光伏资源丰富社区的最优购电协议

Nihan Çiçek van der Heijden, T. Alpcan, E. A. Martinez-Ceseña, F. Suits
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引用次数: 3

摘要

本文研究了希望通过长期购电协议(PPA)和必要时的现货市场直接从发电机购买电力的小型光伏富裕社区。净需求,定义为总需求和太阳能发电量之间的差额,通过PPA和现货市场来满足。由于太阳能发电的随机性,净需求是随机的,难以估计。这种随机性可能导致实际净需求与PPA之间的不匹配。当净需求超过PPA时,以现货市场价格购买电力,导致运营成本增加。为了避免这种不匹配并有效利用太阳能潜力,我们提出了一种利用太阳能发电统计特性的概率优化方法。我们利用历史太阳辐照度数据,将选定地点的每个白天小时的太阳能发电建模为随机变量。通过对随机变量进行变换,得到净需求的概率分布,并提出了一个市场风险敞口概率极限优化问题。优化问题的总体目标是最小化社区的运营成本。与预测和蒙特卡罗模拟不同,我们的方法可以进行市场风险分析,微调PPA以及对太阳能发电统计特性的良好理解。
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Optimal power purchase agreements in PV-rich communities
This paper studies small PV-rich communities that wish to purchase their electricity directly from generators through long-term power purchase agreements (PPA) and the spot market if necessary. Net demand, defined as the difference between total demand and solar generation, is satisfied through PPA and the spot market. Due to the random nature of solar generation, net demand is random and hard to estimate. This randomness may lead to a mismatch between the actual net demand and PPA. When net demand exceeds PPA, electricity is purchased at the spot market prices causing increased operational costs. To avoid this mismatch and utilise solar potential efficiently, we propose a probabilistic optimisation approach using the statistical properties of solar generation. We model solar energy generation as a random variable for each daylight hour at a chosen location using historical solar irradiance data. By transforming random variables, we find the probability distribution of the net demand which is used to propose a market exposure probability limiting optimisation problem. The overall objective of the optimisation problem is to minimise operational costs of communities. Unlike forecasting and Monte Carlo Simulation, our methodology enables market exposure risk analysis, fine-tuning PPA and a good understanding of statistical properties of solar generation.
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