IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i20p7079-d1259185.html
   My bibliography  Save this article

Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers

Author

Listed:
  • Zhiyuan Zeng

    (School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China)

  • Tianyou Li

    (School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China
    Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen 361024, China)

  • Jun Su

    (School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China
    Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen 361024, China)

  • Longyi Sun

    (School of Electrical Engineering and Automation, Xiamen University of Technology, No. 600, Ligong Road, Jimei District, Xiamen 361024, China)

Abstract

Photovoltaic (PV) power generation exhibits stochastic and uncertain characteristics. In order to improve the economy and reliability of a photovoltaic-energy storage system (PV-ESS), it is crucial to optimize both the energy storage capacity size and the charging and discharging strategies of the ESS. An optimal scheduling model for PV-ESS is proposed in this paper, comprehensively considering factors in terms of energy cost and charging/discharging constraints of the PV-ESS. Moreover, the model employs a particle swarm optimization-backpropagation (PSO-BP) neural network to predict the PV power using historical generation data from a factory in Xiamen. The proposed two PV-ESS scheduling strategies are compared under three weather conditions. In the demand management strategy, the ESS can flexibly respond to different weather conditions and load demand changes, and effectively reduce the electricity cost for users.

Suggested Citation

  • Zhiyuan Zeng & Tianyou Li & Jun Su & Longyi Sun, 2023. "Tariff-Based Optimal Scheduling Strategy of Photovoltaic-Storage for Industrial and Commercial Customers," Energies, MDPI, vol. 16(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7079-:d:1259185
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/20/7079/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/20/7079/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
    2. Jose Luis Torres-Moreno & Antonio Gimenez-Fernandez & Manuel Perez-Garcia & Francisco Rodriguez, 2018. "Energy Management Strategy for Micro-Grids with PV-Battery Systems and Electric Vehicles," Energies, MDPI, vol. 11(3), pages 1-13, February.
    3. Wang, Deyun & Yue, Chenqiang & ElAmraoui, Adnen, 2021. "Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. Liu, Luyao & Zhao, Yi & Chang, Dongliang & Xie, Jiyang & Ma, Zhanyu & Sun, Qie & Yin, Hongyi & Wennersten, Ronald, 2018. "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 700-711.
    5. Roberts, Mike B. & Bruce, Anna & MacGill, Iain, 2019. "Impact of shared battery energy storage systems on photovoltaic self-consumption and electricity bills in apartment buildings," Applied Energy, Elsevier, vol. 245(C), pages 78-95.
    6. Sharma, Pavitra & Dutt Mathur, Hitesh & Mishra, Puneet & Bansal, Ramesh C., 2022. "A critical and comparative review of energy management strategies for microgrids," Applied Energy, Elsevier, vol. 327(C).
    7. Hyun Cheol Jeong & Jaesung Jung & Byung O Kang, 2020. "Development of Operational Strategies of Energy Storage System Using Classification of Customer Load Profiles under Time-of-Use Tariffs in South Korea," Energies, MDPI, vol. 13(7), pages 1-17, April.
    Full references (including those not matched with items on IDEAS)

    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. Md Jahidur Rahman & Tahar Tafticht & Mamadou Lamine Doumbia & Iqbal Messaïf, 2023. "Optimal Inverter Control Strategies for a PV Power Generation with Battery Storage System in Microgrid," Energies, MDPI, vol. 16(10), pages 1-36, May.
    2. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    4. Bernadette Fina & Hans Auer, 2020. "Economic Viability of Renewable Energy Communities under the Framework of the Renewable Energy Directive Transposed to Austrian Law," Energies, MDPI, vol. 13(21), pages 1-31, November.
    5. Zhu, Jianquan & Xia, Yunrui & Mo, Xiemin & Guo, Ye & Chen, Jiajun, 2021. "A bilevel bidding and clearing model incorporated with a pricing strategy for the trading of energy storage use rights," Energy, Elsevier, vol. 235(C).
    6. Okay, Kamil & Eray, Sermet & Eray, Aynur, 2022. "Development of prototype battery management system for PV system," Renewable Energy, Elsevier, vol. 181(C), pages 1294-1304.
    7. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
    9. M. M. Hasan & Shakhawat Hossain & M. Mofijur & Zobaidul Kabir & Irfan Anjum Badruddin & T. M. Yunus Khan & Esam Jassim, 2023. "Harnessing Solar Power: A Review of Photovoltaic Innovations, Solar Thermal Systems, and the Dawn of Energy Storage Solutions," Energies, MDPI, vol. 16(18), pages 1-30, September.
    10. Thopil, George Alex & Sachse, Christiaan Eddie & Lalk, Jörg & Thopil, Miriam Sara, 2020. "Techno-economic performance comparison of crystalline and thin film PV panels under varying meteorological conditions: A high solar resource southern hemisphere case," Applied Energy, Elsevier, vol. 275(C).
    11. Jamal, Taskin & Carter, Craig & Schmidt, Thomas & Shafiullah, G.M. & Calais, Martina & Urmee, Tania, 2019. "An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system," Applied Energy, Elsevier, vol. 254(C).
    12. Yu, Min & Niu, Dongxiao & Wang, Keke & Du, Ruoyun & Yu, Xiaoyu & Sun, Lijie & Wang, Feiran, 2023. "Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification," Energy, Elsevier, vol. 275(C).
    13. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    14. Jouttijärvi, Sami & Lobaccaro, Gabriele & Kamppinen, Aleksi & Miettunen, Kati, 2022. "Benefits of bifacial solar cells combined with low voltage power grids at high latitudes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    15. Ana Ogando-Martínez & Xela García-Santiago & Saúl Díaz García & Fernando Echevarría Camarero & Gonzalo Blázquez Gil & Pablo Carrasco Ortega, 2023. "Optimization of Energy Allocation Strategies in Spanish Collective Self-Consumption Photovoltaic Systems," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    16. Işık, Cem & Kuziboev, Bekhzod & Ongan, Serdar & Saidmamatov, Olimjon & Mirkhoshimova, Mokhirakhon & Rajabov, Alibek, 2024. "The volatility of global energy uncertainty: Renewable alternatives," Energy, Elsevier, vol. 297(C).
    17. Fathy, Ahmed & Ferahtia, Seydali & Rezk, Hegazy & Yousri, Dalia & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid," Energy, Elsevier, vol. 247(C).
    18. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    19. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Bianchi, Francesco & Domenghini, Piergiovanni & Cotana, Franco & Wang, Jinwen, 2022. "A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing," Energy, Elsevier, vol. 244(PB).
    20. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & José Matas, 2021. "Individual vs. Community: Economic Assessment of Energy Management Systems under Different Regulatory Frameworks," Energies, MDPI, vol. 14(3), pages 1-27, January.

    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:16:y:2023:i:20:p:7079-:d:1259185. 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.