• Energy Research

The rapid advancements in electric vehicle technology have elevated the lithium-ion battery to the forefront as the paramount energy storage solution. The battery’s health tends to deteriorate gradually as it ages. Due to the inevitable physiochemical reactions that take place inside the battery, it undergoes degradation and at a certain point, it becomes unserviceable. The battery degradation can be estimated using state of health (SOH). This paper employs data-driven techniques to estimate the state of health (SOH) of a battery. To estimate health parameters, a vast quantity of data, such as voltage, current, and temperature, is gathered from the NASA Prognostics Center of Excellence. The data is resampled using the superior Fourier Resampling method and then fed to a machine-learning algorithm. In this study, SOH estimation is carried out using three different machine-learning techniques i.e. Long Short Term Memory (LSTM), Deep Neural Networks (DNN), and Gated Recurrent Unit (GRU). However, the performance and accuracy of SOH estimation using these algorithms are highly dependent on hyperparameter tuning. Therefore, the optimal hyperparameter tuning has been adopted in the present work to reduce the time and complexity of the estimation. Further, the performance of various proposed techniques has been compared against each other using different performance indices such as root mean square error (RMSE), mean absolute error (MAE), and R-square error. GRU technique proved to be excelling with RMSE of 0.003, MAE of 0.003, and R-square error of 0.004 while estimating the SOH of various samples of batteries. This detailed analysis will be helpful for users to evaluate the performance of a battery and plan for maintenance accurately and effectively with minimum downtime.

This work presents a system design for extracting maximum power using the modified maximum power point tracking (MPPT) technique and a novel high-gain DC-DC converter, which was then used to supply a microgrid system with a conventional buck converter. We present a novel structure comprising the MPPT, voltage boosting, and voltage regulating components for a DC microgrid in a single system. The most important features of a photovoltaic (PV) system include a high-gain converter and maximum PV power extraction; considering these, we present a high-gain DC-DC converter that boosts the output voltage to ten times the input voltage. Furthermore, the MPPT technique extracts maximum power from the PV panel based on model predictive control through its better transient response than the conventional incremental conductance method. The MPPT approach was tested with both fixed- and variable-step operations, and the results were compared for load variations. Considering the economics of the system, the proposed approach attempts cost reduction by optimizing the number of sensors to two instead of three. Simulations were conducted under different environmental conditions using MATLAB-Simulink, and the performance differences between the conventional incremental conductance and proposed MPPT-based methods are shown. Next, DC voltage regulation was implemented for the proposed PV and existing systems by considering different load and irradiation conditions while maintaining constant temperature. The simulation results showed the latter system had better performance than the former under different environmental conditions, with persistent results for voltage regulation at different load and irradiation conditions.

AbstractThe development of DC microgrids is reliant on multi-input converters, which offer several advantages, including enhanced DC power generation and consumption efficiency, simplified quality, and stability. This paper describes the development of a multiple input supply based modified SEPIC DC–DC Converter for efficient management of DC microgrid that is powered by two DC sources. Here Multi-Input SEPIC converter offers both versatility in handling output voltage ranges and efficiency in power flow, even under challenging operating conditions like lower duty cycle values. These features contribute to the converter's effectiveness in managing power within a DC microgrid. In this configuration, the DC sources can supply energy to the load together or separately, depending on how the power switches operate. The detailed working states with equivalent circuit diagrams and theoretical waveforms, under steady-state conditions, are shown along with the current direction equations. This paper also demonstrates the typical analysis of large-signal, small-signal, steady-state modeling techniques and detailed design equations. The proposed configuration is validated through the conceptual examination using theoretical and comprehensive MATLAB simulation results. Detailed performance analysis has been done for different cases with various duty ratios. Finally, to show the competitiveness, the multi-input SEPIC topology is compared with similar recent converters.

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor‐ (SC‐) based hybrid energy storage system (HESS) using hybrid proportional and integral‐ (PI‐) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two‐loop voltage and current PI controllers with low‐pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI‐sliding mode controller (SMC) scheme is designed to control the bidirectional DC‐DC converters to overcome the drawbacks of aforementioned issues. The combined PI‐SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB‐Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI‐SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI‐SMC scheme outperforms in transient and steady‐state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set‐up is developed using dSPACE‐1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

Profit maximization is critical in the control of power system networks for both power providers and users. Electrical energy is freely accessible in the electrical grid during off-peak hours, with storage units helping to store excess energy and assist the electrical grid during high-demand situations. Such techniques promote grid stability and ensure safe operation. Because renewable resources are intermittent, energy storage technologies are especially significant in renewable-associated power systems. Vehicle-to-grid (V2G) technology has recently acquired popularity in preserving power grid stability in the presence of renewable resources.V2G technology employs automobiles as mobile storage devices and focuses on the efficient utilization of extra power available during off-peak hours. The goal of this work is to improve the functioning of a V2G system in a power network to reduce energy production costs while increasing system profitability. This study for deregulated power environments also depicts the influence of V2G mixing on system voltage profile and locational marginal pricing (LMP), as well as the performance of the Unified Power Flow Controller (UPFC) on system economics. The MiPower simulation program is used in the study to find the best placement of the power storage unit for the modified IEEE 14-bus system.

This paper presents an energy management peer-to-peer (P2P) and peer-to-grid (P2G) trading strategy for power sharing between prosumers with grid-connected photovoltaic/wind turbine/battery storage systems. The optimal results are obtained using the “Fmincon” solver due to its ability to solve large-scale optimization problems. Two prosumers sectors are considered in this study that is the residential and commercial. The results show that for the residential consumer side, the load is satisfied by the energy from the grid, its own renewable energy system (RES), and the energy from the commercial consumer side RES during the first off-peak period (00:00 AM to 05:00 AM). During the peak pricing period (17:00 PM to 22:00 PM), the load is satisfied by the residential consumer’s battery and the commercial consumer’s battery, and no energy is bought from the grid due to the high price. Furthermore, 37.69% and 39.40% cost savings for residential and commercial, respectively, and a reduction of the bill by $42.26/day and $42.97154/day, respectively, are recorded. Significant grid energy savings for the developed P2P/P2G trading for prosumers and maximum daily cost savings are achieved compared to existing cases such as P2G only and P2P only. Finally, the residential consumer can achieve a greenhouse gases (GHG) emissions reduction of 141.88 kgCO2-eq and 176.99 kgCO2-eq net savings, while the commercial consumer can achieve 122.34 kgCO2-eq GHG emissions mitigation and 196.53 kgCO2-eq net savings.

The usage of renewable energy sources in the power sector has increased day by day to preserve non-renewable resources from extinction. The growth of earnings in the electricity market has also become a critical part as the electricity generated from the renewable integrated system is fluctuating in nature. For a renewable-associated system, energy is abundant during low-demand hours. These excess units are available due to the presence of renewable energy sources like solar, wind, etc. So, a better way to manage this situation is to store the excess energy in the storage devices and use that energy in high-demand hours to enhance the system’s economic profit. This paper presents a comparative study of system economic parameters between regulated and deregulated renewable-associated systems. Solar power has been considered here as a renewable energy source due to its high efficiency and easy availability nature. This work also focused on maximizing the system profit in a deregulated system by the placement of a Thyristor Controlled Series Compensator (TCSC) along with the fuel cell in the presence of a disequilibrium price (DP). The disequilibrium price has occurred due to the disparity of real and expected renewable data which controls the entire renewable-associated system. To achieve maximum profit, it is imperative to reduce generation expenses to a minimum. A flexible alternating current transmission system (FACTS) is used to enhance and increase the system’s power transfer capability and controllability. The system voltage profile and the scenarios of location-based marginal price (LBMP) with and without consideration of TCSC have also been deployed in this work. The study has been performed with different optimization techniques such as Sequential Quadratic Programming (SQP) and Artificial Bee Colony Algorithms (ABC) to analyze the economic risk of the system with a modified IEEE 14-bus system.

The suggested hybrid power plant combines conventional and renewable energy sources along with energy storage devices such as wind, pumped hydro storage (PHS), thermal, and solar-powered electric vehicles (EVs). Its major goal is to improve the electricity network’s revenue and profitability while maintaining grid frequency stability (fG). To achieve this purpose, the approach considers the projected wind velocity (WVProj) in a deregulated market and commits wind farms to meet energy demand appropriately. The functioning of PHS and solar-connected EV power components is closely monitored to reduce the detrimental impact of power system imbalances caused by discrepancies in true (WPTrue) and projected (WPProj) wind production. This operational strategy aims to reduce the unpredictability associated with renewable energy sources cost-effectively. Highlighting the resilience of this technique, the approach incorporates four energy stages of the PHS upper basin (EPHS,max, EPHS,opt, EPHS,low, and EPHS,min) and four energy stages of the EV battery (EEVB,max, EEVB,opt, EEVB,low, and EEVB,min). By considering these energy levels, the approach illustrates the strategy’s long-term effectiveness. Several optimization methods are used for implementation and comparison, including Sequential Quadratic Programming (SQP), BAT algorithm, Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), and Cuckoo Search Algorithm (CSA). The efficacy of the suggested approach is assessed by analyzing and evaluating the IEEE 14-bus power network. The proposed technique deviates from conventional methods by employing the PHS plant to overcome uncertainties related to wind power, guaranteeing that committed generation patterns are reached with the support of solar power and EV-battery storage. Hourly simulations demonstrate that the proposed approach significantly enhances the use of maximum reservoir constraints, which improves system stability and security. The study demonstrates that the proposed two-phase operating technique is successful at improving revenue and profit while stressing stability and security inside the hybrid system. It also provides a feasible option for effective energy management.