Multi-Deployment of Dispersed Power Sources Using RBF Neural Network

Yaser Soliman Qudaih, Takashi Hiyama

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DOI: 10.4236/epe.2010.24032                

Abstract

Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to assess the optimal DG size and deployment for more than one unit, taking the minimum losses and voltage profile as objective functions. A technique called radial basis function (RBF) neural network has been utilized for such target. The method is only depending on the training process; so it is simple in terms of algorithm and structure and it has fast computational speed and high accuracy; therefore it is flexible and reliable to be tested in different target scenarios. The proposed method is designed to find the best solution of multi- DG sizing and deployment in 33-bus IEEE distribution system and create the suitable topology of the system in the presence of DG. Some important results for DG deployment and discussion are involved to show the effectiveness of our proposed method.

Keywords

Dispersed Power Sources Deployment, RBF Neural Network, Power Losses Reduction

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	A. Keane and M. O’Malley, “Optimal Allocation of Embedded Generation on Distribution Networks,” IEEE Transactions on Power Systems, Vol. 20, No. 3, 2005, pp. 1640-1646.
[2]	C. Wang and M. H. Nehrir, “Analytical Approaches for Optimal Placement of Distributed Generation Sources in Power Systems,” IEEE Transactions on Power Systems, Vol. 19, No. 4, 2004, pp. 2068-2076.
[3]	N. Acharia, P. Mahat and N. Mithulananthan, “An Analytical Approach for DG Allocation in Primary Distribution Network,” Electrical Power and Energy Systems, Vol. 28, No. 10, 2006, pp. 669-678.
[4]	K. Nara, Y. Hayashi, B. Deng, K. Ikeda and T. Ashizawa, “Optimal Allocation of Dispersed Generators for Loss Minimization,” Electrical Engineering in Japan, Vol. 136, No. 2, 2001, pp. 1-8.
[5]	M. H. Narazi and M. Parniani, “Determining and Optimizing Power Loss Reduction in Distribution Feeders due to Distributed Generation,” Power Systems Conference and Exposition, IEEE PES, Atlanta, 2006, pp. 1914-1918.
[6]	M. A. Kashem, A. D. T. Le, M. Negnevitsky and G. Ledwich, “Distributed Generation for Minimization of Power Losses in Distribution Systems,” IEEE Power Engineering Society General Meeting, Art. No. 1709179, 2006.
[7]	A. D. T. Le, M. A. Kashem, M. Negnevitsky and G. Ledwich, “Optimal Distributed Generation Parameters for Reducing Losses with Economic Consideration,” IEEE Power Engineering Society General Meeting, Art. No. 4275824, 2007, pp. 1-8.
[8]	D. Gautam and N. Mithulananthan, “Optimal DG Placement in Deregulated Electricity Market,” Electric Power Systems Research, Vol. 77, No. 12, 2007, pp. 1627-1636.
[9]	C. L. T. Borges and D. M. Falca?o, “Optimal Distributed Generation Allocation for Reliability, Losses, and Voltage Improvement,” Electrical Power and Energy Systems, Vol. 28, No. 6, 2006, pp. 413-420.
[10]	D. Trebolle, T. Gómez, R. Cossent and P. Frías, “Distribution Planning with Reliability Options for Distributed Generation,” Electric Power Systems Research, Vol. 80, No. 2, 2010, pp. 222-229.
[11]	M. Fotuhi-Firuzabad and A. Rajabi-Ghahnavie, “An Ana- lytical Method to Consider DG Impacts on Distribution System Reliability,” Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, Art. No. 1547168, 2005, pp. 1-6.
[12]	I.-S. Bae, J.-O. Kim, J.-C. Kim and C. Singh, “Optimal Operating Strategy for Distributed Generation Considering Hourly Reliability Worth,” IEEE Transactions on Power Systems, Vol. 19, No. 1, 2004, pp. 287-292.
[13]	Y. Yue, Q. Kejun and Z. Chengke, “The Effect of Distributed Generation on Distribution System Reliability,” Proceedings of the Universities Power Engineering Conference, Art No. 4469070, 2007, pp. 911-916.
[14]	J. Morren, S. W. H. de Haan and J. A. Ferreira, “Contribution of DG Units to Primary Frequency Control,” European Transaction on Electrical Power, Vol. 16, No. 5, 2006, pp. 507-521.
[15]	V. van Thong, J. Driesen and R. Belmans, “Using Distributed Generation to Support and Provide Ancillary Services for the Power System,” International Conference on Clean Electrical Power, ICCEP’07, Art. No. 4272375, 2007, pp. 159-163.
[16]	S. Rhim, T. Rahman, I. Musirini, S. Azmi, M. Mohamed, M. Hussain, M. Faridun, “Comparing the Network Performance Between the Installation of DG and Compensating Capacitor Using EP,” International Journal of Power, Energy and Artificial Intelligence, Vol. 1, pp 14- 21.
[17]	H. N. Ng, M. M. A. Salama and A. Y. Chikhani, “Classification of Capacitor Allocation Techniques,” IEEE Transactions on Power Delivery, Vol. 15, No.1, 2008, pp. 387-392.
[18]	Y. T. Hsiao and C. Y. Chien, “Optimisation of Capacitor Allocation Using an Interactive Trade-off Method,” IEE Proceedings, Generation, Transmission and Distribution, Vol. 148, No. 4, 2001, pp. 371-374.
[19]	M. Delfanti, G. P. Granelli, P. Marannino and M. Montagna, “Optimal Capacitor Placement Using Deterministic and Genetic Algorithms,” IEEE Transactions on Power Systems, Vol. 15, No. 3, 2000, pp. 1041-1046.
[20]	Y. Qudaih and T. Hiyama, “Reconfiguration of Power Distribution System Using Multi Agent and Hierarchical Based Load Following Operation with Energy Capacitor System,” Proceedings of the 8th International Power Engineering Conference, Singapore, 2007, pp. 263-267.
[21]	V. Calderaro, A. Piccolo and P. Siano, “Maximizing DG Penetration in Distribution Networks by Means of GA Based Reconfiguration,” International Conference on Future Power Systems, Art. No. 1600510, 2005.
[22]	J. N. Fidalgo, J. A. F. M. Torres and M. Matos, “Fair Allocation of Distribution Losses Based on Neural Networks,” International Conference on Intelligent Systems Applications to Power Systems, Art. No. 4441685, 2007, pp. 1-6.
[23]	W. Prommee and W. Ongsakul, “Optimal Multi-Distributed Generation Placement by Adaptive Weight Particle Swarm Optimization,” International Conference on Control, Automation and Systems, Korea, 2008, pp. 1663- 1668.
[24]	A. Mellit and S. A. Kalogirou, “Artificial Intelligence Techniques for Photovoltaic Applications: A review,” Progress in Energy and Combustion Science, Vol. 34, No. 5, 2008, pp. 574-632.
[25]	A. Mellit, S. A. Kalogirou, L. Hontoria and S. Shaari, “Artificial Intelligence Techniques for Sizing Photovoltaic Systems: A review,” Renewable and Sustainable Energy Reviews, Vol. 13, No. 2, 2009, pp. 406-419.
[26]	F. Giraud and Z. M. Salameh, “Analysis of the Effects of a Passing Cloud on a Grid-Interactive Photovoltaic System with Battery Storage Using Neural Networks,” IEEE Transactions on Energy Conversion, Vol. 14, No. 4, 1999, pp. 1572-1577.
[27]	A. Mellit, M. Benghanem and S. A. Kalogirou, “An Adaptive Wavelet-Network Model for Forecasting Daily Total Solar-radiation,” Applied Energy, Vol. 83, No. 7, 2006, pp. 705-722.
[28]	Syafaruddin, E. Karatepe, T. Hiyama, “Feasibility of Artificial Neural Network for Maximum Power Point Estimation of Non Crystalline-Si Photovoltaic Modules,” The 15th ISAP Conference, Curitiba, 2009.
[29]	M. E. Baran and F.Wu, “Network Reconfiguration in Distribution System for Loss Reduction and Load Balancing,” IEEE Transactions on Power Delivery, Vol. 4, No. 2, 1989, pp. 1401-1407.
[30]	R. Srinivasa, S. V. L. Narasimham and M. Ramalingaraju, “Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm,” Proceedings of World Academy of Science, Engineering and Technology, Vol. 35, 2008, pp. 709-715.
[31]	Y. Qudaih and T. Hiyama, “Wealth in DG Diversity for Power Distribution System Operation Improvement,” Transmission & Distribution Conference & Exposition: Asia and Pacific, Dalian, 2009, pp. 1-4.
[32]	A. Mellit, M. Menghanem and M. Bendekhis, “Artificial Neural Network Model for Prediction Solar Radiation Data: Application for Sizing Standalone Photovoltaic Power System,” Proceedings of IEEE Power Engineering Society General Meeting, Vol. 1, 2005, pp. 40-44.