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[ES] El creciente desarrollo e integración de fuentes de energía renovable en el sistema eléctrico presenta grandes desafíos para garantizar un equilibrio constante entre la generación y la demanda de energía. A diferencia de las fuentes de energía convencionales, las energías renovables, como la solar y la eólica, son inherentemente intermitentes y no siempre están disponibles. Esta variabilidad en la generación de energía dificulta la tarea de mantener la estabilidad y fiabilidad operativa del sistema eléctrico y, por tanto, la previsión del desequilibrio a corto plazo se vuelve crucial para lograr este objetivo. Este proyecto de fin de grado se centra en la aplicación de técnicas de Machine Learning en el sistema eléctrico del país de Hungría, con el objetivo principal de mejorar la capacidad de prever dichos desequilibrios. Aunque se explorarán varios algoritmos, la investigación se enfocará en LightGBM, explicando sus características en detalle y evaluando su desempeño mediante métricas adecuadas. Los resultados obtenidos de este estudio proporcionarán información valiosa para operadores de red y entidades reguladoras, permitiéndoles mejorar la eficiencia en la gestión del desequilibrio del sistema eléctrico, lo que resultará en una reducción de los costos operativos y la optimización de la planificación energética., [EN] The increasing development and integration of renewable energy sources into the electrical system present significant challenges in ensuring a constant balance between energy generation and demand. Unlike conventional energy sources, renewable energies, such as solar and wind power, are inherently intermittent and not always available. This variability in energy generation complicates the task of maintaining the operational stability and reliability of the electrical system, and therefore, short-term imbalance forecasting becomes crucial to achieve this goal. This thesis focuses on the application of Machine Learning techniques in the electrical system of Hungary, with the main objective of improving the ability to predict these imbalances. Although various algorithms will be explored, the research will focus on LightGBM, explaining its characteristics in detail and evaluating its performance using appropriate metrics. The results obtained from this study will provide valuable information for grid operators and BRPs, allowing them to improve efficiency in managing the electrical system imbalance, resulting in a reduction of operational costs and optimization of energy planning.

[ES] El creciente desarrollo e integración de fuentes de energía renovable en el sistema eléctrico presenta grandes desafíos para garantizar un equilibrio constante entre la generación y la demanda de energía. A diferencia de las fuentes de energía convencionales, las energías renovables, como la solar y la eólica, son inherentemente intermitentes y no siempre están disponibles. Esta variabilidad en la generación de energía dificulta la tarea de mantener la estabilidad y fiabilidad operativa del sistema eléctrico y, por tanto, la previsión del desequilibrio a corto plazo se vuelve crucial para lograr este objetivo. Este proyecto de fin de grado se centra en la aplicación de técnicas de Machine Learning en el sistema eléctrico del país de Hungría, con el objetivo principal de mejorar la capacidad de prever dichos desequilibrios. Aunque se explorarán varios algoritmos, la investigación se enfocará en LightGBM, explicando sus características en detalle y evaluando su desempeño mediante métricas adecuadas. Los resultados obtenidos de este estudio proporcionarán información valiosa para operadores de red y entidades reguladoras, permitiéndoles mejorar la eficiencia en la gestión del desequilibrio del sistema eléctrico, lo que resultará en una reducción de los costos operativos y la optimización de la planificación energética., [EN] The increasing development and integration of renewable energy sources into the electrical system present significant challenges in ensuring a constant balance between energy generation and demand. Unlike conventional energy sources, renewable energies, such as solar and wind power, are inherently intermittent and not always available. This variability in energy generation complicates the task of maintaining the operational stability and reliability of the electrical system, and therefore, short-term imbalance forecasting becomes crucial to achieve this goal. This thesis focuses on the application of Machine Learning techniques in the electrical system of Hungary, with the main objective of improving the ability to predict these imbalances. Although various algorithms will be explored, the research will focus on LightGBM, explaining its characteristics in detail and evaluating its performance using appropriate metrics. The results obtained from this study will provide valuable information for grid operators and BRPs, allowing them to improve efficiency in managing the electrical system imbalance, resulting in a reduction of operational costs and optimization of energy planning.

[ES] El creciente desarrollo e integración de fuentes de energía renovable en el sistema eléctrico presenta grandes desafíos para garantizar un equilibrio constante entre la generación y la demanda de energía. A diferencia de las fuentes de energía convencionales, las energías renovables, como la solar y la eólica, son inherentemente intermitentes y no siempre están disponibles. Esta variabilidad en la generación de energía dificulta la tarea de mantener la estabilidad y fiabilidad operativa del sistema eléctrico y, por tanto, la previsión del desequilibrio a corto plazo se vuelve crucial para lograr este objetivo. Este proyecto de fin de grado se centra en la aplicación de técnicas de Machine Learning en el sistema eléctrico del país de Hungría, con el objetivo principal de mejorar la capacidad de prever dichos desequilibrios. Aunque se explorarán varios algoritmos, la investigación se enfocará en LightGBM, explicando sus características en detalle y evaluando su desempeño mediante métricas adecuadas. Los resultados obtenidos de este estudio proporcionarán información valiosa para operadores de red y entidades reguladoras, permitiéndoles mejorar la eficiencia en la gestión del desequilibrio del sistema eléctrico, lo que resultará en una reducción de los costos operativos y la optimización de la planificación energética., [EN] The increasing development and integration of renewable energy sources into the electrical system present significant challenges in ensuring a constant balance between energy generation and demand. Unlike conventional energy sources, renewable energies, such as solar and wind power, are inherently intermittent and not always available. This variability in energy generation complicates the task of maintaining the operational stability and reliability of the electrical system, and therefore, short-term imbalance forecasting becomes crucial to achieve this goal. This thesis focuses on the application of Machine Learning techniques in the electrical system of Hungary, with the main objective of improving the ability to predict these imbalances. Although various algorithms will be explored, the research will focus on LightGBM, explaining its characteristics in detail and evaluating its performance using appropriate metrics. The results obtained from this study will provide valuable information for grid operators and BRPs, allowing them to improve efficiency in managing the electrical system imbalance, resulting in a reduction of operational costs and optimization of energy planning.

The agency of incumbents has gained increasing attention in the study of transitions. Recent studies show that besides being inert and resistant to change, incumbents can also support transitions. We focus on the agency of a particular type of incumbent, grid operators. In several countries, these actors play an active role in institutional change in the energy domain. At the same time, they are engaging in activities to maintain the regime. This paper examines the actions of grid operators when performing institutional work, i.e. when creating, maintaining and disrupting institutions. We conducted a qualitative content analysis of Dutch media to analyze the actions of grid operators while engaging with decentralized energy innovations. We conclude that grid operators are both subject and object of institutional work as part of a distributed, collective process of institutional change. Furthermore, our analysis reflects on their paradoxical position as embedded actors engaging in institutional change., Organisation & Governance

To meet new regulations and the demand for a more sustainable energy system, the electricity grid will play an important role. Since the grid owners operate in a monopoly environment a regulating authority, the Swedish Energy Markets Inspectorate (Ei), sets a revenue cap that determines their profits. Changes were made for the regulating period 2016-2019, which now states that grid operators no longer can charge their customers with already depreciated grid components. To spur the grid operators to invest in smarter and more efficient technology, Ei also introduced two new economic incentives to make companies decrease their electricity losses and use the grid more evenly. The first aim of this thesis was to investigate how this new regulatory framework affected the grid operators, by performing interviews and sending out a survey. The result showed that the new regulatory decreases the grid operators’ profitability and also affects their investment strategies. The age of a grid has become more important than both efficient use of the grid and voltage quality when considering investments, since age now has a major impact on the profits. The new incentives however, has almost no effect on the way the grid operators act. The second aim of this study was to investigate how much a grid operator could save with these new incentives. A case study were conducted at Upplands Energi, a small local grid operator, and calculations with their usage data were performed in MATLAB. The results showed that load management by installing control equipment on costumers’ heat pumps and thereby decreasing their power peaks, could save money. By connecting 500 customers Upplands Energi could save about 150 000 SEK per year, and with 5 000 customers they could save almost half a million SEK per year.

To meet new regulations and the demand for a more sustainable energy system, the electricity grid will play an important role. Since the grid owners operate in a monopoly environment a regulating authority, the Swedish Energy Markets Inspectorate (Ei), sets a revenue cap that determines their profits. Changes were made for the regulating period 2016-2019, which now states that grid operators no longer can charge their customers with already depreciated grid components. To spur the grid operators to invest in smarter and more efficient technology, Ei also introduced two new economic incentives to make companies decrease their electricity losses and use the grid more evenly. The first aim of this thesis was to investigate how this new regulatory framework affected the grid operators, by performing interviews and sending out a survey. The result showed that the new regulatory decreases the grid operators’ profitability and also affects their investment strategies. The age of a grid has become more important than both efficient use of the grid and voltage quality when considering investments, since age now has a major impact on the profits. The new incentives however, has almost no effect on the way the grid operators act. The second aim of this study was to investigate how much a grid operator could save with these new incentives. A case study were conducted at Upplands Energi, a small local grid operator, and calculations with their usage data were performed in MATLAB. The results showed that load management by installing control equipment on costumers’ heat pumps and thereby decreasing their power peaks, could save money. By connecting 500 customers Upplands Energi could save about 150 000 SEK per year, and with 5 000 customers they could save almost half a million SEK per year.