Multi-objective distributed generation penetration planning with load model using particle swarm optimization
Abstract
The paper presents an approach for simultaneous optimization of Distributed Generation (DG) penetration level and network performance index to obtain the optimal numbers, sites, and sizes of DG units. Two objective functions are formulated. These are: (II) DG penetration level, (II) network performance index. The minimization of the first objective reduces the capital investment cost of a distribution network owner (DNO) to integrate DG. The minimization of the second objective helps in reduction of network losses and improvement in node voltage profile and line loading. The solution approach provides a set of non-dominated solutions with different values of DG penetration level and network performance index. Thus, it offers more flexibility to a DNO to choose a final solution from the set of solutions according to its strategic decisions, regulatory directives, and budget restrictions. The solution approach used is multi-objective particle swarm optimization. The approach is validated on a 38-node distribution system. The results are compared with some existing approaches
Downloads
References
Bollen, M., & Hassan F. (2011). Integration of distributed generation in the power system. (1. ed.). New York, USA: Wiley- Institute of electrical and electronics engineers press Publishing. DOI: https://doi.org/10.1002/9781118029039
Carpinelli, G., Celli, G., Mocci, S., Pilo, F., & Russo, A. (2005). Optimisation of embedded generation sizing and siting by using a double trade-off method. IEE Proceeding -Generation, Transmission and Distribution, 152(4), 503–513. DOI: https://doi.org/10.1049/ip-gtd:20045129
Celli, G., Ghiani E., Mocci, S., & Pilo, F. (2005). A multiobjective evolutionary algorithm for the sizing and siting of distributed generation. Institute of electrical and electronics engineers Transmission Power Systems, 20(2), 750–757. DOI: https://doi.org/10.1109/TPWRS.2005.846219
Chiradeja, P., Ramakumar, R. (2004). An approach to quantify the technical benefits of distributed generation. Institute of electrical and electronics engineers Transcations on Energy Conversion, 19(4), 764–773. DOI: https://doi.org/10.1109/TEC.2004.827704
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. In Deb, K. Wiley Interscience Series in Systems and Optimization (pp. 501-525). New York, USA: John Wiley Publishing.
De-Souza, B.A., & De-Albuquerque, J. M. C. (2006). Optimal placement of distributed generators networks using evolutionary programming. 2006 Institute of electrical and electronics engineers /PES Transmission and Distribution Conference and Exposition, 1-6. DOI: https://doi.org/10.1109/TDCLA.2006.311571
El-Zonkoly, A. M. (2011). Optimal placement of multi-distributed generation units including different load models using particle swarm optimization. IET Generation Transmission Distribution, 5(7), 760–771. DOI: https://doi.org/10.1049/iet-gtd.2010.0676
Ganguly, S., Sahoo, N. C., & Das, D. (2011). Mono and multi-objective planning of electrical distribution networks using particle swarm optimization. Applied Soft Computing, 11, 2391–2405. DOI: https://doi.org/10.1016/j.asoc.2010.09.002
Li. X. (2003). A nondominated sorting particle swarm optimizer for multiobjective optimization. In Tandish, R., Kendall, G., Wilson, S. (Eds.), Genetic and Evolutionary Computation — GECCO 2003: Genetic and Evolutionary Computation Conference Chicago, IL, USA, Part I (37–48). Berlin: Springer. DOI: https://doi.org/10.1007/3-540-45105-6_4
Mantway A. H., & Al-Muhaini, M. M. (2008). Multi-objective BPSO algorithm for distribution system expansion planning including distributed generation. 2008 Institute of electrical and electronics engineers /PES Transmission and Distribution Conference and Exposition, 1–8. DOI: https://doi.org/10.1109/TDC.2008.4517034
Ochoa, L.F., Feltrin, A. P., & Harrison, G. P. (2006). Evaluating distributed generation impacts with a multiobjective index. Institute of electrical and electronics engineers Transcations on Power Delivery, 21(3), 1452–1458. DOI: https://doi.org/10.1109/TPWRD.2005.860262
Pecas Lopes, J. A., Hatziargyriou, N., Mutale, J., Djapic, P., & Jenkins, N. (2007). Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities. Electric Power Systems Research, 77, 1189–1203. DOI: https://doi.org/10.1016/j.epsr.2006.08.016
Sahoo, N. C., Ganguly, S., & Das, D. (2011). Simple heuristics-based selection of guides for multi-objective PSO with an application to electrical distribution system planning. Engineering Applications of Artificial Intelligence, 24(4), 567–585. DOI: https://doi.org/10.1016/j.engappai.2011.02.007
Singh D., Singh D., & Verma K. S. (2009). Multiobjective optimization for DG planning with load models. Institute of electrical and electronics engineers Transcations on Power Systems, 24(1), 427–436. DOI: https://doi.org/10.1109/TPWRS.2008.2009483
Singh, D., Misra, R. K., & Singh, D. (2007). Effect of load models in distributed generation planning. Institute of electrical and electronics engineers Transcations on Power Systems, 22(4), 2204–2212. DOI: https://doi.org/10.1109/TPWRS.2007.907582
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich, 4-18.
