Specific aspects of forecasting electrical energy losses in electricity networks

Tsygulev, Nikolay; Khlebnikov, Vladimir; Voronina, Yekaterina

doi:10.1088/1755-1315/403/1/012090

Cited by 4 publications

(2 citation statements)

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“…Bhatti et al [17] outline the state of transmission and distribution losses for the Pakistan electrical network and express power losses with a closed-form solution, not taking into account the stochasticity of the problem. Tsygulev et al [18] also forecast what they call technological electricity losses (TEL) with a closed-form solution without specifically focusing on the transmission systems. Closed-form solutions are not suitable for accurately forecasting transmission losses in electrical power systems due to their inability to capture complex relationships between variables and unpredictable external factors.…”

Section: Literature Review and Identified Research Gapsmentioning

confidence: 99%

“…Tsygulev et al. [18] also forecast what they call technological electricity losses (TEL) with a closed‐form solution without specifically focusing on the transmission systems. Closed‐form solutions are not suitable for accurately forecasting transmission losses in electrical power systems due to their inability to capture complex relationships between variables and unpredictable external factors.…”

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

View full text Add to dashboard Cite

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Specific aspects of calculating electrical energy losses in electricity networks

Tsygulev

Khlebnikov

Voronina

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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The paper present modes of operation of 10(6) kV distribution networks, which are determined by the loads of consumers. The network scheme is based on an open circuit, which allows you to perform calculations for each 10(6) kV distribution line (feeder) separately. The calculation of power losses in the electric network is carried out by the method of average loads. The paper determines the integral characteristics of the operational parameters of the head branch, determines the parameters of the equivalent power loss of the feeder equivalent circuit, and the equivalent circuit determines the energy loss in the feeder. The paper compiles the equivalent circuit of the considered network section, calculated the parameters of the equivalent circuit elements and determines the total power loss in all linear sections. The paper presents an algorithm for calculating the steady state using the example of a feeder and shows the sequence of calculation of current distribution in the branches of the equivalent circuit.

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Methods of Calculating Technological Losses Electrical Energy in Electricity Networks

Tsygulev

Khlebnikov

Voronina

2020

2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)

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Specific aspects of forecasting electrical energy losses in electricity networks

Cited by 4 publications

References 0 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

Specific aspects of calculating electrical energy losses in electricity networks

Methods of Calculating Technological Losses Electrical Energy in Electricity Networks

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