IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v97y2016icp169-176.html
   My bibliography  Save this article

Optimization of the investment cost of solar based grid

Author

Listed:
  • Siali, M.
  • Flazi, S.
  • Stambouli, A. Boudghene
  • Fergani, S.

Abstract

A new optimization method of the investment cost of a distribution grid supplied by photovoltaic (PV) sources. This method consists of determining the optimal grid cables cross sections and the optimal grid supply point (GSP) position, such that the sum of the joule losses and the cables investment costs are optimized. The determination of these parameters are performed by the programming of analytical equations using Matlab software, taking into account the influence of technical and economical requirements on the choice of the cables cross sections and the possibility of using several photovoltaic generators (PVG) with separated grids for spaced loads from a long distance, by optimizing the grouping loads combination. The obtained results show that: for a lowest global investment cost, the optimal GSP is referred to the minimum cost center (MCC), for the grid joule losses minimization, it can be in the minimum joule losses cost center (MJLCC); or in the center of the minimum investment cost of cables (CMICC) in the case of the cables investment cost minimization. The adoption of the optimal cross sections, the optimal PVGs (GSPs) number and their optimal positions achieve significant economic gains in terms of the investment cost.

Suggested Citation

  • Siali, M. & Flazi, S. & Stambouli, A. Boudghene & Fergani, S., 2016. "Optimization of the investment cost of solar based grid," Renewable Energy, Elsevier, vol. 97(C), pages 169-176.
  • Handle: RePEc:eee:renene:v:97:y:2016:i:c:p:169-176
    DOI: 10.1016/j.renene.2016.05.077
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116304876
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2016.05.077?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. Xiangang Peng & Lixiang Lin & Weiqin Zheng & Yi Liu, 2015. "Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem," Energies, MDPI, vol. 8(12), pages 1-19, December.
    2. 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. Padmanathan K. & Uma Govindarajan & Vigna K. Ramachandaramurthy & Sudar Oli Selvi T., 2017. "Multiple Criteria Decision Making (MCDM) Based Economic Analysis of Solar PV System with Respect to Performance Investigation for Indian Market," Sustainability, MDPI, vol. 9(5), pages 1-19, May.
    2. Joshua Sunday Riti & Deyong Song & Yang Shu & Miriam Kamah & Agya Adi Atabani, 2018. "Does renewable energy ensure environmental quality in favour of economic growth? Empirical evidence from China’s renewable development," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2007-2030, September.

    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. Adrian Grimm & Patrik Schönfeldt & Herena Torio & Peter Klement & Benedikt Hanke & Karsten von Maydell & Carsten Agert, 2021. "Deduction of Optimal Control Strategies for a Sector-Coupled District Energy System," Energies, MDPI, vol. 14(21), pages 1-13, November.
    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. Zhou, P. & Jin, R.Y. & Fan, L.W., 2016. "Reliability and economic evaluation of power system with renewables: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 537-547.
    4. 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.
    5. Raji Atia & Noboru Yamada, 2016. "Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids," Energies, MDPI, vol. 9(3), pages 1-16, March.
    6. Sulaiman A. Almohaimeed & Siddharth Suryanarayanan & Peter O’Neill, 2021. "Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint," Sustainability, MDPI, vol. 13(17), pages 1-24, August.
    7. Hamed Moazami Goodarzi & Mohammad Hosein Kazemi, 2017. "A Novel Optimal Control Method for Islanded Microgrids Based on Droop Control Using the ICA-GA Algorithm," Energies, MDPI, vol. 10(4), pages 1-17, April.
    8. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
    9. Seung-Ju Lee & Yourim Yoon, 2020. "Electricity Cost Optimization in Energy Storage Systems by Combining a Genetic Algorithm with Dynamic Programming," Mathematics, MDPI, vol. 8(9), pages 1-20, September.
    10. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Bianchi, Francesco & Domenghini, Piergiovanni & Cotana, Franco & Wang, Jinwen, 2022. "A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing," Energy, Elsevier, vol. 244(PB).
    11. Jingmin Fan & Huidong Shao & Yunfei Cao & Lutao Feng & Jianpei Chen & Anbo Meng & Hao Yin, 2022. "Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN," Energies, MDPI, vol. 15(22), pages 1-14, November.
    12. 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).
    13. 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.
    14. 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.
    15. Park, Alex & Lappas, Petros, 2017. "Evaluating demand charge reduction for commercial-scale solar PV coupled with battery storage," Renewable Energy, Elsevier, vol. 108(C), pages 523-532.
    16. Beatrice Marchi & Simone Zanoni & Marco Pasetti, 2019. "Multi-Period Newsvendor Problem for the Management of Battery Energy Storage Systems in Support of Distributed Generation," Energies, MDPI, vol. 12(23), pages 1-13, December.
    17. Ashraf Ramadan & Mohamed Ebeed & Salah Kamel & Almoataz Y. Abdelaziz & Hassan Haes Alhelou, 2021. "Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    18. Morteza Zare Oskouei & Ayşe Aybike Şeker & Süleyman Tunçel & Emin Demirbaş & Tuba Gözel & Mehmet Hakan Hocaoğlu & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "A Critical Review on the Impacts of Energy Storage Systems and Demand-Side Management Strategies in the Economic Operation of Renewable-Based Distribution Network," Sustainability, MDPI, vol. 14(4), pages 1-34, February.
    19. Chaianong, Aksornchan & Bangviwat, Athikom & Menke, Christoph & Breitschopf, Barbara & Eichhammer, Wolfgang, 2020. "Customer economics of residential PV–battery systems in Thailand," Renewable Energy, Elsevier, vol. 146(C), pages 297-308.
    20. 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.

    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:97:y:2016:i:c:p:169-176. 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.