Energy storage in Madeira, Portugal: co-optimizing for arbitrage, self-sufficiency, peak shaving and energy backup

Md Umar Hashmi, Lucas Pereira, A. Bušić
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引用次数: 20

Abstract

Energy storage applications are explored from a prosumer (consumers with generation) perspective for the island of Madeira in Portugal. These applications could also be relevant to other power networks. We formulate a convex co-optimization problem for performing arbitrage under zero feed-in tariff, increasing self-sufficiency by increasing self-consumption of locally generated renewable energy, provide peak shaving and act as a backup power source during anticipated and scheduled power outages. Using real data from Madeira we perform short and long time-scale simulations in order to select end-user contract which maximizes their gains considering storage degradation based on operational cycles. We observe energy storage ramping capability decides peak shaving potential, fast ramping batteries can significantly reduce peak demand charge. The numerical experiment indicates that storage providing backup does not significantly reduce gains performing arbitrage and peak demand shaving. Furthermore, we also use AutoRegressive Moving Average forecasting along with Model Predictive Control for real-time implementation of the proposed optimization problem in the presence of uncertainty.
葡萄牙马德拉的能源存储:套利、自给自足、调峰和能源备份的共同优化
从产消者(消费者与发电)的角度探讨了葡萄牙马德拉岛的储能应用。这些应用也可能与其他电网相关。在零上网电价下进行套利,通过增加本地可再生能源的自用来提高自给自足,在预期和计划停电期间提供调峰和作为备用电源,我们提出了凸协同优化问题。使用来自马德拉的真实数据,我们进行了短期和长期的时间尺度模拟,以选择最终用户合同,考虑到基于操作周期的存储退化,从而最大化他们的收益。我们观察到储能爬坡能力决定了调峰潜力,快速爬坡电池可以显著降低峰值需求电荷。数值实验表明,提供备份的存储不会显著降低执行套利和峰值需求剃须的收益。此外,在存在不确定性的情况下,我们还使用自回归移动平均预测和模型预测控制来实时实现所提出的优化问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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