IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i12p2275-d239661.html
   My bibliography  Save this article

Primal-Dual Learning Based Risk-Averse Optimal Integrated Allocation of Hybrid Energy Generation Plants under Uncertainty

Author

Listed:
  • Xiao Zhao

    (Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430000, China
    School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 430062, China)

  • Xuhui Xia

    (Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430000, China)

  • Guodong Yu

    (School of Management, Shandong University, Jinan 266510, China)

Abstract

A groundswell of opinion in utilizing environmentally friendly energy technologies has been put forth worldwide. In this paper, we consider an energy generation plant distribution and allocation problem under uncertainty to get the utmost out of available developments, as well as to control costs and greenhouse emissions. Different clean and traditional energy technologies are considered in this paper. In particular, we present a risk-averse stochastic mixed-integer linear programming (MILP) model to minimize the total expected costs and control the risk of CO 2 emissions exceeding a certain budget. We employ the conditional value-at-risk (CVaR) model to represent risk preference and risk constraint of emissions. We prove that our risk-averse model can be equivalent to the traditional risk-neutral model under certain conditions. Moreover, we suggest that the risk-averse model can provide solutions generating less CO 2 than traditional models. To handle the computational difficulty in uncertain scenarios, we propose a Lagrange primal-dual learning algorithm to solve the model. We show that the algorithm allows the probability distribution of uncertainty to be unknown, and that desirable approximation can be achieved by utilizing historical data. Finally, an experiment is presented to demonstrate the performance of our method. The risk-averse model encourages the expansion of clean energy plants over traditional models for the reduction CO 2 emissions.

Suggested Citation

  • Xiao Zhao & Xuhui Xia & Guodong Yu, 2019. "Primal-Dual Learning Based Risk-Averse Optimal Integrated Allocation of Hybrid Energy Generation Plants under Uncertainty," Energies, MDPI, vol. 12(12), pages 1-15, June.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2275-:d:239661
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/12/2275/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/12/2275/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alexander Shapiro, 2013. "On Kusuoka Representation of Law Invariant Risk Measures," Mathematics of Operations Research, INFORMS, vol. 38(1), pages 142-152, February.
    2. Lee, Seong Kon & Mogi, Gento & Kim, Jong Wook, 2009. "Energy technology roadmap for the next 10 years: The case of Korea," Energy Policy, Elsevier, vol. 37(2), pages 588-596, February.
    3. Parkinson, Simon C. & Djilali, Ned, 2015. "Long-term energy planning with uncertain environmental performance metrics," Applied Energy, Elsevier, vol. 147(C), pages 402-412.
    4. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    5. Warren B. Powell & Abraham George & Hugo Simão & Warren Scott & Alan Lamont & Jeffrey Stewart, 2012. "SMART: A Stochastic Multiscale Model for the Analysis of Energy Resources, Technology, and Policy," INFORMS Journal on Computing, INFORMS, vol. 24(4), pages 665-682, November.
    6. Yu, Guodong & Haskell, William B. & Liu, Yang, 2017. "Resilient facility location against the risk of disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 82-105.
    7. Dicorato, M. & Forte, G. & Trovato, M., 2008. "Environmental-constrained energy planning using energy-efficiency and distributed-generation facilities," Renewable Energy, Elsevier, vol. 33(6), pages 1297-1313.
    8. Guodong Yu & Fei Li & Yu Yang, 2017. "Robust supply chain networks design and ambiguous risk preferences," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 1168-1182, February.
    9. Xie, Y.L. & Xia, D.H. & Ji, L. & Zhou, W.N. & Huang, G.H., 2017. "An inexact cost-risk balanced model for regional energy structure adjustment management and resources environmental effect analysis-a case study of Shandong province, China," Energy, Elsevier, vol. 126(C), pages 374-391.
    10. Jinye Zhao & Benjamin F. Hobbs & Jong-Shi Pang, 2010. "Long-Run Equilibrium Modeling of Emissions Allowance Allocation Systems in Electric Power Markets," Operations Research, INFORMS, vol. 58(3), pages 529-548, June.
    11. Alanne, Kari & Saari, Arto, 2006. "Distributed energy generation and sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(6), pages 539-558, December.
    12. Yu Huang & Kai Yang & Weiting Zhang & Kwang Y. Lee, 2018. "Hierarchical Energy Management for the MultiEnergy Carriers System with Different Interest Bodies," Energies, MDPI, vol. 11(10), pages 1-18, October.
    13. 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.
    14. Muis, Z.A. & Hashim, H. & Manan, Z.A. & Taha, F.M. & Douglas, P.L., 2010. "Optimal planning of renewable energy-integrated electricity generation schemes with CO2 reduction target," Renewable Energy, Elsevier, vol. 35(11), pages 2562-2570.
    15. Gabriella Dellino & Jack P. C. Kleijnen & Carlo Meloni, 2012. "Robust Optimization in Simulation: Taguchi and Krige Combined," INFORMS Journal on Computing, INFORMS, vol. 24(3), pages 471-484, August.
    16. Chen, Yizhong & He, Li & Li, Jing & Cheng, Xi & Lu, Hongwei, 2016. "An inexact bi-level simulation–optimization model for conjunctive regional renewable energy planning and air pollution control for electric power generation systems," Applied Energy, Elsevier, vol. 183(C), pages 969-983.
    17. Sergio Bruno & Gabriella Dellino & Massimo La Scala & Carlo Meloni, 2019. "A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †," Energies, MDPI, vol. 12(6), pages 1-20, March.
    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. Chen, Yizhong & He, Li & Li, Jing, 2017. "Stochastic dominant-subordinate-interactive scheduling optimization for interconnected microgrids with considering wind-photovoltaic-based distributed generations under uncertainty," Energy, Elsevier, vol. 130(C), pages 581-598.
    2. Ren, Hongbo & Gao, Weijun, 2010. "A MILP model for integrated plan and evaluation of distributed energy systems," Applied Energy, Elsevier, vol. 87(3), pages 1001-1014, March.
    3. Omu, Akomeno & Choudhary, Ruchi & Boies, Adam, 2013. "Distributed energy resource system optimisation using mixed integer linear programming," Energy Policy, Elsevier, vol. 61(C), pages 249-266.
    4. Yu, Guodong & Zhang, Jie, 2018. "Multi-dual decomposition solution for risk-averse facility location problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 70-89.
    5. 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.
    6. Yin, J.N. & Huang, G.H. & Xie, Y.L. & An, Y.K., 2021. "Carbon-subsidized inter-regional electric power system planning under cost-risk tradeoff and uncertainty: A case study of Inner Mongolia, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    7. Mehleri, E.D. & Sarimveis, H. & Markatos, N.C. & Papageorgiou, L.G., 2013. "Optimal design and operation of distributed energy systems: Application to Greek residential sector," Renewable Energy, Elsevier, vol. 51(C), pages 331-342.
    8. Jui-Yuan Lee & Han-Fu Lin, 2019. "Multi-Footprint Constrained Energy Sector Planning," Energies, MDPI, vol. 12(12), pages 1-18, June.
    9. Xiao Zhao & Xuhui Xia & Lei Wang & Guodong Yu, 2018. "Risk-Averse Facility Location for Green Closed-Loop Supply Chain Networks Design under Uncertainty," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    10. Figaj, Rafał & Żołądek, Maciej, 2021. "Experimental and numerical analysis of hybrid solar heating and cooling system for a residential user," Renewable Energy, Elsevier, vol. 172(C), pages 955-967.
    11. de Alegría Mancisidor, Itziar Martínez & Díaz de Basurto Uraga, Pablo & Martínez de Alegría Mancisidor, Iñigo & Ruiz de Arbulo López, Patxi, 2009. "European Union's renewable energy sources and energy efficiency policy review: The Spanish perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 100-114, January.
    12. Eid, Cherrelle & Codani, Paul & Perez, Yannick & Reneses, Javier & Hakvoort, Rudi, 2016. "Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 237-247.
    13. Funcke, Simon & Bauknecht, Dierk, 2016. "Typology of centralised and decentralised visions for electricity infrastructure," Utilities Policy, Elsevier, vol. 40(C), pages 67-74.
    14. Meunier, Guy & Ponssard, Jean-Pierre & Quirion, Philippe, 2014. "Carbon leakage and capacity-based allocations: Is the EU right?," Journal of Environmental Economics and Management, Elsevier, vol. 68(2), pages 262-279.
    15. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    16. Blarke, Morten B., 2012. "Towards an intermittency-friendly energy system: Comparing electric boilers and heat pumps in distributed cogeneration," Applied Energy, Elsevier, vol. 91(1), pages 349-365.
    17. Kannan, Nadarajah & Vakeesan, Divagar, 2016. "Solar energy for future world: - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1092-1105.
    18. Moroni, Stefano & Antoniucci, Valentina & Bisello, Adriano, 2016. "Energy sprawl, land taking and distributed generation: towards a multi-layered density," Energy Policy, Elsevier, vol. 98(C), pages 266-273.
    19. Fonseca, Juan D. & Commenge, Jean-Marc & Camargo, Mauricio & Falk, Laurent & Gil, Iván D., 2021. "Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach," Applied Energy, Elsevier, vol. 290(C).
    20. Ye, Bin & Yang, Peng & Jiang, Jingjing & Miao, Lixin & Shen, Bo & Li, Ji, 2017. "Feasibility and economic analysis of a renewable energy powered special town in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 40-50.

    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:12:y:2019:i:12:p:2275-:d:239661. 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.