Xiangyong Feng;Shunjiang Lin;Yutao Liang;Guansheng Fan;Mingbo Liu
{"title":"Distributed and Risk-Averse ADP Algorithm for Stochastic Economic Dispatch of Power System with Multiple Offshore Wind Farms","authors":"Xiangyong Feng;Shunjiang Lin;Yutao Liang;Guansheng Fan;Mingbo Liu","doi":"10.17775/CSEEJPES.2022.03890","DOIUrl":null,"url":null,"abstract":"With more and more offshore wind power being increasingly connected to power grids, fluctuations in offshore wind speeds result in risks of high operation costs. To mitigate this problem, a risk-averse stochastic economic dispatch (ED) model of power system with multiple offshore wind farms (OWFs) is proposed in this paper. In this model, a novel GlueVaR method is used to measure the tail risk of the probability distribution of operation cost. The weighted sum of the expected operation cost and the GlueVaR is used to reflect the risk of operation cost, which can consider different risk requirements including risk aversion and risk neutrality flexibly by adjusting parameters. Then, a risk-averse approximate dynamic programming (ADP) algorithm is designed for solving the proposed model, in which multi-period ED problem is decoupled into a series of single-period ED problems. Besides, GlueVaR is introduced into the approximate value function training process for risk aversion. Finally, a distributed and risk-averse ADP algorithm is constructed based on the alternating direction method of multipliers, which can further decouple single-period ED between transmission system and multiple OWFs for ensuring information privacy. Case studies on the modified IEEE 39-bus system with an OWF and an actual provincial power system with four OWFs demonstrate correctness and efficiency of the proposed model and algorithm.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":6.9000,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10165662","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CSEE Journal of Power and Energy Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10165662/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
With more and more offshore wind power being increasingly connected to power grids, fluctuations in offshore wind speeds result in risks of high operation costs. To mitigate this problem, a risk-averse stochastic economic dispatch (ED) model of power system with multiple offshore wind farms (OWFs) is proposed in this paper. In this model, a novel GlueVaR method is used to measure the tail risk of the probability distribution of operation cost. The weighted sum of the expected operation cost and the GlueVaR is used to reflect the risk of operation cost, which can consider different risk requirements including risk aversion and risk neutrality flexibly by adjusting parameters. Then, a risk-averse approximate dynamic programming (ADP) algorithm is designed for solving the proposed model, in which multi-period ED problem is decoupled into a series of single-period ED problems. Besides, GlueVaR is introduced into the approximate value function training process for risk aversion. Finally, a distributed and risk-averse ADP algorithm is constructed based on the alternating direction method of multipliers, which can further decouple single-period ED between transmission system and multiple OWFs for ensuring information privacy. Case studies on the modified IEEE 39-bus system with an OWF and an actual provincial power system with four OWFs demonstrate correctness and efficiency of the proposed model and algorithm.
期刊介绍:
The CSEE Journal of Power and Energy Systems (JPES) is an international bimonthly journal published by the Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE (The Institute of Electrical and Electronics Engineers) Inc. Indexed by SCI, Scopus, INSPEC, CSAD (Chinese Science Abstracts Database), DOAJ, and ProQuest, it serves as a platform for reporting cutting-edge theories, methods, technologies, and applications shaping the development of power systems in energy transition. The journal offers authors an international platform to enhance the reach and impact of their contributions.