Quantifying Risk of Wind Power Ramps in ERCOT

Zhao, Jie; Abedi, Sajjad; He, Miao; Du, Pengwei; Sharma, Sandip; Blevins, Bill

doi:10.1109/tpwrs.2017.2678761

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…Recent studies [18] has demonstrated that real-world wind power ramps exhibit heavier tails. In particular, the historical data from wind power generation in ERCOT (Electric Reliability Council of Texas) reveals that less than 5% of hourly wind power ramps had a magnitude greater than three standard deviations [19], [20]. Those extreme values in the tails will require operating reserves during operation to compensate the deviations and to guarantee wind power integration.…”

Section: Modelling Uncertainty With Scenariosmentioning

confidence: 99%

Quantification of operating reserves with high penetration of wind power considering extreme values

Obando¹,

González²,

Moreno³

2020

IJECE

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The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore, the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty in wind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated as a mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching.

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Section: Modelling Uncertainty With Scenariosmentioning

confidence: 99%

Quantification of operating reserves with high penetration of wind power considering extreme values

Obando¹,

González²,

Moreno³

2020

IJECE

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“…e low < e < e up e − e up e > e up e − e low e < e low (15) The ESS rated power is the maximum absolute value of P ESS [i], as shown in formula (16).…”

Section: A the Ess Rated Powermentioning

confidence: 99%

Optimization of Wind Farm Self-Discipline Interval and Energy Storage System Configuration

Zhang

Dong

et al. 2020

IEEE Access

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The uncertain and stochastic output of the wind farm results in a lot of problems when it is connected to the power grid. In order to improve the wind power's friendship to the grid, the wind farm should has a certain self-discipline level. In this paper, it is studied from the perspective of the wind farm self-discipline interval. First, the concept of wind farm self-discipline interval is proposed, followed by a comprehensive index which is used to evaluate the wind farm self-discipline level by comprehensively considering the interval width and the interval accuracy. Second, an optimization method is discussed to obtain the optimal self-discipline interval. This method has general applicability, not only suitable for normal distribution and other known distributions but also for arbitrary distributions (such as non-symmetric, multi-peak distributions). Finally, the size of the energy storage system (ESS) in the wind farm is optimized to guarantee a suitable wind farm self-discipline level. Simulation results show that the proposed method not only effectively improves the self-discipline level of the wind farm but also has general applicability. INDEX TERMS Wind power, self-discipline, energy storage system (ESS), optimization.

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“…This approach was demonstrated for the 50 Hertz Transmission control area, which is in the Eastern part of Germany with 17 GW and 10 GW of installed wind and solar capacity, respectively. An interesting development from ERCOT is the application of extreme value theory for examining the tail characteristics of hourly wind power ramp data, followed by a risk assessment (e.g., value-at-risk based metrics) for wind power up ramps and down ramps separately [126].…”

Section: Communication Of Wind Power Uncertaintymentioning

confidence: 99%

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Bessa

Möhrlen²,

Fundel

et al. 2017

Energies

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Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

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Quantifying Risk of Wind Power Ramps in ERCOT

Cited by 15 publications

References 6 publications

Quantification of operating reserves with high penetration of wind power considering extreme values

Quantification of operating reserves with high penetration of wind power considering extreme values

Optimization of Wind Farm Self-Discipline Interval and Energy Storage System Configuration

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

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