This paper presents both application and comparison of the metaheuristic techniques to Generation Expansion Planning (GEP) problem. The Metaheuristic techniques such as the Genetic Algorithm, Differential Evolution, Evolutionary Programming, Evolutionary Strategy, Ant Colony Optimization, Particle Swarm Optimization, Tabu Search, Simulated Annealing, and Hybrid Approach are applied to solve GEP problem. The original GEP problem is modified using the proposed methods Virtual Mapping Procedure (VMP) and Penalty Factor Approach (PFA), to improve the efficiency of the metaheuristic techniques. Further, Intelligent Initial Population Generation (IIPG), is introduced in the solution techniques to reduce the computational time. The VMP, PFA, and IIPG are used in solving all the three test systems. The GEP problem considered synthetic test systems for 6-year, 14-year, and 24-year planning horizon having five types of candidate units. The results obtained by all these proposed techniques are compared and validated against conventional Dynamic Programming and the effectiveness of each proposed methods has also been illustrated in detail.

This paper describes use of a multiobjective optimization method, elitist nondominated sorting genetic algorithm version II (NSGA-II), to the generation expansion planning (GEP) problem. The proposed model provides for decision maker choice from among the different trade-off solutions. Two different problem formulations are considered. In one formulation, the first objective is to minimize cost; the second objective is to minimize sum of normalized constraint violations. In the other formulation, the first objective is to minimize investment cost; the second objective is to minimize outage cost (or maximize reliability). Virtual mapping procedure is introduced to improve the performance of NSGA-II. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.

No abstract

No abstract

No abstract

No abstract

a b s t r a c tThe issue of balance between the benefits of greater renewable penetration with the cost of adapting conventional base load systems is drawing the attention of power system planners across the globe. The study region, State of Tamilnadu, India, faces acute power shortages and frequent power cuts, though the installed capacity of the state is higher than the peak demand and is planning more solar additions. The impact of the inclusion of solar power plants is analyzed, for 6-year and 14-year planning horizons, using the model formulated, integrating all critical elements of the system, employing Differential Evolution (DE) algorithm. A balanced approach is adopted to understand the long term impact of solar additions by realistically imposing Total Emission Reductions Constraints (TERC), and Emission Treatment Penalty Costs (ETPC) on the remaining portion of pollution. The sensitivity of the system generation mix and the system reliability, to different solar power development and emissions reduction scenarios is also studied. The resulting variations in different cost components are also reported. The study will have greater utility for the planners who are currently involved in the long term planning of systems expected to have increasing proportion of RET plants.

This paper presents an application of elitist nondominated sorting genetic algorithm version II (NSGA-II), a multiobjective algorithm to a constrained single objective optimization problem, the transmission constrained generation expansion planning (TC-GEP) problem. The TC-GEP problem is a large scale and challenging problem for the decision makers (to decide upon site, capacity, type of fuel, etc.) as there exist a large number of combinations. Normally the TC-GEP problem has an objective and a set of constraints. To use NSGA-II, the problem is treated as a two-objective problem. The first objective is the minimization of cost and the second objective is to minimize the sum of normalized soft constraints violation. The hard constraints (must satisfy constraints) are treated as constraints only. To improve the performance of the NSGA-II, two modifications are proposed. In problem formulation the modification is virtual mapping procedure (VMP), and in NSGA-II algorithm, controlled elitism is introduced. The NSGA-II is applied to solve TC-GEP problem for modified IEEE 30-bus test system for a planning horizon of six years. The results obtained by NSGA-II are compared and validated against single-objective genetic algorithm and dynamic programming. The effectiveness of each proposed approach has also been discussed in detail.Index Terms-Combinatorial optimization, dynamic programming, multiobjective, nondominated sorting genetic algorithm (NSGA-II), single-objective genetic algorithm, success rate, transmission constrained generation expansion planning, virtual mapping procedure.