IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p7098-d669182.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/7098/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/21/7098/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Simone Ferrari & Milad Zoghi & Giancarlo Paganin & Giuliano Dall’O’, 2023. "A Practical Review to Support the Implementation of Smart Solutions within Neighbourhood Building Stock," Energies, MDPI, vol. 16(15), pages 1-35, July.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sergey Zhironkin & Fares Abu-Abed & Elena Dotsenko, 2023. "The Development of Renewable Energy in Mineral Resource Clusters—The Case of the Siberian Federal District," Energies, MDPI, vol. 16(9), pages 1-28, April.
    2. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    3. Zekai Xu & Jinghan He & Zhao Liu & Zhiyi Zhao, 2023. "Collaborative Optimization of Transmission and Distribution Considering Energy Storage Systems on Both Sides of Transmission and Distribution," Energies, MDPI, vol. 16(13), pages 1-23, July.
    4. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    5. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2019. "Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation," Energies, MDPI, vol. 12(18), pages 1-19, September.
    6. Qi Huang & Aihua Jiang & Yu Zeng & Jianan Xu, 2022. "Community Flexible Load Dispatching Model Based on Herd Mentality," Energies, MDPI, vol. 15(13), pages 1-18, June.
    7. Savolainen, Rebecka & Lahdelma, Risto, 2022. "Optimization of renewable energy for buildings with energy storages and 15-minute power balance," Energy, Elsevier, vol. 243(C).
    8. Andri Ottesen & Dieter Thom & Rupali Bhagat & Rola Mourdaa, 2023. "Learning from the Future of Kuwait: Scenarios as a Learning Tool to Build Consensus for Actions Needed to Realize Vision 2035," Sustainability, MDPI, vol. 15(9), pages 1-25, April.
    9. King, Marcus & Jain, Anjali & Bhakar, Rohit & Mathur, Jyotirmay & Wang, Jihong, 2021. "Overview of current compressed air energy storage projects and analysis of the potential underground storage capacity in India and the UK," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    10. Di Cosmo, Valeria & Malaguzzi Valeri, Laura, 2018. "Wind, storage, interconnection and the cost of electricity generation," Energy Economics, Elsevier, vol. 69(C), pages 1-18.
    11. Okur, Özge & Voulis, Nina & Heijnen, Petra & Lukszo, Zofia, 2019. "Aggregator-mediated demand response: Minimizing imbalances caused by uncertainty of solar generation," Applied Energy, Elsevier, vol. 247(C), pages 426-437.
    12. Poplavskaya, Ksenia & de Vries, Laurens, 2019. "Distributed energy resources and the organized balancing market: A symbiosis yet? Case of three European balancing markets," Energy Policy, Elsevier, vol. 126(C), pages 264-276.
    13. Wu, Zhaoyuan & Zhou, Ming & Zhang, Ting & Li, Gengyin & Zhang, Yan & Liu, Xiaojuan, 2020. "Imbalance settlement evaluation for China's balancing market design via an agent-based model with a multiple criteria decision analysis method," Energy Policy, Elsevier, vol. 139(C).
    14. Rafael Sánchez-Durán & Joaquín Luque & Julio Barbancho, 2019. "Long-Term Demand Forecasting in a Scenario of Energy Transition," Energies, MDPI, vol. 12(16), pages 1-23, August.
    15. Homeyra Akter & Harun Or Rashid Howlader & Ahmed Y. Saber & Paras Mandal & Hiroshi Takahashi & Tomonobu Senjyu, 2021. "Optimal Sizing of Hybrid Microgrid in a Remote Island Considering Advanced Direct Load Control for Demand Response and Low Carbon Emission," Energies, MDPI, vol. 14(22), pages 1-19, November.
    16. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    17. Pablo Fernández-Bustamante & Oscar Barambones & Isidro Calvo & Cristian Napole & Mohamed Derbeli, 2021. "Provision of Frequency Response from Wind Farms: A Review," Energies, MDPI, vol. 14(20), pages 1-24, October.
    18. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    19. Distaso, Walter & Roccazzella, Francesco & Vrins, Frédéric, 2023. "Business cycle and realized losses in the consumer credit industry," LIDAM Discussion Papers LFIN 2023007, Université catholique de Louvain, Louvain Finance (LFIN).
    20. Jiseong Noh & Hyun-Ji Park & Jong Soo Kim & Seung-June Hwang, 2020. "Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management," Mathematics, MDPI, vol. 8(4), pages 1-14, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7098-:d:669182. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.