Grid-Responsive Control of Aggregated Smart Loads in the Emerging Power Ecosystem V S K V Harish, Avijit Routh Conference Proceedings of International Conference on Sustainable Technology in Energy and Power Systems Stepcon 2025, 2025 The transformation of modern power systems, characterized by high penetration of variable renewable energy sources and declining synchronous inertia, necessitates robust demand-side frequency regulation frameworks. This paper presents a digitalization-driven control strategy leveraging aggregated inverter air-conditioners (IACs) integrated with a single-area reheat steam turbine power system (IAC–SAPS). A generalized small-signal model is formulated that explicitly incorporates nonlinear thermal dynamics, ±30% parametric uncertainties in turbine–governor and load parameters, and communication latency represented through Padé approximation. Structured singular value analysis (μ-analysis) is employed to quantify robust stability margins, and the most destabilizing parameter realization is identified as the worst-case system. Balanced truncation is subsequently used to derive a fourth-order reduced-order worst-case model that preserves dominant oscillatory modes while ensuring computational tractability for controller design. A robust proportional–integral (PI) controller is synthesized for the reduced-order worst-case model by solving a linear–quadratic cost function that penalizes both frequency deviation energy and control effort. The optimized gains are demonstrated to relocate eigenvalues leftward in the complex plane, improving damping ratios and guaranteeing phase/gain margins under delay and uncertainty. Comparative validation against benchmark TCL-based control frameworks highlights the superior resilience of the proposed design, with frequency nadir improved to −1.23 Hz and settling time reduced to ≈399 s. Simulation results confirm that robust, delay-aware PI control of aggregated IACs can significantly enhance transient stability and provide synthetic inertia, establishing demand-side flexibility as a reliable ancillary service in low-inertia grids.
Optimal Frequency Regulation of a single area power system integrated with aggregated inverter air-conditioner systems using Coronavirus Optimization Algorithm V S K V Harish, Avijit Routh Proceedings of 2025 IEEE Artificial Intelligence for Computing Astronomy and Renewable Energy Aicare 2025, 2025 Maintaining frequency stability in low-inertia grids has become a major challenge with the increasing integration of renewable resources. Demand-side participation through inverter air conditioners (IACs) provides a fast and decentralized mechanism to enhance ancillary services. This paper presents the optimal tuning of a Proportional-IntegralDerivative (PID) controller using the Coronavirus Optimization Algorithm (COVID-OA) for frequency regulation of a singlearea reheat steam turbine-based Automatic Generation Control (SArPS) system integrated with aggregated IACs. The IAC dynamic model is developed with both thermal and turbine subsystem parameters considered uncertain within $\pm 30 \%$. A worst-case model of the IAC-integrated SArPS is obtained, and its order is reduced using balanced truncation. This reducedorder transfer function serves as the basis for controller design. The PID controller is tuned by minimizing a performance-based cost function formulated as a linear quadratic function. Simulation studies are carried out for load disturbances of 20% with 20% IAC participation in frequency regulation. Preliminary results show a robust stability margin of $\mathbf{1. 5 0} \boldsymbol{-} \mathbf{1. 5 3}$, with the worst-case model exhibiting complex-conjugate poles at $\pm \mathbf{j} 1.6688$ and a dominant slow pole at $-\mathbf{0. 0 1 5 5}$. The COVID-OA tuned controller significantly suppresses frequency deviations compared to both the uncontrolled system and the Hui-based IAC model, achieving faster damping and reduced overshoot. The proposed framework demonstrates the efficacy of COVIDOA for robust frequency regulation, highlighting the potential of IACs as demand-responsive ancillary resources in future smart grids.
Optimization of PEMFC pressure control using fractional PI/D controller with non-integer order: design and experimental evaluation Avijit Routh, Sankhadeep Ghosh, Indranil Dey, Mehabub Rahaman, Avijit Ghosh Engineering Research Express, 2024 The fuel-based proton exchange membrane (PEM) fuel cell is a promising technology for clean energy production owing to the several advantages including high efficiency (around 80% theoretical), quiet in operation, and almost zero emission as compared to conventional internal combustion engine. Only hydrogen and oxygen are supplied at the anode and cathode, respectively to generate power and water is produced as by product. However, it suffers to achieve its maximum theoretical efficiency due to lack of flow/pressure management of hydrogen and oxygen in the PEMFC stack which also causes flooding within the cell and reduce the performance of the catalyst and reduces the efficiency. The higher efficiency can be achieved with the proper control of the hydrogen and oxygen inlet flow rate and pressure at the PEMFC. Since it’s crucial to maintaining a consistent supply of exponential pressure, the main focus of this work is pressure regulation at the PEMFC cathode side. A fractional PI/D controller is designed to operate the PEMFC system more realistically. There are three primary objectives of this research work. In the first step, monitoring the PEMFC operating pressure to find out the suitable fractional PI-D controller for a given resilience level, which has the lowest Integration Absolute Error (IAE) to disturbances. The robustness level and/or threshold peak is considered as a tuning parameter for the evaluation. Second, compare the best IAE performance of the fractional PI-D controller with that of simple SIMC rules, where a certain level of resilience is achieved by varying the SIMC tuning variable. Through this comparison, the effectiveness of the recommended controller in achieving the optimal plant performance is evaluated. Thirdly, design a non-integer order PEMFC plant with a fractional controller using MATLAB software and compare the results with existing models. This comparison provides insight into the practical performance of the proposed controller. The results shows that the developed fractional PI/D controller is able to control the pressure very efficiently at the PEMFC cathode side. The findings further emphasise on the important to consider the resilience and robustness levels at the time of developing control systems for PEMFCs. The efficacy of the suggested unique technique is further confirmed by contrasting the suggested controller with the developed models.
Dynamic ant colony optimization algorithm for parameter estimation of PEM fuel cell Sankhadeep Ghosh, Avijit Routh, Pintu Hembrem, Mehabub Rahaman, Avijit Ghosh Engineering Research Express, 2024 Proton Exchange Membrane Fuel Cells (PEMFCs) provide a reliable, pollution-free, sustainable, and stable power generating alternative to non-renewable resources, and they do not self-discharge. Proton exchange membrane fuel cells (PEMFCs) necessitate correct parameter estimates for effective investigation, modelling and designing effective fuel cells, highlighting the importance of exact modelling for successful use in many industries. The present research aims to determine the approximate parameters estimation of PEMFC using a modified algorithm derived from the Ant Colony Optimization (ACO) meta-heuristic algorithm. In order to provide justification for the algorithm, it is initially benchmarked against 10 functions. The study compares the outcomes of PEMFC parameter estimation through the Dynamic Ant Colony Optimisation (DACO) algorithm including some additional metaheuristic algorithms such as Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), Artificial Bee Colony (ABC), Differential Evolution (DE) algorithm, and an algorithm known as Grey Wolf Optimisation - Cuckoo Search (GWOCS) which is hybrid in nature. The suggested algorithm’s performance evaluation is based on minimising the Square Error (SSE). The modified proposed optimization algorithm exhibits superior performance compared to other alternative meta-heuristic algorithms due to its minimal SSE value. The effectiveness and efficiency of the modified method based on the Ballard Mark V datasheet were evaluated using statistical error analysis and non-parametric testing. The convergence curves of DACO demonstrate a faster convergence compared to the other optimization algorithms.
Multi objective optimization using artificial neural network to maximize the power output of PEMFCs Sankhadeep Ghosh, Avijit Routh, Mehabub Rahaman, Avijit Ghosh Indian Chemical Engineer, 2024 Designing a PEM fuel cell model is exceedingly challenging because of its multivariate in nature. Optimization is required to achieve highest operating condition. Neural Network Model is one of the possible methods to solve complex problems. The polarisation curve of a PEMFC (Proton Exchange Membrane Fuel Cell) is investigated in this paper in relation to the effects of seven parameters, including temperature, relative humidity in the cathode, relative humidity in the anode, anode stoichiometry, cathode stoichiometry, partial pressure of H2, and partial pressure of O2, using an ANN (artificial neural network) model. Where model geometric parameters i.e. Channel width, Channel depth, Channel length, Rib width, Cell width, GDL thickness, CL thickness, Membrane thickness of PEMFC was constant. Initially single Objective Function (Output Power) is predicted. The research presented here makes predictions about a PEMFC stack's electrical performance under multiple operating conditions. Mathematical model was further verified using laboratory data. Co-efficient of Determination (R2), Mean Square Error (MSE), and Mean Absolute Error (MAE) was determined using the fuel cell stack voltage model and stack power model. The model results show the possibility of using ANN in the implementation of such models to predict the PEMFC system's steady-state behaviour.
Fractional PIλDµ controller design for non-linear PEM fuel cell for pressure control based on a genetic algorithm Avijit Routh, Sankhadeep Ghosh, Mehabub Rahaman, Avijit Ghosh Indian Chemical Engineer, 2023 A fractional-order dynamic model could more accurately model many real scenarios than an integer-order model and provide a more accurate description of numerous genuine dynamical processes. A seventh-order nonlinear proton exchange membrane fuel cell (PEMFC) model is linearised in this research, taking into account correct initial conditions and equilibrium points.We consider the fluctuating load current as a disturbance parameter to affect the system. The goal is to find a control law for the MIMO system using a fractional PID controller based on a genetic algorithm. The controller is a critical part of the fuel cell which controls its functioning and efficiency. The goal is accomplished by designing a fractional controller to adjust the natural response of the fuel cell reactor and maintain the desired Power output in the face of uncertainties and disturbances. The validation results demonstrate that the fractional PIλDµ(FOPID) control method has a smaller overshoot and higher stability than the PID control method. Moreover, it is also observed that the operation efficiency of the PEMFC has risen by 2% with a response timing of less than 0.1 s using the developed fractional PIλDµ(FOPID) control technique.
Modeling and control of a PEM fuel cell performance using Artificial Neural Networks to maximize the real time efficiency Sankhadeep Ghosh, Avijit Routh, Mehabub Rahaman, Avijit Ghosh Uemgreen 2019 1st International Conference on Ubiquitous Energy Management for Green Environment, 2019 In recent years, the proton exchange membrane (PEM) fuel cell is regarded as the best choice in the next generation automobile power source owing to its high fuel conversion efficiency, low noise, almost zero emissions, and low operating temperature. The working condition of PEM fuel cell depends upon several environmental parameters including the flow rate of fuel and oxidant, cell temperature, catalyst activity, and cell fittings. Mostly the data driven techniques are used to predict the voltage and power losses from a fuel cell in particular time. So instead of using a whole analytical model of fuel cell it is better to use Artificial Neural Network (ANN) model due to some of the parameters are very difficult to measure with respect to time. In this present work, it is investigated to develop a PEM fuel cell model using ANN technique. The experimental test on a real time fuel cell has been carried out to validate the ANN model. The different set of operating data is investigated with changing the environmental parameter. The ANN model is applied to emulate real operating conditions such as temperature, hydrogen consumption. After analysis the results it can be concluded that this presented model have good accuracy. Moreover, ANN learning methodology can be implemented to improve the PEM fuel cell stack efficiency. The model is implemented to determine the I-V performance of a single cell PEM fuel cell at different operating settings. The model could obtain the optimized values for the input variables corresponding to the value of objective function. Results showed a consistency between experimental data and the data made by the model. Therefore, it is indicated that the developed model is an effective method, which can predict the performance of fuel cell with high accuracy.
