![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
Abstract
In this article, network reconfiguration and distributed generation allocation in distribution networks are dealt with simultaneously while imposing an objective of minimizing energy loss. The proposed method, which is based on a genetic algorithm, takes into consideration the uncertainty related to renewable distributed generation output power and the load variability. Three scenarios are assessed to analyze the superiority of the proposed method. In the first scenario, distributed generation units are allocated using the base configuration, followed by network reconfiguration. In the second scenario, distributed generations are allocated after network reconfiguration. In the third scenario, distributed generations are allocated simultaneously with network reconfiguration. The constraints involved include voltage limits, line current limits, and radial topology. Both balanced and unbalanced distribution systems are used as case studies.
NOMENCLATURE
| a(i) | = | integer variable indicating size of wind-based distributed generation at bus i as zero (no distributed generation) or multiple of available rating (Prw), assumed to be 1.1 MW herein |
| b(i) | = | integer variable indicating size of biomass-based distributed generation at bus i as zero (no distributed generation) or multiple of available rating (Prdisp), assumed to be 100 kW herein |
| Closses | = | annual energy loss |
| CFw, CFs | = | capacity factors of wind- and solar-based distributed generation, respectively |
| G(ij), B(ij) | = | real and reactive parts of 3 × 3 admittance matrix of branch between nodes i and j |
| I(s,ij) | = | three-phase current matrix of branch ij during state s |
| Imax | = | upper limit of line current as defined by manufacturer |
| n | = | total number of buses |
| Nbr | = | total number of branches |
| nDG | = | set of candidate buses to connect distributed generation units |
| Ns | = | total number of states for each hour |
| nc | = | total number of constraints |
| P | = | set of phases a, b, and c |
| Pdisp(i), Qdisp(i) | = | active and reactive powers of biomass-based distributed generation connected at bus i |
| PMax(i) | = | maximum penetration allowable at bus i |
| Pslack(s), Qslack(s) | = | substation active and reactive powers injected during state s |
| PD(i) | = | rated power demand at bus i |
| PD(s,i), QD(s,i) | = | active and reactive powers of load connected at bus i during state s |
| Ploss(s) | = | power loss for each state s |
| PS(s,i), QS(s,i) | = | active and reactive powers injected during state s of solar-based distributed generation connected at bus i |
| PW(i), PS(i), Pdisp(i) | = | rated power of wind-based, solar-based, and dispatchable distributed generation connected at bus i, respectively |
| PW(s,i), QW(s,i) | = | active and reactive powers injected during state s of wind-based distributed generation connected at bus i |
| R(ij) | = | three-phase resistance matrix of branch ij |
| V(s,i) | = | voltage at bus i during state s |
| Vmax, Vmin | = | maximum and minimum acceptable bus voltages (i.e., 1.05, 0.9 p.u., respectively) |
| x(c) | = | binary variable corresponding to constraint c |
| x(ij) | = | status of branch ij (0: opened, 1: closed) |
| y | = | maximum penetration limit as a percentage of peak load (i.e., equal to 0.3) |
| δ(ij)pm | = | difference in voltage angles between phases p and m of nodes i and j |
Additional information
Notes on contributors
Aboelsood A. Zidan
Aboelsood A. Zidan was born in Sohag, Egypt, in 1982. He received his B.Sc. and M.Sc. in electrical engineering from Assiut University, Egypt, in 2004 and 2007, respectively, and his Ph.D. in electrical engineering from University of Waterloo, Waterloo, Ontario, Canada, in 2013. He is currently an assistant professor at Assiut University, Egypt. His research interests include distribution automation, renewable DG, distribution system planning, and smart grids.
Mostafa F. Shaaban
Mostafa F. Shaaban was born in Virginia, USA, in 1982. He received his B.Sc. and M.Sc. in electrical engineering from Ain Shams University, Cairo, Egypt, in 2004 and 2008, respectively, and his Ph.D. in electrical engineering from University of Waterloo, Waterloo, ON, Canada, in 2014. Currently, he is an assistant professor in the Department of Electrical Engineering, American University in Sharjah, Sharjah, United Arab Emirates. His research interests include smart grid, renewable DG, distribution system planning, electric vehicles, storage systems, and bulk power system reliability.
Ehab F. El-Saadany
Ehab F. El-Saadany was born in Cairo, Egypt, in 1964. He received his B.Sc. and M.Sc. in electrical engineering from Ain Shams University, Cairo, Egypt, in 1986 and 1990, respectively, and his Ph.D. in electrical engineering from University of Waterloo, Waterloo, Ontario, Canada, in 1998. He is currently a professor in the Department of Electrical and Computer Engineering, University of Waterloo. His research interests are distribution system control and operation, power quality, DG, power electronics, digital signal processing applications for power systems, and mechatronics.