Smart control of energy storage system in residential photovoltaic systems for economic and technical efficiency

• Autorzy: Kaczorowska Dominika , Rezmer Jacek , Janik Przemysław , Sikorski Tomasz

Identyfikatory

DOI

10.24425/aee.2023.143691

• Języki publikacji: EN

Abstrakty

EN

In recent years, due to the increasing number of renewable energy sources, which are characterised by the stochastic nature of the generated power, interest in energy storage has increased. Commercial installations use simple deterministic methods with low economic efficiency. Hence, there is a need for intelligent algorithms that combine technical and economic aspects. Methods based on computational intelligence (CI) could be a solution. The paper presents an algorithm for optimising power flow in microgrids by using computational intelligence methods. This approach ensures technical and economic efficiency by combining multiple aspects in a single objective function with minimal numerical complexity. It is scalable to any industrial or residential microgrid system. The method uses load and generation forecasts at any time horizon and resolution and the actual specifications of the energy storage systems, ensuring that technological constraints are maintained. The paper presents selected calculation results for a typical residential microgrid supplied with a photovoltaic system. The results of the proposed algorithm are compared with the outcomes provided by a deterministic management system. The computational intelligence method allows the objective function to be adjusted to find the optimal balance of economic and technical effects. Initially, the authors tested the invented algorithm for technical effects, minimising the power exchanged with the distribution system. The application of the algorithm resulted in financial losses, €12.78 for the deterministic algorithm and €8.68 for the algorithm using computational intelligence. Thus, in the next step, a control favouring economic goals was checked using the CI algorithm. The case where charging the storage system from the grid was disabled resulted in a financial benefit of €10.02, whereas when the storage system was allowed to charge from the grid, €437.69. Despite the financial benefits, the application of the algorithm resulted in up to 1560 discharge cycles. Thus, a new unconventional case was considered in which technical and economic objectives were combined, leading to an optimum benefit of €255.17 with 560 discharge cycles per year. Further research of the algorithm will focus on the development of a fitness function coupled to the power system model.

Słowa kluczowe

EN

economic efficiency; energy storage system; ESS; microgrid management; optimal power flow; OPF; particle swarm optimisation; PSO

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2023
• Tom: Vol. 72, nr 1
• Strony: 81--102
• Opis fizyczny: Bibliogr. 51 poz., rys., tab.

Twórcy

autor

Kaczorowska Dominika

• Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, Poland

autor

Rezmer Jacek

• Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, Poland

autor

Janik Przemysław

• Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, Poland

autor

Sikorski Tomasz

• Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, Poland

Bibliografia

• [1] Dunn B., Kamath H., Tarascon J.M., Electrical energy storage for the grid: A battery of choices, Science (1979), vol. 334, no. 6058, pp. 928–935 (2011), DOI: 10.1126/SCIENCE.1212741.
• [2] Rigo-Mariani R., Sareni B., Roboam X., Integrated Optimal Design of a Smart Microgrid with Storage, IEEE Transactions on Smart Grid, vol. 8, no. 4 (2017), DOI: 10.1109/TSG.2015.2507131.
• [3] Daud M.Z., Mohamed A., Hannan M.A., An improved control method of battery energy storage system for hourly dispatch of photovoltaic power sources, Energy Conversion and Management, vol. 73, pp. 256–270 (2013), DOI: 10.1016/j.enconman.2013.04.013.
• [4] Tran Q.T.T., di Silvestre M.L., Sanseverino E.R., Zizzo G., Pham T.N., Driven primary regulation for minimum power losses operation in islanded microgrids, Energies (Basel), vol. 11, no. 11 (2018), DOI: 10.3390/EN11112890.
• [5] Wang K., Yuan X., Geng Y., Wu X., A practical structure and control for reactive power sharing in microgrid, IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1880–1888 (2019), DOI: 10.1109/TSG.2017.2779846.
• [6] Dall’anese E., Zhu H., Giannakis G.B., Distributed Optimal Power Flow for Smart Microgrids, IEEE Transactions on Smart Grid, vol. 4, no. 3 (2013), DOI: 10.1109/TSG.2013.2248175.
• [7] Leadbetter J., Swan L., Battery storage system for residential electricity peak demand shaving, Energy and Buildings, vol. 55, pp. 685–692 (2012), DOI: 10.1016/J.ENBUILD.2012.09.035.
• [8] Shi W., Xie X., Chu C.-C., Gadh R., Distributed Optimal Energy Management in Microgrids, IEEE Transactions on Smart Grid, vol. 6, no. 3 (2015), DOI: 10.1109/TSG.2014.2373150.
• [9] Moradi M.H., Eskandari M., Mahdi Hosseinian S., Operational Strategy Optimization in an Optimal Sized Smart Microgrid, IEEE Transactions on Smart Grid, vol. 6, no. 3 (2015), DOI: 10.1109/TSG.2014.2349795.
• [10] Zubi G., Dufo-López R., Carvalho M., Pasaoglu G., The lithium-ion battery: State of the art and future perspectives, Renewable and Sustainable Energy Reviews, vol. 89, pp. 292–308 (2018), DOI: 10.1016/J.RSER.2018.03.002.
• [11] Bin Wali S., Hannan M.A., Reza M.S., Jern Ker P., Begum R.A., Abd Rahman M.S., Mansor M., Battery storage systems integrated renewable energy sources: A biblio metric analysis towards future directions, Journal of Energy Storage, vol. 35 (2021), DOI: 10.1016/J.EST.2021.102296.
• [12] Kaczorowska D., Rezmer J., Particle swarm algorithm for microgrid optimization (2018), DOI: 10.1109/IMITEL.2018.8370472.
• [13] Kaczorowska D., Janik P., Jasiński Ł., Rezmer J., Suresh V., Power flow control algorithm in a microgrid with energy storage, Przeglad Elektrotechniczny, vol. 97, no. 7 (2021), DOI: 10.15199/48.2021.07.23.
• [14] Yoldaş Y., Önen A., Muyeen S.M., Vasilakos A.V., Alan İ., Enhancing smart grid with microgrids: Challenges and opportunities, Renewable and Sustainable Energy Reviews, vol. 72, pp. 205–214 (2017), DOI: 10.1016/J.RSER.2017.01.064.
• [15] Terça G., Wozabal D., Economies of Scope for Electricity Storage and Variable Renewables, IEEE Transactions on Power Systems, vol. 36, no. 2 (2021), DOI: 10.1109/TPWRS.2020.3022823.
• [16] Hu Y., Soler Soneira D., Sánchez M.J., Barriers to grid-connected battery systems: Evidence from the Spanish electricity market, Journal of Energy Storage, vol. 35 (2021), DOI: 10.1016/J.EST.2021.102262.
• [17] Nottrott A., Kleissl J., Washom B., Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems, Renewable Energy, vol. 55, pp. 230–240 (2013), DOI: 10.1016/j.renene.2012.12.036.
• [18] Zia M.F., Elbouchikhi E., Benbouzid M., Microgrids energy management systems: A critical review on methods, solutions, and prospects, Applied Energy, vol. 222, pp. 1033–1055 (2018), DOI: 10.1016/J.APENERGY.2018.04.103.
• [19] Azeroual M., Lamhamdi T., el Moussaoui H., el Markhi H., Intelligent energy management system of a smart microgrid using multiagent systems, Archives of Electrical Engineering, vol. 69, no. 1, pp. 23–38 (2020), DOI: 10.24425/AEE.2020.131756.
• [20] Mirtaheri H., Bortoletto A., Fantino M., Mazza A., Marzband M., Optimal Planning and Operation Scheduling of Battery Storage Units in Distribution Systems, 2019 IEEE Milan PowerTech (2019), DOI: 10.1109/PTC.2019.8810421.
• [21] Beltran H., Bilbao E., Belenguer E., Etxeberria-Otadui I., Member S., Rodriguez P., Evaluation of Storage Energy Requirements for Constant Production in PV Power Plants, IEEE Transactions on Industrial Electronics, vol. 60, no. 3, p. 1225 (2013), DOI: 10.1109/TIE.2012.2202353.
• [22] Jia K., Chen Y., Bi T., Lin Y., Thomas D., Sumner M., Historical-Data-Based Energy Management in a Microgrid with a Hybrid Energy Storage System, IEEE Transactions on Industrial Informatics, vol. 13, no. 5 (2017), DOI: 10.1109/TII.2017.2700463.
• [23] Kumar Venayagamoorthy G., Sharma R.K., Gautam P.K., Ahmadi A., Sharma R.K., Dynamic Energy Management System for a Smart Microgrid, IEEE Transactions on Neutral Networks and Learning Systems, vol. 27, no. 8 (2016), DOI: 10.1109/TNNLS.2016.2514358.
• [24] Abdolrasol M.G.M., Mohamed R., Hannan M.A., Al-Shetwi A.Q., Mansor M., Blaabjerg F., Artificial Neural Network Based Particle Swarm Optimization for Microgrid Optimal Energy Scheduling, IEEE Transactions on Power Electronics, vol. 36, no. 11, p. 12151 (2021), DOI: 10.1109/TPEL.2021.3074964.
• [25] Shan Y., Hu J., Member S., Liu H., A Holistic Power Management Strategy of Microgrids Based on Model Predictive Control and Particle Swarm Optimization, IEEE Transactions on Industrial Informatics, vol. 18, no. 8, p. 5115 (2022), DOI: 10.1109/TII.2021.3123532.
• [26] Carpinelli G., Mottola F., Proto D., Russo A., A Multi-Objective Approach for Microgrid Scheduling, IEEE Transactions on Smart Grid, vol. 8, no. 5 (2017), DOI: 10.1109/TSG.2016.2516256.
• [27] Hallmann M., Wenge C., Komarnicki P., Balischewski S., Methods for lithium-based battery energy storage SOC estimation. Part I: Overview, Archives of Electrical Engineering, vol. 71, no. 1, pp. 139–157 (2022), DOI: 10.24425/AEE.2022.140202.
• [28] Hallmann M., Wenge C., Komarnicki P., Methods for lithium-based battery energy storage SOC estimation. Part II: Application and accuracy, Archives of Electrical Engineering, vol. 71, no. 2, pp. 311–323 (2022), DOI: 10.24425/AEE.2022.140713.
• [29] Zhong W., Xie K., Liu Y., Yang C., Xie S., Zhang Y., Online Control and Near-Optimal Algorithm for Distributed Energy Storage Sharing in Smart Grid, IEEE Transactions on Smart Grid, vol. 11, no. 3, pp. 2552–2562 (2020), DOI: 10.1109/TSG.2019.2957426.
• [30] Nayak C.K., Kasturi K., Nayak M.R., Economical management of microgrid for optimal participation in electricity market, Journal of Energy Storage, vol. 21, pp. 657–664 (2019), DOI: 10.1016/J.EST.2018.12.027.
• [31] Saez-de-Ibarra A., Herrera V.I., Milo A., Gaztanaga H., Etxeberria-Otadui I., Bacha S., Padros A., Management Strategy for Market Participation of Photovoltaic Power Plants Including Storage Systems, IEEE Transactions on Industry Applications, vol. 52, no. 5 (2016), DOI: 10.1109/TIA.2016.2585090.
• [32] Kanchev H., Lu D., Colas F., Lazarov V., Francois B., Member S., Energy Management and Operational Planning of a Microgrid with a PV-Based Active Generator for Smart Grid Applications, IEEE Transactions on Industrial Electronics, vol. 58, no. 10, p. 4583 (2011), DOI: 10.1109/TIE.2011.2119451.
• [33] Núñez-Reyes A., Marcos Rodríguez D., Bordons Alba C., Ridao Carlini M.Á., Optimal scheduling of grid-connected PV plants with energy storage for integration in the electricity market, Solar Energy, vol. 144, pp. 502–516 (2017), DOI: 10.1016/J.SOLENER.2016.12.034.
• [34] Perez E., Beltran H., Aparicio N., Rodriguez P., Predictive power control for PV plants with energy storage, IEEE Transactions on Sustainable Energy, vol. 4, no. 2, pp. 482–490 (2013), DOI: 10.1109/TSTE.2012.2210255.
• [35] Garcia-Torres F., Bordons C., Tobajas J., Marquez J.J., Garrido-Zafra J., Moreno-Munoz A., Optimal Schedule for Networked Microgrids under Deregulated Power Market Environment Using Model Predictive Control, IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 182–191 (2021), DOI: 10.1109/TSG.2020.3018023.
• [36] Semero Y.K., Zhang J., Zheng D., Optimal energy management strategy in microgrids with mixed energy resources and energy storage system, IET Cyber-Phys. Syst., Theory Appl., vol. 5, no. 1, pp. 80–84 (2019), DOI: 10.1049/iet-cps.2019.0035.
• [37] Choi J., Shin Y., Choi M., Park W.-K., Lee I.-W., Robust Control of a Microgrid Energy Storage System Using Various Approaches, IEEE Transactions on Smart Grid, vol. 10, no. 3 (2019), DOI: 10.1109/TSG.2018.2808914.
• [38] Elkazaz M.H., Hoballah A., Azmy A.M., Artificial intelligent-based optimization of automated home energy management systems, International Transactions on Electrical Energy Systems, vol. 26, no. 9, pp. 2038–2056 (2016), DOI: 10.1002/ETEP.2195.
• [39] Xu Z., Wang J., Zhu Y., Zhang X., Dong R., Qin W., Optimal Scheduling Strategy of Microgrid Participating in Upper Power Grid Based on Flexibility of Distributed Power, 2020 IEEE Sustainable Power and Energy Conference (iSPEC) (2020), DOI: 10.1109/ISPEC50848.2020.9350976.
• [40] Yousif M., Ai Q., Gao Y., Wattoo A., Jiang Z., Hao R., An Optimal Dispatch Strategy for Distributed Microgrids Using PSO, CSEE Journal of Power and Energy, vol. 6, no. 3 (2020), DOI: 10.17775/CSEE-JPES.2018.01070.
• [41] Riffonneau Y., Bacha S., Barruel F., Ploix S., Optimal Power Flow Management for Grid Connected PV Systems with Batteries, IEEE Transactions on Sustainable Energy, vol. 2, no. 3, p. 309 (2011), DOI: 10.1109/TSTE.2011.2114901.
• [42] Tran T.N., Grid Search of Convolutional Neural Network model in the case of load forecasting, Archives of Electrical Engineering, vol. 70, no. 1, pp. 25–35 (2021), DOI: 10.24425/AEE.2021.136050.
• [43] Ju C., Wang P., Goel L., Xu Y., A Two-Layer Energy Management System for Microgrids with Hybrid Energy Storage Considering Degradation Costs, IEEE Transactions on Smart Grid, vol. 9, no. 6 (2018), DOI: 10.1109/TSG.2017.2703126.
• [44] Du Y., Wu J., Li S., Member S., Long C., Paschalidis I.C., Distributed MPC for Coordinated Energy Efficiency Utilization in Microgrid Systems, IEEE Transactions on Smart Grid, vol. 10, no. 2 (2019), DOI: 10.1109/TSG.2017.2777975.
• [45] Asimakopoulou G.E., Dimeas A.L., Hatziargyriou N.D., Leader-Follower Strategies for Energy Management of Multi-Microgrids, IEEE Transactions on Smart Grid, vol. 4, no. 4 (2013), DOI: 10.1109/TSG.2013.2256941.
• [46] Bahmani R., Karimi H., Jadid S., Stochastic electricity market model in networked microgrids considering demand response programs and renewable energy sources, International Journal of Electrical Power and Energy Systems, vol. 117 (2020), DOI: 10.1016/J.IJEPES.2019.105606.
• [47] Chai Q., Zhang C., Dong Z., Chen W., Optimal Daily Scheduling of Distributed Battery Energy Storage Systems Considering Battery Degradation Cost, 2021 IEEE Power & Energy Society General Meeting (PESGM), pp. 1–5 (2021), DOI: 10.1109/PESGM46819.2021.9638252.
• [48] Li X., Ma R., Yan N., Wang S., Hui D., Research on Optimal Scheduling Method of Hybrid Energy Storage System Considering Health State of Echelon-Use Lithium-Ion Battery, IEEE Transactions on Applied Superconductivity, vol. 31, no. 8 (2021), DOI: 10.1109/TASC.2021.3117752.
• [49] Tran D., Khambadkone A.M., Energy Management for Lifetime Extension of Energy Storage System in Micro-Grid Applications, IEEE Transactions on Smart Grid, vol. 4, no. 3, p. 1289 (2013), DOI: 10.1109/TSG.2013.2272835.
• [50] Kaczorowska D., Rezmer J., Jasinski M., Sikorski T., Suresh V., Leonowicz Z., Kostyla P., Szymanda J., Janik P., A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics, Energies, vol. 13, no. 24, pp. 6670 (2020), DOI: 10.3390/en13246670.
• [51] TAURON Sprzedaż sp. z o.o., Taryfa dla energii elektrycznej dla odbiorców z grup taryfowych G (2021).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).