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

表面上看，来自风和太阳的免费能源带来了不必要的波动，比如夕阳或阵风带来的斜坡。过去，可控发电机已经实现了电力供需平衡，但随着可再生能源的日益普及，这种平衡正变得越来越昂贵。自20世纪80年代以来，一直有人认为应该让消费者参与其中，并认为可以通过管理“需求反应”来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，他们希望得到服务质量（QoS）的一些保证。例如，建筑物或冰箱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。该项目的目标是为“需求调度”创造一门科学，即使用灵活负载的虚拟能源存储。一个重要的成果将是为电网监管创造资源，这些资源就像庞大的电池车队一样可靠和反应灵敏。按照设计，在许多情况下，对电力消费者的影响是无法察觉的；在任何情况下都将保持对QoS的严格限制。这些新资源的潜在经济影响是巨大的。加州计划在电池上花费数十亿美元，这些电池只能提供一小部分可以通过需求调度获得的平衡服务。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实现可持续能源的未来。该项目的目标是通过需求调度创建虚拟储能资源，用于电网级调节、斜坡、峰值平滑，甚至从发电故障等突发事件中恢复，同时确保对消费者的QoS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；消费者期望水的供应不会中断，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。该项目旨在创建一种基于以下基本要素的需求调度科学：(i)需要“本地智能”来确保满足本地QoS约束，同时为电网提供可靠的服务。这是通过每个负载的局部随机控制来实现的，作为整体分布式控制体系结构的一部分。（ii）向网格提供服务的能力是QoS约束的函数。这些关系的本质将部分通过原型硬件的创建来研究。这些实验的一个结果将是创建加载模拟代码，该代码将用作项目的一部分，并与在该领域工作的其他人共享。从成本/服务质量权衡曲线中得出的见解将应用于创造促进消费者参与的市场激励。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，这将是项目的重点。分析基于信息论的相关思想和广义状态空间马尔可夫模型理论概念的扩展。电网级分析需要确定性控制理论的概念，如无源性，以及传统的电力系统技术。要发展的科学基础具有超越权力的应用。所提出的构造局部最优策略的计算工具是新颖的，并且适用于一般类型的随机控制模型。分布式控制体系结构也可能在许多领域找到应用。

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.