Balkan Journal of Electrical and Computer Engineering

2147-284X 2147-284X

MUSA YILMAZ

10.17694/bajece.1529149

Electrical Engineering (Other)

Elektrik Mühendisliği (Diğer)

Grid Integration Strategies for Optimizing Renewable Energy Deployment and Grid Resilience

https://orcid.org/0000-0001-8639-8408

Aykut

Ercan

ISTANBUL GELISIM UNIVERSITY, FACULTY OF ENGINEERING-ARCHITECTURE, DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING

https://orcid.org/0009-0006-6746-1110

Alshuraida

Ihsan

ISTANBUL GELISIM UNIVERSITY

09 30 2024

12 3 247 254 08 07 2024 09 23 2024

2013

Balkan Journal of Electrical and Computer Engineering

This study explores the integration of renewable energy sources, namely, solar and wind, focusing on strategies to optimize their deployment into the electrical grid, and increasing the resiliency of the grid. Using four-year comprehensive data from Spain, including energy consumption, generation, pricing, and the condition of the weather, advanced statistical analysis, regression models, and optimization methods have been employed. Based on the results, it is clear that solar energy is seasonal, and wind energy is variable, with the weather playing a considerable role in the energy output. The optimization analysis showed that when the renewable capacity was increased to include 30 MW of solar and 120 MW of wind, the energy demand would be met at a significantly lower total system cost of $12.60 per unit. The costs related to operation and emissions would also decrease notably. However, with the regression models giving modest values of R² equal to 0.19 for solar and R² equal to 0.21 for wind, the extent of these developments and prediction can be fairly modest. Still, these results provide a strong backbone for the prediction of energy generation and show that modernization of the grid and adaptive management are of crucial importance. The results of the study could provide a guideline for policymakers and energy managers on how these goals can be achieved.

Renewable Energy Integration Grid Resilience Energy Forecasting Optimizing Strategies Sustainable Energy Infrastructure

[1] E. Papadis and G. Tsatsaronis, “Challenges in the decarbonization of the energy sector,” Energy, vol. 205, p. 118025, Aug. 2020, doi: 10.1016/j.energy.2020.118025.

[2] G. O. Atedhor, “Greenhouse gases emissions and their reduction strategies: Perspectives of Africa’s largest economy,” Scientific African, vol. 20, p. e01705, Jul. 2023, doi: 10.1016/j.sciaf.2023.e01705.

[3] O. Akar, Ü. K. Terzi̇, B. K. Tunçalp, and T. Sönmezocak, “Determination of the Optimum Hybrid Renewable Power System: A case study of Istanbul Gedik University Gedik Vocational School,” Balkan Journal of Electrical and Computer Engineering, vol. 7, no. 4, pp. 456–463, Oct. 2019, doi: 10.17694/bajece.623632.

[4] A. T. Hoang, V. V. Pham, and X. P. Nguyen, “Integrating renewable sources into energy system for smart city as a sagacious strategy towards clean and sustainable process,” Journal of Cleaner Production, vol. 305, p. 127161, Jul. 2021, doi: 10.1016/j.jclepro.2021.127161.

[5] K. Moustakas, M. Loizidou, M. Rehan, and A. S. Nizami, “A review of recent developments in renewable and sustainable energy systems: Key challenges and future perspective,” Renewable and Sustainable Energy Reviews, vol. 119, p. 109418, Mar. 2020, doi: 10.1016/j.rser.2019.109418.

[6] E. Aykut and Ü. K. Terzi, “Techno-economic and environmental analysis of grid connected hybrid wind/photovoltaic/biomass system for Marmara University Goztepe campus,” International Journal of Green Energy, vol. 17, no. 15, pp. 1036–1043, Dec. 2020, doi: 10.1080/15435075.2020.1821691.

[7] M. J. B. Kabeyi and O. A. Olanrewaju, “Sustainable Energy Transition for Renewable and Low Carbon Grid Electricity Generation and Supply,” Front. Energy Res., vol. 9, p. 743114, Mar. 2022, doi: 10.3389/fenrg.2021.743114.

[8] O. Çi̇Çek, M. A. M. Millad, and F. Erken, “ENERGY PREDICTION BASED ON MODELLING AND SIMULATION ANALYSIS OF AN ACTUAL GRID-CONNECTED PHOTOVOLTAIC POWER PLANT IN TURKEY,” European Journal of Technic, Dec. 2019, doi: 10.36222/ejt.593250.

[9] D. Mahmood, N. Javaid, G. Ahmed, S. Khan, and V. Monteiro, “A review on optimization strategies integrating renewable energy sources focusing uncertainty factor – Paving path to eco-friendly smart cities,” Sustainable Computing: Informatics and Systems, vol. 30, p. 100559, Jun. 2021, doi: 10.1016/j.suscom.2021.100559.

[10] D. Yang et al., “A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality,” Renewable and Sustainable Energy Reviews, vol. 161, p. 112348, Jun. 2022, doi: 10.1016/j.rser.2022.112348.

[11] Y. Lin, M. K. Anser, M. Y.-P. Peng, and M. Irfan, “Assessment of renewable energy, financial growth and in accomplishing targets of China’s cities carbon neutrality,” Renewable Energy, vol. 205, pp. 1082–1091, Mar. 2023, doi: 10.1016/j.renene.2022.11.026.

[12] J. Wang and W. Azam, “Natural resource scarcity, fossil fuel energy consumption, and total greenhouse gas emissions in top emitting countries,” Geoscience Frontiers, vol. 15, no. 2, p. 101757, Mar. 2024, doi: 10.1016/j.gsf.2023.101757.

[13] Erdiwansyah, Mahidin, H. Husin, Nasaruddin, M. Zaki, and Muhibbuddin, “A critical review of the integration of renewable energy sources with various technologies,” Prot Control Mod Power Syst, vol. 6, no. 1, p. 3, Dec. 2021, doi: 10.1186/s41601-021-00181-3.

[14] A. Razmjoo, L. Gakenia Kaigutha, M. A. Vaziri Rad, M. Marzband, A. Davarpanah, and M. Denai, “A Technical analysis investigating energy sustainability utilizing reliable renewable energy sources to reduce CO 2 emissions in a high potential area,” Renewable Energy, vol. 164, pp. 46–57, Feb. 2021, doi: 10.1016/j.renene.2020.09.042.

[15] S. Aslam, H. Herodotou, S. M. Mohsin, N. Javaid, N. Ashraf, and S. Aslam, “A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids,” Renewable and Sustainable Energy Reviews, vol. 144, p. 110992, Jul. 2021, doi: 10.1016/j.rser.2021.110992.

[16] L. Abualigah et al., “Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques,” Energies, vol. 15, no. 2, p. 578, Jan. 2022, doi: 10.3390/en15020578.

[17] C. Sweeney, R. J. Bessa, J. Browell, and P. Pinson, “The future of forecasting for renewable energy,” WIREs Energy & Environment, vol. 9, no. 2, p. e365, Mar. 2020, doi: 10.1002/wene.365.

[18] “Review of distribution network phase unbalance: Scale, causes, consequences, solutions, and future research direction,” CSEE JPES, 2020, doi: 10.17775/CSEEJPES.2019.03280.

[19] D. Rangel-Martinez, K. D. P. Nigam, and L. A. Ricardez-Sandoval, “Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage,” Chemical Engineering Research and Design, vol. 174, pp. 414–441, Oct. 2021, doi: 10.1016/j.cherd.2021.08.013.

[20] I. Alotaibi, M. A. Abido, M. Khalid, and A. V. Savkin, “A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources,” Energies, vol. 13, no. 23, p. 6269, Nov. 2020, doi: 10.3390/en13236269.

[21] A. Xie, H. Yang, J. Chen, L. Sheng, and Q. Zhang, “A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network,” Atmosphere, vol. 12, no. 5, p. 651, May 2021, doi: 10.3390/atmos12050651.

[22] S. Rahim and P. Siano, “A survey and comparison of leading-edge uncertainty handling methods for power grid modernization,” Expert Systems with Applications, vol. 204, p. 117590, Oct. 2022, doi: 10.1016/j.eswa.2022.117590.

[23] I. Diahovchenko, M. Kolcun, Z. Čonka, V. Savkiv, and R. Mykhailyshyn, “Progress and Challenges in Smart Grids: Distributed Generation, Smart Metering, Energy Storage and Smart Loads,” Iran J Sci Technol Trans Electr Eng, vol. 44, no. 4, pp. 1319–1333, Dec. 2020, doi: 10.1007/s40998-020-00322-8.

[24] O. A. Al-Shahri et al., “Solar photovoltaic energy optimization methods, challenges and issues: A comprehensive review,” Journal of Cleaner Production, vol. 284, p. 125465, Feb. 2021, doi: 10.1016/j.jclepro.2020.125465.

[25] A. Cosic, M. Stadler, M. Mansoor, and M. Zellinger, “Mixed-integer linear programming based optimization strategies for renewable energy communities,” Energy, vol. 237, p. 121559, Dec. 2021, doi: 10.1016/j.energy.2021.121559.

[26] J. Bistline et al., “Energy storage in long-term system models: a review of considerations, best practices, and research needs,” Prog. Energy, vol. 2, no. 3, p. 032001, Jul. 2020, doi: 10.1088/2516-1083/ab9894.

[27] M. Parvin, H. Yousefi, and Y. Noorollahi, “Techno-economic optimization of a renewable micro grid using multi-objective particle swarm optimization algorithm,” Energy Conversion and Management, vol. 277, p. 116639, Feb. 2023, doi: 10.1016/j.enconman.2022.116639.

[28] R. Das et al., “Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services,” Applied Energy, vol. 257, p. 113965, Jan. 2020, doi: 10.1016/j.apenergy.2019.113965.

[29] K. Ullah, G. Hafeez, I. Khan, S. Jan, and N. Javaid, “A multi-objective energy optimization in smart grid with high penetration of renewable energy sources,” Applied Energy, vol. 299, p. 117104, Oct. 2021, doi: 10.1016/j.apenergy.2021.117104.

[30] J. P. Painuly and N. Wohlgemuth, “Renewable energy technologies: barriers and policy implications,” in Renewable-Energy-Driven Future, Elsevier, 2021, pp. 539–562. doi: 10.1016/B978-0-12-820539-6.00018-2.

[31] R. M. Elavarasan et al., “A Comprehensive Review on Renewable Energy Development, Challenges, and Policies of Leading Indian States With an International Perspective,” IEEE Access, vol. 8, pp. 74432–74457, 2020, doi: 10.1109/ACCESS.2020.2988011.

[32] A. Demirtop and O. Sevli, “Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu,” Turkish Journal of Engineering, vol. 8, no. 3, pp. 524–536, Jul. 2024, doi: 10.31127/tuje.1431629.

[33] M. Madhiarasan, “Accurate prediction of different forecast horizons wind speed using a recursive radial basis function neural network,” Prot Control Mod Power Syst, vol. 5, no. 1, p. 22, Dec. 2020, doi: 10.1186/s41601-020-00166-8.