IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i13p7119-d581768.html
   My bibliography  Save this article

Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources

Author

Listed:
  • Abbas Rabiee

    (Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran)

  • Ali Abdali

    (Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran)

  • Seyed Masoud Mohseni-Bonab

    (Digital Systems Department, Hydro-Québec/IREQ, Varennes, QC J3X 1S1, Canada)

  • Mohsen Hazrati

    (Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran)

Abstract

In this paper, a robust scheduling model is proposed for combined heat and power (CHP)-based microgrids using information gap decision theory (IGDT). The microgrid under study consists of conventional power generation as well as boiler units, fuel cells, CHPs, wind turbines, solar PVs, heat storage units, and battery energy storage systems (BESS) as the set of distributed energy resources (DERs). Additionally, a demand response program (DRP) model is considered which has a successful performance in the microgrid hourly scheduling. One of the goals of CHP-based microgrid scheduling is to provide both thermal and electrical energy demands of the consumers. Additionally, the other objective is to benefit from the revenues obtained by selling the surplus electricity to the main grid during the high energy price intervals or purchasing it from the grid when the price of electricity is low at the electric market. Hence, in this paper, a robust scheduling approach is developed with the aim of maximizing the total profit of different energy suppliers in the entire scheduling horizon. The employed IGDT technique aims to handle the impact of uncertainties in the power output of wind and solar PV units on the overall profit.

Suggested Citation

  • Abbas Rabiee & Ali Abdali & Seyed Masoud Mohseni-Bonab & Mohsen Hazrati, 2021. "Risk-Averse Scheduling of Combined Heat and Power-Based Microgrids in Presence of Uncertain Distributed Energy Resources," Sustainability, MDPI, vol. 13(13), pages 1-24, June.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7119-:d:581768
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/13/7119/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/13/7119/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kayal, Partha & Chanda, C.K., 2015. "Optimal mix of solar and wind distributed generations considering performance improvement of electrical distribution network," Renewable Energy, Elsevier, vol. 75(C), pages 173-186.
    2. Younès Dagdougui & Ahmed Ouammi & Rachid Benchrifa, 2020. "Energy Management-Based Predictive Controller for a Smart Building Powered by Renewable Energy," Sustainability, MDPI, vol. 12(10), pages 1-18, May.
    3. Yongli Wang & Yujing Huang & Yudong Wang & Fang Li & Yuanyuan Zhang & Chunzheng Tian, 2018. "Operation Optimization in a Smart Micro-Grid in the Presence of Distributed Generation and Demand Response," Sustainability, MDPI, vol. 10(3), pages 1-25, March.
    4. Kopanos, Georgios M. & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "Energy production planning of a network of micro combined heat and power generators," Applied Energy, Elsevier, vol. 102(C), pages 1522-1534.
    5. Majidi, M. & Mohammadi-Ivatloo, B. & Soroudi, A., 2019. "Application of information gap decision theory in practical energy problems: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 157-165.
    6. Makbul A.M. Ramli & H.R.E.H. Bouchekara & Abdulsalam S. Alghamdi, 2019. "Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources," Sustainability, MDPI, vol. 11(14), pages 1-28, July.
    7. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    8. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
    Full references (including those not matched with items on IDEAS)

    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. Arash Moradzadeh & Sahar Zakeri & Maryam Shoaran & Behnam Mohammadi-Ivatloo & Fazel Mohammadi, 2020. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    2. Lumin Shi & Man-Wen Tian & As’ad Alizadeh & Ardashir Mohammadzadeh & Sayyad Nojavan, 2023. "Information Gap Decision Theory-Based Risk-Averse Scheduling of a Combined Heat and Power Hybrid Energy System," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    3. Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Xiong, Wen & Wang, Li, 2017. "Typical scenario set generation algorithm for an integrated energy system based on the Wasserstein distance metric," Energy, Elsevier, vol. 135(C), pages 153-170.
    4. Taewook Kang, 2020. "BIM-Based Human Machine Interface (HMI) Framework for Energy Management," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    5. Ahmadi, Seyed Ehsan & Sadeghi, Delnia & Marzband, Mousa & Abusorrah, Abdullah & Sedraoui, Khaled, 2022. "Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies," Energy, Elsevier, vol. 245(C).
    6. Razavi, Seyed-Ehsan & Rahimi, Ehsan & Javadi, Mohammad Sadegh & Nezhad, Ali Esmaeel & Lotfi, Mohamed & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Impact of distributed generation on protection and voltage regulation of distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 157-167.
    7. Romero-Quete, David & Garcia, Javier Rosero, 2019. "An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids," Applied Energy, Elsevier, vol. 242(C), pages 1436-1447.
    8. Yu Liu & Shan Gao & Xin Zhao & Chao Zhang & Ningyu Zhang, 2017. "Coordinated Operation and Control of Combined Electricity and Natural Gas Systems with Thermal Storage," Energies, MDPI, vol. 10(7), pages 1-25, July.
    9. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    10. Capuder, Tomislav & Mancarella, Pierluigi, 2014. "Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options," Energy, Elsevier, vol. 71(C), pages 516-533.
    11. Li, Simon & Berrio, Denering & Fang, Yanda, 2022. "Heat balance modelling and simulation of non-mixing buffer tank design for hydronic heating applications," Energy, Elsevier, vol. 244(PB).
    12. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    13. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2015. "Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule," Applied Energy, Elsevier, vol. 149(C), pages 194-203.
    14. Abbassi, Abdelkader & Dami, Mohamed Ali & Jemli, Mohamed, 2017. "A statistical approach for hybrid energy storage system sizing based on capacity distributions in an autonomous PV/Wind power generation system," Renewable Energy, Elsevier, vol. 103(C), pages 81-93.
    15. Ji, Haoran & Wang, Chengshan & Li, Peng & Song, Guanyu & Yu, Hao & Wu, Jianzhong, 2019. "Quantified analysis method for operational flexibility of active distribution networks with high penetration of distributed generators," Applied Energy, Elsevier, vol. 239(C), pages 706-714.
    16. Kools, L. & Phillipson, F., 2016. "Data granularity and the optimal planning of distributed generation," Energy, Elsevier, vol. 112(C), pages 342-352.
    17. Nagaraju Dharavat & Suresh Kumar Sudabattula & Suresh Velamuri & Sachin Mishra & Naveen Kumar Sharma & Mohit Bajaj & Elmazeg Elgamli & Mokhtar Shouran & Salah Kamel, 2022. "Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm," Energies, MDPI, vol. 15(18), pages 1-25, September.
    18. Danyang Guo & Jilai Yu & Mingfei Ban, 2018. "Security-Constrained Unit Commitment Considering Differentiated Regional Air Pollutant Intensity," Sustainability, MDPI, vol. 10(5), pages 1-27, May.
    19. Kiki Ayu & Akilu Yunusa-Kaltungo, 2020. "A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems," Energies, MDPI, vol. 13(8), pages 1-32, April.
    20. Mostafa Rezaeimozafar & Mohsen Eskandari & Mohammad Hadi Amini & Mohammad Hasan Moradi & Pierluigi Siano, 2020. "A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids," Energies, MDPI, vol. 13(7), pages 1-25, April.

    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:jsusta:v:13:y:2021:i:13:p:7119-:d:581768. 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.