Optimal multi-objective distribution system reconfiguration with multi criteria decision making-based solution ranking and enhanced genetic operators
Optimal multi-objective distribution system reconfiguration with multi criteria decision making-based solution ranking and enhanced genetic operators
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DOI：
10.1016/j.ijepes.2013.07.006
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发表时间：
2014
影响因子：
5.2
通讯作者：
A. Mazza;G. Chicco;A. Russo
中科院分区：
文献类型：
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作者：
A. Mazza;G. Chicco;A. Russo
在配电系统优化中，通过使用帕累托前沿分析可以有效解决多个相互冲突的目标的存在。本文讨论了考虑网络损耗和未作为多目标提供的能量的最优重新配置。参考使用基于遗传算法的求解器构建和更新最著名的帕累托前沿，提供了一组原始贡献。交叉算子被扩展以解决多目标解决方案。变异算子被扩展以处理更多的情况。多目标解排序是通过在交叉算子中创建后代时采用多标准决策方法来应用的，并为决策者提供自动支持，以识别最终帕累托前沿中的优选解。所提出的方法应用于两个参考测试网络，其中完整的帕累托前沿是根据整套多目标解决方案计算的。使用基于几何考虑的度量将所得的最著名的帕累托前沿与完整的帕累托前沿进行比较。该比较框架有助于评估多目标优化求解器的性能。
In electrical distribution system optimisation, the presence of multiple conflicting objectives is effectively addressed by using Pareto front analysis. This paper deals with optimal reconfiguration considering network losses and energy not supplied as multi-objectives. A set of original contributions are provided with reference to the construction and updating of the best-known Pareto front using a genetic algorithm-based solver. The crossover operator is extended to address multi-objective solutions. The mutation operator is extended to handle a broader number of cases. Multi-objective solution ranking is applied by resorting to multi criteria decision making methods during the creation of the offsprings in the crossover operator, as well as to provide an automatic support for the decision maker to identify the preferable solution in the final Pareto front. The proposed approach is applied on two reference test networks, for which the complete Pareto front is calculated from the entire set of multi-objective solutions. The resulting best-known Pareto front is compared with the complete Pareto front using a metric based on geometrical considerations. This comparison framework is helpful to assess the performance of the multi-objective optimisation solvers.