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

表面上来自风和太阳的自由能源伴随着不必要的波动性，例如随着夕阳或阵风的斜坡。可控式发电机在过去曾管理过电力供需平衡，但随着可再生能源的普及，这一点的成本越来越高。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，因为他们的想法是，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，他们希望得到一些服务质量(Qos)的保证。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护可靠性。这个项目的目标是创建一门科学，用于“需求调度”，即使用灵活负载的虚拟能量存储。一个主要结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灵敏。根据设计，电力对消费者的影响在许多情况下都是无法检测到的；在所有情况下都将保持严格的服务质量界限。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，这些电池只提供一小部分平衡服务，而这些服务可以通过按需调度获得。开发这种方法和相关技术的潜在影响不亚于基础设施和控制系统的正确组合使可持续能源未来成为可能。该项目的目标是通过需求调度创建虚拟能量存储资源，用于电网级别调节、斜坡、高峰平滑，甚至从发电故障等意外情况中恢复，同时确保用户的服务质量遵守严格的限制。需求分配只能通过设计分布式控制算法来实现，以满足多个潜在冲突的目标：电网需要高质量的资源来进行监管；消费者希望供水不中断，冰箱里的鱼保持新鲜，建筑物内的气候保持在所需的范围内。该项目旨在创建基于以下基本要素的需求调度科学：(I)需要“本地智能”，以确保满足本地服务质量限制，同时为电网提供可靠的服务。这是通过在每个负载上的局部随机控制来实现的，作为整个分布式控制体系结构的一部分。(Ii)对网格的服务能力是服务质量约束的函数。这些关系的性质将在一定程度上通过创建原型硬件来调查。这些实验的一个结果将是创建负载模拟代码，该代码将被用作项目的一部分，并与该领域的其他工作人员共享。(Iii)来自成本/服务质量权衡曲线的见解将被应用于创建消费者参与的市场激励。议题(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。电网水平的分析需要确定性控制理论中的概念，如无源性，以及传统的电力系统技术。有待开发的科学基础具有超越能力的应用。所提出的构造局部最优策略的计算工具是新颖的，适用于一般的随机控制模型。分布式控制体系结构也可能在许多领域得到应用。

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.