• Energy Research

Reservoir and pumped hydro storage facilities represent one of the best options for providing flexibility at low marginal cost and very low life cycle carbon emissions. However, hydropower generation is subject to physical, environmental and regulatory constraints, which introduce complexity in the modelling of hydropower in the context of transition energy analysis. In this article, a probabilistic model for hydropower generation is developed in order to improve an hourly-resolved tool for transition path analysis presented in previous research. The model is based on time series analysis, which exploits the fact that the different constraints affecting hydropower generation were met in the past. The upgraded version of the transition path analysis tool shows a decrease in the hydropower flexibility as compared with previous published results, providing a better picture of the benefits and drawbacks associated with a specific transition path under analysis, for example in terms of assessing the probability of unserved energy. The upgraded version of the tool was employed to analyse the Spanish National Energy and Climate Plan (NECP), finding consistence between proposals associated with the power system and related CO2 reduction and share of renewable electricity targets.

Reservoir and pumped hydro storage facilities represent one of the best options for providing flexibility at low marginal cost and very low life cycle carbon emissions. However, hydropower generation is subject to physical, environmental and regulatory constraints, which introduce complexity in the modelling of hydropower in the context of transition energy analysis. In this article, a probabilistic model for hydropower generation is developed in order to improve an hourly-resolved tool for transition path analysis presented in previous research. The model is based on time series analysis, which exploits the fact that the different constraints affecting hydropower generation were met in the past. The upgraded version of the transition path analysis tool shows a decrease in the hydropower flexibility as compared with previous published results, providing a better picture of the benefits and drawbacks associated with a specific transition path under analysis, for example in terms of assessing the probability of unserved energy. The upgraded version of the tool was employed to analyse the Spanish National Energy and Climate Plan (NECP), finding consistence between proposals associated with the power system and related CO2 reduction and share of renewable electricity targets.

Abstract This paper presents a sequential method for generating synthetic non-homogeneous non-Gaussian turbulent wind fields with a prescribed time-space covariance structure. The proposed methodology is based on the optimisation of restricted multivariate autoregressive (VAR) models, and the quantile-to-quantile transform between statistical distributions. The considered case study is a non-homogeneous non-Gaussian turbulent wind field over the roof of a high rise building simulated with LES. Results show a notably good matching in terms of the reproduced wind statistical distributions, Covariance Matrix Function (CMF) and Cross Power Spectral Density Matrix (CPSDM). In addition, the synthetic wind field reproduced accurately the recirculation bubble close to the roof. The main advantages of the proposed method are that, once the VAR model is computed, the synthesis of several realisations is computationally very cheap, which is useful for performing several aeroelastic simulations of the same analysis case, as suggested by the standards. The critical point is that, to characterise the statistical features for a specific case study (such as wind turbine wakes or turbulence due to obstacles), an LES simulation of the wind field is required as input. The software employed in this work is open source and it is available on GitHub.

Abstract This paper presents a sequential method for generating synthetic non-homogeneous non-Gaussian turbulent wind fields with a prescribed time-space covariance structure. The proposed methodology is based on the optimisation of restricted multivariate autoregressive (VAR) models, and the quantile-to-quantile transform between statistical distributions. The considered case study is a non-homogeneous non-Gaussian turbulent wind field over the roof of a high rise building simulated with LES. Results show a notably good matching in terms of the reproduced wind statistical distributions, Covariance Matrix Function (CMF) and Cross Power Spectral Density Matrix (CPSDM). In addition, the synthetic wind field reproduced accurately the recirculation bubble close to the roof. The main advantages of the proposed method are that, once the VAR model is computed, the synthesis of several realisations is computationally very cheap, which is useful for performing several aeroelastic simulations of the same analysis case, as suggested by the standards. The critical point is that, to characterise the statistical features for a specific case study (such as wind turbine wakes or turbulence due to obstacles), an LES simulation of the wind field is required as input. The software employed in this work is open source and it is available on GitHub.

AbstractFatigue represents a critical issue in many structural applications, and wind turbines are not an exception. Their dynamic response over the years determines the turbine's lifespan, meaning that fatigue loads have a clear impact on the Cost of Energy. Since the direct experimental determination of the loading state is complex or expensive, estimations arising from general operational signals can be explored as an indirect way to acquire knowledge of fatigue loading levels. A case study based on 10‐minute aeroelastic simulations of a wind turbine dynamics is used to develop a Damage Equivalent Load estimation model using operational signals (typically recorded by SCADA systems) as inputs. The focus is on both the input selection and the model configuration, seeking the combination which reaches the lowest error. Three filters and two innovative wrappers (exploration and optimization) were considered within the selection. Linear and Artificial Neural Network models were implemented and compared. Results showed performances in Damage Equivalent Load estimation below 4% in terms of Normalized Root Mean Squared Error, which is promising as compared with related work. Additional conclusions were obtained concerning appropriate Artificial Neural Network configurations (net type, architecture and training algorithm), likewise the potential contribution of a proposed genetic algorithm.

AbstractFatigue represents a critical issue in many structural applications, and wind turbines are not an exception. Their dynamic response over the years determines the turbine's lifespan, meaning that fatigue loads have a clear impact on the Cost of Energy. Since the direct experimental determination of the loading state is complex or expensive, estimations arising from general operational signals can be explored as an indirect way to acquire knowledge of fatigue loading levels. A case study based on 10‐minute aeroelastic simulations of a wind turbine dynamics is used to develop a Damage Equivalent Load estimation model using operational signals (typically recorded by SCADA systems) as inputs. The focus is on both the input selection and the model configuration, seeking the combination which reaches the lowest error. Three filters and two innovative wrappers (exploration and optimization) were considered within the selection. Linear and Artificial Neural Network models were implemented and compared. Results showed performances in Damage Equivalent Load estimation below 4% in terms of Normalized Root Mean Squared Error, which is promising as compared with related work. Additional conclusions were obtained concerning appropriate Artificial Neural Network configurations (net type, architecture and training algorithm), likewise the potential contribution of a proposed genetic algorithm.

Distributed solar photovoltaic (PV) systems are projected to be a key contributor to future energy landscape, but are often poorly represented in energy models due to their distributed nature. They have higher costs compared to utility PV, but offer additional advantages, e.g., in terms of social acceptance. Here, we model the European power network with a high spatial resolution of 181 nodes and a 2-hourly temporal resolution. We use a simplified model of distribution and transmission networks that allows the representation of power distribution losses and differentiates between utility and distributed generation and storage. Three scenarios, including a sector-coupled scenario with heating, transport, and industry are investigated. The results show that incorporating distributed solar PV leads to total system cost reduction in all scenarios (1.4% for power sector, 1.9-3.7% for sector-coupled). The achieved cost reductions primarily stem from demand peak reduction and lower distribution capacity requirements because of self-consumption from distributed solar. This also enhances self-sufficiency for countries. The role of distributed PV is noteworthy in the sector-coupled scenario and is helped by other distributed technologies including heat pumps and electric vehicle batteries.

Distributed solar photovoltaic (PV) systems are projected to be a key contributor to future energy landscape, but are often poorly represented in energy models due to their distributed nature. They have higher costs compared to utility PV, but offer additional advantages, e.g., in terms of social acceptance. Here, we model the European power network with a high spatial resolution of 181 nodes and a 2-hourly temporal resolution. We use a simplified model of distribution and transmission networks that allows the representation of power distribution losses and differentiates between utility and distributed generation and storage. Three scenarios, including a sector-coupled scenario with heating, transport, and industry are investigated. The results show that incorporating distributed solar PV leads to total system cost reduction in all scenarios (1.4% for power sector, 1.9-3.7% for sector-coupled). The achieved cost reductions primarily stem from demand peak reduction and lower distribution capacity requirements because of self-consumption from distributed solar. This also enhances self-sufficiency for countries. The role of distributed PV is noteworthy in the sector-coupled scenario and is helped by other distributed technologies including heat pumps and electric vehicle batteries.