Mohan Lal Kolhe

• Mahakisore, M.; Saravanan, Jaswanth; Ravishanmugam, K.; Sivagnanam, Sharvesh; Shibil, C. V. Muhammed & Satheesh, Rahul [Show all 7 contributors for this article] (2026). Electric vehicle energy consumption prediction using ensemble and deep learning models. Results in Engineering (RINENG). 32, p. 1–22. doi: 10.1016/j.rineng.2026.113531. Full text in Research Archive Show summary

  Electric Vehicles (EVs) are central to the global shift toward sustainable transportation. Yet, accurate prediction of their energy consumption remains a critical challenge due to diverse driving behaviors, environmental conditions, and vehicle dynamics. To address this, we implement a machine learning framework utilising real world data comprising 4630 driving instances of Volkswagen e-Golf and Mitsubishi i-MiEV vehicles recorded between 18 December 2014 and 8 February 2019, of which 4578 records are retained after cleaning. A suite of regression algorithms Random Forest, XGBoost, LightGBM, Multilayer Perceptron (MLP), Deep MLP, AdaBoost, and a Stacked Regressor ensemble are trained and evaluated for energy consumption prediction. Prediction is formulated under an explicit trip conditioned contract in which only quantities knowable before a trip completes are admitted as predictors; attributes that algebraically encode the target are strictly excluded. Under this contract the Stacked Regressor attains the best cross validated performance, with a coefficient of determination (R2) of 0.322 ± 0.048 and a mean absolute error of 1.934 ± 0.067 kWh/100km. LightGBM is statistically indistinguishable from it under a corrected paired test while training approximately thirty times faster, and is accordingly the configuration we recommend for deployment. An empirical ceiling estimated from trips sharing identical attribute values indicates that no more than approximately 0.42 of the variance is recoverable from the attributes available, so the reported accuracy attains some three quarters of what this feature set permits; the remainder requires instrumentation the corpus does not contain. For that recommended configuration the mean absolute percentage error is 15.7%, an aggregate that is right skewed rather than typical: the median absolute percentage error is 8.4%, and 79.0% of trips are predicted within 20% of the recorded value, the mean being governed by a small number of trips of implausibly low recorded consumption. A feature availability ablation demonstrates that admitting a single post trip measurement inflates R2 by approximately 0.37 uniformly across all model families, indicating that a substantial part of the high accuracy reported for this task is attributable to the information set rather than to model architecture. SHAP (SHapley Additive exPlanations) analysis identifies trip distance, tyre type, average speed and auxiliary heating as the dominant contributors to energy consumption. A dedicated thermal analysis quantifies the influence of temperature for the first time in this work: in a sub study of the 1227 Mitsubishi i-MiEV trips for which temperature was recovered, the only vehicle for which it is available, measured ambient temperature correlates with consumption at r=−0.744, and sub zero operation increases consumption by 28.5% relative to mild conditions. Cross vehicle experiments further establish that models trained on one EV architecture do not transfer to another without recalibration. The scope of these findings should be read with the corpus in mind. They concern two production vehicle architectures logged by their owners on a community platform between 2014 and 2019, and the cross vehicle experiments show that they do not transfer even between those two; they are not established for the wider fleet, and the thermal penalties reported here may overstate those of vehicles built since, whose battery thermal management differs substantially. These findings provide realistic insight for EV range estimation, smart energy management, and driver behavior optimization within that scope.

• Moparthi, Janardhan Rao; Bhukya, Krishna Naick; Kethavath, Raghavendra Naik; Kolhe, Mohan Lal & Borut, Jereb (2026). Intelligent Ensemble Learning-Based Fault Diagnosis, Location, and Protection of Series-Compensated Transmission Lines for Smart Power Grid Applications. Energies. 19(16). doi: 10.3390/en19163765. Full text in Research Archive Show summary

  Accurate fault diagnosis and protection of series-compensated transmission lines remain challenging due to the nonlinear behavior of series capacitors and associated protective devices, which degrade the performance of conventional protection relays under varying operating conditions. To address these challenges, this paper proposes an intelligent ensemble learning-based protection framework for fault detection, fault classification, fault section identification, and fault location estimation in fixed series-compensated transmission networks. The proposed framework integrates an Artificial Neural Network (ANN) and a random subspace ensemble classifier (RSEC), where the ANN performs fault detection, classification, and location estimation, while the RSEC identifies the faulted section using a majority-weighted voting strategy. In addition, four fault indices are formulated to effectively characterize fault conditions and improve diagnostic performance. The proposed framework is evaluated on a 400 kV, 50 Hz series-compensated transmission system under diverse fault scenarios and varying operating conditions, including different fault types, fault resistances, fault locations, compensation levels, and noisy measurements. The results demonstrate an average fault detection time of 4.05 ms, 100% fault classification accuracy, 98.646% fault section identification efficiency, a mean signed fault location error of −0.02988%, and a mean absolute location error of 0.0791%, indicating negligible systematic bias and high localization accuracy. Furthermore, real-time validation using the OPAL-RT digital real-time simulator confirms the computational feasibility of the proposed framework, demonstrating its potential as a reliable, accurate, and computationally efficient solution for intelligent protection and monitoring of modern smart transmission networks.

• Suresh, Tejavath; Shah, Varsha A.; Shukla, Akanksha & Kolhe, Mohan Lal (2026). Reliability-Constrained Multi-Objective Planning of PV–BESS-Supported Fast EV Charging Stations in Coupled Power and Transportation Networks. World Electric Vehicle Journal. 17(9). doi: 10.3390/wevj17090491. Full text in Research Archive Show summary

  This paper proposes a two-stage, reliability-driven multi-objective planning framework for fast electric vehicle charging stations (FCSs) integrated with solar photovoltaic (PV) generation and battery energy storage systems (BESS) in coupled power–transportation networks. The framework simultaneously addresses electrical network constraints, transportation-driven charging demand, and techno-economic trade-offs in high EV penetration scenarios. A benchmark IEEE 69-bus radial distribution system is co-simulated with a 25-node transportation network to realistically capture spatial and temporal interactions between EV mobility and grid operation. To quantify the combined impacts of voltage stability, service continuity, and charging uncertainty, a novel average voltage deviation reliability index (AVDRI) is introduced. Spatially and temporally varying EV charging demand is modeled using a hybrid approach that integrates queuing theory with gravity-based traffic interaction models, enabling a realistic representation of stochastic arrival patterns and route-dependent charging behavior. In the first stage, a multi-objective optimization problem is formulated to determine the optimal locations and charging capacities of FCSs, minimizing system power losses, reliability degradation, and total system cost while maximizing EV serviceability. Multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimization (MOGWO), and a proposed hybrid GWOPSO algorithm are comparatively evaluated. In the second stage, a bisection-based sizing strategy is employed to determine the optimal PV and BESS capacities required to mitigate solar intermittency and peak power generation mismatches. Results demonstrate that the proposed hybrid GWOPSO based framework achieves superior convergence characteristics and delivers significant improvements in voltage profile, reliability indices, power loss reduction, and overall techno-economic performance compared to conventional approaches.

• Wadhai, Bhishan; Dhote, Nitin & Kolhe, Mohan Lal (2026). Design of an iterative AI enhanced STATCOM-controlled hybrid renewable energy system with multi-agent coordination and predictive stability intelligence sets. International Journal of Applied Power Engineering. ISSN 2252-8792. 15(3), p. 1147–1156. doi: 10.11591/ijape.v15.i3.pp1147-1156. Full text in Research Archive Show summary

  Renewable energy integration causes intermittency, nonlinear dynamics, and grid-code restrictions in modern power systems. Although hybrid renewable energy systems combining wind, photovoltaic, and fuel cell sources increase energy availability, conventional control approaches often fail to maintain voltage stability, power quality, and rapid fault recovery under varying operating conditions. High renewable penetration and noisy conditions worsen these concerns. Existing methodstypically address voltage regulation, transient stability, fault resilience, and power sharing independently using fixed or offline-tuned controllers, limiting adaptability during grid disturbances. To overcome these challenges, this study proposes an AI-enhanced STATCOM-controlled hybrid renewable energy system with learning-based control, predictive stability assessment, and multi-agent coordination. Adaptive reactive power support, noise-resilient fault detection, renewable source power sharing, predictive voltage regulation, and physiologically inspired transient stability prediction using hierarchical reinforcement learning. The simulation results maintain system voltage deviation within ±2%, harmonic distortion below 2%, fault detection within 7 ms, and transient stability prediction accuracy above 98% across varied operating conditions. Voltage recovery, overshoot suppression, and resource utilization efficiency improve above benchmark techniques. Thus, findings demonstrate that AI-enhanced STATCOM worksas cognitive grid-interfacing agents rather than passive compensators for improving stability, power quality, and operational resilience in various deployment settings.

• Naik, K. Raghavendra; Prasad, M. K.Siva; Kolhe, Mohan Lal & Vasquez, Juan C. (2026). Synergistic integration of multi-energy complementary systems in DC Microgrid-based EV charging stations: a two-layer learning framework for BESS management. Sustainable Energy Technologies and Assessments. ISSN 2213-1388. 93. doi: 10.1016/j.seta.2026.105304. Full text in Research Archive Show summary

  Standalone DC microgrids for EV charging stations are essential for the synergistic integration of energy storage technologies with smart grids and multi-energy complementary systems. However, stochastic dynamic EV charging loads in cold climates often overstress Battery Energy Storage Systems (BESS) integrated with dispatchable small/mini-hydro and renewables within DC microgrid, causing premature degradation of BESS. Conventional strategies struggle to coordinate these nonstationary demands effectively. This paper proposes a Two-Layer Learning Framework for storage-aware management, facilitating a low-carbon transformation of power networks. It integrates a machine learning layer for demand forecasting with a heuristic-based adaptive layer that dynamically adjusts control horizons based on real-time BESS constraints. By prioritizing renewable and hydro dispatch while restricting BESS to corrective support, the approach maintains DC-link stability and operational flexibility. Validation using Oslo’s cold-climate datasets demonstrates that the framework improves system reliability by 12.41% and sustainability by 24.65%. Notably, peak and cumulative BESS loading are reduced by 40% and 61%, respectively. Real-time hardware-in-the-loop (Typhoon HIL-604) validation confirms the framework’s efficacy for creating stable, resilient, and multi-energy standalone power infrastructures.

• Patel, Hemant; Bohre, Aashish Kumar; Sharma, Souvik; Thakkar, Jalpa & Kolhe, Mohan Lal (2026). Operational performance analysis of blockchain-enabled mechanisms for decentralized energy trading in smart grids. AIMS Energy. ISSN 2333-8326. 14(3), p. 710–731. doi: 10.3934/energy.2026029. Full text in Research Archive Show summary

  With the increasing penetration of distributed energy resources, electric vehicles, and prosumers, the demand for secure, scalable, and low-latency transaction platforms in smart grid–based transactive energy systems has grown substantially. Although blockchain technology offers a promising solution for decentralized energy trading, the choice of consensus mechanism critically determines system performance and practical feasibility. This study evaluates the relative performance of Clique, Istanbul Byzantine Fault Tolerance (IBFT), and proof of work (PoW) consensus algorithms to identify the most suitable approach for transactive energy applications in a smart grid environments. Using a five-node blockchain network, key performance indicators (KPIs)—namely, latency, throughput, and transaction failure rate—are quantified for essential market functions, including bidding, market clearing, payment settlement, and balance queries. The results demonstrate that permissioned consensus mechanisms significantly outperform PoW in term of responsiveness, scalability, and reliability. Among the evaluated approaches, IBFT exhibits the highest throughput and the lowest latency, making it the most suitable choice for real-time and near-real-time energy market operations. Clique delivers satisfactory performance in small-scale deployments but exhibits scalability limitations as transaction volumes increase. In contrast, PoW suffers from excessive latency and high failure rates, rendering it unsuitable for smart grid service operations. Overall, the findings indicate that permissioned blockchain platforms employing IBFT can effectively support efficient, secure, and scalable transactive energy markets for future smart grid infrastructures.

• Sireesha, K.; Deepa, K.; Prabhakar, J. Ram; Mikkilineni, Krishna Koushik & Kolhe, Mohan Lal (2026). Physics-informed neural networks combined with SVR for battery health and charge state diagnostics. Discover Applied Sciences. 8(5). doi: 10.1007/s42452-026-08567-4. Full text in Research Archive Show summary

  Precise determination of battery state of charge (SoC) and state of health (SoH) is a key factor in providing safe, reliable, and long-term performance of lithium-ion batteries in electric vehicles and energy storage devices. However, nonlinear electrochemical behaviour, sensor noise, voltage variations, and cycle-dependent degradation make precise state estimation challenging for purely data-driven or purely physics-based methods. This study proposes a deterministic hybrid estimation framework for the SoC and SoH prediction by integrating physics-informed neural networks (PINNs) with machine learning models. The proposed architecture combines a PINN, which enforces charge- conservation consistency through coulomb counting-grounded loss functions, and a support vector regression (SVR) model, that performs deterministic residual learning to correct non-linearities and measurement noise. The framework adopts a two- stage learning process in which the PINN first estimates physically consistent SoC trajectories, followed by SVR-based residual refinement. SoH is subsequently derived from SoC- consistent capacity degradation, forming a unidirectional SoC-SoH diagnostic pipeline. The framework is evaluated using four lithium-ion battery datasets (B0007, B0025, B0028, and B0036) available in the NASA data repository and compared with the traditional machine learning and deep learning models, such as LSTM, GRU, Bi-LSTM, Ind-RNN, PINN, and SVR. Experimental results indicate stable cycle-to-cycle SoC tracking, bias-free and tighter residual distributions, and improved predictive accuracy with reduced MAE and RMSE compared to individual models. The proposed framework produces smooth and physically consistent SoH degradation trajectories across the evaluated cells. With offline training and lightweight online inference, the framework is suitable for real- time deployment in battery management systems.

• Patel, Hemant; Bohre, Aashish Kumar; Yadav, Omkar; Thakkar, Jalpa & Kolhe, Mohan Lal (2026). DG, SOP, and EVCS deployment in a distribution system: Multi-Scenario analysis using HHO and TLBO. AIMS Energy. ISSN 2333-8326. 14(2), p. 358–386. doi: 10.3934/energy.2026016. Full text in Research Archive
• Raja, Ranjith R.; Krishna, Advaith; Satheesh, Rahul; Sreekumar, Sreenu & Kolhe, Mohan Lal (2026). Bayesian-Optimized Predictive Modelling of Electric Arc Furnace Power Consumption for Industrial Deployment. IEEE Access. 14, p. 16282–16295. doi: 10.1109/ACCESS.2026.3655696. Full text in Research Archive
• Khedar, Kamlesh Kumar; Goyal, Govind Rai; Singh, Pushpendra & Kolhe, Mohan Lal (2026). Analysis of Load Profile in the presence of EVs & DERs. The European Physical Journal Conferences. ISSN 2101-6275. 351. doi: 10.1051/epjconf/202635101016. Full text in Research Archive Show summary

  This Paper examines the effects of electric vehicles (EVs) and distributed energy resources (DERs) on distribution networks. The impact of these integrations on load profiles is the main topic of the study. To maximize the load profile, the study focuses on scaling DERs and EV charging/discharging techniques. To measure their individual and combined effects on load variability, system characteristics such as load profile, EV charging pattern. The suggested method for enhancing the load profile and lowering network losses with the irregular electricity produced from DERs and electric vehicle integration has been validated through the analysis of a mathematical model with system & operational restrictions. The analysis’s findings demonstrate that effective EV integration and DER capacity enhance the load profile, enhance voltage control at different distribution network nodes, and significantly lower network losses.

• Wohlgenannt, Philipp; Vetter, Vinzent; Moosbrugger, Lukas; Kolhe, Mohan Lal; Eder, Elias & Kepplinger, Peter (2026). Supervised Imitation Learning for Optimal Setpoint Trajectory Prediction in Energy Management Under Dynamic Electricity Pricing. Energies. 19(6). doi: 10.3390/en19061459. Full text in Research Archive Show summary

  Energy management systems operating under dynamic electricity pricing require fast and cost-optimal control strategies for flexible loads. Mixed-integer linear programming (MILP) can compute theoretically optimal control trajectories but is computationally expensive and typically relies on accurate load forecasts, limiting its practical real-time applicability. This paper proposes a supervised imitation learning (IL) framework that learns optimal setpoint trajectories for a conventional proportional (P) controller directly from electricity price signals and temporal features, thereby eliminating the need for explicit load forecasting. The learned model predicts setpoint trajectories in an open-loop manner, while a lower-level P controller ensures stable closed-loop operation within a two-stage control architecture. The approach is validated in an industrial case study involving load shifting of a refrigeration system under dynamic electricity pricing and benchmarked against MILP optimization, reinforcement learning (RL), heuristic strategies, and various machine learning models. The MILP solution achieves a cost reduction of 21.07% and represents a theoretical upper bound under perfect information. The proposed Transformer model closely approximates this optimum, achieving 19.33% cost reduction while enabling real-time inference. Overall, the results demonstrate that the proposed supervised IL approach can achieve near-optimal control performance with substantially reduced computational effort for real-time energy management applications.

• Divya; Nandanwar, Sonali R.; Deepak, Deva Brinda; Shukla, Tanmay; Patidar, N. P. & Thakkar, Jalpa [Show all 7 contributors for this article] (2026). A Modified H-Bridge 25 level Inverter with only 11 Switches to improve the sinusoidal nature of the output to enhance the performance of Inverter fed AC loads. The European Physical Journal Conferences. ISSN 2101-6275. 351. doi: 10.1051/epjconf/202635101006. Full text in Research Archive Show summary

  The idea of a multiple inverter originated from the extension of inverters to many layers in order to lessen distortion from the basic sinusoidal waveform. Using multiple level inverters has the drawback of requiring the installation of extra switches, which raises the system's bulk and cost and reduces its reliability due to the higher component count. This study proposes a twenty- five-level inverter (25-LI) with fewer switches to address the issue of the system being bigger, more expensive, less dependable, and having less distortion than a symmetrical H-bridged 25-level inverter. The MATLABplatform is used to develop the idea, and analysis is used to evaluate the results' effectiveness.

• T, Deepti; K, Deepa; Sangeetha, S. V.Tresa; T, Porselvi & Kolhe, Mohan Lal (2026). Advanced control optimization for bidirectional DC-DC converters in electric vehicles using Harris Hawk optimization. Journal of King Saud University - Science. ISSN 1018-3647. 38(3). doi: 10.25259/JKSUS_630_2025. Full text in Research Archive
• Panimathi, B.; Deepa, K.; Sangeetha, S. V.Tresa; Porselvi, T. & Kolhe, Mohan Lal (2025). Adaptive neuro-fuzzy control for dual boost converter in fuel cell electric vehicles. AIMS Energy. ISSN 2333-8326. 13(5), p. 1195–1218. doi: 10.3934/energy.2025044. Full text in Research Archive Show summary

  Electric vehicles powered by fuel cells provide clean energy with zero emissions and are highly sustainable. Increased demand for sustainable transport has rendered hydrogen fuel cell electric vehicles (FCEVs) a feasible choice compared to conventional internal combustion engines. However, efficiency is significantly dependent on the strength of power electronic interfaces and smart control. This research combined an adaptive neuro fuzzy inference system (ANFIS) with two switched boost-converters to manage power from a fuel cell to a battery to deliver a constant voltage and to achieve optimal energy use. This improved the voltage regulation capability, particularly during load change. ANFIS combines the learning ability of neural networks with the decision structure of fuzzy logic. The hybrid approach is conducive to faster response, greater flexibility, and better energy efficiency over varying operating conditions. The ANFIS model recorded a value of root mean square error (RMSE) of 0.0024, confirming high accuracy in dynamic loading handling. Simulation outcomes support the effectiveness of the system and better performance when compared with traditional proportional integral (PI) controllers.

• Wohlgenannt, Philipp; Moosbrugger, Lukas; Vetter, Vinzent; Hegenbart, Sebastian; Kolhe, Mohan Lal & Eder, Elias [Show all 7 contributors for this article] (2025). Energy cost and emission optimization in food industry using deep reinforcement learning and Transformer-based load forecasting. Energy Conversion and Management: X. 28. doi: 10.1016/j.ecmx.2025.101364. Full text in Research Archive Show summary

  Given the substantial energy demands of the food industry, this study introduces a data-driven demand response strategy for industrial refrigeration in the food-processing sector, targeting reductions in electricity costs and equivalent CO emissions. To this end, the thermal flexibility of a warehouse is quantified using real measurement data, resulting in an estimated thermal capacity of 182 kWh/K. A temperature bandwidth of 5 K as flexibility corresponds to an equivalent electrical energy storage of 445 kWh. To enable predictive control, a Transformer-Encoder architecture is used for short-term thermal load forecasting. By pretraining the Transformer on historical electrical load data from the entire factory, the model captures broader operational patterns, improving the forecast accuracy despite a limited amount of thermal data. Based on these forecasts, a deep reinforcement learning agent is trained to optimize the set point of a proportional–integral controller within predefined temperature limits. The agent successfully balances conflicting objectives, achieving up to 15.50% cost savings and 9.80% emission reductions compared to a baseline scenario. A Pareto front illustrates the trade-offs between cost and emission minimization. The results demonstrate the effectiveness of combining deep reinforcement learning with Transformer-based load forecasting to simultaneously reduce energy costs and equivalent CO emissions, offering a valuable contribution to the path toward a net-zero industry.

• Patel, Satyam; Patidar, N. P. & Kolhe, Mohan Lal (2025). Probabilistic Analysis of Solar and Wind Energy Potentials at Geographically Diverse Locations for Sustainable Renewable Integration. Energies. 18(22). doi: 10.3390/en18226076. Full text in Research Archive Show summary

  The use of conventional fuel sources from the Earth to generate electrical power leads to several environmental issues such as carbon emissions and ozone depletion. Energy generation from renewable energy sources is one of the most affordable and cleanest techniques. However, the generation of power from non-conventional sources like solar and wind requires the examination of established locations where these resources are plentiful and easily accessible. In this study, an investigation of solar and wind is performed at five different sites in various locations in India. For this examination, data on solar irradiance (W/m2) and wind speed (m/s ) is taken from the “NASA POWER DAV v.2.5.22” Data Access Viewer created by NASA. The data for solar and wind was taken at hourly intervals. The period of the investigation was ten years, i.e., from January 2014 to December 2023. The solar and wind potential analysis was performed in a probabilistic way to determine the parameters that support the installation of solar–PV panels and wind energy generators at the examined sites for the generation of power from these spontaneously available sources, respectively. To examine the potential of solar and wind sites, the Beta and Weibull probability distribution function (PDF) was used. The parameter estimation of the Beta and Weibull PDF was performed via the Maximum Likelihood method. The chosen method is known for its accuracy and efficiency in handling large datasets. Some key performance prediction indicators were analyzed for the investigated solar and wind locations. The findings provide valuable insights that support renewable energy planning and the optimal design of hybrid power systems.

• Mounica, Mallareddy; Rajpathak, Bhooshan Avinash; Kolhe, Mohan Lal; Naik, K. Raghavendra; Moparthi, Janardhan Rao & Kotha, Sravan Kumar [Show all 7 contributors for this article] (2025). Decentralized Energy Management for Efficient Electric Vehicle Charging in DC Microgrids: A Piece-Wise Droop Control Approach. Processes. 13(6). doi: 10.3390/pr13061748. Full text in Research Archive
• Metri, Rajanikant A.; Rajpathak, Bhooshan; Naik, Kethavath Raghavendra & Kolhe, Mohan Lal (2025). A Study of Periodicities in a One-Dimensional Piecewise Smooth Discontinuous Map. Mathematics. 13(15). doi: 10.3390/math13152518. Full text in Research Archive Show summary

  In this study, we investigate the nonlinear dynamical behavior of a one-dimensional linear piecewise-smooth discontinuous (LPSD) map with a negative slope, motivated by its occurrence in systems exhibiting discontinuities, such as power electronic converters. The objective of the proposed research is to develop an analytical approach. Analytical conditions are derived for the existence of stable period-1 and period-2 orbits within the third quadrant of the parameter space defined by slope coefficients 𝑎<0 and 𝑏<0 . The coexistence of multiple attractors is demonstrated. We also show that a novel class of orbits exists in which both points lie entirely in either the left or right domain. These orbits are shown to eventually exhibit periodic behavior, and a closed-form expression is derived to compute the number of iterations required for a trajectory to converge to such orbits. This method also enhances the ease of analyzing system stability by mapping the state–variable dynamics using a non-smooth discontinuous map. The analytical findings are validated using bifurcation diagrams, cobweb plots, and basin of attraction visualizations.

• Kishore, B.; Kabilan, K.; Rahul, L. S.; Priyadarshan, G. Prajwal; Vishnutheerth, E. P. & Satheesh, Rahul [Show all 7 contributors for this article] (2025). Advancing short-term wind power forecasting by AI-driven models for improved accuracy. Electrical engineering (Berlin. Print). ISSN 0948-7921. 107(11), p. 14795–14810. doi: 10.1007/s00202-025-03295-1. Full text in Research Archive
• Nesihath, M. K.; Nafeesa, K.; Sreedharan, Sasidharan; Sulthan, Sheik Mohammed; Satheesh, Rahul & Kolhe, Mohan Lal (2025). Design and implementation of energy management system for optimal hybrid residential microgrid. Electrical engineering (Berlin. Print). ISSN 0948-7921. 108(1). doi: 10.1007/s00202-025-03410-2. Full text in Research Archive Show summary

  Residential microgrid design faces complex challenges, especially in effectively sizing and integrating multiple energy sources to meet growing electricity demand while ensuring reliability. This study proposes a sustainable and economically viable microgrid solution tailored for an urban area in southern India, where energy reliability is a pressing concern. The system combines solar photovoltaic (SPV), wind energy systems (WES), battery energy storage system (BESS), and a diesel generator (DG) to fulfill residential load requirements efficiently. The techno-economic viability of various configurations is assessed using NREL’s HOMER software, identifying an optimal setup that delivers a cost of energy (CoE) of INR 10/kWh and a renewable energy share exceeding 80%, with minimal dependence on both grid and diesel power. To further enhance performance, a custom energy management system (EMS) based on a rule-based control strategy that accounts for battery state of charge (SoC) is implemented. This EMS prioritizes renewable utilization while reducing reliance on external power sources. MATLAB-based simulations under diverse load and generation scenarios confirm the system’s robustness and adaptability. Additionally, a laboratory-scale prototype is developed to test real-time operation in grid-connected mode. Experimental results show a 28% decrease in grid power consumption, a 15% enhancement in battery usage efficiency, and a 22% overall cost reduction, all while maintaining uninterrupted power supply in 96% of scenarios tested. The outcomes underscore the practicality and cost-effectiveness of the proposed microgrid, supporting its application in real-world environments with similar energy demands and challenges.

• Islam, Anfaj; Rudra, Souman & Kolhe, Mohan Lal (2025). Optimizing the placement of distributed energy storage and improving distribution power system reliability via genetic algorithms and strategic load curtailment. Neural Computing & Applications. ISSN 0941-0643. 37, p. 17589–17608. doi: 10.1007/s00521-025-11037-4. Full text in Research Archive Show summary

  As the integration of distributed generation (DG) and smart grid technologies grows, the need for enhanced reliability and efficiency in power systems becomes increasingly paramount. Energy storage systems (ESS) play a crucial role in achieving these objectives, particularly in enabling effective islanding operations during emergencies. This research leverages genetic algorithms to identify optimal combinations of ESS units and strategic load curtailment techniques to mitigate potential contingencies. The results demonstrate that integrating ESS significantly improves network reliability, optimizes infrastructure utilization, and is particularly beneficial in regions with high commercial and industrial load percentages. Moreover, the study underscores the critical role of load shedding in balancing reliability improvements and the sizing of DG units, offering valuable insights for future energy management approaches. By employing binary load curtailment strategies, the research determines the optimal location and size of ESS and DG units within the distribution network. The analysis reveals that ESS integration leads to a reduction in net annual costs while providing valuable insights into network reliability through the assessment of expected energy not supplied. Two case studies comparing various storage technologies with a base case without ESS highlight the cost-effectiveness of enhancing system reliability through distributed storage allocation. The study also emphasizes the significance of load shedding in balancing reliability enhancement and DG sizing, examining the effects of DG placement across different network buses. Finally, this article investigates the impact of diverse load mixes, indicating the need for larger ESS units as the proportion of commercial and industrial loads increases, accompanied by rising costs. Through these comprehensive analyses, the study offers valuable insights into optimizing the placement of distributed storage units and improving the reliability of distribution systems.

• Nishimura, Akira; Ichikawa, Mizuki; Hayakawa, Taisei; Yamada, Souta; Ichii, Ryoma & Kolhe, Mohan Lal (2025). Impact of Membrane Thickness on Characteristics of Biogas Dry Reforming Membrane Reactor Using Pd/Cu Membrane and Ni/Cr/Ru Catalyst. Fuels. 6(1). doi: 10.3390/fuels6010018. Full text in Research Archive Show summary

  The aim of the present study was to clarify the influence of the thickness of the Pd/Cu membrane on the characteristics of biogas dry reforming (BDR) with aNi/Cr/Ru catalyst. We also clarified the impact of the reaction temperature, the molar ratio of CH4:CO2, the differential pressure between the reaction and sweep chambers, and the introduction of a sweep gas on the characteristics of a BDR reactor with a Pd/Cu membrane and a Ni/Cr/Ru catalyst. Through this study’s results, we clarify that the concentration of H2 in the reaction chamber and the sweep chamber increases with the increase in the reaction temperature. In addition, this study clarifies that the highest concentration of H2 in the reaction chamber and the sweep chamber can be obtained with a molar ratio of CH4:CO2 = 1.5:1. This study also clarifies that the highest concentration of H2 can be obtained with a thickness of 40 µm, a molar ratio of CH4:CO2 = 1.5:1, and a differential pressure between the reaction chamber and the sweep chamber of 0 MPa without a sweep gas, which was 4890 ppmV in the reaction chamber and 38 ppmV in the sweep chamber. Under these conditions, CH4 conversion, H2 yield, and thermal efficiency were 75.0%, 0.214%, and 2.92%, respectively

• Lliuyacc-Blas, Ruben; Rheinberger, Klaus; Kolhe, Mohan Lal & Kepplinger, Peter (2025). Sequential linear optimization method with fairness considerations for PV hosting capacity quantification in low-voltage grids. Electric power systems research. ISSN 0378-7796. 248. doi: 10.1016/j.epsr.2025.111843. Full text in Research Archive Show summary

  Hosting capacity quantification intends to provide the maximum aggregated capacity of distributed generation in low-voltage grids. Given the nonlinear nature of this problem, finding a solution in a simple and effective manner remains challenging. Moreover, previous studies have shown that solutions often display high disparities in hosting capacities among individual nodes, penalizing some and favoring others. Such disparities raise fairness concerns. In this paper, we propose a sequential linear optimization method to quantify the PV hosting capacity in low-voltage grids to address these issues. The linear formulation is achieved by using sensitivity matrices that approximate linear relations between changes in voltage and current magnitudes due to changes of PV capacities. To mitigate inaccuracies due to the linear formulation, multiple optimization problems are solved sequentially, updating the grid state at each stage with an AC power flow to realign the solution with the nonlinear problem. The performance of the proposed method is showcased using the CIGRE European low-voltage grid and by comparison with other established methods. To draw conclusions on a large and realistic dataset, the hosting capacities of 5196 low-voltage feeders from Austria are quantified under varying levels of disparity allowances.

• Kumar, Rahul; Bohre, Aashish Kumar; Kolhe, Mohan Lal & Singh, Sri Niwas (2025). Multi-Objective Approach for Optimal Reactive Power Dispatch Employing Sine Cosine Optimization Algorithm. In Bohre, Aashish Kumar; Bhowmik, Partha Sarathee; Dubey, Manisha; Kolhe, Mohan Lal & Singh, Sri Niwas (Ed.), Application of Smart Technologies in Power System and Smart Grid - Select Proceedings of International Conference, CISTEE 2023. Springer. ISSN 9789819799169. p. 437–451. doi: 10.1007/978-981-97-9916-9_30. Full text in Research Archive
• Mane, Sandip; Patil, Rajkumar B; Kolhe, Mohan Lal; Roy, Anindita; Kamble, Amol G & Chaudhari, Amit (2025). Analysis of Thermal Aspect in Hard Turning of AISI 52100 Alloy Steel Under Minimal Cutting Fluid Environment Using FEM. Applied Mechanics. 6(2). doi: 10.3390/applmech6020026. Full text in Research Archive Show summary

  This paper describes a simulation study on the hard turning of AISI 52100 alloy steel with coated carbide tools under minimal cutting fluid conditions using the commercial software AdvantEdge. A finite element analysis coupled with adaptive meshing was carried out to accurately capture temperature gradients. To minimise the number of experiments while optimising the cutting parameters along with fluid application parameters, a cutting speed (v) of 80 m/min, feed rate (f) of 0.05 mm/rev, depth of cut (d) of 0.15 mm, nozzle stand-off distance (NSD) of 20 mm, jet angle (JA) of 30°, and jet velocity (JV) of 50 m/s were observed to be the optimal process parameters based on the combined response’s signal-to-noise ratios. The effects of each parameter on machined surface temperature, cutting force, cutting temperature, and tool–chip contact length were determined using ANOVA. The depth of cut affected cutting force, while cutting speed and jet velocity affected cutting temperature and tool–chip contact length. Cutting speed influenced machined surface temperature significantly, whereas other parameters showed minimal effect. Nozzle stand-off distance exhibited less significant effect. Taguchi optimisation determined the optimal combination of process parameters for minimising thermal effects during hard turning. Cutting temperature and cutting force simulation results were found to be highly consistent with experimental results. On the other hand, the simulated results for the tool–chip contact length and machined surface temperature were very close to the values found in the literature. The result validated the finite element model’s ability to accurately simulate thermal behaviour during hard-turning operations.

• Patel, Hemant; Bohre, Aashish Kumar; Yadav, Omkar; Kolhe, Mohan Lal & Brinda, Deva M (2025). Short Circuit and Harmonics Analysis of IEEE 8 Bus Transmission Systems Using DIgSILENT Power Factory. In Bohre, Aashish Kumar; Bhowmik, Partha Sarathee; Dubey, Manisha; Kolhe, Mohan Lal & Singh, Sri Niwas (Ed.), Application of Smart Technologies in Power System and Smart Grid - Select Proceedings of International Conference, CISTEE 2023. Springer. ISSN 9789819799169. p. 165–176. doi: 10.1007/978-981-97-9916-9_12. Full text in Research Archive
• Krishna, C Jaya; Dash, Ritesh; Mahapatro, Abinash & Kolhe, Mohan Lal (2025). Performance Evaluation of Adaptive Neuro Fuzzy Inference System Based Proportional Integral Controller for Turbine Speed Regulation in Hydroelectric Systems. In Kolhe, Mohan Lal & Liao, Qiangqiang (Ed.), Proceedings of the 10th International Symposium on Hydrogen Energy, Renewable Energy and Materials. Springer. ISSN 9789819632725. p. 65–76. doi: 10.1007/978-981-96-3272-5_7. Full text in Research Archive
• Rajput, Saurabh Kumar; Deo, Arjun; Kulshrestha, Deepansh; Nandanwar, Sonali & Kolhe, Mohan Lal (2025). Impact of Solar Intensity and PV Module Temperature on Power Quality and Transformer Life in Grid-Connected Solar Photovoltaic Power Plants. In Kolhe, Mohan Lal & Liao, Qiangqiang (Ed.), Proceedings of the 10th International Symposium on Hydrogen Energy, Renewable Energy and Materials. Springer. ISSN 9789819632725. p. 77–83. doi: 10.1007/978-981-96-3272-5_8. Full text in Research Archive
• Rajini, V.; Nagarajan, V.S.; Harikrishnan, Karunya & Kolhe, Mohan Lal (2024). Electromagnetic design, sensitivity analysis, optimization and Multiphysics capability of rare-earth-free synchronous reluctance motor for electric trike vehicle. AIMS Energy. ISSN 2333-8326. 12(5), p. 1027–1053. doi: 10.3934/energy.2024049. Full text in Research Archive
• Suresh, Tejavath; Shah, Varsha A; Shukla, Akanksha & Kolhe, Mohan Lal (2024). Placement of EV Charging Stations in a Radial Distribution Network. In XX, XX (Eds.), 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE Press. ISSN 9798350383997. doi: 10.1109/SEFET61574.2024.10718075. Full text in Research Archive
• Mallareddy, Mounica; Rajpathak, Bhooshan; Kolhe, Mohan Lal; Kotha, Sravan Kumar & Naik, K. Raghavendra (2024). Grid-Connected DC Electric Vehicle Charging Station Integrated with Solar PV. In XX, XX (Eds.), 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE Press. ISSN 9798350383997. doi: 10.1109/SEFET61574.2024.10718268. Full text in Research Archive
• Naik, K. Raghavendra; Kolhe, Mohan Lal; Prasad, M. Krishna Siva; Agundis, Gibran David; Vasquez, Juan C & Guerrero, Josep M (2024). Predictive Control Strategy for DC Microgrid Integrated EV Charging Stations in Oslo. In XX, XX (Eds.), 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE Press. ISSN 9798350383997. doi: 10.1109/SEFET61574.2024.10718036. Full text in Research Archive
• Thiagarajan, K; Deepa, T & Kolhe, Mohan Lal (2024). Optimized coil design and advanced neural network control for enhanced wireless power transfer in electric vehicles using Taylor-based Firefly and Dove Swarm Optimization. Wireless Power Transfer. ISSN 2052-8418. 11. doi: 10.48130/wpt-0024-0012. Full text in Research Archive
• Kaur, Paramjeet; Chaturvedi, Krishna Teerath & Kolhe, Mohan Lal (2024). Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques. Energy Engineering. ISSN 0199-8595. 121(3), p. 557–579. doi: 10.32604/ee.2024.043159. Full text in Research Archive
• Worku, Ababay Ketema; Ayele, Delele Worku; Deepak, Deva Brinda; Gebreyohannes, Abaynesh Yihdego; Agegnehu, Segenet Dagmawi & Kolhe, Mohan Lal (2024). Recent Advances and Challenges of Hydrogen Production Technologies via Renewable Energy Sources. Advanced Energy and Sustainability Research. 5(5), p. 1–18. doi: 10.1002/aesr.202300273. Full text in Research Archive
• Nandanwar, Sonali; Patidar, Narayan P; Panda, Sidhartha; Thakkar, Jalpa & Kolhe, Mohan Lal (2024). Synergistic control for enhancing frequency stability in grid-integrated network with decentralized renewable energy resources, energy storage, and electric vehicles. Cleaner Engineering and Technology. 20. doi: 10.1016/j.clet.2024.100757. Full text in Research Archive
• Rani, G. S. Asha; Priya, P. S. Lal; Jayan, Jino; Satheesh, Rahul & Kolhe, Mohan Lal (2024). Data-Driven Energy Management of an Electric Vehicle Charging Station Using Deep Reinforcement Learning. IEEE Access. 12, p. 65956–65966. doi: 10.1109/ACCESS.2024.3398059. Full text in Research Archive
• Nishimura, Akira; Ito, Syogo; Ichikawa, Mizuki & Kolhe, Mohan Lal (2024). Impact of Thickness of Pd/Cu Membrane on Performance of Biogas Dry Reforming Membrane Reactor Utilizing Ni/Cr Catalyst. Fuels. 5(3), p. 439–457. doi: 10.3390/fuels5030024. Full text in Research Archive
• Choudhary, Swapna; Dhote, Nitin K & Kolhe, Mohan Lal (2024). Detection of Plant Leaf Diseases Using Internet of Things and Machine Learning Based Drone System. In XX, XX (Eds.), Image Processing, Electronics and Computers - Proceedings of the 5th Asia-Pacific Conference (IPEC 2024). IOS Press. ISSN 9781643685243. p. 703–712. doi: 10.3233/ATDE240516. Full text in Research Archive Show summary

  With rapid improvement in technology, drones become one of the best fit for agriculture due to their various applications such as spraying pesticides and supervising the yield area of farms. A drone-based system can help in detecting the diseases before they spread to the whole farm. The use of the technology will improve the efficiency of the farming sector. The system is designed using the Internet of Things (IoT) with the help of the ESP32 Cam board, Machine Learning (ML) algorithm, and Google Workspace. ESP-32 Cam module captures the images of plants. Google Drive is used to store captured images. The machine learning algorithm is used to extract features from images and detect possible plant diseases. Workspace is used to create an API that gives G-Drive access to the ML algorithm. The system not only saves human work but also helps in increasing the quality of yield.

• Wohlgenannt, Philipp; Hegenbart, Sebastian; Eder, Elias; Kolhe, Mohan Lal & Kepplinger, Peter (2024). Energy Demand Response in a Food-Processing Plant: A Deep Reinforcement Learning Approach. Energies. 17(24), p. 1–19. doi: 10.3390/en17246430. Full text in Research Archive Show summary

  The food industry faces significant challenges in managing operational costs due to its high energy intensity and rising energy prices. Industrial food-processing facilities, with substantial thermal capacities and large demands for cooling and heating, offer promising opportunities for demand response (DR) strategies. This study explores the application of deep reinforcement learning (RL) as an innovative, data-driven approach for DR in the food industry. By leveraging the adaptive, self-learning capabilities of RL, energy costs in the investigated plant are effectively decreased. The RL algorithm was compared with the well-established optimization method Mixed Integer Linear Programming (MILP), and both were benchmarked against a reference scenario without DR. The two optimization strategies demonstrate cost savings of 17.57% and 18.65% for RL and MILP, respectively. Although RL is slightly less efficient in cost reduction, it significantly outperforms in computational speed, being approximately 20 times faster. During operation, RL only needs 2ms per optimization compared to 19s for MILP, making it a promising optimization tool for edge computing. Moreover, while MILP’s computation time increases considerably with the number of binary variables, RL efficiently learns dynamic system behavior and scales to more complex systems without significant performance degradation. These results highlight that deep RL, when applied to DR, offers substantial cost savings and computational efficiency, with broad applicability to energy management in various applications.

• Suresh, Tejavath; Shah, Varsha A; Shukla, Akanksha & Kolhe, Mohan Lal (2024). Placement of EV Charging Stations in Reconfigured IEEE 33 Bus Distribution Network. In Deng, Mingcong (Eds.), Power, Energy and Electrical Engineering - Proceedings of the 14th International Conference (CPEEE 2024). IOS Press. ISSN 9781643685199. p. 269–279. doi: 10.3233/ATDE240327. Full text in Research Archive
• Jain, Mriga; Singh, Brajesh Kumar & Kolhe, Mohan Lal (2024). A Comprehensive Survey of Machine Learning Techniques for Brain Tumor Detection. In Tiwari, Shailesh; Trivedi, Munesh C.; Kolhe, Mohan Lal & Singh, Brajesh Kumar (Ed.), Advances in Data and Information Sciences - Proceedings of ICDIS 2023. Springer. ISSN 9789819969067. p. 309–319. doi: 10.1007/978-981-99-6906-7_27. Full text in Research Archive
• Mondal, Shubhajyoti; Bohre, Aashish Kumar; Kolhe, Mohan Lal & Thakkar, Jalpa (2024). Steady State and Short Circuit Analysis of Microgrid with Renewables and EVs Using ETAP. In Dhote, Nitin K; Kolhe, Mohan Lal & Rehman, Minhaj (Ed.), Recent Evolutions in Energy, Drives and e-Vehicles - Proceedings of REEDeV-2022. Springer. ISSN 9789819707638. p. 503–523. doi: 10.1007/978-981-97-0763-8_42. Full text in Research Archive
• Ferriday, Thomas Benjamin; Sampathkumar, Suhas Nuggehalli; Mensi, Mounir Driss; Middleton, Peter Hugh; Herle, Jan Van & Kolhe, Mohan Lal (2024). Tuning Stainless Steel Oxide Layers through Potential Cycling─AEM Water Electrolysis Free of Critical Raw Materials. ACS Applied Materials & Interfaces. ISSN 1944-8244. 16(23), p. 29963–29978. doi: 10.1021/acsami.4c01107. Full text in Research Archive
• Dash, Ritesh; K, Jyotheeswara Reddy; Fathima, Misba; L, Anupallavi; Mahapatro, Abinash & Kolhe, Mohan Lal (2024). Optimizing combined heat and power systems for multi-residential buildings: A multi-objective framework for sustainable energy management. Journal of Energy Storage. ISSN 2352-152X. 102(A). doi: 10.1016/j.est.2024.113972. Full text in Research Archive
• Pavon, Wilson; Chamorro, Michael; Michala, Ismael; Guffanti, Diego & Kolhe, Mohan Lal (2024). Optimizing Electrical Systems Stability: A Novel Lyapunov Framework for Harmonic Reduction and Power Quality Enhancement. In XX, XX (Eds.), 2024 3rd International Conference on Power Systems and Electrical Technology (PSET). IEEE Press. ISSN 9798350353204. p. 473–481. doi: 10.1109/PSET62496.2024.10808376. Full text in Research Archive
• Mallareddy, Mounica; Rajpathak, Bhooshan; Kolhe, Mohan Lal; Naik, K. Raghavendra; Moparthi, Janardhan Rao & Kotha, Sravan Kumar (2024). Piece-Wise Droop Controller for Enhanced Stability in DC-Microgrid-Based Electric Vehicle Fast Charging Station. Processes. 12(5). doi: 10.3390/pr12050892. Full text in Research Archive
• Shukla, Hiramani; Nandanwar, Sonali; Patidar, N. P.; Thakkar, Jalpa; Deepak, Deva Brinda & Kolhe, Mohan Lal (2024). Implications of Energy Storage Device in Efficient Frequency Control of Multi-Area Power System. In Malik, Om Parkash & Kolhe, Mohan Lal (Ed.), 2023 2nd International Conference on Clean Energy Storage and Power Engineering (CESPE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350395495. p. 139–144. doi: 10.1109/CESPE60923.2023.00035. Full text in Research Archive
• Vishnutheerth, E.P.; Vijay, Vivek; Satheesh, Rahul & Kolhe, Mohan Lal (2024). A Comprehensive Approach to Wind Power Forecasting Using Advanced Hybrid Neural Networks. IEEE Access. 12, p. 124790–124800. doi: 10.1109/ACCESS.2024.3450096. Full text in Research Archive
• Wohlgenannt, Philipp; Huber, Gerhard; Rheinberger, Klaus; Kolhe, Mohan Lal & Kepplinger, Peter (2024). Comparison of demand response strategies using active and passive thermal energy storage in a food processing plant. Energy Reports. 12, p. 226–236. doi: 10.1016/j.egyr.2024.06.022. Full text in Research Archive Show summary

  Demand side management holds potential for improving energy efficiency and cutting energy consumption within the food industry. This research introduces a demand response approach tailored for an industrial food processing facility, utilizing a chilled water buffer as active thermal energy storage and the plant building as passive thermal energy storage. The plant building and production process are modeled using transient thermal energy balances and the demand side management problem is formulated as a linear program. Model predictive control is employed to manage uncertainties in the optimization process. A simulated case study of an Austrian food processing plant shows reductions in electrical power consumption by up to 18%, electricity costs by up to 24%, and peak load by up to 36% in three distinct optimization scenarios. Simple prediction approaches via averaging historical data already lead to nearly optimal results concerning energy consumption and cost reduction. Highly accurate predictions are necessary for peak load reduction, as considering the simple prediction method only roughly a third of the potential reductions are achieved.

• Nishimura, Akira; Yamada, Souta; Ichii, Ryoma; Ichikawa, Mizuki; Hayakawa, Taisei & Kolhe, Mohan Lal (2024). Hydrogen Yield Enhancement in Biogas Dry Reforming with a Ni/Cr Catalyst: A Numerical Study. Energies. 17(21), p. 1–19. doi: 10.3390/en17215421. Full text in Research Archive Show summary

  This numerical study investigates the impact of the reaction temperature, molar ratio of CH4:CO2, and catalyst porosity (εp) on the H2 yield and H2 selectivity during biogas dry reforming over a Ni/Cr catalyst. Using COMSOL Multiphysics, we conducted detailed simulations to elucidate the underlying reaction characteristics. Our findings reveal that increasing εp from 0.1 to 0.95 significantly provides a 5 times increase in H2 production and a 2.3% increase in H2 selectivity while simultaneously reducing CO selectivity by 2.3%. This effect is attributed to the improved mass transfer within the catalyst bed, leading to more efficient reactant conversion and product formation. Additionally, we observed a strong correlation between higher reaction temperatures and increased H2 yield and H2 selectivity. By optimizing these operational parameters, our results suggest that Ni/Cr catalysts can be effectively employed for the sustainable production of H2 from biogas.

• Sharma, Pooja; Kolhe, Mohan Lal; Torjesen, Stina & Sharma, Arvind (2024). Analysing the Role of Energy Storage in Building Integrated Photovoltaic System: A Techno-Economic Analysis. In Kolhe, Mohan Lal & Liao, Qiangqiang (Ed.), Proceedings of the 9th International Symposium on Hydrogen Energy, Renewable Energy and Materials. Springer. ISSN 9789819736867. p. 67–81. doi: 10.1007/978-981-97-3686-7_6. Full text in Research Archive
• Thaliyadath, Deepti; Kaliyaperumal, Deepa & Kolhe, Mohan Lal (2024). Enhancing Transient Response in a DC-DC Converter for Electric Vehicle DC Fast Charging Applications Using Fractional-Order PI Control. Energies. 17(17). doi: 10.3390/en17174312. Full text in Research Archive
• Mallareddy, Mounica; Rajpathak, Bhooshan; Kolhe, Mohan Lal; Kotha, Sravan Kumar & Naik, K. Raghavendra (2024). Grid-Integrated Solar-Powered DC Fast Charging Station with 50 kW Fast Charger. In Kolhe, Mohan Lal & Liao, Qiangqiang (Ed.), Proceedings of the 9th International Symposium on Hydrogen Energy, Renewable Energy and Materials. Springer. ISSN 9789819736867. p. 83–92. doi: 10.1007/978-981-97-3686-7_7. Full text in Research Archive
• Das, Opy; Zafar, Muhammad Hamza; Sanfilippo, Filippo; Rudra, Souman & Kolhe, Mohan Lal (2024). Advancements in digital twin technology and machine learning for energy systems: A comprehensive review of applications in smart grids, renewable energy, and electric vehicle optimisation. Energy Conversion and Management: X. 24. doi: 10.1016/j.ecmx.2024.100715. Full text in Research Archive
• Ferriday, Thomas Benjamin; Sampathkumar, Suhas Nuggehalli; Middleton, Peter Hugh; Kolhe, Mohan Lal & Herle, Jan Van (2024). A Review of Membrane Electrode Assemblies for the Anion Exchange Membrane Water Electrolyser: Perspective on Activity and Stability. International Journal of Energy Research. ISSN 0363-907X. 2024(1). doi: 10.1155/2024/7856850. Full text in Research Archive
• Sagade, Atul A.; Kolhe, Mohan Lal; Jadhav, Lata; Kumar, Sachin & Tawfik, M.A. (2024). Editorial to the special issue (BEST) of Sustainable energy technologies and assessments. Sustainable Energy Technologies and Assessments. ISSN 2213-1388. doi: 10.1016/j.seta.2024.103765. Full text in Research Archive
• Jalan, Subham Kumar; Babu, B. Chitti & Kolhe, Mohan Lal (2024). High-Flexibility DC-Link Voltage Control for Grid-Tied Solar PV Systems: Maximizing Performance and Power Quality. IEEE transactions on consumer electronics. ISSN 0098-3063. 18(9). doi: 10.1109/TCE.2024.3479329. Full text in Research Archive
• Blas, Rubén Ronald LLiuyacc; Kolhe, Mohan Lal & Kepplinger, Peter (2024). Enhanced violation-mitigation-based method using voltage/current sensitivities for PV hosting capacity quantification in low-voltage grids. Electric power systems research. ISSN 0378-7796. 232, p. 1–11. doi: 10.1016/j.epsr.2024.110430. Full text in Research Archive Show summary

  PV hosting capacity quantification is currently of importance for distribution system operators to manage upcoming PV installation requests, especially within low-voltage grids. The violation-mitigation-based (VMB) method presented a novel downward approach to quantify the PV hosting capacity in low-voltage grids, having the advantage of also quantifying possible expansions in the hosting capacity. The VMB method, however, requires typically hundreds of power flow simulations to quantify the hosting capacity in a low-voltage grid. In this paper, we address this issue by introducing sensitivity matrices to optimize the downward process of quantification. Evaluated in 79 low-voltage feeders from Austria, the enhanced VMB method reduces the number of iterations required from a median of 1764 to 7 while obtaining the same or higher hosting capacity values. The reduced computational effort permits the application of the method proposed at a large-scale. The application of the method proposed on the standard CIGRE residential low-voltage grid is also presented for possible comparison with other methods.

• Ferriday, Thomas Benjamin; Middleton, Peter Hugh; Kolhe, Mohan Lal & Herle, Jan Van (2023). Raising the temperature on electrodes for anion exchange membrane electrolysis - activity and stability aspects. Chemical Engineering Journal Advances. 16. doi: 10.1016/j.ceja.2023.100525. Full text in Research Archive Show summary

  The membrane electrode assembly is the powerhouse of the anion exchange membrane water electrolyser (AEMWE), thereby placing a great importance on the associated preparation conditions. This paper investigated how annealing temperature and time impacted activity and stability for both anode and cathode electrodes with catalyst-PTFE thin-films. The effect of annealing was thoroughly characterised through SEM/EDS, TEM, XRD, Raman spectroscopy and XPS. Moderate heat-treatment (T≤500°C) had a positive effect by improving morphology and enhancing reaction kinetics as seen through three-electrode measurements. Annealing temperature affected hydrogen adsorption, resulting in a change in the hydrogen evolution pathway as shown by hydrogen adsorption peaks and Tafel curves. These beneficial effects were further augmented by an enlarged surface area as shown in both three- and two-electrode measurements. Two electrode measurements revealed a staircase activity-trend, where the annealing temperature yielding the greatest activity declined with increasing annealing time. This resulted in efficient cathodes annealed at 2h-500°C, 3h-400°C and 4h-300°C. The aforementioned cell configurations reached approximately 500 mA cm at 1.73 V, 1.82 V and 2.04 V respectively. Stable electrodes were produced for temperatures ≤500°C, after which their mechanical integrity began to fail due to pyrolysed PTFE. Stability was meticulously characterised and a degradation pathway for carbon catalysts was proposed, where expansion of the carbon black onion layers ultimately lead to catalyst particle detachment.

• Rizvi, Syed Sabir Hussain; Chaturvedi, Krishna Teerth & Kolhe, Mohan Lal (2023). A review on peak shaving techniques for smart grids. AIMS Energy. ISSN 2333-8326. 11(4), p. 723–752. doi: 10.3934/ENERGY.2023036. Full text in Research Archive Show summary

  Peak shaving techniques have become increasingly important for managing peak demand and improving the reliability, efficiency, and resilience of modern power systems. In this review paper, we examine different peak shaving strategies for smart grids, including battery energy storage systems, nuclear and battery storage power plants, hybrid energy storage systems, photovoltaic system installations, the real-time scheduling of household appliances, repurposed electric vehicle batteries, uni- and bi-directional electric vehicle charging, the demand response, the time-of-use pricing, load shedding systems, distributed generation systems and energy-efficient management. We analyze the potential of each strategy to reduce peak demand and shift energy consumption to off-peak hours, as well as identify the key themes critical to the success of peak shaving for smart grids, including effective coordination and communication, data analytics and predictive modeling and clear policy and regulatory frameworks. Our review highlights the diverse range of innovative technologies and techniques available to utilities and power system operators and it emphasizes the need for continued research and development in emerging areas such as blockchain technology and artificial intelligence. Overall, the implementation of peak shaving strategies represents a significant step toward a more sustainable, reliable and efficient power system. By leveraging the latest technologies and techniques available, utilities and power system operators can better manage peak demand, integrate renewable energy sources, and create a more reliable and secure grid for the future. By discussing cutting-edge technologies and methods to effectively manage peak demand and incorporate renewable energy sources, this review paper emphasizes the significance of peak shaving strategies for smart grids as a crucial pathway towards realizing a more sustainable, dependable and efficient power system.

• Ferriday, Thomas Benjamin; Sampathkumar, Suhas Nuggehalli; Middleton, Peter Hugh; Herle, Jan Van & Kolhe, Mohan Lal (2023). How Acid Washing Nickel Foam Substrates Improves the Efficiency of the Alkaline Hydrogen Evolution Reaction. Energies. 16(5). doi: 10.3390/en16052083. Full text in Research Archive Show summary

  Nickel foam substrates are frequently utilised as porous 3D substrates for renewable energyapplications. The preparation of these substrates usually includes an acid-washing step, but thedegree to which this step affects the final electrochemical performance after spray-coating a catalystink is unreported. Herein, we report the effects of acid washing through physicochemical andelectrochemical characterisation. The electrochemical performance was determined through repeatedmeasurements of catalyst-coated nickel foam substrates both with and without the initial step of acidwashing. It was found that acid washing increased the current density by 17.9% for the acid-treatedMoS2-coated nickel foam electrode. This increment was affiliated with an electrochemically activesurface area that increased by 11.2%, and a Tafel analysis indicated that the acid-treated MoS2-coatedelectrodes facilitated the initial water dissociation step of the hydrogen evolution reaction with greaterease. Similar effects were also discovered for acid-treated PtIr(1:3)/C-coated nickel foam substrates.The stability was also improved; the degradation rate was reduced by 18.9% for the acid-treatedMoS2-coated electrodes. This demonstrates the utility of acid washing nickel foam electrodes.

• Nayak, Suchismita; Bohre, Aashish Kumar; Kumar, Prince & Kolhe, Mohan Lal (2023). Modeling and Performance Analysis of an Electric Vehicle. In XX, XX (Eds.), 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON). IEEE Press. ISSN 9781665454995. doi: 10.1109/SMARTGENCON56628.2022.10084234. Full text in Research Archive Show summary

  As compared with fuel-powered vehicles, electric vehicles are more efficient in energy saving, emission reduction and environmental protection. This work present the modelling of Electric Vehicle (EV) and it’s performance analysis including battery charging and discharging conditions. Employing result, it is becoming most important with more applications in the transportation sector. The presented work for EV modeling is implemented using MATLAB/Simulink to study and analyze various parameters’ impacts on vehicle performances. Finally, the results analysis are presented based on vehicle modeling, driver input and battery charging and discharging behavior.

• Talwariya, Akash; Singh, Pushpendra; Jobanputra, Jalpa H. & Kolhe, Mohan Lal (2023). Machine learning based renewable energy generation and energy consumption forecasting. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. ISSN 1556-7036. 45(2), p. 3266–3278. doi: 10.1080/15567036.2023.2194257. Full text in Research Archive Show summary

  Renewable energy generation sources are the need of the hour to mitigate the challenges of fossil fuels, environmental impacts, large transmission and distribution losses, and variable consumption pattern. Renewable energy such as solar PV generation or wind generation is most unpredicted generation and depends upon weather constraints. A promising prediction of generation is a suitable solution for this problem. A machine learning-based conventional neural network algorithm is proposed to forecast the generation and calculate the errors in generation forecast. The generated energy is supplied to consumers, but the consumers consume electricity by their choice and an energy management model is required at the consumer end to supply the available predicted generation to develop a self-sustainable microgrid. The energy consumption at consumer end is essential to predict, and based on the predicted consumption at hourly bases, the cost model for the consumers is developed. The consumption profile is forecasted using long short-term memory as it is a good tool for time-series forecasting using available sequential load consumption data. The demand is forecasted and analyzed for selected load categories (i.e. fixed, non-shift-able, and shift-able loads). The evaluation matrix mean square error (MSE), mean absolute error, and root mean square error (RMSE) are used for the error calculations. The RMSE for solar and wind power generation forecasting have been 2.472 and 3.034, respectively. The RMSE for fixed, non-shiftable, and shiftable demand forecasting have been 0.027, 0.067, and 0.21, respectively. The results show that the proposed methods for predictions of solar, wind power generation and energy consumption forecast achieve better accuracy. The presented methodology can be implemented with energy storage for renewable energy production and demand management for energy from sustainable energy resources.

• Kaur, Paramjeet; Chaturvedi, Krishna Teerath & Kolhe, Mohan Lal (2023). Economic Dispatch of Combined Heat and Power Plant Units within Energy Network Integrated with Wind Power Plant. Processes. 11(4). doi: 10.3390/pr11041232. Full text in Research Archive
• Saw, Bikash Kumar; Bohre, Aashish Kumar; Thakkar, Jalpa & Kolhe, Mohan Lal (2023). Techno-Economic and Environmental Based Approach for Planning of SDG and DSTATCOM with Impact of Network Reconfiguration using APSO and TLBO. Distributed Generation & Alternative Energy Journal. ISSN 2156-3306. 38(5). doi: 10.13052/dgaej2156-3306.38510. Full text in Research Archive
• Sharma, Akshay; Nandanwar, Sonali; Patidar, N.P. & Kolhe, Mohan Lal (2023). Resilience Analysis Through Observation on Grid Parameters in Indian Grid During COVID-19 Pandemic. In xx, xx (Eds.), 2023 3rd International Conference on Energy, Power and Electrical Engineering. IEEE Press. ISSN 9798350318180. p. 995–998. doi: 10.1109/EPEE59859.2023.10351827. Full text in Research Archive
• Konara, Konara Mudiyanselage Sandun Y.; Kolhe, Mohan Lal; Ulltveit-Moe, Nils & Balapuwaduge, Indika A.M. (2023). Reliability Enhancement of Fast Charging Station under Electric Vehicle Supply Equipment Failures and Repairs. Energies. 16(6). doi: 10.3390/en16062933. Full text in Research Archive Show summary

  This work focuses on the enhancement of the charging reliability of both scheduled (SEVs) and opportunistic (UEVs) electric vehicle (EV) users in an EV fast charging station (FCS). The proposed charging coordination strategies allow UEVs to exploit unused charging resources to optimally utilize the limited charging resources of FCS. However, the optimum utilization of limited charging resources of an FCS while assuring a reliable charging process for plugged-in EVs under random failures of electric vehicle supply equipment (EVSE) is a real challenge for the FCS controller. When the FCS admits UEVs in addition to SEVs, assuring a satisfactory quality of service to both EV user categories is also dispensable. Therefore, we analyze the performance of reservation of off-board mobile chargers (MOBCs) to enhance the charging reliability of EV users while achieving high charging resource utilization. This work proposes resource allocation and charging coordination strategies for an FCS where MOBCs are used to enhance the charging reliability of both SEVs and UEVs. Moreover, the proposed dynamic charging resource coordination strategies are analyzed with a continuous time Markov-chain (CTMC). The presented results from the CTMC model demonstrate that the proposed strategies outperform the EV charging process of the FCS in terms of high resource utilization and reliability while guaranteeing a satisfactory quality of service to EV users.

• Konara, Konara Mudiyanselage Sandun Y.; Kolhe, Mohan Lal; Ulltveit-Moe, Nils & Balapuwaduge, Indika A.M. (2023). Optimal Utilization of Charging Resources of Fast Charging Station with Opportunistic Electric Vehicle Users. Batteries. 9(2). doi: 10.3390/batteries9020140. Full text in Research Archive Show summary

  The key challenge with the rapid proliferation of electric vehicles (EVs) is to optimally manage the available energy charging resources at EV fast-charging stations (FCSs). Furthermore, the rapid deployment of fast-charging stations provides a viable solution to the potential driving range anxiety and charging autonomy. Costly grid reinforcements due to extra load caused by fast charging can be omitted using a dedicated energy storage and/or renewable energy system at the FCS. The energy supply and fixed number of EV supply equipment (EVSE) are considered as the limited charging resources of FCS. Amidst various uncertainties associated with the EV charging process, how to optimally utilize limited charging resources with opportunistic ultra-fast charging EV users (UEVs) is studied in this work. This work proposes resource allocation and charging coordination strategies that facilitate UEVs to dynamically exploit these limited charging resources with defined liabilities when pre-scheduled users (SEVs) do not occupy them to utilize limited charging resources maximally. Moreover, the proposed dynamic charging coordination strategies are analyzed with a Monte Carlo simulation (MCS). The presented numerical results reveal that the major drawbacks of under-utilization of limited charging resources by SEVs can be significantly improved through dynamic charging resource allocation and coordination along with UEVs. With the proposed charging coordination strategies in this study, the maximum charging resource utilization of considered FCS with 10 EVSE has been improved to 90%, which bounds to 78% only with SEVs.

• Nishimura, Akira; Mishima, Daiki; Toyoda, Kyohei; Ito, S & Kolhe, Mohan Lal (2023). Numerical Simulation on Effect of Separator Thickness on Coupling Phenomena in Single Cell of PEFC under Higher Temperature Operation Condition at 363 K and 373 K. Energies. 16(2). doi: 10.3390/en16020606. Full text in Research Archive Show summary

  In hydrogen energy systems, the polymer electrolyte fuel cell (PEFC) is an important component. The purpose of this study is to clarify the effect of separator thickness (s.t.) in PEFC on the distributions of mass such as H2, O2, H2O and current density when PEFC is operated at 363 K and 373 K. The relative humidity (RH) of supply gases also impacts the operation. The numerical simulation (using a 3D model) with COMSOL Multiphysics has been conducted to analyze the characteristics of PEFC. It has been observed that the molar concentration of H2 using s.t. of 2.0 mm is smaller compared with the thinner s.t. cases at the initial operation temperature of a cell (Tini) = 363 K and 373 K. The molar concentration of O2 using s.t. of 2.0 mm is smaller compared with the thinner s.t. cases at Tini = 373 K, as well as the case for the RH of supply gases at the anode of 40%RH and cathode of 40%RH (A40%RH/C40%RH) irrespective of Tini. Additionally, it has been clarified that the molar concentration of H2O maintains a low value along with the gas channel at Tini = 373 K using s.t. of 1.5 mm and 1.0 mm. Moreover, it has been clarified that the current density using s.t. of 2.0 mm is the highest among the different s.t. irrespective of Tini, which is the most remarkable in the case of A40%RH&C40%RH.

• Ferriday, Thomas Benjamin; Middleton, Peter Hugh & Kolhe, Mohan Lal (2023). Determining the change in performance from replacing a separator with an anion exchange membrane for alkaline water electrolysis. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2454(012003). doi: 10.1088/1742-6596/2454/1/012003. Full text in Research Archive Show summary

  The emphasis on energy storage has caused renewed interest in alkaline water electrolysis (AWE), where the novel anion exchange membrane (AEM) has opened new pathways to further improve this mature technology. The comparison between the novel and the mature is most commonly performed on uneven grounds, as the ionic conductivity of the 30 wt.% (6.89 M) KOH electrolyte used in AWE is significantly greater than the 1.0 M employed in AEM water electrolysis. Through this paper, the performance of a zero-gap water electrolyser is systematically tested utilising either a separator or an AEM in a 1.0 M KOH electrolyte over several temperatures. Catalysed with only untreated nickel foam, the cell configuration with the AEM displayed predictably enough a notably lower series resistance and thereby a lower overpotential. However, the cell with the separator displayed better innate thermal stability, and showed stable results at 25°C, 40°C and 70°C. These findings exhibit the potential of additional R&D efforts in both separators and AEMs.

• Kaur, Paramjeet; Chaturvedi, Krishna Teerath & Kolhe, Mohan Lal (2023). Combined Heat and Power Economic Dispatching within Energy Network using Hybrid Metaheuristic Technique. Energies. 16(3). doi: 10.3390/en16031221. Full text in Research Archive Show summary

  Combined heat and power (CHP) plants have opportunities to work as distributed power generation for providing heat and power demand. Furthermore, CHP plants contribute effectively to overcoming the intermittence of renewable energy sources as well as load dynamics. CHP plants need optimal solution(s) for providing electrical and heat energy demand simultaneously within the smart network environment. CHP or cogeneration plant operations need appropriate techno-economic dispatching of combined heat and power with minimising produced energy cost. The interrelationship between heat and power development in a CHP unit, the valve point loading effect, and forbidden working regions of a thermal power plant make the CHP economic dispatch’s (CHPED) objective function discontinuous. It adds complexity in the CHPED optimisation process. The key objective of the CHPED is operating cost minimisation while meeting the desired power and heat demand. To optimise the dispatch operation, three different algorithms, like Jaya algorithm, Rao 3 algorithm, and hybrid CHPED algorithm (based on first two) are adopted containing different equality and inequality restrictions of generating units. The hybrid CHPED algorithm is developed by the authors, and it can handle all of the constraints. The success of the suggested algorithms is assessed on two test systems; 5-units and 24-unit power plants.

• Saw, BK; Bohre, Aashish Kumar; Jobanputra, Jalpa H. & Kolhe, Mohan Lal (2023). Solar-DG and DSTATCOM Concurrent Planning in Reconfigured Distribution System Using APSO and GWO-PSO Based on Novel Objective Function. Energies. 16(1). doi: 10.3390/en16010263. Full text in Research Archive Show summary

  The concurrent planning of multiple Distributed Generations (DGs), consisting of solar-DG and DSTATCOM with reconfiguration in IEEE 33 and 69 bus Radial Distribution Network (RDN), using Adaptive Particle Swarm Optimization (APSO) and hybrid Grey Wolf-Particle Swarm Optimization (GWO-PSO), is reported in this paper. For this planning, a novel multiple objective-based fitness-function (𝑀𝑂𝐹𝐹 ) is proposed based on various performance parameters of the system, such as power losses (both active, as well as reactive loss), system voltage profile, short circuit level of line current (SCLLCurrent), and system reliability. The economic perspective of the system has also been considered based on the various costs, such as fix, loss, and Energy Not Supplied (ENS) cost. Two case studies have been presented on IEEE 33 and 69 bus RDN to validate the efficacy of the proposed methodology. The results analysis of the system shows that better performance can be achieved with the proposed technique for 33 and 69 bus RDN, using GWO-PSO rather than APSO. From this results analysis, a vital point is noticed that the SCLLCurrent is reduced, which causes the short-circuit (fault) tolerance capacity (level) of the RDN to become enhanced. Finally, the comparative analysis of the obtained results, using the proposed method with other methods that exist in different literature, reveals that the proposed method has performed better from a techno-economic prospective.

• Mathew, Manuel Sathyajith; Kolhe, Mohan Lal; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2023). Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles. Journal of Cleaner Production. ISSN 0959-6526. 421. doi: 10.1016/j.jclepro.2023.138467. Full text in Research Archive Show summary

  With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

• Opalic, Sven Myrdahl; Palumbo, Fabrizio; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Kolhe, Mohan Lal (2023). COST-WINNERS: COST reduction WIth Neural NEtworks-based augmented Random Search for simultaneous thermal and electrical energy storage control. Journal of Energy Storage. ISSN 2352-152X. 72(B). doi: 10.1016/j.est.2023.108202. Full text in Research Archive Show summary

  The combination of local renewable energy production, dynamic loads, and multiple energy storage systems with different dynamics requires sophisticated control systems to maximize the energy cost efficiency of the combined energy system. Battery and thermal energy storage systems can be combined to increase the local use of on-site renewable energy, reduce peak power demand, and exploit time-of-use energy pricing. In this paper, we focus on how the augmented random search algorithm and artificial neural networks can be used together to solve an energy cost optimization problem involving the control of a battery energy storage system and a thermal energy storage system at the same time in a smart warehouse. As part of this work, a simulated training environment made using the data from the smart warehouse’s operations. In addition to the energy storage systems, the warehouse energy system has integrated a large roof mounted photovoltaic power plant and an industrial-scale cooling system. The developed solution is able to minimize the energy costs by modulating both energy systems, depending on the situation. Additionally, when it is tested against the state-of-the-art solutions, our developed solution at worst matches performance when the alternative algorithm is allowed to increase training time by a factor of nearly three. On average, our presented solution doubles the performance of the benchmark algorithm with much less computational resource expenditure.

• Nishimura, Akira; Hayashi, Yuki; Ito, Syogo & Kolhe, Mohan Lal (2023). Performance Analysis of Hydrogen Production for a Solid Oxide Fuel Cell System Using a Biogas Dry Reforming Membrane Reactor with Ni and Ni/Cr Catalysts. Fuels. 4(3), p. 295–313. doi: 10.3390/fuels4030019. Full text in Research Archive
• Johannesen, Nils Jakob; Kolhe, Mohan Lal & Jacobsen, Andreas Dolven (2023). Correlation Analysis of Potential Solar Photovoltaic Power Plant Integration at Wind Farm with Grid Connection Limits. In XX, XX (Eds.), 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE Press. ISSN 9798350323207. p. 1–6. doi: 10.23919/SpliTech58164.2023.10193149. Full text in Research Archive Show summary

  This work provides investigation on time variability and correlation analysis of a Norwegian wind power plant, which is going to be integrated with solar photovoltaic power plant. This design study of Norwegian Smøla Wind Power Plant is investigating the time variability and correlation analysis. The real operational data of wind power plants and simulated solar photovoltaic power plant data will be analyzed further considering connected power line capacities for investigating the photovoltaic hosting capacity, curtailment and potential for identifying increased capacity of the connected power line. This work will be useful for doing the correlation analysis for solar photovoltaic with wind power plants connected to the grid through common power line, and finding appropriate energy storage. The presented results will be very useful for power system operator for balancing the intermittency of power outputs through dispatchable power generation sources. The typical scenarios are investigated for utilizing the maximum capacity of the common power lines and to minimize the potential curtailments of the renewable energy sources.

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• Lokulwar, Prasad; Verma, Basant Kumar; Sehgal, Mitu; Kumar, Kailash & Kolhe, Mohan Lal (2026). Precision Medicine: Improving Healthcare with Data Science and Machine Learning. Bentham Science Publishers. ISBN 9798898814786. doi: 10.2174/97988988147791260101.
• Bhowmik, Partha Sarathee; Bohre, Aashish Kumar; Kirar, Mukesh Kumar; Kolhe, Mohan Lal & Suwarno, Suwarno (2025). Application of Advance Techniques in Power and Energy Systems - Select Proceedings of International Conference, CISTEE 2023. Springer. ISBN 9789819604760. 470 p. Full text in Research Archive
• Tiwari, Shailesh; Trivedi, Munesh C.; Kolhe, Mohan Lal & Singh, Brajesh Kumar (2025). Advances in Data and Information Sciences - Proceedings of ICDIS 2024, Volume 2. Springer. ISBN 9789819796199. 402 p. Full text in Research Archive
• Bohre, Aashish Kumar; Bhowmik, Partha Sarathee; Dubey, Manisha; Kolhe, Mohan Lal & Singh, Sri Niwas (2025). Application of Smart Technologies in Power System and Smart Grid - Select Proceedings of International Conference, CISTEE 2023. Springer. ISBN 9789819799169. 534 p. Full text in Research Archive
• Kolhe, Mohan Lal & Liao, Qiangqiang (2025). Proceedings of the 10th International Symposium on Hydrogen Energy, Renewable Energy and Materials. Springer. ISBN 9789819632725. 231 p. Full text in Research Archive
• Suwarno, Suwarno; Aziz, Muhammad; Kolhe, Mohan Lal & Yuwono, Tri Yogi (2025). Preface. Springer. ISBN 9789819781997. 541 p. Full text in Research Archive
• Tiwari, Shailesh; Trivedi, Munesh C.; Kolhe, Mohan Lal & Singh, Brajesh Kumar (2025). Advances in Data and Information Sciences - Proceedings of ICDIS 2024, Volume 1. Springer. ISBN 9789819773602. 439 p. Full text in Research Archive
• Kolhe, Mohan Lal (2024). Smart Grid and Renewable Energy Systems - Proceedings of 14th International Conference on Renewable and Clean Energy (ICRCE2024). Springer. ISBN 9789819757824. 103 p. Full text in Research Archive
• Dhote, Nitin K; Kolhe, Mohan Lal & Rehman, Minhaj (2024). Recent Evolutions in Energy, Drives and e-Vehicles - Proceedings of REEDeV-2022. Springer. ISBN 9789819707638. 689 p. Full text in Research Archive
• Tiwari, Shailesh; Trivedi, Munesh C.; Kolhe, Mohan Lal & Singh, Brajesh Kumar (2024). Advances in Data and Information Sciences - Proceedings of ICDIS 2023. Springer. ISBN 9789819969067. 547 p. doi: 10.1007/978-981-99-6906-7. Full text in Research Archive
• Malik, Om Parkash & Kolhe, Mohan Lal (2024). 2023 2nd International Conference on Clean Energy Storage and Power Engineering (CESPE). IEEE (Institute of Electrical and Electronics Engineers). ISBN 9798350395495. 213 p. Full text in Research Archive
• Kolhe, Mohan Lal & Liao, Qiangqiang (2024). Proceedings of the 9th International Symposium on Hydrogen Energy, Renewable Energy and Materials. Springer. ISBN 9789819736867. 312 p. Full text in Research Archive
• Kolhe, Mohan Lal; Bevrani, Hassan & Chen, Zhe (2023). 2023 6th International Conference on Electrical Engineering and Green Energy (CEEGE). IEEE Press. ISBN 9798350312669. 301 p. Full text in Research Archive
• Kolhe, Mohan Lal (2023). Renewable Energy Systems and Sources Proceedings of International Conference on Renewable and Clean Energy (ICRCE) 2023. Springer. ISBN 9789819962891. 138 p. Full text in Research Archive

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• Kolhe, Mohan Lal & Zhang, Ronghui (2025). Letter from the Guest Editors - Focus Issue on Advances in Energy-Efficient Transportation Technologies and Systems. SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy. ISSN 2640-642X. doi: 10.4271/13-06-01-0001. Full text in Research Archive
• Kolhe, Mohan Lal (2025). Advancing Sustainable Electrical Energy Technologies: A Multifaceted Approach Towards SDG Achievement. Processes. 13(1). doi: 10.3390/pr13010210. Full text in Research Archive
• Kolhe, Mohan Lal (2024). Editorial for special issue of Science and Technology for Energy Transition (ISSN: 2804-7699) collection of research articles selected from the 7th International Symposium on Hydrogen Energy, Renewable Energy, and Materials (HEREM) 2021: A multifaceted approach to energy transition: Technological innovations. Science and Technology for Energy Transition (STET). 80. doi: 10.2516/stet/2025001. Full text in Research Archive
• Li, Licheng; Zhou, Xiaoxin; Liu, Huanbin; Cai, Wei; Min, Yonggang & Chen, Jianping [Show all 61 contributors for this article] (2024). Preface. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2703(1). doi: 10.1088/1742-6596/2703/1/011001. Full text in Research Archive
• Kolhe, Mohan Lal (2023). Integrated Renewable Energy System based on Hydrogen. Full text in Research Archive
• Zhang, Ronghui & Kolhe, Mohan Lal (2023). Letter from the Special Issue Editors 13-04-02-0009 SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy V132-13EJ. SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy. ISSN 2640-642X. 4(2), p. 159–160. doi: 10.4271/13-04-02-0009. Full text in Research Archive
• Middleton, Peter Hugh; Kolhe, Mohan Lal; Herle, Jan Van & Ferriday, Thomas Benjamin (2024). Membrane electrode assemblies for anion exchange membrane water electrolysers. Universitetet i Agder. ISSN 9788284271965. Full text in Research Archive
• Konara, Konara Mudiyanselage Sandun Y. & Kolhe, Mohan Lal (2023). Enhancement of Charging Resource Utilization of Electric Vehicle Fast Charging Station with Heterogeneous EV Users. Universitetet i Agder. ISSN 9788284271316. Full text in Research Archive Show summary

  This thesis presents innovative charging resource allocation and coordination strategies that maximize the limited charging resources at FCS with heterogeneous EV users. It allows opportunistic EV users (OEVs) to exploit available charging resources with dynamic event-driven charging resource allocation and coordination strategies apart from primary EV users (PEVs) (registered or scheduled EV users). Moreover, developed strategies focus on the limited charging resources that are allocated for primary/ registered EV users (PEVs) of the FCS who access the FCS with specific privileges according to prior agreements. But the available resources are not optimally utilized due to various uncertainties associated with the EV charging process such as EV mobility-related uncertainties, EVSE failures, energy price uncertainties, etc. Developed strategies consider that idle chargers and vacant space for EVs at the FCS is an opportunity for further utilizing them with OEVs using innovative charging resource coordination strategies. This thesis develops an FCS-centric performance assessment framework that evaluates the performance of developed strategies in terms of charging resource utilization, charging completion and the quality of service (QoS) aspects of EV users. To evaluate QoS of EV charging process, various parameters such as EV blockage, charging process preemptage, mean waiting time, mean charging time, availability of FCS, charging reliability, etc are derived and analyzed. In addition, the developed innovative charging resource allocation and coordination strategies with resource aggregation and demand elasticity further enhance the charging resource utilization while providing a high QoS in EV charging for both PEVs and OEVs.

• Kolhe, Mohan Lal; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Opalic, Sven Myrdahl (2023). Advanced Warehouse Energy Storage System Control Using Deep Supervised- and Reinforcement Learning. Universitetet i Agder. ISSN 9788284271521. Full text in Research Archive

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