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

摘要PI：Chmielewski，Donald和Li，Zuyi提案编号：1511925机构：伊利诺伊理工学院标题：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消耗之间的平衡。由于功率消耗的不确定性（表示为“需求”），具有调度能力的发电机处于恒定的通量状态。可再生能源的引入对缓解整体问题几乎没有帮助，因为可再生能源没有调度能力。因此，能够调度的发电机的峰值功率保持不变，这表明这些（主要是化石）电厂不太可能退役。为了缓解这种调度问题，许多人提倡能量存储和/或需求响应。储能装置在电力过剩时期（可再生能源发电量高而消费者需求低时）收集能量，并在电力短缺时期将能量返回给系统。知识价值需求响应（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降低（创造了淘汰一些具有调度能力的电厂的机会）和差异的减少（表明剩余发电机的负担将减少）。虽然储能和需求响应的电网级效益似乎很明显，但什么激励措施可以证明投资储能设施或需求响应计划是合理的？这些可以在随时间变化的电能价格中找到，这是最近放松管制（或基于市场）的发电机补偿系统的结果。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）分析的随机框架。具体地说，设备设计问题成为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，这种近似失败，如果给定的显着的能量存储和MSP解决方案的方法将需要这些系统。多目标规划问题是工程中最具挑战性的优化问题之一。因此，项目的目标是研究一个新的解决方案的MSP问题的程序。该程序采用了现有的和最近开发的解决方案的方法的新组合，并专门利用智能电网应用的具体特点。该项目是合作性质的。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将需要结合起来，以实现项目目标。在两个领域的边界上，这种智力上的交叉授粉预计将为双方带来意想不到的发现。提出的MSP解决方案的程序，预计有适用性超越智能电网。一个明显的延伸是集成过程设计和控制的长期问题。另一个是过程操作调度，这是非常相似的整数约束机组组合问题，在这个项目中研究。更广泛的影响指导三个REU项目的计划和PI的K-12推广计划将继续进行。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辅导会议以及评论和辅导类出版物，培养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一步，用于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.