Probabilistic short-term transmission-loss forecasting based on a two-point estimate method

Rejc, Matej; Pantoš, Miloš

doi:10.1109/eem.2010.5558710

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Focusing directly on transmission loss forecasting, we explore the following papers of importance. A probabilistic active power loss forecast method was proposed by Rejc et al in [1] which generates forecasts of multiple electricity quantities, ultimately power flows and losses. The continuation of this work is shown in [2] where a fuzzy logic decision approach was used for short-term transmission loss forecasting.…”

Section: Literature Review and Identified Research Gapsmentioning

confidence: 99%

“…This draws the conclusion that the amount of power flowing through the line is the most important feature describing physical transmission losses. Therefore, having accurate line power flow forecasts is essential for accurate transmission loss forecasting as confirmed in [1][2][3][4]. Furthermore, HOPS is legally obligated to procure electricity to cover transmission losses by the Croatian electricity market act [5].…”

Section: Introductionmentioning

confidence: 99%

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On the practical aspects of machine learning based active power loss forecasting in transmission networks

Pandžić,

Sudić,

Capuder

et al. 2024

IET Generation Trans & Dist

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The cost for covering active power losses makes a significant item in transmission system operators (TSO) annual budgets, and still it received limited attention in the existing literature. The focus of accurate power loss forecasting and procurement is of high increase during the past 2 years due to spikes in electricity prices, making the cost of covering the active power losses a dominant factor of TSO operational costs. This paper presents practical aspects of the highly accurate models for transmission loss forecast in the day ahead time frame for the Croatian transmission system. The contributions are two‐fold: 1) Practical insights into usable TSO data are provided, filling a critical research gap and a foundational literature review is established on transmission loss forecasting. 2) A novel method utilizing only electricity transit data as input which outperforms existing practices is presented. For this, several algorithms such as gradient boosted decision tree model (XGB), support vector regressors, multiple linear regression and fully connected feedforward artificial neural networks are developed, and implemented and validated on data obtained from the Croatian TSO. The results show that the XGB model outperforms current TSO model by 32% for 4 months of comparison and TSCNET's commercial solution by 25% during a year‐long testing period. The developed XGB model is also implemented as a software tool and put into everyday operation with the Croatian TSO.

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Section: Literature Review and Identified Research Gapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the practical aspects of machine learning based active power loss forecasting in transmission networks

Pandžić,

Sudić,

Capuder

et al. 2024

IET Generation Trans & Dist

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“…Besides, R represents the set of V θ nodes, T denotes the set of P V and V θ nodes, and, as introduced earlier, S the set of P Q and P V nodes and L the set of P Q buses. Note that the subscripts in (3) indicate which set the corresponding variable belongs to. For example, θ S ∈ R N consists of the voltage angles in set S.…”

Section: B Control-aware Power Flow Modelmentioning

confidence: 99%

“…nodal voltages and branch flows). Probabilistic load flow (PLF) calculations can effectively capture the uncertainties in these states and evaluate the potential operational risk [1], thereby not only becoming a fundamental tool to study the behavior of the system [2], but also being applicable to many areas, including daily system operation [3], [4], short-term/long-term planning [5], [6], etc.…”

Section: Introductionmentioning

confidence: 99%

Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method

Jia¹,

Cao²,

Chen³

et al. 2022

Preprint

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Probabilistic load flow (PLF) calculation, as a fundamental tool to analyze transmission system behavior, has been studied for decades. Despite a variety of available methods, existing PLF approaches rarely take system control into account. However, system control, as an automatic buffer between the fluctuations in random variables and the variations in system states, has a significant impact on the final PLF result. To consider control actions' influence, this paper proposes the first analytical PLF method for the transmission grid that takes into account primary and secondary frequency controls. This method is based on a high-precision linear power flow model, whose precision is even further improved in this paper by an original correction approach. This paper also proves that if the joint probability distribution (JPD) of random variables is expressed by a Gaussian mixture model (GMM), then the JPD of system states (e.g., nodal voltages) is an infinite GMM. By leveraging this proposition, the proposed method can generate the joint PLF of the whole system, is applicable to random variables obeying any distributions, and is capable of capturing their correlation. The high accuracy and satisfactory efficiency of this method are verified on test cases scaling from 14 to 1354 buses.

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Forecasting software for hourly transmission losses

Sulakov¹

2017

2017 15th International Conference on Electrical Machines, Drives and Power Systems (ELMA)

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Probabilistic short-term transmission-loss forecasting based on a two-point estimate method

Cited by 7 publications

References 9 publications

On the practical aspects of machine learning based active power loss forecasting in transmission networks

On the practical aspects of machine learning based active power loss forecasting in transmission networks

Control-aware Probabilistic Load Flow for Transmission Systems: An Analytical Method

Forecasting software for hourly transmission losses

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