IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v130y2019icp774-785.html
   My bibliography  Save this article

A real-time energy management system for smart grid integrated photovoltaic generation with battery storage

Author

Listed:
  • Nge, Chee Lim
  • Ranaweera, Iromi U.
  • Midtgård, Ole-Morten
  • Norum, Lars

Abstract

This paper proposes a real-time energy management system (EMS) suitable for rooftop PV installations with battery storage. The EMS is connected to a smart grid where the price signals indirectly control the power output of the PV/battery system in response to the demand variation of the electricity networks. The objective of the EMS is to maximize the revenue over a given time period while meeting the battery stored energy constraint. The optimization problem is solved using the method of Lagrange multipliers. The uniqueness of the proposed EMS remains in the reactive real-time control mechanism that compensates for the PV power forecast error. The proposed EMS requires only forecasting the average PV power output over the total optimization period. This is in contrast to the predictive power scheduling techniques that require accurate instantaneous PV power forecast. The proposed EMS method is verified by benchmarking against the predictive brute-force dynamic programming (DP) approach. The simulation analysis considers days with varying solar irradiance profiles. The simulation analysis shows the proposed EMS operating under practical assumptions, where the battery storage capacity is subject to constraints and the PV power output is not known a priori.

Suggested Citation

  • Nge, Chee Lim & Ranaweera, Iromi U. & Midtgård, Ole-Morten & Norum, Lars, 2019. "A real-time energy management system for smart grid integrated photovoltaic generation with battery storage," Renewable Energy, Elsevier, vol. 130(C), pages 774-785.
    • Handle: RePEc:eee:renene:v:130:y:2019:i:c:p:774-785
    DOI: 10.1016/j.renene.2018.06.073
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118307250
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.06.073?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. Wang, Chengshan & Jiao, Bingqi & Guo, Li & Tian, Zhe & Niu, Jide & Li, Siwei, 2016. "Robust scheduling of building energy system under uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 366-376.
    2. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    3. 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.
    4. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    5. Greening, Lorna A., 2010. "Demand response resources: Who is responsible for implementation in a deregulated market?," Energy, Elsevier, vol. 35(4), pages 1518-1525.
    6. Torres, David & Crichigno, Jorge & Padilla, Gregg & Rivera, Ruben, 2014. "Scheduling coupled photovoltaic, battery and conventional energy sources to maximize profit using linear programming," Renewable Energy, Elsevier, vol. 72(C), pages 284-290.
    7. Nottrott, A. & Kleissl, J. & Washom, B., 2013. "Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems," Renewable Energy, Elsevier, vol. 55(C), pages 230-240.
    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. Saif Jamal & Nadia M. L. Tan & Jagadeesh Pasupuleti, 2021. "A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications," Sustainability, MDPI, vol. 13(18), pages 1-31, September.
    2. Kotarela, F. & Kyritsis, A. & Papanikolaou, N. & Kalogirou, S.A., 2021. "Enhanced nZEB concept incorporating a sustainable Grid Support Scheme," Renewable Energy, Elsevier, vol. 169(C), pages 714-725.
    3. Sooyoung Jung & Yong Tae Yoon & Jun-Ho Huh, 2020. "An Efficient Micro Grid Optimization Theory," Mathematics, MDPI, vol. 8(4), pages 1-21, April.
    4. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    5. Hassan, Qusay & Khadom, Anees A. & Algburi, Sameer & Al-Jiboory, Ali Khudhair & Sameen, Aws Zuhair & Alkhafaji, Mohamed Ayad & Mahmoud, Haitham A. & Awwad, Emad Mahrous & Mahood, Hameed B. & Kazem, Hu, 2024. "Implications of a smart grid-integrated renewable distributed generation capacity expansion strategy: The case of Iraq," Renewable Energy, Elsevier, vol. 221(C).
    6. Logeswaran, T. & Senthil Raja, M. & Beevi Sahul Hameed, Jennathu & Abdulrahim, Mahabuba, 2022. "Power flow management of hybrid system in smart grid requirements using ITSA-MOAT approach," Applied Energy, Elsevier, vol. 319(C).
    7. Al Essa, Mohammed Jasim M., 2019. "Home energy management of thermostatically controlled loads and photovoltaic-battery systems," Energy, Elsevier, vol. 176(C), pages 742-752.
    8. Abraham Alem Kebede & Maitane Berecibar & Thierry Coosemans & Maarten Messagie & Towfik Jemal & Henok Ayele Behabtu & Joeri Van Mierlo, 2020. "A Techno-Economic Optimization and Performance Assessment of a 10 kW P Photovoltaic Grid-Connected System," Sustainability, MDPI, vol. 12(18), pages 1-29, September.
    9. Prakash, K. & Ali, M. & Hossain, M A & Kumar, Nallapaneni Manoj & Islam, M.R. & Macana, C.A. & Chopra, Shauhrat S. & Pota, H.R., 2022. "Planning battery energy storage system in line with grid support parameters enables circular economy aligned ancillary services in low voltage networks," Renewable Energy, Elsevier, vol. 201(P1), pages 802-820.
    10. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2022. "Grid-connected photovoltaic battery systems: A comprehensive review and perspectives," Applied Energy, Elsevier, vol. 328(C).
    11. Yim, Jaeyun & You, Sesun & Blaabjerg, Frede & Lee, Youngwoo & Gui, Yonghao & Kim, Wonhee, 2024. "Energy management systems for forecasted demand error compensation using hybrid energy storage system in nanogrid," Renewable Energy, Elsevier, vol. 221(C).
    12. Banguero, Edison & Correcher, Antonio & Pérez-Navarro, Ángel & García, Emilio & Aristizabal, Andrés, 2020. "Diagnosis of a battery energy storage system based on principal component analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2438-2449.
    13. Larbi Chrifi-Alaoui & Saïd Drid & Mohammed Ouriagli & Driss Mehdi, 2023. "Overview of Photovoltaic and Wind Electrical Power Hybrid Systems," Energies, MDPI, vol. 16(12), pages 1-35, June.

    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. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    2. Georgiou, Giorgos S. & Christodoulides, Paul & Kalogirou, Soteris A., 2019. "Real-time energy convex optimization, via electrical storage, in buildings – A review," Renewable Energy, Elsevier, vol. 139(C), pages 1355-1365.
    3. Bilsel, R. Ufuk & Ravindran, A., 2011. "A multiobjective chance constrained programming model for supplier selection under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1284-1300, September.
    4. L. Jeff Hong & Zhiyuan Huang & Henry Lam, 2021. "Learning-Based Robust Optimization: Procedures and Statistical Guarantees," Management Science, INFORMS, vol. 67(6), pages 3447-3467, June.
    5. Bordin, Chiara & Anuta, Harold Oghenetejiri & Crossland, Andrew & Gutierrez, Isabel Lascurain & Dent, Chris J. & Vigo, Daniele, 2017. "A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration," Renewable Energy, Elsevier, vol. 101(C), pages 417-430.
    6. DiOrio, Nicholas & Denholm, Paul & Hobbs, William B., 2020. "A model for evaluating the configuration and dispatch of PV plus battery power plants," Applied Energy, Elsevier, vol. 262(C).
    7. 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.
    8. Zhang, Bingying & Li, Qiqiang & Wang, Luhao & Feng, Wei, 2018. "Robust optimization for energy transactions in multi-microgrids under uncertainty," Applied Energy, Elsevier, vol. 217(C), pages 346-360.
    9. Darghouth, Naïm R. & Wiser, Ryan H. & Barbose, Galen & Mills, Andrew D., 2016. "Net metering and market feedback loops: Exploring the impact of retail rate design on distributed PV deployment," Applied Energy, Elsevier, vol. 162(C), pages 713-722.
    10. 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.
    11. Bren, Austin & Saghafian, Soroush, 2018. "Data-Driven Percentile Optimization for Multi-Class Queueing Systems with Model Ambiguity: Theory and Application," Working Paper Series rwp18-008, Harvard University, John F. Kennedy School of Government.
    12. Wang, Guang Chao & Ratnam, Elizabeth & Haghi, Hamed Valizadeh & Kleissl, Jan, 2019. "Corrective receding horizon EV charge scheduling using short-term solar forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 1146-1158.
    13. 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.
    14. Marla, Lavanya & Rikun, Alexander & Stauffer, Gautier & Pratsini, Eleni, 2020. "Robust modeling and planning: Insights from three industrial applications," Operations Research Perspectives, Elsevier, vol. 7(C).
    15. Yanyan Wang & Baiqing Sun, 2022. "Multiperiod optimal emergency material allocation considering road network damage and risk under uncertain conditions," Operational Research, Springer, vol. 22(3), pages 2173-2208, July.
    16. Wang, Qipeng & Zhao, Liang, 2023. "Data-driven stochastic robust optimization of sustainable utility system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    17. Wang, Zeyu & Negash, Ahlmahz & Kirschen, Daniel, 2015. "Assessing the financial impacts of distributed energy on load serving entities," Energy Policy, Elsevier, vol. 86(C), pages 380-392.
    18. Aouam, Tarik & Brahimi, Nadjib, 2013. "Integrated production planning and order acceptance under uncertainty: A robust optimization approach," European Journal of Operational Research, Elsevier, vol. 228(3), pages 504-515.
    19. Gülşen, Ece & Olivetti, Elsa & Freire, Fausto & Dias, Luis & Kirchain, Randolph, 2014. "Impact of feedstock diversification on the cost-effectiveness of biodiesel," Applied Energy, Elsevier, vol. 126(C), pages 281-296.
    20. Grani A. Hanasusanto & Vladimir Roitch & Daniel Kuhn & Wolfram Wiesemann, 2017. "Ambiguous Joint Chance Constraints Under Mean and Dispersion Information," Operations Research, INFORMS, vol. 65(3), pages 751-767, June.

    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:renene:v:130:y:2019:i:c:p:774-785. 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.journals.elsevier.com/renewable-energy .

    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.