基于需求响应的虚拟电厂多时间尺度优化调度

随着我国不断加快新型电力系统建设，风电、光伏等越来越多的分布式资源大规模并网，但其随机性、波动性及分散性的特点给电力系统带来了多重不确定性。提出了一种考虑价格型需求响应的虚拟电厂多时间尺度优化调度方法，在日内调度模型中引入用户价格型需求响应，对用户负荷进行精准调控；采用基于蒙特卡罗与曼哈顿概率距离的场景生成与削减法处理风光出力、电价不确定性，减少日前日内出力偏差，再结合日前调度构成了多时间尺度优化调度模型，并采用滚动优化得到日内优化调度结果。以湖南某小型虚拟电厂系统进行算例仿真，结果表明，所提多时间尺度优化调度策略能够精确预测风光出力及用户负荷，在有效抑制功率波动的同时保证了系统的经济性。

A Multi-Time Scale Optimal Scheduling Strategy of Virtual Power Plants Based on Demand Response

As China continues to accelerate the construction of new power systems， more and more distributed resources such as wind power and photovoltaic are connected to the grid on a large scale， but the randomness， volatility and dispersed characteristics of new energy bring about multiple uncertainties to the power system. In this paper， an improved multi-time scale optimization scheduling method considering price-based demand response is proposed. Firstly， the price based demand response is introduced into the intraday scheduling model to accurately regulate the user load. Secondly， the scenario generation and reduction method based on the Monte Carlo and Manhattan Distance is used to deal with the uncertainty of wind power and electricity price， and the day-ahead and intraday output deviations are reduced. And combined with the day-ahead scheduling， the VPP multi-time scale optimal scheduling model is formed， and the intraday optimal scheduling results are obtained by rolling optimization method. Finally， a small VPP system in Hunan Province is simulated， the results show that the proposed multi-time scale optimization scheduling strategy can accurately predict the wind and solar power output and user load， effectively suppressing power fluctuations while ensuring the system economy.