Proposed research presents a data-driven framework for forecasting municipal solid waste (MSW) generation and emission dynamics in Erode City, India, by employing supervised machine learning algorithms. Leveraging a five-year dataset (2019–2024) comprising socio-economic variables, zonal waste typologies, and historical waste volumes, the model integrates Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers. Feature selection and proximity ranking techniques were applied to identify high-impact variables, with plastic and organic waste emerging as dominant predictors. Data pre-processing included normalization, missing value imputation, and spatial zoning analysis. The model was validated through cross-validation with an 80:20 training-to-testing ratio. Among the tested models, SVM exhibited Superior performance, achieving a prediction accuracy of 96%, lowest mean squared error (MSE = 4860), and minimal computational latency (0.67 seconds), indicating suitability for real-time deployment. The integration of proximity matrix analysis and zonal feature clustering enhanced interpretability and robustness. The proposed framework demonstrates significant potential for scalable waste forecasting applications, enabling emission quantification and strategic decision-making. Future work includes the incorporation of real-time sensor data, temporal decomposition, and hybrid deep learning architectures to optimize waste handling and carbon mitigation strategies.

Deep reinforcement learning (DRL) has emerged as a promising paradigm for the intelligent control of power electronic converters. It offers adaptability, model-free operation, and real-time decision making in complex, nonlinear, and dynamic environments. This review provides a comprehensive analysis of the state-of-the-art in DRL-based control strategies for various power converter applications. It includes voltage regulation in DC-DC converters connected to DC microgrids, speed control of permanent magnet synchronous motors (PMSM), voltage regulation and frequency modulation in dual active bridge (DAB) converters, maximum power point tracking (MPPT) in solar pv systems, and grid-connected inverter control in both grid-following and grid-forming modes. The paper systematically categorizes the recent literature based on converter topology, control objectives, DRL algorithms used, and implementation frameworks, highlighting the strengths and limitations of each approach. Special attention is given to the design of reward functions and action-state representations. Furthermore, the review identifies key challenges including stability assurance, sample inefficiency, hardware deployment constraints, and lack of standardized benchmarking environments. Finally, research gaps and future directions are outlined, emphasizing the need for physics-informed learning, safe exploration strategies, and hybrid model-based approaches to bridge the gap between academic advances and real-world deployment in power electronic systems.

Smart grids (SGs) are technology powered electricity networks that support bidirectional power and data flows. This allows real-time monitoring of demand and enhances the grid’s capability to dynamically adjust the generation and reduce the gap between supply and demand. However, implementing a smart grid in the power network comes with its own set of security challenges, such as cyber-security, distrust in participants and lack of customer engagement due to various cyber-attacks. Such cyber-attacks which will create a distrust among consumers/prosumers to adapt smart grid and distributed energy resources (DER) framework. To circumvent this, a blockchain supported hybrid authentication and handshake algorithm (BSHAHA) for smart grids is proposed in this work, which authenticates data communication between peer-to-peer, aggregators, virtual power plants, and the grid. The algorithm was developed incorporating elliptic-curve cryptography (ECC) and advanced encryption standards (AES) to enhance privacy and session security. The proposed algorithms are verified and tested using formal cyber-security tools, such as Random oracle model and AVISPA as well as by informal security analysis. Furthermore, to simulate a real time test environment, this paper utilized ns-3 network simulator to simulate different smart meter scenario and proposed algorithm are tested for power consumption and scalability, and result are presented. Moreover, blockchain simulation first done in local blockchain using Ganache and truffle IDE and later using the Holesky Ethereum test-network and remix-IDE. Lastly, this paper presented a comparison analysis of power consumption for different consensus mechanisms.

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).