基于服务商引导的综合能源系统源-荷协同优化方法

随着能源市场的逐步开放,大量市场因素涌入传统集中式优化调度策略。在此背景下,以园区综合能源系统(community integrated energy system,CIES)优化运行为应用场景,提出了基于综合能源服务商(integrated energy services provider,IESP)引导的源-荷协同优化运行模型。在需求侧,考虑园区可转移电、气负荷以及建筑物维护结构虚拟储热特性建立综合需求响应(integrated demand response,IDR)策略。在供给侧引入综合能源服务商以代替能源网络引导多能协同灵活交易,综合考虑园区用能需求、响应反馈以及主网与园区联络线电交互功率变化情况优化电-气联合价格信号。通过粒子群结合混合整数线性规划的双层优化算法对上层服务商的价格信号及下层园区需求响应、经济调度等行为进行分层优化,考虑供需双侧互动过程中的相互影响,循环迭代求解出各方追求利益目标时的交互策略。算例仿真表明所提模型可在保障园区的参与满意度、用能经济性的基础上挖掘其响应潜力,对园区与主网间电交互功率进行“削峰填谷”,优化配置系统能源资源,提高系统整体经济性。

Source-Load Collaborative Optimization Method of Integrated Energy System Based on Service Provider Guidance

As the energy market gradually opens up, multiple market factors have been involved in the traditional centralized optimal dispatching approach. In this context, the paper takes the optimal operation of community integrated energy system (CIES) as an application scenario and proposes a source-load collaborative optimization model under the guidance of an integrated energy service provider (IESP). On the demand-side, an integrated demand response (IDR) strategy is established according to the transferable electricity load and gas load as well as the virtual heat storage characteristics of the enclosed structures in the buildings. On the supply-side, an integrated energy service provider is introduced to replace the energy networks and lead the collaborative and flexible trade of multiple energies. Besides, it is able to comprehensively evaluate the community’s energy demand, respond feedback and the situations of interactive power of major grid and tie lines in the community so as to optimize the electricity-gas combined price signals. The bi-level optimization algorithm of particle swarm optimization combined with mixed integer linear programming is used to optimize the price signal of the upper service provider and the demand response and economic dispatching of the lower community. Considering the mutual influence in the process of bilateral interaction between supply-side and demand-side, the interactive strategy of all parties in pursuit of interest goals is solved by cycle iteration. Numerical simulation shows that the model can be used to exploit response potential in the participation satisfaction of the community and energy economy, realize“peak-shaving and valley filling”in the community and the main network, optimize the configuration of energy resources, and increase the overall economy of the system.