Dulal Chandra Das

With the increasing use of renewable energy resources and the demand for a cost-effective, reliable, and uninterrupted supply of electricity, especially in isolated regions, there is a need for an advanced planning and operational control framework for hybrid microgrids. This study proposes a hierarchical two-stage optimization framework for optimal sizing and energy management of an isolated hybrid microgrid comprising solar photovoltaic, hydropower, battery energy storage system, and diesel generation. In the first stage, planning-level optimization is performed using the Harris hawks optimization algorithm to determine optimal system capacities that minimize the annual system cost (ASC) via a 8760-h time-series simulation. In the second stage, an energy management system is developed using a twin-delayed deep deterministic policy gradient method, a reinforcement learning approach, to optimally dispatch power between generation and storage units under stochastic load conditions. The proposed study is conducted using realistic hourly load demand, solar irradiance, and hydropower availability for Bishnupur District, Manipur, India. The system's performance is evaluated based on ASC, loss of power supply probability, renewable energy utilization, diesel fuel consumption, and carbon emissions. The comparative study between independent conventional planning and the operational framework shows that the proposed hierarchical framework delivers improved economic and reliability performance, reduced diesel usage, and lower carbon emissions. The results show the effectiveness of integrating metaheuristic optimization with a reinforcement-learning-based energy management system, enabling intelligent and adaptive decision-making for hybrid microgrid operation under uncertainty conditions.

Electric vehicles (EVs) are rapidly becoming an important part of the power system and pose a high level of uncertainty in the operation of the system while providing a largely untapped distributed energy storage opportunity. Current sequential forecast/schedule approaches can directly result in forecasting errors that impact EV scheduling, resulting in suboptimal grid performance and reduced Vehicle-to-Grid (V2G) penetration in a Virtual Power Plant (VPP) program. This paper introduces an integrated machine-intelligent co-optimization framework that integrates high-accuracy load forecasting and tariff-aware adaptive EV dispatch. The performance of five advanced forecasting models, namely, Long Short-Term Memory (LSTM), hybrid LSTM–XGBoost, TimeMixer++, Variational Mode Decomposition, and Kolmogorov–Arnold Network (KAN), is evaluated using the long-term load data of Andhra Pradesh (2017–2024). Under quasi-stationary conditions, KAN outperforms all compared models, achieving the best mean absolute error (158.2 MW), root mean squared error (209.7 MW), mean absolute percentage error (1.86%), and R2 (0.982), and has the highest anomaly resistance under the COVID-19 shocks. The KAN forecast is used to coordinate bidirectional charging/discharging of 90 000 heterogeneous EVs in a Tariff-Aware Adaptive Vehicle-to-Grid Scheduler that maintains strict State-of-Charge constraints. The framework delivers around a 17% reduction in peak demand, a 21%–25% reduction in daily costs, and an 8.6% Net Revenue Index through V2G arbitrage, resulting in cumulative monthly savings of approximately Rs. 50 crore. The optimality gap of only 17.8% and a stable V2G contribution of 17–18 GWh/month confirm near-optimal economic and operational performance for VPP-enabled power systems.

More than 2 billion people globally depend on biomass for cooking, predominantly using conventional cookstoves. This leads to approximately 4 million annual deaths due to indoor air pollution. Currently, air pollution stands as the biggest environmental health challenge worldwide, having harmful effects on human health, climate, and ecosystems. These health risks can be reduced by improving stove design and combustion characteristics. This article focuses on the design and development of a low-cost IoT-based real-time emission monitoring system (LIEMS) integrated with a thermoelectric generator-integrated biomass cookstove (TIBC). The TIBC is designed to support multi-fuel use, improve user-friendliness, reduce emissions, and enhance overall efficiency. Here, the emission performance evaluation is facilitated by the LIEMS, which monitors pollutants such as particulate matter PM2.5, PM1.0, PM10, carbon monoxide (CO), carbon dioxide (CO2), and volatile organic compounds (VOCs) while also capturing temperature profiles of the cookstove. The LIEMS system is powered entirely by the thermoelectric generator, eliminating the need for an external power source. Built on the ESP-WROOM-32 development board and integrated with the ThingSpeak IoT platform, the system enables real-time data visualization, email alerts for unsafe pollutant levels, and air quality management strategies. Validation results showed measurement discrepancies of ±5.42% for PM2.5, ±5.46% for PM1.0, ±5.49% for PM10, ±23.68% for CO, ±2.38% for CO2, and ±2.17%, ±8.57%, and ±2.17% for the cookstove's hot-side temperature (Thot) and ±8.57% for its cold-side temperature (Tcold). This new integration of IoT technology with TIBC offers a reliable and user-friendly solution for real-time indoor air quality monitoring, advancing household air quality management and sustainability.

The increasing reliance on renewable energy sources (RES) introduces challenges in power system stability due to the inherent variability and intermittency of these sources. Microgrid-integrated virtual power plants (VPPs) offer a promising solution by aggregating diverse energy resources and controllable loads while working as an entity. This study investigates an optimized control strategy for regulating active power within such a system. The proposed hybrid microgrid comprises Solar Photovoltaics (PV), Wind Turbines, Diesel Generators, and controllable loads such as Hybrid Electric Vehicles (HEVs) and Heat Pumps. Utilising a Proportional-Integral-Derivative (PID) controller, optimised through the Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO), the study demonstrates improved system stability and response to power fluctuations. Comparative analysis confirms the superiority of the SSA-based control mechanism over the PSO-based approach. This work underscores the robustness and efficiency of the proposed strategy in ensuring stable power management in dynamic microgrid environments.