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Modeling and Investigation of Demand Response Uncertainty on Reliability Assessment

Author

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  • Jen-Hao Teng

    (Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan)

  • Chia-Hung Hsieh

    (Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan)

Abstract

Demand Response (DR) provides an opportunity for customers to reduce their loads during times of high prices and therefore to shave the peak loads. The power outputs of large-scale generator units can be predicted and controlled easily; therefore, the pricing and reliability of conventional power utilities can be assessed straightforwardly. However, the customer loads are very variable and difficult to predict and control; therefore, the integration of DR might cause uncertainty issues on pricing and reliability and is essential to be further investigated. A novel uncertainty model for load reduction is proposed in this paper. The probability intensities of load reduction are first estimated from the measured load reduction variations. A multi-state probability model is then proposed for load reduction and the Markov process is used to calculate the state probabilities. A stochastic analysis scheme using Monte Carlo simulation for pricing and reliability taking the DR uncertainty into account is then investigated. Several cases are designed to compare the effects of DR uncertainty. Simulation results show that the proposed uncertainty model can be integrated into conventional economic dispatch to precisely evaluate the DR uncertainty on system operation and reliability.

Suggested Citation

  • Jen-Hao Teng & Chia-Hung Hsieh, 2021. "Modeling and Investigation of Demand Response Uncertainty on Reliability Assessment," Energies, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1104-:d:502165
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    References listed on IDEAS

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    1. Luis Alejandro Arias & Edwin Rivas & Francisco Santamaria & Victor Hernandez, 2018. "A Review and Analysis of Trends Related to Demand Response," Energies, MDPI, vol. 11(7), pages 1-24, June.
    2. Wang, Jiadong & Wang, Jianhui & Liu, Cong & Ruiz, Juan P., 2013. "Stochastic unit commitment with sub-hourly dispatch constraints," Applied Energy, Elsevier, vol. 105(C), pages 418-422.
    3. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    4. Venkat Durvasulu & Timothy M. Hansen, 2018. "Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets," Energies, MDPI, vol. 11(12), pages 1-21, December.
    5. Moslehi, Salim & Reddy, T. Agami, 2018. "Sustainability of integrated energy systems: A performance-based resilience assessment methodology," Applied Energy, Elsevier, vol. 228(C), pages 487-498.
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