IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v109y2017icp22-40.html
   My bibliography  Save this article

Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs

Author

Listed:
  • Samimi, Abouzar
  • Nikzad, Mehdi
  • Siano, Pierluigi

Abstract

In this paper, an efficient stochastic framework is proposed to develop a coupled active and reactive market in smart distribution systems. Distributed Energy Resources (DERs) can offer active powers to the market and also offer their reactive powers via a multi-component bidding framework constructed based on their reactive power capability diagrams. Distribution Company (Disco) buys active and reactive powers from a wholesale market and sells them via this market. Aggregators on behalf of responsive loads can participate in the market using a demand buyback program (DBP). The uncertainties of forecasted loads and wind power generation are considered in the proposed framework. To model the stochastic variables, the scenario tree is created using the Weibull and the Gaussian probability density functions (PDFs). The cost objective function of the stochastic coupled market clearing consists of the expected costs of energy and reactive power purchased from the DERs and Disco, the expected penalty cost of CO2 emissions of DERs and the main grid as well as the expected cost of running DBP. The proposed market is cleared through a mixed-integer nonlinear optimization problem solved in GAMS software. The effectiveness of the proposed method is investigated based on a 22-bus 20-kV radial distribution test system.

Suggested Citation

  • Samimi, Abouzar & Nikzad, Mehdi & Siano, Pierluigi, 2017. "Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs," Renewable Energy, Elsevier, vol. 109(C), pages 22-40.
    • Handle: RePEc:eee:renene:v:109:y:2017:i:c:p:22-40
    DOI: 10.1016/j.renene.2017.03.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014811730191X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2017.03.010?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. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Venkatesan, Naveen & Solanki, Jignesh & Solanki, Sarika Khushalani, 2012. "Residential Demand Response model and impact on voltage profile and losses of an electric distribution network," Applied Energy, Elsevier, vol. 96(C), pages 84-91.
    3. Zakariazadeh, Alireza & Homaee, Omid & Jadid, Shahram & Siano, Pierluigi, 2014. "A new approach for real time voltage control using demand response in an automated distribution system," Applied Energy, Elsevier, vol. 117(C), pages 157-166.
    4. Niknam, Taher & Firouzi, Bahman Bahmani & Ostadi, Amir, 2010. "A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators," Applied Energy, Elsevier, vol. 87(6), pages 1919-1928, June.
    5. Mohseni-Bonab, Seyed Masoud & Rabiee, Abbas & Mohammadi-Ivatloo, Behnam, 2016. "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach," Renewable Energy, Elsevier, vol. 85(C), pages 598-609.
    6. Kjetil Høyland & Stein W. Wallace, 2001. "Generating Scenario Trees for Multistage Decision Problems," Management Science, INFORMS, vol. 47(2), pages 295-307, February.
    7. Niknam, Taher, 2011. "A new HBMO algorithm for multiobjective daily Volt/Var control in distribution systems considering Distributed Generators," Applied Energy, Elsevier, vol. 88(3), pages 778-788, March.
    8. Amjady, N. & Rabiee, A. & Shayanfar, H.A., 2010. "A stochastic framework for clearing of reactive power market," Energy, Elsevier, vol. 35(1), pages 239-245.
    9. Gulpinar, Nalan & Rustem, Berc & Settergren, Reuben, 2004. "Simulation and optimization approaches to scenario tree generation," Journal of Economic Dynamics and Control, Elsevier, vol. 28(7), pages 1291-1315, April.
    10. Wang, Qi & Zhang, Chunyu & Ding, Yi & Xydis, George & Wang, Jianhui & Østergaard, Jacob, 2015. "Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response," Applied Energy, Elsevier, vol. 138(C), pages 695-706.
    11. Malekpour, Ahmad Reza & Tabatabaei, Sajad & Niknam, Taher, 2012. "Probabilistic approach to multi-objective Volt/Var control of distribution system considering hybrid fuel cell and wind energy sources using Improved Shuffled Frog Leaping Algorithm," Renewable Energy, Elsevier, vol. 39(1), pages 228-240.
    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. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. 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.
    3. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2019. "Multiobjective robust fuzzy stochastic approach for sustainable smart grid design," Energy, Elsevier, vol. 176(C), pages 929-939.
    4. Firouzi, Mohsen & Samimi, Abouzar & Salami, Abolfazl, 2022. "Reliability evaluation of a composite power system in the presence of renewable generations," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.
    6. Tobajas, Javier & Garcia-Torres, Felix & Roncero-Sánchez, Pedro & Vázquez, Javier & Bellatreche, Ladjel & Nieto, Emilio, 2022. "Resilience-oriented schedule of microgrids with hybrid energy storage system using model predictive control," Applied Energy, Elsevier, vol. 306(PB).
    7. Mimica, Marko & Dominković, Dominik Franjo & Capuder, Tomislav & Krajačić, Goran, 2021. "On the value and potential of demand response in the smart island archipelago," Renewable Energy, Elsevier, vol. 176(C), pages 153-168.
    8. Wentao Lu & Zhenghui Fu & Yang Zhang & Yuxuan Qiao & Lei Yu & Yi Liu, 2021. "Integrated Planning for Regional Development Planning with Low Carbon Development Constraint under Uncertainty: A Case Study of Qingpu District, Shanghai," Sustainability, MDPI, vol. 13(19), pages 1-24, September.
    9. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    10. Bielecki, Sławomir & Skoczkowski, Tadeusz, 2018. "An enhanced concept of Q-power management," Energy, Elsevier, vol. 162(C), pages 335-353.
    11. Nikzad, Mehdi & Samimi, Abouzar, 2021. "Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems," Applied Energy, Elsevier, vol. 282(PA).
    12. Nikpour, Ahmad & Nateghi, Abolfazl & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources," Energy, Elsevier, vol. 227(C).
    13. Lu, W.T. & Dai, C. & Fu, Z.H. & Liang, Z.Y. & Guo, H.C., 2018. "An interval-fuzzy possibilistic programming model to optimize China energy management system with CO2 emission constraint," Energy, Elsevier, vol. 142(C), pages 1023-1039.

    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. Manbachi, Moein & Farhangi, Hassan & Palizban, Ali & Arzanpour, Siamak, 2016. "Smart grid adaptive energy conservation and optimization engine utilizing Particle Swarm Optimization and Fuzzification," Applied Energy, Elsevier, vol. 174(C), pages 69-79.
    2. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Ding, Fei & Wu, Jianzhong, 2018. "A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks," Applied Energy, Elsevier, vol. 228(C), pages 2024-2036.
    3. Fu, Xueqian & Chen, Haoyong & Cai, Runqing & Yang, Ping, 2015. "Optimal allocation and adaptive VAR control of PV-DG in distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 173-182.
    4. Petinrin, J.O. & Shaabanb, Mohamed, 2016. "Impact of renewable generation on voltage control in distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 770-783.
    5. Liu, Pei-chen Barry & Hansen, Mark & Mukherjee, Avijit, 2008. "Scenario-based air traffic flow management: From theory to practice," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 685-702, August.
    6. Tengku Juhana Tengku Hashim & Azah Mohamed, 2017. "Optimal coordinated voltage control in active distribution networks using backtracking search algorithm," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-20, October.
    7. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    8. Wu, Dexiang & Wu, Desheng Dash, 2020. "A decision support approach for two-stage multi-objective index tracking using improved lagrangian decomposition," Omega, Elsevier, vol. 91(C).
    9. Mortazi, Mohammad & Moradi, Ahmad & Khosravi, Mohsen, 2020. "Simultaneous optimization of transformer tap changer and network capacitors to improve the distribution system’s static security considering distributed generation sources," MPRA Paper 109052, University Library of Munich, Germany, revised 01 Jul 2020.
    10. Zare, Mohsen & Niknam, Taher, 2013. "A new multi-objective for environmental and economic management of Volt/Var Control considering renewable energy resources," Energy, Elsevier, vol. 55(C), pages 236-252.
    11. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    12. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    13. Osorio, Maria A. & Gulpinar, Nalan & Rustem, Berc & Settergren, Reuben, 2004. "Post-tax optimization with stochastic programming," European Journal of Operational Research, Elsevier, vol. 157(1), pages 152-168, August.
    14. Niels Blaauwbroek & Phuong Nguyen & Han Slootweg, 2018. "Data-Driven Risk Analysis for Probabilistic Three-Phase Grid-Supportive Demand Side Management," Energies, MDPI, vol. 11(10), pages 1-18, September.
    15. Staino, Alessandro & Russo, Emilio, 2015. "A moment-matching method to generate arbitrage-free scenarios," European Journal of Operational Research, Elsevier, vol. 246(2), pages 619-630.
    16. 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.
    17. Davari-Ardakani, Hamed & Aminnayeri, Majid & Seifi, Abbas, 2014. "A study on modeling the dynamics of statistically dependent returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 35-51.
    18. Iria, José & Soares, Filipe & Matos, Manuel, 2018. "Optimal supply and demand bidding strategy for an aggregator of small prosumers," Applied Energy, Elsevier, vol. 213(C), pages 658-669.
    19. Fengli Jiang & Yichi Zhang & Yu Zhang & Xiaomeng Liu & Chunling Chen, 2019. "An Adaptive Particle Swarm Optimization Algorithm Based on Guiding Strategy and Its Application in Reactive Power Optimization," Energies, MDPI, vol. 12(9), pages 1-14, May.
    20. Osorio, Maria A. & Gulpinar, Nalan & Rustem, Berc, 2008. "A mixed integer programming model for multistage mean-variance post-tax optimization," European Journal of Operational Research, Elsevier, vol. 185(2), pages 451-480, March.

    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:109:y:2017:i:c:p:22-40. 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.