• Energy Research

Faster market integration of new energy technologies could be achieved by use of proper support mechanisms that will create favourable market conditions for these technologies. The best examples for support mechanisms presented in the last two decades are different schemes for promotion of renewable energy sources (RES). In the EU, the most successful supporting scheme are feed in tariffs which significantly increased utilization of renewable energy sources in Germany, Spain, Portugal, Denmark and many other EU countries. Even though of successful feed in tariffs for RES promotion, in many cases the RES penetration is limited by power system requirements linked to the intermittency of RES sources and technical capabilities of the grids. These problems could be solved by implementation of energy storage technologies like reversible or pumped hydro, hydrogen, batteries or some other technology that will be used for balancing, system stabilization or dump load. In these paper feed in tariffs are discussed with proposal for their application for energy storage technologies. After successful application on islands and outmost regions tariffs for energy storages should be also applied in mainland power systems. Increased use of energy storage could optimize the existing assets in the market.

Faster market integration of new energy technologies could be achieved by use of proper support mechanisms that will create favourable market conditions for these technologies. The best examples for support mechanisms presented in the last two decades are different schemes for promotion of renewable energy sources (RES). In the EU, the most successful supporting scheme are feed in tariffs which significantly increased utilization of renewable energy sources in Germany, Spain, Portugal, Denmark and many other EU countries. Even though of successful feed in tariffs for RES promotion, in many cases the RES penetration is limited by power system requirements linked to the intermittency of RES sources and technical capabilities of the grids. These problems could be solved by implementation of energy storage technologies like reversible or pumped hydro, hydrogen, batteries or some other technology that will be used for balancing, system stabilization or dump load. In these paper feed in tariffs are discussed with proposal for their application for energy storage technologies. After successful application on islands and outmost regions tariffs for energy storages should be also applied in mainland power systems. Increased use of energy storage could optimize the existing assets in the market.

Reversible hydro is a competitive storage. The price of hydrogen loop electricity, with free excess renewable is 75 € c/kWh. Price should fall to 1/4 to make it nearly competitive on peak markets and for isolated grids, especially if the price of oil stays high. High oil price will help RES and H2.

Reversible hydro is a competitive storage. The price of hydrogen loop electricity, with free excess renewable is 75 € c/kWh. Price should fall to 1/4 to make it nearly competitive on peak markets and for isolated grids, especially if the price of oil stays high. High oil price will help RES and H2.

Energy utilisation in residential dwellings is stochastic and can worsen the issue of operational planning for energy provisioning. Additionally, planning with intermittent energy sources exacerbates the challenges posed by the uncertainties in energy utilisation. In this work, machine learning regression schemes (random forest and decision tree) are used to train a forecasting model. The model is based on a yearly dataset and its subset seasonal partitions. The dataset is first preprocessed to remove inconsistencies and outliers. The performance measures of mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) are used to evaluate the accuracy of the model. The results show that the performance of the model can be enhanced with hyperparameter tuning. This is shown with an observed improvement of about 44% in accuracy after tuning the hyperparameters of the decision tree regressor. The results further show that the decision tree model can be more suitable for utilisation in forecasting the partitioned dataset.

Energy utilisation in residential dwellings is stochastic and can worsen the issue of operational planning for energy provisioning. Additionally, planning with intermittent energy sources exacerbates the challenges posed by the uncertainties in energy utilisation. In this work, machine learning regression schemes (random forest and decision tree) are used to train a forecasting model. The model is based on a yearly dataset and its subset seasonal partitions. The dataset is first preprocessed to remove inconsistencies and outliers. The performance measures of mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) are used to evaluate the accuracy of the model. The results show that the performance of the model can be enhanced with hyperparameter tuning. This is shown with an observed improvement of about 44% in accuracy after tuning the hyperparameters of the decision tree regressor. The results further show that the decision tree model can be more suitable for utilisation in forecasting the partitioned dataset.

The Netherlands policy on electricity steers towards a carbon neutral power generation in 2050. Among the studies that focus on long term power system research, none use the starting point of a 100% renewable power system in 2050. It is unknown if such a system is technically feasible given the variability of wind and solar. The generation patterns of intermittent renewables differ continuously, causing the remaining power sources in the system to mitigate the variable IRES generation. Additionally, it is also unknown to what extent the energy only market can incentivise investment. By using the PLEXOS electricity market model, the dispatch of CO2 low generation portfolios and the market prices and net profits of generators can be determined in various scenarios. These scenarios vary on demand changes, capacity factors for wind and solar and primary fuel prices. The results show a great sensitivity of an energy only market under the combination of a demand growth and a meteorological year of low capacity factors, resulting in blackouts and high wholesale market prices. Storage and intermittent renewables, however, do not benefit enough from a scenario with a high annual market price. ; Complex Systems Engineering and Management (CoSEM)

The Netherlands policy on electricity steers towards a carbon neutral power generation in 2050. Among the studies that focus on long term power system research, none use the starting point of a 100% renewable power system in 2050. It is unknown if such a system is technically feasible given the variability of wind and solar. The generation patterns of intermittent renewables differ continuously, causing the remaining power sources in the system to mitigate the variable IRES generation. Additionally, it is also unknown to what extent the energy only market can incentivise investment. By using the PLEXOS electricity market model, the dispatch of CO2 low generation portfolios and the market prices and net profits of generators can be determined in various scenarios. These scenarios vary on demand changes, capacity factors for wind and solar and primary fuel prices. The results show a great sensitivity of an energy only market under the combination of a demand growth and a meteorological year of low capacity factors, resulting in blackouts and high wholesale market prices. Storage and intermittent renewables, however, do not benefit enough from a scenario with a high annual market price. ; Complex Systems Engineering and Management (CoSEM)

The goal of this Master thesis project was to design a methodology that helps to analyze a set of indicators that can support the decision-making process, by identifying energy strategies with a temporal horizon 2030-2050 in Cuba. Several sources of data and methodologies were used to fulfill the objective. The energy planning model created by Madrazo during her Ph.D. thesis was used as the main tool in this project. The hourly demand and the wind and solar capacity factors have been used as input data for this model. A post-processing module was created in R code, with the objective of analyzing the behavior of the six technical indicators that the model offers as output. The Principal Component Analysis (PCA) method was used as an exploratory method to identify cluster that later allowed studying in detail the particular characteristics of these indicators. Different graphics were used to visualize the performance of the technical indicators under different conditions, which allows us to propose the best energy strategies to ensure a secure and sustainable energy transition. The methods of “Levelized cost of energy” (LCOE) and the costs associated with the "engineering process design" allowed to assess the economic feasibility of some scenarios of energy transition identified as interesting for decision making. The current cost of the Cuban electro-energetic system was estimated and compared with the costs obtained for future scenarios simulated. After analyzing the behavior of the indicators in detail, it was identified as the most suitable transition scenario for Cuba, where 150% demand is covered by 90% intermittent energy and 10% wind energy. Also, no backup will be required under these conditions, which represents a considerable saving from the current costs required by an electric generation with fossil resources. The energy storage needs will be 0.05 TWh, and the “concrete towers” and “VOSS” technologies can be used in order to minimize installation and operation costs of these facilities.

The goal of this Master thesis project was to design a methodology that helps to analyze a set of indicators that can support the decision-making process, by identifying energy strategies with a temporal horizon 2030-2050 in Cuba. Several sources of data and methodologies were used to fulfill the objective. The energy planning model created by Madrazo during her Ph.D. thesis was used as the main tool in this project. The hourly demand and the wind and solar capacity factors have been used as input data for this model. A post-processing module was created in R code, with the objective of analyzing the behavior of the six technical indicators that the model offers as output. The Principal Component Analysis (PCA) method was used as an exploratory method to identify cluster that later allowed studying in detail the particular characteristics of these indicators. Different graphics were used to visualize the performance of the technical indicators under different conditions, which allows us to propose the best energy strategies to ensure a secure and sustainable energy transition. The methods of “Levelized cost of energy” (LCOE) and the costs associated with the "engineering process design" allowed to assess the economic feasibility of some scenarios of energy transition identified as interesting for decision making. The current cost of the Cuban electro-energetic system was estimated and compared with the costs obtained for future scenarios simulated. After analyzing the behavior of the indicators in detail, it was identified as the most suitable transition scenario for Cuba, where 150% demand is covered by 90% intermittent energy and 10% wind energy. Also, no backup will be required under these conditions, which represents a considerable saving from the current costs required by an electric generation with fossil resources. The energy storage needs will be 0.05 TWh, and the “concrete towers” and “VOSS” technologies can be used in order to minimize installation and operation costs of these facilities.

International audience; The possibility to dry heat-sensitive timber with solar energy or residual energy from industrial processes with low exergy content, may be of double interest: the use of energy sources with very low price and the reduction of thermal pollution. The important issue when using such energies is to adapt the process control to the availability of the energy. Additionally, there is a major concern that this energy saving could be done at the detriment of drying quality or drying time. This paper proposes a numerical investigation of the possibility to use intermittent energy source to dry wood drying by comparing different strategies.