考虑需求侧管理的风光燃储微网两阶段优化调度

针对微电网中源荷匹配较弱及储能应用不充分的问题，计及需求侧管理与碳排放对其源荷储进行协调优化。所研究的并网型微网优化分为需求侧管理与调度两阶段。首先，第一阶段引入需求侧管理模型，结合储能电池并采用纵横交叉算法求解,使微网的净负荷最小与自供率最大。然后，第二阶段依托第一阶段需求侧管理后的信息从经济和环保角度出发，建立以综合成本最小及碳排放量最低为目标的风光燃储微网日前优化调度模型，利用改进多目标灰狼优化算法进行求解。最后，以福建某实际微网为例，通过仿真表明引入需求侧管理可有效利用储能电池改善微网源荷匹配度，充分挖掘需求响应潜力。相比单阶段优化，两阶段优化更有利于提高可再生能源渗透率，降低微网日运行成本与碳排放量，实现微网的低碳经济运行。

Two-stage optimal scheduling of a wind, photovoltaic, gas turbine, fuel cell and storage energy microgrid considering demand-side management

There is weak source-load matching and inadequate energy storage application in a microgrid. It needs to coordinate and optimize the source-load and energy storage considering the demand-side management and carbon emission. In this paper, the optimization of a grid-connected microgrid is divided into two stages including demand-side management and dispatching. First, in the first stage, a demand-side management model is introduced. The energy storage battery is considered and a crisscross optimization algorithm is used to minimize the net load of the microgrid and maximize the self-supply rate. Then, in the second stage, from the economy and environmental protection perspective, a wind, photovoltaic, micro-gas turbine, fuel cell and storage energy microgrid day-ahead optimal dispatching model is established to minimize total costs and carbon emissions. It relies on the information after the demand-side management in the first stage. The above model is analyzed using the improved multi-objective gray wolf optimization algorithm. Finally, taking an actual microgrid in Fujian as an example, simulation shows that the introduction of demand-side management can effectively use energy storage batteries to improve the matching degree of microgrid source-load and fully tap the demand response potential. Compared with single-stage optimization, two-stage optimization is more conducive to improving the penetration rate of renewable energy, reducing the daily operating costs and carbon emissions of microgrids, and realizing their low-carbon economic operation. This work is supported by the National Natural Science Foundation of China (No. 51977039).