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Robust optimization of community energy sharing considering source-load uncertainty and demand response
Community energy consumption is a crucial aspect of the overall societal energy consumption landscape. The allocation rate of distributed photovoltaic (PV) systems within communities is steadily increasing. However, managing and optimizing the consumption of PV resources while mitigating the impact of their inherent randomness and volatility, along with minimizing electricity costs, presents a significant challenge. This paper proposes a mechanism for community energy sharing that utilizes rooftop PV systems, energy storage systems, and bi-directional electric vehicles. To achieve the goal of finding the minimum cost of electricity in the worst scheduling scenarios, a two-stage robust optimization model is established. This model considers the two-sided uncertainty of source and load as well as flexible load demand response. The simulation outcomes prove the proposed method's efficacy in substantially mitigating residential electricity costs and enhancing PV utilization. Notably, during peak summer demand, a substantial 24.78% reduction in electricity costs was achieved, while PV utilization witnessed a significant 16.52% increase.
Robust optimization of community energy sharing considering source-load uncertainty and demand response
Community energy consumption is a crucial aspect of the overall societal energy consumption landscape. The allocation rate of distributed photovoltaic (PV) systems within communities is steadily increasing. However, managing and optimizing the consumption of PV resources while mitigating the impact of their inherent randomness and volatility, along with minimizing electricity costs, presents a significant challenge. This paper proposes a mechanism for community energy sharing that utilizes rooftop PV systems, energy storage systems, and bi-directional electric vehicles. To achieve the goal of finding the minimum cost of electricity in the worst scheduling scenarios, a two-stage robust optimization model is established. This model considers the two-sided uncertainty of source and load as well as flexible load demand response. The simulation outcomes prove the proposed method's efficacy in substantially mitigating residential electricity costs and enhancing PV utilization. Notably, during peak summer demand, a substantial 24.78% reduction in electricity costs was achieved, while PV utilization witnessed a significant 16.52% increase.
Robust optimization of community energy sharing considering source-load uncertainty and demand response
Yang, Di (author) / Lv, Yuntong (author) / Ji, Ming (author) / Wang, Zhitao (author) / Xie, Zhenlin (author) / Hu, Yinlong (author)
2024-09-01
14 pages
Article (Journal)
Electronic Resource
English
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