Dr. B. C. Roy Engineering College (BCREC) - is a private engineering college in Durgapur named after legendary physician Dr. Bidhan Chandra Roy, located in Paschim Bardhaman district, West Bengal, about 160 kilometers from the city of Kolkata, India. It was established on 21 August 2000 with its first batch of students. It offers undergraduate and postgraduate courses in Engineering, Technology and Management. The college is affiliated under All India Council for Technical Education and Maulana Abul Kalam Azad University of Technology formerly known as West Bengal University of Technology (WBUT).BCREC Main BuildingBCREC Library BuildingBCREC Mechanical BuildingBCREC Polytechnic Building.
In the near future, fossil fuel reserves are expected to become progressively depleted. In response, contemporary research efforts worldwide are intensifying the exploration of renewable energy integration into electrical power systems, driven by both environmental imperatives and economic rationale. The principal contribution of the proposed research lies in the development of a scheduling framework for thermal units in coordination with hydro and wind energy sources (HTWS), aimed at minimizing fuel consumption and enhancing economic power generation. A secondary contribution involves the integration of battery energy storage systems (BES) into the HTWS configuration - resulting in the hybrid HTWBS system - to improve voltage stability and optimize economic power delivery under dynamically varying load conditions. Finally, the optimal power flow (OPF) analysis of the hydro-thermal-wind-battery scheduling (HTWBS) within the IEEE-39 bus system is conducted to ensure the most efficient operational outcomes of the integrated power network while reliably meeting load demand. The system's complexity is significantly heightened by non-linear factors such as valve-point loading in thermal units, transmission losses, water availability constraints in hydro units, wind power uncertainties, and the dynamic charging-discharging behavior of batteries. These non-linearities introduce challenges like local optima and slow convergence in scheduling processes, which can be effectively addressed using a relatively recent optimization approach known as the chaotic-opposition-based sine cosine algorithm (COSCA). Through statistical analysis using the ANOVA test and Box plot across three systems, the proposed approach demonstrated minimal variance in mean values and achieved optimal cost outcomes within a tolerance of less than 0.025%, thereby validating its robustness. By effectively reducing generation costs and enhancing the voltage profile, COSCA surpasses alternative optimization strategies, with comparative analysis confirming its superior performance across both test systems.
The optimal integration of renewable energy sources into power systems is a challenging but extremely important task for cost reduction to achieve environmental sustainability. To address the challenges of probabilistic optimal power flow problems, this study proposes a novel marine predators algorithm as an optimization tool. The proposed method used to solving the probabilistic optimal power flow problem on conjunction with a unified power flow controller, incorporating the behavior of wind, PV, and small hydro generation. The proposed framework models the variability of renewable resources using appropriate probability density functions like weibull distribution for wind speeds, lognormal distribution for solar irradiance, and gumbel distribution for water flow rates. The simulation results highlight the efficiency and robustness of marine predators algorithm in solving single-objective optimal power flow problems. The findings show that 5112.5 ($/h) and 1.6823 (t/h) is the ideal fuel cost & emission when considering thermal generators alone; when incorporating renewable energy with thermal, the total cost is 4808 ($/h) and the emission is 1.5131 (t/h). When RES with UPFC both are included, fuel costs 4785.3 ($/h) and emissions are 1.469 (t/h). For research validation, selected conventional generators in the IEEE 57-bus system are replaced with renewable sources. Furthermore, the proposed method enhances the operational efficiency of renewable sources and small hydro systems when integrated with flexible AC transmission system devices like unified power flow controller. Performance evaluation on the IEEE 57-bus test system shows that the proposed approach delivers highly competitive results compared to other optimization.
A mathematical method for quantifying uncertainty in electrical networks using renewable energy, plug-in electric vehicles, and hydrogen energy storage based on Marine Predator Optimization algorithm to solve unit commitment problem is proposed in this research, and the outcomes are compared with Grey Wolf Optimizer (GWO) and Biogeography-Based Optimization (BBO) and Sine Cosine Algorithm (SCA).ANOVA test findings demonstrate the resilience and efficacy of the MPA method in unit commitment. The article illustrates the applicability and validity of the proposed method based on the IEEE-57 bus network. Thermal investigation is the initial step throughout this research, followed by adding the use of renewable energy sources to reduce expenses as well as emissions, and, lastly, hydrogen storage units were added to accommodate rising needs. A total of 100 iterations are counted, and we get the best value after 30 trials in this investigation. Optimizing the one and multiple goals of operations, such as total cost minimization with the valve point effect and emission minimization, as well as simultaneous minimization of cost and emission, is the main aim. The analysis found that between the 13th and 21st hours, when the load is at its maximum, all generators are operating efficiently to meet demand. Important data like the average, median, and surrounding variability are displayed using a Table. This effort concurrently minimized 3.95% (cost) and 8.3% (emission) while achieving a 1.89% cost reduction and a 7.4% emission reduction compared to early research. Goals decreased by 0.58% and 0.72% with the combination of RES and HES, indicating better multifaceted performance.
This paper presents a discrete delta-domain fractional-order PID (FOPID) controller, tuned using dynamic Particle Swarm Optimization (dPSO), for precise DC motor speed control. The proposed approach directly discretizes the FOPID controller in the delta domain, ensuring improved numerical stability and continuous-time-like performance even at fast sampling rates. The controller is optimized for time-domain criteria and implemented on an Atmega328P microcontroller, with comprehensive validation via both simulation and hardware-in-the-loop (HIL) experiments. Compared with the conventional z-domain FOPID and PID controllers, the delta-domain design achieved up to 60% reduction in overshoot and 40% improvement in settling time, while maintaining steady-state error below 1%. These results confirm the practical viability of advanced digital fractional-order control for real-time industrial applications.
Collection of data on simultaneous prevalence of two or more sensitive characteristics is a requirement in various research studies. In this paper, we introduce a two-stage Bivariate Randomized Response Model designed for this purpose, employing a design matrix based on a non-sensitive, unrelated characteristic. The application of the proposed model has been demonstrated via simulation study on real data sets. Additionally, an empirical study has been conducted to compare its efficiency with a standard method. The promising results led to recommendations for practical applications by survey statisticians and practitioners.
