Probabilistic Power Distribution Planning Using Multi-Objective Harmony Search Algorithm

Document Type : Research paper

Authors

1 گروه مهندسی برق-دانشکده مهندسی - دانشگاه کردستان-سنندج ایران

2 گروه مهندسی برق- دانشکده مهندسی- دانشگاه کردستان- سنندج- ایران

3 گروه مهندسی برق- واحد سنندج- دانشگاه آزاد اسلامی- سنندج ایران

Abstract

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. 

Keywords


[1]     A. Sadeghi Yazdankhah and R. Kazemzadeh, “Power management in a utility connected micro-grid with multiple renewable energy sources,” J. Oper. Autom. Power Eng., vol. 5, no. 1, pp. 1-10, 2017.
[2]     P. S. Georgilakis and N. D. Hatziargyriou, “A review of power distribution planning in the modern power systems era: Models, methods and future research,” Electr. Power Syst. Res., vol. 121, pp. 89-100, 2015.
[3]     M. Sadeghi and M. Kalantar, “Clean and polluting DG types planning in stochastic price conditions and DG unit uncertainties,” J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 1-15, 2016.
[4]     M. KN and J. EA, “Optimal integration of distributed generation (DG) resources in unbalanced distribution system considering uncertainty modelling,” Int. Trans. Electr. Energy Syst., vol. 27, no. 1, 2017.
[5]     Z. Wang, B. Chen, J. Wang, and M. M. Begovic, “Stochastic DG placement for conservation voltage reduction based on multiple replications procedure,” IEEE Trans. Power Deliv., vol. 30, no. 3, pp. 1039-1047, 2015.
[6]     I. Kim, “Optimal distributed generation allocation for reactive power control,” IET Gener. Transm. Distrib., vol. 11, no. 6, pp. 1549-1556, 2017.
[7]     E. Ali, S. A. Elazim, and A. Abdelaziz, “Ant lion optimization algorithm for renewable distributed generations,” Energy, vol. 116, pp. 445-458, 2016.
[8]     M. Esmaeili, M. Sedighizadeh, and M. Esmaili, “Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using big bang-big crunch algorithm considering load uncertainty,” Energy, vol. 103, pp. 86-99, 2016.
[9]     M. Ahmadigorji and N. Amjady, “A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorit hm,” Energy, vol. 102, pp. 199-215, 2016.
[10]   S. Singh, T. Ghose, and S. Goswami, “Optimal feeder routing based on the bacterial foraging technique,” IEEE Trans. Power Deliv., vol. 27, no. 1, pp. 70-78, 2012.
[11]   A. Samui, S. Singh, T. Ghose, and S. Samantaray, “A direct approach to optimal feeder routing for radial distribution system,” IEEE Trans. Power Deliv., vol. 27, no. 1, pp. 253-260, 2012.
[12]   E. Naderi, H. Seifi, and M. S. Sepasian, “A dynamic approach for distribution system planning considering distributed generation,” IEEE Trans. Power Deliv., vol. 27, no. 3, pp. 1313-1322, 2012.
[13]   S. M. Mazhari, H. Monsef, and H. Falaghi, “A hybrid heuristic and learning automata‐based algorithm for distribution substations siting, sizing and defining the associated service areas,” Int. Trans. Electr. Energy Syst., vol. 24, no. 3, pp. 433-456, 2014.
[14]   S. N. Ravadanegh, N. Jahanyari, A. Amini, and N. Taghizadeghan, “Smart distribution grid multistage expansion planning under load forecasting uncertainty,” IET Gener. Transm. Distrib., vol. 10, no. 5, pp. 1136-1144, 2016.
[15]   G. Celli, E. Ghiani, G. Soma, and F. Pilo, "Planning of reliable active distribution systems," in Proc. CIGRE, 2012, pp. 1-12.
[16]   M. S. Nazar, M. R. Haghifam, and M. Nažar, “A scenario driven multiobjective primary–secondary distribution system expansion planning algorithm in the presence of wholesale–retail market,” Int. J. Electr. Power Energy Syst., vol. 40, no. 1, pp. 29-45, 2012.
[17]   T.-H. Chen, E.-H. Lin, N.-C. Yang, and T.-Y. Hsieh, “Multi-objective optimization for upgrading primary feeders with distributed generators from normally closed loop to mesh arrangement,” Int J. Electr. Power Energy Syst., vol. 45, no. 1, pp. 413-419, 2013.
[18]   I. Ziari, G. Ledwich, A. Ghosh, and G. Platt, “Optimal distribution network reinforcement considering load growth, line loss, and reliability,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 587-597, 2013.
[19]   A. M. El-Zonkoly, “Multistage expansion planning for distribution networks including unit commitment,” IET Gener. Transm. Distrib., vol. 7, no. 7, pp. 766-778, 2013.
[20]   M. E. Samper and A. Vargas, “Investment decisions in distribution networks under uncertainty with distributed generation—Part II: Implementation and results,” IEEE Trans. Power Syst., vol. 28, no. 3, pp. 2341-2351, 2013.
[21]   E. G. Carrano, F. G. Guimarães, R. H. Takahashi, O. M. Neto, and F. Campelo, “Electric distribution network expansion under load-evolution uncertainty using an immune system inspired algorithm,” IEEE Trans. Power Syst., vol. 22, no. 2, pp. 851-861, 2007.
[22]   C. L. T. Borges and V. F. Martins, “Multistage expansion planning for active distribution networks under demand and distributed generation uncertainties,” Int. J. Electr. Power  Energy Syst., vol. 36, no. 1, pp. 107-116, 2012.
[23]   A. Zidan, M. F. Shaaban, and E. F. El-Saadany, “Long-term multi-objective distribution network planning by DG allocation and feeders’ reconfiguration,” Electr. Power Syst. Res., vol. 105, pp. 95-104, 2013.
[24]   S. Favuzza, G. Graditi, M. G. Ippolito, and E. R. Sanseverino, “Optimal electrical distribution systems reinforcement planning using gas micro turbines by dynamic ant colony search algorithm,” IEEE Trans.Power Syst., vol. 22, no. 2, pp. 580-587, 2007.
[25]   Ž. Popović, V. D. Kerleta, and D. Popović, “Hybrid simulated annealing and mixed integer linear programming algorithm for optimal planning of radial distribution networks with distributed generation,” Electr. Power Syst. Res., vol. 108, pp. 211-222, 2014.
[26]   N. Koutsoukis, P. Georgilakis, and N. Hatziargyriou, “A Tabu search method for distribution network planning considering distributed generation and uncertainties,” Proc. Int. Conf. Probab. Methods Appl. Power Syst., 2014, pp. 1-6.
[27]   R. Hemmati, R.-A. Hooshmand, and N. Taheri, “Distribution network expansion planning and DG placement in the presence of uncertainties,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 665-673, 2015.
[28]   V. F. Martins and C. L. Borges, “Active distribution network integrated planning incorporating distributed generation and load response uncertainties,” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 2164-2172, 2011.
[29]   U. Sultana, A. B. Khairuddin, A. Mokhtar, N. Zareen, and B. Sultana, “Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system,” Energy, vol. 111, pp. 525-536, 2016.
[30]   H. Doagou-Mojarrad, G. Gharehpetian, H. Rastegar, and J. Olamaei, “Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm,” Energy, vol. 54, pp. 129-138, 2013.
[31]   Y.-Y. Hong and S.-Y. Ho, “Determination of network configuration considering multiobjective in distribution systems using genetic algorithms,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 1062-1069, 2005.
[32]   X.-F. Wang, Y. Song, and M. Irving, Modern power systems analysis. Springer Science & Business Media, 2010.
[33]   Z. W. Geem, J. H. Kim, and G. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60-68, 2001.
[34]   D. Manjarres, I. Landa-Torres, S.  Gil-Lopez, J. D. Ser, M. N. Bilbao, S. S. Sanz and Z. W. Geem, “A survey on applications of the harmony search algorithm,” Engineering Applications of Artificial Intelligence, vol. 26, no. 8, pp. 1818-1831, 2013.
[35]   M. Mahdavi, M. Fesanghary, and E. Damangir, “An improved harmony search algorithm for solving optimization problems,” Applied mathematics and computation, vol. 188, no. 2, pp. 1567-1579, 2007.
[36]   B. Huang, B. Buckley, and T.-M. Kechadi, “Multi-objective feature selection by using NSGA-II for customer churn prediction in telecommunications,” Expert Systems with Applications, vol. 37, no. 5, pp. 3638-3646, 2010.
[37]   K. Deb, Multi-objective optimization using evolutionary algorithms. John Wiley & Sons, 2001.
[38]   M. Haghifam, H. Falaghi, and O. Malik, “Risk-based distributed generation placement,” IET Generation Transmission and Distribution, vol. 2, no. 2, pp. 252-260, 2008.
[39]   H. Falaghi, C. Singh, M.-R. Haghifam, and M. Ramezani, “DG integrated multistage distribution system expansion planning,” International Journal of Electrical Power & Energy Systems, vol. 33, no. 8, pp. 1489-1497, 2011.
[40]   V. Quintana, H. Temraz, and K. Hipel, “Two-stage power system distribution planning algorithm,” IEE Proc. C (Gener., Trans. Distrib.), vol. 140, no. 1, pp. 17-29, 1993.
[41]   H. Falaghi and M.-R. Haghifam, “ACO based algorithm for distributed generation sources allocation and sizing in distribution systems,” in Power Tech, 2007 IEEE Lausanne, 2007, pp. 555-560.
[42]   M. Rabiee, M. Zandieh, and P. Ramezani, “Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches,” Int.l J. Produc. Res., vol. 50, no. 24, pp. 7327-7342, 2012.
Volume 6, Issue 1
June 2018
Pages 111-125
  • Receive Date: 09 August 2017
  • Revise Date: 24 October 2017
  • Accept Date: 15 February 2018
  • First Publish Date: 01 June 2018