Collaborative Research: EAGER: Renewables: A function space theory for continuous-time flexibility scheduling in electricity markets

多年来，现行的电网运行程序在补偿电力负荷的变化性和不确定性方面运行良好。这为向数百万用户提供可靠、经济的电力做出了贡献。然而，注入电网的可再生能源发电水平不断上升，增加了更高水平的变异性和不确定性。此外，在积极追求绿色能源的几个市场中，大规模、快速和意想不到的电力变化正导致对斜坡发电的频繁突然需求，即所谓的斜坡稀缺事件，同时增加了系统的运行成本。该项目采用了一种新的模型，预计将产生用于发电资源调度的算法，该算法对于具有高可再生渗透率的系统更有效。这项工作的主要焦点是所谓的机组组合问题，它涉及到发电机组的调度，以补偿电力需求的变化。虽然目前机组组合是根据每小时变化的发电计划来考虑的，但该项目考虑在较短的时间间隔内进行调度，以充分跟踪高度多变的电网中不断变化的供需情况。这项研究可以消除大规模可再生能源整合的根本障碍，从而为可再生电力资源的可持续、可靠和经济整合铺平道路。这将有助于实现能源独立和减少温室气体排放的国家目标。虽然该方法提供了完全不同的观点，但它没有从根本上改变批发市场的体系结构，也没有从根本上改变调度问题的复杂性，因此该项目的想法在实际市场中的集成预计是实际可行的。这项工作中的主要假设是，斜坡稀缺事件是严重瓶颈的证据，该瓶颈总体上存在于电力系统运行问题的普遍离散时间公式中，特别是与两个相互依赖的因素有关：1)机组组合(UC)问题决策空间结构背后的近似值，以及2)发电机组和其他灵活资源的运行成本函数的结构，他们被允许竞标能源，但不允许竞标坡道。当前的UC决策空间仅包括小时承诺决策点和小时发电计划，它们形成了每个发电机组的分段恒定发电轨迹。这些轨迹是填充实际UC决策空间的高阶连续时间对应轨迹的零级近似。事实上，在每小时UC模型中，关于净负荷变化的信息捕捉得很差，并且丢失了大量关于净负荷变化的信息。为了解决增加的斜坡需求，不是将决策空间限制到承诺状态和发电轨迹，还包括发电轨迹的一阶导数，即斜坡轨迹，作为自由度中的决策变量，这将是有利的，从而打开了接收在每个时刻捕捉发电和斜坡的联合成本的竞争性报价的大门。考虑到连续时间轨迹具有额外的自由度，可以选择作为优化决策空间的一部分，提出了一种新的方法，该方法引入了直接代表额外自由度的变量，并可以帮助对它们进行适当的定价。所使用的概念是公认的函数空间概念，它允许UC问题被描述为混合整数线性规划(MILP)问题，该问题目前很流行，但具有额外的自由度来平衡可变性。初步结果表明，引入显式坡道变量改变了调度方案中不同机组的优先顺序，降低了总运行成本，显著减少了坡道稀缺事件。还需要注意的是，与函数空间表示法相比，引入每小时一次的决策变量更加复杂，并且导致效率降低，而函数空间表示法是为了提高表示目标和约束的准确性而定制的。

Current electric power grid operating procedures have worked well for many years in compensating for the variability and uncertainty of electric power load by programmed changes in generation. This has contributed to the reliable and economic delivery of electric power to millions of customers. However, the rising level of renewable generation injected into the power grid adds a higher level of variability and uncertainty. Moreover, in several markets that are aggressively pursuing green energy, large, fast, and unexpected power changes are leading to frequent sudden demands for ramping power generation, so-called ramping scarcity events, while increasing the operating cost of the systems. This project takes a new modeling that is expected to yield algorithms for scheduling of generation resources that is more effective for systems with high renewable penetration. The main focus of the work is what is known as the unit commitment problem, which involves scheduling of generating units to compensate for variability in power demand. While currently unit commitment is considered in terms of generation schedules that change on an hourly basis, the project considers a scheduling over shorter time intervals to adequately track changing supply and demand in highly variable power networks. This research can eliminate a fundamental barrier to large-scale renewable integration, thus paving the way to sustainable, reliable, and economic integration of renewable electricity resources. This would contribute to reaching national targets on energy independence and greenhouse gas reductions. While the approach offers a radically different point of view, it does not fundamentally alter the architecture of the wholesale market, nor the complexity of the scheduling problem, so the integration of the project's ideas in real markets is expected to be practically feasible.The main hypothesis in this work is that ramping scarcity events are evidence of a severe bottleneck that lies in the prevalent discrete time formulation of the power system operation problem in general, and in particular to two interdependent factors: 1) the approximation behind the structure of the unit commitment (UC) problem decision space, and 2) the structure of the operating cost functions of the generating units and other flexible resources, who are allowed to bid for energy but not for ramping. The current UC decision space includes only hourly commitment decision points and hourly generation schedules, which form a piecewise constant generation trajectory for each generating unit. These trajectories are a zero-order approximation of their higher-order continuous-time counterparts that populate the actual UC decision space. In fact, the information about the variability of the net-load is poorly captured in the hourly UC model, and a wealth of information about the variations of the net-load is lost. In order to address the increased ramping demand, instead of limiting the decision space to the commitment state and generation trajectory, it would be advantageous to also include the first derivative of the generation trajectory, i.e. the ramping trajectory, as a decision variable among the degrees of freedom, opening the door to receiving competitive offers that capture the joint cost of generation and of ramping at each time instant. Recognizing that a continuous-time trajectory bears additional degrees of freedom that could be chosen as part of the optimization decision space, a new approach is proposed that incorporates variables that directly represent additional degrees of freedom and can facilitate appropriately pricing them. The notion utilized is the well-established notion of function space that allows the UC problem to be formulated as a Mixed Integer Linear Programming (MILP) problem, currently in vogue, but with additional degrees of freedom to balance the variability. Preliminary results clearly show that the introduction of explicit ramping trajectory variables alter the priority given to different units in the schedule, reduces the total operation cost, and considerably reduces ramping scarcity events. It is also noticed that introducing sub-hourly decision variables is more complex and leads to decreased efficiency compared to the function space representation, which is tailored to increase the accuracy in representing both objectives and constraints.