Electric vehicle (EV) technology has revolutionized the transportation sector in the last few decades. The adoption of EVs, along with the advancement of smart grid technologies and Renewable Energy Sources (RES), has introduced new concepts in the automobile and power industries. Vehicle-Grid Integration (VGI) or Vehicle-to-Grid (V2G) is a technology revolutionizing both the transport and electric power sectors. From a V2G perspective, these sectors are complementary and mutually beneficial. For the power sector, mitigation of voltage and frequency excursions and the prospect of grid stabilization on the brink of uncertainties owing to the dynamics in the grid scenario are very important. This article focuses on various aspects of EV-power grid integration. The tremendous benefits of this technology, as presented in the literature, are reviewed. Furthermore, the concerns and the implementation challenges are reviewed in detail in this work.

Optimization Techniques for Operation and Control of Microgrids – Review

Energy is the basic prerequisite of any industry, but global warming, climate change, and pollution are increasing due to digitization and industrialization. Rising electricity demand, combined with the integration of electric vehicles, has made it increasingly challenging to rely solely on conventional centralized power systems. To meet this current changing energy scenario, it becomes essential to introduce green energy into conventional power systems and allow bi-directional energy flow. Microgrids, smart grids, and virtual power plants will play an important role in making this massive shift from a centralized system to a decentralized power system. A virtual power plant is a cloud-based energy system incorporating various microgrids, energy storage, distributed energy resources, and weather forecasting. Since this system is virtual, it could lead to cyber threats. To the best of the authors’ knowledge, this review article complies with recent data from ten major research libraries, offering consolidated insights into the virtual power plant (VPP) framework that will enhance customer participation and encourage them to become prosumers. Additionally, a blockchain-based VPP framework is presented along with two very prominent scenarios of blockchain-based distributed VPP involving P2P transactions and NW trading discussed for building futuristic NZEGs aimed at reducing carbon footprints and providing a foundation for future net-zero energy grids (NZEZs).

The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO-\(\lambda\)) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.

The global energy landscape is undergoing a fundamental transformation from centralized generation to a decentralized sustainable energy system. This paradigm shift is primarily driven by the rapid proliferation of distributed energy resources (DERs) such as solar photovoltaics, wind turbines, battery energy storage, responsive real-time demand response, and energy management. The virtual power plant (VPP) concept has emerged as an advanced framework to integrate, coordinate, and optimize these diverse DERs, enabling them to operate as a unified, flexible power entity. However, conventional centralized power plants face many challenges in terms of scalability, trust, security, and transparent coordination among all operating units. Blockchain technology, with its decentralized ledger, immutability, and smart contract capabilities, offers a transformative solution to these limitations. When such revolutionary technology is integrated with VPPs, it facilitates transparent peer-to-peer (P2P) energy trading, Nega-watt trading, and kilo watt trading. Hence, such a blockchain-based VPP (BVPP) framework establishes a decentralized energy management system that ensures trustless coordination, data integrity, and real-time optimization of renewable assets. This chapter explores the architecture, operational mechanisms, and potential applications of BVPP in the context of futuristic power systems. It discusses how blockchain enhances energy democratization, fosters prosumer participation, and optimizes transactive energy models. Furthermore, it addresses critical technical, economic, and regulatory challenges such as scalability, interoperability, and governance and proposes viable solutions for large-scale BVPP deployment.

The Automatic Self Balancing Robot for Monitoring of Crop is a novel robotic system designed for the agricultural industry to improve crop monitoring and management. The robot is equipped with sensors, cameras, and machine learning algorithms that enable it to navigate through fields, collect da-ta on plant health, and identify potential issues here in this case fruit ripening (Early ripe, partially ripe, Ripe and Decay) with the help of camera. The self-balancing feature of the robot allows it to operate on uneven terrain, ensuring stable and accurate data gathering. Data gathered by the robot such as tempera-ture, humidity and soil moisture are sent to a central system for analysis, ena-bling agricultural producers to decide on crop management. The use of this ro-botic system can increase productivity, reduce employment costs, and improve overall harvest yields PID control, a feedback control loop, modifies the robot's behaviors in response to the discrepancy between the desired and actual states. A mathematical process called Kalman filtering is used to calculate and remove noise from sensor measurements, enabling more precise data interpretation. To improve the overall impression of the robot's orientation, sensor fusion com-bines data from several sensors, such as gyroscopes and accelerometers. IMUs are sensor bundles with integrated sensors that provide information on rotation, acceleration, and magnetic field strength, allowing for accurate measurement and control.