CAJ | 학술논문

以污染气体排放量、网损最小为目标，建立多目标电力系统最优潮流数学模型，并提出一种基于人工蜂群的多目标算法对其进行求解。该算法利用外部存档技术来保存进化过程中已经找到的Pareto最优解，并在每次迭代后更新。最后根据模糊集理论从Pareto最优解集中选取最优折衷解，为决策者提供科学的决策依据。通过IEEE-30节点系统及IEEE-57节点系统的仿真，验证了该算法在求解大规模电力系统多目标问题上的有效性，相比其他多目标算法能有效避免局部收敛。
이오염기체배방량、망손최소위목표，건립다목표전력계통최우조류수학모형，병제출일충기우인공봉군적다목표산법대기진행구해。해산법이용외부존당기술래보존진화과정중이경조도적Pareto최우해，병재매차질대후경신。최후근거모호집이론종Pareto최우해집중선취최우절충해，위결책자제공과학적결책의거。통과IEEE-30절점계통급IEEE-57절점계통적방진，험증료해산법재구해대규모전력계통다목표문제상적유효성，상비기타다목표산법능유효피면국부수렴。
Taking the minimum pollutant emission and active loss as objective functions, this paper builds a multi-objective optimization model for power flow optimization of power system. In the proposed algorithm, an external archive of non-dominated solutions is kept which is updated at each iteration. Moreover, a method based on fuzzy set theory is employed to extract one of the Pareto-optimal solutions set as the best compromise one to provide the scientific decision basis for decision-makers. Simulation of IEEE-30 bus system and IEEE-57 bus system testify that this algorithm can avoid the local convergence effectively compared with other multi-objective optimization algorithm.