IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i3p610-d135557.html
   My bibliography  Save this article

Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs

Author

Listed:
  • Pouria Sheikhahmadi

    (Department of Electrical and Computer Engineering, University of Kurdistan, Sanandaj 66131, Iran)

  • Ramyar Mafakheri

    (Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj 66131, Iran)

  • Salah Bahramara

    (Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj 66131, Iran)

  • Maziar Yazdani Damavandi

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal)

  • João P. S. Catalão

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal
    INESC TEC and the Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal
    INESC-ID, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal)

Abstract

The operation problem of a micro-grid (MG) in grid-connected mode is an optimization one in which the main objective of the MG operator (MGO) is to minimize the operation cost with optimal scheduling of resources and optimal trading energy with the main grid. The MGO can use incentive-based demand response programs (DRPs) to pay an incentive to the consumers to change their demands in the peak hours. Moreover, the MGO forecasts the output power of renewable energy resources (RERs) and models their uncertainties in its problem. In this paper, the operation problem of an MGO is modeled as a risk-based two-stage stochastic optimization problem. To model the uncertainties of RERs, two-stage stochastic programming is considered and conditional value at risk (CVaR) index is used to manage the MGO’s risk-level. Moreover, the non-linear economic models of incentive-based DRPs are used by the MGO to change the peak load. The numerical studies are done to investigate the effect of incentive-based DRPs on the operation problem of the MGO. Moreover, to show the effect of the risk-averse parameter on MGO decisions, a sensitivity analysis is carried out.

Suggested Citation

  • Pouria Sheikhahmadi & Ramyar Mafakheri & Salah Bahramara & Maziar Yazdani Damavandi & João P. S. Catalão, 2018. "Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs," Energies, MDPI, vol. 11(3), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:610-:d:135557
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/3/610/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/3/610/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Justo, Jackson John & Mwasilu, Francis & Lee, Ju & Jung, Jin-Woo, 2013. "AC-microgrids versus DC-microgrids with distributed energy resources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 387-405.
    2. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    3. Nwulu, Nnamdi I. & Xia, Xiaohua, 2017. "Optimal dispatch for a microgrid incorporating renewables and demand response," Renewable Energy, Elsevier, vol. 101(C), pages 16-28.
    4. Siyoung Lee & Younggyu Jin & Gilsoo Jang & Yongtae Yoon, 2016. "Optimal Bidding of a Microgrid Based on Probabilistic Analysis of Island Operation," Energies, MDPI, vol. 9(10), pages 1-14, October.
    5. Ting-Chia Ou & Wei-Fu Su & Xian-Zong Liu & Shyh-Jier Huang & Te-Yu Tai, 2016. "A Modified Bird-Mating Optimization with Hill-Climbing for Connection Decisions of Transformers," Energies, MDPI, vol. 9(9), pages 1-12, August.
    6. Chongxin Huang & Dong Yue & Song Deng & Jun Xie, 2017. "Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization," Energies, MDPI, vol. 10(3), pages 1-23, March.
    7. Ou, Ting-Chia & Hong, Chih-Ming, 2014. "Dynamic operation and control of microgrid hybrid power systems," Energy, Elsevier, vol. 66(C), pages 314-323.
    8. Ting-Chia Ou & Kai-Hung Lu & Chiou-Jye Huang, 2017. "Improvement of Transient Stability in a Hybrid Power Multi-System Using a Designed NIDC (Novel Intelligent Damping Controller)," Energies, MDPI, vol. 10(4), pages 1-16, April.
    9. Zhongwen Li & Chuanzhi Zang & Peng Zeng & Haibin Yu, 2016. "Combined Two-Stage Stochastic Programming and Receding Horizon Control Strategy for Microgrid Energy Management Considering Uncertainty," Energies, MDPI, vol. 9(7), pages 1-16, June.
    10. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    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. Yang Zhang & Zhenghui Fu & Yulei Xie & Qing Hu & Zheng Li & Huaicheng Guo, 2020. "A Comprehensive Forecasting–Optimization Analysis Framework for Environmental-Oriented Power System Management—A Case Study of Harbin City, China," Sustainability, MDPI, vol. 12(10), pages 1-26, May.
    2. Nenad Jovanović & Javier García-González & Santiago Cerisola & Julián Barquín, 2018. "Impact of Risk Aversion on the Operation of Hydroelectric Reservoirs in the Presence of Renewable Energy Sources," Energies, MDPI, vol. 11(6), pages 1-20, May.
    3. Haseeb Javed & Hafiz Abdul Muqeet & Moazzam Shehzad & Mohsin Jamil & Ashraf Ali Khan & Josep M. Guerrero, 2021. "Optimal Energy Management of a Campus Microgrid Considering Financial and Economic Analysis with Demand Response Strategies," Energies, MDPI, vol. 14(24), pages 1-24, December.
    4. 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).
    5. Daniel Kitamura & Leonardo Willer & Bruno Dias & Tiago Soares, 2023. "Risk-Averse Stochastic Programming for Planning Hybrid Electrical Energy Systems: A Brazilian Case," Energies, MDPI, vol. 16(3), pages 1-16, February.
    6. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    7. Mahmood Hosseini Imani & Shaghayegh Zalzar & Amir Mosavi & Shahaboddin Shamshirband, 2018. "Strategic Behavior of Retailers for Risk Reduction and Profit Increment via Distributed Generators and Demand Response Programs," Energies, MDPI, vol. 11(6), pages 1-24, June.
    8. Yulei Xie & Linrui Wang & Guohe Huang & Dehong Xia & Ling Ji, 2018. "A Stochastic Inexact Robust Model for Regional Energy System Management and Emission Reduction Potential Analysis—A Case Study of Zibo City, China," Energies, MDPI, vol. 11(8), pages 1-24, August.
    9. Ng, Rong Wang & Begam, K.M. & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2022. "A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions," Energy, Elsevier, vol. 248(C).
    10. Luis Alejandro Arias & Edwin Rivas & Francisco Santamaria & Victor Hernandez, 2018. "A Review and Analysis of Trends Related to Demand Response," Energies, MDPI, vol. 11(7), pages 1-24, 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. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    2. Fei Wang & Lidong Zhou & Hui Ren & Xiaoli Liu, 2017. "Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimizat," Energies, MDPI, vol. 10(12), pages 1-23, November.
    3. Pengfei Wang & Jialiang Yi & Mansoureh Zangiabadi & Pádraig Lyons & Phil Taylor, 2017. "Evaluation of Voltage Control Approaches for Future Smart Distribution Networks," Energies, MDPI, vol. 10(8), pages 1-17, August.
    4. Andrés Henao-Muñoz & Andrés Saavedra-Montes & Carlos Ramos-Paja, 2018. "Optimal Power Dispatch of Small-Scale Standalone Microgrid Located in Colombian Territory," Energies, MDPI, vol. 11(7), pages 1-20, July.
    5. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    6. Mohammed Elsayed Lotfy & Tomonobu Senjyu & Mohammed Abdel-Fattah Farahat & Amal Farouq Abdel-Gawad & Hidehito Matayoshi, 2017. "A Polar Fuzzy Control Scheme for Hybrid Power System Using Vehicle-To-Grid Technique," Energies, MDPI, vol. 10(8), pages 1-25, July.
    7. Hongyue Li & Xihuai Wang & Jianmei Xiao, 2018. "Differential Evolution-Based Load Frequency Robust Control for Micro-Grids with Energy Storage Systems," Energies, MDPI, vol. 11(7), pages 1-19, June.
    8. Chen, J.J. & Zhao, Y.L. & Peng, K. & Wu, P.Z., 2017. "Optimal trade-off planning for wind-solar power day-ahead scheduling under uncertainties," Energy, Elsevier, vol. 141(C), pages 1969-1981.
    9. Geng, Zhiqiang & Li, Yanan & Han, Yongming & Zhu, Qunxiong, 2018. "A novel self-organizing cosine similarity learning network: An application to production prediction of petrochemical systems," Energy, Elsevier, vol. 142(C), pages 400-410.
    10. Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
    11. Qian Liu & Rui Wang & Yan Zhang & Guohua Wu & Jianmai Shi, 2018. "An Optimal and Distributed Demand Response Strategy for Energy Internet Management," Energies, MDPI, vol. 11(1), pages 1-16, January.
    12. Kai Ma & Congshan Wang & Jie Yang & Qiuxia Yang & Yazhou Yuan, 2017. "Economic Dispatch with Demand Response in Smart Grid: Bargaining Model and Solutions," Energies, MDPI, vol. 10(8), pages 1-17, August.
    13. Jaewan Suh & Sungchul Hwang & Gilsoo Jang, 2017. "Development of a Transmission and Distribution Integrated Monitoring and Analysis System for High Distributed Generation Penetration," Energies, MDPI, vol. 10(9), pages 1-15, August.
    14. Qinliang Tan & Yihong Ding & Yimei Zhang, 2017. "Optimization Model of an Efficient Collaborative Power Dispatching System for Carbon Emissions Trading in China," Energies, MDPI, vol. 10(9), pages 1-19, September.
    15. Van-Hai Bui & Akhtar Hussain & Hak-Man Kim, 2017. "Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System," Energies, MDPI, vol. 10(10), pages 1-16, September.
    16. Mohammed H. Alsharif, 2017. "Comparative Analysis of Solar-Powered Base Stations for Green Mobile Networks," Energies, MDPI, vol. 10(8), pages 1-25, August.
    17. Nikos Kampelis & Elisavet Tsekeri & Dionysia Kolokotsa & Kostas Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2018. "Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions," Energies, MDPI, vol. 11(11), pages 1-22, November.
    18. Changcheng Li & Jinghan He & Pei Zhang & Yin Xu, 2017. "A Novel Sectionalizing Method for Power System Parallel Restoration Based on Minimum Spanning Tree," Energies, MDPI, vol. 10(7), pages 1-21, July.
    19. Wang, Jianzhou & Yang, Wendong & Du, Pei & Li, Yifan, 2018. "Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system," Energy, Elsevier, vol. 148(C), pages 59-78.
    20. Reza Sirjani, 2017. "Optimal Capacitor Placement in Wind Farms by Considering Harmonics Using Discrete Lightning Search Algorithm," Sustainability, MDPI, vol. 9(9), pages 1-20, September.

    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:11:y:2018:i:3:p:610-:d:135557. 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.