Enhancing Aggregator profitability in the Electricity Market using modern metaheuristic optimization techniques

Year 2025, Volume: 9 Issue: 4, 661 - 669, 08.10.2025

Sumit Banker , Jaydeep Chakravorty Chetan Bariya Tejal Chaudhary Mitesh Priyadarshi

Abstract

Exponentially increasing use of the DERs (Distributed Energy Resources) has also increased the complexity and problems associated to the power system. This research paper is a step towards the profit maximization of the aggregator, which is a representative entity on behalf of the consumers and prosumers. To enhance the profit of aggregators, the operating cost of the Electricity Market needs to be minimized. In this research paper metaheuristic optimization techniques, RCEDUMDA (Ring-Cellular Encode-Decode Univariate Marginal Distribution Algorithm) and CSO (Cuckoo Search Optimization) are implemented to solve the aggregator profit maximization problem. The system adopted for the problem is the IEEE 30-bus test system with the inclusion of the DERs and considering the optimal dispatch of generation within the system constraints limits. The framework opted for this paper incorporates the cost-based objective function, including the income from the electricity sales and generator operational cost. Both the metaheuristic optimization techniques are tested on the mentioned framework on the bases of convergence characteristics, computational efficiency, and solution quality. Simulation results reflect that the RCEDUMDA significantly outperforms CSO by 12% higher aggregator profit with 35.5% less convergence time and better stability. The results point out the potential of RCEDUMDA metaheuristic optimization technique in enhancing the aggregator decision making and profit in Electricity Market.

Keywords

Electricity market , Metaheuristic optimization , Aggregator , DERs , RCEDUMDA , CSO

References

• Vardhan, B. V. S., Khedkar, M., & Srivastava, I. (2022). Effective energy management and cost effective day ahead scheduling for distribution system with dynamic market participants. Sustainable Energy Grids and Networks, 31(100706), 100706. doi:10.1016/j.segan.2022.100706.
• Temiz, R., & Tür, M. R. (2024). Investment technique for ensuring energy supply continuity in ring grids. Turkish Journal of Engineering, 8(2), 186–195. doi:10.31127/tuje.1357643.
• Wen, X., Abbes, D., & Francois, B. (2021). Stochastic optimization for security-constrained day-ahead operational planning under PV production uncertainties: Reduction analysis of operating economic costs and carbon emissions. IEEE Access: Practical Innovations, Open Solutions, 9, 97039–97052. doi:10.1109/access.2021.3093653.
• Batar, M. (2022). Word-based game development on Android with an efficient graphical data structure. Turkish Journal of Engineering, 6(3), 256–261. doi:10.31127/tuje.987141.
• Bakir, H. (2024). Optimal power flow analysis with circulatory system-based optimization algorithm. Turkish Journal of Engineering, 8(1), 92–106. doi:10.31127/tuje.1282429.
• IEEE Staff, 2010 IEEE Power and Energy Society General Meeting. IEEE, 2010.
• Mochi, P., Pandya, K. S., Faia, R., Dabhi, D., Soares, J., & Vale, Z. (2023). Empowering customers in local electricity market: A prosumer segmentation and operating envelope strategy for joint cost reduction and profit maximization. Electric Power Systems Research, 226, 109908. https://doi.org/10.1016/j.epsr.2023.109908
• Mochi, P., Pandya, K. S., Faia, R., & Soares, J. (2023). Prosumer segmentation Strategies for local electricity market partaking by monetary reward. IEEE Access, 11, 62879–62891. https://doi.org/10.1109/access.2023.3287323.
• El-Qasery, M., Abbou, A., Laamim, M., Id-Khajine, L., & Rochd, A. (2025). Comparative Analysis of GA and PSO Algorithms for Optimal Cost Management in On-Grid Microgrid Energy Systems with PV-Battery Integration. Global Energy Interconnection. https://doi.org/10.1016/j.gloei.2025.05.003.
• Mwitia, S. M., & Segera, D. R. (2022). An aggressive cuckoo search algorithm for optimum power allocation in a CDMA-Based cellular network. The Scientific World JOURNAL, 2022, 1–30. https://doi.org/10.1155/2022/5443160
• Rodríguez-González, A. Y., Aranda, R., Álvarez-Carmona, M. Á., Martínez-López, Y., & Madera-Quintana, J. (2022, July 9). Applying ring cellular encode-decode UMDA to risk-based energy scheduling. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Presented at the GECCO ’22: Genetic and Evolutionary Computation Conference, Boston Massachusetts. doi:10.1145/3520304.3534055.
• Banker, S. (2024). Metaheuristic optimization algorithms comparison adopted for the profit maximization of electricity market participants. Journal of Electrical Systems, 20(6s), 1032–1042. doi:10.52783/jes.2835.
• Tang, Z., Hill, D. J., & Liu, T. (2020). Distributed control of active distribution networks to support voltage control in subtransmission networks. International Journal of Electrical Power & Energy Systems, 117(105715), 105715. doi:10.1016/j.ijepes.2019.105715.
• Alhaj Omar, F., Pamuk, N., & Kulaksiz, A. A. (2023). A critical evaluation of maximum power point tracking techniques for PV systems working under partial shading conditions. Turkish Journal of Engineering, 7(1), 73–81. doi:10.31127/tuje.1032674.
• Bayhan, B., & Arslan, G. (2018). Applicability of solar and wind energy technologies for a non-residential building. Turkish Journal of Engineering, 2(1), 27–34. doi:10.31127/tuje.341462.
• Eltamaly, A. M. (2021). An improved cuckoo search algorithm for maximum power point tracking of photovoltaic systems under partial shading conditions. Energies, 14(4), 953. doi:10.3390/en14040953.
• Harsh, P., & Das, D. (2022). Optimal coordination strategy of demand response and electric vehicle aggregators for the energy management of reconfigured grid-connected microgrid. Renewable and Sustainable Energy Reviews, 160(112251), 112251. doi:10.1016/j.rser.2022.112251.
• Elbaş, H., & Bilgin, T. T. (2025). Forecasting electricity demand in Türkiye: A comprehensive review of methods, determinants, and policy implications. Journal of Energy Systems, 9(1), 121–147. doi:10.30521/jes.1549293.
• Rammohan, S., Marathe, R. R., & Sudarsanam, N. (2022). Profitable market mechanism for platform-based aggregator taxi services. Transportation Research Interdisciplinary Perspectives, 16(100687), 100687. doi:10.1016/j.trip.2022.100687.
• Cancro, C., Delcea, C., Fabozzi, S., Ferruzzi, G., Graditi, G., Palladino, V., & Valenti, M. (2022). A profitability analysis for an aggregator in the ancillary services market: An Italian case study. Energies, 15(9), 3238. doi:10.3390/en15093238..
• Faqiry, M., Edmonds, L., Zhang, H., Khodaei, A., & Wu, H. (2017). Transactive-market-based operation of distributed electrical energy storage with grid constraints. Energies, 10(11), 1891. doi:10.3390/en10111891.
• Valova, R., & Brown, G. (2022). Distributed energy resource interconnection: An overview of challenges and opportunities in the United States. Solar Compass, 2(100021), 100021. doi:10.1016/j.solcom.2022.100021.
• Ongsakul, W., & Dieu, V. N. (2016). Artificial intelligence in power system optimization. doi:10.1201/b14906.
• Liu, G., Xu, Y., & Tomsovic, K. (2016). Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization. IEEE Transactions on Smart Grid, 7(1), 227–237. doi:10.1109/tsg.2015.2476669.
• Rodríguez-González, A. Y., Aranda, R., Álvarez-Carmona, M. Á., Martínez-López, Y., & Madera-Quintana, J. (2022, July 9). Applying ring cellular encode-decode UMDA to risk-based energy scheduling. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Presented at the GECCO ’22: Genetic and Evolutionary Computation Conference, Boston Massachusetts. doi:10.1145/3520304.3534055.
• Rodríguez-González, A. Y., Barajas, S., Aranda, R., Martínez-López, Y., & Madera-Quintana, J. (2021, July 7). Ring cellular encode-decode UMDA. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Presented at the GECCO ’21: Genetic and Evolutionary Computation Conference, Lille France. doi:10.1145/3449726.3463278.
• He, Q., Liu, H., Ding, G., & Tu, L. (2023). A modified Lévy flight distribution for solving high-dimensional numerical optimization problems. Mathematics and Computers in Simulation, 204, 376–400. doi:10.1016/j.matcom.2022.08.017.
• Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2013). Erratum to: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(2), 245–245. doi:10.1007/s00366-012-0308-4.
• Dey, B., Sharma, G., & Bokoro, P. N. (2024). A novel hybrid crow search arithmetic optimization algorithm for solving weighted combined economic emission dispatch with load-shifting practice. Algorithms, 17(7), 313. doi:10.3390/a17070313.
• Airoboman, A., Idris Araga, & Mohammad-Ashafa, J. (2024). Reliability Improvement of Distribution System Network using Network Reconfiguration. Engineering Applications, 3(3), 214–225. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/1581
• İncekara, Çetin Önder. (2023). Industrial internet of things (IIoT) in energy sector. Advanced Engineering Science, 3, 21–30. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/839
• Hida, A., Bualoti, R., & Qosja, P. (2024). Optimal design, cost analysis and impact of a tracked bifacial PV plant in distribution system. Advanced Engineering Science, 4, 65–75. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/1495
• Admasie, S., Song, J.-S., & Kim, C.-H. (2023). Optimal coordinated generation scheduling considering day‐ahead PV and wind power forecast uncertainty. IET Generation, Transmission and Distribution, 17(11), 2545–2562. doi:10.1049/gtd2.12868.
• Çeli̇k, İ., Yildiz, C., & Şekkeli̇, M. (2021). Wind power plant layout optimization using particle swarm optimization. Turkish Journal of Engineering, 5(2), 89–94. doi:10.31127/tuje.698856.
• Ahlawat, A., & Das, D. (2023). Optimal sizing and scheduling of battery energy storage system with solar and wind DG under seasonal load variations considering uncertainties. Journal of Energy Storage, 74(109377), 109377. doi:10.1016/j.est.2023.109377.
• Harizaj, M., & Bisha, I. (2023). Integration in internet of things of electric vehicle charging infrastructure. Engineering Applications, 2(2), 136–145. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/885
• Acar, A. ., & Kaygusuz, A. . (2022). Effect of energy storage on power system stability. Engineering Applications, 1(1), 91–98. Retrieved from https://publish.mersin.edu.tr/index.php/enap/article/view/321
• Yüksek, G., Muratoğlu, Y., & Alkaya, A. (2022). Modelling of supercapacitor by using parameter estimation method for energy storage system. Advanced Engineering Science, 2, 67–73. Retrieved from https://publish.mersin.edu.tr/index.php/ades/article/view/98
• Kaya, H. (2024). An analysis of market size trends forecasting and range prediction in Electric Vehicles using machine learning algorithms. Turkish Journal of Forecasting. doi:10.34110/forecasting.1485136.
• Koytsoumpa, E. I., Bergins, C., & Kakaras, E. (2018). Flexible operation of thermal plants with integrated energy storage technologies. Warme- Und Stoffubertragung [Heat and Mass Transfer], 54(8), 2453–2460. doi:10.1007/s00231-017-2148-7