• Energy Research

Abstract The incorporation of phase change material (PCM) into the building integrated semi-transparent photovoltaic (BISTPV) system is a promising technology to regulate the enhanced surface temperature of the photovoltaic (PV) system. In this work, Sodium Sulfate Decahydrate (Na2SO4·10H2O) and Zinc Nitrate Hexahydrate (N2O6Zn·6H2O) were mixed to form the binary eutectic PCM by heating mixing method. The results of Differential Scanning Calorimeter (DSC) characterization of those eutectic mixtures showed that the molar mass proportion of 70% weight of Na2SO4·10H2O and 30% weight N2O6Zn·6H2O was an optimum eutectic mixture for the solar energy applications. The developed eutectic mixture was employed in the specially designed and fabricated building-integrated semi-transparent photovoltaic phase change material (BISTPV-PCM) system to regulate BISTPV cell temperature. The experimentation was carried out at the outdoor environmental conditions in the region of Kovilpatti (9°10′0″N, 77°52′0″E), Tamilnadu, India throughout the year of 2018. The instantaneous peak temperature was reduced up to 12 ᵒC for the BISTPV-PCM system compared to the non-PCM counterpart. The annual output power generated from the BISTPV module was 34,287 W h/year which increased to 37,024 W h/year by using PCM.

Abstract The incorporation of phase change material (PCM) into the building integrated semi-transparent photovoltaic (BISTPV) system is a promising technology to regulate the enhanced surface temperature of the photovoltaic (PV) system. In this work, Sodium Sulfate Decahydrate (Na2SO4·10H2O) and Zinc Nitrate Hexahydrate (N2O6Zn·6H2O) were mixed to form the binary eutectic PCM by heating mixing method. The results of Differential Scanning Calorimeter (DSC) characterization of those eutectic mixtures showed that the molar mass proportion of 70% weight of Na2SO4·10H2O and 30% weight N2O6Zn·6H2O was an optimum eutectic mixture for the solar energy applications. The developed eutectic mixture was employed in the specially designed and fabricated building-integrated semi-transparent photovoltaic phase change material (BISTPV-PCM) system to regulate BISTPV cell temperature. The experimentation was carried out at the outdoor environmental conditions in the region of Kovilpatti (9°10′0″N, 77°52′0″E), Tamilnadu, India throughout the year of 2018. The instantaneous peak temperature was reduced up to 12 ᵒC for the BISTPV-PCM system compared to the non-PCM counterpart. The annual output power generated from the BISTPV module was 34,287 W h/year which increased to 37,024 W h/year by using PCM.

Suspended particle device (SPD) switchable glazing can change optical transmission from "opaque" state to "transparent" state in the presence of an alternating current (AC) power supply. It can be applied to control internal temperatures in buildings. Thermal characterisation of both SPD and same area of a double-glazing sample was accomplished using an outdoor test cell in Dublin, Ireland. The overall heat transfer coefficients (U value) were calculated for both systems from the experimental data. The average U values for SPD and double glazing samples were found to be 5.9W/m2K and 2.98W/m2K, respectively. Addition of double-glazing to this SPD switchable single glazing offered a U value of 1.99W/m2K.

Suspended particle device (SPD) switchable glazing can change optical transmission from "opaque" state to "transparent" state in the presence of an alternating current (AC) power supply. It can be applied to control internal temperatures in buildings. Thermal characterisation of both SPD and same area of a double-glazing sample was accomplished using an outdoor test cell in Dublin, Ireland. The overall heat transfer coefficients (U value) were calculated for both systems from the experimental data. The average U values for SPD and double glazing samples were found to be 5.9W/m2K and 2.98W/m2K, respectively. Addition of double-glazing to this SPD switchable single glazing offered a U value of 1.99W/m2K.

Strategic selection of glazing, its window-to-wall ratio, and wall thickness of building reduce the energy consumption in the built environment. This paper presents the experimental results of solar optical properties of five glasses: clear, tinted bronze, tinted green, bronze reflective, and polymer dispersed liquid crystal glasses. Laterite room models were modeled with four different thicknesses and four different glasses using Design Builder, and thermal simulation tests were carried out using Energy Plus. The energy savings and carbon emission mitigation prospective of a building’s glazing variety, window-to-wall ratio (WWR), and wall thickness were investigated. The results revealed that among the five window glasses studied, the polymer dispersed liquid crystal glazing window (PDLCGW) was found to be the most energy-efficient for low heat gain in laterite rooms. The laterite room with 0.23 m wall thickness and 40% PDLCGW WWR reduced 18.9% heat gain in comparison with the laterite room with 0.23 m wall thickness and 40% clear glass WWR. The laterite room of 0.23 m wall thickness with PDLCGW glazing of 40% WWR enhanced cooling cost savings up to USD 31.9 compared to the laterite room of 0.08 m wall thickness with 40% PDLCGW. The laterite room of 0.23 m wall thickness with PDLCGW glazing of 40% WWR also showed improved carbon mitigation of 516 kg of CO2/year compared to the 0.23 m wall thickness laterite room of 40% WWR with clear glass glazing. The results also showed that the laterite room with 0.23 m wall thickness and 100% clear glass WWR increased heat gain by 28.2% in comparison with the laterite room with 0.23 m wall thickness and 20% clear glass WWR. The results of this article are essential for the strategic design of buildings for energy saving and emission reduction.

Strategic selection of glazing, its window-to-wall ratio, and wall thickness of building reduce the energy consumption in the built environment. This paper presents the experimental results of solar optical properties of five glasses: clear, tinted bronze, tinted green, bronze reflective, and polymer dispersed liquid crystal glasses. Laterite room models were modeled with four different thicknesses and four different glasses using Design Builder, and thermal simulation tests were carried out using Energy Plus. The energy savings and carbon emission mitigation prospective of a building’s glazing variety, window-to-wall ratio (WWR), and wall thickness were investigated. The results revealed that among the five window glasses studied, the polymer dispersed liquid crystal glazing window (PDLCGW) was found to be the most energy-efficient for low heat gain in laterite rooms. The laterite room with 0.23 m wall thickness and 40% PDLCGW WWR reduced 18.9% heat gain in comparison with the laterite room with 0.23 m wall thickness and 40% clear glass WWR. The laterite room of 0.23 m wall thickness with PDLCGW glazing of 40% WWR enhanced cooling cost savings up to USD 31.9 compared to the laterite room of 0.08 m wall thickness with 40% PDLCGW. The laterite room of 0.23 m wall thickness with PDLCGW glazing of 40% WWR also showed improved carbon mitigation of 516 kg of CO2/year compared to the 0.23 m wall thickness laterite room of 40% WWR with clear glass glazing. The results also showed that the laterite room with 0.23 m wall thickness and 100% clear glass WWR increased heat gain by 28.2% in comparison with the laterite room with 0.23 m wall thickness and 20% clear glass WWR. The results of this article are essential for the strategic design of buildings for energy saving and emission reduction.

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.