• Energy Research
• 11. Sustainability close
• Applied Energy close

Applied Energy, 258 ISSN:0306-2619 ISSN:1872-9118

Abstract Within residences, normative messaging interventions have encouraged households to engage in various pro-environmental behaviors. In norm-based intervention campaigns, it is hypothesized that more personally relevant reference groups increase norm adherence, thus improving the effectiveness of normative messaging interventions. Advanced energy grid infrastructure, such as smart meters and cloud computing, enables the creation of highly personalized behavioral reference groups in a non-invasive manner by dynamically classifying households into highly similar user groups based on usage patterns. Unfortunately, it remains unclear how readily available data on household energy use and housing characteristics affect the classification performance of dynamic behavioral reference groups. Therefore, this research evaluates the classification performance of dynamic behavioral reference groups using readily available data. An energy-cyber-physical system for personalized normative messaging interventions is trained and tested using one-year of energy use data from 2248 households in Holland, Michigan. Dynamic behavioral reference group classification proved very accurate, 94.7–95.9% for weekly feedback and 89.9–93.1% for monthly feedback using only readily available data. In addition, using more historical energy use data contributes to enhancing classification accuracy. Lastly, high classification performance for each behavioral reference group is achieved at 97.6% of precision, recall and F1-score. With the proposed system, it is possible to dynamically assign highly personalized behavioral reference groups to households every billing cycle even if behavioral patterns are subject to change. Thus, interveners will be able to deploy personalized normative feedback messages on a large scale.

Abstract As our energy systems are transitioning towards low-carbon energy sources and their environmental and economic sustainability are assessed, their potential social impacts must also be determined. These social impacts may be disproportionate to a population, leading to energy justice concerns. The social life cycle assessment framework can be used to comprehensively address energy justice concerns by different stakeholder groups and at all life cycle stages associated with a low-carbon energy system. Indicators for a social life cycle assessment framework that addresses energy justice are introduced and discussed. These indicators are organized by four categories of stakeholders for electrical energy systems: workers, electricity consumers, local communities, and society as a whole. The social life cycle assessment framework allows for variations in justice and equity to be determined not only at the generation stage, but through multiple points in the life cycle of the same energy system, from raw material extraction, through manufacturing, transportation, distribution, electricity generation, and waste management. This framework can address potential energy justice issues along the life cycle of new energy systems and assist in their design and planning for optimizing their social sustainability without overlooking vulnerable populations.

Abstract Contrarily to what happens in northern European countries, buildings in the Mediterranean region are prone to overheating. Consequently, it is important to better understand the role that the thermal transmittance of the building envelope elements has on air-conditioning consumptions. This paper analyzes the effect of different U-values on building design in the Mediterranean area. 192 000 residential buildings were randomly generated for sixteen distinct locations and the energy consumption was assessed for each. It was found that in northern Mediterranean locations, as U-values decreased, energy consumption also decreased. However, in warmer climates, low thermal transmittances tended to significantly increase energy consumption. Hence, the lower the latitude, the higher the U-values should be, in order to prevent increasing the cooling demands. Additionally, geometry-based indexes were correlated with the building’s energy performance. For high U-values, it was found that bigger buildings worsen the energy performance and larger windows tended to improve it. For low U-values, bigger north-facing windows were beneficial. There is an adequate interval of values for which the geometry has a lower impact, which is wider and higher for lower latitudes, thus meaning that not only does the building performance improve but architects are also freer to explore alternative designs.

Abstract This study focuses on improving the efficiency of the microwave (MW) pretreatment of waste activated sludge (WAS) through deflocculation mediated by sodium tripolyphosphate (STPP), a cationic binding agent. Deflocculated sludge was subjected to MW pretreatment to assess its impact on biomass disintegration. At the optimised energy for MW pretreatment (14,000 kJ/kg TS), the chemical oxygen demand (COD) solubilisation was 28% and 21% and the reduction in suspended solids (SS) was 38% and 26%, respectively, for deflocculated (treated with a cationic binding agent followed by microwaves) and flocculated (treated by microwaves alone) sludge samples. The formation of volatile fatty acids in the deflocculated sludge medium (840 mg/L) was comparatively higher than that in the flocculated sludge (420 mg/L) and the control (62 mg/L). This study indicates that deflocculated sludge is more amenable to hydrolysis. The results of a test of biochemical methane potential also confirmed the greater amenability of deflocculated sludge for anaerobic degradation.

Abstract Agriculture plays an important role in global climate change. The interaction and efficiency of use of land, water, and energy in agricultural activities are the principal factors affecting greenhouse gas (GHG) emissions and food production. However, comprehensive analysis exploring the mechanism of the land–water–energy system in agricultural production remains lacking. This study developed such a framework based on regional agricultural GHG emissions by combining top-down analysis that considered cross-sectoral interactions with bottom-up analysis that addressed the context-specific conditions of resources and technology. We employed the proposed framework to analyze the interaction of land–water–energy and factors influencing agricultural GHG emissions and to explore mitigation measures based on a case study of the Sanjiang Plain (China). Results showed cropland on the Sanjiang Plain produced 1.8 million tonnes of protein and released 10.9 million tonnes of CO2eq in 2015 using 3.0 million ha of arable land, 12.1 billion m3 of water, and 100.4 PJ of energy. Owing to their high input of resources and flooded cultivation, rice fields produced 29% of total crop protein but consumed 51% of total crop water use, 43% of total crop energy use, and emitted 54% of total crop GHG (CO2eq). Structural adjustment through conversion of half the paddy fields into dryland crops (e.g., wheat) could mitigate GHG emissions by 18.8% in 2020 compared with the baseline scenario. However, such change would be almost impossible given the Sanjiang Plain is one of China’s most important rice-producing areas. If integrated technology improvements were adopted, e.g., advanced crop–soil nutrition management, groundwater protection measures, water-saving irrigation technology, and low-carbon energy technology, GHG emissions could be reduced by 23.9% without sacrificing food production. This study used the nexus approach to analyze agricultural GHG emissions, providing a framework for sustainable agricultural management and a reference for understanding the land–water–energy nexus.

Due to growing interest in wind energy harvesting offshore as well as in the urban environment, vertical axis wind turbines (VAWTs) have recently received renewed interest. Their omni-directional capability makes them a very interesting option for use with the frequently varying wind directions typically encountered in the built environment while their scalability and low installation costs make them highly suitable for offshore wind farms. However, they require further performance optimization to become competitive with horizontal axis wind turbines (HAWTs) as they currently have a lower power coefficient (CP). This can be attributed both to the complexity of the flow around VAWTs and the significantly smaller amount of research they have received. The pitch angle is a potential parameter to enhance the performance of VAWTs. The current study investigates the variations in loads and moments on the turbine as well as the experienced angle of attack, shed vorticity and boundary layer events (leading edge and trailing edge separation, laminar-to-turbulent transition) as a function of pitch angle using Computational Fluid Dynamics (CFD) calculations. Pitch angles of −7° to +3° are investigated using Unsteady Reynolds-Averaged Navier-Stokes (URANS) calculations while turbulence is modeled with the 4-equation transition SST model. The results show that a 6.6% increase in CP can be achieved using a pitch angle of −2° at a tip speed ratio of 4. Additionally, it is found that a change in pitch angle shifts instantaneous loads and moments between upwind and downwind halves of the turbine. The shift in instantaneous moment during the revolution for various pitch angles suggests that dynamic pitching might be a very promising approach for further performance optimization.

Electric vehicles (EVs) are considered a substitute for fossil-fueled vehicles due to rising fossil fuel prices and accompanying environmental concerns, and their use is predicted to increase dramatically shortly. However, the widespread use of EVs and their large-scale integration into the energy system will present several operational and technological hurdles. In the energy industry, an innovative solution known as the EVs smart parking lot (SPL) is introduced to handle EV charging and discharging electricity and energy supply challenges. This paper investigates social equity access and mobile charging stations (MCSs) for EVs, where the owner of MCSs is the EV parking lot. Accordingly, a new self-scheduling model for SPLs is presented in this paper that incorporates scheduling of the MCSs as temporary charging infrastructures while considering social equity access and optimizes SPL energy generation and storage schedule. The main objectives of this research are to (i) develop MCSs accessibility measures and quantify the equity impacts of MCSs locations by modeling prioritized demand based on several indices; (ii) determine the optimal set-points of SPL components (i.e., combined heat and power (CHP), photovoltaic system, electrical and heat-energy storage, and MCSs) to manage electrical peak demand and to maximize the economic benefits of SPLs. Results indicate that the proposed demand prioritization function model can meet the required EV charging demands for prioritized events, and the self-scheduling model for SPLs satisfies the charging demand of the EVs in the SPL location. Also, the social equity access to the EV charging stations is satisfied by analyzing the operation of MCSs around the prioritized demand of the prioritized events and social equity access indices.