IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i5p4607-4628d49897.html
   My bibliography  Save this article

Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration

Author

Listed:
  • Sam Weckx

    (Division Electrical Energy and Computer Architectures, Department of Electrical Engineering, Katholieke Universiteit Leuven (ELECTA, KU Leuven), Kasteelpark Arenberg 10, bus 2445, 3001 Leuven, Belgium
    Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boerentang 200, 2400 Mol, Belgium)

  • Reinhilde D'hulst

    (Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boerentang 200, 2400 Mol, Belgium)

  • Johan Driesen

    (Division Electrical Energy and Computer Architectures, Department of Electrical Engineering, Katholieke Universiteit Leuven (ELECTA, KU Leuven), Kasteelpark Arenberg 10, bus 2445, 3001 Leuven, Belgium)

Abstract

In this paper, a locational marginal pricing algorithm is proposed to control the voltage in unbalanced distribution grids. The increasing amount of photovoltaic (PV) generation installed in the grid may cause the voltage to rise to unacceptable levels during periods of low consumption. With locational prices, the distribution system operator can steer the reactive power consumption and active power curtailment of PV panels to guarantee a safe network operation. Flexible loads also respond to these prices. A distributed gradient algorithm automatically defines the locational prices that avoid voltage problems. Using these locational prices results in a minimum cost for the distribution operator to control the voltage. Locational prices can differ between the three phases in unbalanced grids. This is caused by a higher consumption or production in one of the phases compared to the other phases and provides the opportunity for arbitrage, where power is transferred from a phase with a low price to a phase with a high price. The effect of arbitrage is analyzed. The proposed algorithm is applied to an existing three-phase four-wire radial grid. Several simulations with realistic data are performed.

Suggested Citation

  • Sam Weckx & Reinhilde D'hulst & Johan Driesen, 2015. "Locational Pricing to Mitigate Voltage Problems Caused by High PV Penetration," Energies, MDPI, vol. 8(5), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:5:p:4607-4628:d:49897
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/5/4607/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/5/4607/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bokyung Ko & Nugroho Prananto Utomo & Gilsoo Jang & Jaehan Kim & Jintae Cho, 2013. "Optimal Scheduling for the Complementary Energy Storage System Operation Based on Smart Metering Data in the DC Distribution System," Energies, MDPI, vol. 6(12), pages 1-17, December.
    2. Bingtuan Gao & Wenhu Zhang & Yi Tang & Mingjin Hu & Mingcheng Zhu & Huiyu Zhan, 2014. "Game-Theoretic Energy Management for Residential Users with Dischargeable Plug-in Electric Vehicles," Energies, MDPI, vol. 7(11), pages 1-20, November.
    3. Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
    4. Naveed Ul Hassan & Muhammad Adeel Pasha & Chau Yuen & Shisheng Huang & Xiumin Wang, 2013. "Impact of Scheduling Flexibility on Demand Profile Flatness and User Inconvenience in Residential Smart Grid System," Energies, MDPI, vol. 6(12), pages 1-28, December.
    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. Ashraf Ramadan & Mohamed Ebeed & Salah Kamel & Ahmed M. Agwa & Marcos Tostado-Véliz, 2022. "The Probabilistic Optimal Integration of Renewable Distributed Generators Considering the Time-Varying Load Based on an Artificial Gorilla Troops Optimizer," Energies, MDPI, vol. 15(4), pages 1-22, February.
    2. José Miguel Paredes-Parra & Antonio Mateo-Aroca & Guillermo Silvente-Niñirola & María C. Bueso & Ángel Molina-García, 2018. "PV Module Monitoring System Based on Low-Cost Solutions: Wireless Raspberry Application and Assessment," Energies, MDPI, vol. 11(11), pages 1-20, November.
    3. Xiaoqing Hu & Beibei Wang & Shengchun Yang & Taylor Short & Lei Zhou, 2015. "A Closed-Loop Control Strategy for Air Conditioning Loads to Participate in Demand Response," Energies, MDPI, vol. 8(8), pages 1-32, August.
    4. 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.
    5. Kongrit Mansiri & Sukruedee Sukchai & Chatchai Sirisamphanwong, 2018. "Fuzzy Control for Smart PV-Battery System Management to Stabilize Grid Voltage of 22 kV Distribution System in Thailand," Energies, MDPI, vol. 11(7), pages 1-19, July.
    6. Su Su & Yong Hu & Tiantian Yang & Shidan Wang & Ziqi Liu & Xiangxiang Wei & Mingchao Xia & Yutaka Ota & Koji Yamashita, 2018. "Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities," Energies, MDPI, vol. 11(2), pages 1-19, February.
    7. 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.

    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. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.
    2. Thijs Klauw & Marco E. T. Gerards & Johann L. Hurink, 2017. "Resource allocation problems in decentralized energy management," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 749-773, July.
    3. Simona-Vasilica Oprea & Adela Bâra & Adriana Reveiu, 2018. "Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders," Energies, MDPI, vol. 11(1), pages 1-31, January.
    4. 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.
    5. Muhammad Riaz & Aamir Hanif & Haris Masood & Muhammad Attique Khan & Kamran Afaq & Byeong-Gwon Kang & Yunyoung Nam, 2021. "An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-12, December.
    6. Huang, Pei & Wu, Hunjun & Huang, Gongsheng & Sun, Yongjun, 2018. "A top-down control method of nZEBs for performance optimization at nZEB-cluster-level," Energy, Elsevier, vol. 159(C), pages 891-904.
    7. Jun Liu & Jinchun Chen & Chao Wang & Zhang Chen & Xinglei Liu, 2020. "Market Trading Model of Urban Energy Internet Based on Tripartite Game Theory," Energies, MDPI, vol. 13(7), pages 1-24, April.
    8. Zhang, Sheng & Huang, Pei & Sun, Yongjun, 2016. "A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties," Energy, Elsevier, vol. 94(C), pages 654-665.
    9. Seong-Kyu Kim & Jun-Ho Huh, 2018. "A Study on the Improvement of Smart Grid Security Performance and Blockchain Smart Grid Perspective," Energies, MDPI, vol. 11(8), pages 1-22, July.
    10. 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.
    11. Rodrigo Verschae & Takekazu Kato & Takashi Matsuyama, 2016. "Energy Management in Prosumer Communities: A Coordinated Approach," Energies, MDPI, vol. 9(7), pages 1-27, July.
    12. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    13. Mengmeng Qiao & Zexu Yu & Zhenhai Dou & Yuanyuan Wang & Ye Zhao & Ruishuo Xie & Lianxin Liu, 2022. "Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-31, July.
    14. Mohammad Ali Taghikhani & Behnam Zangeneh, 2022. "Optimal energy scheduling of micro-grids considering the uncertainty of solar and wind renewable resources," Journal of Scheduling, Springer, vol. 25(5), pages 567-576, October.
    15. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    16. Xia, Yuanxing & Xu, Qingshan & Tao, Siyu & Du, Pengwei & Ding, Yixing & Fang, Jicheng, 2022. "Preserving operation privacy of peer-to-peer energy transaction based on Enhanced Benders Decomposition considering uncertainty of renewable energy generations," Energy, Elsevier, vol. 250(C).
    17. Verschae, Rodrigo & Kawashima, Hiroaki & Kato, Takekazu & Matsuyama, Takashi, 2016. "Coordinated energy management for inter-community imbalance minimization," Renewable Energy, Elsevier, vol. 87(P2), pages 922-935.
    18. Saffari, Mohammad & de Gracia, Alvaro & Fernández, Cèsar & Belusko, Martin & Boer, Dieter & Cabeza, Luisa F., 2018. "Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV," Applied Energy, Elsevier, vol. 211(C), pages 604-616.
    19. Yuttana Kongjeen & Krischonme Bhumkittipich, 2018. "Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis," Energies, MDPI, vol. 11(6), pages 1-16, June.
    20. Sarah O’Connell & Marcus Martin Keane, 2021. "Development of a Framework for Activation of Aggregator Led Flexibility," Energies, MDPI, vol. 14(16), pages 1-15, August.

    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:8:y:2015:i:5:p:4607-4628:d:49897. 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.