An intelligent alternating current-optimal power flow for reduction of pollutant gases with incorporation of variable generation resources
DOI:
https://doi.org/10.17159/2413-3051/2020/v31i1a7008Keywords:
combined economic and emission dispatch, modified constricted coefficient particle swarm optimisation, metaheuristic optimal power flow, variable generation resourcesAbstract
Frequent escalations in fuel costs, environmental concerns, and the depletion of non-renewable fuel reserves have driven the power industry to significant utilisation of renewable energy resources. These resources cannot satisfy the entire system load demand because of the intermittent nature of variable generation resources (VGRs) such as wind and solar. Therefore, there is a need to optimally schedule the generating units (thermal and VGRs) to reduce the amount of fuel used and the level of emissions produced. In this study, an AC-power flow in conjunction with combined economic and environmental dispatch approach through the implementation of a modified constricted coefficient particle swarm optimisation was used to minimise the fuel cost and the level of emission gases produced. The approach was applied to the Institute of Electric and Electronic Engineers 30 bus test system through three different load conditions: base-load, increase-load and critical-load. The results showed the practicality of the proposed approach for the simultaneous reduction of the total generation cost and emission levels on a large electrical power grid while maintaining all the physical and operational constraints of the system.
Downloads
References
Goudarzi, A., Li, Y., & Xiang, J. 2020. A hybrid non-linear time-varying double-weighted particle swarm opti-mization for solving non-convex combined environmental economic dispatch problem. Applied Soft Compu-ting, 86, 105894. https://doi.org/10.1016/j.asoc.2019.105894.
Goudarzi, A., Swanson, A. G., Tooryan, F., & Ahmadi, A. 2017. Non-convex optimization of combined envi-ronmental economic dispatch through the third version of the cultural algorithm. IEEE Texas Power and En-ergy Conference. https://doi.org/10.1109/tpec.2017.7868281.
Blumsack, S. 2018. Variable energy resources and three economic challenges. The Pennsylvania State Univer-sity. [Online]. Available: https://www.e-education.psu.edu/eme801/node/539. [Accessed 16 July 2018].
Goudarzi, A., Viray, Z. N. C., Siano, P., Swanson, A. G., Coller, J. V., & Kazemi, M. 2017. A probabilistic deter-mination of required reserve levels in an energy and reserve co-optimized electricity market with variable generation. Energy, 130, 258–275. https://doi.org/10.1016/j.energy.2017.04.145.
Velaga, S. & Padma, k. 2013. Combined economic and emission dispatch using multi-objective particle swarm optimization with svc installation. International Journal of Advanced Computer Research, 3(11): 13 – 18. https://doi.org/10.1.1.405.9669.
Monteiro, C., Bessa, R., Miranda, V., Botterud, A., Wang. J., & Conzelmann, G. 2009. Wind power forecasting: state-of-the-art. Decision and Information Services Division, Argonne National Laboratory. 1 – 216. https://doi.org/10.2172/968212.
Borhanazad, H., Mekhilef, S., Gounder Ganapathy, V., Modiri-Delshad, M., & Mirtaheri, A. 2014. Optimization of micro-grid system using mopso. Renewable Energy, 71, 295–306. https://doi.org/10.1016/j.renene.2014.05.006.
Makhloufi, S., Mekhaldi, A., Teguar, M., Koussa, D.S. & Djoudi, A. 2013. Optimal power flow solution includ-ing wind power generation into isolated adrar power system using psogsa, Revue des Energies Renouvelables, 16(4): 721 – 732. https://www.semanticscholar.org/.
Abuella, M. A. & Hatziadoniu, C. J. 2015. The economic dispatch for integrated wind power systems using particle swarm optimization. IEEE Conference in Charlotte, 1 – 6. https://arxiv.org/pdf/1509.01693.
Suresh, V. & Suresh, S. 2015. Economic dispatch and cost analysis on a power system network interconnected with solar farm. International Journal of Renewable Energy Research, 5(4):1099 – 1105. https://www.ijrer.org/.
ElDesouky, A. A. 2013. Security and stochastic economic dispatch of power system including wind and solar resources with environmental consideration. International Journal of Renewable Energy Research, 3(4): 951 – 958. https://www.ijrer.org/.
Saxena, N., & Ganguli, S. 2015. Solar and wind power estimation and economic load dispatch using firefly algorithm. Procedia Computer Science, 70, 688–700. https://doi.org/10.1016/j.procs.2015.10.106.
Saadat, H. 1999. Power System Analysis. New York, WCB/McGraw-Hill. https://www.mheducation.com/.
Sereeter, B., Vuik, C., & Witteveen, C. 2019. On a comparison of Newton–Raphson solvers for power flow problems. Journal of Computational and Applied Mathematics, 360, 157–169. https://doi.org/10.1016/j.cam.2019.04.007.
Aslam, M. U., Cheema, M. U., Samran, M., & Cheema, M. B. 2014. Optimal power flow based upon genetic algorithm deploying optimum mutation and elitism. The 1st International Conference on Information Technolo-gy, Computer, and Electrical Engineering. https://doi.org/10.1109/icitacee.2014.7065767.
Goudarzi, A., Ahmadi, A., Swanson, A. G., & Van Coller, J. 2016. Non-convex optimisation of combined envi-ronmental economic dispatch through cultural algorithm with the consideration of the physical constraints of generating units and price penalty factors. SAIEE Africa Research Journal, 107(3), 146–166. https://doi.org/10.23919/saiee.2016.8532239.
Pranava, G., & Prasad, P. V. 2013. Constriction coefficient particle swarm optimization for economic load dis-patch with valve point loading effects. International Conference on Power, Energy and Control. https://doi.org/10.1109/icpec.2013.6527680.
Fahad, S., Mahdi, A. J., Tang, W. H., Huang, K., & Liu, Y. 2018. Particle swarm optimization based dc-link volt-age control for two stage grid connected pv inverter. International Conference on Power System Technology. https://doi.org/10.1109/powercon.2018.8602128.
Gharib, A., Benhra, J. & Chaouqi, M. 2018. A performance comparison of genetic algorithm and particle swarm optimization applied to tsp. International Journal of Recent Trends in Engineering and Research, 4(4), 529–536. https://doi.org/10.23883/ijrter.2018.4270.s3bvz.
Caboz, J. 2018. These are the 5 biggest green energy projects in SA – all wind farms. Business Inside. [Online]. Available: https://www.businessinsider.co.za/5-massive-new-renewable-energy-projects-that-transformed-south-africas-landscape-2018-4. [Accessed 7 September 2018].
Suzlon Energy Limited. Suzlon powering a greener tomorrow. classic fleet, [Online]. Available: https://www.suzlon.com/in-en/energy-solutions/classic-fleet-wind-turbines. [Accessed 17 September 2018].
The Nordex Group. AW3000 – Nordex. [Online]. Available: http://www.nordex-online.com/fileadmin/MEDIA/AW/AW3000_oct17_EU-EN.pdf&ved=2ahUKEwiVhJCIyffdAhUDXsAKHQfzCvMQFjAAegQIAhAB&usg=AOvVaw0oLQyNN65v-IWmyamss9Kz. [Accessed 8 September 2018].
The Nordex Group. Nordex: N117/3000. [Online]. Available: www.nordex-online.com/en/produkte-service/wind-turbines/n117-30-mw.html. [Accessed 8 September 2018].
South African Wind Energy Association. Wind energy. [Online]. Available: www.sawea.org.za. [Accessed 10 September 2018].
Southern African Universities Radiometric Network. Solar radiometric data for the public sauran, Durban. http://www.sauran.net/ShowStation.aspx?station=2.
Aryal, A., & Bhattarai, N. 2018. Modelling and simulation of 115.2 kwp grid-connected solar pv system using pvsyst. Kathford Journal of Engineering and Management, 1(1), 31–34. https://doi.org/10.3126/kjem.v1i1.22020.
Oubbati, Y., Mohammed, A. & Arif, S. 2016. Improved pso applied to the optimal power flow with transient stability constraints. Journal of Electrical Systems, 12(4): 672 – 686. https://creativecommons.org/licenses/by-nc/4.0/.
Reddy, S. S., & Momoh, J. A. 2016. Minimum emission dispatch in an integrated thermal and wind energy conservation system using self-adaptive differential evolution. IEEE PES Power Africa. https://doi.org/10.1109/powerafrica.2016.7556615.
Panda, S.R. 2013. Distributed slack bus model for qualitative economic load dispatch. National Institute of Technology, Rourkela, 1 – 45. https://www.semanticscholar.org/.
Augusteen, W. A., Geetha, S., & Rengaraj, R. 2016. Economic dispatch incorporation solar energy using parti-cle swarm optimization. 3rd International Conference on Electrical Energy Systems. https://doi.org/10.1109/icees.2016.7510618.
Garnham, B. L. 2016. Mercury emissions from South Africa’s coal-fired power stations. Clean Air Journal, 26(2), 14–20. https://doi.org/10.17159/2410-972x/2016/v26n2a8.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Sumant Lalljith, Andrew G. Swanson, Arman Goudarzi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png){kind=spacer}