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

Illinois Institute Of Technology, Chicago IL

Investigators

Abstract

Abstract PIs: Chmielewski, Donald and Li, Zuyi Proposal #: 1511925 Institution: Illinois Institute of Technology Title: A Novel Approach to Multistage Stochastic Programming for Smart Grid Applications The 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.