风-光-储和需求响应协同的虚拟电厂日前经济调度优化

Day-ahead Economic Dispatch Optimization of Virtual Power Plant Based on Wind-photovoltaic-energy Storage and Demand Response Synergy

目前可再生能源直接并入电网仍然面临稳定性和经济性问题,经过虚拟电厂整合可以缓解对电网的影响。以系统整合后最终运行成本达到最小作为目标,进行新能源出力和负荷在未来24 h的预测,计及电网侧在不同时间内的电价变化情况,采用反向学习的混沌映射自适应粒子群算法对风-光-储能和需求响应不同组合搭配的5种调度方案进行探讨,与原始粒子群算法相比,所提算法可以跳出局部最优解而找到全局最优解。计算结果表明,风-光-储和需求响应都参与供电相比风-光-储供电可以将运行成本降低4.47%,用户舒适度提高3.51%。

At present,the direct integration of renewable energy into the power grid still faces stability and economic problems,and the impact on the power grid can be mitigated by virtual power plant integration.This paper takes the minimum total operating cost after system integration as the objective for predicting new energy output and load in the next 24 hours.It also considers the changes in electricity prices on the grid side at different times and uses the chaotic mapping adaptive particle swarm optimization algorithm based on opposition-based learning to discuss five scheduling schemes with different combinations of wind,photovoltaic,energy storage and demand response.Compared with the original particle swarm algorithm,this algorithm can jump out of local optimal solution and find out the global optimal solution.The calculation results show that the operating cost can be reduced by 4.47%when both wind,photovoltaic and energy storage and the demand response are involved in power supply,as well as improve user comfort by 3.51%compared with only wind photovoltaic and energy storage participating in power supply.