不确定因素下建筑集群冷热电联供系统多目标优化

为降低建筑楼宇的能源消耗,研究建筑集群中的多个建筑楼宇共享冷热电联供系统、热能存储装置以及电池时的能源调度优化问题.考虑到建筑楼宇的能源需求和能源价格具有随机性,并且每个建筑楼宇以各自的费用最小化为目标,从随机规划和多目标的角度,建立建筑集群供能系统的两阶段多目标随机规划模型.为了提高模型的求解效率,提出将线性规划松弛与Benders分解算法相结合,从而获得建筑楼宇共享能源系统的Pareto最优解集.算例分析中通过CPLEX软件求解,对比分析不同随机因素对最优化建筑集群供能系统总费用以及建筑楼宇各自费用的影响程度,结果表明了所提出算法的有效性以及所构建的模型可以有效提高最优化决策的准确性.

Multi-objective programming for building clusters combined cooling, heating and power system under uncertainty

To achieve the energy consumption reduction of buildings, the energy operation strategy of combined cooling, heating and power (CCHP) system, thermal storage and battery shared by buildings in the building clusters is addressed. The energy demands and energy prices are stochastic, and each building aims to minimize its cost. The stochastic programming approach and multi-objective theory are applied to formulate a two-stage multi-objective stochastic programming model. In order to obtain the Pareto optimal solutions, the LP-Relaxation and Benders decomposition algorithm are adopted to solve the energy schedule model with high solving efficiency. Finally, the mathematical model is solved by CPLEX software, and comparative studies are performed to evalute the effect of the different stochastic parameters on the optimal building cluster total cost and every building cost. The experimental results show that the hybrid algorithm is effective for solving the optimal model and the multi-objective stochastic programming model can significantly improve the accuracy of the optimal decisions.