Probabilistic Power Distribution Planning Using Multi-Objective Harmony Search Algorithm

In this paper, power distribution planning (PDP) considering distributed generators (DGs) is investigated as a dynamic multi-objective optimization problem. Moreover, Monte Carlo simulation (MCS) is applied to handle the uncertainty in electricity price and load demand. In the proposed model, investment and operation costs, losses and purchased power from the main grid are incorporated in the first objective function, while pollution emission due to DGs and the grid is considered in the second objective function. One of the important advantages of the proposed objective function is a feeder and substation expansion in addition to an optimal placement of DGs. The resulted model is a mixed-integer non-linear one, which is solved using a non-dominated sorting improved harmony search algorithm (NSIHSA). As multi-objective optimization problems do not have a unique solution, to obtain the final optimum solution, fuzzy decision making analysis tagged with planner criteria is applied. To show the effectiveness of the proposed model and its solution, it is applied to a 9-node distribution system. Keyword: Fuzzy decision-making, Harmony search algorithm, Monte Carlo simulation, Power distribution planning. NOMENCLATURE Indices and sets / t t  Index/Set of time period CDS / y  Index/Set of candidate distribution substations / F   Index/Set of existing and candidate lines/feeders , / B N i j  Index/Set of nodes DG / k  Index/Set of DGs EDS / h  Index/Set of existing distribution substation GE / m  Index/Set of gaseous emission Parameters d The discount rate B C Base MVA of system C  Investment cost of line/feeder ($) y C Investment cost of distribution substation ($) INV k C Investment cost of kth DG technology ($/kW) OP k C Operation cost of kth DG technology ($/kWh) pf Penalty factor DG , k m E Emission factor of type m in kth DG technology (kg/kWh) G m E Emission factor of type m associated with electricity taken from the grid (kg/kWh) s  Electricity market Price ($/kWh) TPH Total planning horizon CAP k P Capacity limit of kth DG technology (kW) Min i U Minimum voltage at node i Max i U Maximum voltage at node i SS-Max h P Distribution substation capacity limit (MVA) Max ij P Thermal capacity of line/feeder connecting node i to node j (kW) cos Power factor ij Z Impedance of line/feeder connecting node i to node j R Resistance of the feeder (Ω/phase/km) X Reactance of the feeder (Ω/phase/km) Di, t load demand at node i in time period t (kW) Variables , t n  Number of lines/feeders must be installed in time period t Received: 09 Aug. 2017 Revised: 24 Oct. 2017 Accepted: 15 Feb. 2018 Corresponding author: E-mail: j.moshtagh@uok.ac.ir (J. Moshtagh) Digital object identifier: 10.22098/joape.2018.3908.1309  2018 University of Mohaghegh Ardabili. All rights reserved. A. Rastgou, J. Moshtagh, S. Bahramara: Probabilistic Power Distribution Planning... 112 . t y n Number of substations must be installed in time period t OP , , t i k P Operation generation of kth DG technology at node i in time period t (kW) PS , t h P Purchased power from substation h in time period t (kW) , t ij P Power flow in line/feeder connecting node i to node j in time period t (kW) , t i U Voltage of node i in time period t COF Cost of lines/feeders ($) CDS Cost of distribution substation ($) ICD Cost of DGs ($) OCD Operation cost of DGs ($) COL Cost of losses ($) CPP Cost of purchased power from main grid ($) PEA Pollution emission amount (ton/h) TSC Total social cost ($)

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