Published in IET Generation, Transmission & Distribution Received on 19th March 2010 Revised on 17th October 2010 doi: 10.1049/iet-gtd.2010.0199 ISSN 1751-8687 Effective method for optimal allocation of distributed generation units in meshed electric power systems M.F. Akorede H. Hizam I. Aris M.Z.A. Ab Kadir Electrical & Electronic Engineering Department, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia E-mail: makorede@ieee.org Abstract: Improper placement of distributed generation (DG) units in power systems would not only lead to an increased power loss, but could also jeopardise the system operation. To avert these scenarios and tackle this optimisation problem, this study proposes an effective method to guide electric utility distribution companies (DISCOs) in determining the optimal size and best locations of DG sources on their power systems. The approach, taking into account the system constraints, maximises the system loading margin as well as the profit of the DISCO over the planning period. These objective functions are fuzzified into a single multi-objective function, and subsequently solved using genetic algorithm (GA). In the GA, a fuzzy controller is used to dynamically adjust the crossover and mutation rates to maintain the proper population diversity (PD) during GA’s operation. This effectively overcomes the premature convergence problem of the simple genetic algorithm (SGA). The results obtained on IEEE 6-bus and 30-bus test systems with the proposed method are evaluated with the simulation results of the classical grid search algorithm, which confirm its robustness and accuracy. This study also demonstrates DG’s economic viability relative to upgrading substation and feeder facilities, when the incremental cost of serving additional load is considered. Nomenclature C DG in installation cost of DG, $/MW C DG iv investment cost of DG, $/MW C DG O&M&T DG operation and maintenance and tax cost, $/MWh C DG pc purchase cost of DG, $/MW C E cost of energy, $/MWh C F cost of fuel, $/MMBtu dr discount rate f A average fitness f B best fitness f W worst fitness F k (z) overall objective function at node k i, k, y bus indices kG g power increase factor for g central generator L N number of load level N B number of system buses N L number of load buses where DG can be placed N DG number of DG units N G number of central generation units N p population size P c crossover rate P D i j active power demand at node i at load level j P DG k j DG active power injected at node k at load level j P G g power generated from g central generator P G g 0 power generated from g central generator at basecase loading P Lm j network power loss on line m at jth load level P Lm DG j network power loss on line m at jth load level after DG installation P m mutation rate P R profit feasible region of solutions S feasible region of solutions S iy apparent power flow from node i to y S R selection rate T d duration of load per day, h T p horizon planning period, years V i voltage at bus i W weighting factor z ∗ x ideal objective vector z x nad nadir objective vector mF x (z) normalised new objective function g t present worth factor at year t DP G g power generation increase from g central generator DP D i j power demand increase at i bus at jth load level DP Lm j change in system loss on line m at jth load level 276 IET Gener. Transm. Distrib., 2011, Vol. 5, Iss. 2, pp. 276–287 & The Institution of Engineering and Technology 2011 doi: 10.1049/iet-gtd.2010.0199 www.ietdl.org