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

表面上来自风能和太阳能的免费能源带有不必要的波动性，例如夕阳或阵风的斜坡。可控发电机在过去管理了电力的供需平衡，但随着可再生能源的渗透率越来越高，这变得越来越昂贵。自20世纪80年代以来，人们一直认为，消费者应该参与其中，认为可以管理“需求反应”，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，并且期望对他们接收的服务质量（QoS）有一些保证。例如，建筑物或冰箱中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保持可靠性。该项目的目标是创建一门“需求调度”科学，即使用灵活负载的虚拟储能。一个主要的成果将是为电网监管创造资源，这些资源就像巨大的电池舰队一样可靠和响应迅速。通过设计，对电力消费者的影响在许多情况下将是不可检测的;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。加州计划在电池上花费数十亿美元，而这些电池只能提供使用需求调度可以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来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服从严格的约束。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，冰箱中的鱼保持新鲜，建筑物内的气候保持在期望的范围内。该项目旨在创建一个科学的需求调度的基础上，这些基本要素：（一）“本地智能”是必需的，以确保本地的QoS约束得到满足，同时提供可靠的服务，电网。这是通过在每个负载的局部随机控制作为整体分布式控制架构的一部分来实现的。(ii)网格的服务能力是QoS约束的函数。这些关系的性质将部分通过原型硬件的创建进行调查。这些实验的结果之一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权衡曲线的见解将被应用于创造市场激励消费者参与。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。分析是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的概念，如无源性，沿着传统的电力系统技术。有待开发的科学基础具有超越电力的应用。所提出的构造局部最优策略的计算工具是新颖的，并适用于一般类的随机控制模型。分布式控制体系结构也可能在许多领域中找到应用。

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.