• Energy Research

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. A novel model called Sailfish Whale Optimization-based Deep Long Short- Term memory (SWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SWO is designed by integrating the Sailfish Optimizer (SO) with the Whale Optimization Algorithm (WOA). The Hilbert-Schmidt Independence Criterion (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm is implemented in MATLAB software package and the study was done using real-time data. The optimal features are trained using Deep LSTM model. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves percentage improvements of 10%, 9.5%,6%, 4% and 3% in terms of Mean Squared Error (MSE) and 26%, 21%, 16%, 12% and 6% in terms of Root Mean Square Error (RMSE) for Bootstrap-based Extreme Learning Machine approach (BELM), Direct Quantile Regression (DQR), Temporally Local Gaussian Process (TLGP), Deep Echo State Network (Deep ESN) and Deep LSTM respectively. The hybrid approach using the optimization algorithm with the deep learning model leads to faster convergence rate during the training process and enables the small-scale decentralized systems to address the challenges of distributed energy resources. The time series datasets of different utilities are trained using the hybrid model and the temporal dependencies in the sequence of data are predicted with point of interval as 5 years-head. Energy autonomy of the country till the year 2048 is assessed and compared.

With the growing consumer demand in the electronics field, sustainable and effective cooling approaches are imperative to maximize operational efficiency. Heat pipes shave a major consideration in the field of heat transfer in a modern era of miniaturization of equipment. In current trends, the proportion of custom-designed electronic chips is increasing, given the space constraints of the application. Additionally, the use of nanofluids in heat pipes has drawn considerable attention because of their exceptional performance in heat transfer. This research is proposed primarily to investigate the effect of nanofluids on the performance of the partially flattened heat pipe. Here, the evaporator portion forms flat shape which is mostly suitable for fixing easily in electronic circuits. The remaining portions, such as the adiabatic and condenser, are left as circular. This work also covers the development of flattened heat pipes and analyzes their performance. Pure water, Titanium Oxide (TiO2), and Aluminum Oxide (Al2O3)-water-based nanofluids have been used in this research as working fluids. The heat transfer analysis on the customized partially flattened heat pipe was performed, and the results have been compared with fully flattened and circular heat pipes. The heat transfer parameters, such as the heat transfer coefficient and thermal resistance, have been determined from the heat input, evaporator temperature, and condenser temperature for various inclination angles including 0°, 45°, and 90° with the heat input varied between 50–300 W. The results have shown that the flattened heat pipe performed better with Al2O3 nanofluid at an inclination angle of 45° at all of the heat inputs and provided better thermal resistance compared with the other combinations. At 45°, the resistance of the heat pipe was reduced by 2% and 8% with Al2O3 nanofluid compared with water and TiO2 nanofluid. Furthermore, the heat transfer coefficient was found higher by 4 W/m2-K and 4.6 W/m2-K with Al2O3 and gives better results in terms of resistance and heat transfer coefficient.

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.

Energy efficiency has emerged as a crucial focal point in global agendas, being recognized for its pivotal role in combatting climate change, bolstering energy security, and fostering economic growth. Governments worldwide are formulating ambitious targets and enacting comprehensive strategies to optimize energy utilization across various sectors. This involves the formulation of policies, provision of incentives, and facilitation of collaborations to encourage energy-efficient practices, ultimately steering towards a sustainable and energy-efficient future. Notably, the residential sector stands as a pivotal component in these efforts due to its substantial share of energy consumption. This paper evaluates the strategic vision of Morocco concerning energy efficiency within the residential sector from its inception to the projected initiatives up to 2030. The analysis focuses on the current iteration of thermal regulations and its implications. Although specific numerical outcomes are not discussed herein, the implementation of these regulations is observed to yield notable benefits, including reductions in energy bills and gains in annual primary energy. These advantages are estimated to result in a substantial decrease in final energy consumption, equating to significant savings for end-users. Additionally, to cover the expenses associated with building repairs and thermal enhancements, an extra fee is levied, varying based on building typology and climatic region. Despite this additional investment, the associated costs typically exhibit a favorable payback period, on average, underscoring the efficacy of regulatory and profitability measures in driving energy efficiency within the residential sector. This paper examines Morocco’s strategic approach to energy efficiency in the residential sector, focusing on its thermal building regulation RTCM (Moroccan thermal regulation on construction). Energy efficiency is recognized as essential for reducing GHG (greenhouse gas) emissions, enhancing energy security, and lowering costs. Using simulation models across six climatic zones and three residential building types, the study highlights RTCM’s significant impact—achieving national energy savings between 39% and 68%. Despite added costs for thermal improvements, the measures show favorable payback periods, confirming RTCM’s strong energy and economic performance and its potential role in shaping future policies.

Significant changes in society were emphasized as being required to achieve Sustainable Development Goals, a need which was further intensified with the emergence of the pandemic. The prospective society should be directed towards sustainable development, a process in which technology plays a crucial role. The proposed study discusses the technological potential for attaining the Sustainable Development Goals via disruptive technologies. This study further analyzes the outcome of disruptive technologies from the aspects of product development, health care transformation, a pandemic case study, nature-inclusive business models, smart cities and villages. These outcomes are mapped as a direct influence on Sustainable Development Goals 3, 8, 9 and 11. Various disruptive technologies and the ways in which the Sustainable Development Goals are influenced are elaborated. The investigation into the potential of disruptive technologies highlighted that Industry 5.0 and Society 5.0 are the most supportive development to underpin the efforts to achieve the Sustainable Development Goals. The study proposes the scenario where both Industry 5.0 and Society 5.0 are integrated to form smart cities and villages where the prospects of achieving Sustainable Development Goals are more favorable due to the integrated framework and Sustainable Development Goals’ interactions. Furthermore, the study proposes an integrated framework for including new age technologies to establish the concepts of Industry 5.0 and Society 5.0 integrated into smart cities and villages. The corresponding influence on the Sustainable Development Goals are also mapped. A SWOT analysis is performed to assess the proposed integrated approach to achieve Sustainable Development Goals. Ultimately, this study can assist the industrialist, policy makers and researchers in envisioning Sustainable Development Goals from technological perspectives.

This work presents an economic analysis of a hybrid renewable energy source (HRES) integrated with an energy storage system (ESS) using batteries with a new proposed strategy. Here, the HRES system comprises wind turbines (WT) and a photovoltaic (PV) system. The hybrid WT, PV and energy storage system with battery offer several benefits, in particular, high wind generation utilization rate, and optimal generation for meeting supply-demand gaps. The real recorded data of various parameters of a 22 KV hybrid ‘Regen’ feeder of 110/22 KV Vagarai Substation of TANTRANSCO in Palani of Tamilnadu in India was gathered, studied for the entire year of 2018, and utilized in this paper. The proposed strategy is the hybridization of two algorithms called Radial Basis Function Neural Network (RBFNN) and Oppositional Elephant Herding Optimization (OEHO) named the RBFNOEHO technique. With the help of RBFNN, the continuous load demand required for the HRES and be tracked. OEHO is used to optimize a perfect combination of HRES with the predicted load demand. The aim of the proposed hybrid RBFNOEHO is to study the cost comparison of the HRES system with the existing conventional base method, energy storage method (ESS) with batteries and with HOMER. The proposed Hybrid RBFNOEHO technique is evaluated by comparing it with the other techniques; it is found that the proposed method yields a more optimal solution than the other techniques.

Wind Energy Conversion System (WECS) plays an inevitable role across the world. WECS consist of many components and equipment’s such as turbines, hub assembly, yaw mechanism, electrical machines; power electronics based power conditioning units, protection devices, rotor, blades, main shaft, gear-box, mainframe, transmission systems and etc. These machinery and devices technologies have been developed on gradually and steadily. The electrical machine used to convert mechanical rotational energy into electrical energy is the core of any WECS. Many electrical machines (generator) has been used in WECS, among the generators the Permanent Magnet Synchronous Generators (PMSGs) have gained special focus, been connected with wind farms to become the most desirable due to its enhanced efficiency in power conversion from wind energy turbine. This article provides a review of literatures and highlights the updates, progresses, and revolutionary trends observed in WECS-based PMSGs. The study also compares the geared and direct-driven conversion systems. Further, the classifications of electrical machines that are utilized in WECS are also discussed. The literature review covers the analysis of design aspects by taking various topologies of PMSGs into consideration. In the final sections, the PMSGs are reviewed and compared for further investigations. This review article predominantly emphasizes the conceptual framework that shed insights on the research challenges present in conducting the proposed works such as analysis, suitability, design, and control of PMSGs for WECS.