IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v161y2016icp197-205.html
   My bibliography  Save this article

Decision tree aided planning and energy balancing of planned community microgrids

Author

Listed:
  • Moutis, Panayiotis
  • Skarvelis-Kazakos, Spyros
  • Brucoli, Maria

Abstract

Planned Communities (PCs) present a unique opportunity for deployment of intelligent control of demand-side distributed energy resources (DER) and storage, which may be organized in Microgrids (MGs). MGs require balancing for maintaining safe and resilient operation. This paper discusses the implications of using MG concepts for planning and control of energy systems within PCs. A novel tool is presented, based on decision trees (DTs), with two potential applications: (i) planning of energy storage systems within such MGs and (ii) controlling energy resources for energy balancing within a PC MG. The energy storage planning and energy balancing methodology is validated through sensitivity case studies, demonstrating its effectiveness. A test implementation is presented, utilizing distributed controller hardware to execute the energy balancing algorithm in real-time.

Suggested Citation

  • Moutis, Panayiotis & Skarvelis-Kazakos, Spyros & Brucoli, Maria, 2016. "Decision tree aided planning and energy balancing of planned community microgrids," Applied Energy, Elsevier, vol. 161(C), pages 197-205.
    • Handle: RePEc:eee:appene:v:161:y:2016:i:c:p:197-205
    DOI: 10.1016/j.apenergy.2015.10.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915012295
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.10.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stadler, M. & Groissböck, M. & Cardoso, G. & Marnay, C., 2014. "Optimizing Distributed Energy Resources and building retrofits with the strategic DER-CAModel," Applied Energy, Elsevier, vol. 132(C), pages 557-567.
    2. Johnson, Jeremiah X. & De Kleine, Robert & Keoleian, Gregory A., 2014. "Assessment of energy storage for transmission-constrained wind," Applied Energy, Elsevier, vol. 124(C), pages 377-388.
    3. Shen, Bo & Ghatikar, Girish & Lei, Zeng & Li, Jinkai & Wikler, Greg & Martin, Phil, 2014. "The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges," Applied Energy, Elsevier, vol. 130(C), pages 814-823.
    4. Huisman, Ronald & Huurman, Christian & Mahieu, Ronald, 2007. "Hourly electricity prices in day-ahead markets," Energy Economics, Elsevier, vol. 29(2), pages 240-248, March.
    5. McIlveen-Wright, David R. & Huang, Ye & Rezvani, Sina & Redpath, David & Anderson, Mark & Dave, Ashok & Hewitt, Neil J., 2013. "A technical and economic analysis of three large scale biomass combustion plants in the UK," Applied Energy, Elsevier, vol. 112(C), pages 396-404.
    6. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
    7. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    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. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
    2. Krkoleva Mateska, Aleksandra & Borozan, Vesna & Krstevski, Petar & Taleski, Rubin, 2018. "Controllable load operation in microgrids using control scheme based on gossip algorithm," Applied Energy, Elsevier, vol. 210(C), pages 1336-1346.
    3. Dania Ortiz & Vera Migueis & Vitor Leal & Janelle Knox-Hayes & Jungwoo Chun, 2022. "Analysis of Renewable Energy Policies through Decision Trees," Sustainability, MDPI, vol. 14(13), pages 1-31, June.
    4. Huang, Guizao & Wu, Guangning & Yang, Zefeng & Chen, Xing & Wei, Wenfu, 2023. "Development of surrogate models for evaluating energy transfer quality of high-speed railway pantograph-catenary system using physics-based model and machine learning," Applied Energy, Elsevier, vol. 333(C).
    5. Siwei Lou & Wenqiang Chen & Danny H.W. Li & Mo Wang & Hainan Chen & Isaac Y.F. Lun & Dawei Xia, 2019. "Tilted Photovoltaic Energy Outputs in Outdoor Environments," Sustainability, MDPI, vol. 11(21), pages 1-17, October.
    6. Hartmann, Bálint & Divényi, Dániel & Vokony, István, 2018. "Evaluation of business possibilities of energy storage at commercial and industrial consumers – A case study," Applied Energy, Elsevier, vol. 222(C), pages 59-66.
    7. Liu, Jiangfeng & Zhang, Qi & Li, Hailong & Chen, Siyuan & Teng, Fei, 2022. "Investment decision on carbon capture and utilization (CCU) technologies—A real option model based on technology learning effect," Applied Energy, Elsevier, vol. 322(C).
    8. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    9. Huo, Yuchong & Bouffard, François & Joós, Géza, 2021. "Decision tree-based optimization for flexibility management for sustainable energy microgrids," Applied Energy, Elsevier, vol. 290(C).
    10. Du, Yan & Wang, Zhiwei & Liu, Guangyi & Chen, Xi & Yuan, Haoyu & Wei, Yanli & Li, Fangxing, 2018. "A cooperative game approach for coordinating multi-microgrid operation within distribution systems," Applied Energy, Elsevier, vol. 222(C), pages 383-395.
    11. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
    12. Meena, Nand K. & Yang, Jin & Zacharis, Evan, 2019. "Optimisation framework for the design and operation of open-market urban and remote community microgrids," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    13. Huo, Yuchong & Bouffard, François & Joós, Géza, 2022. "Integrating learning and explicit model predictive control for unit commitment in microgrids," Applied Energy, Elsevier, vol. 306(PA).
    14. Adolfo Crespo Márquez & Antonio de la Fuente Carmona & Sara Antomarioni, 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency," Energies, MDPI, vol. 12(18), pages 1-25, September.
    15. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    16. Mehmet Efe Biresselioglu & Muhittin Hakan Demir, 2022. "Constructing a Decision Tree for Energy Policy Domain Based on Real-Life Data," Energies, MDPI, vol. 15(7), pages 1-15, March.
    17. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
    18. Kou, Peng & Liang, Deliang & Gao, Lin, 2017. "Distributed EMPC of multiple microgrids for coordinated stochastic energy management," Applied Energy, Elsevier, vol. 185(P1), pages 939-952.

    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. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Theresa Liegl & Simon Schramm & Philipp Kuhn & Thomas Hamacher, 2023. "Considering Socio-Technical Parameters in Energy System Models—The Current Status and Next Steps," Energies, MDPI, vol. 16(20), pages 1-19, October.
    3. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    4. Leif Hanrahan, Brian & Lightbody, Gordon & Staudt, Lawrence & G. Leahy, Paul, 2014. "A powerful visualization technique for electricity supply and demand at industrial sites with combined heat and power and wind generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 860-869.
    5. Azofra, D. & Martínez, E. & Jiménez, E. & Blanco, J. & Saenz-Díez, J.C., 2014. "Comparison of the influence of biomass, solar–thermal and small hydraulic power on the Spanish electricity prices by means of artificial intelligence techniques," Applied Energy, Elsevier, vol. 121(C), pages 28-37.
    6. Fleischhacker, Andreas & Lettner, Georg & Schwabeneder, Daniel & Auer, Hans, 2019. "Portfolio optimization of energy communities to meet reductions in costs and emissions," Energy, Elsevier, vol. 173(C), pages 1092-1105.
    7. Xu, Bing & Nayak, Amar & Gray, David & Ouenniche, Jamal, 2016. "Assessing energy business cases implemented in the North Sea Region and strategy recommendations," Applied Energy, Elsevier, vol. 172(C), pages 360-371.
    8. 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.
    9. Thakur, Jagruti & Chakraborty, Basab, 2016. "Demand side management in developing nations: A mitigating tool for energy imbalance and peak load management," Energy, Elsevier, vol. 114(C), pages 895-912.
    10. Villa-Arrieta, Manuel & Sumper, Andreas, 2018. "A model for an economic evaluation of energy systems using TRNSYS," Applied Energy, Elsevier, vol. 215(C), pages 765-777.
    11. Sayegh, M.A. & Danielewicz, J. & Nannou, T. & Miniewicz, M. & Jadwiszczak, P. & Piekarska, K. & Jouhara, H., 2017. "Trends of European research and development in district heating technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1183-1192.
    12. Marchetti, Isabella & Rego, Erik Eduardo, 2022. "The impact of hourly pricing for renewable generation projects in Brazil," Renewable Energy, Elsevier, vol. 189(C), pages 601-617.
    13. Mara Madaleno & Carlos Pinho, 2010. "Hedging Performance and Multiscale Relationships in the German Electricity Spot and Futures Markets," JRFM, MDPI, vol. 3(1), pages 1-37, December.
    14. Sinha, Pankaj & Mathur, Kritika, 2016. "Empirical Analysis of Developments in the Day Ahead Electricity Markets in India," MPRA Paper 72969, University Library of Munich, Germany.
    15. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    16. Thao Pham & Killian Lemoine, 2020. "Impacts of subsidized renewable electricity generation on spot market prices in Germany : Evidence from a GARCH model with panel data," Working Papers hal-02568268, HAL.
    17. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    18. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    19. Stadler, M. & Groissböck, M. & Cardoso, G. & Marnay, C., 2014. "Optimizing Distributed Energy Resources and building retrofits with the strategic DER-CAModel," Applied Energy, Elsevier, vol. 132(C), pages 557-567.
    20. Thomaidis, Nikolaos S. & Biskas, Pandelis N., 2021. "Fundamental pricing laws and long memory effects in the day-ahead power market," Energy Economics, Elsevier, vol. 100(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:eee:appene:v:161:y:2016:i:c:p:197-205. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.