Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability.

Farm machinery like water sprinklers (WS) and pesticide sprayers (PS) are becoming quite popular in the agricultural sector. The WS and PS are two distinct types of machinery, mostly powered using conventional energy sources. In recent times, the battery and solar-powered WS and PS have also emerged. With the current WS and PS, the main drawback is the lack of intelligence on water and pesticide use decisions and autonomous control. This paper proposes a novel multi-purpose smart farming robot (MpSFR) that handles both water sprinkling and pesticide spraying. The MpSFR is a photovoltaic (PV) powered battery-operated internet of things (IoT) and computer vision (CV) based robot that helps in automating the watering and spraying process. Firstly, the PV-powered battery-operated autonomous MpSFR equipped with a storage tank for water and pesticide drove with a programmed pumping device is engineered. The sprinkling and spraying mechanisms are made fully automatic with a programmed pattern that utilizes IoT sensors and CV to continuously monitor the soil moisture and the plant’s health based on pests. Two servo motors accomplish the horizontal and vertical orientation of the spraying nozzle. We provided an option to remotely switch the sprayer to spray either water or pesticide using an infrared device, i.e., within a 5-m range. Secondly, the operation of the developed MpSFR is experimentally verified in the test farm. The field test’s observed results include the solar power profile, battery charging, and discharging conditions. The results show that the MpSFR operates effectively, and decisions on water use and pesticide are automated.

The automobile industry and technology are putting a great significance in improving vehicles to become more fuel economical, but with incremental costs relative to conventional vehicle technologies; these new vehicles are electric vehicles (EV), plug-in hybrid electric vehicles (PHEV), and hybrid electric vehicles (HEV). However, their significant capabilities to reduce petroleum consumption and achieve efficiency over their life cycles offer economic benefits for customers, industry, carmakers, and policymakers. In this paper, an HEV concept based on renewable energy resources (RERs) is proposed. The proposed HEV design utilizes solar PV energy, wind energy, fuel cell, and a supercapacitor (PV + WE + FC + SC) which generates electrical energy via a proton exchange membrane (PEM) and an SC to cater for strong torque requirements. The vehicle incorporates a battery pack in conjunction with an SC for the power demands and an FC as the backup energy supply. An alternator connected to turbine blades runs by wind energy while the car is moving forward, which produces electricity through the alternator to charge the battery. The design aims to ensure zero carbon emission and improved energy efficiency, is lightweight, and incorporates in-wheel motors to eliminate the mechanical transmissions. Modeling and simulation were carried out for each subsystem using MATLAB^® and Simulink^® packages. ANSYS Fluent simulation was used to analyze wind energy. The standard analysis, e.g., pressure, velocity, and vector contour, were also considered while designing the final model. To regulate the power supply and demand, the selection of energy sources was controlled by a rule-based supervisory controller following a logical sequence that prioritizes energy sources with the SC as a source in-vehicle stop-and-go situations while the battery acts as the primary source, FC as a backup supply, and wind and solar power to recharge the battery. Solar charging is switched on automatically once the vehicle is parked, and the controller controls the energy flow from the alternator during that period.

Several studies state that phase change material (PCM) improves the electrical power and efficiency of the photovoltaic (PV) module. To find the suitable PCM for tropical climatic conditions, multi-PCMs are examined simultaneously with melting temperatures of 31 °C, 35 °C, 37 °C, and 42 °C. In this study, PCM containers are integrated behind the PV module with a thickness of 50 mm. The performance of the multi PV-PCMs is monitored year-round and compared with PV-noPCM. The experimental results show that the selected four PCMs performed the cooling process autonomously in all the climates, such as PCM with a melting temperature of 37 °C and 42 °C enhanced the higher cooling rate in summer, and the same PCMs failed to achieve a higher cooling rate in winter. The lowest temperature drop was noted for pre-monsoon and monsoon seasons due to the low irradiance. On the other hand, the highest temperature drop of 16.33 °C is observed for pre-summer (March) and 15.7 °C, and 17.14 °C for summer (April) as compared to PV-noPCM. The results of the present investigation highlight the requirement for choosing the proper PCM melting temperature based on optimal year-round performance. Further, it is recommended that a single PCM melting temperature for cooling the PV modules year-round in tropical climates is inappropriate, and instead, a cascaded structure with different PCM melting temperatures is recommended.

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.

As the global interest in renewable energy generation continues, the need to develop new and innovative solutions is being explored every day throughout the world by researchers and innovators. Hybrid renewable energy innovations are gaining progressive interest not only because of the threat of climate change but also due to the technological advancements seen in renewables. Ocean waves have immense potential as a renewable energy source, and related technologies have advanced continuously over the past few decades. In response, this paper extensively studies wave energy converters (WECs) based on the power take-off (PTO) technique, and presents a novel hybrid wave-plus-photon energy (HWPE) harvester called Wavevoltaics, based on wave and solar energy capture systems for coastal communities’ power needs, in line with decarbonization measures. The HWPE harvester uses a simple rack-and-pinion mechanism in combination with solar cell technology to convert the wave energy into usable electrical energy in a water column structural design. This novel HWPE device can be used to provide power for lighting and gadgets for coastal communities that rely heavily on fossil fuels for their lighting and electrical needs. Later in the paper, the challenges faced in hybrid wave energy development are presented.