Demand-Response PEV Charging Control in Smart Grids for Renewable Supply Utilization and Hedging and Transfer of Forecast Risk

目前，电力市场有两大发展趋势。在供应方面，越来越多地推动使用可再生能源，但这与能源供应的高度可变率有关。在需求方面，插电式（混合）电动汽车（pev / phev）的部署越来越多，这不仅会显著增加平均用电量，而且会产生非常激增的需求模式。幸运的是，pev在能源消耗率和时间表方面也提供了很大的灵活性，我们相信，利用这种灵活性，供应方面的可变性可以部分地“吸收”。这构成了本项目的广泛目标，其目的是建立有效利用可变费率可再生能源的高效PEV需求调度机制，并对冲电力公司（集成商）的预测风险。这些机制将允许与能源生产/供应的可变性相关的风险部分转移——从电力供应商和集成商（反过来是发电机）到消费者（PEV用户），以换取降低的充电价格。该项目为电动汽车充电环境提供的解决方案也适用于其他弹性负载，这些负载在一段时间内与部分可预测的能源消耗量相关。该项目所考虑的问题对于电网的稳定性和盈利运行至关重要，因为它要应对不断增加的pev部署，并试图大规模整合可再生能源发电。更具体地说，本项目研究了智能电网中电动汽车充电的两种需求响应控制策略。在第一种方法中，公用事业公司（或聚合器）为电动汽车充电设定与时间和地点相关的价格，而让电动汽车（或与电动汽车充电相关的智能电表）根据个人最大利益和电动汽车所有者的约束来进行调度。在第二种方法中，公用事业公司（在市场上）提供硬合同和软合同，在指定的时间间隔内向pev收取一定水平的费用，pev根据自己的需求和偏好购买；然后由公用事业公司自己完成电动汽车充电的计划，以遵守其出售给电动汽车的合同条款。在此背景下，将探讨两个广泛的相互关联的研究问题。第一个问题涉及这些合约的设计，以及相关的“合约组合优化”问题——这涉及决定公用事业公司应该提供多少硬合同和多少软合同，以达到预期的风险回报权衡点。第二个涉及通过拍卖和其他“激励相容”的市场清算机制提供这些合同的过程（即，调用pev的真实反应），并导致最终能源分配的整体“社会效率”。在这两种定价/调度框架下，电动汽车充电的预测、需求响应控制带来了具有挑战性的问题，可以使用随机博弈论、拍卖机制设计、随机微积分和风险管理中的工具和技术进行建模，但必须适当地进行调整和扩展，以考虑电动汽车充电和可再生能源供应过程的模型、约束和要求。

There are currently two major developments shaping the electricity market. On the supply side, there is increasing push towards the use of renewable energy sources, which are however associated with a highly variable rate of energy supply. On the demand side, there is increasing deployment of plug-in (hybrid) electric vehicles (PEVs/PHEVs), which will not only increase the average electricity consumption significantly, but also generate very bursty demand patterns. Fortunately, PEVs also provide significant flexibility in terms of their energy consumption rates and schedules, and we believe that variability in the supply side can be partly "absorbed" using the flexibility. This constitutes the broad goal of this project, which aims at efficient PEV demand scheduling mechanisms for effective utilization of variable-rate renewable energy sources, and hedging of forecast risks by electric utilities (aggregators). These mechanisms would allow the partial transfer of risk associated with variability of energy generation/supply -- from the electricity suppliers and aggregators (and in turn the generators) to the consumers (PEV users), in exchange of a reduced price of charging. The solutions resulting from this project for the PEV charging context would also be applicable to other elastic loads that are associated with a partly predictable amount of energy consumption over a time span. The issues considered in this project are critical for the stability and profitable operation of the electric grid as it copes up with the increased deployments of PEVs, and attempts to integrate renewable energy generation at a large scale.More specifically, this project investigates two demand-response control strategies for charging PEVs in the smart grid. In the first method, the utility (or aggregator) sets time- and location-dependent prices for PEV charging, while letting the PEVs (or the smart meters associated with PEV charging) mostly do the scheduling, based on the individual best interests and constraints of the PEV owners. In the second method, the utility offers (in the market) hard and soft contracts to charge PEVs up to certain levels within a specified interval, which the PEVs purchase based on their needs and preferences; the scheduling of the PEV charging is then done by the utility itself such that adheres to the terms of the contracts that it has sold to the PEVs. Two broad inter-related research issues will be explored in this context. The first involves the design of these contracts, as well as the associated "contract portfolio optimization" question - which involves deciding on the how many hard and soft contracts the utility should offer to attain its desired risk-return tradeoff point. The second involves the process of offering these contracts through auctions and other market clearing mechanisms that are "incentive-compatible" (i.e., invoke truthful response from the PEVs), and result in overall "social efficiency" of the resulting energy allocation. Predictive, demand-response control within these two pricing/scheduling frameworks for PEV charging brings about challenging issues that can be modeled using tools and techniques in stochastic game theory, auction mechanism design, stochastic calculus, and risk management, but must be suitably adapted and extended to consider the models, constraints and requirements of the PEV charging and renewable energy supply processes.