基于模型预测控制的虚拟电厂储能系统能量协同优化调控方法

随着可再生能源在传统电网中的渗透率逐渐增高，虚拟电厂的概念被提出，旨在有效整合并利用可再生能源。提出了一种基于模型预测控制的虚拟电厂储能系统能量协同优化调控方法，使用长短期记忆神经网络来获取未来一天内虚拟电厂管辖范围内的负荷、风电、光伏出力预测值。在模型预测控制的框架下，以虚拟电厂运行调度的成本最小化为目标，使用一种改进的粒子群寻优算法求解优化过程。仿真结果表明所提方法的有效性。

Model Predictive Control Based Energy Collaborative Optimization Control Method for Energy Storage System of Virtual Power Plant

With the high penetration rate of renewable energy in traditional power grid，the concept of virtual power plant （VPP） is proposed to integrate and utilize renewable energy. Energy collaborative optimization control method for energy storage system of virtual power plant is proposed based on model predictive control. Long-short term memory neural network is used to obtain the one day-ahead forecasting information， such as load， wind and photovoltaic within the jurisdiction of virtual power plant. With the minimum economic cost as the optimization goal，the optimal scheduling is solved by an improved particle swarm optimization algorithm under the framework of model predictive control. The effectiveness of the proposed scheme has been validated by simulation results.