Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generation

Wald, Dylan; Johnson, Kathryn; King, Jennifer; Comden, Joshua; Bay, Christopher J.; Chintala, Rohit; Vijayshankar, Sanjana; Vaidhynathan, Deepthi

doi:10.1016/j.adapen.2023.100131

Cited by 14 publications

(1 citation statement)

References 30 publications

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“…However, the uncertainty and volatility of renewable energy and loads pose substantial challenges to the safety and stability of multi-microgrid operation in industrial parks [2,3]. ES can alleviate the impact of photovoltaic (PV) and load uncertainty on the park to a certain extent but the high cost of ES is still a limiting factor for its widespread implementation [4]. Consequently, it becomes crucial to investigate the integration of PV generation and load management within industrial park smart microgrids [5,6], especially for the optimization control of smart microgrids [7].…”

Section: Introductionmentioning

confidence: 99%

Information Gap Decision Theory-Based Stochastic Optimization for Smart Microgrids with Multiple Transformers

et al. 2023

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Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park’s peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids.

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