Generation Expansion Planning (GEP) is one of the most important decision-making activities in electric utilities. Least-cost GEP is to determine the minimum-cost capacity addition plan (i.e., the type and number of candidate plants) that meets forecasted demand within a pre specified reliability criterion over a planning horizon. In this paper, Differential Evolution (DE), and Oppositionbased ...

Generation expansion planning is a challenging problem due to its large-scale, long-term, non-linear, and discrete nature of generation unit size. Since the computation revolution, there are several emerging techniques proposed to solve large scale optimization problem. Many of these techniques have been reported to use in generation expansion planning. This paper will describe these emerging o...

In recent years the expansion planning problem has become increasingly complex. As expansion planning (sometimes called composite or integrated resource planning) is a non-linear and nonconvex optimization problem, researchers have traditionally focused on approximate models of power flows to solve the problem. The problem has also been split into generation expansion planning (GEP) and transmi...

The Generation expansion planning (GEP) problem is a large scale, mixed integer and the most complicated optimization problem. So we are using Multi-objective differential evolution (MODE) for solving the GEP problem. To calculate for best combinations of conventional sources with and without wind farm, GEP problem is considered for test system of and satisfy the constraints by calculating EENS...

Electric Power Generation Expansion Planning (GEP) is the problem of determining an optimal construction and generation plan of both new and existing electric power plants to meet future electricity demand. We consider a stochastic optimization approach for this capacity expansion problem under demand and fuel price uncertainty. In a two-stage stochastic optimization model for GEP, the capacity...

This paper presents a development of an improved genetic algorithm (IGA) and its application to a least-cost generation expansion planning (GEP) problem. Least-cost GEP problem is concerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration, a process which is computationally impossible in a real-world GEP problem. In this pape...

This paper describes use of a new approach to solve multi-period multi-objective optimization of generation expansion planning problem in deregulated power systems over a long term planning horizon. Traditionally, the objective in undertaking aGeneration Expansion Planning (GEP) process has been tominimize the expected sum of yearly discounted costs(incorporate i.e., construction costs, operati...

This paper describes the application of an evolutionary algorithm, Restart Covariance Matrix Adaptation Evolution Strategy (RCMAES) to the Generation Expansion Planning (GEP) problem. RCMAES is a class of continuous Evolutionary Algorithm (EA) derived from the concept of self-adaptation in evolution strategies, which adapts the covariance matrix of a multivariate normal search distribution. The...