Common dimensioning of frequency restoration reserve capacities for European load-frequency control blocks

Knorr, Kaspar; Dreher, Alexander; Böttger, Diana

doi:10.1016/j.epsr.2019.01.037

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…However, these methods only consider the reserves needed in single-area power systems and the possibility to share reserves among LFC areas is hence not considered. In [11], a method for dynamic dimensioning considering reserve sharing between areas is presented. However, the study do not consider transmission limitations between areas.…”

Section: B Modeling Perspective and Literature Reviewmentioning

confidence: 99%

Investigating Reserve Dimensioning Approaches for Multi-Area Reserve Capacity Markets: A Nordic Case Study

Khodadadi¹,

Nordström²,

Eriksson³

et al. 2023

Preprint

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<p>This paper investigates Frequency Restoration Reserve (FRR) allocation and dimensioning approaches in a multi-area context, focusing on the Nordic Load-Frequency Control (LFC) block. We aim to (i) provide a framework for multi-area FRR dimensioning applicable to dynamic as well as static approaches, and (ii) enhance the understanding of how the available information impacts the need for FRR capacity. We model the optimal FRR dimensioning and allocation in the entire Nordic LFC block, considering sharing of FRR between areas. We describe how FRR can be dimensioned dynamically, analyse the benefits of moving from a static to a dynamic approach, and investigate the impact of when FRR is dimensioned in terms of the available information. The proposed FRR dimensioning procedure includes a new application of a methodology to simulate imbalance scenarios and a novel reformulation of a chance-constrained optimization problem. Case study results show that dynamic dimensioning of FRR leads to a reduction in total reserve needs compared to the static approach and hence lower balancing costs, especially if FRR is dimensioned after the clearing of the day-ahead market. </p>

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Section: B Modeling Perspective and Literature Reviewmentioning

confidence: 99%

Investigating Reserve Dimensioning Approaches for Multi-Area Reserve Capacity Markets: A Nordic Case Study

Khodadadi¹,

Nordström²,

Eriksson³

et al. 2023

Preprint

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“…(See [7] for more recent development). As an example, the Graf-Haubrich method used in Germany constructs a probability distribution function (PDF), which takes into account all important factors of imbalance, such as power plant outages, load variations, forecast errors, etc., and calculates the necessary control reserves including the tertiary control reserve (TCR) corresponding to a specified percentile of the PDF in order to maintain a certain security level [8]. Probabilistic solar power forecasting has been studied by many researchers for efficient reserve power procurement [9], on the other hand, there is also the case where simple schemes that use extreme errors in historical data for reserve power procurement is adopted.…”

mentioning

confidence: 99%

Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

2022

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Although the recent development of solar power forecasting through machine learning approaches, such as the machine learning models based on numerical weather prediction (NWP) data, has been remarkable, their extreme error requires an increase in the amount of reserve capacity procurement used for the power system safety. Hence, a reduction of the serious overestimation is necessary for efficient grid operation. However, despite the importance of the above issue, few studies have focused on the model design, suppressing serious errors, to the best of the authors’ knowledge. This study investigates a prediction model that can reduce the huge overestimation of the solar irradiance, which poses a risk to the power system. The specific approaches used are as follows: the employment of Support Vector Quantile Regression (SVQR), the utilization of Meso-scale Ensemble Prediction System (MEPS, Meso-scale EPS for the regions of Japan) data, which is based on the forecasts from Meso-scale Model (MSM) as explanatory variables, and the hyperparameter adjustment. The performance of the models is verified in the one day-ahead forecasting for surface solar irradiance at five sites in the Kanto region as the numerical simulation, where their forecasting errors are measured by the root mean square error (RMSE) and the 3σ error, which corresponds to the 99.87% quantile error of the order statistics. The test results indicate the following findings: the SVRs’ RMSE and 3σ error tend to be trade-offs in the case of varying the penalty of the regularization term; by using SVR as a post-processing tool for MSM or MEPS data, both of the score of their metrics can be improved from original data; the MEPS-based SVQR (MEPS-SVQR) could provide superior performance in both metrics in comparison with the MSM-based SVQR (MSM-SVQR) if the parameters are properly adjusted. Although the time period and the type of MEPS data used for the validation are limited, our report is expected to help the design of NWP-based machine learning models to enable short-term solar power forecasts with a low risk of overestimation.

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Reconfiguration and reinforcement allocation as applied to hourly medium‐term load forecasting of distribution feeders

Paredes¹,

Vargas²,

Maldonado

2020

IET Generation, Transmission & Distribution

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Common dimensioning of frequency restoration reserve capacities for European load-frequency control blocks

Cited by 6 publications

References 8 publications

Investigating Reserve Dimensioning Approaches for Multi-Area Reserve Capacity Markets: A Nordic Case Study

Investigating Reserve Dimensioning Approaches for Multi-Area Reserve Capacity Markets: A Nordic Case Study

Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation

Reconfiguration and reinforcement allocation as applied to hourly medium‐term load forecasting of distribution feeders

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