运筹与管理 ›› 2022, Vol. 31 ›› Issue (6): 32-39.DOI: 10.12005/orms.2022.0179

• 理论分析与方法探讨 • 上一篇    下一篇

计及需求响应不确定性的综合能源系统多目标优化调度管理

王俐英, 董厚琦, 宋美琴, 林嘉琳, 曾鸣   

  1. 华北电力大学 经济与管理学院,北京 102206
  • 收稿日期:2020-09-13 出版日期:2022-06-25 发布日期:2022-07-20
  • 作者简介:王俐英(1997-),女,内蒙古人,博士研究生,研究方向:能源互联网,综合能源系统;董厚琦(1993-),男,山东人,博士研究生,研究方向:能源互联网,综合能源系统;宋美琴(1998-),吉林人,硕士研究生,研究方向:能源互联网,综合能源系统;林嘉琳(1998-),福建人,硕士研究生,研究方向:能源互联网,综合能源系统;曾鸣(1957-),山西人,教授、博导,研究方向:能源互联网,综合能源系统。
  • 基金资助:
    国家重点研发计划(2021YFB2400704);国家社会科学基金重大项目(19ZDA081);中央高校基本科研业务费专项资金资助(2020MS067)

Multi-objective Optimal Dispatch Management of Integrated Energy System Considering Uncertainty of Demand Response

WANG Li-ying, DONG Hou-qi, SONG Mei-qin, LIN Jia-lin, ZENG Ming   

  1. College of Economics and Management, North China Electric Power University, Beijing 102206, China
  • Received:2020-09-13 Online:2022-06-25 Published:2022-07-20

摘要: 需求响应作为电力系统的重要调节手段,可显著提升系统灵活性和经济性。利用价格弹性构建了包含价格与激励措施的需求响应模型,并在此基础上考虑需求响应的不确定性,以综合能源系统经济性和环保性为优化目标,构建了综合能源系统多目标优化调度模型。利用E约束法将多目标优化模型转化为单目标优化模型,得到Pareto最优解集,运用模糊决策法从中选取最优方案。基于实际案例进行测算,结果表明价格型与激励型需求响应手段的结合能够实现削峰填谷,有效降低系统的运行成本和碳排放量。

关键词: 需求响应, 综合能源系统, 多目标优化, 不确定性

Abstract: As an important regulation means of power system, demand response can significantly improve system flexibility and economy. Based on the time-of-use electricity price mechanism, the demand response model including price and incentive measures is constructed by using the price elasticity matrix, and on this basis, the uncertainty of demand response is considered, and the multi-objective optimization scheduling model of the integrated energy system is constructed by taking the economy and environmental protection of the integrated energy system as the optimization objectives. The constraint method is used to transform the multi-objective optimization model into the single-objective optimization model, Pareto optimal solution set is obtained, and the fuzzy decision method is used to select the optimal scheme. Based on the actual case, the calculation results show that the combination of price and incentive demand response means can achieve peak load cutting and effectively reduce the operating cost and carbon emissions of the system.

Key words: demand response, integrated energy system, multi-objective optimization, uncertainty

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