Two-layer Optimal Scheduling Method for Shared Energy Storage and Integrated Energy Microgrid Systems Based on Adaptive Mutation Particle Swarm Optimization Algorithm

In the context of increasing renewable energy installations, developing energy storage technologies is a necessary measure to address demand matching issues and reduce the impact of uncertainty in wind and solar power generation on the grid. This paper introduces shared energy storage into integrate...

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Bibliographic Details
Published in:2024 4th International Conference on Energy, Power and Electrical Engineering (EPEE) pp. 1189 - 1197
Main Authors: Zou, Chao, Zhang, Yunjie, Liu, Wei
Format: Conference Proceeding
Language:English
Published: IEEE 20.09.2024
Subjects:
Adaptation models
component
Electricity
Energy storage
Lead acid batteries
Microgrid system
Microgrids
Micromechanical devices
Optimal scheduling
Particle swarm optimization
Particle Swarm Optimization Algorithm
Pricing
Resistance heating
Shared energy storage
stackelberg game
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Summary:In the context of increasing renewable energy installations, developing energy storage technologies is a necessary measure to address demand matching issues and reduce the impact of uncertainty in wind and solar power generation on the grid. This paper introduces shared energy storage into integrated energy microgrid systems and describes the system operation framework. Operational models are established for the microgrid operator, the shared energy storage service provider, and the user aggregator. To take into account the interests of all parties involved, a two-layer optimization scheduling strategy for the integrated energy microsystem considering shared energy storage under a leader-follower game strategy is proposed. The upper-layer pricing model of the microgrid operator is solved using an adaptive mutation particle swarm optimization algorithm, updating the electricity and heat selling prices of the upper-level leader. The lower-level problem is solved using the CPLEX solver, optimizing equipment output, demand response, and electricity purchase and sale plans. The combined method of the solver and the particle swarm optimization algorithm optimizes the operational strategies of shared energy storage and the microgrid alliance, and the effectiveness of the proposed model is demonstrated through simulation examples.
DOI:10.1109/EPEE63731.2024.10875082