IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v191y2020ics0360544219321929.html
   My bibliography  Save this article

Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources

Author

Listed:
  • Jiao, P.H.
  • Chen, J.J.
  • Peng, K.
  • Zhao, Y.L.
  • Xin, K.F.

Abstract

Standalone renewable energy system holds the most promising solution to the electrification of remote areas without utility grid access as well as to reduce fossil fuel consumption and environmental pollution. However, the random volatility and unpredictability of renewable energy are key factors to restrict its large-scale accommodation. In the present study, a multi-objective mean-semi-entropy model is proposed for a standalone micro-grid with photovoltaic-wind-battery-diesel generator hybrid system, with the aim of providing a trade-off solution between maximum profits and minimum risk in consideration of photovoltaic and wind uncertainties. Then, the preference-inspired co-evolutionary algorithm, along with Pareto optimality concept, is used for the system techno-economic optimization, i.e., to maximize the profits defined as the mean value of the return and to minimize the risk defined as the semi-entropy simultaneously. Subsequently, the preference ranking organization method is used for decision making to determine the optimal trade-off dispatch solution. Simulation results show that the multi-objective mean-semi-entropy model is well applicable to deal with standalone micro-grid operation, considering the integration of uncertain renewable energy resources.

Suggested Citation

  • Jiao, P.H. & Chen, J.J. & Peng, K. & Zhao, Y.L. & Xin, K.F., 2020. "Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321929
    DOI: 10.1016/j.energy.2019.116497
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219321929
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.116497?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. Wang, Bo & Wang, Shuming & Zhou, Xianzhong & Watada, Junzo, 2016. "Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties," Energy, Elsevier, vol. 111(C), pages 18-31.
    2. Tabar, Vahid Sohrabi & Abbasi, Vahid, 2019. "Energy management in microgrid with considering high penetration of renewable resources and surplus power generation problem," Energy, Elsevier, vol. 189(C).
    3. Hong, Ying-Yi & Lin, Jie-Kai, 2013. "Interactive multi-objective active power scheduling considering uncertain renewable energies using adaptive chaos clonal evolutionary programming," Energy, Elsevier, vol. 53(C), pages 212-220.
    4. Li, Y.Z. & Wu, Q.H. & Li, M.S. & Zhan, J.P., 2014. "Mean-variance model for power system economic dispatch with wind power integrated," Energy, Elsevier, vol. 72(C), pages 510-520.
    5. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    6. Duan Li & Wan‐Lung Ng, 2000. "Optimal Dynamic Portfolio Selection: Multiperiod Mean‐Variance Formulation," Mathematical Finance, Wiley Blackwell, vol. 10(3), pages 387-406, July.
    7. Jiang, Ping & Yang, Hufang & Heng, Jiani, 2019. "A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting," Applied Energy, Elsevier, vol. 235(C), pages 786-801.
    8. Chen, J.J. & Zhuang, Y.B. & Li, Y.Z. & Wang, P. & Zhao, Y.L. & Zhang, C.S., 2017. "Risk-aware short term hydro-wind-thermal scheduling using a probability interval optimization model," Applied Energy, Elsevier, vol. 189(C), pages 534-554.
    9. Chen, J.J. & Wu, Q.H. & Zhang, L.L. & Wu, P.Z., 2017. "Multi-objective mean–variance–skewness model for nonconvex and stochastic optimal power flow considering wind power and load uncertainties," European Journal of Operational Research, Elsevier, vol. 263(2), pages 719-732.
    10. Pfeifer, Antun & Dobravec, Viktorija & Pavlinek, Luka & Krajačić, Goran & Duić, Neven, 2018. "Integration of renewable energy and demand response technologies in interconnected energy systems," Energy, Elsevier, vol. 161(C), pages 447-455.
    11. 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.
    12. Sharafi, Masoud & ELMekkawy, Tarek Y., 2014. "Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach," Renewable Energy, Elsevier, vol. 68(C), pages 67-79.
    13. Abedi, S. & Alimardani, A. & Gharehpetian, G.B. & Riahy, G.H. & Hosseinian, S.H., 2012. "A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1577-1587.
    14. Gazijahani, Farhad Samadi & Salehi, Javad, 2018. "Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach," Energy, Elsevier, vol. 161(C), pages 999-1015.
    15. Zhao, M. & Chen, Z. & Blaabjerg, F., 2006. "Probabilistic capacity of a grid connected wind farm based on optimization method," Renewable Energy, Elsevier, vol. 31(13), pages 2171-2187.
    16. Zhang, Jingrui & Wu, Yihong & Guo, Yiran & Wang, Bo & Wang, Hengyue & Liu, Houde, 2016. "A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints," Applied Energy, Elsevier, vol. 183(C), pages 791-804.
    17. Keshta, H.E. & Ali, A.A. & Saied, E.M. & Bendary, F.M., 2019. "Real-time operation of multi-micro-grids using a multi-agent system," Energy, Elsevier, vol. 174(C), pages 576-590.
    18. Khalilpour, Rajab & Vassallo, Anthony, 2016. "Planning and operation scheduling of PV-battery systems: A novel methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 194-208.
    19. Rabiee, Abbas & Mohseni-Bonab, Seyed Masoud, 2017. "Maximizing hosting capacity of renewable energy sources in distribution networks: A multi-objective and scenario-based approach," Energy, Elsevier, vol. 120(C), pages 417-430.
    20. Bertrand Mareschal & Jean Pierre Brans & Philippe Vincke, 1986. "How to select and how to rank projects: the Prométhée method," ULB Institutional Repository 2013/9307, ULB -- Universite Libre de Bruxelles.
    21. Brans, J. P. & Vincke, Ph. & Mareschal, B., 1986. "How to select and how to rank projects: The method," European Journal of Operational Research, Elsevier, vol. 24(2), pages 228-238, February.
    22. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Rocco, Vittorio & Rossi, Joao Luis, 2018. "Hybrid renewable energy systems for renewable integration in microgrids: Influence of sizing on performance," Energy, Elsevier, vol. 152(C), pages 744-758.
    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. Zhao, Yuehao & Li, Zhiyi & Ju, Ping & Zhou, Yue, 2023. "Two-stage data-driven dispatch for integrated power and natural gas systems by using stochastic model predictive control," Applied Energy, Elsevier, vol. 343(C).
    2. Chen, J.J. & Qi, B.X. & Rong, Z.K. & Peng, K. & Zhao, Y.L. & Zhang, X.H., 2021. "Multi-energy coordinated microgrid scheduling with integrated demand response for flexibility improvement," Energy, Elsevier, vol. 217(C).
    3. Yuan, Zhi & Li, Ji, 2024. "Photovoltaic-penetrated power distribution networks’ resiliency-oriented day-ahead scheduling equipped with power-to-hydrogen systems: A risk-driven decision framework," Energy, Elsevier, vol. 299(C).
    4. Ting Wang & Qiya Wang & Caiqing Zhang, 2021. "Research on the Optimal Operation of a Novel Renewable Multi-Energy Complementary System in Rural Areas," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    5. Ren, Xiaojun & Wu, Yongtang & Hao, Dongmin & Liu, Guoxu & Zafetti, Nicholas, 2021. "Analysis of the performance of the multi-objective hybrid hydropower-photovoltaic-wind system to reduce variance and maximum power generation by developed owl search algorithm," Energy, Elsevier, vol. 231(C).
    6. Safder, Usman & Hai, Tra Nguyen & Loy-Benitez, Jorge & Yoo, ChangKyoo, 2022. "Nationwide policymaking strategies to prevent future electricity crises in developing countries using data-driven forecasting and fuzzy-SWOT analyses," Energy, Elsevier, vol. 259(C).
    7. Shahbazbegian, Vahid & Shafie-khah, Miadreza & Laaksonen, Hannu & Strbac, Goran & Ameli, Hossein, 2023. "Resilience-oriented operation of microgrids in the presence of power-to-hydrogen systems," Applied Energy, Elsevier, vol. 348(C).
    8. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2021. "Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation," Applied Energy, Elsevier, vol. 290(C).
    9. Al-Quraan, A. & Al-Mhairat, B., 2024. "Economic predictive control-based sizing and energy management for grid-connected hybrid renewable energy systems," Energy, Elsevier, vol. 302(C).

    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. Li, Y.Z. & Wu, Q.H. & Li, M.S. & Zhan, J.P., 2014. "Mean-variance model for power system economic dispatch with wind power integrated," Energy, Elsevier, vol. 72(C), pages 510-520.
    2. Jiao, P.H. & Chen, J.J. & Cai, X. & Zhao, Y.L., 2024. "Fuzzy semi-entropy based downside risk to low-carbon oriented multi-energy dispatch considering multiple dependent uncertainties," Energy, Elsevier, vol. 287(C).
    3. Li, Y.Z. & Li, K.C. & Wang, P. & Liu, Y. & Lin, X.N. & Gooi, H.B. & Li, G.F. & Cai, D.L. & Luo, Y., 2017. "Risk constrained economic dispatch with integration of wind power by multi-objective optimization approach," Energy, Elsevier, vol. 126(C), pages 810-820.
    4. 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.
    5. Li, M.S. & Lin, Z.J. & Ji, T.Y. & Wu, Q.H., 2018. "Risk constrained stochastic economic dispatch considering dependence of multiple wind farms using pair-copula," Applied Energy, Elsevier, vol. 226(C), pages 967-978.
    6. Zopounidis, C., 1999. "Multicriteria decision aid in financial management," European Journal of Operational Research, Elsevier, vol. 119(2), pages 404-415, December.
    7. Ridha, Hussein Mohammed & Gomes, Chandima & Hizam, Hashim & Ahmadipour, Masoud & Heidari, Ali Asghar & Chen, Huiling, 2021. "Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2019. "Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators," European Journal of Operational Research, Elsevier, vol. 278(3), pages 942-960.
    9. Zhang, Jingrui & Zhu, Xiaoqing & Chen, Tengpeng & Yu, Yanlin & Xue, Wendong, 2020. "Improved MOEA/D approach to many-objective day-ahead scheduling with consideration of adjustable outputs of renewable units and load reduction in active distribution networks," Energy, Elsevier, vol. 210(C).
    10. Chen, J.J. & Qi, B.X. & Peng, K. & Li, Y. & Zhao, Y.L., 2020. "Conditional value-at-credibility for random fuzzy wind power in demand response integrated multi-period economic emission dispatch," Applied Energy, Elsevier, vol. 261(C).
    11. Ma, Shuai & Ma, Xiaoteng & Xia, Li, 2023. "A unified algorithm framework for mean-variance optimization in discounted Markov decision processes," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1057-1067.
    12. Lin, Zhenjia & Chen, Haoyong & Wu, Qiuwei & Li, Weiwei & Li, Mengshi & Ji, Tianyao, 2020. "Mean-tracking model based stochastic economic dispatch for power systems with high penetration of wind power," Energy, Elsevier, vol. 193(C).
    13. Yongqi Zhao & Jiajia Chen, 2021. "A Quantitative Risk-Averse Model for Optimal Management of Multi-Source Standalone Microgrid with Demand Response and Pumped Hydro Storage," Energies, MDPI, vol. 14(9), pages 1-17, May.
    14. Yi Peng, 2015. "Regional earthquake vulnerability assessment using a combination of MCDM methods," Annals of Operations Research, Springer, vol. 234(1), pages 95-110, November.
    15. Denys Yemshanov & Frank H. Koch & Yakov Ben‐Haim & Marla Downing & Frank Sapio & Marty Siltanen, 2013. "A New Multicriteria Risk Mapping Approach Based on a Multiattribute Frontier Concept," Risk Analysis, John Wiley & Sons, vol. 33(9), pages 1694-1709, September.
    16. Xiangyu Cui & Xun Li & Duan Li & Yun Shi, 2014. "Time Consistent Behavior Portfolio Policy for Dynamic Mean-Variance Formulation," Papers 1408.6070, arXiv.org, revised Aug 2015.
    17. Guh, Yuh-Yuan, 1997. "Introduction to a new weighting method -- Hierarchy consistency analysis," European Journal of Operational Research, Elsevier, vol. 102(1), pages 215-226, October.
    18. Hajkowicz, Stefan, 2006. "Taking a closer look at multiple criteria analysis and economic evaluation," 2006 Conference (50th), February 8-10, 2006, Sydney, Australia 139785, Australian Agricultural and Resource Economics Society.
    19. Meløn, Mønica García & Aragonés Beltran, Pablo & Carmen González Cruz, M., 2008. "An AHP-based evaluation procedure for Innovative Educational Projects: A face-to-face vs. computer-mediated case study," Omega, Elsevier, vol. 36(5), pages 754-765, October.
    20. Briec, Walter & Kerstens, Kristiaan, 2009. "Multi-horizon Markowitz portfolio performance appraisals: A general approach," Omega, Elsevier, vol. 37(1), pages 50-62, February.

    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:energy:v:191:y:2020:i:c:s0360544219321929. 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/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.