IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i13p4608-d846408.html
   My bibliography  Save this article

Solar Energy Demand-to-Supply Management by the On-Demand Cumulative-Control Method: Case of a Childcare Facility in Tokyo

Author

Listed:
  • Hiromasa Ijuin

    (Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi 182-8585, Japan)

  • Satoshi Yamada

    (Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi 182-8585, Japan)

  • Tetsuo Yamada

    (Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi 182-8585, Japan)

  • Masato Takanokura

    (Department of Industrial Engineering and Management, Engineering Research Institute, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama-shi 221-8686, Japan)

  • Masayuki Matsui

    (Department of Industrial Engineering and Management, Engineering Research Institute, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama-shi 221-8686, Japan)

Abstract

In recent years, environmental and energy issues relating to global warming have become more serious, and there is a need to shift from conventional power generation, which emits an abundance of carbon dioxide, to renewable energy sources without emissions, such as solar and wind. However, solar power generation, which is one of the renewable energies, changes dynamically, depending on real time weather conditions. Thus, power supplied mainly by solar power generation is often unstable, and an appropriate on-demand energy management for demand-to-supply is required to ensure a stable power supply. Demand-to-supply management methods include inventory management analysis and on-demand inventory management analysis. The cumulative-control method has been used as one of the production management methods to visually manage inventory status in factories and warehouses, while the on-demand cumulative-control method is an extension of inventory management analysis. This study models a demand-to-supply management method for a solar power generation system by using the on-demand cumulative-control method in an actual case. First, a demand-to-supply management method is modeled by an on-demand cumulative-control method, using actual power data from a childcare facility in Tokyo. Next, the on-demand cumulative-control method is adopted to the case without batteries, and the amount of electricity to be purchased is estimated. Finally, the effectiveness of the maximum battery capacity and the amount of the initial charge are examined and discussed by sensitivity analysis.

Suggested Citation

  • Hiromasa Ijuin & Satoshi Yamada & Tetsuo Yamada & Masato Takanokura & Masayuki Matsui, 2022. "Solar Energy Demand-to-Supply Management by the On-Demand Cumulative-Control Method: Case of a Childcare Facility in Tokyo," Energies, MDPI, vol. 15(13), pages 1-23, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4608-:d:846408
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/13/4608/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/13/4608/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pham, An & Jin, Tongdan & Novoa, Clara & Qin, Jin, 2019. "A multi-site production and microgrid planning model for net-zero energy operations," International Journal of Production Economics, Elsevier, vol. 218(C), pages 260-274.
    2. Rentizelas, Athanasios A. & Tolis, Athanasios I. & Tatsiopoulos, Ilias P., 2012. "Investment planning in electricity production under CO2 price uncertainty," International Journal of Production Economics, Elsevier, vol. 140(2), pages 622-629.
    3. Wichmann, Matthias Gerhard & Johannes, Christoph & Spengler, Thomas Stefan, 2019. "Energy-oriented Lot-Sizing and Scheduling considering energy storages," International Journal of Production Economics, Elsevier, vol. 216(C), pages 204-214.
    4. Jahanpour, Ehsan & Ko, Hoo Sang & Nof, Shimon Y., 2016. "Collaboration protocols for sustainable wind energy distribution networks," International Journal of Production Economics, Elsevier, vol. 182(C), pages 496-507.
    5. Trappey, Amy J.C. & Trappey, Charles V. & Liu, Penny H.Y. & Lin, Lee-Cheng & Ou, Jerry J.R., 2013. "A hierarchical cost learning model for developing wind energy infrastructures," International Journal of Production Economics, Elsevier, vol. 146(2), pages 386-391.
    6. Xydis, George, 2013. "A techno-economic and spatial analysis for the optimal planning of wind energy in Kythira island, Greece," International Journal of Production Economics, Elsevier, vol. 146(2), pages 440-452.
    7. Uhlemair, Harald & Karschin, Ingo & Geldermann, Jutta, 2014. "Optimizing the production and distribution system of bioenergy villages," International Journal of Production Economics, Elsevier, vol. 147(PA), pages 62-72.
    8. Victor Santana-Viera & Jesus Jimenez & Tongdan Jin & Jose Espiritu, 2015. "Implementing factory demand response via onsite renewable energy: a design-of-experiment approach," International Journal of Production Research, Taylor & Francis Journals, vol. 53(23), pages 7034-7048, December.
    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. Ursavas, Evrim, 2017. "A benders decomposition approach for solving the offshore wind farm installation planning at the North Sea," European Journal of Operational Research, Elsevier, vol. 258(2), pages 703-714.
    2. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    3. Razm, Sobhan & Brahimi, Nadjib & Hammami, Ramzi & Dolgui, Alexandre, 2023. "A production planning model for biorefineries with biomass perishability and biofuel transformation," International Journal of Production Economics, Elsevier, vol. 258(C).
    4. Büyüközkan, Gülçin & Güleryüz, Sezin, 2016. "An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey," International Journal of Production Economics, Elsevier, vol. 182(C), pages 435-448.
    5. Akihiko Takada & Hiromasa Ijuin & Masayuki Matsui & Tetsuo Yamada, 2023. "Seasonal Analysis and Capacity Planning of Solar Energy Demand-to-Supply Management: Case Study of a Logistics Distribution Center," Energies, MDPI, vol. 17(1), pages 1-23, December.
    6. Magni, Carlo Alberto & Marchioni, Andrea & Baschieri, Davide, 2022. "Impact of financing and payout policy on the economic profitability of solar photovoltaic plants," International Journal of Production Economics, Elsevier, vol. 244(C).
    7. Irawan, Chandra Ade & Jones, Dylan & Hofman, Peter S. & Zhang, Lina, 2023. "Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders," European Journal of Operational Research, Elsevier, vol. 308(2), pages 864-883.
    8. Kumar, Patanjal & Baraiya, Rajendra & Das, Debashree & Jakhar, Suresh Kumar & Xu, Lei & Mangla, Sachin Kumar, 2021. "Social responsibility and cost-learning in dyadic supply chain coordination," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    9. Wei Chen & Yongle Tian & Kaiming Zheng & Nana Wan, 2023. "Influences of mechanisms on investment in renewable energy storage equipment," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12569-12595, November.
    10. Sebastian Schär & Jutta Geldermann, 2021. "Adopting Multiactor Multicriteria Analysis for the Evaluation of Energy Scenarios," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    11. Ren, Shuyun & Luo, Fengji & Lin, Lei & Hsu, Shu-Chien & LI, Xuran Ivan, 2019. "A novel dynamic pricing scheme for a large-scale electric vehicle sharing network considering vehicle relocation and vehicle-grid-integration," International Journal of Production Economics, Elsevier, vol. 218(C), pages 339-351.
    12. Rentizelas, Athanasios & Georgakellos, Dimitrios, 2014. "Incorporating life cycle external cost in optimization of the electricity generation mix," Energy Policy, Elsevier, vol. 65(C), pages 134-149.
    13. Dafni Despoina Avgoustaki & George Xydis, 2020. "Indoor Vertical Farming in the Urban Nexus Context: Business Growth and Resource Savings," Sustainability, MDPI, vol. 12(5), pages 1-18, March.
    14. Zhang, Shichen & Zhang, Jianxiong, 2018. "Contract preference with stochastic cost learning in a two-period supply chain under asymmetric information," International Journal of Production Economics, Elsevier, vol. 196(C), pages 226-247.
    15. Andrea Marchioni & Carlo Alberto Magni & Davide Baschieri, 2020. "Investment and Financing Perspectives for a Solar Photovoltaic Project," MIC 2020: The 20th Management International Conference,, University of Primorska Press.
    16. Federica Cucchiella & Idiano D Adamo & Massimo Gastaldi, 2015. "Profitability Analysis for Biomethane: A Strategic Role in the Italian Transport Sector," International Journal of Energy Economics and Policy, Econjournals, vol. 5(2), pages 440-449.
    17. Hyungguen Park & Changhee Kim, 2018. "Do Shifts in Renewable Energy Operation Policy Affect Efficiency: Korea’s Shift from FIT to RPS and Its Results," Sustainability, MDPI, vol. 10(6), pages 1-14, May.
    18. Sgouridis, Sgouris & Ali, Mohamed & Sleptchenko, Andrei & Bouabid, Ali & Ospina, Gustavo, 2021. "Aluminum smelters in the energy transition: Optimal configuration and operation for renewable energy integration in high insolation regions," Renewable Energy, Elsevier, vol. 180(C), pages 937-953.
    19. Yu Sang Chang & Dosoung Choi & Hann Earl Kim, 2017. "Dynamic Trends of Carbon Intensities among 127 Countries," Sustainability, MDPI, vol. 9(12), pages 1-21, December.
    20. Shen, Xiaojun & Li, Xingyi & Yuan, Jiahai & Jin, Yu, 2022. "A hydrogen-based zero-carbon microgrid demonstration in renewable-rich remote areas: System design and economic feasibility," Applied Energy, Elsevier, vol. 326(C).

    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:15:y:2022:i:13:p:4608-:d:846408. 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.