电力建设2024，Vol.45Issue(7)：167-178,12.DOI:10.12204/j.issn.1000-7229.2024.07.015
储能辅助下基于多重博弈的社区光伏用户电能交易模型
Energy Trading Model for Community Photovoltaic Users Based on Multi-Game Theory with Energy Storage Support
摘要
Abstract
The participation of small power users in electricity trading can promote the development of the electricity sales market.However,the characteristics of small power users are not fully considered in the current distributed transaction methods,and the potential of user energy storage in the transaction needs to be further explored.Therefore,a two-stage day-ahead and intraday trading framework for community photovoltaic users participating in energy storage is proposed.In the day-ahead stage,the trading volume is increased by energy storage charging and discharging,whereas in the intraday stage,the influence of prediction error is reduced by energy storage regulation to improve users'trading income and relieve the operating pressure of the power grid.First,the user identity was divided,and an electricity sales bidding model was constructed by considering the purchase and sale of power in the energy storage operation to formulate the energy storage day-ahead operation plan.Second,considering the diverse interest interactions,the trading behavior was modeled based on multiple games to solve the trading strategy of each user.Third,model predictive control was used to correct the intraday energy storage operation plan and alleviate the influence of the forecast error balance on the power grid.Finally,an example was analyzed to verify that the proposed model can further increase the power-trading volume of users and reduce the dependence on the power grid based on the traditional transaction mode.关键词
社区光伏用户/分布式交易/储能运行优化/多重博弈/模型预测控制Key words
community photovoltaic user/distributed transaction/energy storage optimization operation/multigame/model predictive control分类
信息技术与安全科学引用本文复制引用
伍宇铜,刘洋,许立雄,李金鸿,胡开鑫..储能辅助下基于多重博弈的社区光伏用户电能交易模型[J].电力建设,2024,45(7):167-178,12.基金项目
This work is supported by Sichuan Provincial Science and Technology Program Funding(No.2023YFG0132). 四川省科技计划资助项目(2023YFG0132) （No.2023YFG0132）
