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Introduction: The integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging. Methods: To address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis. Results: The proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (N PC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case. Discussion: Seasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption. [ABSTRACT FROM AUTHOR]

Household electricity consumption (HEC) is changing over time, depends on multiple factors, and leads to effects on the prediction accuracy of the model. The objective of this work is to propose a novel methodology for improving HEC prediction accuracy. This study uses two original datasets, namely questionnaire survey (QS) and monthly consumption (MC), which contain data from 225 consumers from Maharashtra, India. The original datasets are combined to create three additional datasets, namely QS + MC, QS equation (QsEq) + next month's consumptions, and QsEq + MC. Furthermore, the HEC prediction accuracy is boosted by applying different approaches, like correlation methods, feature engineering techniques, data quality assessment, heterogeneous ensemble prediction (HEP), and the hybrid model. Five HEP models are created using dataset combinations and machine learning algorithms. Based on the MC dataset, the random forest provides the best prediction of RMSE (36.18 kWh), MAE (25.73 kWh), and R2 (0.76). Similarly, QsEq + MC dataset adaptive boosting provides a better prediction of RMSE (36.77 kWh), MAE (26.18 kWh), and R2 (0.76). This prediction accuracy is further increased using the proposed hybrid model to RMSE (22.02 kWh), MAE (13.04 kWh), and R2 (0.92). This research work benefits researchers, policymakers, and utility companies in obtaining accurate prediction models and understanding HEC. [ABSTRACT FROM AUTHOR]

The growing industry and its complex and large information sets require Big Data (BD) technology and its open-source frameworks (Apache Hadoop) to (1) collect, (2) analyze, and (3) process the information. This information usually ranges in size from gigabytes to petabytes of data. However, processing this data involves web consoles and communication channels which are prone to intrusion from hackers. To resolve this issue, a novel machine learning (ML)-based security-centric approach has been proposed to evade cyber-attacks on the Hadoop ecosystem while considering the complexity of Big Data in Cloud (BDC). An Apache Hadoop-based management interface "Ambari" was implemented to address the variation and distinguish between attacks and activities. The analyzed experimental results show that the proposed scheme effectively (1) blocked the interface communication and retrieved the performance measured data from (2) the Ambari-based virtual machine (VM) and (3) BDC hypervisor. Moreover, the proposed architecture was able to provide a reduction in false alarms as well as cyber-attack detection. [ABSTRACT FROM AUTHOR]

The hybrid microgrid (HMG) is an arrangement for empowering the society by using the renewable energy sources (RESs) connected with common DC bus terminal. The effective operation of the HMG relies on the transient response of the DC bus as well as the stability of the system state that may change due to the stochastic behavior of RESs and industrial loads. This work is motivated to develop a two‐level decentralized control strategy consisting of a non‐linear and linear control scheme to augment the transient stability and tracking performance of an HMG. Firstly, a non‐linear partial feedback linearization control framework combined with a PI regulator (PFLC‐PI) is designed for the transient stability enhancement of the common bus. Secondly, a linear unified controller (UC) is developed to guarantee the constant load voltages over the industrial loads through the control of the load‐side inverter. The UC effectiveness is measured by varying the load parameters associated with single‐ and three‐phase load networks. The obtained results are then compared with the existing controllers to guarantee the augmented performance of proposed two‐level controller. Additionally, an external parametric uncertainty is intentionally added to the single‐phase load network dynamics and studied to ensure the robustness of the controller. [ABSTRACT FROM AUTHOR]