Evolutionary Algorithms for Resource Allocation in Smart Grid




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De Montfort University


Thesis or dissertation

Peer reviewed


As an upgrade and development direction of the traditional grid, the smart grid provides a feasible solution for the global low-carbon targets. The smart grid's bidirectional communication architecture and decentralization control methods contribute to integrating distributed energy sources, energy storage equipment, and interaction or coordination between energy supply and demand. However, resource allocation and energy management are getting more complex due to the heterogeneous of various components, information, and data. At the same time, the penetration of massive electric vehicles generated by traffic electrification has significantly influenced the energy system. Implementing an appropriate strategy to orchestrate the interplay between the smart grid and energy consumers or devices will enhance the resource allocation and management of the energy system. Demand response has recently been proven a reliable management approach for energy systems integrating electric vehicles. In addition, the management decision of the energy system has always been a very complex optimisation problem. Choosing and implementing an appropriate optimisation technology can broaden the smart grid's management ideas and application effects. Therefore, this thesis is dedicated to designing proper energy management strategies and applying evolutionary algorithms in smart grid resource allocation and management. With the massive literature review and analysis, the research mainly focuses on the linkage management of microgrids and electric vehicles, and three specific management scenarios have been studied. Firstly, for the energy management of small microgrids, this paper proposes a novel energy management method, which not only considers the charging demand of electric vehicles parked in the area but also regards electric vehicles as expandable energy storage devices under direct control by the energy system. At the same time, when applying the evolutionary algorithm to search for optimal solutions, the working idea of combining with other strategies to improve the optimisation ability of the algorithm is analysed. Secondly, considering the differences in electricity prices, energy-consuming behaviours and service objectives in different regions, this paper proposes a new idea of using commuter electric vehicles as energy transfer devices for multi-region microgrids based on price demand responses to improve the performance of multiple energy systems. In this scenario, the main consideration in applying evolutionary algorithms is improving the algorithm's performance by adjusting algorithm parameters according to the actual case. Finally, this paper proposes an energy interactive management method to optimize the energy management of community microgrids by coordinating large-scale electric vehicles. In the first stage, the microgrid and electric vehicles freely handle their energy demand and make pre-scheduling plans; in the second stage, the energy supply and demand balance is maintained, and the economic costs of both energy system and electric vehicles owners are reduced through real-time data update and market interaction. In this case, evolutionary algorithms are used to find decision schemes for large-scale optimisation problems. The experimental results show that the evolutionary algorithm can effectively solve complex optimisation problems in the energy system, and the proposed energy system management method also shows its unique advantages.





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