分时电价影响下的多能源虚拟电厂双层优化调度

随着风光等可再生能源的大规模接入,其随机性、波动性的出力特点使得电力系统的调度变得困难甚至会造成严重的弃风弃光问题。为减小大规模新能源接入对电网带来的冲击,提出了一种基于分时电价的虚拟电厂双层优化调度模型。在上层模型中,以风光预测出力为计划出力进行申报,针对实际出力与预测出力之间的偏差,燃气轮机和储能电池协调配合对其进行平抑,在风光消纳最大的基础上,求得偏差最小的补偿方案,然后将上层出力传递至下层模型。在下层模型中,基于分时电价对抽水储能装置制定控制策略,以减小净负荷波谷差,然后采用自适应粒子群算法对各个火电机组的出力进行寻优。最后对比分析了不同风光预测误差对虚拟电厂经济性的影响,研究了抽水蓄能装置给净负荷曲线所带来的改变。结果表明:模型可以提升新能源消纳水平,基于分时电价的虚拟电厂能够实现收益最大化,保证区域内供需平衡。

Bi level optimal dispatching of multi energy virtual power plant influenced by TOU price

With the large-scale access of renewable energy sources, its randomness and volatility make it difficult to dispatch the power system and even cause the discarding of the wind. In order to reduce the impact of large-scale new energy access on the power grid, a two-layer optimal scheduling model of virtual power plant based on time sharing price is proposed. In the upper model, the wind and scenery prediction force is declared for the planned force. In view of the deviation between the actual force and the forecast force, the gas turbine and the energy storage battery are coordinated to suppress it. On the basis of the maximum wind and scenery, the minimum deviation compensation scheme is obtained, and the upper force is transferred to the lower layer model. In the lower layer model, the control strategy is set up based on the time sharing price to reduce the net load trough difference, and then the adaptive particle swarm optimization is used to optimize the output of each thermal power unit. Finally, the impact of different wind and solar forecast errors on the economy of the virtual power plant is compared and analyzed, and the change of net load curve caused by pumped storage device is studied. The results show that the model can improve the level of new energy consumption, and the virtual power plant based on TOU price can maximize the profit and ensure the balance of supply and demand in the region. This work is supported by National Natural Science Foundation of China (No. 51767023).