IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v227y2021ics0360544221007209.html
   My bibliography  Save this article

A multi-objective formulation of improving flexibility in the operation of electric power systems: Application to mitigation measures during the coronavirus pandemic

Author

Listed:
  • Alvarez, Gonzalo E.

Abstract

The coronavirus pandemic has infected more than 23 million people worldwide by August 2020 along with more than 800,000 deaths. To face this pandemic, a joint effort among different areas has been required. In this context, the correct supply of basic services is the key to enhance the complex circumstance. The operation of the electric power systems is needed to ensure the answer to the situation. Basic services in areas of health, security, food, and communications depend on the electricity supply. Consequently, this paper introduces a multi-objective procedure that enhances the operations of power systems under these circumstances. It considers geographical areas that are affected by coronavirus cases and their effects on the personnel of power plants. To obtain the best combinations of total cost and protection of the workers, lexicographic optimization is implemented. The effectiveness of this approach is studied by solving two test cases: a 6-bus system and the Argentine Electric System with real data about the infection cases. The effects on the electricity generation and transportation stages are studied. The results allow identifying critical areas and proposing corrective actions. The method can reach feasible solutions with a low computational requirement.

Suggested Citation

  • Alvarez, Gonzalo E., 2021. "A multi-objective formulation of improving flexibility in the operation of electric power systems: Application to mitigation measures during the coronavirus pandemic," Energy, Elsevier, vol. 227(C).
  • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221007209
    DOI: 10.1016/j.energy.2021.120471
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221007209
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.120471?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. Yuan, Xiaohui & Zhang, Binqiao & Wang, Pengtao & Liang, Ji & Yuan, Yanbin & Huang, Yuehua & Lei, Xiaohui, 2017. "Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm," Energy, Elsevier, vol. 122(C), pages 70-82.
    2. Reza Norouzi, Mohammad & Ahmadi, Abdollah & Esmaeel Nezhad, Ali & Ghaedi, Amir, 2014. "Mixed integer programming of multi-objective security-constrained hydro/thermal unit commitment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 911-923.
    3. Halicioglu, Ferda, 2007. "Residential electricity demand dynamics in Turkey," Energy Economics, Elsevier, vol. 29(2), pages 199-210, March.
    4. Andruszkiewicz, Jerzy & Lorenc, Józef & Weychan, Agnieszka, 2020. "Seasonal variability of price elasticity of demand of households using zonal tariffs and its impact on hourly load of the power system," Energy, Elsevier, vol. 196(C).
    5. Thatcher, Marcus J., 2007. "Modelling changes to electricity demand load duration curves as a consequence of predicted climate change for Australia," Energy, Elsevier, vol. 32(9), pages 1647-1659.
    6. Mazidi, Mohammadreza & Monsef, Hassan & Siano, Pierluigi, 2016. "Design of a risk-averse decision making tool for smart distribution network operators under severe uncertainties: An IGDT-inspired augment ε-constraint based multi-objective approach," Energy, Elsevier, vol. 116(P1), pages 214-235.
    7. Wang, Xianxun & Virguez, Edgar & Xiao, Weihua & Mei, Yadong & Patiño-Echeverri, Dalia & Wang, Hao, 2019. "Clustering and dispatching hydro, wind, and photovoltaic power resources with multiobjective optimization of power generation fluctuations: A case study in southwestern China," Energy, Elsevier, vol. 189(C).
    8. El Sehiemy, Ragab A. & Selim, F. & Bentouati, Bachir & Abido, M.A., 2020. "A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems," Energy, Elsevier, vol. 193(C).
    9. Alvarez, Gonzalo E., 2020. "Operation of pumped storage hydropower plants through optimization for power systems," Energy, Elsevier, vol. 202(C).
    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. Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
    2. Halbrügge, Stephanie & Buhl, Hans Ulrich & Fridgen, Gilbert & Schott, Paul & Weibelzahl, Martin & Weissflog, Jan, 2022. "How Germany achieved a record share of renewables during the COVID-19 pandemic while relying on the European interconnected power network," Energy, Elsevier, vol. 246(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. Guo, Su & Zheng, Kun & He, Yi & Kurban, Aynur, 2023. "The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps," Renewable Energy, Elsevier, vol. 202(C), pages 1169-1189.
    2. Razavi, Seyed-Ehsan & Esmaeel Nezhad, Ali & Mavalizadeh, Hani & Raeisi, Fatima & Ahmadi, Abdollah, 2018. "Robust hydrothermal unit commitment: A mixed-integer linear framework," Energy, Elsevier, vol. 165(PB), pages 593-602.
    3. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Alharthi, Mosleh M. & Ghoneim, Sherif S.M. & Ginidi, Ahmed R., 2021. "Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework," Energy, Elsevier, vol. 237(C).
    4. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
    5. Omri, Anis, 2014. "An international literature survey on energy-economic growth nexus: Evidence from country-specific studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 951-959.
    6. Shahbaz, Muhammad & Lean, Hooi Hooi, 2012. "Does financial development increase energy consumption? The role of industrialization and urbanization in Tunisia," Energy Policy, Elsevier, vol. 40(C), pages 473-479.
    7. Ahmed, Khalid, 2015. "The sheer scale of China’s urban renewal and CO2 emissions: Multiple structural breaks, long-run relationship and short-run dynamics," MPRA Paper 71035, University Library of Munich, Germany.
    8. Dergiades, Theologos & Tsoulfidis, Lefteris, 2008. "Estimating residential demand for electricity in the United States, 1965-2006," Energy Economics, Elsevier, vol. 30(5), pages 2722-2730, September.
    9. Acaravici, Ali, 2010. "Structural Breaks, Electricity Consumption and Economic Growth: Evidence from Turkey," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 140-154, July.
    10. Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Short term scheduling of hydrothermal power systems with photovoltaic and pumped storage plants using quasi-oppositional turbulent water flow optimization," Renewable Energy, Elsevier, vol. 191(C), pages 459-492.
    11. Kim, Kyungah & Choi, Jihye & Lee, Jihee & Lee, Jongsu & Kim, Junghun, 2023. "Public preferences and increasing acceptance of time-varying electricity pricing for demand side management in South Korea," Energy Economics, Elsevier, vol. 119(C).
    12. Muhammad Shahbaz & Mete Feridun, 2012. "Electricity consumption and economic growth empirical evidence from Pakistan," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(5), pages 1583-1599, August.
    13. Psiloglou, B.E. & Giannakopoulos, C. & Majithia, S. & Petrakis, M., 2009. "Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment," Energy, Elsevier, vol. 34(11), pages 1855-1863.
    14. Stéphane Auray & Vincenzo Caponi & Benoît Ravel, 2019. "Price Elasticity of Electricity Demand in France," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 513, pages 91-103.
    15. Zhang, Tianhao & Dong, Zhe & Huang, Xiaojin, 2024. "Multi-objective optimization of thermal power and outlet steam temperature for a nuclear steam supply system with deep reinforcement learning," Energy, Elsevier, vol. 286(C).
    16. Zhao, Ziwen & Yuan, Yichen & He, Mengjiao & Jurasz, Jakub & Wang, Jianan & Egusquiza, Mònica & Egusquiza, Eduard & Xu, Beibei & Chen, Diyi, 2022. "Stability and efficiency performance of pumped hydro energy storage system for higher flexibility," Renewable Energy, Elsevier, vol. 199(C), pages 1482-1494.
    17. Shahenda Sarhan & Ragab El-Sehiemy & Amlak Abaza & Mona Gafar, 2022. "Turbulent Flow of Water-Based Optimization for Solving Multi-Objective Technical and Economic Aspects of Optimal Power Flow Problems," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
    18. Brantley Liddle, 2017. "Accounting for Nonlinearity, Asymmetry, Heterogeneity, and Cross-Sectional Dependence in Energy Modeling: US State-Level Panel Analysis," Economies, MDPI, vol. 5(3), pages 1-11, August.
    19. He, Xianghui & Yang, Jiandong & Yang, Jiebin & Zhao, Zhigao & Hu, Jinhong & Peng, Tao, 2023. "Evolution mechanism of water column separation in pump turbine: Model experiment and occurrence criterion," Energy, Elsevier, vol. 265(C).
    20. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2016. "A new approach to modeling the effects of temperature fluctuations on monthly electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 206-216.

    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:227:y:2021:i:c:s0360544221007209. 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.