Keywords
low power sources
multi-criteria decision-making
energy community
optimization
How to Cite
Abstract
The objective of this study is to establish a methodology for the equitable distribution of loads among participants in an energy community consisting of low-power renewable energy sources. This is achieved through the utilization of a modified resource allocation algorithm. The proposed approach effectively considers multiple criteria, limited initial information, and the need for flexible assignment and reprioritization of objectives and constraints. To achieve this, linguistic characteristics are employed. Furthermore, under specific conditions, the list of objective functions can be expanded to capture the diverse interests of actors in the electricity market, such as suppliers and operators. To validate the effectiveness of the proposed methodology, statistical data on the operation of renewable energy sources supplying electricity to consumers within a specific region was utilized. Optimal operating modes for these sources were investigated. The focal point of this research is to develop a tool that ensures the optimal functioning of the network comprising low-power sources. To attain this, optimization criteria encompassing technical, economic, and environmental aspects were employed to derive the optimal operating modes for the renewable energy community. Bibl. 8, fig. 11, tabl. 2.
References
Veremiichuk Y., Yarmoliuk O., Pustovyi A., Mahnitko A.,Zicmane I., Lomane T. Features of electricity distribu-tion using energy storage in solar photovoltaic structure. Latvian journal of Physics and Technical Sciences. 2020. No. 5. Pp. 48–59. DOI: https://doi.org/10.2478/lpts-2020-0024
Groppi D., Pfeifer А., Garcia D.A., Krajačić G., Duić N.A review on energy storage and demand side management solutions in smart energy islands. Renewable and Sustainable Energy Reviews. 2021. Vol. 135. No. 110183. Pp. 1–14. DOI: https://doi.org/10.1016/j.rser.2020.11018
Lowitzsch J., Hoicka C.E., Tulder van F.J. Renewable energy communities under the 2019 European Clean Energy Package – Governance model for the energy clusters of the future? Renewable and Sustainable Energy Re-views2020. Vol. 122. Art. 109489. DOI: https://doi.org/10.1016/j.rser.2019.109489
Zharkin A.F., Novskiy V.O., Popov V.A., Yarmoliuk O.S., Hawkar Ahmed Noory. Review of technologies for controlling the modes of operation of 6...20 kV electrical networks with distributed energy sources. Electronic Modeling. 2021. Vol. 43. No. 1. Pp. 46–66. DOI: https://doi.org/10.15407/emodel.43.01.046 (Ukr)
Reis I.F., GoncËalves I., Lopes M.A., Antunes C.H., Amulti-agent system approach to exploit demand-side flexibility in an energy community. Utilities Policy. 2020. Vol. 67. Pp. 101–114. DOI: https://doi.org/10.1016/j.jup.2020.101114
Gjorgievski V.Z., Cundeva S., Georghiou G.E. Social arrangements, technical designs and impacts of energy com-munities: A review. Renewable Energy. 2021. Vol. 169. Pp. 1138–1156. DOI: https://doi.org/10.1016/j.renene.2021.01.078
Ekel P., Galperin E. Box-triangular multiobjective linear programs for recourse allocation with application to load management and energy market problems. Mathematical and Computer Modelling. 2003. Vol. 37 (1). Is. 1–2. Pp. 1–17. DOI: https://doi.org/10.1016/S0895-7177(03)80001-8
Yarmoliuk O., Veremiichuk Y., Opryshko V., Mahnitko A., Zicmane I., Lomane T. Distribution processes optimization in power supply systems with low power energy sources. Proc. IEEE 63th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). 2022. Pp. 1–5. DOI: https://doi.org/10.1109/RTUCON56726.2022.9978794
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