Distributed Control for Demand Dispatch: The Creation of Virtual Energy Storage from Flexible Loads

需求调度的分布式控制：灵活负载创建虚拟储能

• 批准号: 1609131
• 负责人: Sean Meyn
• 金额: $ 38万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609131&HistoricalAwards=false
• 关键词: Distributed; Control; Demand; Dispatch; Creation

Ostensibly free energy from the wind and the sun comes with unwanted volatility, such as ramps with the setting sun or with gusts of wind. Controllable generators have managed supply-demand balance of power in the past, but this is becoming increasingly costly with increasing penetration of renewable energy. It has been argued since the 1980s that consumers should be put in the loop, with the idea that "demand response" can be managed to help to create the needed supply-demand balance. However, consumers use power for a reason, and expect some guarantees on the quality of service (QoS) they receive. For example, the temperature in a building or refrigerator must remain within strict bounds. Moreover, the behavior of some consumers is unpredictable, while the grid operator requires predictable controllable resources to maintain reliability. The goal of this project is to create a science for "demand dispatch," which is virtual energy storage using flexible loads. A major outcome will be the creation of resources for grid regulation that are as reliable and responsive as giant fleets of batteries. By design, the impact to consumers of electricity will be undetectable in many cases; strict bounds on QoS will be maintained in all cases. The potential economic impact of these new resources is enormous. California plans to spend billions of dollars on batteries that will provide only a small fraction of the balancing services that can be obtained using demand dispatch. The potential impact of developing this methodology and associated technology is no less than a sustainable energy future becoming possible with the right mix of infrastructure and control systems.The goal of this project is to create virtual energy storage resources via demand dispatch to be used for grid-level regulation, ramping, peak smoothing, and even recovery from contingencies such as generation faults, while ensuring that QoS to consumers obeys strict constraints. Demand dispatch can only be realized by devising distributed control algorithms that meet multiple, potentially conflicting objectives: the grid needs high quality resources for regulation; the consumer expects that water supply is not interrupted, fish in the refrigerator stays fresh, and the climate within a building remains within desired bounds. The project aims to create a science for demand dispatch based on these essential ingredients:(i) "Local intelligence" is required to ensure local QoS constraints are met, while simultaneously providing reliable service to the grid. This is realized through local stochastic control at each load as part of an overall distributed control architecture. (ii) Capacity of service to the grid is a function of QoS constraints. The nature of these relationships will be investigated in part through the creation of prototype hardware. One outcome of these experiments will be the creation of load simulation code that will be used as part of the project, and shared with others working in this field.(iii) Insight from cost/QoS tradeoff curves will be applied in the creation of market incentives for consumer engagement. Topic (i) presents significant scientific challenges. This will require the development of stochastic control / Markov Decision Process techniques that will be a focus of the project. Analysis is based on related ideas from information theory and extensions of concepts from the theory of general state space Markov models. Grid-level analysis requires concepts from deterministic control theory such as passivity, along with traditional power systems technology. The scientific foundations to be developed have applications beyond power. The proposed computational tools for constructing local optimal policies are novel, and applicable to general classes of stochastic control models. The distributed control architecture is also likely to find applications in many fields.

表面上来自风和太阳的免费能量会带来不必要的波动，例如夕阳或阵风带来的坡道。过去，可控发电机已经实现了电力供需平衡，但随着可再生能源渗透率的不断提高，其成本也越来越高。自 20 世纪 80 年代以来，人们一直认为应该让消费者参与其中，认为可以管理“需求响应”以帮助创造所需的供需平衡。然而，消费者使用电力是有原因的，并期望他们收到的服务质量 (QoS) 得到一些保证。例如，建筑物或冰箱内的温度必须保持在严格的范围内。此外，一些消费者的行为是不可预测的，而电网运营商需要可预测的可控资源来维持可靠性。该项目的目标是创建“需求调度”科学，即使用灵活负载的虚拟能源存储。一个主要成果将是为电网监管创造资源，这些资源像巨型电池组一样可靠且反应灵敏。根据设计，在许多情况下对电力消费者的影响是无法察觉的；在所有情况下都将保持对 QoS 的严格限制。这些新资源的潜在经济影响是巨大的。加州计划花费数十亿美元购买电池，而这些电池只能提供通过需求调度获得的平衡服务的一小部分。开发这种方法和相关技术的潜在影响不亚于通过基础设施和控制系统的正确组合使可持续能源的未来成为可能。该项目的目标是通过需求调度创建虚拟能源存储资源以用于电网级调节、爬坡、峰值平滑，甚至从发电故障等意外情况中恢复，同时确保消费者的服务质量遵守严格的约束。需求调度只能通过设计满足多个潜在冲突目标的分布式控制算法来实现：电网需要高质量的资源进行调节；消费者期望供水不会中断，冰箱里的鱼保持新鲜，建筑物内的气候保持在理想的范围内。该项目旨在基于以下基本要素创建一门需求调度科学：(i) 需要“本地智能”来确保满足本地服务质量约束，同时为电网提供可靠的服务。这是通过每个负载的本地随机控制作为整体分布式控制架构的一部分来实现的。 (ii) 网格服务容量是 QoS 约束的函数。这些关系的本质将通过创建原型硬件进行部分研究。这些实验的一个成果将是创建负载模拟代码，该代码将用作项目的一部分，并与该领域的其他工作人员共享。(iii) 从成本/QoS 权衡曲线中获得的见解将应用于创建市场消费者参与的激励措施。主题（i）提出了重大的科学挑战。这将需要开发随机控制/马尔可夫决策过程技术，这将是该项目的重点。分析基于信息论的相关思想和一般状态空间马尔可夫模型理论的概念扩展。电网级分析需要确定性控制理论（例如无源性）以及传统电力系统技术的概念。待开发的科学基础具有超越电力的应用。所提出的用于构建局部最优策略的计算工具是新颖的，并且适用于一般类别的随机控制模型。分布式控制架构也可能在许多领域得到应用。