IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i14p3715-d386732.html
   My bibliography  Save this article

Modelling Energy Distribution in Residential Areas: A Case Study Including Energy Storage Systems in Catania, Southern Italy

Author

Listed:
  • Alberto Fichera

    (Department of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy)

  • Alessandro Pluchino

    (Department of Physics and Astronomy “Ettore Majorana”, University of Catania, 95124 Catania, Italy)

  • Rosaria Volpe

    (Department of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy)

Abstract

Complexity is a widely acknowledged feature of urban areas. Among the different levels to which this definition applies, the energy sector is one of the most representative of this way of conceiving cities. An evidence of this complexity can be detected in the growing impact of prosumers. Prosumers produce energy to meet their own demands, distribute it directly to neighbors and, eventually, store the energy neither consumed nor distributed. The modelling of distribution networks is a challenging task that requires ad hoc models to simulate the mutual energy exchanges occurring among prosumers. To serve at this scope, this paper proposes an agent-based model aiming at determining which operating conditions enhance the energy distribution among prosumers and diminish the supply from traditional power plants. An application of the model within a residential territory is then presented and simulations are conducted under two scenarios: the first investigating the distribution among prosumers equipped with photovoltaics (PV) systems, the second integrating energy storage systems to PV panels. Both scenarios are studied at varying the installed PV capacity within the territory, the allowed distance of connection among prosumers, as well as the rate of utilization of the links of the network. Results from the simulated case study reveal that the energy distribution among prosumers can be enhanced by providing short-range links for the electricity exchange. Similar advantages can be achieved by integrating storage systems to PV, along with a significant reduction in the electricity requested to the centralized grid.

Suggested Citation

  • Alberto Fichera & Alessandro Pluchino & Rosaria Volpe, 2020. "Modelling Energy Distribution in Residential Areas: A Case Study Including Energy Storage Systems in Catania, Southern Italy," Energies, MDPI, vol. 13(14), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3715-:d:386732
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/14/3715/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/14/3715/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hafiz, Faeza & Rodrigo de Queiroz, Anderson & Fajri, Poria & Husain, Iqbal, 2019. "Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach," Applied Energy, Elsevier, vol. 236(C), pages 42-54.
    2. Falke, Tobias & Krengel, Stefan & Meinerzhagen, Ann-Kathrin & Schnettler, Armin, 2016. "Multi-objective optimization and simulation model for the design of distributed energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1508-1516.
    3. Klein, Martin & Ziade, Ahmad & de Vries, Laurens, 2019. "Aligning prosumers with the electricity wholesale market – The impact of time-varying price signals and fixed network charges on solar self-consumption," Energy Policy, Elsevier, vol. 134(C).
    4. Umberto Berardi, 2012. "Sustainability Assessment in the Construction Sector: Rating Systems and Rated Buildings," Sustainable Development, John Wiley & Sons, Ltd., vol. 20(6), pages 411-424, November.
    5. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimization framework for distributed energy systems with integrated electrical grid constraints," Applied Energy, Elsevier, vol. 171(C), pages 296-313.
    6. Kabir, M.N. & Mishra, Y. & Ledwich, G. & Xu, Z. & Bansal, R.C., 2014. "Improving voltage profile of residential distribution systems using rooftop PVs and Battery Energy Storage systems," Applied Energy, Elsevier, vol. 134(C), pages 290-300.
    7. Ma, Xiandong & Wang, Yifei & Qin, Jianrong, 2013. "Generic model of a community-based microgrid integrating wind turbines, photovoltaics and CHP generations," Applied Energy, Elsevier, vol. 112(C), pages 1475-1482.
    8. Graditi, G. & Ippolito, M.G. & Telaretti, E. & Zizzo, G., 2016. "Technical and economical assessment of distributed electrochemical storages for load shifting applications: An Italian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 515-523.
    9. Barbour, Edward & Parra, David & Awwad, Zeyad & González, Marta C., 2018. "Community energy storage: A smart choice for the smart grid?," Applied Energy, Elsevier, vol. 212(C), pages 489-497.
    10. Alberto Fichera & Elisa Marrasso & Maurizio Sasso & Rosaria Volpe, 2020. "Energy, Environmental and Economic Performance of an Urban Community Hybrid Distributed Energy System," Energies, MDPI, vol. 13(10), pages 1-19, May.
    11. Orehounig, Kristina & Evins, Ralph & Dorer, Viktor, 2015. "Integration of decentralized energy systems in neighbourhoods using the energy hub approach," Applied Energy, Elsevier, vol. 154(C), pages 277-289.
    12. Antonio Gagliano & Francesco Nocera & Giuseppe Tina, 2020. "Performances and economic analysis of small photovoltaic–electricity energy storage system for residential applications," Energy & Environment, , vol. 31(1), pages 155-175, February.
    13. Erdinc, Ozan & Paterakis, Nikolaos G. & Pappi, Iliana N. & Bakirtzis, Anastasios G. & Catalão, João P.S., 2015. "A new perspective for sizing of distributed generation and energy storage for smart households under demand response," Applied Energy, Elsevier, vol. 143(C), pages 26-37.
    14. Comodi, Gabriele & Giantomassi, Andrea & Severini, Marco & Squartini, Stefano & Ferracuti, Francesco & Fonti, Alessandro & Nardi Cesarini, Davide & Morodo, Matteo & Polonara, Fabio, 2015. "Multi-apartment residential microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies," Applied Energy, Elsevier, vol. 137(C), pages 854-866.
    15. Mehleri, Eugenia D. & Sarimveis, Haralambos & Markatos, Nikolaos C. & Papageorgiou, Lazaros G., 2012. "A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level," Energy, Elsevier, vol. 44(1), pages 96-104.
    16. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2015. "Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving," Applied Energy, Elsevier, vol. 147(C), pages 246-257.
    17. Ibrahim, H. & Ilinca, A. & Perron, J., 2008. "Energy storage systems--Characteristics and comparisons," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(5), pages 1221-1250, June.
    18. Inturri, Giuseppe & Le Pira, Michela & Giuffrida, Nadia & Ignaccolo, Matteo & Pluchino, Alessandro & Rapisarda, Andrea & D'Angelo, Riccardo, 2019. "Multi-agent simulation for planning and designing new shared mobility services," Research in Transportation Economics, Elsevier, vol. 73(C), pages 34-44.
    19. Yang, Yun & Zhang, Shijie & Xiao, Yunhan, 2015. "Optimal design of distributed energy resource systems coupled with energy distribution networks," Energy, Elsevier, vol. 85(C), pages 433-448.
    20. Fossati, Juan P. & Galarza, Ainhoa & Martín-Villate, Ander & Fontán, Luis, 2015. "A method for optimal sizing energy storage systems for microgrids," Renewable Energy, Elsevier, vol. 77(C), pages 539-549.
    21. Cedillos Alvarado, Dagoberto & Acha, Salvador & Shah, Nilay & Markides, Christos N., 2016. "A Technology Selection and Operation (TSO) optimisation model for distributed energy systems: Mathematical formulation and case study," Applied Energy, Elsevier, vol. 180(C), pages 491-503.
    22. Blarke, M.B. & Lund, H., 2008. "The effectiveness of storage and relocation options in renewable energy systems," Renewable Energy, Elsevier, vol. 33(7), pages 1499-1507.
    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. Eid Gul & Giorgio Baldinelli & Pietro Bartocci, 2022. "Energy Transition: Renewable Energy-Based Combined Heat and Power Optimization Model for Distributed Communities," Energies, MDPI, vol. 15(18), pages 1-18, September.
    2. Volpe, R. & Catrini, P. & Piacentino, A. & Fichera, A., 2022. "An agent-based model to support the preliminary design and operation of heating and power grids with cogeneration units and photovoltaic panels in densely populated areas," Energy, Elsevier, vol. 261(PB).
    3. 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. Fichera, Alberto & Frasca, Mattia & Volpe, Rosaria, 2017. "Complex networks for the integration of distributed energy systems in urban areas," Applied Energy, Elsevier, vol. 193(C), pages 336-345.
    2. Alberto Fichera & Mattia Frasca & Rosaria Volpe, 2020. "A cost-based approach for evaluating the impact of a network of distributed energy systems on the centralized energy supply," Energy & Environment, , vol. 31(1), pages 77-87, February.
    3. Urban, Kristof L. & Scheller, Fabian & Bruckner, Thomas, 2021. "Suitability assessment of models in the industrial energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    4. Zhigang Duan & Yamin Yan & Xiaohan Yan & Qi Liao & Wan Zhang & Yongtu Liang & Tianqi Xia, 2017. "An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand," Energies, MDPI, vol. 11(1), pages 1-23, December.
    5. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2017. "Decarbonizing the electricity grid: The impact on urban energy systems, distribution grids and district heating potential," Applied Energy, Elsevier, vol. 191(C), pages 125-140.
    6. Prasanna, Ashreeta & Dorer, Viktor & Vetterli, Nadège, 2017. "Optimisation of a district energy system with a low temperature network," Energy, Elsevier, vol. 137(C), pages 632-648.
    7. Marquant, Julien F. & Evins, Ralph & Bollinger, L. Andrew & Carmeliet, Jan, 2017. "A holarchic approach for multi-scale distributed energy system optimisation," Applied Energy, Elsevier, vol. 208(C), pages 935-953.
    8. Jie Ji & Xin Xia & Wei Ni & Kailiang Teng & Chunqiong Miao & Yaodong Wang & Tony Roskilly, 2019. "An Experimental and Simulation Study on Optimisation of the Operation of a Distributed Power Generation System with Energy Storage—Meeting Dynamic Household Electricity Demand," Energies, MDPI, vol. 12(6), pages 1-16, March.
    9. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    10. Zwickl-Bernhard, Sebastian & Auer, Hans, 2021. "Open-source modeling of a low-carbon urban neighborhood with high shares of local renewable generation," Applied Energy, Elsevier, vol. 282(PA).
    11. Yang, Dongfeng & Jiang, Chao & Cai, Guowei & Yang, Deyou & Liu, Xiaojun, 2020. "Interval method based optimal planning of multi-energy microgrid with uncertain renewable generation and demand," Applied Energy, Elsevier, vol. 277(C).
    12. Braeuer, Fritz & Kleinebrahm, Max & Naber, Elias & Scheller, Fabian & McKenna, Russell, 2022. "Optimal system design for energy communities in multi-family buildings: the case of the German Tenant Electricity Law," Applied Energy, Elsevier, vol. 305(C).
    13. Paulo Rotella Junior & Luiz Célio Souza Rocha & Sandra Naomi Morioka & Ivan Bolis & Gianfranco Chicco & Andrea Mazza & Karel Janda, 2021. "Economic Analysis of the Investments in Battery Energy Storage Systems: Review and Current Perspectives," Energies, MDPI, vol. 14(9), pages 1-29, April.
    14. Wang, Wei & Jing, Rui & Zhao, Yingru & Zhang, Chuan & Wang, Xiaonan, 2020. "A load-complementarity combined flexible clustering approach for large-scale urban energy-water nexus optimization," Applied Energy, Elsevier, vol. 270(C).
    15. Kang, Jing & Wang, Shengwei, 2018. "Robust optimal design of distributed energy systems based on life-cycle performance analysis using a probabilistic approach considering uncertainties of design inputs and equipment degradations," Applied Energy, Elsevier, vol. 231(C), pages 615-627.
    16. Jing, Rui & Wang, Meng & Zhang, Zhihui & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2019. "Distributed or centralized? Designing district-level urban energy systems by a hierarchical approach considering demand uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    17. Novoa, Laura & Flores, Robert & Brouwer, Jack, 2019. "Optimal renewable generation and battery storage sizing and siting considering local transformer limits," Applied Energy, Elsevier, vol. 256(C).
    18. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
    19. Christina Papadimitriou & Marialaura Di Somma & Chrysanthos Charalambous & Martina Caliano & Valeria Palladino & Andrés Felipe Cortés Borray & Amaia González-Garrido & Nerea Ruiz & Giorgio Graditi, 2023. "A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks," Energies, MDPI, vol. 16(10), pages 1-46, May.
    20. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(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:13:y:2020:i:14:p:3715-:d:386732. 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.