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A novel cluster-based spinning reserve dynamic model for wind and PV power reinforcement

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  • Nikolaidis, Pavlos
  • Poullikkas, Andreas

Abstract

The share of low-carbon energy in the electricity-production industry is increasing, creating reliability disturbances in modern power systems. Globally, the various renewable resources are distinguished by their origin into firm, variable and uncertain. To cope with the impact of variable and uncertain renewables on residual load, system operators need to plan-ahead adequate spinning reserves. In this work, we introduce a new paradigm for addressing the dynamic spinning reserve formulation, that is capable of accounting for the largely unaddressed challenge of the volatile behavior of different power inputs in the presence of storage. Based on realistic models and spinning reserve clusters, our solution leverages widely adopted robust approaches in the field, providing optimum cost/risk trade-off without deteriorating the computational burden. The proposed framework relies on a hybrid optimization mechanism to enable the effective unit commitment and allow for the minimization of spinning reserve deficits, renewable energy curtailment and load shedding. In the presence of storage, our formulation improves not only the annual total cost, but also allows for renewable generation enhancement at the maximum reliability level. The annual improvement accounts for 1.18% increases in renewable penetration, reduced costs in the range of €9M-€27 M and 3.75–9.45 GWh of services that are not withheld.

Suggested Citation

  • Nikolaidis, Pavlos & Poullikkas, Andreas, 2021. "A novel cluster-based spinning reserve dynamic model for wind and PV power reinforcement," Energy, Elsevier, vol. 234(C).
  • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221015188
    DOI: 10.1016/j.energy.2021.121270
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    References listed on IDEAS

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    4. Giorgos S. Georgiou & Pavlos Nikolaidis & Soteris A. Kalogirou & Paul Christodoulides, 2020. "A Hybrid Optimization Approach for Autonomy Enhancement of Nearly-Zero-Energy Buildings Based on Battery Performance and Artificial Neural Networks," Energies, MDPI, vol. 13(14), pages 1-23, July.
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    Cited by:

    1. Maria Carmen Falvo & Stefano Panella & Mauro Caprabianca & Federico Quaglia, 2021. "A Review on Unit Commitment Algorithms for the Italian Electricity Market," Energies, MDPI, vol. 15(1), pages 1-14, December.
    2. Pavlos Nikolaidis, 2023. "Solar Energy Harnessing Technologies towards De-Carbonization: A Systematic Review of Processes and Systems," Energies, MDPI, vol. 16(17), pages 1-39, August.
    3. Muhammad Asghar Majeed & Furqan Asghar & Muhammad Imtiaz Hussain & Waseem Amjad & Anjum Munir & Hammad Armghan & Jun-Tae Kim, 2022. "Adaptive Dynamic Control Based Optimization of Renewable Energy Resources for Grid-Tied Microgrids," Sustainability, MDPI, vol. 14(3), pages 1-14, February.
    4. Pavlos Nikolaidis & Andreas Poullikkas, 2022. "A Thorough Emission-Cost Analysis of the Gradual Replacement of Carbon-Rich Fuels with Carbon-Free Energy Carriers in Modern Power Plants: The Case of Cyprus," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    5. Grasu Stelian, 2023. "Is Hydrogen the Future Golden Boy of Maritime Transportation?," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 73-79, July.
    6. Valeri Mladenov & Vesselin Chobanov & George Calin Seritan & Radu Florin Porumb & Bogdan-Adrian Enache & Vasiliki Vita & Marilena Stănculescu & Thong Vu Van & Dimitrios Bargiotas, 2022. "A Flexibility Market Platform for Electricity System Operators Using Blockchain Technology," Energies, MDPI, vol. 15(2), pages 1-26, January.
    7. Sai, Wei & Pan, Zehua & Liu, Siyu & Jiao, Zhenjun & Zhong, Zheng & Miao, Bin & Chan, Siew Hwa, 2023. "Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms," Applied Energy, Elsevier, vol. 352(C).

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