A VDS-NSGA-II algorithm for multiyear multiobjective dynamic generation and transmission expansion planning

Abstract

The chapter develops a dynamic biobjective model for the generation expansion planning together with the transmission system expansion planning. A virtual database-supported nondominated sorting genetic algorithm, known as “VDS-NSGA-II,” is designed to tackle the multiyear multiobjective dynamic generation and transmission expansion planning (MMDGTEP) framework. The MMDGTEP is formulated as a biobjective optimization problem in this chapter, while the objective functions are defined as total cost minimization and also minimizing the expected energy not supplied (EENS) at the hierarchy level II, known as EENSHL-II. The first objective function is comprised of the investment and operating costs. The proposed hybrid model is decomposed into two programming problems: master problem and slave problem. In the first level, that is the master level, a virtual mapping procedure (VMP) is incorporated in the VDS-NSGA-II to evaluate the contrast of each capacity additions in the planning horizon. In the second level, that is the slave problem, a linear programming approach is employed to assess the objectives of the problem. The virtual database helps reduce the computational burden. By avoiding the monotonous calculation in the proposed framework, the convergence time is reduced, appropriately. After obtaining the optimal Pareto set, the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) decision maker is used to pick the most desired Pareto-optimal solution. The presented long-term planning model is simulated on a test power system to verify the effectiveness and efficiency of the framework.