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Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration

Author

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  • Ilias G. Marneris

    (Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Pandelis N. Biskas

    (Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Anastasios G. Bakirtzis

    (Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

Abstract

The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-ahead scheduling results are tested against different real-time dispatch regimes, with none or limited look-ahead capability, or with the use of the variability reserve, utilizing a modified version of the Greek power system. The results demonstrate the enhanced reliability achieved by applying the multi-timing scheduling concept and explicitly considering the variability reserve, and certain features regarding the allocation and deployment of reserves are discussed.

Suggested Citation

  • Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:140-:d:88541
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    1. Anthony Papavasiliou & Shmuel S. Oren, 2013. "Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network," Operations Research, INFORMS, vol. 61(3), pages 578-592, June.
    2. Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
    3. Deane, J.P. & Drayton, G. & Ó Gallachóir, B.P., 2014. "The impact of sub-hourly modelling in power systems with significant levels of renewable generation," Applied Energy, Elsevier, vol. 113(C), pages 152-158.
    4. Ying-Yi Hong & Ti-Hsuan Yu & Ching-Yun Liu, 2013. "Hour-Ahead Wind Speed and Power Forecasting Using Empirical Mode Decomposition," Energies, MDPI, vol. 6(12), pages 1-16, November.
    5. Kyung-bin Kwon & Hyeongon Park & Jae-Kun Lyu & Jong-Keun Park, 2016. "Cost Analysis Method for Estimating Dynamic Reserve Considering Uncertainties in Supply and Demand," Energies, MDPI, vol. 9(10), pages 1-16, October.
    6. PAPAVASILIOU, Anthony & OREN, Schmuel S., 2013. "Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network," LIDAM Reprints CORE 2500, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, vol. 5(3), pages 1-37, March.
    8. Simone Sperati & Stefano Alessandrini & Pierre Pinson & George Kariniotakis, 2015. "The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation," Energies, MDPI, vol. 8(9), pages 1-26, September.
    9. Beibei Wang & Xiaocong Liu & Feng Zhu & Xiaoqing Hu & Wenlu Ji & Shengchun Yang & Ke Wang & Shuhai Feng, 2015. "Unit Commitment Model Considering Flexible Scheduling of Demand Response for High Wind Integration," Energies, MDPI, vol. 8(12), pages 1-22, December.
    10. Pinson, P. & Girard, R., 2012. "Evaluating the quality of scenarios of short-term wind power generation," Applied Energy, Elsevier, vol. 96(C), pages 12-20.
    11. Gerardo J. Osório & Jorge N. D. L. Gonçalves & Juan M. Lujano-Rojas & João P. S. Catalão, 2016. "Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term," Energies, MDPI, vol. 9(9), pages 1-19, August.
    12. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    13. Isemonger, Alan G., 2009. "The evolving design of RTO ancillary service markets," Energy Policy, Elsevier, vol. 37(1), pages 150-157, January.
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    5. Miguel Carrión & Rafael Zárate-Miñano & Ruth Domínguez, 2018. "A Practical Formulation for Ex-Ante Scheduling of Energy and Reserve in Renewable-Dominated Power Systems: Case Study of the Iberian Peninsula," Energies, MDPI, vol. 11(8), pages 1-22, July.
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    7. Gang Wang & Dahai You & Zhe Zhang & Li Dai & Qi Zou & Hengwei Liu, 2018. "Network-Constrained Unit Commitment Based on Reserve Models Fully Considering the Stochastic Characteristics of Wind Power," Energies, MDPI, vol. 11(2), pages 1-20, February.
    8. Alshawaf, Mohammad & Poudineh, Rahmatallah & Alhajeri, Nawaf S., 2020. "Solar PV in Kuwait: The effect of ambient temperature and sandstorms on output variability and uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
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