in uncertainty estimation, energy storage planning, and investment cost calculations in this paper. Furthermore, further enhancement is needed in terms of quantification theories supporting the economic evaluation of shared energy storage siting and sizing. Overall, this research aims to provide an optimization approach for the shared energy storage siting and sizing problem, incorporating scenario generation techniques and grid constraints, to promote stable operation and economic development of power systems with high penetration of renewable energy.

关键词： renewable energy sources Uncertainty Costs Optimization models Robustness Planning Scenario generation

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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on energy Storage System

SSRN

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SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Shijiazhuang Gaocheng District Electric Supply Company No. 76 Changsheng South Street Gaocheng District Shijiazhuang052160 China

Large wind farms connected to the grid through MMC-HVDC systems can cause overloading of the non-faulty converter station after a DC fault. This short communication proposes a system fault ride-through method by coordinating and controlling the energy storage system, the non-faulty converter station and the wind farm to jointly consume the unbalanced power during the fault. The model is created in the PSCAD simulation platform, and the correctness and effectiveness of the method are verified by simulation. © 2023, The Authors. All rights reserved.

关键词： energy storage

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Dc Fault Ride-Through control for Mmc-Hvdc Grid-Connected Wind Farm Based on energy Storage System

SSRN

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SSRN 2023年

作者： Sun, Yinfeng Guo, Yuhang Lu, Yuze Lu, Mingzhi Key Laboratory of Modern Power System Simulation and Control& Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China State GridShijiazhuang Gaocheng District Electric Supply Company No. 76Changsheng SouthStreet Gaocheng District Shijiazhuang052160 China

关键词： energy storage

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Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach

arXiv

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arXiv 2022年

作者： Li, Yang Bu, Fanjin Li, Yuanzheng Long, Chao Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China School of Artifcial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Water Energy and Environment Cranfield University CranfieldMK43 0AL United Kingdom

Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands. To address these challenges, a comprehensive scheduling framework is proposed by introducing a model-free deep reinforcement learning (DRL) approach based on modeling an island integrated energy system (IES). In response to the shortage of freshwater on islands, in addition to the introduction of seawater desalination systems, a transmission structure of "hydrothermal simultaneous transmission" (HST) is proposed. The essence of the IES scheduling problem is the optimal combination of each unit’s output, which is a typical timing control problem and conforms to the Markov decision-making solution framework of deep reinforcement learning. Deep reinforcement learning adapts to various changes and timely adjusts strategies through the interaction of agents and the environment, avoiding complicated modeling and prediction of multi-uncertainties. The simulation results show that the proposed scheduling framework properly handles multi-uncertainties from power sources and loads, achieves a stable demand supply for various resources, and has better performance than other real-time scheduling methods, especially in terms of computational efficiency. In addition, the HST model constitutes an active exploration to improve the utilization efficiency of island freshwater. © 2022, CC BY-NC-ND.

关键词： Desalination

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Deep Sequence to Sequence Attention Network Approach for Short-Term Electricity Price Forecasting

SSRN

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SSRN 2022年

作者： Chen, Haipeng Wu, Huayue Chen, Jian Kan, Tianyang Miao, Sen Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Chengde Power Supply Company State Grid Jibei Electric Power Co. Ltd. Chengde067000 China

With increasing renewable energy penetration and the extension of the power system scale, accurate electricity price prediction has long become a challenging task. The complexity of electricity market information, insufficient model training, and the lack of model generalization ability limit the prediction accuracy of the existing electricity price forecasting methods. This paper proposes a Sequence-to-Sequence Attention algorithm based on the Double Deep Q Network optimization method for short-term electricity price prediction. This algorithm first conducts the maximum information coefficient correlation analysis to select the input dataset for the prediction model of electricity price. Secondly, the Sequence-to-Sequence Attention model is proposed for point forecasting and probabilistic forecasting of short-term electricity prices. Finally, the hyperparameters of the model are optimized by the Double Deep Q Network method, which improves the prediction accuracy and the generalization ability of the prediction model of electricity price. Simulations are carried out on Pennsylvania–New Jersey–Maryland market data and the New South Wales electricity market. The results show that the proposed method has superior performance than the existing prediction methods, which is appliable for the short-term electricity price prediction and provides a powerful decision-making basis for market participants and regulators. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Optimal Scheduling Strategy of a Regional Integrated energy System Considering renewable energy Uncertainty and Heat Network Transmission Characteristics

SSRN

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SSRN 2022年

作者： Chen, Haipeng Gao, Lin Zhang, Yongling Zhao, Chang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Department of Electrical Engineering Northeast Electric Power University Jilin132012 China Gongqing Institute of Science and Technology Gongqing City 332020 China National New Energy Group Jilin Jiangnan Thermal Power Limited Company Jilin132012 China

In order to overcome the curtailment of wind energy caused by "power determined by heat" of combined heat and power units, this study proposes an integrated demand response strategy and optimization method for heat-electric energy dispatching in a regional integrated energy system. Firstly, a scene analysis model based on Latin Hypercube Sampling (LHS) and Backward Reduction (BR) is constructed to analyze the probabilistic properties of wind power output in various scenes, and further provide a necessary reference for integrated energy scheduling. Secondly, the schedulable electrical power and thermal demand are evaluated with the consideration of the transmission characteristics of the heat supply network, then the integrated demand response of electric-thermal coordination is adopted to facilitate suppress the heat load fluctuation and wind power consumption. Finally, the optimal scheduling model of regional integrated energy system is established considering the source-load coordination, and the optimal output of each power unit is determined with the goal of minimizing overall cost during the scheduling period. The simulation results demonstrated that the proposed scheduling method reduces the operation cost of the integrated energy system and effectively promotes the wind energy consumption. Furthermore, it serves as a benchmark for the coordinated operation of wind turbines and power grids. © 2022, The Authors. All rights reserved.

关键词： Electric power utilization

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Research on the Grid-connected Photovoltaic power Scheduling Strategy Considering the Flexible Load

Research on the Grid-connected Photovoltaic Power Scheduling...

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energy Engineering and power Systems (EEPS), International Conference on

作者： Tao Meng Chenggang Li Dexin Li Jiajun Zhang Electric Power Research Institute State Grid Jilin Electric Power Co. Ltd Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education School of Electrical Engineering Northeast Electric Power University Changchun China Electric Power Research Institute State Grid Jilin Electric Power Co. Ltd Changchun China

With the target of "peak carbon dioxide emission" and "carbon neutrality", the installed capacity of renewable energy power generation is increasing rapidly. To improve the absorption capacity of renewable energy, the frequency regulation ability of flexible load is considered. Diffusion strategy is an optimal strategy for the distributed multi-agent system. Considering the flexible load, the grid-connected photovoltaic power applies the diffusion strategy to optimize the scheduling strategy. The effectiveness of the proposed optimal algorithm is verified by a simulation example. The results show that the proposed algorithm can realize frequency regulation, and improve the installed capacity of photovoltaic power generation.

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Ultra-Short-Term Multi-Step Prediction of Wind power Based on modernTCN and Multi-Task Learning

Ultra-Short-Term Multi-Step Prediction of Wind Power Based o...

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IEEE International Conference on power and power and renewable energy (ICPRE)

作者： Mao Yang Xiangyu Li Zifen Han Yong Sun Xiqiang Chang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China State Grid Gansu Electric Power Company Gansu China State Grid Jilinsheng Economic And Technological Research Institute Jilin China State Grid Xinjiang Electric Power CO. LTD Xinjiang China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

The intermittent and fluctuating nature of wind energy makes it difficult to accurately predict wind power, leading to low penetration in the wind power grid. To address this problem, this paper designs ultra-short-term multi-step prediction of wind power based on modernTCN and multi-task learning to improve the prediction accuracy. Firstly, modernTCN's ability to have a large receptive field and capture cross-variate timing dependencies is exploited to obtain multivariate timing feature information and establish a multi-step mapping of input features to power. Then it is inputted into the MMoE multi-task learning method, and the prediction results are corrected to obtain the ultra-short-term prediction of wind power using multiple expert networks and gating units. The experimental results show that the method proposed in this paper can effectively improve the ultra-short-term prediction accuracy of wind power, and can provide a favourable reference for the scheduling centre to adjust the power generation plan in real-time response to the power demand.

关键词： renewable energy sources Accuracy Wind energy Wind power generation Predictive models Multitasking Feature extraction Real-time systems Timing Wind forecasting

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Wind power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach

arXiv

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arXiv 2022年

作者： Li, Yang Wang, Ruinong Li, Yuanzheng Zhang, Meng Long, Chao The Key Laboratory of Modern Power System Simulation Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China The State Grid Jilin Electric Power Co. Ltd Jilin Power Supply Company Chuanying District Jilin132001 China The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The School of National Key Laboratory of Science and Technology Vessel Integrated Power System Naval University of Engineering Wuhan430033 China The School of Water Energy and Environment Cranfield University United Kingdom

In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasting methods raise concerns regarding data privacy-preserving and data islands problem. To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL). Firstly, this paper uses the deep deterministic policy gradient (DDPG) algorithm as the basic forecasting model to improve prediction accuracy. Secondly, we integrate the DDPG forecasting model into the framework of federated learning. The designed FedDRL can obtain an accurate prediction model in a decentralized way by sharing model parameters instead of sharing private data which can avoid sensitive privacy issues. The simulation results show that the proposed FedDRL outperforms the traditional prediction methods in terms of forecasting accuracy. More importantly, while ensuring the forecasting performance, FedDRL can effectively protect the data privacy and relieve the communication pressure compared with the traditional centralized forecasting method. In addition, a simulation with different federated learning parameters is conducted to confirm the robustness of the proposed scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： Electric load dispatching

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Fuzzy control Based Virtual Synchronous Generator for Self-Adaptative control in Hybrid Microgrid

SSRN

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SSRN 2022年

作者： Lyu, Ling Wang, Xuesong Zhang, Liang Zhang, Zhe Koh, Leong Hai Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Hebei Electric Power Company Cangzhou Power Supply Company Cangzhou061000 China School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore National Metrology Centre 138635 Singapore

When there is a large penetration of renewable energy sources, maintaining the stability of a low-inertia microgrid becomes a major difficulty. However, in such systems, the virtual inertia values are often fixed constants, and the choice of their values will significantly affect the frequency and voltage stability of the microgrid. Higher frequency and voltage oscillations may occur due to improper selection of fixed virtual inertia values. Therefore, virtual inertia-based control has attracted a lot of attention. In this paper, an adaptive virtual inertia control system using a fuzzy system is proposed through the setting of fuzzy logic rules and affiliation functions to provide adaptive inertia control for the system to ensure the stability of frequency and voltage. In the proposed adaptive control strategy, the virtual inertia values are automatically adjusted according to the signal deviation and rate of change of the actual system, avoiding the selection of inappropriate inertia values and providing fast inertial response. Simulation and experimental results show that the proposed adaptive control algorithm, by combining the advantages of large inertia and small inertia, enables effective improvement of the dynamic response of the system voltage and frequency in both rectifier and inverter modes. The effectiveness of the proposed control strategy is verified. © 2022, The Authors. All rights reserved.

关键词： Fuzzy control

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