含多能微网群的区域电热综合能源系统分层自治优化调度

分布式能源与负荷聚合形成多能微网群连接至区域供热系统和上级配电网,构成区域电热综合能源系统.为保护多利益主体隐私并满足其利益诉求,提出了基于分布自治、集中协调设计架构的区域电热综合能源系统分层优化调度方法.在上层,计及多能流异质动态特性,提出了区域电热综合能源系统的动态经济调度模型;在下层,考虑用户柔性热需求和分布式光伏不确定性,建立了多能微网自治能量管理的机会约束模型.基于目标级联分析法将上下层解耦,层间仅需交换边界电功率和热功率,通过微网并行计算与层间迭代求解获得全局最优策略.算例结果证明,该方法能够在保证收敛性的前提下,为含多能微网群的区域电热综合能源系统提供高精度的多能互补优化调度结果.此外,对缩放因子进行灵敏度分析,讨论了算法误差来源并说明了算法最优性.

Hierarchical Autonomous Optimal Dispatching of District Integrated Heating and Power System with Multi-energy Microgrids

Multi-energy microgrids integrating distributed energy resources and loads are connected to the district heating system and superior distribution network to form district integrated heating and power system. In order to protect the privacy of multi-stakeholders and satisfy their interest demands, a hierarchical optimal dispatching method for the district integrated heating and power system is proposed based on the framework of distributed autonomy and centralized coordination. At the upper layer, a dynamic economic dispatching model is proposed for the district integrated heating and power system considering the heterogeneous dynamic characteristics of multi-energy flows. At the lower layer, a chance constrained model is established for the autonomous energy management of multi-energy microgrids considering the flexible thermal demand of users and uncertainty of the distributed photovoltaic. Analytical target cascading theory is applied to decouple the upper and lower layers, and only the boundary electric power and thermal power between layers need to be exchanged. The global optimal strategy is obtained through parallel calculation of microgrids and iterative computation between the layers. The simulation results verify that the proposed method can provide high-precision multi-energy complementary optimal dispatching results for the district integrated heating and power system with multi-energy microgrids on the premise of ensuring convergence. In addition, the sensitivity analysis of the scaling factor is carried out, the source of algorithm error is discussed and the optimality of the algorithm is explained.