基于混合粒子群算法的源-荷-储协调优化调度

Optimal scheduling of source-load-storage coordination based on hybrid particle swarm optimization

为了提高新型电力系统中对风电和光伏的消纳能力,降低电力系统运行成本,将火电机组、光伏、风电、需求响应负荷和储能系统作为调度资源建立了基于源-荷-储协调的优化调度模型。以火电机组运行成本、弃风弃光成本和需求响应负荷调度成本最小为目标,提出了一种两阶段优化方法。第一阶段优化采用离散二进制粒子群优化算法,使火电机组启动成本和弃风弃光成本之和最小。在第一阶段优化结果的基础上,第二阶段的优化采用双层连续粒子群优化算法使基于电价的需求响应负荷调度成本和燃料成本之和最小。算例结果验证了该优化调度模型的可行性和有效性。

An optimal scheduling model based on source-load-storage coordination was established with thermal power units, photovoltaics, wind power, demand response loads and energy storage systems function as scheduling resource modelling, so as to improve the absorption capacity of wind power and photovoltaics in the new power system, and to reduce the operational cost of the power system as well. A two-stage optimization approach was proposed with an aim to minimize operational cost of thermal power units, the cost of discarding wind and photovoltaic power, and the cost of scheduling the load on demands. A discrete binary particle swarm optimization algorithm was adopted for optimization at the first stage to minimize the sum of the start-up cost of thermal power units and the cost of discarding wind and photovoltaic power. Based on the result of optimization at the first stage, a double-layer particle swarm optimization algorithm was applied at the second stage to minimize the sum of the cost of scheduling the load on price-based demands and the fuel cost. The results of the experiments have proven feasibility and validity of the optimal scheduling model.