• Energy Research
• 2021-2025close
• 13. Climate action close
• 11. Sustainability close
• Applied Energy close

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training. Manuscript accepted for publication in Applied Energy

Abstract Computable general equilibrium models are becoming popular for simulating emissions trading systems. However, these models make various assumptions about the production structure and the possibilities of substituting energy for productive factors or inputs. Therefore, this study aims to analyze how different options to incorporate energy into the nested production structure of a computable general equilibrium model affect the obtained impacts when an emissions trading system is implemented. A flexible computable general equilibrium model called ECOMODEL is developed, which is the first model that allows choosing any of the three nested production structures most used in the literature (KEL-M, KL-EM, or KLE-M). The Chilean economy is used as a case study since there is a current database with high disaggregation of the energy sector for calibrating the computable general equilibrium model. The results show that the simulated impacts with KEL-M and KL-EM structures are the best when elasticities of substitution equal to the values ​​most frequently used in previous studies are chosen. However, the KEL-M structure that considers energy as a substitute for capital provides overly optimistic environmental results when high elasticities of substitution are used, obtaining the lowest reduction in Gross Domestic Product and the lowest price of the emissions trading system. Furthermore, the KLE-M structure gives unrealistic results regardless of the elasticities of substitution used. In consequence, a KL-EM nested production structure should be prioritized to simulate an emissions trading system since it provides realistic results and is less sensitive to the values of the elasticities of substitution.

Abstract The urge to increase renewable energy penetration into the power supply mix has been frequently highlighted in response to climate change. South Korea was analyzed as a case study for which the government has shown motivation to increase renewable energy penetration. Herein, a hybrid renewable energy system (HRES) including solar and wind energies were selected due to their relatively stable and mature technology. In addition, Power-to-X has been incorporated to cover other renewable energy options such as hydrogen and synthetic natural gas (SNG). Therefore, an approach of forecasting the weather characteristics and demand loading over a relatively long timeframe was implemented via deep learning techniques (LSTM and GRU) and statistical approaches (Fbprophet and SARIMA), respectively. A deployment strategy incorporating HRES and Power-to-X is then proposed in correspondence to the forecasted results of the 15 regions considered in this study. An extension of this, the reliability of the designed system is further assessed based on the probability of the demand losses with the aid of Monte-Carlo simulation. With the proposed deployment strategy, a total annual cost of 9.88 × 1011 $/year and a greenhouse gas reduction of 1.24 × 106 tons/year are expected for a 35% renewable energy penetration. However, only SNG shows relatively competitive cost (at 23.20 $/m3 SNG), whereas the average costs of electricity (0.133 $/kWh) and hydrogen (7.784 $/kg H2) across the regions are yet to be competitive compared to the current market prices. Nonetheless, the priority of deployment across regions has been identified via TOPSIS.

Abstract Integrating low-carbon technologies (e.g. heat pumps, photovoltaic systems) in buildings influences the stability of the low-voltage grid, which therefore often requires to be reinforced. This article proposes a techno-economic methodology to identify the reinforcements needed to maintain grid stability at the lowest life-cycle cost. Novel contributions include the consideration of three-phase connection of low-carbon technologies as a reinforcement option and the fact that we study to what extent grid reinforcements can mitigate voltage unbalance issues. Additionally, to reduce computing time, a dummy island approach is used, whereby one feeder is modelled in detail and the remainder of the distribution island is represented by an aggregated load. Finally, random repetitions are proposed, to consider uncertainties related to building properties, occupants and the location of low-carbon technologies in the feeders. The methodology is applied to investigate the integration of heat pumps and photovoltaic systems in typical Belgian rural and urban grids. For the rural grid, heat pumps may lead to significant reinforcement costs (up to 1230 €/dwelling), mainly due to voltage stability problems. For the urban grid, heat pump and photovoltaic integration causes low reinforcement cost (

Abstract Global demand for methanol as both a chemical precursor and a fuel additive is rising. At the same time, numerous renewable methanol production pathways are under development, which, if commercialized, could provide significant environmental benefits over traditional methanol synthesis pathways. However, it is difficult to compare technologies at different maturity levels, with differing feedstocks, and with significant differences in overall process design. Thus, there is a need to harmonize the analyses of renewable pathways using a consistent techno-economic approach to evaluate the potential for commercialization of various pathways. This analysis uses a novel cross-comparison method to assess near-term and long-term viability of both low- and high-maturity level technologies. The techno-economic assessment considers cost factors critical to market acceptance combined with carbon- and energy-efficiency assessments of three renewable pathways compared with a commercial baseline. We find that biomass gasification to methanol represents a near-term viable pathway with a high technology readiness level and commercially competitive market price. If cost-reducing technological improvements can be realized and scaled up in the CO2 electrolysis pathways, the potential for higher carbon efficiencies may help drive market adoption of these more modular, direct conversion pathways in future markets as they present an opportunity to better support global decarbonization efforts through efficient waste carbon utilization.

Thin-film photovoltaics (PV) cells offer several benefits over conventional first-generation PV technologies, including lighter weight, flexibility, and lower power generation cost. Among the competing thin-film technologies, chalcogenide solar cells offer promising performance on efficiency and technological maturity level. However, in order to appraise the performance of the technology thoroughly, issues such as raw materials scarcity, toxicity, and environmental impacts need to be investigated in detail. This paper therefore, for the first time, presents a cradle to gate life cycle assessment for four different emerging chalcogenide PV cells, and compares their results with copper zinc tin sulfide (CZTS) and the commercially available CIGS to examine their effectiveness in reducing the environmental impacts associated with PV technologies. To allow for a full range of indicators, life cycle assessment methods CML 2001, IMPACT 2002+, and ILCD 2011 were used to analyse the results. The results identify environmental hotspots associated with different materials and components and demonstrate that using current efficiencies, the environmental impact of copper indium gallium selenide (CIGS) for generating 1kWh electricity was lower than that of the other studied cells. However, at comparable efficiencies the antimony-based cells offered the lowest environmental impacts in all impact categories. The effect of materials used was also found to be lower than the impact of electricity consumed throughout the manufacturing process, with the absorber layer contributing the most to the majority of the impact categories examined. In terms of chemicals consumed, cadmium acetate contributed significantly to the majority of the environmental impacts. Stainless steel in the substrate/insulating layer and molybdenum in the back contact both contributed considerably to the toxicity and ozone depletion impact categories. This paper demonstrates considerable environmental benefits associated with non-toxic chalcogenide PV cells suggesting that the current environmental concerns can be addressed effectively using alternative materials and manufacturing techniques if current efficiencies are improved. Peer Reviewed