基于分布式神经动力学算法的微电网多目标优化

针对微电网多目标优化计算量较大的问题,提出了一种考虑需求响应的微电网分布式神经动力学优化算法.首先,考虑平均效率函数、微电网的排放、需求响应引起的不满意度以及总利润函数等因素建立多目标优化模型.其次,应用单目标积公式将多目标优化问题转换为单目标优化问题,并证明了最优解是原始多目标问题的帕累托最优点.再次,使用对数障碍物惩罚因子处理不等式约束,利用Lasalle的不变性原理和Lyapunov函数证明所提出的算法可以收敛到最优解.最后,通过仿真验证了本文算法可以在保证优化精度与收敛性条件下,大大降低计算成本.

Multiple Objective Optimization of Microgrid Based on Distributed Neural Dynamics Algorithm

In order to solve the problem of large amount of calculation in multi-objective optimization of microgrid, a distributed neural dynamic optimization algorithm considering demand response was proposed. Firstly, the multi-objective optimization model was established considering the average efficiency function, micro grid emissions, demand response induced dissatisfaction and total profit function. Then, the multi-objective optimization problem was transformed into a single objective optimization problem by using the single objective product formula, and it was proved that the optimal solution was the Pareto best of the original multi-objective problem. Furthermore, the logarithmic obstacle penalty factor was used to deal with inequality constraints, and the invariance principle of LaSalle and Lyapunov function were used to prove that the proposed algorithm can converge to the optimal solution. Finally, the simulation results show that the proposed method can greatly reduce the calculation cost under the condition of ensuring the optimization accuracy and convergence.