Convex and Non-convex Heat Curve Parameters Estimation Using Cuckoo Search

Abstract

Cuckoo search-based algorithm is presented for accurate estimation of thermal power plant heat curve (or fuel cost function) parameters. The fuel cost function of power plant reveals some of its economical characteristics that greatly impact many operational practices. Some of influential factors that affect the input–output characteristics of thermal power plants are ambient operating temperature and aging of generating units. Periodical and accurate extraction of fuel cost function characteristics is very important as it directly affects optimal power flow and economic dispatch calculations which in turn enhances the overall operational and economical practices. Convex and non-convex or smooth and non-smooth models that describe the input–output relationship of thermal units are considered including the one that accounts for the valve loading point. The objective is to minimize the total estimation error using cuckoo search algorithm via proper estimation of fuel cost function parameters. The proposed approach relieves some of the mathematical restrictions typically imposed on system modeling since it does not require convexity nor differentiability like in the case of many conventional estimation techniques. Various study cases are considered in this work to test the performance of the method. Results obtained are compared to those computed using competing estimation methods. Comparison results are in favor of Cuckoo search algorithm in all study cases considered.