Recent advancements in green building technologies (GBTs) have grown substantially, as an outcome of the environmental, economic and societal benefits. It has the potential to move toward sustainable development, specifically related to climate change. In GBTs, the main objective is to use energy, water and other resources in a balanced way, without using them extensively. This will improve the environmental conditions. Green buildings (GBs) are beneficial when it comes to energy consumption and emissions; low maintenance and operation costs; boosting health and productivity; etc. There is a lack of a critical review of the past or present research work in the area of the Green Building Technology (GBT) sector to identify the future roadmap for sustainable green building technologies. A critical review, with the help of proper research methodology, was identified. The scope of this study is to analyze the existing work on different issues, and find different key issues in green building research, which has minimal use of natural resources, is cost-effective and is designed and constructed for a longer duration, considering future prospects. This paper examines the state of green building construction today and makes recommendations for further study and development which will be necessary for a sustainable future. In order to encourage research, this study also identified a few possible future research directions in sustainable development.

This paper proposes an effective approach to solve renewable distributed generators (RDGs) and electric vehicle charging station (EVCS) allocation problems in the distribution system (DS) to reduce power loss (P_Loss) and enhance voltage profile. The RDGs considered for this work are solar, wind and fuel cell. The uncertainties related to RDGs are modelled using probability distribution functions (PDF). These sources’ best locations and sizes are identified by the voltage stability index (VSI) and political optimization algorithm (POA). Furthermore, EV charging strategies such as the conventional charging method (CCM) and optimized charging method (OCM) are considered to study the method’s efficacy. The developed approach is studied on Indian 28 bus DS. Different cases are considered, such as a single DG, multiple DGs and a combination of DGs and EVs. This placement of multiple DGs along with EVs, considering proper scheduling patterns, minimizes P_Loss and considerably improves the voltage profile. Finally, the proposed method is compared with other algorithms, and simulated results show that the POA method produces better results in all aspects.

This paper made an attempt to put forward the comparative performance analysis of different energy storage devices (ESDs), such as redox flow batteries (RFBs), superconducting magnetic energy storage (SMES) device and ultra-capacitors (UCs), in the combined frequency and voltage stabilization of a multi-area interconnected power system (MAIPS). The investigative power system model comprises two areas, and each area consists of the power-generating sources of thermal, hydro and gas units. The intelligent control mechanism of fuzzy PID was used as a secondary controller optimized with a hybridized approach of the artificial electric field algorithm (HAEFA) subjected to the minimization of integral time absolute error (ITAE) objective function. However, the superiority of fuzzy PID in dampening the deviations of combined load frequency control (LFC) and automatic voltage regulator (AVR) responses was revealed upon comparison with conventional PI and PID. Further, the LFC-AVR combined analysis was extended to incorporate different ESDs one after the other. The simulation results reveal the efficacy of incorporating ESDs with the LFC-AVR system and the supremacy of RFBs in damping out the fluctuations in frequency and voltage.

Energy is an indicator of the socio-economic development of any country and has become an indispensable part of modern society. Despite several renewable sources of energy generation, biomass sources are still under-utilized due to the absence of standard policies of estimation of resources at the country level. This paper attempts to estimate the gross crop residue and surplus residue potential for all provinces of the agricultural country, India. In India, the total area under crop production is 94,305 thousand hectares and the yield from all significant crops is 309,133 kg per hectare. It is estimated that total gross crop residue generation in the country is 480 million tonnes. Subsequently, after consumption of crop residues for numerous applications, the surplus crop residues are 121 million tonnes. The bioenergy potential from the surplus residues is estimated as 1988 PJ, which offers a huge potential energy source, from materials otherwise treated as waste. The Indian province Punjab, rich in agricultural sources and covering only 7% of the total cropping area of the country, generates 11% of the total surplus crop which could be used for further efficient use as bio briquettes.

The increased popularity of small-scale DER has replaced the well-established concept of conventional generating plants around the world. In the present energy scenario, a significant share of energy production now comes from the grid integrated DERs installed at various consumer premises. These DERs are being renewable-based generates only intermittent power, which in turn makes the scheduling of electrical dispatch a tough task. The Virtual Power Plant (VPP) is a potential solution to this challenge, which coordinates and aggregates the DERs generation into a single controllable profile. In this paper, a modified PSO-based multi-objective optimization is proposed for the VPP scheduling in distribution network applications such as energy cost minimization, peak shaving, and reliability improvement. For feasibility analysis of the VPP, a case study of state power utility is taken, which includes a 90 bus industrial feeder with grid integrated PVs as DER. The optimized results are computed in both grid-connected and autonomous mode reveal that the operating cost, peak demand, and EENS are declined by 31.70%, 23.59%, and 62.30% respectively. The overall results obtained are compared by the results obtained from other well-established optimization techniques and it is found that the proposed technique is comparatively more cost-effective than others.