An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm

Shi, Yong; Cheng, Yin; Su, Ji; Mao, Meiqin

doi:10.1002/ese3.1407

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…In such circumstances, the integration of an energy storage unit can not only accomplish peak shaving and valley filling, but also address the fluctuation issues associated with wind power and photovoltaic generation. Additionally, it enhances the microgrid's capacity to absorb energy generated by wind and photovoltaic sources 3 . Hence, in the microgrid system design process, the initial step involves addressing the capacity configuration challenge within the microgrid system.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, it enhances the microgrid's capacity to absorb energy generated by wind and photovoltaic sources. 3 Hence, in the microgrid system design process, the initial step involves addressing the capacity configuration challenge within the microgrid system. This stands as a prominent and challenging issue in ongoing research on the optimization and design of microgrid systems.…”

Section: Introductionmentioning

confidence: 99%

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Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

Wang,

Niu

et al. 2024

Energy Science & Engineering

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How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

Wang,

Niu

et al. 2024

Energy Science & Engineering

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Modelling and analysis of chatter in the heavy-load multi-DoF plastic forming process

Han,

Lu,

Zheng

et al. 2025

Mechanical Systems and Signal Processing

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Optimal planning of wind and solar complementary AC/DC microgrids under distributed power capacity constraints

Li,

Hao

et al. 2024

J. Phys.: Conf. Ser.

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The conventional AC/DC microgrid wind-solar complementary optimization planning method mainly uses the CvaR (conditional value at risk) risk value stochastic model to calculate the randomness of output electricity price, which is vulnerable to changes in load expectations, resulting in the per unit value of photovoltaic load output not meeting the actual demand of the microgrid. Therefore, under the constraints of distributed generation capacity, an optimal planning method of wind-solar complementation for AC/DC microgrids is designed. That is to say, considering the distributed generation capacity constraints, the optimal planning model of wind-solar complementation of AC and DC microgrids is constructed, and the bidirectional adjustment equation of the optimal planning of wind-solar complementation of AC and DC microgrids is generated, thus realizing the optimal planning of wind-solar complementation of AC and DC microgrids. The experimental results show that the PV output per unit value is higher and the load output per unit value is lower in the design of the wind-solar complementary optimization planning method of AC/DC microgrid considering distributed generation capacity constraints, which meets the requirements of efficient operation of the microgrid and has certain economic value.

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An online identification method for establishing a microgrid equivalent model based on the hybrid particle swarm optimization butterfly algorithm

Cited by 5 publications

References 12 publications

Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

Research on multiobjective capacity configuration optimization of grid‐connected wind–solar–storage microgrid system based on improved BWO algorithm

Modelling and analysis of chatter in the heavy-load multi-DoF plastic forming process

Optimal planning of wind and solar complementary AC/DC microgrids under distributed power capacity constraints

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