Multi-Objective Optimization of Integrated Power System Expansion Planning with Renewable Energy-Based Distributed Generation

Abstract

Multi-objective-based power system expansion planning considering distributed generation is presented in this study. An integrated model of electrical power system expansion planning with renewable energy-based distributed generation is proposed. Two objective functions, cost and the emission objective function, are implemented in the proposed model. The cost function comprises investment cost and operation cost. The investment cost covers the cost of the new generation unit, the new distributed generation unit, and the investment for the new transmission circuit. The operation cost covers the operation cost of the installed and new generation unit and the operation cost of the installed and new distributed generation unit. Two emission gasses, CO2 and NOx, are considered in the proposed model. These two gasses are expressed in the same unit as Global Warming Potential. The developed model is implemented into the IEEE 14 bus system. Lexicographic optimization combined with the epsilon constraint technique is used to solve the multi-objective problem. A Pareto optimal solution is generated by this method. Then, the fuzzy decision-making process is implemented to select the best solution from the Pareto set. The simulation results show that the distributed generation significantly reduces the overall cost of power system expansion planning. With a 30% DG penetration level, overall planning costs can be reduced by 12.62% compared to without DG penetration with an equal weighting factor for each objective function. Details of the capacity expansion of the generation unit, distributed generation unit, and transmission line are presented in this study.