基于共享储能的多工业用户综合能源系统两阶段鲁棒优化调度

Two-Stage Robust Optimal Scheduling for Integrated Energy Systems of Multiple Industrial Users with Shared Energy Storage

中文关键词:共享储能  两阶段鲁棒优化  工业用户  综合能源系统

keywords:shared energy storage  two-stage robust optimization  industrial users  integrated energy system

中文摘要:针对共享储能参与工业用户综合能源系统时，分布式能源出力与负荷需求的双重不确定性引发的系统配置与调度策略鲁棒性不足的问题，提出一种考虑负荷和可再生能源出力不确定性的两阶段鲁棒优化调度策略。首先，构建包含光伏、风机、燃气轮机及共享储能电站的多工业用户综合能源系统架构，并建立两阶段优化模型：第一阶段以系统四季典型日运行成本最小为目标，优化共享储能容量与功率配置；第二阶段基于配置结果对系统进行实时优化调度，实现储能规划与运行的协同决策；其次，模型目标函数综合考虑购电和购气成本、共享储能服务费、设备运维成本、投资成本及碳排放成本，采用盒式不确定集刻画风光出力和负荷波动特性，并运用列约束生成算法进行模型求解；最后，通过算例分析验证策略的有效性。结果表明，所提策略可有效降低高峰时段购电量，提升系统可再生能源消纳水平，在保障系统经济性与低碳性的同时显著增强鲁棒性。

Abstract:When shared energy storage is integrated into the integrated energy system（IES） of industrial users，the dual uncertainties of distributed energy output and load demand often lead to insufficient robustness of system configuration and scheduling strategies. To tackle this problem，a two-stage robust optimal scheduling strategy considering the uncertainties of load and renewable energy output is proposed in this paper. Firstly，an architecture of the integrated energy system for multiple industrial users is constructed，which incorporates photovoltaic systems，wind turbines，gas turbines and a shared energy storage station； a two-stage optimization model is established: the first stage optimizes the capacity and power configuration of shared energy storage with the objective of minimizing the system’s operating costs on typical days across four seasons； the second stage performs real-time optimal scheduling of the system based on the configuration results to realize collaborative decision-making for energy storage planning and operation. Secondly，the objective function of the model comprehensively takes into account the electricity purchase cost，shared energy storage service fee，equipment operation and maintenance cost，investment cost and carbon emission cost. A box uncertainty set is adopted to characterize the output fluctuations of wind and photovoltaic energy as well as load variations，and the column-and-constraint generation algorithm is used to solve the model. Finally，the effectiveness of the proposed strategy is verified through case studies. The results show that the proposed strategy can effectively reduce the electricity purchase volume during peak periods and improve the renewable energy consumption rate of the system，which significantly enhances the system robustness while ensuring its economic efficiency and low-carbon performance.