Department of Electrical and Electronics Engineering

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    Digital twin simulation and comparative study to understand the microgrid operation and control
    (IEEE, 2025-01) Mathur, Hitesh Datt; Gautam, Aditya R.
    Solar photovoltaic (PV) microgrid (MG) systems are the future of the growing electrical industry. However, these systems have a dependency on the environment. To mitigate this dependency, better control and technologies are required. The digital twin (DT) technology can be utilized to make these MGs more sustainable and promising. In this paper, the DT of the installed MG has been developed on the Simulink platform. The irradiance data, and load power consumption data have been used as input for the DT of the MG. Moreover, it is found that the DT of the MG is working stably with the given data. On the output side, the grid power data of the physical MG has been compared with the output grid power of DT. Further, it has been found that the solar power generation, load power consumption data, and grid power data have matched the results of the physical MG.
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    Techno-economic and reliability assessment of an off-grid solar-powered energy system
    (Elsevier, 2024-10) Mathur, Hitesh Datt; Gautam, Aditya R.
    In the current era of the world, electricity has become a basic need of every individual. Therefore, the global energy demand has doubled from 77 trillion kWh to nearly 155 trillion kWh since 1980. However, despite the rising energy demand in developing countries, the per capita energy consumption only grew by about 14% globally. It is mainly because of the rural and remote areas where grid supply infrastructure is unavailable or inaccessible. Therefore, a solar-based microgrid (MG) can be considered an alternative energy solution. These MGs with energy storage support can be considered a viable solution because of the intermittent nature of solar photovoltaic (PV). In this regard, this paper describes the design, development, and deployment of a solar rooftop microgrid (SRMG) at the commercial building of BITS Pilani, India. Further, a comprehensive economic analysis is performed using a novel method of levelized cost of energy (LCOE) and payback period, and the results are compared with the traditional method's results. In order to improve the reliability of the SRMG, a demand side management (DSM) strategy is developed and implemented. It can effectively cater the unbalance between load and generation by shifting the non-critical loads to unused solar PV generation hours. It was found from the results that a significant energy of BESS is conserved which is reflected by the high state of charge (SOC) value. The developed design, economic analysis, and proposed DSM make the SRMG an economical and reliable energy solution.
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    Multi-Agent-Based Forecast Update Methods for Profit Enhancement of Intermittent Distributed Generators in a Smart Microgrid
    (Taylor & Francis, 2018-12) Patel, Ashish
    Uncertainty in generation from intermittent sources makes a strong case to have effective forecasting methods. However, errors in forecast lead to losses to the distributed generation (DG) owners. In this article, multi-agent-based forecast update methods are proposed which minimize the forecast errors. The effectiveness of the proposed methods in enhancing the profit of intermittent generators and microgrid operational cost is analyzed using a microgrid with two scenarios, namely simple ownership and multiple ownership. A modified IEEE 13 bus system is used as the case study system and the system simulation for the microgrid is performed on the OpenDSS platform and the proposed multi-agent system is developed using JAVA Agent DEvelopment (JADE) framework. From the simulation results, the proposed approaches are found effective in increasing profit margins for the investors or owners of the DGs.
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    Decentralized Renewable Resource Redistribution and Optimization for Beyond 5G Small Cell Base Stations: A Machine Learning Approach
    (IEEE, 2023-03) Chamola, Vinay
    Optimal resource provisioning and management of the next generation communication networks are crucial for attaining a seamless quality of service with reduced environmental impact. Considering the ecological assessment, urban and rural telecommunication infrastructure is moving toward deploying green cellular base stations to cater to the needs of the ever-growing traffic demands of heterogeneous networks. In such scenarios, the existing learning-based renewable resource provisioning methods lack intelligent and optimal resource management at the small cell base stations (SCBS). Therefore, in this article, we present a novel machine learning-based framework for intelligent resource provisioning mechanisms for micro-grid connected green SCBSs with a completely modified ring parametric distribution method. In addition, an algorithmic implementation is proposed for prediction-based renewable resource re-distribution with energy flow control unit mechanism for grid-connected SCBS, eliminating the need for centralized hardware. Moreover, this modeling enables the prediction mechanism to estimate the future on-demand traffic provisioning capability of SCBS. Furthermore, we present the numerical analysis of the proposed framework showcasing the systems’ ability to attain a balanced energy convergence level of all the SCBS at the end of the periodic cycle, signifying our model’s merits.
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    Development of a Novel Strategy with Electrical Vehicles to Mitigate Frequency Aberration in Microgrid
    (IEEE, 2018) Mathur, Hitesh Datt
    Power system complexity is growing rapidly with changing scenario of load characteristic. Microgrid is a feasible solution to cater to varying load for maintaining power quality parameters especially frequency and voltage. The microgrid, consisting of different types of intermittent sources and loads, is liable to a substantially high frequency aberration. This needs to be mitigated at a faster rate in order to supply quality power supply to consumers. This paper focuses on development of a novel control strategy for quick active power support by electric vehicles (EV) to suppress frequency deviation caused due to fluctuating load. This approach senses the frequency change and communicates with EV aggregator to supply required amount of active power to microgrid. It is simulated on MATLAB/Simulink platform and results obtained are encouraging in terms of critical parameters i.e. settling time and peak over/undershoot
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    Digital twin of a commercial building microgrid: Economic & Environmental sustainability analysis
    (IEEE, 2022) Mathur, Hitesh Datt
    Digital twin of a commercial building microgrid: Economic & Environmental sustainability analysis - CentraleSupélec Accéder directement au contenu Accéder directement à la navigation Nouvelle interface Toggle navigation HAL Accueil Dépôt Je dépose Questions pratiques Questions juridiques C'est quoi l'OA ? Consultation Consultation par période Consultation par auteurs Consultation par collections Outils Créer son IdHAL et CVHAL
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    Optimal energy management in microgrid including stationary and mobile storages based on minimum power loss and voltage deviation
    (Wiley, 2021-10) Mathur, Hitesh Datt; Mishra, Puneet
    The grid-connected microgrid (MG) may depend on the utility grid (UG) during peak load hours to meet its unequaled load demand. Also, the rapid increase in penetration of plug-in hybrid electric vehicles (PHEV) affects the system's stability and the load demand. Therefore, it is an indisputable fact that MG demands, an effective energy management strategy to manage the peak load that occurred due to PHEV charging. To address this challenge, an optimal energy management strategy (OEMS) is proposed that aims at maximum utilization of DERs, promotes energy trading, and diminishes the dependency of MG on UG. It is achieved by optimizing the energy exchanged between MG and UG with simultaneously minimizing the active power losses (APL) and voltage deviation (VD) of a MG. Further, a state flow-based coordinated charging/discharging scheduling algorithm is also proposed to improve the performance, lifetime, and utilization of an energy storage system. The performance of OEMS is evaluated for different PHEV penetration levels (EVPL) and simulation results are compared with the recently reported strategy. The obtained results reveal that the proposed OEMS is superior to other strategy as it fulfills the MG's load with the minimum APL and VD and by optimally feeding energy to the UG it increases the MG operator's (MGO) profit in the range of 8.4%–13%. Lastly, the peak load management of MG is achieved and the efficacy of OEMS is further improved by introducing Governed charging/discharging mode of PHEVs in the system. The economic analysis discloses that OEMS with GCDM has increased the MGO's profit by 10.8%–29.5% depending on EVPL.
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    Electric Vehicle Impact Analysis in a Microgrid Using Optimized Bio-Inspired Non-Integer Controller
    (IEEE, 2019) Mathur, Hari Om
    During peak hours of load demand in micro-grid (MG), the power frequency faces more excursion from a nominal value thereby deteriorating the power quality. The main idea is to utilize the EV power during peak load in to a MG system to limit the under frequency deviation and to charge the EV during off peak hours when the generation is surplus. This paper simulates a MG model in MATLAB/Simulink with EV as one of the sources using PID and fractional order PID (FOPID) controller to regulate the frequency deviation. The parameters of PID and FOPID controller are being optimized using genetic algorithm (GA) for improvement in the time response of frequency deviation of MG. The total energy model (TEM) of EV has been used for MG simulation. The optimized parameters obtained for PID, GAPID, FOPID and GAFOPID has been recorded for analysis. The GAFOPID based controller gives the better response for the defined objective function.
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    Frequency Excursion Mitigation in a Multi-source Islanded Energy System Using Meta-Heuristic Optimization Strategies
    (IEEE, 2020) Mathur, Hitesh Datt
    Distributed Energy Resources and Electric vehicles are getting more popular in the energy market. They are emerging as promising concepts that can bring a revolution in the field of energy resources and transportation systems. The integration of different renewable energy resources and electric vehicles leads to a comprehensive microgrid (MG). It is a fact that an increase in power demand leads to a drop in power frequency and vice-versa, which adversely affects the power quality of the system. Therefore, the primary idea of this research work is to regulate frequency by controlling the wind power and available EV power. In this paper, a detailed MG model is simulated in MATLAB/Simulink with wind and EV as one of the sources using fractional order PID (FOPID) controller. Further, parameters of FOPID controller is optimized by using Ant Colony Optimization(ACO) and Particle Swarm Optimization (PSO). The frequency responses, wind power, and EV power results are compared for three cases; FOPID without optimization, ACO based FOPID, and PSO based FOPID. The generalized wind model and total energy model (TEM) are used for simulating wind source and EV, respectively. Optimizer based FOPID controller provides a better response for the defined objective function.
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    Forecasting of solar and wind power using LSTM RNN for load frequency control in isolated microgrid
    (Taylor & Francis, 2020) Mathur, Hitesh Datt; Bhanot, Surekha
    Renewable sources such as solar PV and wind are stochastic in nature, hence their integration with emerging isolated microgrid (MG) is challenging especially with regards to stability issues. An accurate prediction model of wind and solar sources is necessary to analyze the uncertainty in MG system and to encourage the reliable participation of wind and solar power in the energy market. The advancement in deep learning methods has made it possible to develop a multi-step forecasting model unlike shallow neural networks (SNNs). The time series forecasting using SNN and Recurrent Neural Network (RNN) suffers from the problem of vanishing/exploding gradient while training. To eliminate this problem the long short-term memory (LSTM) RNN has been used in this study for wind speed and solar irradiance prediction. The forecasted solar and wind power is applied to analyze the load frequency behavior and the response of nonrenewable sources for sudden rise and fall in load power demand and PI controller is used to mitigate frequency deviation to ensure the stability of the MG power system.