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Globally, renewable energy-based power generation is experiencing exponential growth due to concerns over the environmental impacts of traditional power generation methods. Microgrids (MGs) are commonly employed to integrate renewable sources due to their distributed nature, with batteries often used to compensate for power fluctuations caused by the intermittency of renewable energy sources. However, sudden fluctuations in the power supply can negatively impact battery performance, making it challenging to select an appropriate battery energy storage system (BESS) at the design stage of an MG. The cycle count of a battery in relation to battery stress is a useful measure for determining the general health of a battery and can aid in BESS selection. An accurate digital replica of an MG is required to determine the required cycle count and stress levels of a BESS. The Digital Twin (DT) concept can be used to replicate the dynamics of the MG in a virtual environment, allowing for the estimation of required cycle numbers and applied stress levels to a BESS. This paper presents a Microgrid Digital Twin (MGDT) model that can estimate the required cycle count and stress levels of a BESS without considering any unique battery type. Based on the results, designers can select an appropriate BESS for the MG, and the MGDT can also be used to roughly estimate the health of the currently operating BESS, allowing for cost-effective predictive maintenance scheduling for MGs. [ABSTRACT FROM AUTHOR]

Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are being researched, including thermal imaging, image processing, sensors, cameras with IoT, machine learning, and deep learning. In this study, a new dataset of images of dusty and clean panels is introduced and applied to the current state-of-the-art (SOTA) classification algorithms. Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2%. Hence, both the dataset and SolNet can be used as benchmarks for future research endeavors. Furthermore, the classes of the dataset can also be expanded for multiclass classification. At the same time, the SolNet model can be fine-tuned by tweaking the hyperparameters for further improvements. [ABSTRACT FROM AUTHOR]

This paper considers the problem of false data injection attacks (FDIAs) on load frequency control of interconnected smart grids (ISGs) with delayed electric vehicles (EVs) and renewable energies. By intruding incorrect information, unauthorised users can corrupt the system information leading to degradation in the performance and disruptions of ISGs. In this paper, a model of ISGs subject to FDIAs in aggregator of EVs and power plants is first presented. This mathematical representation comprises dynamic interactions of power plants, delayed EVs, renewable energies and FDIAs on both system states and outputs. Based on recent advanced techniques on functional observers and matrix inequalities for time‐delay systems, then a new distributed functional observers based scheme is developed to realise the tasks of detecting and isolating FDIAs. Also, an effective procedure presented in tractable linear matrix inequalitiesis build with an optimisation process for the synthesis of the detector. The proposed detector is distributed, of reduced order, avoids the risk of centralised malicious incidents, therefore easy for implementation and monitoring tasks. The stability of ISGs and contribution of EVs subject to FDIAs are also discussed. Comprehensive simulations are given to demonstrate the effectiveness of our proposed method by using three‐area ISGs. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced control scheme for a direct drive variable speed wind turbine with an interior permanent magnet (IPM) synchronous generator. The proposed control scheme incorporates maximum torque per ampere (MTPA) trajectory and maximum power extraction, which ensures the generation of required torque for maximum power extraction with minimum stator current. This in turn minimizes stator copper loss and excessive heat generated in the IPM synchronous generator. The performance of the proposed control scheme has been validated through rigours simulation and experimental studies under varying wind speed conditions. The simulation and experimental results demonstrate the efficacy of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper aims to develop the generalized dynamical model of multiple photovoltaic (PV) units connected to the grid along with the dynamic interaction analysis among different PV units. The dynamical models of multiple PV units are developed by considering three different configurations through which these PV units are connected to the grid. These configurations include: (a) the direct connection of multiple PV units to the grid; (b) the connection of multiple PV units to the grid through a point of common coupling (PCC); and (c) the connection of PV units without a PCC. The proposed modeling framework provides meaningful insights for analyzing dynamic interaction analysis where these interactions from other PV units are expressed in terms of voltages and line impedances rather than the dynamics of currents. The dynamic interactions among different PV units for all these configurations are analyzed using both analytical and simulation studies. Simulations are carried out on an IEEE 15-bus test system and dynamic interactions are analyzed from the total harmonic distortions (THDs) in the current responses of different PV units. Both analytical and simulation studies clearly indicate that the effects of dynamic interactions are prominent with the increase in PV units. [ABSTRACT FROM AUTHOR]

This paper presents an analytical framework to develop a hierarchical energy management system (EMS) for energy sharing among neighbouring households in residential microgrids. The houses in residential microgrids are categorized into three different types, traditional, proactive and enthusiastic, based on the inclusion of solar photovoltaic (PV) systems and battery energy storage systems (BESSs). Each of these three houses has an individual EMS, which is defined as the primary EMS. Two other EMSs (secondary and tertiary) are also considered in the proposed hierarchical energy management framework for the purpose of effective energy sharing. The intelligences of each EMS are presented in this paper for the purpose of energy sharing in a residential microgrid along with the priorities. The effectiveness of the proposed hierarchical framework is evaluated on a residential microgrid in Australia. The analytical results clearly reflect that the proposed scheme effectively and efficiently shares the energy among neighbouring houses in a residential microgrid. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to identify the real and reactive power transfer between generators and load using modified nodal equations. Based on available load flow results, the method partitions the Y-bus matrix to decompose the current of the load buses as a function of the generators' current and load voltages. Then, it uses the modified admittance matrix to decompose the load voltage-dependent term into components of generator dependent terms. By using these two decompositions of current and voltage terms, the real and reactive power transfer between loads and generators are obtained. The effectiveness of the proposed method is demonstrated by using a simple 5-bus system and the modified IEEE 30-bus system. The proposed methodology provides reasonable and acceptable results to real and reactive power transfer allocation. [ABSTRACT FROM AUTHOR]

In this paper, partial feedback linearizing model predictive controllers are designed for grid-connected systems comprising multiple photovoltaic (PV) units where these units are connected through a point of common coupling (PCC). The proposed controllers are designed for voltage source inverters (VSIs) based on comprehensive dynamical models of grid-connected PV systems with the proposed topology. The proposed partial feedback linearization scheme decouples multiple PV units in the forms of several reduced-order subsystems and enables linear controller design through the linear continuous-time receding horizon model predictive control scheme. The proposed partial feedback linearization scheme also considers dynamic interactions among multiple PV units as external noises or disturbances and decouples these noises. This paper includes the noise decoupling capability of the partial feedback linearization for grid-connected PV systems with multiple PV units which are connected through a PCC. Simulation results clearly demonstrate the effectiveness of the proposed scheme under different operating conditions as compared to an existing proportional integral controller. [ABSTRACT FROM AUTHOR]