Mohan Lal Kolhe

Professor

Department of Engineering Sciences

Email: mohan.l.kolhe@uia.no
Phone: +47 37233293
Mobile phone: +4793414532

Office H4031 (Jon Lilletuns vei 9, 4879 Grimstad, Norway)

Research groups

Areas of responsibility

Prof. Dr. Mohan Lal Kolhe is a full professor in smart grid and renewable energy at the Faculty of Engineering and Science of the University of Agder (Norway). He is a leading renewable energy technologist with three decades of academic experience at the international level and previously held academic positions at the world's prestigious universities, e.g., University College London (UK / Australia), University of Dundee (UK); University of Jyvaskyla (Finland); Hydrogen Research Institute, QC (Canada); etc. In addition, he was a member of the Government of South Australia’s first Renewable Energy Board (2009-2011) and worked on developing renewable energy policies.

Professor Kolhe is an expert evaluator of many prestigious international research councils (e.g., European Commission: Erasmus+ Higher Education – International Capacity Building, Royal Society London (UK), Engineering and Physical Sciences Research Council (EPSRC UK), Cyprus Research Foundation, etc.). In addition, many international organizations have invited him to deliver keynote addresses, expert lectures, workshops, etc. He has also been a member of many academic promotional committees.

Professor Kolhe has been successful in obtaining competitive research funding from prestigious research bodies (such as the Norwegian Research Council, EU, EPSRC, BBSRC, NRP, etc.) for his work on sustainable energy systems. His work on energy systems and electrical & electronic engineering has been recognised in the top 2% of scientists worldwide consistently from 2020 to 2023, according to Stanford University matrices based on Elsevier data. His top 10 publications have received an average of more than 200 citations each, making him an acknowledged pioneer in his profession on a global scale.

Research

Academic interests

Sustainable Electrical Energy Systems
Smart Grid and Grid Integration of Renewable Energy Systems
Electric Vehicles
Hydrogen Energy Technlogies
Solar Photovoltaic and Wind Energy Systems Engineering
Electro-Mechanical-Chemical Energy Conversion and Managment
Power System Dynamics, Electrical Machines, Energy Conversion and Management
Techno-Economics of Energy Systems

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. ISSN 2352-4847. 12, p. 226–236. doi: 10.1016/j.egyr.2024.06.022.
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. ISSN 2352-4847. 12, p. 226–236. doi: 10.1016/j.egyr.2024.06.022.
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 979-8-3503-9549-5. p. 139–144. doi: 10.1109/CESPE60923.2023.00035.
Ferriday, Thomas Benjamin; Sampathkumar, Suhas Nuggehalli; Mensi, Mounir Driss; Middleton, Peter Hugh; Van Herle, Jan & 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.
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. ISSN 2666-7908. 20. doi: 10.1016/j.clet.2024.100757.
Asha Rani, G. S.; Lal Priya, P. S.; Jayan, Jino; Satheesh, Rahul & Kolhe, Mohan Lal (2024). Data-Driven Energy Management of an Electric Vehicle Charging Station Using Deep Reinforcement Learning. IEEE Access. ISSN 2169-3536. 12, p. 65956–65966. doi: 10.1109/ACCESS.2024.3398059.
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. ISSN 2227-9717. 12(5). doi: 10.3390/pr12050892.
Lliuyacc-Blas, Ruben; 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(110430), p. 1–11. doi: 10.1016/j.epsr.2024.110430.
Ferriday, Thomas Benjamin; Sampathkumar, Suhas Nuggehalli; Middleton, Peter Hugh; Kolhe, Mohan Lal & Van Herle, Jan (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.
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.
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. ISSN 2699-9412. 5(5), p. 1–18. doi: 10.1002/aesr.202300273.
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.
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.
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 979-83-503-2320-7. p. 1–6. doi: 10.23919/SpliTech58164.2023.10193149. 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.
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. ISSN 2673-3994. 4(3), p. 295–313. doi: 10.3390/fuels4030019.
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.
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.
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. 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.
Nandanwar, Sonali; Patidar, Narayan P; Brinda, Deva M & Kolhe, Mohan Lal (2023). Real-time computing of power flows and node voltages in electrical energy network using decision trees. Cleaner Engineering and Technology. ISSN 2666-7908. 15. doi: 10.1016/j.clet.2023.100654. Full text in Research Archive
Ferriday, Thomas Benjamin; Middleton, Peter Hugh; Kolhe, Mohan Lal & Van Herle, Jan (2023). Raising the temperature on electrodes for anion exchange membrane electrolysis - activity and stability aspects. Chemical Engineering Journal Advances. ISSN 2666-8211. 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.
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 978-1-6654-5499-5. doi: 10.1109/SMARTGENCON56628.2022.10084234. 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.
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. ISSN 2227-9717. 11(4). doi: 10.3390/pr11041232.
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. 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.
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. ISSN 1996-1073. 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.
Johannesen, Nils Jakob; Kolhe, Mohan Lal & Goodwin, Morten (2023). Vertical Approach Anomaly Detection Using Local Outlier Factor. In Alhelou, Hassan Haes; Hatziargyriou, Nikos & Dong, Zhao Yang (Ed.), Power Systems Cybersecurity Methods, Concepts, and Best Practices. Springer. ISSN 978-3-031-20360-2. p. 297–310. doi: 10.1007/978-3-031-20360-2_12. Full text in Research Archive Show summary
Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. In addition anomaly detection can be used as a tool to detect unwanted outliers, caused by operational failures and technical faults, for the pre-processing of data for machine learning, to detect concept drift as well as enhancing cyber-security in smart electrical grid operations. It is known that anomalies are defined through their contextual appearance. Hence, anomalies are divided into point, conceptual and contextual anomalies. In this work the contextual anomaly detection is examined, through a novel type of load forecasting known as vertical approach. This chapter explores the use of anomaly detection in the relevant learning systems for machine learning in smart electrical grid operation and management through data from New South Wales region in Australia. The presented vertical time approach uses seasonal data for training and inference, as opposed to continuous time approach that utilizes all data in a continuum from the start of the dataset until the time used for inference. It is observed that Local Outlier Factor identifies different local outliers given different vertical approaches. In addition, the local outlier factor score vary vertically. An anomaly is defined as a deviation from an established normal pattern. Spotting an anomaly depends on the ability to defy what is normal. Anomaly detection systems aim at finding these anomalies. Anomaly detection systems are in high demand, despite the fact that there is no clear validation approach. These systems rely on deep domain expertise.
Daniel, Leena; Chaturvedi, Krishna Teerath & Kolhe, Mohan Lal (2023). Genetic Algorithm for Economic Load Dispatch with Microgrid to Save Environment by Reduction of CO2 Emission. In Khosla, Anita & Kolhe, Mohan Lal (Ed.), Renewable Energy Optimization, Planning and Control Proceedings of ICRTE 2022. Springer. ISSN 978-981-19-8963-6. p. 29–39. doi: 10.1007/978-981-19-8963-6_3. Show summary
The continuous reduction in fossil fuel resources, the Distributed Generation Technologies have recently fascinated more attention. Microgrid technologies are also employed to join such sources into the main network by pointedly enhancing energy utilization through local production and load control. As a result, quality and reliability have improved. Most of such network studies focus on operating and investment expenses but ignore the environmental impact. An optimization model is developed based on these two criteria to estimate the feasibility and environmental involvement of microgrid. Renewable energy sources have a high penetration rate in this model. The genetic algorithm is utilized to perform hourly optimizations on microgrid in order to achieve environmental benefits as well as financial gains.
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. ISSN 1996-1073. 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.
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. ISSN 1996-1073. 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.
Kaur, Paramjeet; Chaturvedi, Krishna Teerath & Kolhe, Mohan Lal (2023). Combined Heat and Power Economic Dispatching within Energy Network using Hybrid Metaheuristic Technique. Energies. ISSN 1996-1073. 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.
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. ISSN 2313-0105. 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.
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. 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.
Ferriday, Thomas Benjamin; Nuggehalli Sampathkumar, Suhas; Middleton, Peter Hugh; Van Herle, Jan & Kolhe, Mohan Lal (2023). How Acid Washing Nickel Foam Substrates Improves the Efficiency of the Alkaline Hydrogen Evolution Reaction. Energies. ISSN 1996-1073. 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.
Johannesen, Nils Jakob; Kolhe, Mohan Lal & Goodwin, Morten (2022). Recurrent neural networks for electrical load forecasting to use in demand response. In Alhelou, Hassan Haes; Moreno-Muñoz, Antonio & Siano, Pierluigi (Ed.), Industrial Demand Response: Methods, best practices, case studies, and applications. IET Digital Library. ISSN 9781839535628. p. 41–58. doi: 10.1049/PBPO215E_ch3. Show summary
Electric load forecasting is a fundamental technique to understand end-user behavior and therefore a crucial factor in the design of demand response (DR) programs. Load forecasting will also identify the appropriate design of DR programs. In this chapter, a range of different machine learning applications are studied to represent the influential factors for electrical load demand forecast in a DR context, with a variety of different data scenarios, temporal and technical scenario. This chapter explores and compares the load prediction analysis through basic recurrent neural networks (RNNs); Vanilla RNN, gated recurrent units (GRU), and long short-term memory (LSTM), using principal component analysis (PCA). It is found that PCA can be used to reduce the number of principal components for Vanilla RNN, GRU, and LSTM networks. Reducing the number of principal components using PCA is one of the techniques that is used in dimensionality reduction. Reduction in dimensionality will relieve the computational burden. In this work, the dimensionality reduction improves the predictive output. It is observed that for electric load demand forecasting, the preferred technique is GRU, trained with a principal component. The performance is evaluated through mean absolute percentage error (MAPE), which is relatively lower than other techniques.
Singh, Aman; Dev, Maha; Singh, Brajesh K.; Kumar, Anuj & Kolhe, Mohan Lal (2022). A Review of Content-Based Image Retrieval Based on Hybrid Feature Extraction Techniques. In Tiwari, Shailesh; Trivedi, Munesh C.; Kolhe, Mohan Lal & Singh, Brajesh K. (Ed.), Advances in Data and Information Sciences Proceedings of ICDIS 2022. Springer. ISSN 978-981-19-5292-0. p. 303–313. doi: 10.1007/978-981-19-5292-0_28. Show summary
Reviewing the literature is important to gain knowledge of a particular field before undertaking any research. In today's world, images are ever-evolving, making it difficult to get a relevant image. CBIR is used to search any image in the stock image. It extracts features from input images and retrieves images from datasets with similar features. Many techniques have been developed for CBIR, some of which are illustrated in this survey. A multimedia content examination is utilized in an assortment of real-world computer vision applications, and digital images make up a critical piece of multimedia data. Observing a reasonable image in a file is a troublesome research challenge for the computer vision field. Most web crawlers bring pictures utilizing ordinary text-put-together strategies that depend on respect to subtitles and metadata. CBIR and image arrangement-based models depict undeniable level image sees utilizing feature vectors made out of mathematical qualities. The research exhibits that an impressive separation exists between image feature portrayal and human visual understanding. Different strategies have been utilized by different authors like convolutional neural network, deep learning, multi-scale LTP, DL-TLCF, DBN, etc., with great precision on publically available datasets like ImageNet, Caltech256, Cifar10, IRMA, UK Bench, ILSVRC-2010, and ILSVRC-2012. In this paper, competitively study of deep learning model-based CBIR framework with general query processing module which can handle (text, video, and image query). Similar analyses in view of strategies, model, dataset, and precision have been performed, and they accomplish great exactness also.
Dassanayake, D. Janith Kavindu; Dilhani, M.H.M.R.S.; Konara, Konara Mudiyanselage Sandun Y. & Kolhe, Mohan Lal (2022). Day-Ahead Solar Power Forecasting Using Artificial Neural Network with Outlier Detection. In Kolhe, Mohan Lal; Karande, Kailash J. & Deshmukh, Sampat G. (Ed.), Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications. CRC Press. ISSN 9781032115023. p. 51–63. doi: 10.1201/9781003220176-4. Show summary
Photovoltaic (PV) energy is a highly intermittent renewable energy source. Due to this intermittent nature, when integrating the PV system with a power system, the reliability and the stability of the power system will decrease. Therefore, predicting the PV generation will help to maintain the power system in a stable way without any uncontrollable and unanticipated variations. Thus, the task of solar power forecasting becomes vital. Furthermore, during the operation of the PV system, a considerable number of outliers will occur due to array anomalies and faults, sensor failure and irregular array shutdowns. These outliers will cause the accuracy of the forecasting model to decrease. Therefore, data pre-processing should be done before modelling the forecast model. This chapter presents interpolation and exponential smoothing as data pre-processing techniques, and an artificial neural network (ANN) is trained to create a day-ahead solar forecasting model.
Naik, R; Rajpathak, Bhooshan; Mitra, Arghya & Kolhe, Mohan Lal (2022). Renewable Energy Integrated DC Microgrid for EV Charging Station. In XX, XX (Eds.), 2021 IEEE Transportation Electrification Conference (ITEC-India 2021). IEEE Press. ISSN 9781665421478. doi: 10.1109/ITEC-India53713.2021.9932500. Show summary
Renewable energy based DC microgrid (MG) can be used for EV fast charging stations. Integration of EV charging station to DC MG is a reliable solution to cope up with the rapid increasing load demand of EVs. But, due to intermittent nature of solar PV and EV load the power management of such MGs is going to be stimulant. Developed countries like Norway are operating small hydro generator (SHG) -solar PV-battery storage system (BSS) based DC MGs to solve the power management issues of EV charging stations. But, the power flow oscillations with rapid solar PV dynamics during peak EV load demand cannot be compensated exhaustively due to slow operating time of SHG and C-rate restraint of BSS. Despite these constraints, a smart energy management strategy (SEMS) is proposed in this work to address power management objective. By predicting the EV load dynamics on iteration basis, the proposed SEMS is going to operate the SHG against its slow operating time and ensures the sustainable power flow control of EV charging station interfaced DC MG. Moreover, the configured DC MG model with SEMS is validated in OPAL-RT real time simulator.
Konara, Konara Mudiyanselage Sandun Y. & Kolhe, Mohan Lal (2022). Charging Coordination of Opportunistic EV Users at Fast Charging Station with Adaptive Charging. In XX, XX (Eds.), 2021 IEEE Transportation Electrification Conference (ITEC-India 2021). IEEE Press. ISSN 9781665421478. doi: 10.1109/ITEC-India53713.2021.9932507. Show summary
Rapid deployment of fast-charging stations (FCSs) with innovative charging coordination strategies can alleviate the potential driving range anxiety and long charging periods that make sever bottlenecks for the proliferation of Electric Vehicles (EVs). Therefore, this work proposes dynamic resource allocation and charging coordination strategies to exploit unused charging resources by registered slow charging users (SCUs) for charging opportunistic fast charging users (FCUs) using adaptive charging. This work analyzes strategies that maximize the charging resource utilization and charging completion while adhering all the constraints enforced by the utility grid. In this work, we use a queue to freeze the charging process of SCUs for attracting more FCUs till SCUs become critical. The proposed techniques are going to be useful for maximizing the overall profit of the FCS.
Talwariya, Akash; Singh, Pushpendra; Brinda, Deva M; Jobanputra, Jalpa H. & Kolhe, Mohan Lal (2022). Domestic Energy Consumption Forecasting using Machine Learning, 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE conference proceedings. ISSN 978-1-6654-8828-0. doi: 10.23919/SpliTech55088.2022.9854296. Show summary
Energy management is essential maintain the demand at consumer end, reduce the wastage of electricity during off peak hours, environmental constrains, and large transmission and distribution losses. Consumers enabled with renewable energy sources and electric vehicles are the key for the intelligent and green grid. Renewable energy sources installed near load centres to manage the demand and electric vehicles are used as backup source of energy and supply to grid during peak hours. Demand of consumers is random and to maintain the demand with renewable energy sources and electric vehicles, connected appliances at consumer end are categorized as shiftable, non-shiftable and curatable appliances. To manage the demand for a reliable grid, energy consumption prediction is essential at consumer end for the demand. To. forecast the demand a long short term memory algorithm is used and evaluate the error's obtained in the forecasting. In future this model can integrate with game theory for energy bidding.
Sarkar, Rishiraj; Bohre, Aashish Kumar; Bera, Tushar Kanti; Bhowmik, Partha Sarathee; Kolhe, Mohan Lal & Hassanabad, Armin Hassanzadeh (2022). Effect of Shading on System Parameters and CO2 Emission of grid-connected PV System. In XX, XX (Eds.), 2022 IEEE 10th Power India International Conference (PIICON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-5930-3. doi: 10.1109/PIICON56320.2022.10045311.
Gill, Amandeep; Singh, Pushpendra; Jobanputra, Jalpa H. & Kolhe, Mohan Lal (2022). Placement analysis of combined renewable and conventional distributed energy resources within a radial distribution network. AIMS Energy. ISSN 2333-8326. 10(6), p. 1216–1229. doi: 10.3934/ENERGY.2022057. Full text in Research Archive Show summary
System islanding, relay tripping, and reverse power flow-like issues in the distribution network are all caused by randomly placed distributed energy resources. To minimize such problems, distributed energy resource (DER) optimal placement in the radial distribution network (RDN) is essential to reduce power loss and enhance the voltage profile. When placing DERs, consideration of constraints like size, location, number, type, and power factor (PF) should be considered. For optimal placement, renewable and nonrenewable DERs are considered. The effects of different types and PFs of DER placements have been tested on the IEEE 33 bus RDN to satisfy all limitations. Using various intelligent techniques, distributed energy resource units of optimal type, PF, size, quantity, and position were placed in the IEEE 33 bus RDN. These intelligent strategies for minimizing power loss, enhancing the voltage profile, and increasing the convergence rate are based on an adaptive neuro-fuzzy inference system, a genetic algorithm, and enhanced particle swarm optimization.
Talwariya, Akash; Singh, Pushpendra & Kolhe, Mohan Lal (2022). A Stackelberg Game Theory Based Demand Response Algorithm for Domestic Consumers. Electric power components and systems. ISSN 1532-5008. doi: 10.1080/15325008.2022.2150914. Show summary
A stackelberg game algorithm is proposed to optimize real time demand response equilibrium between consumers and utilities with the integration of renewable energy sources. Stackelberg game is played between one leader and N followers to provide interaction between utility and consumers in the presence of energy management center. The objective of consumers is to minimize their energy tariffs by scheduling their appliances. Appliances are categorized as non-shiftable, shiftable and curtailable appliances based on their applications. The objective of the utility is to maximize its profit by managing peak to average energy consumption ratio, penetration of renewable energy sources as a primary source and during peak hours, integrate electric vehicles as a backup. The existence of a unique stackelberg equilibrium that provides an optimal energy consumption scheme to each appliance at the consumer end was demonstrated. The simulation results showed the optimal real time demand response using stackelberg game algorithm is effective for achieving the optimal load control.
Solanki, Surendra; Dehalwar, Vasudev; Choudhary, Jaytrilok; Kolhe, Mohan Lal & Ogura, Koki (2022). Spectrum Sensing in Cognitive Radio Using CNN-RNN and Transfer Learning. IEEE Access. ISSN 2169-3536. 10, p. 113482–113492. doi: 10.1109/ACCESS.2022.3216877. Full text in Research Archive Show summary
Cognitive radio has been proposed to improve spectrum utilization in wireless communication. Spectrum sensing is an essential component of cognitive radio. The traditional methods of spectrum sensing are based on feature extraction of a received signal at a given point. The development in artificial intelligence and deep learning have given an opportunity to improve the accuracy of spectrum sensing by using cooperative spectrum sensing and analyzing the radio scene. This research proposed a hybrid model of convolution and recurrent neural network for spectrum sensing. The research further enhances the accuracy of sensing for low SNR signals through transfer learning. The results of modelling show improvement in spectrum sensing using CNN-RNN compared to other models studied in this field. The complexity of an algorithm is analyzed to show an improvement in the performance of the algorithm.
Opalic, Sven Myrdahl; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Kolhe, Mohan Lal (2022). Augmented Random Search with Artificial Neural Networks for energy cost optimization with battery control. Journal of Cleaner Production. ISSN 0959-6526. 380(2). doi: 10.1016/j.jclepro.2022.134676.
Wohlgenannt, Philipp; Preissinger, Markus; Kolhe, Mohan Lal & Kepplinger, Peter (2022). Demand Side Management of a Battery-Supported Manufacturing Process with On-Site Generation, 2022 IEEE 7th International Energy Conference (ENERGYCON). IEEE conference proceedings. ISSN 978-1-6654-7982-0. doi: 10.1109/ENERGYCON53164.2022.9830336. Show summary
Due to the global competition in manufacturing, flexibility to provide for individually customized products is considered an important selling point. Constantly changing manufacturing processes face higher production costs than well known reoccurring schedules. To lower these costs in general, we propose a model predictive control concept to reduce manufacturing energy costs in particular, using an existing digital twin to estimate the load of the different manufacturing steps. Based on a mixed integer linear programming formulation of the batterysupported manufacturing process, the system makes optimum use of the on-site photovoltaic generation by production scheduling and adaptive battery control. A simulation study considering a time of use and a real-time pricing scenario provides a proof of concept.
Jawale, Sayali Ashok; Singh, Sanjay Kumar; Singh, Pushpendra & Kolhe, Mohan Lal (2022). Priority Wise Electric Vehicle Charging for Grid Load Minimization. Processes. ISSN 2227-9717. 10(9). doi: 10.3390/pr10091898.
Mathew, Manuel Sathyajith & Kolhe, Mohan Lal (2022). Performance Modelling of Renewable Energy Systems Using kNN Algorithm for Smart Grid Applications, 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE conference proceedings. ISSN 978-1-6654-8828-0. doi: 10.23919/SpliTech55088.2022.9854321. Show summary
Share of energy from renewable source like solar and wind in the electric grids are steadily increasing in the recent years. Understanding the performance of power systems based on these stochastic energy sources, under various operating environments, is essential for the management of renewable integrated smart grids. In this paper, we propose such performance models for wind and solar energy systems, based on k Nearest Neighbor (kNN) algorithm. Operational data from a 2.3 MW wind turbine were used for the wind power model whereas the solar PV performance was modelled based on the data from a 800 kWp monocrystalline solar PV system. The data were cleaned for possible outliers and noises using Z score and DBSCAN clustering method. Model features were identified based on the Pearson and Spearman Correlations. The kNN models for the solar and wind power systems were then developed with optimal k values of 30 and 28 respectively. Performance of the models were evaluated using different error matrices. With RMSEs of 0.03 and 0.06 respectively for the wind and solar performance models, the proposed method could accurately quantify the generation from these systems under varying operating environments.
Johannesen, Nils Jakob; Kolhe, Mohan Lal & Goodwin, Morten (2022). Evaluating Anomaly Detection Algorithms through different Grid scenarios using k-Nearest Neighbor, iforest and Local Outlier Factor, 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE conference proceedings. ISSN 978-1-6654-8828-0. doi: 10.23919/SpliTech55088.2022.9854355. Full text in Research Archive Show summary
Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. The available advanced information and communicating platform and computational capability renders smart grid prone to attacks with extreme social, financial and physical effects. The smart network enables energy management of smart appliances contributing support for ancillary services. Cyber threats could affect operation of smart appliances and hence the ancillary services, which might lead to stability and security issues. In this work, an overview is presented of different methods used in anomaly detection, performance evaluation of 3 models, the k-Nearest Neighbor, local outlier factor and isolated forest on recorded smart meter data from urban area and rural region
Nishimura, Akira; Mishima, Daiki; Kono, Nozomu; Toyoda, Kyohei & Kolhe, Mohan Lal (2022). Impact of Separator Thickness on Temperature Distribution in Single Polymer Electrolyte Fuel Cell Based on 1D Heat Transfer. Energy and Power Engineering. ISSN 1947-3818. 14(7), p. 248–273. doi: 10.4236/epe.2022.147014.
Nishimura, Akira; Kono, Nozomu; Toyoda, Kyohei; Mishima, Daiki & Kolhe, Mohan Lal (2022). Impact of Separator Thickness on Temperature Distribution in Single Cell of Polymer Electrolyte Fuel Cell Operated at Higher Temperature of 90 °C and 100 °C. Energies. ISSN 1996-1073. 15(12), p. 1–25. doi: 10.3390/en15124203. Full text in Research Archive
Muhandiram Arachchige, Ireshika Subodha Tharangi; Rheinberger, Klaus; LLiuyacc Blas, Rubén Ronald; Kolhe, Mohan Lal; Preißinger, Markus & Kepplinger, Peter (2022). Optimal power tracking for autonomous demand side management of electric vehicles. Journal of Energy Storage. ISSN 2352-152X. 52(B). doi: 10.1016/j.est.2022.104917. Full text in Research Archive Show summary
Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.
Ari, Arnab; Bohre, Aashish Kumar; Chaturvedi, Pradyumn; Kolhe, Mohan Lal & Singh, Sri Niwas (2022). Techno-Economic Analysis of Hybrid Renewable Energy Systems — A Review with Case Study. In Bohre, Aashish Kumar; Chaturvedi, Pradyumn; Kolhe, Mohan Lal & Singh, Sri Niwas (Ed.), Planning of Hybrid Renewable Energy Systems, Electric Vehicles and Microgrid: Modeling, Control and Optimization. Springer. ISSN 978-9811909788. p. 227–264. doi: 10.1007/978-981-19-0979-5_11. Show summary
Although renewable sources of energy provide multiple benefits, their intermittent nature makes it difficult for application as individual sources of energy. A hybrid renewable energy system integrates different non-renewable and renewable sources along with storage systems to overcome this drawback. This work aims to shed light on the various techno-economic aspects of HRES discussed in recent papers. Also the different components and their mathematical modelling have been highlighted. Diverse range of optimizing techniques used for sizing and optimization of cost, reliability and environmental parameters has been discussed. A brief look at the applications studied in recent papers is taken and future prospects have been discussed. Additionally, In this work, the case study of rural area of West Bengal is presented. The study location is Digha village (21° 37.6’N, 87° 30.4’E), West Bengal, India. The techno-economic analysis using HOMER software of hybrid renewable energy system is presented for possible best optimized solution based on Reliability, Net present costs and Levelized Cost of Energy.
Rai, Ramendra Kumar; Bohre, Aashish Kumar; Chaturvedi, Pradyumn; Kolhe, Mohan Lal & Singh, Sri Niwas (2022). Planning and Impact of Electric Vehicle Charging Stations in Distribution System Using Optimization Techniques. In Bohre, Aashish Kumar; Chaturvedi, Pradyumn; Kolhe, Mohan Lal & Singh, Sri Niwas (Ed.), Planning of Hybrid Renewable Energy Systems, Electric Vehicles and Microgrid: Modeling, Control and Optimization. Springer. ISSN 978-9811909788. p. 177–191. doi: 10.1007/978-981-19-0979-5_9. Show summary
Global warming concerns have led developing as well as developed countries to promote the use of clean & green energy more than ever. Electric vehicle seems to be one of the most adaptable option to counter the concern. But to tackle environmental issues with electric vehicles, there are other factors that need to be addressed and understood. This chapter will showcase the impact of electric vehicle penetration on the power distribution grid and how the impact can be minimized using an optimization technique. The effects of introducing electric vehicles on the voltage profile, cost of generation, active and reactive power losses are discussed in view of emerging markets like India. A 69–bus system is considered for this study and different loads are applied to each branch of the system. A gradual increase in the load is provided and using AC- Optimal Power Flow (OPF) method, behaviour and impacts of different parameters is calculated and compared graphically. Finally, the Artificial Bee Colony (ABC) optimization technique has been employed to find the optimal location for EV charging stations Graphical comparisons are also done between base cases, scenario cases and systems with EV charging stations to reach a conclusion.
Bohre, Aashish Kumar; Chaturvedi, Pradyumn; Kolhe, Mohan Lal & Singh, Sri Niwas (2022). Introduction. In Bohre, Aashish Kumar; Chaturvedi, Pradyumn; Kolhe, Mohan Lal & Singh, Sri Niwas (Ed.), Planning of Hybrid Renewable Energy Systems, Electric Vehicles and Microgrid: Modeling, Control and Optimization. Springer. ISSN 978-9811909788. p. 1–7. doi: 10.1007/978-981-19-0979-5_1.
Rasul, Mohamed Jemsy Mohamed Ashik; Alwis, Naleen de & Kolhe, Mohan Lal (2022). Cost-Benefit and Short-Term Power Flow Analysis of Grid Integrated Residential Photovoltaic-Battery Energy System. In Tomar, Anuradha; Nguyen, Phuong & Mishra, Sukumar (Ed.), Control of Smart Buildings: An Integration to Grid and Local Energy Communities. Springer. ISSN 978-981-19-0375-5. p. 251–270. doi: 10.1007/978-981-19-0375-5_11. Show summary
A recent progressing trend on grid-connected residential photovoltaic (PV) system has been reported all around the world with the focuses on various aspects, mainly on the reduction of grid consumption and demand-side management with peak shaving and peak shifting. Subsequently, a huge impact on the distribution network is caused due to the power quality problems arising from grid-connected PV systems. As the most promising solution, the integration of battery energy storage with the PV system is going to improve the operation of distribution network by enhancing self-consumption as well as in demand side management. The purpose of this study is to investigate the technical performance of a battery-coupled grid-connected PV system and to assess the economic viability of such integrated renewable energy systems in Sri Lanka. In this work, a residential PV-battery energy system is designed and developed considering a control algorithm for energy efficient system operation at conditions to maximize the PV self-consumption through battery energy throughput and to reduce the utility grid consumption accordingly. The system is analysed for dynamic load profile under variable PV conditions with dynamic battery state of charge (SOC) conditions. The results are analysed for system performance and stability conditions. Overall system performance indicated a stable system operation for each investigated condition by effectively managing the PV, battery and grid systems to meet the real-time residential demand. The economic viability of the proposed PV system is analysed using the discounted cash flow (DCF) approach using the indices of benefit-cost ratio (B/C), net present value (NPV), internal rate of return (IRR), and payback period, which revealed to be viable in regular Sri Lankan grid connected PV-battery energy residential system.
Kaur, Paramjeet; Chaturvedi, Krishna Teerath & Kolhe, Mohan Lal (2022). Techno-Economic Power Dispatching of Combined Heat and Power Plant Considering Prohibited Operating Zones and Valve Point Loading. Processes. ISSN 2227-9717. 10(5). doi: 10.3390/pr10050817.
Dehalwar, Vasudev; Kolhe, Mohan Lal; Deoli, Shreya & Jhariya, Mahendra Kumar (2022). Blockchain-based trust management and authentication of devices in smart grid. Cleaner Engineering and Technology. ISSN 2666-7908. 8. doi: 10.1016/j.clet.2022.100481. Full text in Research Archive Show summary
The digitalization of the power grid and advancement in intelligent technologies have enabled the service provider to convert the existing electrical grid into a smart grid. The transformation of the grid will help in integrating cleaner energy technologies with energy management to improve power network efficiency. Internet of things (IoT) and various network components need to be deployed to harness the full potential of the smart grid. Also, integrating intermittent renewable energy sources, energy storage, intelligent control of selected power-intensive loads, etc will improve energy efficiency. But deployment of this information and communication technologies will make the grid more vulnerable to cyber attacks from hackers. In this work, blockchain-based self-sovereign identification and authentication technique is presented to avert identity theft and masquerading. The proposed approach can minimize the chances of identity-based security breaches in the smart grid. This paper provides an overview of the model of identification and authentication of IoT devices in Smart Grid based on Blockchain technology. The Blockchain based implementation of identification and authentication of devices is proposed to validate the model in the distributed electrical energy network. The model is able to authenticate the device using Blockchain in a trusted model. The system works according to plan validating the authenticity of transaction in a node in log(n) time, which justifies presented result.
LLiuyacc Blas, Rubén Ronald; Nyberg, Svein Olav Glesaaen; Muhandiram Arachchige, Ireshika Subodha Tharangi; Kolhe, Mohan Lal & Kepplinger, Peter (2022). PV Hosting Capacity Estimation in Low Voltage Feeders Through Bayesian Statistical Inference. In XX, XX (Eds.), 2022 12th International Conference on Power, Energy and Electrical Engineering (CPEEE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-2049-5. p. 250–255. doi: 10.1109/CPEEE54404.2022.9738661. Show summary
PV hosting capacity provides utilities the knowledge of the maximum amount of solar installations possible to accommodate in low voltage grids such that no operational problems arise. As the quantification of the hosting capacity requires data collection, grid modelling, and often time-consuming simulations, simplified estimations for large-scale applications are of interest. In this paper, Bayesian statistical inference is applied to estimate the hosting capacities of more than 5000 real feeders in Austria. The results show that the hosting capacity of 95% of the total feeders can be estimated with a mean error below 20% by only having knowledge of a random sample of 5%. Moreover, the hosting capacity estimation at a regional level shows a maximum error below 9%, also relying on a random sample of 5% of the total feeders. Furthermore, the approach proposed provides a methodology to assess new parameters aiming to improve the accuracy of the hosting capacity estimation at a feeder level.
Dassanayake, D.J.K.; Ruwanthi, H.M.A.P.; Karunanayaka, K.K.D.I.; Konara, Konara Mudiyanselage Sandun Y.; Perera, P.D.C. & Kolhe, Mohan Lal (2022). Controller for the Power Injection from a Grid-Interfaced Energy Storage within an AC Micro Grid. In Kolhe, Mohan Lal; Jaju, S B & Daigavane, P M (Ed.), Smart Technologies for Energy, Environment and Sustainable Development, Vol 1. Springer. ISSN 978-981-16-6875-3. p. 337–355. doi: 10.1007/978-981-16-6875-3_28. Show summary
AC microgrids can work in either grid-connected or isolated mode. In the grid-connected operation, the power flow between the grid should be controlled. For this purpose, an accurate power flow controller is needed. The main focus of the study is to design an innovative power flow controller which is capable of injecting controlled power to the grid through a three-level neutral point clamped inverter integrated with a battery storage. The control strategies are developed in the d-q synchronous frame. The controller comprises of four main control blocks serving four major functions. Conversion of natural frame parameters into d-q synchronous reference frame, phased locked loop strategy to track grid frequency, generation of current references, current controlling loops to control active and reactive power, generation of voltage references and pulse generation with sinusoidal pulse width modulation scheme are the functions handled by the control blocks. The proposed controller is capable of handling power injection under both fixed and varying power demands from the grid.
Daniel, Leena; Chaturvedi, Krishna Teerath & Kolhe, Mohan Lal (2022). Economic Load Dispatch in Power System by Using Elephant Herding Optimization Technique. In Kolhe, Mohan Lal; Jaju, S B & Daigavane, P M (Ed.), Smart Technologies for Energy, Environment and Sustainable Development, Vol 1. Springer. ISSN 978-981-16-6875-3. p. 357–368. doi: 10.1007/978-981-16-6875-3_29. Show summary
The goal of economic load dispatch (ELD) is to cut the generator’s fuel consumption rate by which reducing the operating cost of the power system by defining the output power of each generator with consideration of different constraints to meet the load demands of the connected network. Economic load dispatch is a most communal problem that comes upon in power system studies. ELD is always formulated as nonlinear optimization problem with various constraints. This work proposes a metaheuristic optimization method for the solution of ELD-related issues. The name of this method is elephant herding optimization (EHO). This technique is primarily stimulated by the navigation nature of elephant cluster. During the journey of any elephant’s life, it belongs to many diverse fraternities. The group of elephants is always following their leader, and usually, elder female elephant leads the group. When male elephants mature, they isolate from origin cluster and live separately. These are the two different behaviors and can be exhibited in two operators, i.e., clan updating operator and separating operator. Each clan of the elephants is updated and modified by their current position and matriarch by clan updating operator. To establish its effectiveness, EHO is applied on 13-unit test system and compared with GSA and CEP. The outcomes display that the EHO gives better results as compared to other metaheuristic algorithms.
Sharma, Pooja; Kolhe, Mohan Lal; Torjesen, Stina & Sharma, Arvind (2022). Role of Subsidies for PV Solar Installations in India: A Cost–Benefit Analysis. In Kolhe, Mohan Lal; Jaju, S B & Daigavane, P M (Ed.), Smart Technologies for Energy, Environment and Sustainable Development, Vol 1. Springer. ISSN 978-981-16-6875-3. p. 369–380. doi: 10.1007/978-981-16-6875-3_30. Show summary
The paper highlights the role of subsidies in the PV solar installments both from the lenders and from the receiver’s perspective, evaluating the existing financial incentives contributing to the adoption of PV solar technology. It evaluates the costs and benefits accumulated by the installation of grid-connected PV solar panel of size 2.5 and 10 kW using the method of cost–benefit analysis and net present value. The paper observes a significant role of subsidies in realizing the benefits in case of PV solar installations. The results of cost–benefit analysis reveal that the internal rate of return is greater in case of subsidy than without subsidy indicating the role of subsidies in incentivizing the investment in PV solar systems. An internal rate of return is 13% for smaller plant and 19.94% for larger plant with subsidy. However, the slow growth in investment observed in past years reflects the vulnerability of the PV solar system on the financial structure. The PV solar costs that are highly vulnerable to technological innovations, and the subsidies in such cases are bound to accumulate financial liabilities for government in terms of opportunity cost foregone by not investing in new technology. Moreover, the reduction in the costs of PV solar technology is contributing in shorten the gestation period and achieving break even hinders investors in making investment decisions. The present situation of PV solar development in India needs not only a periodic assessment process to tackle the issue of rapid innovation in technology but also relevant policies and procedures promoting buyback options to integrate upgradation of technology.
Naik, K. Raghavendra; Rajpathak, Bhooshan; Mitra, Arghya; Sadanala, C & Kolhe, Mohan Lal (2022). Power management scheme of DC micro-grid integrated with photovoltaic - Battery - Micro hydro power plant. Journal of Power Sources. ISSN 0378-7753. 525. doi: 10.1016/j.jpowsour.2022.230988. Show summary
Appropriate power management strategy for DC microgrid (MG) is required for integrating intermittent renewable energy sources and providing controllable power flow from dispatchable sources during load dynamics. In this work, DC MG with photovoltaic – battery - micro hydro power plant (MHPP) is considered. It is critical to have sustainable power flow in a DC MG. In considered DC MG, due to technical constraints (e.g. mechanical response time of MHPP, C-rate limitation of battery, PV intermittency), load dynamics cannot be compensated instantaneously. In this work, an innovative supervised power management scheme (SPMS) is proposed for sustainable power flow distribution within the DC MG sources against aforementioned supply constraints. The presented SPMS strategically operates MHPP of DC MG in such a way that sustainable power flow can be managed from dispatchable sources with maximum participation of MHPP during load transients with due consideration of C-rate limitations of battery storage. The effectiveness of proposed SPMS for MHPP centered DC MG is tested for load dynamics, and results are validated through hardware-in-loop experimental system. Moreover, performance of proposed SPMS, to maintain the stable power flow control of DC MG during transient load, is investigated with voltage stability analysis.
Johannesen, Nils Jakob; Kolhe, Mohan Lal & Goodwin, Morten (2022). Load prediction of rural area Nordic holiday resorts for microgrid development, Residential Microgrids and Rural Electrifications. Academic Press. ISSN 978-0-323-90177-2. p. 163–181. doi: 10.1016/B978-0-323-90177-2.00005-0.
Ogura, Koki; Chandhaket, Srawouth; Kolhe, Mohan Lal; Sakphrom, Siraporn & Mekhilef, Saad (2022). Simple Lossless Inductive Snubbers-Assisted Series Load Resonant Inverter Operating under ZCS-PDM Scheme for High-Frequency Induction Heating Fixed Roller. Applied Sciences. ISSN 2076-3417. 12(3). doi: 10.3390/app12031122. Full text in Research Archive Show summary
This paper presents a high-frequency pulse-density-modulated (PDM) soft-switching series load resonant inverter for use in induction heating (IH) fixed roller applications, which is used in copy and printing machines. The proposed simple high-frequency resonant inverter uses an asymmetrical pulse pattern PDM control scheme to achieve complete zero-current soft-switching commutations over a wide output range of input power regulation. Additionally, when the printer toner requires operation in very light load conditions, this causes difficulty in achieving zero-voltage or zero-current soft-switching operations in the IH high-frequency resonant inverters with pulse frequency modulation or pulse width modulation control schemes. The proposed resonant inverter demonstrates the capability to accomplish highly efficient power conversions. In this work, a fixed roller for printing machines is developed for doing experiments to verify the efficiency of the proposed circuit topology and its PDM control schemes. The inverter’s steady-state and transient operating principles are analyzed based on the proposed control strategy at a high-frequency PDM. Operating conditions such as power loss analysis, power conversion efficiency and temperature rise characteristics of the proposed inverter are presented and analyzed through experimental results. Finally, from a practical viewpoint, a comparative study of a conventional halogen lamp heater and the proposed IH fixed roller is deliberated.
Ferriday, Thomas Benjamin; Middleton, Peter Hugh & Kolhe, Mohan Lal (2021). Review of the Hydrogen Evolution Reaction—A Basic Approach. Energies. ISSN 1996-1073. 14(24), p. 1–42. doi: 10.3390/en14248535. Full text in Research Archive
Johannesen, Nils Jakob; Kolhe, Mohan Lal & Goodwin, Morten (2021). Comparing recurrent neural networks using principal component analysis for electrical load predictions, 2021 6th International Conference on Smart and Sustainable Technologies - SpliTech. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-953-290-112-2. doi: 10.23919/SpliTech52315.2021.9566357.
Konara, Konara Mudiyanselage Sandun Y. & Kolhe, Mohan Lal (2021). Queue Based Dynamic Charging Resource Allocation and Coordination for Heterogeneous Traffic in an Electrical Vehicle Charging Station. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. ISSN 1556-7036. doi: 10.1080/15567036.2021.1974983.
Johannesen, Nils Jakob & Kolhe, Mohan Lal (2021). Application of Regression Tools for Load Prediction in Distributed Network for Flexible Analysis. In Alhelou, Hassan Haes; Heydarian-Forushani, Ehsan & Siano, Pierluigi (Ed.), Flexibility in Electric Power Distribution Networks. CRC Press. ISSN 9781003122326. p. 67–94. doi: 10.1201/9781003122326.
Naik, K. Raghavendra; Rajpathak, Bhooshan; Mitra, Arghya & Kolhe, Mohan Lal (2021). Adaptive energy management strategy for sustainable voltage control of PV-hydro-battery integrated DC microgrid. Journal of Cleaner Production. ISSN 0959-6526. 315. doi: 10.1016/j.jclepro.2021.128102. Full text in Research Archive
Nishimura, Akira; Kono, Nozomu; Toyoda, Kyohei; Kojima, Yuya & Kolhe, Mohan Lal (2021). Impact Analysis of MPL on a PEFC Cell’s Temperature Distribution with Thin PEM and GDL for Operating at Higher Temperature than Usual . Journal of Energy and Power Engineering. ISSN 1934-8975. 15, p. 39–51. doi: 10.17265/1934-8975/2021.02.001.
Nishimura, Akira; Takada, Tomohiro; Ohata, Satoshi & Kolhe, Mohan Lal (2021). Biogas Dry Reforming for Hydrogen through Membrane Reactor Utilizing Negative Pressure. Fuels. ISSN 2673-3994. 2(2), p. 194–209. doi: 10.3390/fuels2020012.
Naik, K. Raghavendra; Rajpathak, Bhooshan; Mitra, Arghya & Kolhe, Mohan Lal (2021). Assessment of energy management technique for achieving the sustainable voltage level during grid outage of hydro generator interfaced DC Micro-Grid. Sustainable Energy Technologies and Assessments. ISSN 2213-1388. 46, p. 1–18. doi: 10.1016/j.seta.2021.101231.

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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 979-8-3503-9549-5. 213 p.
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.
Kolhe, Mohan Lal; Bevrani, Hassan & Chen, Zhe (2023). 2023 6th International Conference on Electrical Engineering and Green Energy (CEEGE). IEEE Press. ISBN 979-8-3503-1266-9. 301 p.
Kolhe, Mohan Lal (2023). Renewable Energy Systems and Sources Proceedings of International Conference on Renewable and Clean Energy (ICRCE) 2023. Springer. ISBN 978-981-99-6289-1. 138 p.
Khosla, Anita & Kolhe, Mohan Lal (2023). Renewable Energy Optimization, Planning and Control Proceedings of ICRTE 2022. Springer. ISBN 978-981-19-8963-6. 466 p.
Kolhe, Mohan Lal; Karande, Kailash J.; Deshmukh, Sampat G. & Mulani, Altaf O. (2022). Computational Intelligence in Engineering Systems. AIP Publishing (American Institute of Physics). ISBN 978-0-7354-4226-9.
Tiwari, Shailesh; Trivedi, Munesh C.; Kolhe, Mohan Lal & Singh, Brajesh K. (2022). Advances in Data and Information Sciences Proceedings of ICDIS 2022. Springer. ISBN 978-981-19-5292-0. 522(1). 567 p.
Kolhe, Mohan Lal (2022). Renewable Energy Systems in Smart Grid Select Proceedings of International Conference on Renewable and Clean Energy (ICRCE) 2022. Springer. ISBN 978-981-19-4360-7. 938(1). 168 p.
Kolhe, Mohan Lal; Karande, Kailash J. & Deshmukh, Sampat G. (2022). Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications. CRC Press. ISBN 9781032115023. 316 p.
Kolhe, Mohan Lal; Muhammad, Aziz; Kharbachi, Abdel El & Yuwono, Tri Yogi (2022). Recent Advances in Renewable Energy Systems Select Proceedings of ICOME 2021. Springer Nature. ISBN 978-981-19-1581-9. 386 p.
Bohre, Aashish Kumar; Chaturvedi, Pradyumn; Kolhe, Mohan Lal & Singh, Sri Niwas (2022). Planning of Hybrid Renewable Energy Systems, Electric Vehicles and Microgrid: Modeling, Control and Optimization. Springer. ISBN 978-9811909788. 1009 p.
Tiwari, Shailesh; Trivedi, Munesh C.; Kolhe, Mohan Lal; Mishra, K.K. & Singh, Brajesh K. (2022). Advances in Data and Information Sciences Proceedings of ICDIS 2021. Springer. ISBN 978-981-16-5689-7. 745 p.
Kolhe, Mohan Lal; Jaju, S B & Daigavane, P M (2022). Smart Technologies for Energy, Environment and Sustainable Development, Vol 2. Springer. ISBN 978-981-16-6879-1. 2(1). 595 p.
Kolhe, Mohan Lal; Jaju, S B & Daigavane, P M (2022). Smart Technologies for Energy, Environment and Sustainable Development, Vol 1. Springer. ISBN 978-981-16-6875-3. 1(1). 695 p.
Goyal, Vishal; Gupta, Manish; Trivedi, Aditya & Kolhe, Mohan Lal (2021). Proceedings of International Conference on Communication and Artificial Intelligence. Springer. ISBN 978-981-336-546-9. 611 p.

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Kolhe, Mohan Lal (2023). Integrated Renewable Energy System based on Hydrogen.
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.
Kolhe, Mohan Lal (2023). Journal of Physics: Conference Series, Volume 2430, The 8th International Symposium on Hydrogen Energy, Renewable Energy and Materials (HEREM 2022). Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2430(011001). doi: 10.1088/1742-6596/2430/1/011001.
Kolhe, Mohan Lal (2023). Journal of Physics: Conference Series Volume 2442, 2022 International Conference on Energy and Power Engineering (EPE 2022). Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2442(011001). doi: 10.1088/1742-6596/2442/1/011001.
Kolhe, Mohan Lal; Karande, Kailash J.; Deshmukh, Sampat G. & Mulani, Altaf O. (2022). Preface: Computational Intelligence in Engineering Systems. AIP Conference Proceedings. ISSN 0094-243X. 2494. doi: 10.1063/12.0012465.
Perković, Toni; Šolić, Petar; Čoko, Duje; Andrić, Ivo & Kolhe, Mohan Lal (2022). Smart Environments. Journal of Communication Software and Systems. ISSN 1845-6421. 18(2), p. 182–183. doi: 10.24138/jcomss-2022-0079.
Goyal, Vishal; Gupta, Manish; Trivedi, Aditya & Kolhe, Mohan Lal (2021). Preface. Lecture Notes in Networks and Systems. ISSN 2367-3370. 192, p. v–vi.
Kolhe, Mohan Lal (2021). Engineering: Integrated Sustainable Electrical Energy Systems (Topical Collection of SN Applied Sciences). SN Applied Sciences. ISSN 2523-3963. Show summary
The topical collection on ‘integrated sustainable electrical energy system’ is going to cover the peer-reviewed articles with key aspects of renewable energy technologies and their integration within the grid, innovative technologies for up-gradation of electrical energy system, new innovation and development for sustainable grid operation and management including forthcoming demand of electric vehicles, techno-economics, engineering policies and business models analysis for sustainable electrical energy system
Middleton, Peter Hugh; Kolhe, Mohan Lal; Van Herle, Jan & Ferriday, Thomas Benjamin (2024). Membrane electrode assemblies for anion exchange membrane water electrolysers. Universitetet i Agder. ISSN 978-82-8427-196-5.
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 978-82-8427-152-1.
Kolhe, Mohan Lal; Kepplinger, Peter & Muhandiram Arachchige, Ireshika Subodha Tharangi (2023). Autonomous Demand Side Management of Electric Vehicles. Universitetet i Agder. ISSN 978-82-8427-125-5.
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 978-82-8427-131-6. Full text in Research Archive
Johannesen, Nils Jakob; Kolhe, Mohan Lal & Goodwin, Morten (2022). Machine Learning Applications for Load Predictions in Electrical Energy Network. Universitetet i Agder. ISSN 978-82-8427-086-9. Full text in Research Archive

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Published Apr. 16, 2024 11:12 AM