UNS: A Novel Approach to Multistage Stochastic Programming for Smart Grid Applications

【摘要】作者：Chmielewski， Donald和Li， zuyi提案#:1511925机构：伊利诺伊理工学院题目：智能电网应用的多阶段随机规划新方法电网必须始终保持发电和用电之间的平衡。由于电力消耗（表示为“需求”）的不确定性，具有调度能力的发电机处于不断变化的状态。由于可再生能源没有调度能力，引进可再生能源对缓解整体问题几乎没有作用。因此，具有调度能力的发电机的峰值功率保持不变，这表明这些电厂（主要是化石电厂）不太可能退役。为了缓解这一调度问题，许多人提倡储能和/或需求响应。储能单元在电力过剩时期（可再生能源发电量高而消费者需求低）收集能量，并在电力短缺时期将能量返回系统。需求响应（DR）的概念是争取一部分消费者（需求）对可再生能源以及其他消费者的需求做出响应。预期的影响是峰值功率的减少（为淘汰一些有调度能力的电厂创造了机会）和方差的减少（表明剩余的发电机将承受更少的负担）。虽然储能和需求响应在电网层面的好处似乎很明显，但什么激励措施证明投资储能设施或需求响应计划是合理的呢？这可以在电力价格随时间变化中找到，这是最近解除对发电机的管制（或基于市场的）补偿制度的结果。核心问题是不确定性（价格、需求或可再生能源发电）将迫使净现值（NPV）分析进入随机框架。具体来说，设备设计问题变成了一个多阶段随机规划（MSP）。在许多情况下，时间尺度的分离将MSP转换为简单的两阶段问题。不幸的是，如果这些系统需要大量的能量存储和MSP解决方法，这种近似就会失败。MSP是所有工程中最具挑战性的优化问题之一。因此，该项目的目标是研究一种新的MSP问题的求解程序。该程序采用了现有和最近开发的解决方案方法的新颖组合，并针对智能电网应用的特定特征进行了量身定制。这个项目是合作性的。来自两个研究团体（化学过程控制和电力系统优化，由PI和co-PI代表）的概念和解决方案技术需要结合起来实现项目目标。在两个领域的边界上，这种智力的交叉授粉有望在这两个领域产生意想不到的发现。所提出的MSP解决方案程序有望在智能电网之外具有适用性。一个明显的延伸是长期存在的集成过程设计和控制问题。另一个是过程作业调度问题，它与本课题研究的整数约束单元承诺问题非常相似。更广泛的影响计划对三个REU项目进行指导，PI的K-12外展计划将继续进行。此外，该项目的一个主要目标是通过在化学工程会议、多社区研讨会和小型会议上组织特邀指导会议，以及审查和指导型出版物，培育化学工程智能电网社区。开发计算上易于处理的MSP解决方案方法被认为是技术转让的重要步骤，以广泛投资于储能设施和采用需求响应参与。

AbstractPIs: Chmielewski, Donald and Li, ZuyiProposal #: 1511925 Institution: Illinois Institute of TechnologyTitle: A Novel Approach to Multistage Stochastic Programming for Smart Grid ApplicationsThe electric grid must maintain a balance between power generation and consumption at all times. Due to the uncertainty of power consumption (denoted as 'demand'), dispatch capable power generators are in a constant state of flux. Introduction of renewable power does little to alleviate the overall problem because renewable power has no dispatch capability. Thus peak power from the dispatch capable generators remains the same, indicating that retirement of these (mostly fossil) plants is unlikely. To alleviate this dispatch problem, many advocate energy storage and / or demand response. Energy storage units collect energy during periods of excess power (when renewable generation is high and consumer demand is low) and return energy to the system during times of scarcity.Intellectual Merit The notion of Demand Response (DR) is to enlist a subset of consumers (the demand) to respond to renewable sources as well as demand from other consumers. The expected impact is a reduction in peak power (creating an opportunity to retire some dispatch capable plants) and a reduction of the variance (indicating that remaining generators will experience fewer burdens). While the grid-level benefits of energy storage and demand response seem clear, what incentives justify investment in a storage facility or a demand response program? These can be found in the time-dependent electric energy prices, a result of a recently deregulated (or market based) compensation system for power generators. The central issue is that uncertainty (in price, demand or renewable generation) will force the Net Present Value (NPV) analysis to a stochastic framework. Specifically, the equipment design question becomes a Multistage Stochastic Program (MSP). In many cases, a separation of timescales converts the MSP to a simple two-stage problem. Unfortunately, this approximation fails if given significant energy storage and MSP solution methods will be required for these systems. MSP is one of the most challenging optimization problems in all of engineering. Thus, the project objective is to investigate a new solution procedure for MSP problems. This procedure employs a novel combination of existing and recently developed solution methods and is tailored to exploit the specific characteristics of the smart grid application. The project is collaborative in nature. Concepts and solution techniques from two research communities (chemical process control and power system optimization, represented by the PI and co-PI), will need to be combined to achieve project goals. This intellectual cross-pollination at the boundary of two fields is expected to yield unforeseen discoveries for both. The proposed MSP solution procedure is expected to have applicability beyond smart grid. A clear extension is to the longstanding problem of integrated process design and control. Another is process operations scheduling, which is very much similar to the integer constrained unit commitment problem studied in this project.Broader Impacts Mentoring of three REU projects is planned and the PI's K-12 outreach program will be continued. In addition, a major goal of the project is to foster a chemical engineering smart grid community, through the organization of invited tutorial sessions at chemical engineering conferences, multi-community workshops and mini-conferences, and review and tutorial type publications. Development of computationally tractable MSP solution methods is perceived as an essential step to the transfer of technology for widespread investment in energy storage facilities and adoption of demand response participation.