IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i23p6112-d1536707.html
   My bibliography  Save this article

School Start Times for Solar Alignment: Evaluating the Benefits of Schedule Optimisation for Peak and Cost Reduction

Author

Listed:
  • Terhemba Michael-Ahile

    (Electrical and Electronic Engineering, Stellenbosch University, Matieland 7602, South Africa)

  • Jason Avron Samuels

    (Department of Industrial Engineering, Stellenbosch University, Matieland 7602, South Africa)

  • Marthinus Johannes Booysen

    (Electrical and Electronic Engineering, Stellenbosch University, Matieland 7602, South Africa
    Department of Industrial Engineering, Stellenbosch University, Matieland 7602, South Africa)

Abstract

The global push towards sustainable energy usage and the increasing adoption of renewable energy sources, such as solar power, requires innovative approaches to energy management, particularly in energy-intensive sectors such as education. This study proposes a change in school start time from 7 a.m. to 9 a.m. to align operational hours with periods of off-peak electricity demand and maximum solar availability. Four scenarios are compared: baseline (current schedule without solar), shifted schedule without solar, baseline with solar, and shifted schedule with solar integration. The analysis reveals that shifting the school’s operational hours alone leads to a peak demand reduction of 40%, mitigating strain on the grid during high-demand periods. Solar integration without schedule has a less pronounced effect on peak demand (26%). The combination of schedule shifting and solar integration delivers the most significant benefits, with the highest cost reductions (28%) and peak demand reductions (60%). This study demonstrates that synchronised solar energy generation and optimised scheduling can enhance energy efficiency and long-term financial savings, offering a practical solution for reducing operational costs and improving sustainability in schools. This study demonstrates how public institutions can contribute to the energy transition by adapting their operational schedules to align with renewable energy availability, rather than relying on conventional fixed schedules.

Suggested Citation

  • Terhemba Michael-Ahile & Jason Avron Samuels & Marthinus Johannes Booysen, 2024. "School Start Times for Solar Alignment: Evaluating the Benefits of Schedule Optimisation for Peak and Cost Reduction," Energies, MDPI, vol. 17(23), pages 1-20, December.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6112-:d:1536707
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/23/6112/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/23/6112/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kumar, R. Seshu & Raghav, L. Phani & Raju, D. Koteswara & Singh, Arvind R., 2021. "Intelligent demand side management for optimal energy scheduling of grid connected microgrids," Applied Energy, Elsevier, vol. 285(C).
    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. Nguyen, Hai-Tra & Safder, Usman & Loy-Benitez, Jorge & Yoo, ChangKyoo, 2022. "Optimal demand side management scheduling-based bidirectional regulation of energy distribution network for multi-residential demand response with self-produced renewable energy," Applied Energy, Elsevier, vol. 322(C).
    2. Huang, Yan & Ju, Yuntao & Ma, Kang & Short, Michael & Chen, Tao & Zhang, Ruosi & Lin, Yi, 2022. "Three-phase optimal power flow for networked microgrids based on semidefinite programming convex relaxation," Applied Energy, Elsevier, vol. 305(C).
    3. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    4. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    5. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).
    6. An, Xiangxin & Si, Guojin & Xia, Tangbin & Wang, Dong & Pan, Ershun & Xi, Lifeng, 2023. "An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs," Applied Energy, Elsevier, vol. 336(C).
    7. Phani Raghav, L. & Seshu Kumar, R. & Koteswara Raju, D. & Singh, Arvind R., 2022. "Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid," Applied Energy, Elsevier, vol. 306(PB).
    8. O’Reilly, Ryan & Cohen, Jed & Reichl, Johannes, 2024. "Achievable load shifting potentials for the European residential sector from 2022–2050," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    9. Tang, Hong & Wang, Shengwei, 2022. "A model-based predictive dispatch strategy for unlocking and optimizing the building energy flexibilities of multiple resources in electricity markets of multiple services," Applied Energy, Elsevier, vol. 305(C).
    10. Jianwei Gao & Yu Yang & Fangjie Gao & Haoyu Wu, 2022. "Two-Stage Robust Economic Dispatch of Regional Integrated Energy System Considering Source-Load Uncertainty Based on Carbon Neutral Vision," Energies, MDPI, vol. 15(4), pages 1-16, February.
    11. Kumar, Abhishek & Deng, Yan & He, Xiangning & Singh, Arvind R. & Kumar, Praveen & Bansal, R.C. & Bettayeb, M. & Ghenai, C. & Naidoo, R.M., 2023. "Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index," Applied Energy, Elsevier, vol. 339(C).
    12. Masoud Dashtdar & Aymen Flah & Seyed Mohammad Sadegh Hosseinimoghadam & Hossam Kotb & Elżbieta Jasińska & Radomir Gono & Zbigniew Leonowicz & Michał Jasiński, 2022. "Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony," Sustainability, MDPI, vol. 14(11), pages 1-26, May.
    13. Hanaa Feleafel & Jovana Radulovic & Michel Leseure, 2024. "Should We Have Selfish Microgrids?," Energies, MDPI, vol. 17(16), pages 1-23, August.
    14. Zhou, Xu & Ma, Zhongjing & Zou, Suli & Zhang, Jinhui, 2022. "Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions," Applied Energy, Elsevier, vol. 324(C).
    15. Marcel Antal & Vlad Mihailescu & Tudor Cioara & Ionut Anghel, 2022. "Blockchain-Based Distributed Federated Learning in Smart Grid," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    16. José Luis Ruiz Duarte & Neng Fan, 2022. "Operation of a Power Grid with Embedded Networked Microgrids and Onsite Renewable Technologies," Energies, MDPI, vol. 15(7), pages 1-24, March.
    17. Hwang Goh, Hui & Shi, Shuaiwei & Liang, Xue & Zhang, Dongdong & Dai, Wei & Liu, Hui & Yuong Wong, Shen & Agustiono Kurniawan, Tonni & Chen Goh, Kai & Leei Cham, Chin, 2022. "Optimal energy scheduling of grid-connected microgrids with demand side response considering uncertainty," Applied Energy, Elsevier, vol. 327(C).
    18. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    19. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Turky, Rania A. & Jurado, Francisco, 2022. "Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach," Applied Energy, Elsevier, vol. 310(C).
    20. Ali M. Jasim & Basil H. Jasim & Habib Kraiem & Aymen Flah, 2022. "A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System," Sustainability, MDPI, vol. 14(16), pages 1-28, August.

    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:17:y:2024:i:23:p:6112-:d:1536707. 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.