Day-Ahead Optimization of Energy Internet System for Low-Carbon Parks Accounting for V2G and Dynamic Electricity Tariffs
Hao Zhou, Zao Tang, Pingliang Zeng, Nengjie Fang, Yibo Zhu · 2024
The low-carbon park energy internet (PEI) system utilizes various renewable energy sources and efficient energy utilization technologies to integrate multiple forms of energy, thereby reducing reliance on fossil fuels. This approach aims to achieve low carbon emissions, improve energy efficiency, and promote sustainable development. However, the existing energy market mechanisms may struggle to meet the demands of low-carbon parks, particularly in terms of renewable energy trading and subsidies, necessitating new market models for support. Moreover, the success of low-carbon parks heavily relies on active participation and collaboration from users; however, users’ awareness and acceptance of low-carbon technologies may impact their willingness to engage. Additionally, the tight supply of electricity resources affects the normal operation of the system. This paper addresses the trading mechanisms and optimization issues of the low-carbon PEI system, focusing on day-ahead optimization that accounts for vehicle-to-grid (V2G) interactions and dynamic electricity tariffs. It analyzes the economically driven electric vehicle (EV)-PEI interaction, constructs a V2G trading mechanism based on dynamic pricing, and considers constraints such as energy conservation, energy storage, and EV limitations. A model of the low-carbon PEI system is established, taking into account dual uncertainties of source and load, and solved using the CPLEX solver. The results indicate that the proposed method effectively reduces carbon emissions, minimizes the energy gap, and lowers the total cost of the system.