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© 2020 Elsevier Ltd Free cooling of buildings uses the nocturnal outdoor air as a heat sink via a ventilation process. This could be performed by storing the night coolness for use during the daytime as appropriate. Due to the latent heat capacity, phase change material (PCM) could play anessential role in the effective operation of the free cooling systems by shifting the daytime peak load to the night. However, there is a scarceness on the technology application in hot climates. This paper presents results of a parametric investigation into the application of PCMs as thermal energy storage (TES) to provide sustainable cooling to buildings in hot arid climate by making use of the night-time free cooling. The proposed TES medium comprises an arrangement of metallic modules filled with RT28HC PCM. Numerous geometrical configurations and operational parameters have been assessed. A transient CFD simulation has been employed using ANSYS Fluent software. Validation of the numerical results with experimental data has shown a good agreement. The results have demonstrated that the temperature difference between the PCM and the air, at appropriate air flow rate would have a significant impact on the performance of the system. A free cooling system based on the proposed arrangement has the potential to meet around 42% of a typical building cooling load and has the ability to save up to 67% of building cooling energy load in summer season compared to conventional air-conditioning systems in hot arid climates.

Decarbonizing the building sector is extremely important to mitigating climate change as the sector contributes 40% of the overall energy consumption and 36% of the total greenhouse gas emissions in the world. Net-zero energy buildings are one of the promising decarbonization attempts due to their potential of decreasing the use of energy and increasing the total share of renewable energy. To achieve a net-zero energy building, it is necessary to decrease the energy demand by applying efficiency enhancement measures and using renewable energy sources. Net-zero energy buildings can be classified into four models (Net-Zero Site Energy buildings, Net-Zero Emissions buildings, Net-Zero Source Energy buildings, and Net-Zero Cost Energy buildings). A variety of technical, financial, and environmental factors should be considered during the decision-making process of net-zero energy building development, justifying the use of multi-criteria decision analysis methods for the design of net-zero energy buildings. This paper also discussed the contributions of renewable energy generation (hydropower, wind energy, solar, heat pumps, and bioenergy) to the development of net-zero energy buildings and reviewed its role in tackling the decarbonization challenge. Cost-benefit analysis and life cycle assessment of building designs were reviewed to shape the priorities of future development. It is important to develop a universal decision instrument for optimum design and operation of net-zero energy buildings.

AbstractBioenergy projects can lead to direct and indirect land use change (LUC), which can substantially affect greenhouse gas balances with both beneficial and adverse outcomes for bioenergy's contribution to climate change mitigation. The causes behind LUC are multiple, complex, interlinked, and change over time. This makes quantification uncertain and sensitive to many factors that can develop in different directions—including land use productivity, trade patterns, prices and elasticities, and use of by‐products associated with biofuels production. Quantifications reported so far vary substantially and do not support the ranking of bioenergy options with regard to LUC and associated emissions. There are however several options for mitigating these emissions, which can be implemented despite the uncertainties. Long‐rotation forest management is associated with carbon emissions and sequestration that are not in temporal balance with each other and this leads to mitigation trade‐offs between biomass extraction for energy use and the alternative to leave the biomass in the forest. Bioenergy's contribution to climate change mitigation needs to reflect a balance between near‐term targets and the long‐term objective to hold the increase in global temperature below 2°C (Copenhagen Accord). Although emissions from LUC can be significant in some circumstances, the reality of such emissions is not sufficient reason to exclude bioenergy from the list of worthwhile technologies for climate change mitigation. Policy measures to minimize the negative impacts of LUC should be based on a holistic perspective recognizing the multiple drivers and effects of LUC.This article is categorized under: Bioenergy > Economics and Policy Bioenergy > Climate and Environment

Abstract With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

The energy considered as waste heat in industrial furnaces owing to inefficiencies represents a substantial opportunity for recovery by means of thermal energy storage (TES) implementation. Although conventional systems based on sensible heat are used extensively, these systems involve technical limitations. Latent heat storage based on phase change materials (PCMs) results in a promising alternative for storing and recovering waste heat. Within this scope, the proposed PCM-TES allows for demonstrating its implementation feasibility in energy-intensive industries at high temperature range. The stored energy is meant to preheat the air temperature entering the furnace by using a PCM whose melting point is 885 °C. In this sense, a heat transfer model simulation is established to determine an appropriate design based on mass and energy conservation equations. The thermal performance is analysed for the melting and solidification processes, the phase transition and its influence on heat transference. Moreover, the temperature profile is illustrated for the PCM and combustion air stream. The obtained results prove the achievability of very high temperature levels (from 700 to 865 °C) in the combustion air preheating in a ceramic furnace; so corroborating an energy and environmental efficiency enhancement, compared to the initial condition presenting an air outlet at 650 °C.

Biomass plays an important role in the world primary energy supplies, currently providing ∼14% of the world's primary energy needs and being the fourth largest contributor following coal, oil and natural gas. Over the past decade, domestic biomass heating has received more governmental and public supports than ever before in many developed countries, such as the UK. Although biomass combustion releases some combustion pollutants, biomass is renewable and produces little net CO 2 emissions to the atmosphere. Owing to the low sulphur and low nitrogen contents of many biomass materials, substituting biomass for fossil fuels, particularly coal, can reduce SO x and NO x emissions. This study investigated flue gas emissions, particularly carbon monoxide and nitrogen oxides, of a domestic biomass boiler under various operating conditions. The biomass boiler used in this study satisfies the current EU regulation (EN 303-05) on emissions of domestic biomass boilers. Emissions of the boiler had been measured not only under normal combustion conditions, but also under 'idle' combustion conditions when the boiler was not in but was ready for full operation. The experimental results are analysed and presented in this paper. Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

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.