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The increasing depletion of natural resources, combined with a wider set of pressures on the environment, has, in recent years, highlighted the need for a more efficient use of energy and a development process that involves alternative energy sources. Energy efficiency has received much attention as a solution, implying both monetary and emissions savings. However, the latter may be partially offset by the income and demand effects of the former, both in more efficient sectors and in spreading to the wider economy. This is the problem of rebound effects. Taking Spain as a case study, and introducing an energy-related CGE model that develops the inclusion of renewables, this paper evaluates a combination of efficiency initiatives to deliver both reduced energy use by households and a more sustainable supply of energy. Our findings suggest that a package aimed at improving efficiency in household electricity and petroleum use, combined with a more competitive supply of energy from renewable sources, may be the only way to get reductions in all energy use, and thus benefit the economy. Specifically, we consider how this package may lead to positive economic impacts and associated rebound effects, where the latter are focused on a greener energy supply.

Abstract Effect of inclination angle to the thermal performance of a heat pipe photovoltaic/thermal system (HP-PV/T) system was rarely reported. In the present study, a HP-PV/T system was firstly constructed in an Enthalpy Difference Laboratory, where inclination angle was experimentally managed as the only variable. Meanwhile, a comprehensive numerical model for the HP-PV/T system was developed to validate the experiments. Particularly, a 3D model for the inclined heat pipe was also firstly involved. The simulation results show that liquid film thickness within the condenser or the evaporator stabilizes at a constant value at inclining condition. The relative filmwise thermal resistance of the condenser decreases first and then increases with inclination angle; while the evaporator shows an opposite trend to the condenser. The overall thermal resistance of solar heat pipe is mainly determined by the evaporator while the evaporator is mainly determined by the effective height of the liquid pool. The experimental and simulation results both indicate that the optimum inclination angle is 40°. The proposed model agrees well with the experimental results at big inclination angles (≥20°), it is practicable to reveal the influence of inclination angle to the thermal performance of a HP-PV/T system.

This paper presents a new framework for optimal planning of electrical, heating, and cooling distributed energy resources and networks considering smart buildings' contribution scenarios in normal and external shock conditions. The main contribution of this paper is that the impacts of smart buildings' commitment scenarios on the planning of electrical, heating, and cooling systems are explored. The proposed iterative four-stage optimization framework is another contribution of this paper, which utilizes a self-healing performance index to assess the level of resiliency of the multi-carrier energy system. In the first stage, the optimal decision variables of planning are determined. Then, in the second stage, the smart buildings and parking lots contribution scenarios are explored. In the third stage, the optimal hourly scheduling of the energy system for the normal condition is performed considering the self-healing performance index. Finally, in the fourth stage, the optimization process determines the optimal scheduling of system resources and the switching status of electrical switches, heating, and cooling pipelines’ control valves. The proposed method was successfully assessed for the 123-bus IEEE test system. The proposed framework reduced the expected values of aggregated system costs and energy not supplied costs by about 49.92% and 93.64%, respectively, concerning the custom planning exercise. ; © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; fi=vertaisarvioitu|en=peerReviewed|

Abstract A novel integrated energy system based on a geothermal heat source and a liquefied natural gas heat sink is proposed in this study for providing heating, cooling, electricity power, and drinking water simultaneously. The arrangement is a cascade incorporating a flash-binary geothermal system, a regenerative organic Rankine cycle, a simple organic Rankine cycle, a vapor compression refrigeration cycle, a regasification unit, and a reverse osmosis desalination system. Energy, exergy, and exergoeconomic methods are employed to analyze the suggested system. A parametric study based on decision variables is carried out to better assess the system performance. Four different multi-objective optimization problems are also carried out. At the most excellent trade-off solution specified by the TOPSIS method, the system attains 29.15% exergy efficiency and 1.512 $/GJ total product cost per exergy unit. The main output products are consequently calculated to be 101.07 kg/s cooling water, 570.44 kW net output power, and 81.57 kg/s potable water.

Abstract In energy demand forecasting, the objective function is often symmetric, implying that over-prediction errors and under-prediction errors have the same consequences. In practice, these two types of errors generally incur very different costs. To accommodate this, we propose a machine learning algorithm with a cost-oriented asymmetric loss function in the training procedure. Specifically, we develop a new support vector regression incorporating a linear-linear cost function and the insensitivity parameter for sufficient fitting. The electric load data from the state of New South Wales in Australia is used to show the superiority of our proposed framework. Compared with the basic support vector regression, our new asymmetric support vector regression framework for multi-step load forecasting results in a daily economic cost reduction ranging from 42.19 % to 57.39 % , depending on the actual cost ratio of the two types of errors.

Abstract In the paper a novel approach to thermochemical utilization of low rank coal, flotation concentrates and municipal refuse derived fuels was presented. The economic attractiveness of low rank coals and flotation concentrates is limited and that is why they are commonly stored at excavation heaps causing additional costs and the risk of endogenous fires occurrence. One of the crucial parameters determining the attractiveness and usability of a fuel in the gasification process is its reactivity. In the study several low rank coals, flotation concentrates and municipal refuse derived fuels were tested in terms of their reactivity in the process of steam gasification. The reactivity of low rank coal and flotation concentrates at 50% of carbon conversion, R50, varied between 1.46·10−4 and 2.39·10−4 s−1, whereas the maximum reactivity, Rmax, from 3.28·10−4 to 4.62·10−4 s−1. Advanced mathematical models were developed to investigate the similarities and dissimilarities between the studied fuels as well as the relationships between the physical and chemical parameters and the reactivities of fuel chars in steam gasification. On this basis, a low rank coal was selected and blended with 20%w/w of municipal refuse derived fuel in co-gasification experiments. The aim of the research was to utilize the low rank coal characterized by the lowest reactivities (R50 and Rmax of 1.46·10−4 and 3.28·10−4 s−1, respectively) in steam co-gasification to hydrogen-rich gas with an alternative fuel in a fixed bed reactor at the temperature of 800 °C. The selected low rank coal was blended with 20%w/w of municipal refuse derived and the resulting fuel yielded the average concentration of hydrogen in the produced gas of 58.99%vol.

Abstract Accurate installed capacity forecasting can provide effective decision-making support for planning development strategies and establishing national electricity policies. First, considering the data limitation in quantity and accuracy, this paper proposes a multi-factor installed capacity forecasting framework combining the fuzzy time series method and support vector regression. Compared with four benchmark models, the proposed model shows advantages in installed capacity prediction. Second, the predictability dynamics of national installed capacity are explored from the perspective of country clusters. It is revealed that highly predictable countries usually obtain high forecasting accuracy with all forecasting models and are less sensitive to forecasting models. Using the k-means clustering method, this paper divides 136 sample countries into four categories according to the predictability. Third, based on the mean impact value analysis, this paper differentiates and ranks the importance of input variables on installed capacity development. The two most important factors influencing installed capacity are installed capacity development in the previous period and population. Overall, these results are of practical value to the operating decisions of electric power enterprises and the electricity plans of governments.