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Power and Energy Management of a DC Microgrid for a Renewable Curtailment Case Due to the Integration of a Small-Scale Wind Turbine

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  • Jamila Aourir

    (AVENUES, Université de Technologie de Compiègne, Centre Pierre Guillaumat-CS 60 319, 60203 Compiègne, France)

  • Fabrice Locment

    (AVENUES, Université de Technologie de Compiègne, Centre Pierre Guillaumat-CS 60 319, 60203 Compiègne, France)

  • Manuela Sechilariu

    (AVENUES, Université de Technologie de Compiègne, Centre Pierre Guillaumat-CS 60 319, 60203 Compiègne, France)

Abstract

Economic dispatch optimization and power management are the main concerns for a microgrid (MG). They are always studied and are considered to achieve an efficient operation of the MG by simplifying the control process and decreasing losses. The integration of a small-scale wind turbine (SSWD) into a direct current (DC) MG has an impact on its power and energy management. Excess power produced by renewable energy sources (RESs) is one of the problems that face the reliability of the MG and should be resolved. For this reason, a supervisory system is suggested to manage the excess of power. During the supervision process, some criteria, such as the physical limits and tariffs of the components are taken into account. Then, the suggested power management strategy aims to achieve an instantaneous power balance considering a rule-based power and depends on the above-mentioned criteria. To better meet the power balance, it is necessary to explore the constraints related to the control and supervision of the studied DC MG. Performance measures include the overall system energy cost and renewable curtailment (renewable energy that cannot be utilized and should be limited). Thus, the power limitation strategy consists of using two types of “shedding coefficients”, α and γ , to calculate the power that should be limited from each RES in the case of energy surplus. Simulation tests are carried out using two power management strategies: optimization and without optimization (i.e., storage priority). The results reveal that the coefficient γ reduces the overall energy cost and whatever the applied coefficient, optimization still provides good performances and significantly reduces the global energy cost.

Suggested Citation

  • Jamila Aourir & Fabrice Locment & Manuela Sechilariu, 2022. "Power and Energy Management of a DC Microgrid for a Renewable Curtailment Case Due to the Integration of a Small-Scale Wind Turbine," Energies, MDPI, vol. 15(9), pages 1-24, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3421-:d:810452
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    References listed on IDEAS

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    1. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa & Zhao, Jun-Hong, 2009. "Review on multi-criteria decision analysis aid in sustainable energy decision-making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2263-2278, December.
    2. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2016. "Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building," Applied Energy, Elsevier, vol. 171(C), pages 153-171.
    3. Alarcon-Rodriguez, Arturo & Ault, Graham & Galloway, Stuart, 2010. "Multi-objective planning of distributed energy resources: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(5), pages 1353-1366, June.
    4. Comodi, Gabriele & Giantomassi, Andrea & Severini, Marco & Squartini, Stefano & Ferracuti, Francesco & Fonti, Alessandro & Nardi Cesarini, Davide & Morodo, Matteo & Polonara, Fabio, 2015. "Multi-apartment residential microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies," Applied Energy, Elsevier, vol. 137(C), pages 854-866.
    5. Hu, Ming-Che & Lu, Su-Ying & Chen, Yen-Haw, 2016. "Stochastic programming and market equilibrium analysis of microgrids energy management systems," Energy, Elsevier, vol. 113(C), pages 662-670.
    6. Nogueira, Carlos Eduardo Camargo & Vidotto, Magno Luiz & Niedzialkoski, Rosana Krauss & de Souza, Samuel Nelson Melegari & Chaves, Luiz Inácio & Edwiges, Thiago & Santos, Darlisson Bentes dos & Wernck, 2014. "Sizing and simulation of a photovoltaic-wind energy system using batteries, applied for a small rural property located in the south of Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 151-157.
    7. Rajanna, S. & Saini, R.P., 2016. "Development of optimal integrated renewable energy model with battery storage for a remote Indian area," Energy, Elsevier, vol. 111(C), pages 803-817.
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    Cited by:

    1. Marvin Lema & Wilson Pavon & Leony Ortiz & Ama Baduba Asiedu-Asante & Silvio Simani, 2022. "Controller Coordination Strategy for DC Microgrid Using Distributed Predictive Control Improving Voltage Stability," Energies, MDPI, vol. 15(15), pages 1-15, July.

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