Integration of renewable type of distributed generation (DG) system specifically solar and wind has provided multiple benefits including positive environmental impact. However, improper allocation may result in increased voltage fluctuations and system losses. In this paper, comparison of optimal allocation for different solar and wind DG scenarios based from loss reduction and voltage improvement was made. DG and load models were considered to determine their effect in the distribution network. Simulations were implemented to IEEE 37-bus test system using Genetic Algorithm in the Component Object Model interface of MATLAB and OpenDSS. The study shows that the integration of both solar and wind DGs with constant power factor lagging model resulted in the most significant improvement in a system with commercial load model.
Keywords: Optimization of wind and solar DGs, renewable distributed generation, solar DG modeling, wind DG modeling, genetic algorithm, MATLAB, OpenDSS[1] U.S. Energy Information Administration, “International Energy Outlook 2016,” [Online]. Available: https://www.eia.gov/outlooks/ieo/pdf/0484(2016).pdf. [Accessed: 14-Dec-2017].
[2] Department of Energy and Electric Power Industry Management Bureau, “Power Supply and Demand Highlights (January-June 2017),” [Online]. Available: https://www.doe.gov.ph/sites/default/files/pdf/electric_power/power_supply_demand_highlights_jan_jun_2017.pdf. [Accessed: 25-Aug-2018].
[3] K. B. Reddy, K. H. Reddy, and P. S. Babu, “Optimal Location and Size of Distributed Generations Using Kalman Filter Algorithm for Reduction of Power Loss and Voltage Profile Improvement,” International Journal of Engineering Research and Development, vol. 10, no. 9, pp. 19-28, 2014.
[4] T. J. Sahib, M. R. Ab. Ghani, Z. Jano, and I. H. Mohamed, “Optimum Allocation of Distributed Generation using PSO: IEEE Test Case Studies Evaluation,” International Journal of Applied Engineering Research, vol. 12, no. 11, pp. 2900–2906, 2017.
[5] D. Rama Prabha and T. Jayabarathi, “Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm,” Ain Shams Engineering Journal, vol. 7, no. 2, pp. 683–694, 2016.
[6] M. Shahzad, I. Ahmad, W. Gawlik, and P. Palensky, “Load Concentration Factor Based Analytical Method for Optimal Placement of Multiple Distribution Generators for Loss Minimization and Voltage Profile Improvement,” Energies , vol. 9, no. 4, pp. 1-21, 2016.
[7] P. Sharma and A. Tandon, “Techniques for optimal placement of DG in radial distribution system: A review,” in 2015 Communication, Control and Intelligent Systems (CCIS), 2015, pp. 453–458.
[8] IRENA, “Renewable Power Generation Costs in 2017,” International Renewable Energy Agency, 2018. [Online]. Available: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Jan/IRENA_2017_Power_Costs_2018.pdf. [Accessed: 02-Feb-2019].
[9] F. Rezaei and S. Esmaeili, “Decentralized reactive power control of distributed PV and wind power generation units using an optimized fuzzy-based method,” International Journal of Electrical Power & Energy Systems, vol. 87, pp. 27–42, 2017.
[10] G.V.N. Lakshmi, A.J. Laxmi, and V.V. Reddy, “Optimal Allocation and Sizing of Multiple Distributed Generators Using Genetic Algorithm,” in International Conference on Advances in Communication, Network, and Computing, 2014, pp. 305-312.
[11] S. Remha, S. Chettih, and S. Arif, “Optimal placement of different DG units type in distribution networks based on voltage stability maximization and minimization of power losses,” in 2016 8th International Conference on Modelling, Identification and Control (ICMIC), 2016, pp. 867–873.
[12] J. K. L. Caasi and R. A. Aguirre, “Comparative analysis of the optimal siting and sizing on different solar distributed generation models through stochastic method,” in 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016, pp. 485–490.
[13] L. D. T. Narcise and R. A. Aguirre, “Comparative analysis of optimal allocation for different wind distributed generation models using stochastic approach,” in 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016, pp. 46–51.
[14] H. Nasiraghdam and S. Jadid, “Load model effect assessment on optimal distributed generation (DG) sizing and allocation using improved harmony search algorithm,” in 2013 Smart Grid Conference (SGC), 2013, pp. 210–218.
[15] D. Singh, D. Singh, and K. S. Verma, “Multiobjective Optimization for DG Planning With Load Models,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 427–436, 2009.
[16] H. Qamar, H. Qamar, A. Vaccaro, and N. Ahmed, “Reactive power control for voltage regulation in the presence of massive pervasion of distributed generators,” in 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2017, pp. 1–5.
[17] Y. M. Atwa, E. F. El-Saadany, M. M. A. Salama, and R. Seethapathy, “Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 360–370, 2010.
[18] A. Ellis, R. Nelson, E. Von Engeln and R. Walling, “Reactive power performance requirements for wind and solar plants,” in 2012 IEEE Power and Energy Society General Meeting, 2012, pp. 1–8.
[19] G. J. Shirek and B. A. Lassiter, “Solar plant modeling impacts on distribution systems PV case study,” in 2012 Rural Electric Power Conference, 2012, pp. B5-1-B5-10.
[20] Y. Zhang, S. Zhu, R. Sparks, and I. Green, “Impacts of solar PV generators on power system stability and voltage performance,” in 2012 IEEE Power and Energy Society General Meeting, 2012, pp. 1–7.
[21] M. Alotaibi, A. Almutairi, and M. M. A. Salama, “Effect of wind turbine parameters on optimal DG placement in power distribution systems,” in 2016 IEEE Electrical Power and Energy Conference (EPEC), 2016, pp. 1–4.
[22] P. Wais, “Two and three-parameter Weibull distribution in available wind power analysis,” Renewable Energy, vol. 103, pp. 15–29, 2017.
[23] V. Gupta, S. R. Donepudi, and N. Subrahmanyam, “Optimal placement of distributed generators in distribution system using backtracking search optimization for various load models,” in 2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), 2015, pp. 350–354.
[24] Electric Power Research Institute, “Simulation Tool - Open DSS,” [Online]. Available: http://smartgrid.epri.com/SimulationTool.aspx. [Accessed: 20-April-2018].
[25] G. Lindfield and J. Penny, Introduction to Nature-Inspired Optimization, Academic Press, 2017.
[26] K. Deep and M. Thakur, “A new mutation operator for real coded genetic algorithms,” Applied Mathematics and Computation, vol. 193, no. 1, pp. 211–230, 2007.
[27] W. H. Kersting, “Radial distribution test feeders,” IEEE Transactions on Power Systems, vol. 6, no. 3, pp. 975–985, 1991.
[28] K. Corfee, G. Stevens, and S. Goffri, “Distributed Generation Resource Assessment for Long-Term Planning Study,” [Online]. Available: http://www.pacificorp.com/content/dam/pacificorp/doc/Energy_Sources/Integrated_Resource_Plan/2015IRP/2015IRPStudy/Navigant_Distributed-Generation-Resource-Study_06-09-2014.pdf. [Accessed: 25-June-2018].
[29] Astronergy, “Datasheet Crystalline PV Module ASM6610P Series,” [Online]. Available: http://www.astronergy.com/attch/product/ASM6610P_US201507.pdf. [Accessed: 12-Mar-2018].