Análisis cualitativo del impacto de la respuesta de la demanda en los cargos por uso del sistema de distribución

(Eng) Electric power systems are subject to high electricity demand variations during short periods due to consumption habits of end-users. In these periods, the operation of distribution networks is expensive, energy losses are increased and voltages may drop for buses located far from feeders. These negative effects can be avoided when Demand Response (DR) schemes are considered. Currently, policies for promoting DR in Colombia rise and it is fundamental the establishment of criteria for identifying the impact of DR in Colombian electricity market. This work tends to classify and identify the impact of DR on Usage Costs of Distribution Systems. Based on the current rate scheme, results show that this impact can be classified in five aspects: equipment capacity, energy sales, energy losses, payments between network operators and energy service quality. Finally, parameters of current methodology for the calculation of Usage Costs that are sensitive to DR are identified and classified.(Spa) Los sistemas eléctricos enfrentan grandes demandas de electricidad durante cortos periodos debido a los hábitos de consumo de los usuarios. En estos periodos, la operación del sistema de distribución es costosa, se incrementan las pérdidas de energía y disminuyen las tensiones en los nodos retirados de los alimentadores. Una forma de evitar estos efectos negativos es la Respuesta de la Demanda (RD). Actualmente se adelantan políticas que pretenden estimular la RD en Colombia, y resulta fundamental establecer criterios que permitan predecir el impacto que tendría en el mercado eléctrico. Este trabajo tiene como objetivos clasificar e identificar el impacto que podría tener la RD en los cargos por uso del sistema de distribución. Los resultados muestran que, bajo el actual esquema tarifario, es posible clasificar el impacto de la RD sobre estos cargos en cinco aspectos: Capacidad de equipos, ventas de energía, pérdidas de energía, pagos entre operadores de red y calidad del servicio. Finalmente, se identifican y clasifican aquellos parámetros que hacen parte de la metodología del cálculo de cargos por uso del sistema de distribución que son sensibles a la RD según la regulación vigente

Short-term forecasting of wind energy: A comparison of deep learning frameworks

Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented

Wind power long-term scenario generation considering spatial-temporal dependencies in coupled electricity markets

This article belongs to the Section A3: Wind, Wave and Tidal EnergyWind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France

Air temperature forecasting using machine learning techniques: a review

Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined

Estimación del consumo eléctrico colombiano en el corto y largo plazo empleando regresión multivariable y series temporales

The forecast electricity consumption constitutes an important step towards the development of new technologies to meet the energy consumption in the coming decades. This paper intends to make a projection by means of a time series and multivariate regression which allows to connect the country’s economic growth with its electric consumption. To validate the proposed metho­dology, the error is calculated in respect to official information provided by the UPME. La previsión de consumo de energía eléctrica constituye un pilar importante para desarrollar proyectos de expansión en generación, transmisión y distribución. En este trabajo se propone realizar una proyec­ción de demanda para el consumo de energía eléctrica en el sector residencial colombiano por medio de una serie temporal y una regresión multivariable que relacione el crecimiento económico del país con su consumo eléctrico. Para validar la metodología propuesta, se compararán los resultados obte­nidos con la información oficial suministrada por la Unidad de Planeación Minero-Energética (UPME)

Impacto de un programa de respuesta de la demanda eléctrica en el sector de gas natural

El objetivo de este trabajo es desarrollar una metodología que muestre cómo los programas de respuesta en demanda en el sector eléctrico son una alternativa para aumentar los tiempos de suministro de gas natural ante condiciones de falla o mantenimientos programados en un gasoducto. La metodología se basa en la solución de dos problemas: la programación óptima de unidades y la programación de la producción de gas natural. Para solucionar el problema de la programación óptima de unidades, se propone un modelo lineal entero mixto que permite incluir un programa de respuesta en demanda. En contraste, para la programación de la producción de gas natural, se ha propuesto un modelo no lineal entero mixto, el cual permite incluir en el análisis, el almacenamiento en gasoductos ante condiciones de falla o mantenimientos programados. La metodología propuesta ha sido evaluada sobre un sistema de prueba de ocho nodos de gas y seis nodos eléctricos. Los resultados muestran que los tiempos de suministro derivados de almacenamientos en gasoductos podrían ajustarse a las necesidades del sistema con una combinación adecuada entre los programas de respuesta en demanda y la capacidad de almacenamiento

Estimación del consumo eléctrico colombiano en el corto y largo plazo empleando regresión multivariable y series temporales

The forecast electricity consumption constitutes an important step towards the development of new technologies to meet the energy consumption in the coming decades. This paper intends to make a projection by means of a time series and multivariate regression which allows to connect the country’s economic growth with its electric consumption. To validate the proposed metho­dology, the error is calculated in respect to official information provided by the UPME. La previsión de consumo de energía eléctrica constituye un pilar importante para desarrollar proyectos de expansión en generación, transmisión y distribución. En este trabajo se propone realizar una proyec­ción de demanda para el consumo de energía eléctrica en el sector residencial colombiano por medio de una serie temporal y una regresión multivariable que relacione el crecimiento económico del país con su consumo eléctrico. Para validar la metodología propuesta, se compararán los resultados obte­nidos con la información oficial suministrada por la Unidad de Planeación Minero-Energética (UPME)

Estimación del consumo eléctrico colombiano en el corto y largo plazo empleando regresión multivariable y series temporales

The forecast electricity consumption constitutes an important step towards the development of new technologies to meet the energy consumption in the coming decades. This paper intends to make a projection by means of a time series and multivariate regression which allows to connect the country’s economic growth with its electric consumption. To validate the proposed metho­dology, the error is calculated in respect to official information provided by the UPME. La previsión de consumo de energía eléctrica constituye un pilar importante para desarrollar proyectos de expansión en generación, transmisión y distribución. En este trabajo se propone realizar una proyec­ción de demanda para el consumo de energía eléctrica en el sector residencial colombiano por medio de una serie temporal y una regresión multivariable que relacione el crecimiento económico del país con su consumo eléctrico. Para validar la metodología propuesta, se compararán los resultados obte­nidos con la información oficial suministrada por la Unidad de Planeación Minero-Energética (UPME)

Semianalytic integral method for the fast solution of circulating currents in power transformers

Power transformer design normally includes an optimization process which involves the assessment of a great number of design alternatives. This calculation process normally requires a high computation time and its reduction is always a desirable goal. Circulating currents in parallel connected conductors in transformer windings are a critical design aspect to be analyzed. This article presents a new methodology for the fast calculation of circulating currents for parallel connected conductors in power transformers. The formulation for solid conductor modeling has been developed using the same calculation strategy as in Semianalytic Integral Method (SAIM) [1], which allows a significant reduction of computational effort. A realistic case study of a 25 MVA transformer was used to validate the proposed methodology. As for the accuracy of the calculations, the comparison of the results obtained by the proposed methodology and those calculated using the Finite Element Method (FEM) shows an excellent agreement between both approaches. However, the computational performance of the new approach was found to be much higher than that of FEM. This makes the proposed method much more efficient for transformer design purposes.Fil: Diaz, Guillermo. Universidad de La Salle; ColombiaFil: Mombello, Enrique Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Marulanda, Geovanny A.. Universidad de La Salle; Colombi

Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France