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The Virtual Power Plant (VPP) is a promising solution to power systems' challenges. However, its energy management system (EMS) faces challenges due to centralized Big-Data analysis, i.e., complexity and high computational cost. Decentralized strategies, e.g., federated learning (FL), have been proposed to mitigate these challenges. Although conventional FL can be implemented in VPPs, its effectiveness is limited by its typical structure, which relies on single-pattern modeling. Indeed, this particular structure leads to delays and inaccuracies in optimizing the heterogeneous generator dispatch patterns, particularly renewables. Hence, addressing this critical hindrance is essential for FL's efficient and effective deployment in VPPs. To overcome this drawback, this paper proposes novel VPP modeling utilizing multi-task FL modernized by multi-pattern modeling to decentralize the EMS processing. The proposed FL defines electricity generators' agents as clients collaborating to train the decentralized local data coordinated by a central server. The innovative idea is to create a novel multi-pattern-multi-task FL for electricity generation prediction so that the generator's agents learn to provide an optimized dispatch. Thus, the conventional multi-task FL is improved by proposing an optimized neural-network clustering technique. Simulation results demonstrate that multi-pattern-multi-task FL is 39%-41% faster than the best-reported methods, i.e., teaching–learning-based and honey-bee-mating optimization algorithms.© 2017 Elsevier Inc. All rights reserved.

The most important feature of load forecasting is enabling the building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has increased during the COVID-19 period, as compared to normal times. Therefore, the performance of forecasting methods is impacted, and further tuning of parameters is required to cope with energy consumption changes due to COVID-19. This paper proposes a new adaptive neuro-fuzzy 2 inference system (ANFIS2) for energy usage forecasting in residential buildings for both normal and COVID-19 periods. The particle swarm optimization (PSO) method has been implemented for parameter optimization, and subtractive clustering is used for data training for the proposed ANFIS2 system. Two modifications in terms of input and parameters of ANFIS2 are made to cope with the change in the consumption pattern and reduce the prediction errors during the COVID-19 period. Simulation results obtained by MATLAB software validate the efficacy of the proposed ANFIS2 in residential load forecasting during both normal and COVID-19 periods. Moreover, the performance of the proposed method is better than that of the existing adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), and random forest (RF) approaches.

Conventional evolutionary optimization techniques often struggle with finding global optima, getting stuck in local optima instead, and can be sensitive to initial conditions and parameter settings. Efficient Distributed Generation (DG) allocation in distribution systems hinges on streamlined optimization algorithms that handle complex energy operations, support real-time decisions, adapt to dynamics, and improve system performance, considering cost and power quality. This paper proposes the Simulated-Annealing-Quasi-Oppositional-Teaching-Learning-Based Optimization Algorithm to efficiently allocate DGs within a distribution test system. The study focuses on wind turbines, photovoltaic units, and fuel cells as prominent DG due to their growing usage trends. The optimization goals include minimizing voltage losses, reducing costs, and mitigating greenhouse gas emissions in the distribution system. The proposed algorithm is implemented and evaluated on the IEEE 70-bus test system, with a comparative analysis conducted against other evolutionary methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Honey Bee Mating Optimization (HBMO), and Teaching-Learning-Based Optimization (TLBO) algorithms. Results indicate that the proposed algorithm is effective in allocating the DGs. Statistical testing confirms significant results (probability < 0.1), indicating superior optimization capabilities for this specific problem. Crucially, the proposed algorithm excels in both accuracy and computational speed compared to other methods studied.

Abstract The surging usage of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS) with millisecond range latency and massive machine to machine communications where 5G could act. However, 5G suffers from inherent protocols, hardware, and software vulnerabilities that seriously threaten the communicating entities' cyber‐physical security. To overcome these limitations in the EVCS system, this paper analyses the impact of False Data Injection (FDI) and Distributed Denial of Services (DDoS) attacks on the operation of EVCS. This work is an extension of the previously published conference paper about the EVCS. As new features, this paper simulates the FDI attack and the syn flood DDoS attacks on 5G enabled remote Supervisory Control and Data Acquisition (SCADA) system that controls the solar photovoltaics (PV) controller, Battery Energy Storage (BES) controller, and EV controller of the EVCS. The extent of delay has been increased to more than 500 ms with the severe DDoS attack via 5G. The attacks make the EVCS system oscillate or shift the DC operating point. The frequency of oscillation, its damping, and the system's resiliency are found to be related to the attacks' intensity and the target controller. Finally, the novel stacked Long Short‐Term Memory (LSTM) based intrusion detection systems (IDS) are proposed solely based on the electrical fingerprint. This model can detect the stealthy cyberattacks that bypass the cyber layer and go unnoticed in the monitoring system with nearly 100% detection accuracy.

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification; however, the EVCS has various vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCSs open up large attack surfaces for local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose data-driven model-free digital clones based on multiple independent agents deep reinforcement learning (IADRL) that uses the Twin Delayed Deep Deterministic Policy Gradient (TD3) to efficiently learn the control policy to mitigate the cyberattacks on the controllers of EVCS. Also, the proposed digital clones trained with TD3 are compared against the benchmark Deep Deterministic Policy Gradient (DDPG) agent. The attack model considers the APT designed to malfunction the duty cycles of the EVCS controllers with Type-I low-frequency attacks and Type-II constant attacks. The proposed model restores the EVCS operation under threat incidence in any/all controllers by correcting the control signals generated by the legacy controllers. Our experiments verify the superior control policies and actions of TD3-based clones compared to the DDPG-based clones. Also, the TD3-based controller clones solve the problem of incremental bias, suboptimal policy, and hyperparameter sensitivity of the benchmark DDPG-based digital clones, enforcing the efficient mitigation of the impact of cyberattacks on EVCS controllers.

This work proposes two non-linear and one linear equation-based system for residential load forecasting considering heating degree days, cooling degree days, occupancy, and day type, which are applicable to any residential building with small sets of smart meter data. The coefficients of the proposed nonlinear and linear equations are tuned by particle swarm optimization (PSO) and the multiple linear regression method, respectively. For the purpose of comparison, a subtractive clustering based adaptive neuro fuzzy inference system (ANFIS), random forests, gradient boosting trees, and long-term short memory neural network, conventional and modified support vector regression methods were considered. Simulations have been performed in MATLAB environment, and all the methods were tested with randomly chosen 30 days data of a residential building in Memphis City for energy consumption prediction. The absolute average error, root mean square error, and mean average percentage errors are tabulated and considered as performance indices. The efficacy of the proposed systems for residential load forecasting over the other systems have been validated by both simulation results and performance indices, which indicate that the proposed equation-based systems have the lowest absolute average errors, root mean square errors, and mean average percentage errors compared to the other methods. In addition, the proposed systems can be easily practically implemented.

Aggressive voltage scaling into the subthreshold operating region holds great promise for applications with strict energy budget. However, it has been established that higher speed superthreshold device is not suitable for moderate performance subthreshold circuits. The design constraint for selecting Vth and TOX is much more flexible for subthreshold circuits at low voltage level than superthreshold circuits. In order to obtain better performance from a device under subthreshold conditions, it is necessary to investigate and optimize the process and geometry parameters of a Si MOSFET at nanometer technology node. This paper calibrates the fabrication process parameters and electrical characteristics for n- and p-MOSFETs with 35 nm physical gate length. Thereafter, the calibrated device for superthreshold application is optimized for better performance under subthreshold conditions using TCAD simulation. The device simulated in this work shows 9.89% improvement in subthreshold slope and 34% advantage in ION/IOFF ratio for the same drive current.

E-learning does not function properly if the system is not in accordance with user needs. This study aims to establish an evaluation model for e-learning user interface according to user acceptance. The model is designed based on three categories: user learning style, usability and user benefits. Results of measurements of the three categories will determine the level of user acceptance of the e-learning interface. The data were taken using a questionnaire which was distributed to 125 ELS students from various countries. Then processed using SEM and Lisrel v8.80. This paper presents experimental set up for the general research and some results for technology acceptance theories.

This paper analyses standard 6T and 7T SRAM (static random access memory) cell in light of process, voltage and temperature (PVT) variations to verify their functionality and robustness. The 7T SRAM cell consumes higher hold power due to its extra cell area required for its functionality constraint. It shows 60% improvement in static noise margin (SNM), 71.4% improvement in read static noise margin (RSNM) and 50% improvement in write static noise margin (WSNM). The 6T cell outperforms 7T cell in terms of read access time (TRA) by 13.1%. The write access time (TWA) of 7T cell for writing "1" is 16.6 x longer than that of 6T cell. The 6T cell proves it robustness against PVT variations by exhibiting narrower spread in TRA (by 1.2 x) and Twa (by 3.4x). The 7T cell offers 65.6% saving in read power (RPWR) and 89% saving in write power (WPWR). The RPWR variability indicates that 6T ell is more robust against process variation by 3.9x. The 7T cell shows 1.3x wider write power (WPWR) variability indicating 6T cell's robustness against PVT variations. All the results are based on HSPICE simulation using 32 nm CMOS Berkeley Predictive Technology Model (BPTM).

Battery energy storage systems (BESSs) play a crucial role in integrating renewable energy sources into microgrids. However, robust BESS controllers are needed to carry out this function properly. Existing controllers suffer from overshoots and slow convergence issues. Moreover, as electrical grid networks become increasingly connected, the risk of cyberattacks grows, and traditional physics-based anomaly detection methods face challenges such as reliance on predefined models, high computational demands, and limited scalability for complex, large-scale data. To address the limitations of the existing approaches, this paper first proposes a novel sigma-delta modulation (SDM) controller for BESSs in solar photovoltaic (PV)-connected microgrids. The performance of SDM has been compared with those of the proportional–integral (PI) controller and fuzzy logic controller (FLC). Also, this paper proposes an improved ensemble-based method to detect the false data injection (FDI) and denial-of-service (DoS) attacks on the BESS controller. The performance of the proposed detection method has been compared with that of the traditional ensemble-based method. Four PV-connected microgrid systems, namely the solar DC microgrid, grid-connected solar AC microgrid, hybrid AC microgrid with two BESSs, and hybrid AC microgrid with a single BESS, have been considered to show the effectiveness of the proposed control and detection methods. The MATLAB/Simulink-based results show the effectiveness and better performance of the proposed controller and detection methods. Numerical results demonstrate the improved performance of the proposed SDM controller, with a 35% reduction in AC bus voltage error compared to the conventional PI controller and FLC. Similarly, the proposed SAMME AdaBoost detection method achieves superior accuracy with an F1 score of 95%, outperforming the existing ensemble approaches. [ABSTRACT FROM AUTHOR]