Distributed
generation (DG) is gaining in importance due to the growing demand for
electrical energy and the key role it plays in reducing actual energy losses,
lowering operating costs and improving voltage stability. In this paper, we
propose to inject distributed power generation into a distribution system while
minimizing active energy losses. This injection should be done at a grid node
(which is a point where energy can be injected into or recovered from the grid)
that will be considered the optimal node when total active losses in the radial
distribution system are minimal. The focus is on meeting energy demand using
renewable energy sources. The main criterion is the minimization of active
energy losses during injection. The method used is the algorithm of bee colony
(ABC) associated with Newtonian energy flow transfer equations. The method has
been implemented in MATLAB for optimal node search in IEEE 14, 33 and 57 nodes
networks. The active energy loss results of this hybrid algorithm were compared
with the results of previous searches. This comparison shows that the proposed
algorithm allows to have reduced losses with the power injected that we have
found.
References
[1]
Tajayouti, M. (2015) Electrical Network, Power Quality and Study of the Impact of PV Energy Injection Book. Mohammed V University, Rabat Higher School of Technology.
[2]
Dang, X.L. (2014) Contribution to the Study of Distributed PV/Storage Systems: Impact of Their Integration into a Fragile Network. Doctoral Thesis, ENS, Cachan.
[3]
Slimani, L. (2009) Contribution to the Application of Optimization by Metaheuristic Methods to the Optimal Power Flow in a Deregulated Electricity Environment. Doctoral Thesis, Batna University, Batna.
[4]
Hamza, L. (2017) Study of Integration of Decentralized Production in an Electrical Distribution Network. Master’s Thesis, Academic Kasdi Merbah Ouargla University, Ouargla.
[5]
Borges, C.L.T. and Falcao, D.M. (2006) Optimal Distributed Generation Allocation for Reliability, Losses and Voltage Improvement. Journal of Power and Energy Systems, 28, 413-420. https://doi.org/10.1016/j.ijepes.2006.02.003
[6]
Ochoa, L.F. and Harrison, G.P. (2011) Minimizing Energy Losses: Optimal Accommodation and Smart Operation of Renewable Distributed Generation. IEEE Transactions on Power Systems, 26, 198-205.
https://doi.org/10.1109/TPWRS.2010.2049036
[7]
El-Zonkoly, A.M. (2011) Optimal Placement of Multidistributed Generation Units Including Different Load Models Using Particle Swarm Optimization. Swarm and Evolutionary Computation, 1, 50-59. https://doi.org/10.1049/iet-gtd.2010.0676
[8]
Jain, N., et al. (2013) A Generalized Approach for DG Planning and Viability Analysis under Market Scenario. IEEE Transactions on Industrial Electronics, 60, 5075-5085. https://doi.org/10.1109/TIE.2012.2219840
[9]
Nadhir, K. (2013) Distributed Generation Location and Size Determination to Reduce Power Losses of a Distribution Feeder by Firefly Algorithm. International Journal of Advanced Science and Technology, 56, 61-72.
[10]
Karaboga, D. (2007) A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization, 39, 459-471. https://doi.org/10.1007/s10898-007-9149-x
[11]
Kumar, N., Singh Parmar, K.P. and Dahiya, S. (2012) A Genetic Algorithm Approach for the Solution of Economic Load Dispatch Problem. International Journal on Computer Science and Engineering, 4, 1063-1068.
[12]
Srinivasa, R. (2008) Optimization of Distribution Network Configuration for Loss Reduction Us in Artificial Bee Colony Algorithm. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 2, 1964-1970.
[13]
Ngueouele, Y.B. (2012) Modeling of Power Injections of a PV System on a Public Memory Network. Doctoral Thesis, International Institute 2IE.
[14]
Mladjao, M.M. (2017) Contribution to the Modeling and Optimization of Multi-Source and Multi-Charge Energy Systems. Doctoral Thesis, Université De Lorraine, Lorraine.
[15]
Loubière, P. (2016) Improvement of Optimization Metaheuristics Using Sensitivity Analysis. Doctoral Thesis, Paris-Est University, Paris.
[16]
Anurag, D. and Laxmi, S. (2017) Optimal Placement and Sizing of Distributed Generation Units for Power Loss Reduction Using Moth-Flame Optimization Algorithm. International Conference on Intelligent Computing, Instrumentation and Control Technologies, p. 1577.
[17]
Gargi, T., Anikumar, M. and Praghnesh, B. (2019) Optimal Sizing and Placement of Multiple Distributed Generators Using Teaching Learning Based Optimization Algorithm in Radial Distributed Network. 6th International Conference on Control, Decision and Information Technologies, Paris, 23-26 April 2019, 960.
[18]
Thanatchai, K. (2009) Simplified Newton-Raphson Power-Flow Solution Method, Power System Research Unit. School of Electrical Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon.
[19]
Busayo, H.A., Abdoulie, S.M. and Ahmed, S.T. (2017) An Improved Artificial Bee Colony Using Cultural Algorithm for Optimization Problem. International Journal of Computer Applications, 160, 14-18.
[20]
Dusan, T. (2009) Bee Colony Optimization (BCO). University of Belgrade, Belgrade.
[21]
Sandeep, K., Sharma, V.K. and Kumari, R. (2013) A Novel Hybrid Crossover Based Artificial Bee Colony Algorithm for Optimization Problem. International Journal of Computer Applications, 82, 18-25.
[22]
Sahoo, N.C. and Prasad, K. (2006) A Fuzzy Genetic Approach for Network Reconfiguration to Enhance Voltage Stability in Radial Distribution Systems. Energy Conversion and Management, 47, 3288-3306.
https://doi.org/10.1016/j.enconman.2006.01.004
[23]
Kansal, S., Kumar, V. and Tyagi, B. (2016) Hybrid Approach for Optimal Placement of Multiple DGs of Multiple Types in Distribution Networks. International Journal of Electrical Power & Energy Systems, 75, 226-235.
https://doi.org/10.1016/j.ijepes.2015.09.002
[24]
El-Fergany, A. (2015) Optimal Allocation of Multi-Type Distributed Generators Using Backtracking Search Optimization Algorithm. International Journal of Electrical Power & Energy Systems, 64, 1197-1205.
https://doi.org/10.1016/j.ijepes.2014.09.020
