Incentivizing Reliable Demand Response with Customers' Uncertainties and Capacity Planning

Joshua Comden, Zhenhua Liu, Yue Zhao
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Abstract

One of the major issues with the integration of renewable energy sources into the power grid is the increased uncertainty and variability that they bring. If this uncertainty is not sufficiently addressed, it will limit the further penetration of renewables into the grid and even result in blackouts. Compared to energy storage, Demand Response (DR) has advantages to provide reserves to the load serving entities (LSEs) in a cost-effective and environmentally friendly way. DR programs work by changing customers' loads when the power grid experiences a contingency such as a mismatch between supply and demand. Uncertainties from both the customer-side and LSE-side make designing algorithms for DR a major challenge. This paper makes the following main contributions: (i) We propose DR control policies based on the optimal structures of the offline solution. (ii) A distributed algorithm is developed for implementing the control policies without efficiency loss. (iii) We further offer an enhanced policy design by allowing flexibilities into the commitment level. (iv) We perform real world trace based numerical simulations which demonstrate that the proposed algorithms can achieve near optimal social cost. Details can be found in our extended version.
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基于客户不确定性和容量规划的可靠需求响应激励
将可再生能源纳入电网的主要问题之一是它们带来的不确定性和可变性增加。如果不充分解决这种不确定性,将限制可再生能源进一步渗透到电网中,甚至导致停电。与储能相比,需求响应(DR)以经济、环保的方式向负荷服务实体(lse)提供备用电力具有优势。当电网出现供需不匹配等突发情况时,DR计划通过改变客户的负荷来发挥作用。来自客户端和伦敦证交所的不确定性使得DR算法的设计成为一项重大挑战。本文的主要贡献如下:(i)提出了基于离线解决方案最优结构的容灾控制策略。(ii)开发了一种分布式算法,在不损失效率的情况下实现控制策略。(iii)我们进一步提供更完善的政策设计,在承诺层面允许灵活性。(iv)我们进行了基于真实世界轨迹的数值模拟,证明了所提出的算法可以实现接近最优的社会成本。细节可以在我们的扩展版本中找到。
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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