Flexible Ramping Services in Power Systems: Background, Challenges, and Procurement Methods

Kaheh, Zohreh; Kazemzadeh, Reza Baradaran; Sheikh‐El‐Eslami, Mohammad Kazem

doi:10.1007/s40998-020-00336-2

Cited by 8 publications

(2 citation statements)

References 62 publications

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“…Ref. [35] presents reviews on issues ranging from quantifying flexibilities and modeling flexibility boundaries to procurement and management services from the energy components in the distribution network. Techniques based on optimal power flow [36,37] and economical generation dispatch [38,39] are commonly used in literature to estimate the flexibility operating region for different generation scenarios.…”

Section: Review Of Relevant Literaturementioning

confidence: 99%

A Bi-Level Security Constrained Model for Optimal Flexibility Operation

2022

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Section: Review Of Relevant Literaturementioning

confidence: 99%

A Bi-Level Security Constrained Model for Optimal Flexibility Operation

2022

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“…Therefore, flexibility is introduced as one of the main concerns of renewable-dominated power systems. One of main resources of ramp flexibility is demand-side resources [1][2][3][4]. Demand-side flexibility refers to the voluntary alterations in the grid electricity demand, which are activated by some sort of incentives.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Demand Response Behavior of Residential Consumers through a Bayesian Forecasting Method

Kaheh

Arasteh

2023

Preprint

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Demand-side resources have been recognized as a cost-effective approach for providing the required flexibility in power systems with high penetrations of renewable resources. Almost all existing demand response (DR) models are established based on the assumption that end-users are always rational and predictable agents with well-defined preferences. This assumption has resulted in apparently unaccountable divergence between modelled and observed results for DR approaches. However, due to the nature of the prompt responses of DR providers to DR signals, it is of paramount importance to consider the effect of impulsive decisions on providing the flexibility. This paper investigates the effects of impulsive decisions on the frequency of being unresponsive to DR signals and the collaboration rate in 5-minutes ahead DRX through a Bayesian Forecasting Method. The analyses have demonstrated considering the provided flexibility in similar days of week as explanatory variable in Bayesian structural time series can be helpful for the detection of impulsive behavior to attain more accurate DR behavior forecasting.

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