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

合作研究：EAGER：可再生能源：电力市场连续时间灵活性调度的函数空间理论

• 批准号: 1549923
• 负责人: Anna Scaglione
• 金额: $ 14.96万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2017-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549923&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Renewables; function

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.

当前的电网运行程序多年来在通过发电的编程变化来补偿电力负荷的可变性和不确定性方面一直运行良好。这有助于向数百万客户提供可靠且经济的电力输送。然而，注入电网的可再生能源发电水平不断上升，增加了更高程度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导致频繁突然增加发电需求，即所谓的增加稀缺事件，同时增加了系统的运营成本。该项目采用了一种新的模型，预计将产生对于可再生能源渗透率高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，其中涉及发电机组的调度以补偿电力需求的变化。虽然目前机组承诺是根据每小时变化的发电时间表来考虑的，但该项目考虑了更短时间间隔的调度，以充分跟踪高度可变的电力网络中不断变化的供应和需求。这项研究可以消除大规模可再生能源并网的根本障碍，从而为可持续、可靠和经济的可再生电力资源整合铺平道路。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，因此该项目的想法在实际市场中的整合预计是可行的。这项工作中的假设是，逐渐稀缺事件是电力系统运行问题普遍存在的离散时间公式中存在严重瓶颈的证据，特别是两个相互依赖的因素：1）机组结构背后的近似承诺（UC）问题决策空间；2）发电机组和其他灵活资源的运营成本函数的结构，允许它们竞标能源，但不允许竞标。目前的UC决策空间仅包括每小时的承诺决策点和每小时的发电计划，它们形成了每个发电单元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阶近似。事实上，每小时 UC 模型很难捕获有关净负荷变化的信息，并且丢失了有关净负荷变化的大量信息。为了解决增加的斜坡需求，不将决策空间限制为承诺状态和发电轨迹，有利的是还包括发电轨迹的一阶导数，即斜坡轨迹，作为度数中的决策变量自由，打开了接受竞争性报价的大门，这些报价捕获了每个时刻的发电和爬坡的联合成本。认识到连续时间轨迹具有可以选择作为优化决策空间的一部分的附加自由度，提出了一种新方法，该方法结合了直接表示附加自由度的变量，并且可以促进对其进行适当定价。使用的概念是完善的函数空间概念，它允许将 UC 问题表述为当前流行的混合整数线性规划 (MILP) 问题，但具有额外的自由度来平衡可变性。初步结果清楚地表明，显式爬坡轨迹变量的引入改变了调度中不同单元的优先级，降低了总运营成本，并大大减少了爬坡稀缺事件。还值得注意的是，与函数空间表示相比，引入每小时决策变量更加复杂，并且导致效率降低，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。