考虑用户响应度模糊控制的需求响应双层博弈模型 |
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作者姓名: | 谢雨奇 曾伟 马瑞 |
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作者单位: | 长沙理工大学电气与信息工程学院 湖南 长沙,国网江西省电力有限公司电力科学研究院,长沙理工大学电气与信息工程学院 湖南 长沙 |
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基金项目: | 多能流系统故障耦合传播机理及其负荷裕度演变风险特征研究 (51977012)国家自然科学基金项目(面上项目,重点项目,重大项目) |
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摘 要: | 配电网中分布式新能源、可控负荷等柔性资源参与电网需求响应已成为新型电力系统削峰填谷的重要手段,而如何考虑用户响应度和平衡多主体利益十分关键。为此,文中首先建立用户响应度的模糊控制模型,并给出考虑用户响应度模糊性的供电方、负荷聚合商、用户等多主体需求响应参与方效益函数模型;进而以日负荷曲线偏差最小和系统成本最小为优化目标,上层优化采用供电方最优需求响应方案,下层优化在供电方及负荷聚合商之间求得最优任务分配,从而建立供电方、负荷聚合商、用户等多主体协同需求响应双层模型,并提出基于Stackelberg博弈理论和k-means聚类算法的求解方法;最后以某地区历史数据进行模拟仿真。结果表明文中模型在考虑用户响应度和协同多主体利益下能有效筛选优质需求响应资源,平抑负荷波动。
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关 键 词: | 多目标优化 Stackelberg博弈 需求响应 k-means聚类算法 不确定性 模糊控制 |
收稿时间: | 2022/4/6 0:00:00 |
修稿时间: | 2022/9/6 0:00:00 |
Two-layer game model for demand response considering fuzzy control of user responsiveness |
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Authors: | XIE Yuqi ZENG Wei MA Rui |
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Affiliation: | changsha university of science and technology,State Grid Jiangxi Electric Power Co., LTD. Electric Power Research Institute, |
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Abstract: | In the distribution network, flexible resources such as distributed energy and controllable load are mined and aggregated to participate in demand response, which is an important measure of load adjusting in the new power system. How to consider the uncertainty of user responsiveness and balance the interests of multiple participants is very important. In this paper, the fuzzy control model of user responsiveness is established firstly, and the benefit function model of multi-agent demand response participants is built considering the fuzziness of user responsiveness. Furthermore, with the minimum deviation of daily load curve and the minimum system cost as the optimization objective, the upper optimization takes the best demand response scheme of the power supplier. The lower optimization obtains the best task allocation between the power supplier and the load aggregator, so as to establish the multi-agent collaborative demand response two-layer model of the power supplier, the load aggregator and the user. A solution method based on Stackelberg game theory and k-means algorithm is proposed. The simulation results of historical data in a certain area show that the model can effectively screen high-quality demand response resources to suppress load fluctuation under the consideration of uncertainty of user responsiveness and cooperation of multi-agent interests. |
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Keywords: | Multi-objective optimization Stackelberg game Demand response Power system Uncertainty Fuzzy control |
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