多时间尺度下计及多主体的综合能源系统经济优化调度OA
Economic optimization scheduling of integrated energy systems considering multiple agents at multiple time scales
在"双碳"背景下,为应对源荷不确定性、电网灵活调节能力不足等问题,文中建立一种计及多主体交互和多能响应的综合能源系统经济优化调度模型,以实现综合能源系统经济低碳运行.基于模型预测控制方法,在多时间尺度下考虑绿证-奖惩阶梯式碳交易机制,建立以综合能源系统运营商日净收益最大为目标的优化模型.将非线性模型转化为混合整数线性规划模型并拓展到日内和实时阶段,运用 MATLAB 平台调用 Gurobi 求解.同时采用拉丁超立方抽样法和 Kantorovich场景削减法处理新能源出力的不确定性.设置不同模型进行对比分析,结果表明,所提策略下综合能源系统运营商和需求响应聚合商的预期日运行利润明确,能够有效促进新能源消纳、降低碳排放、提升运营商收益.如模型(1)的净利润相比模型(2)增加 3 382.97元,碳交易支出下降约 17.2%;实时阶段求解时间缩短,负荷需求高峰时段的收益更高.多时间尺度调度可应对新能源和负荷预测误差,基于模型预测控制的调度策略有助于挖掘需求响应资源.考虑绿证-奖惩阶梯式碳交易机制及综合需求响应的优化调度可兼顾经济性和低碳性,能够有效平抑源荷波动,提高各主体效益.
Under the"dual carbon"goal,an economic optimization scheduling model is proposed for integrated energy systems that incorporates multiple agent interaction and multiple energy demand response to address challenges such as source-load uncertainty and insufficient grid flexibility,thereby achieving coordinated economic and low-carbon operation.Based on the model predictive control method,considering the green certificate reward and punishment tiered carbon trading mechanism at multiple time scales,an optimization model is established with the goal of maximizing the daily net profit of the integrated energy system operators.The nonlinear model is reformulated as a mixed integer linear programming model and extended to the intraday and real-time stages,which is then efficiently solved using the Gurobi solver via the MATLAB platform.Meanwhile,Latin hypercube sampling method and Kantorovich scenario reduction method are used to deal with the uncertainty of new energy output.Comparative analysis of different models shows that under the proposed strategy,the expected daily operating profits of the integrated energy system operator and the demand response aggregator are well-defined,effectively promoting new energy consumption,reducing carbon emissions,and increasing operator revenue.For example,the net profit of scenario(1)is 3 382.97 yuan higher than that of scenario(2),while carbon trading costs decrease by approximately 17.2%.Additionally,the solution time in the real-time stage is shortened,and higher revenues are achieved during peak load demand periods.Multiple time scales scheduling can cope with forecasting errors of new energy and load.The scheduling strategy based on model predictive control can explore demand response resources.The optimal dispatch considering the green certificate reward and punishment tiered carbon trading mechanism and integrated demand response can balance economic efficiency and low-carbon performance,effectively smooth source load fluctuations,and improve the benefits of all agents.
曾艾东;仲凯;盛昊;王明深;臧连桢;汪子睿
南京工程学院电力工程学院、沈国荣学院,江苏 南京 211167||江苏省配电网智能技术与装备协同创新中心,江苏 南京 211100南京工程学院电力工程学院、沈国荣学院,江苏 南京 211167南京工程学院电力工程学院、沈国荣学院,江苏 南京 211167国网江苏省电力有限公司电力科学研究院,江苏 南京 211103南京工程学院电力工程学院、沈国荣学院,江苏 南京 211167南京工程学院电力工程学院、沈国荣学院,江苏 南京 211167
信息技术与安全科学
多主体多时间尺度绿证-奖惩阶梯式碳交易机制经济优化调度模型模型预测控制源荷波动
multiple agentsmultiple time scalesgreen certificate reward and punishment tiered carbon trading mechanismeconomic optimization scheduling modelmodel predictive controlsource load fluctuation
《电力工程技术》 2026 (8)
24-35,12
国家自然科学基金资助项目(52477101)江苏省自然科学基金资助项目(BK20210932)
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