• Energy Research
• 7. Clean energy close
• 12. Responsible consumption close
• 6. Clean water close
• CN close
• GB close
• DE close
• AT close

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

To study the heating performance of a solar-assisted cold-water phase-change-energy heat pump system, its heating performance under series and parallel connections is simulated for a community in Harbin, the influence of ice thickness on the different operation modes is analyzed, and the economy of the system is calculated for series and parallel connections in this paper. The results show that the water supply temperature is higher and more uniform in the parallel operation, and more terminal heat is supplied; the ice thickness has more of an influence on the series connection compared to the parallel connection; and the dynamic payback period is 6.72 years for the series connection and 7.28 years for the parallel connection. This case study can serve as a guide for practical engineering application projects and act as a reference for heating and economic data for the promotion of this heat pump system.

SummaryNumerous wind turbines form large‐scale wind farms, which are complex nonlinear systems with uncertain parameters. The issue of maximum wind energy capture and coordinated control has always been a research hotspot. In this article, under the condition of limited input and uncertain parameters, the preset controller and the adaptive cooperation control are designed to realize the maximum wind energy capture for every wind turbine and the adaptive cooperation control of multiple wind turbines. The research gap lies in that the Hamilton model of wind power generation system is established with uncertain parameters, and the preset controller (method) is designed to capture the maximum wind energy. Under the hypothesis that the uncertain part can be expressed as a linear form about unknown parameter, and using the saturation function processing method in the diagonal matrix, an adaptive feedback controller with limited input is designed to realize the adaptive cooperation control of multiple wind turbines. The simulation results show that under the conditions such as variable wind speed, limited input and uncertain parameters, the wind turbine remain normal operation at the desired angular velocity. It can be concluded that not only the maximum wind energy capture is realized under the condition of variable wind speed, in which the wind turbine can operate on the optimal power curve to improve the utilization of wind energy, but also the adaptive cooperation control of multiple wind turbines can be achieved with limited input and parameter perturbation.

Abstract The price effect of the rising share of renewable electricity, which is called ‘merit-order-effect’, leads to noticeable changes in the German power industry and debates about the electricity market design. This paper estimates the merit-order-effect induced by variable renewable energy in the German-Austrian electricity sector with a multivariate regression model. The research focus lies on the impact of the estimated effects on the marketability of variable renewable electricity generation. The results show a systematic decline of the average market revenues for wind and photovoltaic plants in the period from January 2011 to December 2013. Current market data shows a continuation of this trend into 2016. According to the German long term goals for the use of renewables, wind and solar power will play a crucial role in the future electricity generation mix. If investments in these technologies will be profitable without any regulatory remuneration mechanisms in addition to the market revenues, depends on the further cost degression and the development of the merit-order-effect.

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

Abstract Electricity markets in Europe become increasingly interconnected due to new grid connections and market coupling regulations. This paper examines the interdependencies between the Swiss electricity market and those of neighbouring countries. The Swiss market serves as a good example for a smaller electricity market which is increasingly affected by developments in the large neighbouring countries. To study these cross-border effects, especially those on Swiss electricity prices, we apply two different methodologies, an econometric and a Nash-Cournot equilibrium model. The analyses show that the Swiss electricity price correlates strongly with the German electricity price in the summer, but tends to follow the French electricity price in the winter. Another finding is that gas prices and the electricity load of neighbouring countries have a significant influence on prices. In particular, the load of France and Italy is driving up Swiss prices in the winter, while the German electricity demand and renewable energy generation have a larger influence on Swiss prices in the summer.

Biofuels have been identified as a mid-term GHG emission abatement solution for decarbonising the transport sector. This study examines the techno-economic analysis of biofuel production via biomass fast pyrolysis and subsequent bio-oil upgrading via zeolite cracking. The aim of this study is to compare the techno-economic feasibility of two conceptual catalyst regeneration configurations for the zeolite cracking process: (i) a two-stage regenerator operating sequentially in partial and complete combustion modes (P-2RG) and (ii) a single stage regenerator operating in complete combustion mode coupled with a catalyst cooler (P-1RGC). The designs were implemented in Aspen Plus® based on a hypothetical 72 t/day pine wood fast pyrolysis and zeolite cracking plant and compared in terms of energy efficiency and profitability. The energy efficiencies of P-2RG and P-1RGC were estimated at 54% and 52%, respectively with corresponding minimum fuel selling prices (MFSPs) of £7.48/GGE and £7.20/GGE. Sensitivity analysis revealed that the MFSPs of both designs are mainly sensitive to variations in fuel yield, operating cost and income tax. Furthermore, uncertainty analysis indicated that the likely range of the MFSPs of P-1RGC (£5.81/GGE  £11.63/GGE) at 95% probability was more economically favourable compared with P-2RG, along with a penalty of 2% reduction in energy efficiency. The results provide evidence to support the economic viability of biofuel production via zeolite cracking of pyrolysis-derived bio-oil.