高可再生能源整合保险合同

Dongwei Zhao, Hao Wang, Jianwei Huang, Xiaojun Lin
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引用次数: 2

摘要

可再生能源的日益普及对电网的可靠性提出了重大挑战。人们对利用保险等金融工具来帮助最终用户对冲因可再生能源变化而导致的潜在负荷损失风险的兴趣越来越大。有了保险,用户向公用事业公司支付保险费,这样如果他的需求没有得到充分满足,他就会得到补偿。一个合适的保险设计需要解决以下两个挑战:(1)用户的可靠性偏好是隐私信息;(2)保险设计与可再生能源投资决策紧密耦合。为了解决这些问题,我们采用契约理论来引出用户的私人可靠性偏好,并研究了公用事业公司如何共同优化保险合同和可再生能源规划。一个关键的分析挑战是,保险设计和可再生能源规划的联合优化是非凸的。我们利用两个基准问题:无保险基准和社会最优基准,揭示了最优解的重要结构性质,从而解决了这一困难。通过与无保险基准的比较,证明了在最优契约下,社会成本和用户总能源成本始终不大于无保险基准。仿真结果表明,在中等电价、低类型异质性和高可再生不确定性条件下,保险合同的最大效益能够实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Insurance Contract for High Renewable Energy Integration
The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information; and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users' private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users' total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty.
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