Multi-objective optimization models for power load balancing in distributed energy systems

• Published in: Energy Informatics Vol. 8; no. 1; pp. 104 - 22
• Main Authors: Wang, Zhuo, Luo, Yuchen, Wu, Wei, Cao, Lei, Li, Zhun
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2025; Springer Nature B.V; SpringerOpen
• Subjects: Alternative energy sources; Artificial intelligence; Climate change; Computer Science; Deep reinforcement model grid, energy sources; Distributed generation; Efficiency; Electrical loads; Electricity; Electricity distribution; Emissions; Energy consumption; Energy management; Energy resources; Energy storage; Information Systems and Communication Service; Investigations; Multi-objective optimization; Multiple objective analysis; Operating costs; Optimization; Optimization models; Photovoltaics; Power load, distributed generation; Power supply; Probability density functions; Renewable energy sources; Renewable resources; Solar energy; Supply & demand; Uncertainty; Wind power; Power load, distributed generation; Multi-objective optimization; Deep reinforcement model grid, energy sources
• DOI: 10.1186/s42162-025-00526-4
• ISSN: 2520-8942

When it comes to smart power grid (SPG) reliability and energy balancing, multi-objective energy optimization is a must. Uncertainty and several competing criteria on the demand and generation sides make multi-objective optimization difficult. Selecting a model capable of resolving scheduling issues related to loads and dispersed energy sources is, therefore, essential. This study details a concept for optimizing the SPG’s operating cost and pollutant emissions using renewable electricity. Renewable energy sources, such as solar photovoltaic and wind power, are inherently unpredictable and subject to change. Uncertainty around renewable energy is handled by the suggested approach via the use of a probability density function (PDF). In order to address a multi-objective optimization (MOCO) issue, the model that was built relies on a MOCO method. A benchmark model for energy management and control is used to verify the performance of the suggested model, which is a multi-objective deep reinforcement learning (DRL) method. According to the results, MOCO reduces operating costs by 15% and environmental emissions by 8%. The results show that compared to the comparison models, the proposed model achieves the aims better.