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Adaptive constraint differential evolution for optimal power flow
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DOI：10.1016/j.energy.2021.121362.png)
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Abstract
En 中文
The optimal power flow (OPF) problem featured as a non-linear, non-convex, large-scale and constrained, still remains a popular and challenging work in power systems optimization. Although various optimization algorithms have been devoted to solving this problem, they suffer from some weak points such as insufficient accuracy as well as most of them are unconstrained optimization algorithms that result in optimal solutions that violate certain security operational constraints. To this end, this paper presents an adaptive constraint differential evolution (ACDE) algorithm, in which the novelty lies primarily in these three points: i) the crossover rate (CR) sorting mechanism is employed to build the relationship of CR and individual fitness values; ii) reusing successful evolution direction is proposed to guide the individual evolution towards promising regions; iii) an advanced constraint handling technique named superiority of feasible solutions (SF) is introduced to effectively deal with constraints in power systems. In order to verify the performance of the presented approach to the OPF problem, the standard IEEE-30 bus system is selected as the test case, in which six optimization objectives including total fuel cost, total fuel cost considering the valve-point effect, real active power losses, voltage deviation, voltage stability and emission are studied. The experimental results demonstrate that the presented approach can provide the smaller cost (800.41132$/h), reducing by up to 3.76% compared to the MPIO-COSR. In terms of the emission, ACDE emits the least emissions (0.204817ton/h). In addition, the proposed method also obtains the best results on the real active power losses (3.084041 MW) and voltage deviation (0.085636p.u.) when compared with other state-of-the-art methods. (c) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Optimal power flow
Power systems
Differential evolution
Successful evolution direction
Constraint handling
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