Optimization and Operation of Microgrid Based on Multi-strategy Collaborative Optimization Salp Swarm Algorithm
DOI:
https://doi.org/10.13052/spee1048-5236.44410Keywords:
Microgrid, optimal scheduling, salp swarm algorithm, chaotic mapping, reverse learning strategyAbstract
Microgrid systems include various micro power sources that need to meet a large number of constraints. Traditional optimization algorithms often encounter challenges in escaping local optima, making it difficult to achieve optimal solutions. To address such issues, a multi-strategy collaborative optimization algorithm called the multi-strategy collaborative salp swarm algorithm (MSSSA) is proposed, which takes into account both operation and environmental pollution. With the objective function of comprehensive cost, constraints such as power balance, climbing rate, and interaction power extreme value of tie lines are set. Then, the MSSSA is employed to solve
the microgrid scheduling model. By comparing the simulation results, the superiority of the MSSSA over other algorithms, as well as the rationality of optimizing microgrid systems, is verified.
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