Optimal Allocation of Distributed Generation Using Evolutionary Multi-objective Optimization

This paper proposes a new long-term planning methodology for Multi-objective Distributed Generation Placement and Sizing (MO-DGPS) aiming at a minimum energy loss, CO2 emission, overall cost, besides enhancing the system voltage stability and reliability. The MO-DGPS problem has been reformulated to incorporate the uncertainties such as intermittent power generation of renewable DG (RDG), and forced outages of Distributed Generation (DG) units, the cost of reactive power imports from the substation with the conventional DGPS problem description. Moreover, the problem also considers time-varying loads, and load growth. The objective of the proposed modified problem formulation is to identify the optimal DG placement, sizing and the selling price of its generated power to the utility by minimizing the Distribution Companies cost and maximizing the DG Investor's profits simultaneously, considering various constraints and uncertainties. A fast-elitist Non-dominated Sorting Genetic Algorithm-II has been employed to solve the reformulated MO-DGPS problem. The IEEE 33-Node and practical Tamil Nadu Electricity Board 84-Node radial distribution systems have been utilized as test systems in order to validate the proposed methodology. Performance analysis has been done on the results of a MO-DGPS problem for different combinations of RDG with conventional DG units. The results of these different scenarios have been compared with the results of recent research reports too. Among investigations of various scenarios, it is found that the right combination of biomass and conventional DG units provides better objective values and performance indices, minimum energy loss and CO2 emission.