基于CCP-AGABP双层综合能源系统调度策略

Optimal Scheduling Strategy of Dual-level Integrated Energy System Based on CCP-AGABP

针对综合能源系统(IES)中可再生能源与电热冷负荷因不确定影响引起功率偏差问题,实现IES优化调度,将机会约束规划(CCP)的随机模型和自适应遗传算法改进的反向传播神经网络(AGABP)的预测模型相结合,提出了双层IES优化调度策略。上层利用CCP对日前优化调度中的不确定性问题进行处理,缓解因可再生能源与多能负荷预测误差引起的功率较大偏差的问题,下层基于AGABP的预测模型进行日内优化,对日前优化调度偏差进行修正。针对随机优化模型难以求解的问题,采用确定性等价类的方法,将模型中的机会约束等价转换为确定性约束,并通过交替方向乘子法(ADMM)对改进的模型进行优化求解。最后,通过算例仿真验证了所提调度策略的有效性。

Aiming at the problem of power deviation caused by uncertain influence of renewable energy and electric heating and cooling loads in the integrated energy system(IES)to realize integrated energy system optimal dispatch,a double-layer IES optimal dispatch strategy was proposed by combining the stochastic model of chance constrained programming(CCP)and the prediction model of back propagation neural network improved by adaptive genetic algorithm(AGABP).The upper layer used chance constrained programming to deal with the uncertainty problem in the day-ahead optimal dispatch to relieve the problem of large power deviation caused by the forecast error of renewable energy and multi-energy load,and the lower layer performed intra-day optimization based on the prediction model improved by adaptive genetic algorithm to correct the deviation of day-ahead optimal dispatch.To solve the problem that the stochastic optimization model is difficult to solve,the deterministic equivalence class method was used to transform the chance constraints in the model into deterministic constraints,and then the alternating direction multiplier method(ADMM)was used to optimize the improved model.Finally,an example simulation verified the effectiveness of the proposed scheduling strategy.