SUSTAINING THE DISTRIBUTION GRID NETWORK RELIABILITY WITH DISTRIBUTED WIND TURBINE GENERATIONS
DOI:
https://doi.org/10.11113/aej.v15.23220Keywords:
Active Power Loss, Distribution Network, Optimization Function, Reliability index, Wind TurbineAbstract
Distributed generations are practically operated at the rated maximum power output. The locking off and on into the grid network of the intermittent power generating units is on availability basis. There are significant integration challenges posed by wind power generation due to its intermittency nature which seriously affect power grid stability and reliability issues related to grid power quality and voltage profile. Several reliability indices can be established to assess the distribution network reliability; they include ASAI, SAIFI, AENS and EENS. This study had the main purpose of optimizing placement and sizing of wind distributed generations through the application of the Particle Swarm Optimization algorithm to attain voltage profile improvement as well as network reliability enhancement by power loss reduction for radial distribution network - IEEE 33-bus system. The proposed PSO-based algorithm adequacy was investigated using MATLAB simulation on a radial distribution network - IEEE standard 33-bus test system, with a case study of Genetic Algorithm being used for validation of the solution techniques and model developed. The RDN test system is connected to a total of 3.32 MW and 2.71 MVAr; both real and reactive loads respectively. Modelling of the wind power generation considered for grid integration was variable reactive power model. A cut-off wind speed of ≥ 6 m/s on average was considered for power generation. Results from the analysis yield 0.2115 p.u of average active power produced by the wind turbine generator. 206.87 kW and 139.13 kVAr were the APL and RPL initial network configurations obtained respectively at 0.83pf when no DG was integrated. When wind DG is integrated at optimal placement and capacity, the reduction of the overall network power loss is 68.12% and 61.43% for real and reactive power loss accordingly. The voltage profile improved by 8.17% on installation of wind DG. The network ASAI reliability index value before wind DG integration was 0.99743 p.u. which improved by 1.81% after their installation. In general, network power losses are minimized besides acceptable voltage profile being maintained for a sustained distribution system reliability when several wind DGs are integrated.
References
Banhthasit, B., C. Jamroen, and S. Dechanupaprittha. 2018. Optimal generation scheduling of power system for maximum renewable energy harvesting and power loss minimization: International journal of electrical and computer engineering (IJECE). 8(4): 1954-1966, ISSN: 2088-8740, DOI: 10.11591/ijece.v8i4.pp1954-1966.
Chandra, Y., and M. S. Kumari. 2012. Optimal placement of Multi DGs in Distributed System with Considering the Bus Available Limits. Energy and Power. 2: 13-21, DOI: 10.5923/j.ep.2012020.03.
Rohit, F., and S. B. Jitendra. 2015. Optimal placement of Multi DG in 33 Bus System. International Journal of Advance Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE). 4(4): 3758-3765.
Shadrack, O. O. 1996. Power Systems Generation Scheduling and Optimization Using Evolutionary Computation Techniques. Brunel University.
Rind, M. H., M. K. Rathi, A. A. Hashmani, and A. A. Lashari. 2019. Optimal placement and sizing of DG in Radial distribution system. Sindh university Research Journal (Science Series).v51(04): 653-660.
Pisica, I., C. Balac, and M. Eremia. 2014. Optimal distributed generation location and sizing using genetic algorithm. Research gate: IEEE Xplore.
Osborn, J., and C. Kawann. 2016. Reliability of the U.S. Electricity System: recent trends and current issues. U.S. Department of Energy.
Wikipedia. 2020. Intermittent energy source. Available https://en.wikipedia.org./wiki/Intermittent-energy-source. Retrieve date: April 15, 2023.
American Physical Society. 2010. Integrating Renewable Electricity on the Grid: https://www.aps.org/policy/reports/popa reports/upload/integratingelec-exsum. Retrieved date: May 13, 2023.
Peter, T. 2015. When the grid says ‘no’ to wind and solar power, this company’s technology helps it say ‘yes’ again; April 07, 12:30, PM EDT.
Eberhart, R. C., and Y. Shi. 2000. Comparing inertia weights and constraint factor in particle swarm optimization. Proceedings of the 2000 International Congress of Evaluation Computation. San Diego, California, IEEE Service Center, Piscataway: 84 – 88
Bernard, M. 2017. What is the impact of intermittent energy sources on the grid? https://www.quora.com/What-is-the-impact-of-intermittent-energy-sources-on-the-grid. Retrieved date: Sept 24, 2022.
Bird, L., M. Milliga, and D. Lew. 2013. Integrating variable renewable energy: Challenges and Solutions. National Renewable Energy Laboratory.
Craig, M., and C. Brancucci. 2021. Impact of variable renewable energy sources on bulk power system planning and operation. Handbook of energy economics and policy. US Department of Energy.
Curry, J. 2016. Renewables and grid reliability. http://www.renewables-and-grid-reliability. American Clean Power
Electric Power Research Institute. 2016. Making distribution grids stronger, more resilient. Epri Journal. 6: 1-8. https://eprijournal.com/simulating-future-grid-reliability
Febrizio, M. 2016. Why intermittent wind can never match power dispatched on demand. Institute for Energy Research.
Harnett, S. 2016. Optimization methods for power grid reliability. Columbia University Press.
Kahouli, O., H. Alsaif, Y. Bouteraa, N. B. Ali, and M. Chaabene. 2021. Power systems reconfiguration in distribution network for improving reliability using genetic algorithm and particle swarm optimization. Journal: Applied science. 11(7): 3092, DOI: 10.3390/app11073092.
Milligan, M., and B. Kirby. 2010. Characteristics for Effective Integration of Variable Generation in the Western Interconnection. NREL/TP-550-48192.
Nazir, M. S., F. Alturise, S. Alshmrany, H. M. J. Nazir, M. Bilal, N. Abdalla, P. Sanjeevikumar, and Z. M. Ali. 2020. Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend. 12: 3778, DOI: 10.3390/su12093778.www.mdpi.com/journal/sustainability.
Ravi, U., S. Amit, S. S. Ahmed, S. K. Goyal, and N. Kanwar. 2020. A Wind Farm Modelling in IEEE – 33 Bus Reliability Test System on DigSilent Power Factory. International Intelligent Engineering and Management-India.
] Sarita, K., S. Kumar, A. S. S. Vardhan, R. M. Elavarasan, R. K. Saket, and N. Das. 2020. Reliability Assessment of wind integrated distribution system using electrical loss minimization technique. Energies. 13(21): 5631-5650, DOI: 10.3390/en13215631.
Zonkoly, A. M. E. 2011. Optimal placement of multi-distributed generation units including different load models using particle swarm optimization. Institute of Engineering Technology Generation, Transmission & Distribution. 5(7): 760 – 771.
