A novel framework for photovoltaic energy optimization based on supply–demand constraints
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/3131141Utgivelsesdato
2023Metadata
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Originalversjon
Sun, Y., Liu, N., Khan, I., Park, Y.-C., Byun, Y.-C., & Madsen, D. Ø. (2023). A novel framework for photovoltaic energy optimization based on supply–demand constraints. Frontiers in Energy Research, 11, Artikkel 1267579. https://doi.org/10.3389/fenrg.2023.1267579Sammendrag
Introduction: Distributed power supply has increasingly taken over as the energy industry’s primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources.
Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master–slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster.
Results: Simulation results show that the proposed framework has better performance under different operating conditions.
Discussion: The energy storage operators and numerous energy storage users can implement master–slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win–win conditions.