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已经很好地通过发电的程序化变化来补偿电力负荷的可变性和不确定性。这有助于为数百万客户提供可靠和经济的电力。然而，注入电网的可再生能源发电水平的上升增加了更高水平的可变性和不确定性。此外，在一些积极追求绿色能源的市场中，大规模、快速和意外的电力变化正在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营成本。该项目采用了一种新的模型，该模型有望产生对具有高可再生渗透率的系统更有效的发电资源调度算法。这项工作的主要焦点是所谓的机组承诺问题，它涉及到发电机组的调度，以补偿电力需求的变化。虽然目前的机组承诺是根据每小时变化的发电计划来考虑的，但该项目考虑了一个更短时间间隔的计划，以充分跟踪高度可变的电网中不断变化的供需。本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温室气体减排的国家目标。虽然该方法提供了一个完全不同的观点，但它并没有从根本上改变批发市场的架构，也没有改变调度问题的复杂性，因此将项目的想法整合到实际市场中预计是切实可行的。这项工作的主要假设是，日益严重的稀缺事件是一个严重瓶颈的证据，这个瓶颈存在于普遍存在的电力系统运行问题的离散时间公式中，特别是两个相互依存的因素：1)机组承诺（UC）问题决策空间结构背后的近似；2)发电机组和其他灵活资源的运行成本函数的结构，允许发电机组和其他灵活资源投标能源，但不允许爬坡。当前的UC决策空间仅包括每小时承诺决策点和每小时发电计划，它们构成了每个发电机组分段不变的发电轨迹。这些轨迹是它们的高阶连续时间对应物的零阶近似值，这些对应物填充了实际的UC决策空间。事实上，关于净负荷变化的信息在每小时UC模型中捕捉得很差，并且关于净负荷变化的大量信息丢失了。为了解决不断增加的爬坡需求，与其将决策空间限制在承诺状态和发电轨迹上，还可以将发电轨迹的一阶导数即爬坡轨迹作为自由度中的决策变量，从而打开获得捕获每个时刻的发电和爬坡联合成本的竞争性报价的大门。认识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，该方法包含直接表示额外自由度的变量，并可以方便地对它们进行适当定价。所使用的概念是公认的函数空间概念，它允许将UC问题公式化为当前流行的混合整数线性规划（MILP）问题，但增加了额外的自由度来平衡可变性。初步结果清楚地表明，引入明确的坡道轨迹变量改变了计划中不同单元的优先级，降低了总运行成本，并大大减少了坡道稀缺事件。还注意到，与函数空间表示相比，引入次小时决策变量更复杂，导致效率降低，而函数空间表示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.