考虑天气类型的新型HDP智能楼宇储能优化调度

Energy optimal scheduling for new HDP intelligent building considering weather classification

针对智能楼宇微网系统存在的非线性、时变、分布式发电不确定性等导致的建模困难问题,提出了一种基于启发式动态规划(Heuristic Dynamic Programming,HDP)的储能系统调度算法。在考虑储能系统寿命、用户实时电价(Residential Real-TimePrice,RRTP)的基础上根据天气分类使用两种神经网络来训练HDP模型,使得它能够适应自身所在环境而进行自我更新。通过与微分进化算法的对比分析,结果表明,所提出的储能优化调度算法能够有效地节约用电成本、避免蓄电池深度充放电,具有良好的经济收益;在与环境的学习过程中逐步寻求最优解的特性使得该算法对模型依赖度低,有效缓解了建模困难问题;在均衡负载、削峰填谷方面也起到了较好的效果。

In view of the modeling difficulties due to the nonlinear, time-varying and distributed generation uncertainty of intelligent building micro-grid system, a scheduling algorithm for energy storage system based on heuristic dynamic programming (HDP) is proposed in this paper. According to weather classification, two neural networks are used to train the HDP model, so that it can adapt itself to the environment and self-update considering the service life of the energy storage system and the real-time price of users. Compared with differential evolution algorithms, the results show that the proposed energy storage optimization scheduling algorithm can effectively save electricity costs and avoid deep charge and discharge of energy storage systems, and has good economic benefits. In the process of learning from the environment, the characteristics of the optimal solution are gradually sought to make the algorithm less dependent on the model and effectively alleviate the difficulty of modeling. Moreover, it also has a good effect in balancing load, cutting peak and filling valley.