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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

This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate. To prevent the battery from performance degradation at low temperatures, a thermal management (TM) system has been considered, including a high-voltage coolant heater (HVCH) for the battery and cabin compartment heating. Accordingly, an optimal control problem (OCP) has been formulated in the form of a nonlinear program (NLP), aiming at minimising the total energy consumption of the battery. The main focus here is to develop a computationally efficient approach, mimicking the optimal preheating behavior without a noticeable increase in the total energy consumption. The proposed algorithm is simple enough to be implemented in a low-level electronic control unit of the vehicle, by eliminating the need for solving the full NLP in the cost of only 1Wh increase in the total energy consumption.

Abstract Flow-accelerated corrosion (FAC) is a degradation mechanism that affects carbon steel piping in power plants. The failures and degradation due to FAC have necessitated numerous replacements in many power plants. Several computer codes around the world were developed as part of a systematic program or process to control FAC in power plant utilities. The typical plant model requires the input of the flow parameters, piping configuration and the plant water chemistry. The results on FAC rate are considered the key to proper selection of components for inspection. The lack of information on the effect of the upstream components located in the proximity limited the accuracy of the FAC prediction tools and hence will affect the accuracy in identifying potential inspection locations. In the present study 211 inspection data for 90° carbon steel elbows from several nuclear power plants were used to determine the effect of the proximity between two components on the FAC wear rate. The effect of the velocity as well as the distance between the elbows and the upstream components is discussed in the present analysis. Based on the analyzed trends obtained from the inspection data, significant increase in the wear rate of approximately 70% on average is identified to be due to the proximity.

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.

The performance capabilities of an axially laminated anisotropic rotor (ALA) in a high-speed synchronous reluctance motor (SynRM) were studied. A 12 kW ALASynRM was designed as an alternative to a high-speed induction motor (IM) with a solid rotor. The electromagnetic design was implemented taking into account possible issues related to the new manufacturing methods, which require thicker rotor layers than in a typical ALA. The ALASynRM shows a higher efficiency than the corresponding IM with a smooth or slitted solid rotor equipped with copper end rings. To verify the design method, a prototype IM with a smooth solid rotor was built and tested. In the analysis, it was found that, similar to IMs, in an ALASynRM a considerable part of losses takes place in the rotor despite the absence of slip-related losses in the SynRM. The distribution of eddy current losses in the ALA rotor is significantly uneven. The torque ripple in the ALASynRM is considerably larger than the corresponding ripple in IMs.

Short term load forecasting plays an increasingly important role in Smart Grid. Short term load forecasting is also an important part of enterprise power system management. Providing accurate load time series data for a certain period of time in the future can enable enterprises to ensure the smooth operation of production while making a reasonable power plan, reducing power consumption and basic electricity charges, thus reducing the production cost of enterprises. In addition, lower electricity consumption means lower carbon dioxide emissions, which has far-reaching implications for sustainable development strategies. This paper presents a short-term load forecasting method based on time series. The model divides the time series data into four parts: trend item, period item, holiday item and error item. In the experiment part, this paper provides a set of preprocessing method flow. Aiming at the problem that the sampling rate of the current smart grid data is not constant, a data smoothing algorithm is proposed.

Abstract The central role of occupants for achieving energy savings in residential buildings is increasingly recognised. Simulation programmes able to take into account occupant behaviour are considered to be powerful tools for bridging the gap between the predicted and the actual energy consumption for new buildings. Nevertheless, the majority of residential buildings that will constitute the housing stock in 2050 have already been built today, therefore occupant behaviour and building simulation tools need to be fully exploited for supporting the renovation of existing housing stock. The aim of this paper is to explore the role of occupant behaviour modelling in supporting decision-makers dealing with the design of renovation strategies for residential buildings. An Italian multi-family public housing building is assumed as case study to estimate the influence of three dimensions linked with occupant behaviour – management of the thermostat, management of the heating system, variation of building characteristics – on energy heating consumption. The results show that, while the occupant behaviour influences the heating loads up to 1/3 in case of high level of building retrofit, the less the building is renovated, the higher is the behavioural impact in absolute terms of energy reduction. Therefore, in order to be effective, renovation strategies are required to design appropriate informative instruments at an early stage to support behaviour changes towards responsible energy consumption.