Multi-Source Collaborative Optimization Scheduling Technology for New Energy Microgrid Based on Improved Particle Swarm Algorithm
DOI:
https://doi.org/10.4108/ew.9800Keywords:
New Energy Microgrid, Improved Particle Swarm Algorithm, Multi-source Collaborative Optimization Scheduling, Dynamic Inertia Weight, Adaptive Learning FactorAbstract
INTRODUCTION: New energy microgrids face significant challenges in multi-source coordinated dispatching, primarily due to the high uncertainty in renewable energy output. Traditional optimization methods often suffer from local optimality and extended computation times, limiting their effectiveness in real-time or complex environments. There is a critical need for enhanced strategies to improve both the economic efficiency and operational robustness of microgrids.
OBJECTIVES: This paper aims to propose and validate an optimization dispatching strategy based on an Improved Particle Swarm Optimization (IPSO) algorithm. The core objectives are to reduce the system's comprehensive operating cost, increase computational efficiency, and enhance the absorption rate of new energy within microgrid systems.
METHODS: The IPSO algorithm enhances the conventional PSO by incorporating dynamically adjusted inertia weights and adaptive learning factors, improving its global search ability and convergence speed. A multi-source collaborative optimization model is formulated with a primary objective of minimizing the total operating cost. The model accounts for fluctuations in load demand and constraints related to the charging and discharging efficiency of the energy storage system (ESS). The strategy is implemented and tested with consideration of Distributed Energy Resources (DERs)..
RESULTS: The improved IPSO algorithm demonstrated a 22.7% improvement in computational efficiency. It also achieved an average 16.2% reduction in the system’s comprehensive operating cost and increased the average absorption rate of new energy to 92.4%.
CONCLUSION: The proposed IPSO-based optimization strategy significantly enhances the economic and operational efficiency of new energy microgrids. By effectively integrating DER characteristics and operational constraints, this method provides a viable technical pathway for advancing the utilization of renewable energy and improving the overall performance of microgrid systems.
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