A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm

A multi-resource balanced allocation method using a genetic-heuristic fusion algorithm is proposed to address the imbalance in distributed power generation resource allocation and the over-generation problem in virtual power plants. By establishing models of wind, solar, storage, and controllable lo...

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Vydané v:	International journal of computational intelligence systems Ročník 18; číslo 1; s. 200 - 22
Hlavní autori:	Li, Haifeng, Jin, Tao, Xu, Xian, Shi, Lin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 04.08.2025 Springer Nature B.V Springer
Predmet:	Alternative energy Ant colony Ant colony optimization Artificial Intelligence Collaboration Computational Intelligence Consumption Control Controllability Convergence Costs Distributed generation Distributed generation resource allocation Efficiency Electricity Energy management Energy resources Energy storage Engineering Flexibility Fuzzy logic Genetic algorithms Genetic-heuristic algorithm Heuristic Heuristic methods Local optimization Mathematical Logic and Foundations Mechatronics Optimization algorithms Optimization models Optimization techniques Power plants Power supply Renewable resources Resource allocation Robotics Scheduling Supply & demand Systems stability Virtual power plant Virtual power plants Genetic-heuristic algorithm Ant colony Virtual power plant Distributed generation resource allocation
ISSN:	1875-6883, 1875-6891, 1875-6883
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Shrnutí:	A multi-resource balanced allocation method using a genetic-heuristic fusion algorithm is proposed to address the imbalance in distributed power generation resource allocation and the over-generation problem in virtual power plants. By establishing models of wind, solar, storage, and controllable load characteristics, an optimization model is constructed with objectives of resource allocation balance and minimization of call costs, subject to constraints such as power balance. Combining the global search capability of a genetic algorithm and the local optimization capability of an ant colony algorithm, the genetic algorithm stage adopts real-number encoding and a dynamic crossover-mutation strategy, while the ant colony algorithm stage optimizes the pheromone update mechanism to avoid premature convergence. The experimental results show that this method achieves 100% accurate allocation of resources without any over-generation occurrences and reduces the resource allocation deviation rate by 32–67% compared to alternative methods. The algorithm demonstrates fast convergence, yielding solutions in less than 0.6 s across 14 repeated experiments, with an average convergence time reduction of 42% compared to traditional algorithms. Under a comprehensive fluctuation scenario with 30% renewable energy fluctuation rate and 15% load forecasting error, the system stability index remains at 0.865, demonstrating the algorithm’s efficiency and robustness under complex conditions and providing an effective approach for optimizing virtual power plant resource allocation.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1875-6883 1875-6891 1875-6883
DOI:	10.1007/s44196-025-00941-1