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Method for Determining the Optimal Capacity of Energy Storage Systems with a Long-Term Forecast of Power Consumption

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  • Nikita Dmitrievich Senchilo

    (Department of Electrical Engineering, Saint Petersburg Mining University, 2 21st Line, 199106 Saint Petersburg, Russia)

  • Denis Anatolievich Ustinov

    (Department of Electrical Engineering, Saint Petersburg Mining University, 2 21st Line, 199106 Saint Petersburg, Russia)

Abstract

The unevenness of the electricity consumption schedule at enterprises leads to a peak power increase, which leads to an increase in the cost of electricity supply. Energy storage devices can optimize the energy schedule by compensating the planned schedule deviations, as well as reducing consumption from the external network when participating in a demand response. However, during the day, there may be several peaks in consumption, which lead to a complete discharge of the battery to one of the peaks; as a result, total peak power consumption does not decrease. To optimize the operation of storage devices, a day-ahead forecast is often used, which allows to determine the total number of peaks. However, the power of the storage system may not be sufficient for optimal peak compensation. In this study, a long-term forecast of power consumption based on the use of exogenous parameters in the decision tree model is used. Based on the forecast, a novel algorithm for determining the optimal storage capacity for a specific consumer is developed, which optimizes the costs of leveling the load schedule.

Suggested Citation

  • Nikita Dmitrievich Senchilo & Denis Anatolievich Ustinov, 2021. "Method for Determining the Optimal Capacity of Energy Storage Systems with a Long-Term Forecast of Power Consumption," Energies, MDPI, vol. 14(21), pages 1-25, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7098-:d:669182
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    References listed on IDEAS

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    1. Chie Hoon Song, 2021. "Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape," Energies, MDPI, vol. 14(18), pages 1-20, September.
    2. Ann-Kathrin Klaas & Hans-Peter Beck, 2021. "A MILP Model for Revenue Optimization of a Compressed Air Energy Storage Plant with Electrolysis," Energies, MDPI, vol. 14(20), pages 1-21, October.
    3. Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2022. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," International Journal of Forecasting, Elsevier, vol. 38(1), pages 97-116.
    4. Tatiana Nevzorova & Vladimir Kutcherov, 2021. "The Role of Advocacy Coalitions in Shaping the Technological Innovation Systems: The Case of the Russian Renewable Energy Policy," Energies, MDPI, vol. 14(21), pages 1-24, October.
    5. Yuriy Leonidovich Zhukovskiy & Daria Evgenievna Batueva & Alexandra Dmitrievna Buldysko & Bernard Gil & Valeriia Vladimirovna Starshaia, 2021. "Fossil Energy in the Framework of Sustainable Development: Analysis of Prospects and Development of Forecast Scenarios," Energies, MDPI, vol. 14(17), pages 1-28, August.
    6. Stefan Ungureanu & Vasile Topa & Andrei Cristinel Cziker, 2021. "Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market," Energies, MDPI, vol. 14(21), pages 1-26, October.
    7. Mohammad Alipour & Rodney A. Stewart & Oz Sahin, 2021. "Beyond the Diffusion of Residential Solar Photovoltaic Systems at Scale: Allegorising the Battery Energy Storage Adoption Behaviour," Energies, MDPI, vol. 14(16), pages 1-12, August.
    8. van der Veen, Reinier A.C. & Hakvoort, Rudi A., 2016. "The electricity balancing market: Exploring the design challenge," Utilities Policy, Elsevier, vol. 43(PB), pages 186-194.
    9. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    10. Marcin Szott & Marcin Jarnut & Jacek Kaniewski & Łukasz Pilimon & Szymon Wermiński, 2021. "Fault-Tolerant Control in a Peak-Power Reduction System of a Traction Substation with Multi-String Battery Energy Storage System," Energies, MDPI, vol. 14(15), pages 1-23, July.
    11. Pingping Yun & Yongfeng Ren & Yu Xue, 2018. "Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method," Energies, MDPI, vol. 11(12), pages 1-23, December.
    12. Piotr Gajewski & Krzysztof Pieńkowski, 2021. "Control of the Hybrid Renewable Energy System with Wind Turbine, Photovoltaic Panels and Battery Energy Storage," Energies, MDPI, vol. 14(6), pages 1-23, March.
    13. Hou, Qingchun & Zhang, Ning & Du, Ershun & Miao, Miao & Peng, Fei & Kang, Chongqing, 2019. "Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China," Applied Energy, Elsevier, vol. 242(C), pages 205-215.
    14. Kendall Mongird & Vilayanur Viswanathan & Patrick Balducci & Jan Alam & Vanshika Fotedar & Vladimir Koritarov & Boualem Hadjerioua, 2020. "An Evaluation of Energy Storage Cost and Performance Characteristics," Energies, MDPI, vol. 13(13), pages 1-53, June.
    15. Nayeem Chowdhury & Fabrizio Pilo & Giuditta Pisano, 2020. "Optimal Energy Storage System Positioning and Sizing with Robust Optimization," Energies, MDPI, vol. 13(3), pages 1-20, January.
    16. Kyo Beom Han & Jaesung Jung & Byung O Kang, 2021. "Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea," Energies, MDPI, vol. 14(19), pages 1-17, October.
    17. Xiaotong Qie & Rui Zhang & Yanyong Hu & Xialing Sun & Xue Chen, 2021. "A Multi-Criteria Decision-Making Approach for Energy Storage Technology Selection Based on Demand," Energies, MDPI, vol. 14(20), pages 1-29, October.
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    2. Yuriy Leonidovich Zhukovskiy & Margarita Sergeevna Kovalchuk & Daria Evgenievna Batueva & Nikita Dmitrievich Senchilo, 2021. "Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response," Sustainability, MDPI, vol. 13(24), pages 1-26, December.
    3. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    4. Yuriy Zhukovskiy & Anastasia Koshenkova & Valeriya Vorobeva & Daniil Rasputin & Roman Pozdnyakov, 2023. "Assessment of the Impact of Technological Development and Scenario Forecasting of the Sustainable Development of the Fuel and Energy Complex," Energies, MDPI, vol. 16(7), pages 1-23, March.
    5. Marina A. Nevskaya & Semen M. Raikhlin & Amina F. Chanysheva, 2024. "Assessment of Energy Efficiency Projects at Russian Mining Enterprises within the Framework of Sustainable Development," Sustainability, MDPI, vol. 16(17), pages 1-20, August.

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