The structure analysis of the energy balance allows use optimal of available resources and infrastructure to maximize energy production, this is especially important for territorial communities with limited resources. The article was devoted to conduct a statistical analysis of combined energy production from wind-solar power plants and sources of thermal generation as part of a typical system of centralized electricity supply and heat for the territorial community of Smila, Cherkasy region, Ukraine. To promote the use of distributed resources, the concept of a microenergy system (MES) has been proposed and its basic structure with energy production, conversion and storage devices. Based on indeterminacy MES leaded a probability distribution model. The results showed that the MES could integrate different types of energy, such as wind, photovoltaic and gas. Multiple energy cycles were achieved through energy conversion and storage devices, and different energy needs were met. Seasonality imposed significant restrictions on the use of heat power plant on solid biofuel (HPP) and biofuel cogeneration gas-piston plant (BCP) in the summer, even without taking into account possible maneuvering modes. The level of losses increased from 19.51% till 40.83%, which leads to an increase in the cost of electricity generation. Average values of the specific cost of electricity on the monthly interval for the selected structure sources (SPP – 1.0 mW, WPP – 1.6 mW, HPP – 8.5 mW, BCP – 1.0 mW) grew in the summer from 23.63% till 53.37%. At the same time, Fe5(YL) reached the highest value in the non-heating season 0.689. The research can be used for practical purposes in the dispatching of generated electric and thermal energy
energy supply of territorial communities, energy balance, micropower system, combined production of electricity and heat
[1] Bagheri, M., Delbari, S.H., Pakzadmanesh, M., & Kennedy, C.A. (2019). City-integrated renewable energy design for low-carbon and climate-resilient communities. Applied Energy, 239, 1212-1225. doi: 10.1016/j. apenergy.2019.02.031.
[2] Bloess, A., Schill, W.-P., & Zerrahn, A. (2018). Power-to-heat for renewable energy integration: A review of technologies, modeling approaches, and flexibility potentials. Applied Energy, 212, 1611-1626. doi: 10.1016/j.apenergy.2017.12.073.
[3] Connolly, D., Lund, H., Mathiesen, B.V., Werner, S., Möller, B., Persson, U., Boermans, T., Trier, D., Østergaard, P.A., & Nielsen S. (2014). Heat roadmap Europe: Combining district heating with heat savings to decarbonise the eu energy system. Energy Policy, 65, 475-489. doi: 10.1016/j.enpol.2013.10.035.
[4] European Directive 2018/2001/EU of the European Parliament and of the Council on the promotion of the use of energy from renewable sources (recast). (2018, December). Retrieved from https://eur-lex.europa.eu/eli/dir/2018/2001/oj.
[5] Fellah, K., & Abbou, R. (2024). Modelling and formal verification approach for microgrid energy management systems under random load. IFAC-PapersOnLine, 58(1), 270-275. doi: 10.1016/j.ifacol.2024.07.046.
[6] Gabbar, H.A., Runge, J., Bondarenko, D., Bower, L., Pandya, D., Musharavati, F., & Pokharel, S. (2015). Performance evaluation of gas-power strategies for building energy conservation. Energy Conversion and Management, 93, 187-196. doi: 10.1016/j.enconman.2014.12.060.
[7] Ghiani, E., Giordano, A., Nieddu, A., Rosetti, L., & Pilo F. (2019). Planning of a smart local energy community: the case of Berchidda Municipality (Italy), Energies, 12(24), article number 4629. doi: 10.3390/en12244629.
[8] Guandalini, G., Robinius, M., Grube, T., Campanari, S., & Stolten, D. (2017). Long-term power-to-gas potential from wind and solar power: A country analysis for Italy. International Journal of Hydrogen Energy, 42(19), 13389-13406. doi: 10.1016/j.ijhydene.2017.03.081.
[9] Hahnel, U.J.J., Herberz, M., Pena-Bello, A., Parra, D., & Brosch, T. (2020). Becoming prosumer: Revealing trading preferences and decision-making strategies in peer-to-peer energy communities, Energy Policy, 137, article number 111098. doi: 10.1016/j.enpol.2019.111098.
[10] Heredia, F.J., Rider, M.J., & Corchero, C. (2010). Optimal bidding strategies for thermal and generic programming units in the day-ahead electricity market. IEEE Transactions on Power Systems, 25(3), 1504-1518. doi: 10.1109/TPWRS.2009.2038269.
[11] Jiang, Y.-W., Chen, C., & Wen, B.-Y. (2008). Application of stochastic simulation’s particle swarm algorithm in the compensation of reactive power for wind farms. Proceedings of the Chinese Society of Electrical Engineering, 28, 47-52.
[12] Ju, L., Li, H., Zhao, J., Chen, K., Tan, Q., & Tan, Z. (2016). Multi-objective stochastic scheduling optimization model for connecting a virtual power plant to wind-photovoltaic-electric vehicles considering uncertainties and demand response. Energy Conversion and Management, 128, 160-177. doi: 10.1016/j.enconman.2016.09.072.
[13] Kaplun, V. (2023). Principles of resource-process modeling of territorial communities combined energy supply in the climate change prevention context. System Research in Energy, 4(75), 54-64. doi: 10.15407/srenergy2023.04.055.
[14] Kaplun, V., & Osypenko, V. (2020). Energy efficiency analyses in polygeneration microgrids with renewable sources 2020. In IEEE 7th international conference on energy smart systems (ESS) (pp. 139-143). Kyiv: IEEE. doi: 10.1109/ESS50319.2020.9160346.
[15] Karimi, A., Nayeripour, M., & Abbasi, A.R. (2024). Coordination in islanded microgrids: Integration of distributed generation, energy storage system, and load shedding using a new decentralized control architecture. Journal of Energy Storage, 98(B), article number 113199. doi: 10.1016/j.est.2024.113199.
[16] Koirala, B.P., Koliou, E., Friege, J., Hakvoort, R.A., & Herder, P.M. (2016). Energetic communities for community energy: A review of key issues and trends shaping integrated community energy systems. Renewable and Sustainable Energy Reviews, 56, 722-744. doi: 10.1016/j.rser.2015.11.080.
[17] Li, J., Mo, H., Sun, Q., Wei, W., & Yin, K. (2024). Distributed optimal scheduling for virtual power plant with high penetration of renewable energy. International Journal of Electrical Power & Energy Systems, 160, article number 110103. doi: 10.1016/j.ijepes.2024.110103.
[18] Mehigan, L., Deane, J.P., Gallachóir, B.P.Ó., & Bertsch, V. (2018). A review of the role of distributed generation (DG) in future electricity systems. Energy, 163, 822-836. doi: 10.1016/j.energy.2018.08.022.
[19] Mohammadi, J., Rahimikian, A., & Ghazizadeh, M.S. (2007). Aggregated wind power and flexible load offering strategy. IET Renewable Power Generation, 5(6), 439-447. doi: 10.1049/iet-rpg.2011.0066.
[20] Saeed, N., Wen, F., & Afzal, M.Z. (2024). Decentralized peer-to-peer energy trading in microgrids: Leveraging blockchain technology and smart contracts. Energy Reports, 12, 1753-1764. doi: 10.1016/j.egyr.2024.07.053.
[21] Smila city territorial community. (n.d.). Statistical data on energy consumption. Retrieved from https://www.rayrada.ck.ua/8-silski-rady/5605-smilianska-miska-terytorialna-hromada.html.
[22] Stojiljkovic, M.M. (2017). Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics. Energy, 137, 1231-1251. doi: 10.1016/j.energy.2017.06.037.
[23] Tan, Z.-F., Ju, L.-W., Li, H.-H., Li, J.-Y., & Zhang, H.-J. (2014). A two-stage scheduling optimization model and solution algorithm for wind power and energy storage system considering uncertainty and demand response. International Journal of Electrical Power & Energy Systems, 63, 1057-1069. doi: 10.1016/j.ijepes.2014.06.061.
[24] Yang, H., Yi, D., Zhao, J., Luo, F., & Dong, Z. (2014). Distributed optimal dispatch of virtual power plant based on ELM transformation. Journal of Industrial and Management Optimization, 10(4), 1297-1318. doi: 10.3934/jimo.2014.10.1297.
[25] Zhang, X., Yang, J., Wang, W., Zhang, M., & Jing, T. (2018). Integrated optimal dispatch of a rural micro-energy-gridwith multi-energy stream based on model predictive control. Energies, 11, article number 3439. doi: 10.3390/en11123439.
[26] Zhao, X., Yang, L., Qu, X., & Yan, W. (2018). An improved energy flow calculation method for integrated electricity and natural gas system. Transactions of China Electrotechnical Society, 33, 467-477.