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.

当前的电力电网操作程序在弥补了通过编程的发电变化来弥补电力负载的可变性和不确定性方面已经有效了很多年。这有助于向数百万客户提供可靠和经济的电力。但是，注入功率电网的可再生生成水平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，大型，快速和意外的电力变化导致对发电的发电，所谓的越来越稀缺事件的频繁突然需求，同时增加了系统的运行成本。该项目采用了一种新的建模，预计将产生算法来安排发电资源，这对于具有较高可再生渗透的系统更有效。工作的主要重点是所谓的单位承诺问题，其中涉及安排生成单位以弥补电源需求的可变性。尽管目前的单位承诺是根据每小时变化的生成计划来考虑的，但该项目考虑了时间间隔较短的时间间隔，以充分跟踪高度可变功率网络中的供求。这项研究可以消除大规模可再生一体化的基本障碍，从而为可再生电力资源的可持续，可靠和经济整合铺平了道路。这将有助于达到国家对能源独立和减少温室气体的目标。尽管该方法提供了根本不同的观点，但它并没有从根本上改变批发市场的体系结构，也不会改变调度问题的复杂性，因此，预计该项目思想在实际市场中的整合实际上是可行的。这项工作的主要假设是，涉及稀缺事件的证据是，涉及到的稀缺性均在于涉及的涉及时间，并且在既定的机构中都存在委托机构，而这些措施是既定的，并且是派遣时间的，这些既定是在不断挑战的。相互依存的因素：1）单位承诺（UC）问题决策空间的结构背后的近似值，以及2）生成单元和其他灵活资源的操作成本功能的结构，这些资源被允许竞标能量，但不能竞标。当前的UC决策空间仅包括每小时的承诺决策点和小时生成时间表，该计划构成了每个生成单元的分段恒定生成轨迹。这些轨迹是其高阶连续时间对应物的零级近似，该轨迹填充了实际的UC决策空间。实际上，有关净载荷可变性的信息在小时UC模型中捕获很差，并且有关净负载变化的大量信息丢失了。为了解决不断增加的需求，而不是将决策空间限制在承诺状态和发电轨迹上，还可以包括一代轨迹的第一个衍生产品，即坡道轨迹，作为自由度之间的决策变量，为捕获每种竞争成本的竞争成本和在每种时间上的共同成本上打开竞争力的竞争力，并在每种时间上都可以升级。认识到连续的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，因此提出了一种新方法，该方法结合了直接代表额外自由度的变量，并可以促进适当的定价。所使用的概念是功能空间的公认概念，它允许将UC问题作为混合整数线性编程（MILP）问题提出，目前处于Vogue，但具有额外的自由度来平衡可变性。初步结果清楚地表明，显式坡道轨迹变量的引入改变了时间表中不同单位的优先级，减少了总运行成本，并大大减少了坡度稀缺事件。还注意到，与功能空间表示相比，引入子小时决策变量更为复杂，并且导致效率降低，该功能空间表示量是为了提高代表目标和约束的准确性。