A Micro Energy Grid Optimal Design and Economic Operation Using Genetic Algorithms

Ahmed Eldessouky; Abdallah Fahmy

doi:10.38032/jea.2023.03.005

Authors

Ahmed Eldessouky Faculty of Energy and Environment Engineering, BUE, Cairo, Egypt
Abdallah Fahmy Canadian International College, Cairo, Egypt

DOI:

https://doi.org/10.38032/jea.2023.03.005

Keywords:

Micro Energy Grid, Energy Grid Operation, Optimal Structure of Energy Grid, Genetic Algorithms, Renewable Energy Sources

Abstract

This paper presents an optimal design procedure and economic operational scheduling of micro energy grids (MEGs). The optimization objectives are to minimize cost, carbon dioxide emissions, and energy deficiency. The energy sources and conversion technologies included in this study are renewable-based sources (wind and photovoltaic), a furnace, an electrical heater, a main power grid, and a local power station. Two proposed control levels are applied to control the operation of the MEG. The supervisor control level selects the energy supplier based on price and/or availability. The inner control level dynamically matches the demand profile with the supply profile. The control loops guarantee dynamic matching between the demand profile and supply profile. Two scenarios are simulated, zero interest rates and 5.25% interest rates. The results showed renewables contribute with a significant share as an energy source, however, higher interest rates would negatively impact this contribution. It also confirms that carbon taxes can reduce the use of fossil fuels as an energy source.

References

Eckhart M., El-Ashry M., Hales D., Hamilton K., and Rae P., 2020 "Renewables, Global Status report," REN21.

Eckhart M., El-Ashry M., Hales D., Hamilton K., and Rae P., 2018 "Renewables, Global Status report," REN21.

"Summary Report: 2012 DOE Microgrid Workshop.," 2012.

Parhizi, S., Lotfi, H., Khodaei, A. and Bahramirad, S., 2015. State of the art in research on microgrids: A review. IEEE access, 3, pp.890-925. DOI: https://doi.org/10.1109/ACCESS.2015.2443119

Ma, T., Wu, J. and Hao, L., 2017. Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub. Energy conversion and management, 133, pp.292-306. DOI: https://doi.org/10.1016/j.enconman.2016.12.011

Gabbar, H.A. and Zidan, A., 2016. Optimal scheduling of interconnected micro energy grids with multiple fuel options. Sustainable Energy, Grids and Networks, 7, pp.80-89. DOI: https://doi.org/10.1016/j.segan.2016.06.006

Han, Y. and Shen, P., 2014. Modeling, control and electromagnetic transient simulation of the doubly fed induction generator-based wind energy generation system. Simulation, 90(3), pp.275-289. DOI: https://doi.org/10.1177/0037549713516516

Katzenbach, R., Clauss, F. and Zheng, J., 2015, May. Potentials of sustainable energy management in buildings. In 2015 5th International Youth Conference on Energy (IYCE) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/IYCE.2015.7180832

Luque, A. and Hegedus, S. eds., 2011. Handbook of photovoltaic science and engineering. John Wiley & Sons. DOI: https://doi.org/10.1002/9780470974704

Jiang, X. and Xiao, C., 2019. Household energy demand management strategy based on operating power by genetic algorithm. IEEE Access, 7, pp.96414-96423. DOI: https://doi.org/10.1109/ACCESS.2019.2928374

Raghavan, A., Maan, P. and Shenoy, A.K., 2020. Optimization of day-ahead energy storage system scheduling in microgrid using genetic algorithm and particle swarm optimization. Ieee Access, 8, pp.173068-173078. DOI: https://doi.org/10.1109/ACCESS.2020.3025673

Teo, T.T., Logenthiran, T., Woo, W.L., Abidi, K., John, T., Wade, N.S., Greenwood, D.M., Patsios, C. and Taylor, P.C., 2020. Optimization of fuzzy energy-management system for grid-connected microgrid using NSGA-II. IEEE transactions on cybernetics, 51(11), pp.5375-5386. DOI: https://doi.org/10.1109/TCYB.2020.3031109

Moghaddam, A.A., Seifi, A., Niknam, T. and Pahlavani, M.R.A., 2011. Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source. energy, 36(11), pp.6490-6507. DOI: https://doi.org/10.1016/j.energy.2011.09.017

Díaz, G., Planas, E., Andreu, J. and Kortabarria, I., 2015. Joint cost of energy under an optimal economic policy of hybrid power systems subject to uncertainty. Energy, 88, pp.837-848. DOI: https://doi.org/10.1016/j.energy.2015.07.003

Zare, M., Niknam, T., Azizipanah-Abarghooee, R. and Ostadi, A., 2016. New stochastic bi-objective optimal cost and chance of operation management approach for smart microgrid. IEEE Transactions on Industrial Informatics, 12(6), pp.2031-2040. DOI: https://doi.org/10.1109/TII.2016.2585379

Kouloura, T.C., Genikomsakis, K.N. and Protopapas, A.L., Systemic assessment of measures for sustainable energy management in buildings: The case of a student dormitory, ECOS2006, Crete-Greece, July 2006. In Conference Proceedings (pp. 861-868).

Zidan, A., Gabbar, H.A. and Eldessouky, A., 2015. Optimal planning of combined heat and power systems within microgrids. Energy, 93, pp.235-244. DOI: https://doi.org/10.1016/j.energy.2015.09.039

International Energy Agency, 2022. Renewable Energy Market Update: Outlook for 2022 and 2023. OECD Publishing.

Spertino, F., Di Leo, P., Ilie, I.S. and Chicco, G., 2012. DFIG equivalent circuit and mismatch assessment between manufacturer and experimental power-wind speed curves. Renewable Energy, 48, pp.333-343. DOI: https://doi.org/10.1016/j.renene.2012.01.002

Wiser, R.H. and Bolinger, M., 2019. 2018 wind technologies market report. DOI: https://doi.org/10.2172/1559241

Siyal, S.H., Mentis, D. and Howells, M., 2016. Mapping key economic indicators of onshore wind energy in Sweden by using a geospatial methodology. Energy conversion and management, 128, pp.211-226. DOI: https://doi.org/10.1016/j.enconman.2016.09.055

Davis M., Sylvia L.M., Zoe G., Sagar C., Caitlin C., Matthew S., Matt I., Elissa P., and Chris S., "Solar Market Insight Report 2022 Year in Review," Solar Energy Industries Association (SEIA), Washington,, 2022.

REN21, P.R.S., 2022. Renewables 2022 global status report. Renewable Energy Policy Network for the 21st Century (REN21).

Feldman, D., Ramasamy, V., Fu, R., Ramdas, A., Desai, J. and Margolis, R., 2021. US solar photovoltaic system and energy storage cost benchmark (Q1 2020) (No. NREL/TP-6A20-77324). National Renewable Energy Lab.(NREL), Golden, CO (United States).

Tjengdrawira, C. and Richter, M., 2016. Review and Gap Analyses of Technical Assumptions in PV Electricity Cost Report on Current Practices in How Technical Assumptions Are Accounted in PV Investment Cost Calculation. No. Solar Bankability WP3 Deliverable D, 3.

Ufluoğlu, E.E. and Kayakutlu, G., 2016. Mathematical model for a microgrid consisting of wind turbine, PV panels, and energy storage unit. Journal of Renewable and Sustainable Energy, 8(5). DOI: https://doi.org/10.1063/1.4964309

Feldman, D., Ramasamy, V., Fu, R., Ramdas, A., Desai, J., and Margolis, R., 2021. U.S. Solar Photovoltaic System Cost Benchmark: Q1 2020. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-77324. DOI: https://doi.org/10.2172/1764908

How much does it cost to install an electric furnace? Fixr.com | Electric Furnace Cost | Electric Furnace Replacement Cost. Available at: https://www.fixr.com/costs/electric-furnace.

Canada, E. and C.C. (2021) Government of Canada, Canada.ca. Available at: https://www.canada.ca/en/environment-climate-change/services/climate-change/pricing-pollution-how-it-will-work/industry/pricing-carbon-pollution.html.

"Heating with electricity," Natural Resources Canada’s, Office of Energy Efficiency, 2003. https://natural-resources.canada.ca/sites/www.nrcan.gc.ca/files/energy/pdf/energystar/Heating_with_Electricity.pdf

NSRDB. Available at: https://nsrdb.nrel.gov/.