Faculty Publications

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Author: search.filters.author.Gaonkar, D.N.Subject: search.filters.subject.Solar power generation

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    Power control of PV/fuel cell/supercapacitor hybrid system for stand-alone applications
    (International Journal of Renewable Energy Research, 2016) Sabhahit, N.S.; Gaonkar, D.N.; Nempu, P.B.
    This paper presents modeling and control of photovoltaic/fuel cell/supercapacitor hybrid power system for stand-alone applications. The hybrid power system uses solar photovoltaic array and fuel cell as the main sources. These sources share their power effectively to meet the load demand. The supercapacitor bank is used to supply or absorb the power during load transients. The main control system comprises of controller for maximum power tracking from photovoltaic system, a DC-DC boost converter with controller for fuel cell system for power management and inverter controller to regulate voltage and frequency. The stand-alone hybrid system aims to provide quality power supply to the consumers with a constant voltage and frequency along with proper power management using simple control techniques. The modeling and control strategies of the hybrid system are realized in MATLAB/Simulink.
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    Integrated power flowand voltage regulation of stand-alone PV-fuel cell system with supercapacitors
    (Acta Press journals@actapress.com, 2017) Sabhahit, J.N.; Gaonkar, D.N.; Nempu, P.B.
    The output of the solar cell is fluctuating due to intermittency of solar irradiation. Hybridizing the solar photovoltaic (PV) system with other sources and appropriate storage devices is essential to generate electricity continuously. This paper presents the control strategies for a PV-fuel cell hybrid power system with supercapacitor bank for isolated load applications. Supercapacitor bank is controlled using a bidirectional DC/DC converter so as to regulate voltage at a DC link and to keep the system stable under transient load variations. The H-bridge inverter is controlled to regulate voltage and frequency across the load. The PV system is controlled to extract maximum power using the maximum power point tracking algorithm. This paper aims to provide a single-phase supply with constant voltage and frequency to the consumers with proper power sharing among different sources. The hybrid system is realized in Matlab/Simulink environment.
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    Intermittent power smoothing control for grid connected hybrid wind/PV system using battery-EDLC storage devices
    (Polish Academy of Sciences 12 Smetna Street Krakow 31-343, 2020) Sabhahit, N.S.; Gaonkar, D.N.; Karthik, R.P.; Prasanna, P.
    Wind and solar radiation are intermittent with stochastic fluctuations, which can influence the stability of operation of the hybrid system in the grid integrated mode of operation. In this research work, a smoothing control method for mitigating output power variations for a grid integrated wind/PV hybrid system using a battery and electric double layer capacitor (EDLC) is investigated. The power fluctuations of the hybrid system are absorbed by a battery and EDLC during wide variations in power generated from the solar and wind system, subsequently, the power supplied to the grid is smoothened. This makes higher penetration and incorporation of renewable energy resources to the utility system possible. The control strategy of the inverter is realized to inject the power to the utility system with the unity power factor and a constant DC bus voltage. Both photovoltaic (PV) and wind systems are controlled for extracting maximum output power. In order to observe the performance of the hybrid system under practical situations in smoothing the output power fluctuations, one-day practical site wind velocity and irradiation data are considered. The dynamic modeling and effectiveness of this control method are verified in the MATLAB/Simulink environment. The simulation results show that the output power variations of the hybrid wind/PV system can be significantly mitigated using the combination of battery and EDLC based storage systems. The power smoothing controller proposed for the hybrid storage devices is advantageous as compared to the control technique which uses either battery or ultracapacitor used for smoothing the fluctuating power. © 2020. The Author(s).
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    Optimal operation of multi-source electric vehicle connected microgrid using metaheuristic algorithm
    (Elsevier Ltd, 2022) Sabhahit, N.S.; Jadoun, V.K.; Gaonkar, D.N.; Shrivastava, A.; Kanwar, N.; Nandini, K.K.
    In this paper, a multi-source microgrid (MG) has been considered which inducts power from solar photovoltaic (PV), wind turbine, pumped hydro storage system (PHSS) and diesel generator (DG). A problem formulation is proposed on a multi-source MG considering an electric vehicle (EV) as source and load demand. A modified operation strategy is proposed to achieve the lowest possible fuel usage of DG and to optimize the operation of multi-sources used in the MG. When the sum of PV, wind power production and EV discharge is less than the load requirement, the required deficit power should be delivered by DG and PHS. This work considers PV and wind as the primary energy supplying sources, while DG, EV and PHS as the additional energy suppliers with EV and PHS as energy storage systems. By properly coordinating EVs, they can become a major contributor to the successful execution of the MG concept. In this work, a modified charging/discharging algorithm is presented to check the effect of EVs to supply a portion of peak loads with PHS to reduce the fuel consumption of DG in three diverse modes of operation. A modified whale optimization algorithm (WOA) and teaching learning-based optimization (TLBO) are applied to effectively solve this proposed complex problem using the MATLAB platform. The optimum solutions obtained after different independent trials by both the techniques are compared with the latest published techniques. It can be observed that modified WOA performs better than TLBO and other recently published methods on the base case and proposed multi-source MG case in three diverse modes of operation. The outcomes of the simulation confirm the effectiveness of modified WOA in reducing fuel consumption. © 2022 Elsevier Ltd
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    Operation and control of multiple electric vehicle load profiles in bipolar microgrid with photovoltaic and battery energy systems
    (Elsevier Ltd, 2023) Nisha, K.S.; Gaonkar, D.N.; Sabhahit, N.S.
    Charging of electric vehicles is going to be a major electrical load in the near future, as more and more population shift to electric auto-motives from conventional internal combusted engine-powered vehicles. Integration of electric vehicle charging stations (EVCS) might even burden the existing grid to a point of collapse or grid failure. Establishing charging stations interfaced with bipolar DC microgrids along the roads and highways is the most realistic and feasible solution to avoid the overburdening of the existing power system. The bipolar DC microgrid is a far better microgrid structure than the unipolar microgrid structure in many aspects like reliability, flexibility, and controllability. It can provide multiple voltage level interfaces according to the load demands, which is very apt for different charging levels of electric vehicles (EVs). Operation of multiple sources and multiple loads connected to bipolar DC microgrid will affect DC voltage regulation, capacitance-voltage balancing, and overall stable operation of the grid. In order to mitigate these power quality problems arising in multi-node bipolar DC microgrids, a decentralized model predictive control is proposed in this paper. EV charging load profiles are modeled and developed by considering standard driving cycles, state of charge, and power demand of multiple vehicles to study the effect of unpredictable varying EV loads in the bipolar DC microgrid. EVCS thus modeled are connected to solar photovoltaic-battery energy storage fed bipolar DC microgrid with three-level/bipolar converters and analyzed under dynamic conditions for capacitance–voltage unbalance mitigation, voltage regulation, and the stability of operation with model predictive control. Simulation studies are carried out in MATLAB/Simulink to verify the effectiveness of the system. © 2022 Elsevier Ltd
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    Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method
    (MDPI, 2023) Krishnamurthy, N.K.; Sabhahit, J.N.; Jadoun, V.K.; Gaonkar, D.N.; Shrivastava, A.; Rao, V.S.; Kudva, G.
    In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network’s operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 × 106 for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20; 16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs’ optimal placement at bus numbers 2, 18, 30; 60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm. © 2023 by the authors.
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    Nataf-KernelDensity-Spline-based point estimate method for handling wind power correlation in probabilistic load flow
    (Elsevier Ltd, 2024) Mahmmadsufiyan, M.; Gaonkar, D.N.; Nuvvula, R.S.S.; Muyeen, S.M.; Shezan, S.A.; Shafiullah, G.M.
    Modern power systems integrated with renewable energies (REs) contain many uncertainties. The proposed method introduces a novel approach to address the challenges associated with wind power generation uncertainty in probabilistic load flow (PLF) studies. Unlike conventional methods that use wind speed as an input, the paper advocates for utilizing wind generator output power (WGOP) as an input to the point estimate method (PEM) in solving PLF. The uniqueness lies in recognizing the distinct behavior of wind power uncertainty, where not all random samples of wind speed contribute to actual wind power production. The paper suggests a Nataf-KernelDensity-Spline-based PEM, combining the Nataf transformation, Kernel density estimation (KDE), and cubic spline interpolation. This innovative integration effectively manages wind power correlation within the analytical framework. By incorporating spline interpolation and kernel density estimation into the traditional PEM, the proposed method significantly enhances accuracy. To validate the effectiveness of the proposed approach, the method is applied to IEEE-9 and IEEE-57 bus test systems, considering uncertainties related to load, wind power generation (WPG), solar power generation (SPG), and conventional generator (CoG) outages. Comparative analysis with Monte Carlo simulation (MCS) results demonstrates that the proposed method outperforms the conventional PEM in terms of accuracy. Overall, the paper contributes a pioneering solution that not only highlights the importance of using WGOP as an input in PLF but also introduces a sophisticated method that surpasses traditional approaches, improving accuracy in power system studies involving renewable energy integration. The accuracy of the proposed method is validated by comparing its results with those obtained through Monte Carlo simulation (MCS), where the proposed method yields more accurate results than the conventional PEM. © 2023 Elsevier Ltd