IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i3p611-d315000.html
   My bibliography  Save this article

Coordination Mechanism for PV Battery Systems with Local Optimizing Energy Management

Author

Listed:
  • Manuel Kersic

    (Belectric GmbH, Industriestraße 65, D-01129 Dresden, Germany)

  • Thilo Bocklisch

    (Chair of Energy Storage Systems, Institute of Power Engineering, Technische Universität Dresden, Helmholtzstraße 9, D-01062 Dresden, Germany)

  • Michael Böttiger

    (Chair of Energy Storage Systems, Institute of Power Engineering, Technische Universität Dresden, Helmholtzstraße 9, D-01062 Dresden, Germany)

  • Lisa Gerlach

    (Chair of Energy Storage Systems, Institute of Power Engineering, Technische Universität Dresden, Helmholtzstraße 9, D-01062 Dresden, Germany)

Abstract

This publication presents a coordination mechanism for neighboring photovoltaic (PV) battery systems with local optimizing energy management (EM). The aim of this coordination is a high degree of self-sufficiency for the neighborhood while maintaining a high individual degree of self-sufficiency and relieving the grid. A financial incentive to increase the energy exchanged within the neighborhood is introduced. The local EM of the individual PV battery system uses model predictive control based on deterministic dynamic programming in order to minimize the individual economic costs and extreme grid power values. By using a coordination algorithm involving a central information processing unit, the neighboring PV battery systems are given information about the sum of the planned consumption and feed-in power profiles of the neighborhood, as well as the neighborhood tariffs. Based on these data, the PV battery systems successively optimize the operation of their batteries until either convergence or a maximum count of iterations is achieved. The operating principle of the distributed EM concept with coordination is demonstrated through a simulation of a residential neighborhood comprising eight households with different load profiles and varying PV peak powers and battery capacities. Its performance is compared with three EM concepts: two distributed concepts without coordination and another one with central optimizing EM representing ideal coordination. The resulting power flow distributions are analyzed, and the benefits and weaknesses of the developed coordination mechanism are discussed based on a number of evaluation criteria.

Suggested Citation

  • Manuel Kersic & Thilo Bocklisch & Michael Böttiger & Lisa Gerlach, 2020. "Coordination Mechanism for PV Battery Systems with Local Optimizing Energy Management," Energies, MDPI, vol. 13(3), pages 1-25, January.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:611-:d:315000
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/3/611/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/3/611/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ranaweera, Iromi & Midtgård, Ole-Morten & Korpås, Magnus, 2017. "Distributed control scheme for residential battery energy storage units coupled with PV systems," Renewable Energy, Elsevier, vol. 113(C), pages 1099-1110.
    2. Ratnam, Elizabeth L. & Weller, Steven R. & Kellett, Christopher M., 2015. "An optimization-based approach to scheduling residential battery storage with solar PV: Assessing customer benefit," Renewable Energy, Elsevier, vol. 75(C), pages 123-134.
    3. Ronny Gelleschus & Michael Böttiger & Thilo Bocklisch, 2019. "Optimization-Based Control Concept with Feed-in and Demand Peak Shaving for a PV Battery Heat Pump Heat Storage System," Energies, MDPI, vol. 12(11), pages 1-16, June.
    4. Khalilpour, Rajab & Vassallo, Anthony, 2016. "Planning and operation scheduling of PV-battery systems: A novel methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 194-208.
    5. Celik, Berk & Roche, Robin & Suryanarayanan, Siddharth & Bouquain, David & Miraoui, Abdellatif, 2017. "Electric energy management in residential areas through coordination of multiple smart homes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 260-275.
    6. Ranaweera, Iromi & Midtgård, Ole-Morten, 2016. "Optimization of operational cost for a grid-supporting PV system with battery storage," Renewable Energy, Elsevier, vol. 88(C), pages 262-272.
    7. Yu-Shan Cheng & Yi-Hua Liu & Holger C. Hesse & Maik Naumann & Cong Nam Truong & Andreas Jossen, 2018. "A PSO-Optimized Fuzzy Logic Control-Based Charging Method for Individual Household Battery Storage Systems within a Community," Energies, MDPI, vol. 11(2), pages 1-18, February.
    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. Karol Bot & Inoussa Laouali & António Ruano & Maria da Graça Ruano, 2021. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques," Energies, MDPI, vol. 14(18), pages 1-27, September.
    2. Etedadi, Farshad & Kelouwani, Sousso & Agbossou, Kodjo & Henao, Nilson & Laurencelle, François, 2023. "Consensus and sharing based distributed coordination of home energy management systems with demand response enabled baseboard heaters," Applied Energy, Elsevier, vol. 336(C).
    3. Insu Do & Siyoung Lee, 2022. "Optimal Scheduling Model of a Battery Energy Storage System in the Unit Commitment Problem Using Special Ordered Set," Energies, MDPI, vol. 15(9), pages 1-14, April.
    4. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.

    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. Azuatalam, Donald & Paridari, Kaveh & Ma, Yiju & Förstl, Markus & Chapman, Archie C. & Verbič, Gregor, 2019. "Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 555-570.
    2. Leithon, Johann & Werner, Stefan & Koivunen, Visa, 2021. "Energy optimization through cooperative storage management: A calculus of variations approach," Renewable Energy, Elsevier, vol. 171(C), pages 1357-1370.
    3. Leithon, Johann & Werner, Stefan & Koivunen, Visa, 2020. "Cost-aware renewable energy management: Centralized vs. distributed generation," Renewable Energy, Elsevier, vol. 147(P1), pages 1164-1179.
    4. Vieira, Filomeno M. & Moura, Pedro S. & de Almeida, Aníbal T., 2017. "Energy storage system for self-consumption of photovoltaic energy in residential zero energy buildings," Renewable Energy, Elsevier, vol. 103(C), pages 308-320.
    5. Vakilifard, Negar & A. Bahri, Parisa & Anda, Martin & Ho, Goen, 2018. "A two-level decision making approach for optimal integrated urban water and energy management," Energy, Elsevier, vol. 155(C), pages 408-425.
    6. Talent, Orlando & Du, Haiping, 2018. "Optimal sizing and energy scheduling of photovoltaic-battery systems under different tariff structures," Renewable Energy, Elsevier, vol. 129(PA), pages 513-526.
    7. Aotian Song & Lin Lu & Zhizhao Liu & Man Sing Wong, 2016. "A Study of Incentive Policies for Building-Integrated Photovoltaic Technology in Hong Kong," Sustainability, MDPI, vol. 8(8), pages 1-21, August.
    8. Koskela, Juha & Rautiainen, Antti & Järventausta, Pertti, 2019. "Using electrical energy storage in residential buildings – Sizing of battery and photovoltaic panels based on electricity cost optimization," Applied Energy, Elsevier, vol. 239(C), pages 1175-1189.
    9. Jessica Thomsen & Christoph Weber, "undated". "How the design of retail prices, network charges, and levies affects profitability and operation of small-scale PV-Battery Storage Systems," EWL Working Papers 1903, University of Duisburg-Essen, Chair for Management Science and Energy Economics.
    10. Lee, J. & Bérard, Jean-Philippe & Razeghi, G. & Samuelsen, S., 2020. "Maximizing PV hosting capacity of distribution feeder microgrid," Applied Energy, Elsevier, vol. 261(C).
    11. Gopinath Subramani & Vigna K. Ramachandaramurthy & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg & Josep M. Guerrero, 2017. "Grid-Tied Photovoltaic and Battery Storage Systems with Malaysian Electricity Tariff—A Review on Maximum Demand Shaving," Energies, MDPI, vol. 10(11), pages 1-17, November.
    12. Langenmayr, Uwe & Wang, Weimin & Jochem, Patrick, 2020. "Unit commitment of photovoltaic-battery systems: An advanced approach considering uncertainties from load, electric vehicles, and photovoltaic," Applied Energy, Elsevier, vol. 280(C).
    13. O'Shaughnessy, Eric & Cutler, Dylan & Ardani, Kristen & Margolis, Robert, 2018. "Solar plus: A review of the end-user economics of solar PV integration with storage and load control in residential buildings," Applied Energy, Elsevier, vol. 228(C), pages 2165-2175.
    14. Li, Dacheng & Guo, Songshan & He, Wei & King, Marcus & Wang, Jihong, 2021. "Combined capacity and operation optimisation of lithium-ion battery energy storage working with a combined heat and power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    15. Babacan, Oytun & Ratnam, Elizabeth L. & Disfani, Vahid R. & Kleissl, Jan, 2017. "Distributed energy storage system scheduling considering tariff structure, energy arbitrage and solar PV penetration," Applied Energy, Elsevier, vol. 205(C), pages 1384-1393.
    16. Sani Hassan, Abubakar & Cipcigan, Liana & Jenkins, Nick, 2017. "Optimal battery storage operation for PV systems with tariff incentives," Applied Energy, Elsevier, vol. 203(C), pages 422-441.
    17. Khalilpour, Kaveh R. & Lusis, Peter, 2020. "Network capacity charge for sustainability and energy equity: A model-based analysis," Applied Energy, Elsevier, vol. 266(C).
    18. Moradi Amani, A. & Sajjadi, S.S. & Al Khafaf, N. & Song, H. & Jalili, M. & Yu, X. & Meegahapola, L. & McTaggart, P., 2023. "Technology balancing for reliable EV uptake in distribution grids: An Australian case study," Renewable Energy, Elsevier, vol. 206(C), pages 939-948.
    19. Anilkumar, T.T. & Simon, Sishaj P. & Padhy, Narayana Prasad, 2017. "Residential electricity cost minimization model through open well-pico turbine pumped storage system," Applied Energy, Elsevier, vol. 195(C), pages 23-35.
    20. Jeddi, Babak & Mishra, Yateendra & Ledwich, Gerard, 2021. "Distributed load scheduling in residential neighborhoods for coordinated operation of multiple home energy management systems," Applied Energy, Elsevier, vol. 300(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:3:p:611-:d:315000. 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.