IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p101-d1010944.html
   My bibliography  Save this article

Optimal Scheduling of Controllable Resources in Energy Communities: An Overview of the Optimization Approaches

Author

Listed:
  • Emely Cruz-De-Jesús

    (AICIA (Andalusian Association for Research and Industrial Cooperation), 41092 Seville, Spain)

  • Jose L. Martínez-Ramos

    (Department of Electrical Engineering, Universidad de Sevilla, 41092 Seville, Spain)

  • Alejandro Marano-Marcolini

    (Department of Electrical Engineering, Universidad de Sevilla, 41092 Seville, Spain)

Abstract

In recent years, there has been a growing interest in the study of energy communities. This new definition refers to a community sharing energy resources of different types to meet its needs and reduce the associated costs. Optimization is one of the most widely used techniques for scheduling the operation of an energy community. In this study, we extensively reviewed the mathematical models used depending on the objectives and constraints considered. The models were also classified according to whether they address uncertainty and the inclusion of flexibility constraints. The main contribution of this study is the analysis of the most recent research on the mathematical formulation of optimization models for optimal scheduling of resources in energy communities. The results show that the most commonly used objectives are profit maximization and cost minimization. Additionally, in almost all cases, photovoltaic generation is one of the main energy sources. Electricity prices, renewable generation, and energy demand are sources of uncertainty that have been modeled using stochastic and robust optimization. Flexibility services using demand response are often modeled using interruptible loads and shiftable loads. There is still considerable room for further research on the distribution of benefits among the participants of the energy community and the provision of flexibility services to the electricity grid.

Suggested Citation

  • Emely Cruz-De-Jesús & Jose L. Martínez-Ramos & Alejandro Marano-Marcolini, 2022. "Optimal Scheduling of Controllable Resources in Energy Communities: An Overview of the Optimization Approaches," Energies, MDPI, vol. 16(1), pages 1-15, December.
  • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:101-:d:1010944
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/101/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/1/101/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Simoiu, Mircea Stefan & Fagarasan, Ioana & Ploix, Stéphane & Calofir, Vasile, 2022. "Modeling the energy community members’ willingness to change their behaviour with multi-agent systems: A stochastic approach," Renewable Energy, Elsevier, vol. 194(C), pages 1233-1246.
    2. Gjorgievski, Vladimir Z. & Cundeva, Snezana & Georghiou, George E., 2021. "Social arrangements, technical designs and impacts of energy communities: A review," Renewable Energy, Elsevier, vol. 169(C), pages 1138-1156.
    3. Rosato, Antonello & Panella, Massimo & Andreotti, Amedeo & Mohammed, Osama A. & Araneo, Rodolfo, 2021. "Two-stage dynamic management in energy communities using a decision system based on elastic net regularization," Applied Energy, Elsevier, vol. 291(C).
    4. Adamantios G. Papatsounis & Pantelis N. Botsaris & Stefanos Katsavounis, 2022. "Thermal/Cooling Energy on Local Energy Communities: A Critical Review," Energies, MDPI, vol. 15(3), pages 1-20, February.
    5. Álvaro Manso-Burgos & David Ribó-Pérez & Manuel Alcázar-Ortega & Tomás Gómez-Navarro, 2021. "Local Energy Communities in Spain: Economic Implications of the New Tariff and Variable Coefficients," Sustainability, MDPI, vol. 13(19), pages 1-18, September.
    6. Giacomo Talluri & Gabriele Maria Lozito & Francesco Grasso & Carlos Iturrino Garcia & Antonio Luchetta, 2021. "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, MDPI, vol. 14(24), pages 1-23, December.
    7. Urmila Diwekar, 2008. "Introduction to Applied Optimization," Springer Optimization and Its Applications, Springer, number 978-0-387-76635-5, September.
    8. Juan M. Morales & Antonio J. Conejo & Henrik Madsen & Pierre Pinson & Marco Zugno, 2014. "Integrating Renewables in Electricity Markets," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-9411-9, September.
    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. Carlos Pereyra-Mariñez & José Andrickson-Mora & Victor Samuel Ocaña-Guevera & Félix Santos García & Alexander Vallejo Diaz, 2023. "Energy Supply Systems Predicting Model for the Integration of Long-Term Energy Planning Variables with Sustainable Livelihoods Approach in Remote Communities," Energies, MDPI, vol. 16(7), pages 1-17, March.
    2. Emanuele Cutore & Alberto Fichera & Rosaria Volpe, 2023. "A Roadmap for the Design, Operation and Monitoring of Renewable Energy Communities in Italy," Sustainability, MDPI, vol. 15(10), pages 1-26, May.

    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. Tomin, Nikita & Shakirov, Vladislav & Kurbatsky, Victor & Muzychuk, Roman & Popova, Ekaterina & Sidorov, Denis & Kozlov, Alexandr & Yang, Dechang, 2022. "A multi-criteria approach to designing and managing a renewable energy community," Renewable Energy, Elsevier, vol. 199(C), pages 1153-1175.
    2. Heilmann, Jakob & Wensaas, Marthe & Crespo del Granado, Pedro & Hashemipour, Naser, 2022. "Trading algorithms to represent the wholesale market of energy communities in Norway and England," Renewable Energy, Elsevier, vol. 200(C), pages 1426-1437.
    3. Wen, Xin & Abbes, Dhaker & Francois, Bruno, 2021. "Modeling of photovoltaic power uncertainties for impact analysis on generation scheduling and cost of an urban micro grid," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 116-128.
    4. Lazzari, Florencia & Mor, Gerard & Cipriano, Jordi & Solsona, Francesc & Chemisana, Daniel & Guericke, Daniela, 2023. "Optimizing planning and operation of renewable energy communities with genetic algorithms," Applied Energy, Elsevier, vol. 338(C).
    5. Pedro Faria & Zita Vale, 2022. "Realistic Load Modeling for Efficient Consumption Management Using Real-Time Simulation and Power Hardware-in-the-Loop," Energies, MDPI, vol. 16(1), pages 1-15, December.
    6. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    7. Sima, Catalina Alexandra & Popescu, Claudia Laurenta & Popescu, Mihai Octavian & Roscia, Mariacristina & Seritan, George & Panait, Cornel, 2022. "Techno-economic assessment of university energy communities with on/off microgrid," Renewable Energy, Elsevier, vol. 193(C), pages 538-553.
    8. Lorenzo De Vidovich & Luca Tricarico & Matteo Zulianello, 2023. "How Can We Frame Energy Communities’ Organisational Models? Insights from the Research ‘Community Energy Map’ in the Italian Context," Sustainability, MDPI, vol. 15(3), pages 1-25, January.
    9. Fioriti, Davide & Frangioni, Antonio & Poli, Davide, 2021. "Optimal sizing of energy communities with fair revenue sharing and exit clauses: Value, role and business model of aggregators and users," Applied Energy, Elsevier, vol. 299(C).
    10. Isabel Martins & Mujing Ye & Miguel Constantino & Maria Conceição Fonseca & Jorge Cadima, 2014. "Modeling target volume flows in forest harvest scheduling subject to maximum area restrictions," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 343-362, April.
    11. Taylor, Josh A. & Dhople, Sairaj V. & Callaway, Duncan S., 2016. "Power systems without fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1322-1336.
    12. Kamila Słupińska & Marek Wieruszewski & Piotr Szczypa & Anna Kożuch & Krzysztof Adamowicz, 2022. "Public Perception of the Use of Woody Biomass for Energy Purposes in the Evaluation of Content and Information Management on the Internet," Energies, MDPI, vol. 15(19), pages 1-11, September.
    13. Elena Andriollo & Alberto Caimo & Laura Secco & Elena Pisani, 2021. "Collaborations in Environmental Initiatives for an Effective “Adaptive Governance” of Social–Ecological Systems: What Existing Literature Suggests," Sustainability, MDPI, vol. 13(15), pages 1-29, July.
    14. Feng, Wenxiu & Ruiz Mora, Carlos, 2023. "Risk Management of Energy Communities with Hydrogen Production and Storage Technologies," DES - Working Papers. Statistics and Econometrics. WS 36274, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Kubli, Merla & Puranik, Sanket, 2023. "A typology of business models for energy communities: Current and emerging design options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    16. Gabrielli, Paolo & Gazzani, Matteo & Martelli, Emanuele & Mazzotti, Marco, 2018. "Optimal design of multi-energy systems with seasonal storage," Applied Energy, Elsevier, vol. 219(C), pages 408-424.
    17. Ricardo M. Lima & Antonio J. Conejo & Loïc Giraldi & Olivier Le Maître & Ibrahim Hoteit & Omar M. Knio, 2022. "Risk-Averse Stochastic Programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1795-1818, May.
    18. Haji Bashi, Mazaher & De Tommasi, Luciano & Le Cam, Andreea & Relaño, Lorena Sánchez & Lyons, Padraig & Mundó, Joana & Pandelieva-Dimova, Ivanka & Schapp, Henrik & Loth-Babut, Karolina & Egger, Christ, 2023. "A review and mapping exercise of energy community regulatory challenges in European member states based on a survey of collective energy actors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    19. Jesús Fraile Ardanuy & Roberto Alvaro-Hermana & Sandra Castano-Solis & Julia Merino, 2022. "Carbon-Free Electricity Generation in Spain with PV–Storage Hybrid Systems," Energies, MDPI, vol. 15(13), pages 1-20, June.
    20. Guoqiang Sun & Weihang Qian & Wenjin Huang & Zheng Xu & Zhongxing Fu & Zhinong Wei & Sheng Chen, 2019. "Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach," Energies, MDPI, vol. 12(10), pages 1-23, May.

    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:2022:i:1:p:101-:d:1010944. 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.