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Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm
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DOI：10.1109/ACCESS.2022.3193371.png)
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Abstract
En 中文
This paper proposes a new multi-objective method that efficiently solves the multi-objective optimal power flow (MOOPF) problem in power systems. The objective of solving the MOOPF problem is to concurrently optimize the fuel cost, emissions, and active power loss. The proposed multi-objective search group algorithm (MOSGA) is an effective method that combines the merits of the original search group algorithm with fast nondominated sorting, crowding distance, and archive selection strategies to acquire a nondominated set in a single run. The MOSGA is employed on IEEE 30-bus and 57-bus systems to validate its robustness and efficiency. It was found that implementing MOSGA to solve the MOOPF significantly enhanced the performance of power systems in terms of economic, environmental, and technical benefits. As for Case 6, the fuel cost, emissions, and active power loss were reduced by 16.5707%, 52.0605%, and 60.9443%, respectively. The simulation results were analyzed and compared with those of previously reported studies based on the best individual solutions, compromise solutions, and performance indicators. The comparative results confirmed the potential and advantage of MOSGA when solving the MOOPF problem efficiently and MOSGA had high-quality optimal solutions.
Keywords:
Costs
Fuels
Optimization
Generators
Sorting
Search problems
Linear programming
Multi-objective search group algorithm
multi-objective optimal power flow
fuel cost
emissions
Journal
IF:
3.6
Papers:
9.8W
Citations:
33.8W
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