Load estimation for distribution systems with minimum information

Borozan, V.; Rajaković, Nikola

doi:10.1002/etep.26

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…L'adjonction de données socio-économiques aux données de consommation peut apporter une amélioration des prédictions comme le suggèrent les publications récentes [3][4][5]. D'autres stratégies visant à modéliser d'abord les consommations journalières et hebdomadaires participent à cette amélioration.…”

Section: Discussionunclassified

Modélisation de la demande en énergie électrique au Togo

Adjamagbo,

Ngae,

Vianou

et al. 2023

J. Ren. Energies

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Quatre modèles paramétriques sont présentés dans ce travail, pour déterminer la consommation mensuelle de l’énergie électrique au Togo. Les données retenues pour cette modélisation portent sur la période 2001 - 2005 et sont constituées des relevés de consommation électrique, de température et d’humidité relative. Ces données ont été bootstrapées afin d’évaluer sur un jeu de données conséquent les paramètres des différents modèles. La démarche de modélisation a porté sur l’identification (reconstruction) et la simulation (prévision) de la consommée. Elle est concluante au regard des résultats obtenus par l’ensemble des modèles. Les modèles ARX se sont avérés les mieux indiqués pour l’identification, avec une erreur quadratique moyenne de 105 kWh, soit 1 % de la consommation minimale durant cette période. Pour ce qui est de la prévision, le modèle saisonnier a été le plus efficace, avec une erreur de 3.26 105 kWh.

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Section: Discussionunclassified

Modélisation de la demande en énergie électrique au Togo

Adjamagbo,

Ngae,

Vianou

et al. 2023

J. Ren. Energies

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“…Such attributes are for each transformer, their power rating and their year of installation; for each urban infrastructure, their radius of the service area, their electricity consumption, and their expected growth in load; and for each future urban infrastructure, their expected growth in load. These two databases allow obtaining the initial grid cells' states for the year base: Load density : this can represent the installed power or the maximum demand of grouped consumers in the grid cell . The load density is considered as the installed power per unit area, that is, the sum of the power rating in the grid cell divided by the grid cell's area.…”

Section: Simulation Methods Using Power‐law Distribution With Fractal mentioning

confidence: 99%

“…• Load density: this can represent the installed power or the maximum demand of grouped consumers in the grid cell [18]. The load density is considered as the installed power per unit area, that is, the sum of the power rating in the grid cell divided by the grid cell's area.…”

Section: Input Datamentioning

confidence: 99%

Grid-based simulation method for spatial electric load forecasting using power-law distribution with fractal exponent

Melo

Carreno

Padilha‐Feltrin

et al. 2015

Int. Trans. Electr. Energ. Syst.

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SUMMARYA grid-based simulation method to forecast the spatial growth of load density in a distribution utility service zone is presented. The future load density is simulated considering a city's dynamic growth. A power-law distribution with fractal exponent is used to determine how much the load density will increase/decrease depending on different factors like: natural growth following patterns, spontaneous new loads without predefined patterns, and urban poles growth that attract/repel new consumers. These factors are simulated using two modules that exchange information. The final result will display a map with future load density considering a heterogeneous distribution, which is the main contribution of this work. The method is tested on a real distribution system in a medium-sized Brazilian city. The most important characteristic of this method is its high efficiency in spatial load density recognition with less input data than traditional spatial load forecasting methodologies.

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“…For instance, refs. [8][9][10] focus on estimating the loading condition taking into account the real-time measurements at the feeders.…”

Section: Introductionmentioning

confidence: 99%

Modeling of photovoltaic and wind turbine based distributed generation in state estimation

Ranković

Sarić

2012

2012 15th International Power Electronics and Motion Control Conference (EPE/PEMC)

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This paper proposes an algorithm for distribution state estimation with included unmonitored (or partially monitored) photovoltaic generation and wind turbine&asynchronous generator based models, as well as distributed loads. These data are treated as pseudo measurements and included in the state estimation with lower weights. Initial state estimations for unmonitored (partially monitored) distributed generation and loads are calculated from the weather forecast data or daily load profiles, respectively. The pseudo measurements are additionally re-adjusted by the optimal load/generation reallocation procedure to fit the real-time measurements inside the weighted least square (WLS) based state estimation algorithm. The results and practical aspects of the proposed methodology are demonstrated on characteristic real-life urban distribution network with 40 buses.

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Load estimation for distribution systems with minimum information

Cited by 8 publications

References 16 publications

Modélisation de la demande en énergie électrique au Togo

Modélisation de la demande en énergie électrique au Togo

Grid-based simulation method for spatial electric load forecasting using power-law distribution with fractal exponent

Modeling of photovoltaic and wind turbine based distributed generation in state estimation

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