IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i8p591-d74849.html
   My bibliography  Save this article

Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

Author

Listed:
  • Nan Zhou

    (State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Nian Liu

    (State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Jianhua Zhang

    (State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Jinyong Lei

    (Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510080, Guangdong Province, China)

Abstract

In order to solve the influence of uncertain photovoltaic power (PV) on the stable operation of microgrid (MG), demand response (DR) and battery energy storage system (BESS) need to be introduced simultaneously into the operation optimal scheduling of PV-based microgrid (PV-MG). Therefore, it is of great significance for commercial investment decisions of PV-MG to consider the influence of DR on BESS optimal sizing. Under the peak-valley time-of-use (TOU) price, this paper builds cross-time DR models based on price elasticity matrix. Furthermore, through the introduction of DR and BESS into PV-MG scheduling optimization, the MG investment and benefit model is proposed. Considering the constraint condition such as co-ordination of supply and demand, electricity price elasticity and energy loss of storage system, the improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimal allocation model of the BESS with the target of maximum PV consumptive rate and annual net profits. The optimization method was applied to a PV-MG in Guangdong. Through the regulation and control effect of demand response and BESS on load distribution, the uncertainties PV power can be suppressed so as to improve the PV system consumptive level, which is of great guiding significance for BESS optimal sizing under this situation.

Suggested Citation

  • Nan Zhou & Nian Liu & Jianhua Zhang & Jinyong Lei, 2016. "Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response," Energies, MDPI, vol. 9(8), pages 1-24, July.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:591-:d:74849
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/8/591/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/9/8/591/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Walawalkar, Rahul & Blumsack, Seth & Apt, Jay & Fernands, Stephen, 2008. "An economic welfare analysis of demand response in the PJM electricity market," Energy Policy, Elsevier, vol. 36(10), pages 3692-3702, October.
    2. Liu, Nian & Chen, Zheng & Liu, Jie & Tang, Xiao & Xiao, Xiangning & Zhang, Jianhua, 2014. "Multi-objective optimization for component capacity of the photovoltaic-based battery switch stations: Towards benefits of economy and environment," Energy, Elsevier, vol. 64(C), pages 779-792.
    3. Liu, Nian & Tang, Qingfeng & Zhang, Jianhua & Fan, Wei & Liu, Jie, 2014. "A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids," Applied Energy, Elsevier, vol. 129(C), pages 336-345.
    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. Jiaxin Lu & Weijun Wang & Yingchao Zhang & Song Cheng, 2017. "Multi-Objective Optimal Design of Stand-Alone Hybrid Energy System Using Entropy Weight Method Based on HOMER," Energies, MDPI, vol. 10(10), pages 1-17, October.
    2. Federica Cucchiella & Idiano D’Adamo & Massimo Gastaldi, 2017. "The Economic Feasibility of Residential Energy Storage Combined with PV Panels: The Role of Subsidies in Italy," Energies, MDPI, vol. 10(9), pages 1-18, September.
    3. Nian Liu & Cheng Wang & Minyang Cheng & Jie Wang, 2016. "A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids," Energies, MDPI, vol. 9(12), pages 1-18, December.
    4. Paraskevas Panagiotidis & Andrew Effraimis & George A Xydis, 2019. "An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids," Energy & Environment, , vol. 30(1), pages 63-80, February.
    5. Donato Morea & Lucilla Bittucci & Arturo Cafaro & Fabiomassimo Mango & Pina Murè, 2021. "Can the Current State Support Mechanisms Help the Growth of Renewable Energies in Wind Markets?," Sustainability, MDPI, vol. 13(21), pages 1-17, November.
    6. Khezri, Rahmat & Mahmoudi, Amin & Aki, Hirohisa, 2022. "Optimal planning of solar photovoltaic and battery storage systems for grid-connected residential sector: Review, challenges and new perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    7. Idiano D’Adamo, 2018. "The Profitability of Residential Photovoltaic Systems. A New Scheme of Subsidies Based on the Price of CO 2 in a Developed PV Market," Social Sciences, MDPI, vol. 7(9), pages 1-21, August.
    8. Manish Mohanpurkar & Yusheng Luo & Danny Terlip & Fernando Dias & Kevin Harrison & Joshua Eichman & Rob Hovsapian & Jennifer Kurtz, 2017. "Electrolyzers Enhancing Flexibility in Electric Grids," Energies, MDPI, vol. 10(11), pages 1-17, November.
    9. Ibrahim Alsaidan & Abdulaziz Alanazi & Wenzhong Gao & Hongyu Wu & Amin Khodaei, 2017. "State-Of-The-Art in Microgrid-Integrated Distributed Energy Storage Sizing," Energies, MDPI, vol. 10(9), pages 1-14, September.
    10. Hun-Chul Seo, 2017. "New Configuration and Novel Reclosing Procedure of Distribution System for Utilization of BESS as UPS in Smart Grid," Sustainability, MDPI, vol. 9(4), pages 1-16, March.
    11. Domenico Campisi & Simone Gitto & Donato Morea, 2018. "Shari’ah-Compliant Finance: A Possible Novel Paradigm for Green Economy Investments in Italy," Sustainability, MDPI, vol. 10(11), pages 1-12, October.
    12. Muhammad Adil Khan & Kamran Zeb & P. Sathishkumar & Himanshu & S. Srinivasa Rao & Chandu V. V. Muralee Gopi & Hee-Je Kim, 2018. "A Novel Off-Grid Optimal Hybrid Energy System for Rural Electrification of Tanzania Using a Closed Loop Cooled Solar System," Energies, MDPI, vol. 11(4), pages 1-22, April.
    13. Federica Cucchiella & Idiano D’Adamo & Massimo Gastaldi, 2017. "Economic Analysis of a Photovoltaic System: A Resource for Residential Households," Energies, MDPI, vol. 10(6), pages 1-15, June.
    14. Hafiz Abdul Muqeet & Hafiz Mudassir Munir & Haseeb Javed & Muhammad Shahzad & Mohsin Jamil & Josep M. Guerrero, 2021. "An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges," Energies, MDPI, vol. 14(20), pages 1-34, October.
    15. Kongrit Mansiri & Sukruedee Sukchai & Chatchai Sirisamphanwong, 2018. "Fuzzy Control for Smart PV-Battery System Management to Stabilize Grid Voltage of 22 kV Distribution System in Thailand," Energies, MDPI, vol. 11(7), pages 1-19, July.
    16. P. Sathishkumar & T. N. V. Krishna & Himanshu & Muhammad Adil Khan & Kamran Zeb & Hee-Je Kim, 2018. "Digital Soft Start Implementation for Minimizing Start Up Transients in High Power DAB-IBDC Converter," Energies, MDPI, vol. 11(4), pages 1-18, April.
    17. Su Su & Yong Hu & Tiantian Yang & Shidan Wang & Ziqi Liu & Xiangxiang Wei & Mingchao Xia & Yutaka Ota & Koji Yamashita, 2018. "Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities," Energies, MDPI, vol. 11(2), pages 1-19, February.
    18. Khalilpour, Kaveh R. & Lusis, Peter, 2020. "Network capacity charge for sustainability and energy equity: A model-based analysis," Applied Energy, Elsevier, vol. 266(C).
    19. Md. Mahamudul Hasan & Boris Berseneff & Tim Meulenbroeks & Igor Cantero & Sajib Chakraborty & Thomas Geury & Omar Hegazy, 2022. "A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology," Energies, MDPI, vol. 15(15), pages 1-21, July.
    20. Thebelt, Alexander & Tsay, Calvin & Lee, Robert M. & Sudermann-Merx, Nathan & Walz, David & Tranter, Tom & Misener, Ruth, 2022. "Multi-objective constrained optimization for energy applications via tree ensembles," Applied Energy, Elsevier, vol. 306(PB).
    21. Diju Gao & Xuyang Wang & Tianzhen Wang & Yide Wang & Xiaobin Xu, 2018. "An Energy Optimization Strategy for Hybrid Power Ships under Load Uncertainty Based on Load Power Prediction and Improved NSGA-II Algorithm," Energies, MDPI, vol. 11(7), pages 1-14, July.
    22. Qi Wang & Ping Chang & Runqing Bai & Wenfei Liu & Jianfeng Dai & Yi Tang, 2019. "Mitigation Strategy for Duck Curve in High Photovoltaic Penetration Power System Using Concentrating Solar Power Station," Energies, MDPI, vol. 12(18), pages 1-16, September.
    23. Hassan, Aakash & Al-Abdeli, Yasir M. & Masek, Martin & Bass, Octavian, 2022. "Optimal sizing and energy scheduling of grid-supplemented solar PV systems with battery storage: Sensitivity of reliability and financial constraints," Energy, Elsevier, vol. 238(PA).
    24. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2017. "Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids," Energies, MDPI, vol. 10(7), pages 1-19, June.
    25. Mihai Sanduleac & Irina Ciornei & Mihaela Albu & Lucian Toma & Marta Sturzeanu & João F. Martins, 2017. "Resilient Prosumer Scenario in a Changing Regulatory Environment—The UniRCon Solution," Energies, MDPI, vol. 10(12), pages 1-22, November.

    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. Wenxia Liu & Shuya Niu & Huiting Xu & Xiaoying Li, 2016. "A New Method to Plan the Capacity and Location of Battery Swapping Station for Electric Vehicle Considering Demand Side Management," Sustainability, MDPI, vol. 8(6), pages 1-17, June.
    2. Moore, J. & Woo, C.K. & Horii, B. & Price, S. & Olson, A., 2010. "Estimating the option value of a non-firm electricity tariff," Energy, Elsevier, vol. 35(4), pages 1609-1614.
    3. Nian Liu & Cheng Wang & Minyang Cheng & Jie Wang, 2016. "A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids," Energies, MDPI, vol. 9(12), pages 1-18, December.
    4. Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
    5. Ibrahim, Amin & Rahnamayan, Shahryar & Vargas Martin, Miguel & Yilbas, Bekir, 2014. "Multi-objective thermal analysis of a thermoelectric device: Influence of geometric features on device characteristics," Energy, Elsevier, vol. 77(C), pages 305-317.
    6. 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.
    7. Asad Rasheed & Kalyana C. Veluvolu, 2024. "Respiratory Motion Prediction with Empirical Mode Decomposition-Based Random Vector Functional Link," Mathematics, MDPI, vol. 12(4), pages 1-20, February.
    8. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Cohen, Miri Weiss & Reis, Agnaldo J.R. & Silva, Sidelmo M. & Souza, Marcone J.F. & Fleming, Peter J. & Guimarães, Frederico G., 2016. "Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid," Renewable Energy, Elsevier, vol. 89(C), pages 730-742.
    9. Montuori, Lina & Alcázar-Ortega, Manuel, 2021. "Demand response strategies for the balancing of natural gas systems: Application to a local network located in The Marches (Italy)," Energy, Elsevier, vol. 225(C).
    10. García-Triviño, Pablo & Torreglosa, Juan P. & Fernández-Ramírez, Luis M. & Jurado, Francisco, 2016. "Control and operation of power sources in a medium-voltage direct-current microgrid for an electric vehicle fast charging station with a photovoltaic and a battery energy storage system," Energy, Elsevier, vol. 115(P1), pages 38-48.
    11. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    12. Rious, Vincent & Perez, Yannick & Roques, Fabien, 2015. "Which electricity market design to encourage the development of demand response?," Economic Analysis and Policy, Elsevier, vol. 48(C), pages 128-138.
    13. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    14. 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).
    15. Nian Liu & Minyang Cheng, 2017. "Effectiveness Evaluation for a Commercialized PV-Assisted Charging Station," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    16. Hyun, Minwoo & Kim, Yeong Jae & Eom, Jiyong, 2020. "Assessing the impact of a demand-resource bidding market on an electricity generation portfolio and the environment," Energy Policy, Elsevier, vol. 147(C).
    17. Tu, Chunming & He, Xi & Shuai, Zhikang & Jiang, Fei, 2017. "Big data issues in smart grid – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1099-1107.
    18. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    19. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
    20. Khan, Aftab Ahmed & Razzaq, Sohail & Khan, Asadullah & Khursheed, Fatima & Owais,, 2015. "HEMSs and enabled demand response in electricity market: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 773-785.

    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:9:y:2016:i:8:p:591-:d:74849. 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.