1949-3053 (c) 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TSG.2016.2608508, IEEE Transactions on Smart Grid 1 A Planning Approach for the Network Configuration of AC-DC Hybrid Distribution Systems Haytham M. A. Ahmed, Student Member, IEEE; Ayman B. Eltantawy, Member, IEEE; and M. M. A. Salama, Fellow, IEEE Abstract—This paper proposes a novel stochastic planning model for AC-DC hybrid distribution systems (DSs). Taking into account the possibility of each line/bus being AC or DC, the model finds the optimal AC-DC hybrid configuration of buses and lines in the DS. It incorporates consideration of the stochastic behavior of load demands and renewable-based distributed generators (DGs). The stochastic variations are addressed using a Monte-Carlo simulation technique. The objective of the planning model is the minimization of DS installation and operation costs. The optimal planning solution is obtained by dividing the hybrid planning problem into two nested optimization problems: 1) the main problem is formulated using a genetic algorithm (GA) to search for the optimal AC-DC configuration, and 2) the subproblem is used for determining the optimal power flow solution for each configuration generated by the GA. The proposed model has been employed for finding the optimal configuration for a suggested case study that included photovoltaic panels, wind-based DG, and electric vehicle charging stations. The same case study was also solved using a traditional AC planning technique in order to evaluate the effectiveness of the proposed model and the associated cost-savings. The results demonstrate the advantages offered by the proposed model. The proposed framework represents an effective technique that can be used by DS operators to identify the optimal AC-DC network configuration of future DSs. Index Terms—Distribution system planning, AC-DC distribution systems, hybrid power systems, voltage source converter, electric vehicle charging stations, renewable distributed generation. NOMENCLATURE A. Sets I ac Number of AC generators. J dc Number of DC generators. N b Number of buses in the network. N c Number of converters in the system. T P Number of years in the planning horizon. B. Indices c Index of converters. i Index of AC generators. j Index of DC generators. n, m Index of buses. t Index of years. C. Parameters C ac G i Energy cost of the AC generator i, in $/MWh. C dc G j Energy cost of the DC generator j , in $/MWh. d Discount rate, as a %. B nm Susceptance of the AC line connecting buses n and m, p.u. G nm Conductance of the AC line connecting buses n and m, p.u. Haytham M. A. Ahmed, Ayman B. Eltantawy, and M. M. A. Salama are with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, N2L 3G1 Canada (e-mail: h2abdelr@uwaterloo.ca; abahgat@uwaterloo.ca; msalama@uwaterloo.ca) G dc nm Conductance of the DC line connecting buses n and m, p.u. K c Constant of the voltage source converter (VSC). R dc Resistance of a DC line, equal to (1/G dc ). S Base Base power for the AC-DC network, in MVA. V ac Base Base value of the AC voltage, in kV. V dc Base Base value of the DC voltage, in kV. β Annual maintenance cost as a percentage of IC . ε A selected small tolerance. η c-nm-i Efficiency of the VSC (c) that functions as an inverter between buses n and m, as a %. η c-nm-r Efficiency of the VSC (c) that functions as a rectifier between buses n and m, as a %. η c-n-i Efficiency of the converter (c) that functions as an inverter at bus n, as a %. η c-n-r Efficiency of the converter (c) that functions as a rectifier at bus n, as a %. ϕ c-nm Power factor angle of the VSC (c) connected at bus n between buses n and m. λ f The number of feasible OPF solutions as a per- centage of the total number of solutions for dif- ferent MCS scenarios. ✦ max Maximum limit of the variable ✦. ✦ min Minimum limit of the variable ✦. D. Variables AOMC t Annual operation and maintenance cost at year t. C OPF ,t Stochastic variable representing the optimal oper- ation cost for different MCS scenarios, at year t. D nm Binary element of the line (n, m) in the line-type matrix (D). E(C OPF ,t ) Expected value of the stochastic variable C OPF ,t . IC Installation costs for lines and converters. L max Maximum permissible number of lines that can be connected to any bus in the system. L min Minimum permissible number of lines that can be connected to any bus in the system. M nm Modulation index of the VSC connected at bus n, between buses n and m. PCV Present cost value of the project. P inj n Active power injected into bus n. P cal n Calculated active power at bus n. P nm Active power transmitted from bus n to bus m. P dc Gn Output power of the DC generator at bus n. P ac Gn Active power of the AC generator at bus n. P dc Ln Power demand of the DC load at bus n. P ac Ln Active power demand of the AC load at bus n. P c Active power at the AC side of the VSC (c). Q inj n Reactive power injected into bus n. Q cal n Calculated reactive power at bus n.