A Novel Genetic-based Optimization for Transmission Constrained Generation Expansion Planning

Abstract

Transmission constrained generation expansion planning (TC-GEP) problem involves decisions on site, capacity, type of fuel, and etc. of new generation units, which should be installed over a planning horizon to meet the expectations of energy demand. Th is may lead to adding or lightening transmission lines congestion. This paper presents an application of genetic algorith m (GA ) to TC-GEP problem for simu ltaneously determinat ion of new generation site, capacity and fuel type for a mu lti-period generation expansion plan. The object ive function in this paper is to minimize the total generation cost which is co mposed of generation capital investment costs, operation and maintenance (O&M) costs, outage cost, transmission losses costs and transmission enhancement costs. In this paper, also a new method is proposed for computing transmission enhancement costs. In addition a new approach is presented in this paper to determine site and number of co mb ined cycle power plants regarding to candidate units. The GA is applied to solve TC-GEP problem for 4 bus test system fro m Grainger & Stevenson for a planning horizon of one year and the results are co mpared and validated against Enumeration Method (EM). Then GA is applied to solve TC-GEP , kb CIGV related to fuel supply piping cost($) , kb CIGVGC Part of , kb CIGV related to interconnection cost to the main grid ($) worst t IT Transmission capital investment costs ($) in worst period of year t (period with the worst conditions) of the study period A Novel Genetic-based Optimization for Transmission Constrained Generation Expansion Planning