Abstract
Distributed Generation (DG), with respect to its ability in utilizing the alternative resources of energy, provides a promising future for power generation in electrical networks. Distributed generators contribution to the power systems includes improvement in energy efficiency and power quality to reliability and security. These benefits are only achievable with optimal allocation of distributed resources that considers the objective function, constraints, and employs a suitable optimization algorithm. In this chapter, a quantum inspired computational intelligence is exercised for the optimal allocation of distributed generators. The fact that most of power system optimization problems, when modelled accurately, are of non-convex and sometimes discrete nature has encouraged many researchers to develop optimization techniques to overcome such difficulties. The basic Particle Swarm Optimization (PSO) is one of the most favored optimization techniques with many attractive features. Early experiments of employing PSO in many applications in power systems have indicated its promising potential. Consequently, the more advanced alternatives of this algorithm such as Quantum behaved PSO (Q-PSO) may show the same or even better performance in power system problems. The aforementioned algorithm has already been employed for different optimization objectives in power systems such as: short-term non-convex economic scheduling, unit commitment problems, loss of power minimization, economic load dispatch, smart building energy management and power system operations. Nevertheless, the algorithm has never been used for optimal allocation of distributed generation units. In this chapter the above problem will be solved with a quantum behaved particle swarm optimization algorithm. The chapter will be started with an introduction to the optimal allocation of DG then the power system, including the DG units will be modeled. On the next step the Q-PSO will be adopted for the optimal allocation. Finally, the results and discussions will be presented.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ackermann, T., Andersson, G., Söder, L.: Distributed generation: a definition. Electr. Power Syst. Res. 57(3), 195–204 (2001)
Chiradeja, P., Ramakumar, R.: An approach to quantify the technical benefits of distributed generation. IEEE Trans. Energy Convers. 19(4), 764–773 (2004)
El-Khattam, W., Salama, M.M.: Distributed generation technologies, definitions and benefits. Electr. Power Syst. Res. 71(2), 119–128 (2004)
Pepermans, G., Driesen, J., Haeseldonckx, D., Belmans, R., Dhaeseleer, W.: Distributed generation: definition, benefits and issues. Energy Policy 33(6), 787–798 (2005)
Hung, D.Q., Mithulananthan, N., Bansal, R.: An optimal investment planning framework for multiple distributed generation units in industrial distribution systems. Appl. Energy 124, 62–72 (2014)
HA, M.P., Huy, P.D., Ramachandaramurthy, V.K.: A review of the optimal allocation of distributed generation: objectives, constraints, methods, and algorithms. Renew. Sustain. Energy Rev. (2016)
Walling, R., Saint, R., Dugan, R.C., Burke, J., Kojovic, L.A.: Summary of distributed resources impact on power delivery systems. IEEE Trans. Power Deliv. 23(3), 1636–1644 (2008)
Ault, G.W., McDonald, J.R.: Planning for distributed generation within distribution networks in restructured electricity markets. IEEE Power Eng. Rev. 20(2), 52–54 (2000)
Dugan, R.C., McDermott, T.E., Ball, G.J.: Planning for distributed generation. IEEE Ind. Appl. Mag. 7(2), 80–88 (2001)
Rau, N.S., Wan, Y.-H.: Optimum location of resources in distributed planning. IEEE Trans. Power Syst. 9(4), 2014–2020 (1994)
Padma Lalitha, M., Veera Reddy, V., Sivarami Reddy, N.: Application of fuzzy and abc algorithm for dg placement for minimum loss in radial distribution system. Iran. J. Electr. Electron. Eng. 6(4), 248–257 (2010)
Popović, D., Greatbanks, J., Begović, M., Pregelj, A.: Placement of distributed generators and reclosers for distribution network security and reliability. Int. J. Electr. Power Energy Syst. 27(5), 398–408 (2005)
Ochoa, L.F., Dent, C.J., Harrison, G.P.: Distribution network capacity assessment: variable dg and active networks. IEEE Trans. Power Syst. 25(1), 87–95 (2010)
Dent, C.J., Ochoa, L.F., Harrison, G.P., Bialek, J.W.: Efficient secure ac opf for network generation capacity assessment. IEEE Trans. Power Syst. 25(1), 575–583 (2010)
Kumar, A., Gao, W.: Optimal distributed generation location using mixed integer non-linear programming in hybrid electricity markets. IET Gener. Trans. Distrib. 4(2), 281–298 (2010)
López-Lezama, J.M., Padilha-Feltrin, A., Contreras, J., Muñoz, J.I.: Optimal contract pricing of distributed generation in distribution networks. IEEE Trans. Power Syst. 26(1), 128–136 (2011)
Ghosh, S., Ghoshal, S.P., Ghosh, S.: Optimal sizing and placement of distributed generation in a network system. Int. J. Electr. Power Energy Syst. 32(8), 849–856 (2010)
Wang, D.T.-C., Ochoa, L.F., Harrison, G.P.: Dg impact on investment deferral: network planning and security of supply. IEEE Trans. Power Syst. 25(2), 1134–1141 (2010)
Atwa, Y., El-Saadany, E., Salama, M., Seethapathy, R.: Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans. Power Syst. 25(1), 360–370 (2010)
Khodr, H., Silva, M.R., Vale, Z., Ramos, C.: A probabilistic methodology for distributed generation location in isolated electrical service area. Electr. Power Syst. Res. 80(4), 390–399 (2010)
Dent, C.J., Ochoa, L.F., Harrison, G.P.: Network distributed generation capacity analysis using opf with voltage step constraints. IEEE Trans. Power Syst. 25(1), 296–304 (2010)
Keane, A., O’Malley, M.: Optimal allocation of embedded generation on distribution networks. IEEE Trans. Power Syst. 20(3), 1640–1646 (2005)
Kim, J., Nam, S., Park, S., Singh, C.: Dispersed generation planning using improved hereford ranch algorithm. Electr. Power Syst. Res. 47(1), 47–55 (1998)
Gandomkar, M., Vakilian, M., Ehsan, M.: A combination of genetic algorithm and simulated annealing for optimal dg allocation in distribution networks. In: Canadian Conference on Electrical and Computer Engineering, 2005, pp. 645–648. IEEE (2005)
Acharya, N., Mahat, P., Mithulananthan, N.: An analytical approach for dg allocation in primary distribution network. Int. J. Electr. Power Energy Syst. 28(10), 669–678 (2006)
Murty, V., Kumar, A.: Optimal placement of dg in radial distribution systems based on new voltage stability index under load growth. Int. J. Electr. Power Energy Syst. 69, 246–256 (2015)
Borges, C.L., Falcao, D.M.: Optimal distributed generation allocation for reliability, losses, and voltage improvement. Int. J. Electr. Power Energy Syst. 28(6), 413–420 (2006)
Vatani, M., Alkaran, D.S., Sanjari, M.J., Gharehpetian, G.B.: Multiple distributed generation units allocation in distribution network for loss reduction based on a combination of analytical and genetic algorithm methods. IET Gener. Transm. Distrib. 10(1), 66–72 (2016)
Nguyen, T.T., Truong, A.V., Phung, T.A.: A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network. Int. J. Electr. Power Energy Syst. 78, 801–815 (2016)
Kowsalya, M., et al.: Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization. Swarm Evol. Comput. 15, 58–65 (2014)
Pesaran, M., Mohd Zin, A.A., Khairuddin, A., Shariati, O.: Optimal sizing and siting of distributed generators by a weighted exhaustive search. Electr. Power Compon. Syst. 42(11), 1131–1142 (2014)
Nazari-Heris, M., Mohammadi-Ivatloo, B.: Application of heuristic algorithms to optimal pmu placement in electric power systems: An updated review. Renew. Sustain. Energy Rev. 50, 214–228 (2015)
Nazari-Heris, M., Mohammadi-Ivatloo, B., Gharehpetian, G.: Short-term scheduling of hydro-based power plants considering application of heuristic algorithms: a comprehensive review. Renew. Sustain. Energy Rev. 74, 116–129 (2017)
Mahdi, F.P., Vasant, P., Rahman, M.M., Abdullah-Al-Wadud, M., Watada, J., Kallimani, V.: Quantum particle swarm optimization for multiobjective combined economic emission dispatch problem using cubic criterion function. In: 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 1–5. IEEE (2017)
Mohammadi-Ivatloo, B., Moradi-Dalvand, M., Rabiee, A.: Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electr. Power Syst. Res. 95, 9–18 (2013)
Yuan, X., Wang, L., Yuan, Y.: Application of enhanced pso approach to optimal scheduling of hydro system. Energy Convers. Manag. 49(11), 2966–2972 (2008)
Yare, Y., Venayagamoorthy, G.K., Aliyu, U.: Optimal generator maintenance scheduling using a modified discrete pso. IET Gener. Transm. Distrib. 2(6), 834–846 (2008)
Mohammadi, M., Hosseinian, S., Gharehpetian, G.: Optimization of hybrid solar energy sources/wind turbine systems integrated to utility grids as microgrid (mg) under pool/bilateral/hybrid electricity market using pso. Solar Energy 86(1), 112–125 (2012)
Yu, X.-M., Xiong, X.-Y., Wu, Y.-W.: A pso-based approach to optimal capacitor placement with harmonic distortion consideration. Electr. Power Syst. Res. 71(1), 27–33 (2004)
Krueasuk, W., Ongsakul, W.: Optimal placement of distributed generation using particle swarm optimization. In: Proceedings of Power Engineering Conference in Australasian Universities, Australia. Citeseer (2006)
Moradi, A., Fotuhi-Firuzabad, M.: Optimal switch placement in distribution systems using trinary particle swarm optimization algorithm. IEEE Trans. Power Deliv. 23(1), 271–279 (2008)
Saravanan, M., Slochanal, S.M.R., Venkatesh, P., Abraham, P.S.: Application of pso technique for optimal location of facts devices considering system loadability and cost of installation. In: The 7th International Power Engineering Conference, IPEC 2005, pp. 716–721. IEEE (2005)
Zhao, B., Guo, C., Cao, Y.: A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Trans. Power Syst. 20(2), 1070–1078 (2005)
Moghaddas Tafreshi, S., Hakimi, S.: Optimal sizing of a stand-alone hybrid power system via particle swarm optimization (pso). In: International Power Engineering Conference, IPEC 2007, pp. 960–965. IEEE (2007)
Al-Kazemi, B., Mohan, C.: Discrete multi-phase particle swarm optimization. In: Information Processing with Evolutionary Algorithms, pp. 305–327. Springer (2005)
Yang, S., Wang, M.: A quantum particle swarm optimization. In: Congress on Evolutionary Computation, et al.: CEC2004, vol. 1, pp. 320–324. IEEE (2004)
Moore, P., Venayagamoorthy, G.K.: Evolving combinational logic circuits using a hybrid quantum evolution and particle swarm inspired algorithm. In: 2005 NASA/DoD Conference on Evolvable Hardware, 2005, pp. 97–102. Proceedings. IEEE (2005)
Sun, J., Feng, B., Xu, W.: Particle swarm optimization with particles having quantum behavior. In: Congress on Evolutionary Computation, CEC2004, vol. 1, pp. 325–331. IEEE (2004)
Sun, J., Xu, W., Feng, B.: A global search strategy of quantum-behaved particle swarm optimization. In: 2004 IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 111–116. IEEE (2004)
Sun, J., Lai, C.-H., Wu, X.-J.: Particle Swarm Optimisation: Classical and Quantum Perspectives. CRC Press (2011)
Lu, S., Sun, C., Lu, Z.: An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling. Energy Convers. Manag. 51(3), 561–571 (2010)
dos Santos Coelho, L., Mariani, V.C.: Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects. Energy Convers. Manag. 49(11), 3080–3085 (2008)
Jeong, Y.-W., Park, J.-B., Jang, S.-H., Lee, K.Y.: A new quantum-inspired binary pso: application to unit commitment problems for power systems. IEEE Trans. Power Syst. 25(3), 1486–1495 (2010)
Wang, J., Liu, Z., Lu, P.: Electricity load forecasting based on adaptive quantum-behaved particle swarm optimization and support vector machines on global level. In: International Symposium on Computational Intelligence and Design, ISCID’08, vol. 1, pp. 233–236. IEEE (2008)
Tian, S., Tuanjie, L.: Short-term load forecasting based on rbfnn and qpso. In: Power and Energy Engineering Conference, APPEEC 2009. Asia-Pacific. IEEE (2009)
Badawy, R., Heßler, A., Albayrak, S., Hirsch, B., Yassine, A.: Quantum-inspired evolution for smart building energy management in future power networks. In: EngOpt2014, p. 226 (2014)
Esmin, A.A., Lambert-Torres, G., De Souza, A.Z.: A hybrid particle swarm optimization applied to loss power minimization. IEEE Trans. Power Syst. 20(2), 859–866 (2005)
Ibrahim, A.A., Mohamed, A., Shareef, H., Ghoshal, S.P.: An effective power quality monitor placement method utilizing quantum-inspired particle swarm optimization. In: 2011 International Conference on Electrical Engineering and Informatics (ICEEI), pp. 1–6. IEEE (2011)
Sun, J., Fang, W., Palade, V., Wu, X., Xu, W.: Quantum-behaved particle swarm optimization with gaussian distributed local attractor point. Appl. Math. Comput. 218(7), 3763–3775 (2011)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Metropolis, N., Ulam, S.: The monte carlo method. J. Am. Stat. Assoc. 44(247), 335–341 (1949)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Nazari-Heris, M., Madadi, S., Pesaran Hajiabbas, M., Mohammadi-Ivatloo, B. (2018). Optimal Distributed Generation Allocation Using Quantum Inspired Particle Swarm Optimization. In: Hassanien, A., Elhoseny, M., Kacprzyk, J. (eds) Quantum Computing:An Environment for Intelligent Large Scale Real Application . Studies in Big Data, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-319-63639-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-63639-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63638-2
Online ISBN: 978-3-319-63639-9
eBook Packages: EngineeringEngineering (R0)