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

• Published in: International journal of computational intelligence systems Vol. 18; no. 1; pp. 1 - 22
• Main Authors: Li, Haifeng, Jin, Tao, Xu, Xian, Shi, Lin
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 04.08.2025; Springer Nature B.V; Springer
• Subjects: Ant colony; Ant colony optimization; Artificial Intelligence; Computational Intelligence; Control; Controllability; Convergence; Distributed generation; Distributed generation resource allocation; Engineering; Genetic algorithms; Genetic-heuristic algorithm; Heuristic methods; Local optimization; Mathematical Logic and Foundations; Mechatronics; Optimization models; Power plants; Resource allocation; Robotics; Systems stability; Virtual power plant; Virtual power plants; Genetic-heuristic algorithm; Ant colony; Virtual power plant; Distributed generation resource allocation
• DOI: 10.1007/s44196-025-00941-1
• ISSN: 1875-6883; 1875-6891

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.