Conditional generative adversarial network-driven bi-level sizing for community microgrids with RTP-based demand response and V2G-enhanced EV charging orchestration
Source abstract
Abstract
The increasing disorderly charging of electric vehicles (EVs) is challenging the stability of community power systems. However, microgrids offer a pathway for integrating renewable energy and managing EV loads. To optimize capacity allocation in such scenarios, this paper proposes a two-level optimization strategy based on a real-time pricing demand response mechanism. The upper level plans energy storage, photovoltaic, and wind power capacities to minimize annual comprehensive costs, while the lower level optimizes daily scheduling plans. To address renewable energy, conditional generative adversarial networks and an improved k-medoids algorithm are employed for scenario generation and simplification. Simulation results demonstrate that this strategy reduces microgrid operating costs by 5.3% and diminishes peak-to-off-peak load differences by 158.85 kW. Analysis of the impact of the electric vehicle participation scale in vehicle-to-grid interactions on system dispatch effectiveness identified an optimal participation level of 120 electric vehicles. This approach was validated for enhancing economic benefits and promoting sustainable energy utilization.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electric Vehicles and Infrastructure · Smart Grid Energy Management · Transportation and Mobility Innovations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
International Joint Research Center for Smart Grid Optimization and Control Technologies, Hunan University 1 , Changsha 410082,
School of Electrical and Information Engineering, Hunan Institute of Engineering 2 , Xiangtan 411104,
Kaili Power Supply Bureau of Guizhou Power Grid Co., Ltd. 3 , Kaili 556099,
Funding
General Project of Hunan Natural Science Foundation
References (52)
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