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Improvement of Wind Energy Production through HVDC Systems

Author

Listed:
  • Morris Brenna

    (Department of Energy, Politecnico di Milano, Via La Masa, 34-20156 Milano (MI), Italy)

  • Federica Foiadelli

    (Department of Energy, Politecnico di Milano, Via La Masa, 34-20156 Milano (MI), Italy)

  • Michela Longo

    (Department of Energy, Politecnico di Milano, Via La Masa, 34-20156 Milano (MI), Italy)

  • Dario Zaninelli

    (Department of Energy, Politecnico di Milano, Via La Masa, 34-20156 Milano (MI), Italy)

Abstract

Variable and non-programmable resources, such as solar and wind, have undergone a stunning growth in recent years and are likely to gain even more importance in the future. Their strong presence in the national electricity mix has created issues in many countries regarding the secure operation of the power system. In order to guarantee the stability of the system, several TSOs have resorted to wind energy curtailment, which represents a waste of clean energy and an economic loss. In order to analyze this issue, a model of the Italian power system was developed, a program able to simulate the electricity dispatching mechanism. The model was, then, used to evaluate possible solutions to reduce wind curtailment. In particular, a proposal for the construction of an HVDC line linking Southern and Northern Italy was studied.

Suggested Citation

  • Morris Brenna & Federica Foiadelli & Michela Longo & Dario Zaninelli, 2017. "Improvement of Wind Energy Production through HVDC Systems," Energies, MDPI, vol. 10(2), pages 1-25, January.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:157-:d:88930
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    References listed on IDEAS

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    2. Xuehan Zhang & Yongju Son & Sungyun Choi, 2022. "Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources," Energies, MDPI, vol. 15(6), pages 1-18, March.
    3. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.
    4. Roland Ryndzionek & Łukasz Sienkiewicz, 2020. "Evolution of the HVDC Link Connecting Offshore Wind Farms to Onshore Power Systems," Energies, MDPI, vol. 13(8), pages 1-17, April.
    5. Mohsin Ali Koondhar & Ghulam Sarwar Kaloi & Abdul Sattar Saand & Sadullah Chandio & Wonsuk Ko & Sisam Park & Hyeong-Jin Choi & Ragab Abdelaziz El-Sehiemy, 2023. "Critical Technical Issues with a Voltage-Source-Converter-Based High Voltage Direct Current Transmission System for the Onshore Integration of Offshore Wind Farms," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    6. Peláez-Rodríguez, C. & Pérez-Aracil, J. & Fister, D. & Prieto-Godino, L. & Deo, R.C. & Salcedo-Sanz, S., 2022. "A hierarchical classification/regression algorithm for improving extreme wind speed events prediction," Renewable Energy, Elsevier, vol. 201(P2), pages 157-178.

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