Demand-side management program planning using stochastic load forecasting with extreme value theory

Abstract

Demand-side management (DSM) is easy to apply to reduce system peak load by a utility and it can be a convenient way to control and change amount of electric usage by end-use customers. Planning and operating techniques for a DSM program are required to efficiently manage and operate the program. This paper is focused on planning technique for an incentive-based DSM program. This paper describes a stochastic model that can estimate the operating days, hours, and total capacity for efficiently planning a DSM program. A temperature stochastic process, from weather derivatives, is used in the proposed method. Temperature sensitivity is proposed to improve load forecasting accuracy. The generalized extreme value distribution is also proposed for estimating stochastic results. The results of case studies are presented to show the effectiveness of the proposed method.