SGER: Optimal Stochastic Unit Response Subject to Ramp and Network Constraints

University Of Maryland, College Park, College Park MD

Investigators

Abstract

Unit commitment (UC) is an important optimization problem for power utilities to economically schedule generating resources to achieve cost minimization in the regulated environment. Although the electricity industry is moving toward deregulation, the importance of the UC does not diminish along with the restructuring trend. On the other hand, new features and requirements are now considered in UC models such as price uncertainty, which significantly increase the complexity of the problem. Some centralized markets (such as the PJM) still perform UC-like optimization to conduct electricity auctions. Also the industry is recently considering "re-regulation" due to incidents such as California crisis and Enron and emphasizing centralized unit commitment. This research will address the optimal response of a thermal plant to price and network uncertainties. In the problem, the operator of a power plant maximizes total profit by optimally committing the unit to generate power to sell in spot markets via transmission network. The unit commitment is subject to physical constraints including the ramp constraints. These operational constraints can impact the capability of how the unit can quickly respond to profitable opportunities. Technical Merits The challenge of this research is twofold: the ramp constraints have been notorious for making the unit operation problem non-separable in time even without considering uncertainty, and the large dimensionality of the network uncertainties considered, such as nodal and transmission prices, and transmission congestion. We intend to develop an efficient and theoretically sound approach with a polynomial time complexity to solve the deterministic ramp-constrained operation optimization. We will also develop a new approach integrating the Monte Carlo method and the least squares regression to solve this stochastic unit response optimization. The proposed new method is general, effective, and efficient for large-scale multi-stage stochastic optimization. Broader Impact of the Proposed Activity Through the proposed research, the capacity expansion process in a deregulated power market will be studied. The research has many benefits: 1. The solution procedure developed in this proposal can be used to help independent power producers achieve optimal commitment and dispatch decisions in the competitive marketplace. It can also contribute to the traditional UC optimization. 2. This research will provide an efficient yet theoretically sound approach to handle the notorious ramping constraints in the unit commitment problems. Recently the electric industry is being considered "re-regulated" due to incidents such as California crisis and Enron, and is refocusing on centralized unit commitment. This research will make a fundamental contribution to the development of more efficient solution algorithms. Furthermore, since we will develop a polynomial time algorithm, it will be practical and applicable for large-scale real cases. 3. We will tackle this research from both technical and financial perspective. In the technical side, we will develop efficient algorithms to aid optimal operation. In the financial side, we hope to provide a fair valuation tool to help investors to make appropriate investment decisions. In the long run, customers also benefit from the improved societal efficiency.