Abstract: Aiming at the problems of load-side users’ electricity bills, new energy consumption rate, and electricity peak-valley difference, an improved adaptive decomposition-based multi-objective evolutionary algorithm is proposed to optimize the controllable load dispatch of contracted households in the building microgrid. By analyzing the power consumption characteristics of the load, the load is divided into five categories, which establish mathematical models, optimize objective functions and constraints. A new adaptive weight vector is generated by combining generalized decomposition with uniform distribution, which makes the non dominated solution closer to the real Pareto front; the idea of historical experience is adopted to realize adaptive selection strategy by counting the contribution rate of SBX and DE crossover operators to external archive and using roulette. The subgeneration points are modified by the characteristic constraint mapping, which indirectly expands the search space of the algorithm and improves the diversity of the population. The convergence and superiority of the improved AWS-MOEA/D algorithm are verified by the test function; the simulation experiment results of the dispatching of the residents in a residential area show that the improved algorithm can save more electricity bills and effectively increase new energy consumption rate after dispatching.

摘要： 针对负荷侧用户用电电费、新能源消纳率和用电峰谷差等问题，提出了一种改进的自适应基于分解的多目标进化算法，进行楼宇微电网签约住户可控负荷优化调度；通过分析负荷的用电特性，将用电负荷分为五类并分类建立数学模型、优化目标函数和约束条件；将广义分解与均匀分配相结合产生新的自适应权重向量使算法非支配解更接近真实帕累托前沿；采用历史经验的思想通过计数SBX和DE两种交叉算子对外部存档的贡献率，运用轮盘赌的方式实现自适应选择策略；通过特性约束条件映射对产生的子代点进行修正，间接地扩大了算法搜索空间，提高了种群多样性。通过测试函数验证了改进的AWS-MOEA/D算法的收敛性和优越性；在某小区楼宇住户调度仿真实验结果表明，所改进的算法在调度后能节省更多的电费，并有效地提高了新能源消纳率。