Search

With the gradual transformation of energy industries around the world, the trend of industrial reform led by clean energy has become increasingly apparent. As a critical link in the new energy industry chain, lithium-ion (Li-ion) battery energy storage system plays an irreplaceable role. Accurate estimation of Li-ion battery states, especially state of charge (SOC) and state of health (SOH), is the core to realize the safe and efficient utilization of energy storage systems. This paper presents a systematic and comprehensive evaluation and summary of the most advanced Li-ion battery state estimation methods proposed in the past 3 years, focusing on analyzing data-driven state estimation algorithms. At the same time, the latest Li-ion battery data sets and data selection methods are analyzed, and future research trends and possible challenges are proposed. This review will provide a valuable reference for future academic research in Li-ion battery state estimation. [ABSTRACT FROM AUTHOR]

Power electronic converters are widely implemented in many types of power applications such as microgrids. Power converters can make a physical connection between the power resources and the power application. To control a power converter, required data such as the voltage and the current of that should be measured to be used in a control application. Therefore, a communication-based structure including sensors and communication links can be used to measure the desired data and transmit that to the controllers. So, a power converter-based system can be considered as a type of cyber-physical system, and it can be vulnerable to cyber-attacks. Then, it can strongly be recommended to use a strategy for a power converter-based system to monitor the system and identify the existence of cyber-attack in the system. In this study, artificial intelligence (AI) is deployed to calculate the value of the false data (i.e., constant false data, and time-varying false data) and detect false data injection cyber-attacks on power converters. Besides, to have a precise technical evaluation of the proposed methodology, that is evaluated under other issues, i.e., noise, and communication link delay. In the case of noise, the proposed strategy is examined under noises with different signal-to-noise ratios. Further, for the case of the communication delay, the system is examined under both symmetrical (i.e., same communication delay on all inputs) and unsymmetrical communication delays (i.e., different communication delay/delays on the inputs). In this work, artificial neural networks are implemented as the AI-based application, and two types of the networks, i.e., feedforward (as a basic type) and long short-term memory (LSTM)-based network as a more complex network are tested. Finally, three important AI-based techniques (regression, classification, and clustering) are examined. Based on the obtained results, this work can properly identify and calculate the false data in the system. [ABSTRACT FROM AUTHOR]

The air-supported membrane coal shed is widely used in bulk cargo terminals. It not only effectively protects goods from adverse weather conditions but also helps reduce coal dust and harmful gas emissions, promoting the green and sustainable development of ports. However, in practical engineering, the design parameters of the coal shed are often based on experience, making it difficult to accurately assess the quality of the structural design. The flexibility of the membrane material also makes the structure susceptible to deformation or tearing. This paper mainly focuses on modeling and solving the optimization design issues of air-supported membrane coal shed structures. According to the evaluation criteria for the form of air-supported membrane coal sheds, a multi-objective structural optimization model is established to minimize the maximum stress on the membrane surface, ensure uniform stress distribution, maximize structural stiffness, and minimize costs. The study utilizes a combined optimization approach using ANSYS 19.0 and MATLAB 2016a, incorporating an improved NSGA-II algorithm program developed in MATLAB into ANSYS for structural form analysis and load calculation. The research results indicate that the optimal solution reduces the maximum stress on the loaded membrane surface by 5.36%, shortens the maximum displacement by 30.3%, and saves on economic costs by 9.85%. Compared to traditional empirical design methods, the joint use of MATLAB and ANSYS for optimization design can provide more superior solutions, helping ports to achieve environmental protection and economic efficiency goals. [ABSTRACT FROM AUTHOR]

In the context of integrating Renewable Energy Sources, Microgrid (MG) development is pivotal, particularly as a foundational technology for Smart-Grid evolution. Despite advancements in control techniques, challenges persist in ensuring system stability and accurate power sharing across diverse operational conditions and load types. The objective of this research is to control numerous paralleled inverters-based distributed generators (DGs) that contribute to power sharing in an island MG. The proposed methodology involves developing an innovative small-signal model for islanding MGs that incorporate virtual impedances. Subsequently, optimization algorithms based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are proposed and compared for designing the virtual impedances. These algorithms analyze all potential operating points, aiming to minimize reactive power mismatches while maximizing MG stability. The suggested objective function facilitates the simultaneous achievement of these objectives. The proposed approaches were tested using MATLAB-Simulink software, and the comparison of the results between conventional approach and the proposed optimal approaches shows significant improvement in terms of the dynamic response during load changes, such as a decrease in response time by up to 20%, a reduction in overshoot percentage by approximately 15%, and a settling time improvement of nearly 25%. These quantified improvements highlight the effectiveness of the GA and PSO methods in minimizing the reactive power-sharing error while optimizing MG performance and stability. [ABSTRACT FROM AUTHOR]

In the context of global carbon neutrality, ports face significant electricity demand for cargo handling and pressure to reduce carbon emissions. The abundant wind energy resources in port areas make wind power highly promising for port applications. The optimal selection of site and turbine types for wind power systems can effectively reduce emissions in ports, achieving sustainability and improving economic benefits. The practical implementation of wind energy systems considering practical constraints holds significant research significance. Taking Ningbo-Zhoushan Port as an example, this paper analyzes the wind energy resources in the port area and provides an overview of wind power system construction sites. Based on the actual conditions of the port area, this paper comprehensively reviews the site selection of wind turbines from the perspectives of wind resources, specific climates, and noise impacts. With the consideration of engineering preferences, this paper selects performance indicators based on the four mainstream turbine models and proposes a comprehensive weight determination method using the entropy weight method and analytic hierarchy process (AHP) to determine the weights of the indicators. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is then employed to score and compare four turbine plans, enabling the turbine selection process to consider both engineering preferences and objectivity, thereby enhancing the accuracy and reliability of wind turbine planning and achieving significant ecological and economic benefits through benefit analysis. [ABSTRACT FROM AUTHOR]

Fossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced a notable decrease in the production of coal and natural gas energy, juxtaposed with a significant rise in the production of renewable energy sources. The study employed neural networks, ANNs (artificial neural networks), and LSTM (long short-term memory), as well as CNN (convolutional neural network) and hybrid CNN-LSTM designs, to assess Turkey's energy potential. Real-time outcomes were produced by integrating these models with meteorological data. The objective was to design strategies for enhancing performance by comparing various models of outcomes. The data collected for Turkey as a whole are based on average values. Machine learning approaches were employed to mitigate the error rate seen in the acquired outcomes. Comparisons were conducted across light gradient boosting machine (LightGBM), gradient boosting regressor (GBR), and random forest regressor (RF) techniques, which represent machine learning models, alongside deep learning models. Based on the findings of the comparative analyses, it was determined that the machine learning model, LightGBM, exhibited the most favorable performance in enhancing the accuracy of predictions. Conversely, the hybrid model, CNN-LSTM, had the greatest rate of inaccuracy. This study will serve as a guide for renewable energy researchers, especially in developing countries such as Turkey that have not switched to a smart grid system. [ABSTRACT FROM AUTHOR]

The transition to sustainable power infrastructure necessitates integrating various renewable energy sources efficiently. Our study introduces the deterministic balanced method (DBM) for optimizing hybrid energy systems, with a particular focus on using hydrogen for energy balance. The DBM translates the sizing optimization problem into a deterministic one, significantly reducing the number of iterations compared to state-of-the-art methods. Comparative analysis with HOMER Pro demonstrates a strong alignment of results, with deviations limited to a 5% margin, confirming the precision of our method in sizing determinations. Utilizing solar and wind data, our research includes a case study of Cairo International Airport, applying the DBM to actual energy demands. [ABSTRACT FROM AUTHOR]

In recent years, electric vehicles (EVs) have become increasingly popular, bringing about fundamental shifts in transportation to reduce greenhouse effects and accelerate progress toward decarbonization. The role of EVs has also experienced a paradigm shift for future energy networks as an active player in the form of vehicle-to-grid, grid-to-vehicle, and vehicle-to-vehicle technologies. EVs spend a significant part of the day parked and have a remarkable potential to contribute to energy sustainability as backup power units. In this way, EVs can be connected to the grid as stationary power units, providing a range of services to the power grid to increase its reliability and resilience. The available systems show that EVs can be used as alternative energy sources for various network systems like smart grids, microgrids, and virtual power plants besides transportation. While the grid–EV connection offers various contributions, it also has some limitations and effects. In this context, the current study highlights the power system impacts and key contributions of EVs connected to smart grids. Regarding the power system impacts in case of EV integration into smart grids, the challenges and difficulties are categorized under the power system stability, voltage/current distortions, load profile, and power losses. Voltage/current distortions like sags, unbalances, harmonics, and supraharmonics are also detailed in the study. Subsequently, the key contributions to the smart grid in terms of energy management, grid-quality support, grid balancing, and socio-economic impacts are explained. In the energy management part, issues such as power flow, load balancing, and renewable energy integration are elaborated. Then, the fault ride-through capability, reactive power compensation, harmonic mitigation, and grid loss reduction are presented to provide information on power quality enhancement. Lastly, the socio-economic impacts in terms of employment, net billing fees, integration with renewable energy sources, and environmental effects are elucidated in the present study. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks. [ABSTRACT FROM AUTHOR]

Earlier protection methods mainly focused on using communication channels to transmit trip signals between the protective devices (PDs), with no solutions provided in the case of communication failure. Therefore, this paper introduces a dual-layer protection system to ensure secure protection against fault events in the Distribution Systems (DSs), particularly in light of communication failures. The initial layer uses the Total Harmonic Distortion (THD), the estimates of the amplitude voltages, and the zero-sequence grid voltage components, functioning as a fault sensor, to formulate an adaptive algorithm based on a Finite State Machine (FSM) for the detection and isolation of faults within the grid. This layer primarily relies on communication protocols for effective coordination. A Second-Order Generalized Integrator (SOGI) expedites the derivation of the estimated variables, ensuring fast detection with minimal computational overhead. The second layer uses the behavior of the positive- and negative-sequence components of the grid voltages during fault events to locate and isolate these faults. In the event that the first layer exposes a communication failure, the second layer will automatically be activated to ensure secure protection as it operates, using the local information of the Protective devices (PDs), without the need for communication channels to transmit trip signals between the PDs. The proposed protection system has been assessed using simulations with MATLAB/Simulink and providing experimental results considering an IEEE 9-bus standard radial system. The obtained results confirm the capability of the system for identifying and isolating different types of faults, varying conditions, and modifications to the grid configuration. The results show good behavior of the initial THD-based layer, with fast time responses ranging from 6 to 8.5 ms in all the examined scenarios. In contrast, the sequence-based layer exhibits a protection time response of approximately 150 ms, making it a viable backup option in the event of a communication failure. [ABSTRACT FROM AUTHOR]

The DC microgrid is comprised of a considerable number of electronically regulated power electronic loads that act as constant power loads (CPLs). These power electronic devices have a high bandwidth regulation capability as well as a high-power conversion efficiency. Specifically, the high bandwidth control for the output of the converter load, when paired with the system's filtering components, results in negatively damped oscillations. These features, even if needed, may cause system instability and, finally, system failure if not avoided. To achieve effective power flow control in a DC microgrid, it is crucial to eliminate the undesired behaviour of the CPLs. The control objective requires the assessment of the power for uncertain loads, which vary with time. This paper proposes an adaptive controller linked to a cubature Kalman filter(CKF) for a DC microgrid with time-varying non-ideal CPLs. The controller utilises the neuro-fuzzy inference system(ANFIS) to make the design adaptive. The CKF method is used to determine the instantaneous value of time-varying load power. The assessed power is afterward sent to an ANFIS-based controller, which aims to modify the energy storage systems (ESS) injected current adaptively. The suggested controller not only maintains overall stability when the CPLs vary significantly, but it has a rapid dynamic response and accurate tracking across a wide operating range as well. The simulation results demonstrate that the proposed adaptive controller can improve the DC microgrid's transient response while also increasing the stability margin. [ABSTRACT FROM AUTHOR]

For a lithium-ion (Li-ion) battery to operate safely and reliably, an accurate state of health (SOH) estimation is crucial. Data-driven models with manual feature extraction are commonly used for battery SOH estimation, requiring extensive expert knowledge to extract features. In this regard, a novel data pre-processing model is proposed in this paper to extract health-related features automatically from battery-discharging data for SOH estimation. In the proposed method, one-dimensional (1D) voltage data are converted to two-dimensional (2D) data, and a new data set is created using a 2D sliding window. Then, features are automatically extracted in the machine learning (ML) training process. Finally, the estimation of the SOH is achieved by forecasting the battery voltage in the subsequent cycle. The performance of the proposed technique is evaluated on the NASA public data set for a Li-ion battery degradation analysis in four different scenarios. The simulation results show a considerable reduction in the RMSE of battery SOH estimation. The proposed method eliminates the need for the manual extraction and evaluation of features, which is an important step toward automating the SOH estimation process and developing battery digital twins. [ABSTRACT FROM AUTHOR]

In this research paper, a comprehensive performance analysis was carried out for a 48-watt transformerless DC-DC boost converter using a Proportional–Integral–Derivative (PID) controller through dynamic modeling. In a boost converter, the optimal design of the magnetic element plays an important role in efficient energy transfer. This research paper emphasizes the design of an inductor using the Area Product Technique (APT) to analyze factors such as area product, window area, number of turns, and wire size. Observations were made by examining its response to changes in load current, supply voltage, and load resistance at frequency levels of 100 and 500 kHz. Moreover, this paper extended its investigation by analyzing the failure rates and reliability of active and passive components in a 48-watt boost converter, providing valuable insights about failure behavior and reliability. Frequency domain analysis was conducted to assess the controller's stability and robustness. The results conclusively underscore the benefits of incorporating the designed PID controller in terms of achieving the desired regulation and rapid response to disturbances at 100 and 500 kHz. The findings emphasize the outstanding reliability of the inductor, evident from the significantly low failure rates in comparison to other circuit components. Conversely, the research also reveals the inherent vulnerability of the switching device (MOSFET), characterized by a higher failure rate and lower reliability. The MATLAB® Simulink platform was utilized to investigate the results. [ABSTRACT FROM AUTHOR]

Summary: Accurate state of charge (SOC) estimation is based on a precise battery model and is the focus of the battery management system (BMS). First, based on the second‐order RC equivalent circuit model and Grunwald–Letnikov (G‐L) definition, the high‐precision fractional‐order hysteresis‐equivalent circuit model (FH‐ECM) is established considering the open‐circuit voltage hysteresis effect. Then, the global parameters of the battery model are identified using a particle swarm algorithm optimized by the genetic algorithm (GA‐PSO). Third, a fractional‐order adaptive unscented Kalman filter (FOAUKF) algorithm is derived to estimate the SOC of lithium‐ion batteries. Finally, the feasibility of the model and algorithm is verified under complex working conditions. Under the dynamic stress test (DST) condition, the accuracy of model terminal voltage has been improved by 37.83%, and the error of SOC estimation has been reduced by 11.28%. Under Beijing bus dynamic stress test (BBDST) condition, the model terminal voltage accuracy has been improved by 51.44%, and the SOC estimation error has been reduced by 35.71%. The experimental results fully confirm the accuracy of the fractional‐order hysteresis‐equivalent circuit modeling method. [ABSTRACT FROM AUTHOR]

This work analyzes the stability and performance of an offshore solar-concentrated ocean thermal energy conversion system (SC-OTEC) tied to an onshore AC grid. The OTEC is a system where electricity is generated using small temperature differences between the warm surface and deep cold ocean water. Existing control methods for SC-OTEC systems lack coordination, hindering dynamic stability and effective damping for the synchronous generator (SG). These methods struggle to quickly adapt to sudden disturbances and lack the capability to adequately reject or compensate for such disturbances due to complex control constraints and computational demands. To this regard, a control strategy combining sliding mode control (SMC) and a power system stabilizer (PSS) to improve the SC-OTEC dynamic stability and damping features for the SG. Moreover, an auxiliary secondary automatic voltage regulator is assembled with a non-linear exciter system to provide damping features. The proposed PID-PSS and secondary AVR controller gains are adaptively tuned using a modified whale optimization algorithm with the balloon effect modulation. The studied SC-OTEC is tested through MATLAB/Simulink under a severe 3ϕ short-circuit fault, solar radiation variations, and a change in surface seawater temperature as well as changes in local loads. The final findings approved that the proposed control strategy preserves a strong performance and can mimic effectively the proposed SC-OTEC damping compared to the conventional system. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the operation management of a multi-node community microgrid (MG), emphasizing power flow constraints and the integration of photovoltaic (PV) and battery systems. This study formulates MG operation management as a multi-objective optimal power flow problem, aiming to minimize costs (maximize profits) and emissions simultaneously. The multi-objective particle swarm optimization (MPSO) method is employed to tackle this complex optimization challenge, yielding a Pareto optimal front that represents the trade-offs between these conflicting objectives. In addition to the normative operation scenarios, this research investigates the robustness of the MG system in the face of abnormal situations. These abnormal scenarios include damage to the PV system, sudden increases in the MG load, and the loss of connection to the main electricity grid. This study focuses on Lombok Island, Indonesia as a practical case study, acknowledging the ongoing efforts to implement the community MG concept in this region. It is observed that when the access to the electricity grid is limited, the energy not served (ENS) increases to 2.88 MWh. During the fault scenario in which there is a 20% increase in the hourly load of each MG, a total of 4.5 MWh ENS is obtained. It is concluded that a resilient operation management system is required to ensure a consistent and reliable energy supply in community MGs in the face of disruptions. [ABSTRACT FROM AUTHOR]

In recent years, community microgrids have expanded their power systems with many aims. One of the most important goals of microgrids is to increase resiliency. The main objective of this paper is to develop effective planning tools for community microgrids within electrical distribution networks, with a specific focus on ensuring the provision of critical loads during natural disasters. Additionally, this paper emphasized emphasises long-term planning considerations by using DIgSILENT 15.2 tools. The primary goal of this issue is to create the best planning tools for community microgrids in order to increase the network's resilience to natural disasters, with a focus on important loads like hospitals and hotels. Also, the second goal is an optimization that seeks to reduce overall expenses. Finally, we'll talk about how to get two results: one is to choose a few microgrid-based loads to link to the electrical distribution network, and the other is to show that each microgrid has the power needed to support both its local loads and the system's vital loads in the event of a natural disaster. In this paper, the use of community microgrids for energy access against natural disasters in Indonesia is investigated by considering the case study of microgrids on Lombok Island. The study results, using the proposed framework, show that the presence of a microgrid structure in the distribution network expansion planning helps to improve operating conditions and supply critical loads in natural disasters. [ABSTRACT FROM AUTHOR]

In light of the growing integration of renewable energy sources in power systems, the adoption of DC microgrids has become increasingly popular, due to its simple structure, having no frequency, power factor concerns. However, the dependence of DC microgrids on cyber-networks also makes them susceptible to cyber-attacks. Potential cyberattacks can disrupt power system facilities and result in significant economic and loss of life. To address this concern, this paper presents an attack-resilient control strategy for microgrids to ensure voltage regulation and power sharing with stable operation under cyber-attack on the actuators. This paper first formulates the cyber-security problem considering a distributed generation based microgrid using the converter model, after which an attack-resilient control is proposed to eliminate the actuator attack impact on the system. Steady state analysis and root locus validation illustrate the feasibility of the proposed method. The effectiveness of the proposed control scheme is demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue on computational methods and artificial intelligence applications in low-carbon energy systems. The editorial highlights the urgent need for advanced computing and artificial intelligence in the clean energy transition to improve system reliability, economics, and sustainability. The special issue includes 19 original research articles covering topics such as energy forecasting, situational awareness, multi-energy system dispatch, and power system operation. The articles present state-of-the-art methods and techniques in these areas, including wind power forecasting, demand-side flexibility, fault diagnosis of photovoltaic strings, and energy management strategies. The authors express their gratitude to the participating authors and anonymous reviewers for their contributions to the special section. [Extracted from the article]

Rural electrification is a crucial component of meeting sustainable development goals. In Pakistan, load shedding and power outages are frequent. According to the current data, Pakistan is experiencing a shortfall of power between 6000 and 6500 MW. Microgrid technology has the potential to provide a solution to this problem in an efficient and low‐cost manner. This paper proposes the development of a hybrid microgrid system (HMGS) for rural communities. For that purpose, a technological analysis of the HMGS system for rural electrification is performed. Solar photovoltaic (PV) and wind resource‐based renewable energy systems are considered in this work for the electrification of rural areas of Pakistan. A hybrid PV/wind system is designed using MATLAB software. Simulation results show that a 230‐V sinusoidal output voltage has been produced by the proposed model. The advantage of this model is that it minimizes the impact of transients and provides a sinusoidal output waveform. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed generation (DG) to power distribution networks mainly induces weaknesses in the sensitivity and selectivity of protection systems. In this manner, conventional protection systems often fail to protect active distribution networks (ADN) in the case of short-circuit faults. To overcome these challenges, the accurate detection of faults in a reasonable fraction of time appears as a critical issue in distribution networks. Machine learning techniques are capable of generating efficient analytical expressions that can be strong candidates in terms of reliable and robust fault detection for several operating scenarios of ADNs. This paper proposes a deep group method of data handling (GMDH) neural network based on a non-pilot protection method for the protection of an ADN. The developed method is independent of the DG capacity and achieves accurate fault detection under load variations, disturbances, and different high-impedance faults (HIFs). To verify the improvements, a test system based on a real distribution network that includes three generators with a capacity of 6 MW is utilized. The extensive simulations of the power network are performed using DIgSILENT Power Factory and MATLAB software. The obtained results reveal that a mean absolute percentage error (MAPE) of 3.51% for the GMDH-network-based protection system is accomplished thanks to formulation via optimized algorithms, without requiring the utilization of any feature selection techniques. The proposed method has a high-speed operation of around 20 ms for the detection of faults, while the conventional OC relay performance is in the blinding mode in the worst situations for faults with HIFs. [ABSTRACT FROM AUTHOR]

Ports in China are facing significant pressure to reduce carbon emissions in alignment with carbon peak and carbon neutrality goals. Onshore power supply (OPS) is regarded as a promising approach to accomplish these targets, necessitating a thorough evaluation of its impact for port authorities to make informed decisions regarding its adoption. This research focuses on Ningbo Zhoushan Port, the largest port globally, as a case study. Two metrics are proposed to quantify the energy efficiency of ships powered by onshore energy while berthed. The installation and connection status of OPS in the port area are analyzed. Subsequently, the energy demand of berthed ships is assessed, and the potential for energy efficiency improvement resulting from OPS implementation is evaluated using Monte Carlo methods. The findings reveal untapped potential in the studied port area, with OPS demonstrating the ability to improve energy efficiency of berthed ships at a rate parallel to the connection rate, excluding indirect emissions. However, considering indirect emissions and energy loss diminishes the effectiveness of OPS. The paper discusses practical implications for enhancing the energy efficiency of OPS, enabling port authorities to make well-informed decisions. These findings are invaluable for Chinese port authorities striving to achieve carbon reduction goals and enhance sustainability in the maritime industry. [ABSTRACT FROM AUTHOR]

Wind energy is an effective way to reduce emissions in ports. However, port wind power generation exhibits strong intermittency and randomness. Predicting port wind speed enables timely scheduling of port operations and improves wind energy utilization efficiency. To achieve high accuracy and rapid prediction of port wind speed, this paper proposes a wind speed prediction model based on the Sparrow Search Algorithm (SSA) optimized Wavelet Neural Network (WNN). Firstly, the SSA is used to optimize the Mean Squared Error (MSE) as the fitness function during the training process of the WNN model, obtaining the optimal fitness value corresponding to the network parameters. Then, the obtained parameters are used as the network model parameters of WNN for wind speed prediction. To validate the effectiveness of the proposed method, the model is validated using the measured wind speed data from the Chuanshan Port Area of Ningbo-Zhoushan Port throughout 2022, and its performance is compared with three other models: SSA–BP, SSA–LSTM, and WNN. The results demonstrate that the proposed prediction model exhibits good performance in port wind speed prediction and outperforms the other comparative models in terms of prediction accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel virtual inertia controller for converters in power systems, which can solve the system's nonlinearity for frequency support. First, the system dynamics are formulated as a nonlinear state-space, in which the reciprocal of inertia is modeled as control input. Correspondingly, a cost function is defined by considering frequency deviation and rate of change of the frequency, which can preserve a tradeoff between critical frequency limits and respective control energy. Following, the optimal frequency regulation problem is solved by using an online adaptive dynamic programming method, where the actor and critic neural networks are constructed to approximate the optimal control input and optimal cost function, respectively. After that, the small-signal analysis is provided to identify the stability of the converter under the proposed controller. Finally, simulation results verify that the frequency response of the system is significantly improved, while retaining more DC side energy. [ABSTRACT FROM AUTHOR]

Energy efficiency and operational safety practices on ships have gained more importance due to the rules set by the International Maritime Organization in recent years. While approximately 70% of the fuel consumed on a commercial ship is utilized for the propulsion load, a significant portion of the remaining fuel is consumed by the auxiliary generators responsible for the ship's onboard load. It is crucial to comprehend the impact of the electrical load on the ship's generators, as it significantly assists maritime operators in strategic energy planning to minimize the chance of unexpected electrical breakdowns during operation. However, an appropriate handling mechanism is required when there are massive datasets and varied input data involved. Thus, this study implements data-driven approaches to estimate the load of a chemical tanker ship's generator using a 1000-day real dataset. Two case studies were performed, namely, single load prediction for each generator and total load prediction for all generators. The prediction results show that for the single generator load prediction of DG1, DG2, and DG3, the decision tree model encountered the least errors for MAE (0.2364, 0.1306, and 0.1532), RMSE (0.2455, 0.2069, and 0.2182), and MAPE (17.493, 5.1139, and 7.7481). In contrast, the deep neural network outperforms all other prediction models in the case of total generation prediction, with values of 1.0866, 2.6049, and 14.728 for MAE, RMSE, and MAPE, respectively. [ABSTRACT FROM AUTHOR]

The integration of Distributed Generators (DGs) into distribution systems (DSs) leads to more reliable and efficient power delivery for customers. However, the possibility of bi-directional power flow creates new technical problems for protection schemes. This poses a threat to conventional strategies because the relay settings have to be adjusted depending on the network topology and operational mode. As a solution, it is important to develop novel fault protection techniques to ensure reliable protection and avoid unnecessary tripping. In this regard, Total Harmonic Distortion (THD) can be used as a key parameter for evaluating the grid's waveform quality during fault events. This paper presents a comparison between two DS protection strategies that employ THD levels, estimated amplitude voltages, and zero-sequence components as instantaneous indicators during the faults that function as a kind of fault sensor to detect, identify, and isolate faults. The first method uses a Multiple Second Order Generalized Integrator (MSOGI) to obtain the estimated variables, whereas the second method uses a single SOGI for the same purpose (SOGI-THD). Both methods rely on communication lines between protective devices (PDs) to facilitate coordinated protection. The effectiveness of these methods is assessed by using simulations in MATLAB/Simulink considering various factors such as different types of faults and DG penetrations, different fault resistances and fault locations in the proposed network. Moreover, the performance of these methods is compared with conventional overcurrent and differential protections. The results show that the SOGI-THD method is highly effective in detecting and isolating faults with a time interval of 6–8.5 ms using only three SOGIs while requiring only 447 processor cycles for execution. In comparison to other protection methods, the SOGI-THD method exhibits a faster response time and a lower computational burden. Furthermore, the SOGI-THD method is robust to harmonic distortion, as it considers pre-existing harmonic content before the fault and avoids interference with the fault detection process. [ABSTRACT FROM AUTHOR]

A microgrid is an autonomous electrical system that consists of renewable energy and efficiently achieves power balance in a network. The complexity in the distribution network arises due to the intermittent nature of renewable generation units and varying power. One of the important objectives of a microgrid is to perform energy management based on situational awareness and solve an optimization problem. This paper proposes an enhanced multi-objective multi-verse optimizer algorithm (MOMVO) for stochastic generation power optimization in a renewable energy-based islanded microgrid framework. The proposed algorithm is utilized for optimum power scheduling among various available generation sources to minimize the microgrid's generation costs and power losses. The performance of MOMVO is assessed on a 6-unit and 10-unit test system. Simulation results show that the proposed algorithm outperforms other metaheuristic algorithms for multi-objective optimization. [ABSTRACT FROM AUTHOR]

In the distribution system, decentralized generation is incorporated to maximize the system reliability and economic stability, and to avoid redundancy in system. The efficient implementation of distributed‐generations (DGs) in a distribution‐system (DS) can be done by employing demand‐side management (DSM). In this research proposal, location and size selection of DGs in a DS considering DSM is accomplished using a hybrid Jaya‐Luus‐Jaakola (JLJ) algorithm. The hybridization of Jaya algorithm (JA) with Luus‐Jaakola algorithm (LJA) is done with a view to improving the rate of convergence of the algorithm. Three design objectives of minimization of active power loss and voltage deviation along with maximization of voltage stability index are considered in this research proposal. These design objectives under consideration are integrated together to form a problem of a single objective by assignments of weights. The formulated problem is minimized using proposed hybrid algorithm subjected to system constraints such as balance of power, limits on bus voltage and line capacity, and generation level of DGs. A static penalty is added to the formulated problem in case of any constraint violations. A photo‐voltaic‐based DG is used in the 33‐bus standard radial‐distribution‐system (RDS). A DSM scenario is created by shifting some loads of peak hours to off‐peak hours in view of minimizing the total cost of generation. The potency and superiority of the proposed algorithm is validated with the help of comparison of the results obtained by exploiting proposed method with results acquired from available state‐of‐art algorithms in literature. The comparison proves the comparative superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Dispatchable energy storage system (ESS) plays a critical role in the smart grid through energy shift and power support. However, it exhibits different operational strategies and economic benefits in different application scenarios due to its inherent degradation behaviour. This paper aims to explore the technical and economic feasibility of the flexible traction power supply system (FTPSS) integrating ESS and renewable energy sources (RES) based on the traction load characteristics. First, a battery degradation model applicable in its frequent charging and discharging operating conditions is derived. Then this paper develops an operational‐sizing co‐optimization framework for the ESS in the FTPSS, where the operation decisions are made considering the degradation costs varying with the sizes and energy throughput. To solve this large‐scale non‐linear intertemporal decision‐making problem, an iterative method with a linear programming (LP) core is proposed to achieve the trade‐off between computational efficiency and accuracy. The results of the extensive comparative cases show that the proposed approach can achieve approximately 10% higher economic benefits than the existing bi‐level sizing strategies for FTPSS. [ABSTRACT FROM AUTHOR]

As autonomous electric vehicles and car-sharing services are becoming more popular, the contribution of shared autonomous electric vehicles (SAEVs) to the future of urban transportation is getting more achievable. Like conventional electric vehicles, SAEVs can provide power grids with ancillary services. This article proposes a new scheduling scheme for SAEV fleets within a cooperative plan to let power distribution networks benefit from the energy storage of vehicle batteries in recovering critical loads after a predictable extreme event. According to a long-term contract, the detailed request of the distribution system operator (DSO), together with desired constraints and perquisites, is sent to the SAEVs aggregator (SA) prior to the landfall of a predictable extreme event. Afterward, SA runs a targeted algorithm to schedule trip assignments and charging cycles of SAEVs so that the required constraints of DSO are satisfied. The SAEV participants will continue carrying passengers within the scheduled time horizon in addition to delivering energy to the distribution network at the scheduling deadline declared by DSO. This deadline is the time instant when the capacity of the SAEV fleet may be no more applicable to enhance the system preparedness against the approaching event. Numerical results illustrated that the proposed scheme helps improve the power grid resilience by delivering 2396.1 kWh of energy to the distribution network in addition to increasing the total income of each participant SAEV by about 130%. Thus, it is implied that the proposed method offers a win-win situation for both entities. [ABSTRACT FROM AUTHOR]

Despite significant advances in distributed renewable energy systems (DRES), the technology still faces several substantial challenges that prevent the large-scale adoption of these systems into a country's energy sector. The intermittency of renewables, uncertainties associated with real-time multi-horizon weather and load forecasts, and lack of comprehensive control systems are among the main technical and regulatory challenges for the real-world adoption of DRES. This paper outlines the current state of knowledge in the real-world operation of DRES and also describes pathways and methodologies that enable and facilitate the uptake of DRES in a country's energy sector. [ABSTRACT FROM AUTHOR]

Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids. [ABSTRACT FROM AUTHOR]

Over the last few decades, lithium-ion batteries have grown in importance for the use of many portable devices and vehicular applications. It has been seen that their life expectancy is much more effective if the required conditions are met. In one of the required conditions, accurately estimating the battery's state of charge (SOC) is one of the important factors. The purpose of this research paper is to implement the probabilistic filter algorithms for SOC estimation; however, there are challenges associated with that. Generally, for the battery to be effective the Bayesian estimation algorithms are required, which are recursively updating the probability density function of the system states. To address the challenges associated with SOC estimation, the research paper goes further into the functions of the extended Kalman filter (EKF) and sigma point Kalman filter (SPKF). The function of both of these filters will be able to provide an accurate estimation. Further studies are required for these filters' performance, robustness, and computational complexity. For example, some filters might be accurate, might not be robust, and/or not implementable on a simple microcontroller in a vehicle's battery management system (BMS). A comparison is made between the EKF and SPKF by running simulations in MATLAB. It is found that the SPKF has an obvious advantage over the EKF in state estimation. Within the SPKF, the sub-filter, the central difference Kalman filter (CDKF), can be considered as an alternative to the EKF for state estimation in battery management systems for electric vehicles. However, there are implications to this which include the compromise of computational complexity in which a more sophisticated micro-controller is required. [ABSTRACT FROM AUTHOR]

From the perspective of propagation dynamics in complex networks, failure propagation in cyber-physical power systems is analogous to the spread of diseases; subsequently, the cyber nodes and power nodes are regarded as individuals in each of their groups. In this study, a two-layer interdependent network model of the cyber-physical power system is proposed, where each subnetwork adopts the Susceptible-Infected-Susceptible (SIS) epidemic-spreading model. On this basis, we construct a failure cooperation propagation model of cyber-physical power systems. Furthermore, we introduce the node protection mechanism to ensure the normal operation of key nodes. The generated scale-free cyber network and IEEE118-bus power system are used for simulation to analyze the influence of the coupling effect between them on the final failure scale. [ABSTRACT FROM AUTHOR]

Due to the unbalanced load connection in the sensitive load bus (SLB), the ac microgrid may suffer from voltage unbalance in the system. To overcome this issue, this article presents a new distributed control scheme to achieve the unbalanced voltage compensation for islanded ac microgrid with limited noncontinuous communication among the distributed generation (DG) units. In this article, a two-layer control framework, including the cyber layer and the primary control layer, is employed. The proposed control strategy that is applied in the cyber layer implements an event-triggered communication to effectively compensate for the unbalanced voltage in the SLB. In contrast with the centralized control strategy, the proposed control strategy is fully distributed, and the information exchange among the DG units at only the event-triggered times. As a result, the communication burdens can be measurably reduced while the control performance is not affected. The stability is analyzed with the Lyapunov function in this article. Finally, several experimental case studies are provided to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In islanded ac microgrids, the mismatched impedances of the interfacing feeders between the inverters and the load bus cause poor reactive power sharing when the conventional frequency and voltage droop control technique is employed. Such operation endangers the whole microgrid reliability as it may lead to overloading certain inverters and, consequently, triggering protection relays and causing cascaded failure. Thus, this article proposes an accurate reactive power sharing strategy that considers the mismatched feeder impedances in islanded ac microgrids. It is based on the optimal tuning of the virtual complex impedance for each inverter. The proposed strategy has several advantages. First, it has a physical meaning as it establishes an explicit relationship between the mismatched values of the actual resistive–inductive feeders and the assigned values for the proposed virtual complex impedance to each inverter. Hence, further degradations in the microgrid voltages are prevented. Second, there is no need for prior knowledge about the actual feeder impedances in the design stage as they are estimated online from the available measurements. Finally, the proposed control is reliable and fault-tolerant as it copes with unexpected failures such as failure of (or sudden switching off) some inverters and communication disruptions/delays. It ensures accurate power sharing even under the primary controllers after losing communication links with the secondary controller. The simulation and experimental verification results are presented to validate the performance of the proposed technique. [ABSTRACT FROM AUTHOR]

Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid's distribution sector as well as fault location methods recommended to improve resilience, which will aid readers in choosing methods for their own research. Finally, conclusions are given after discussing the trends in fault location and detection techniques. [ABSTRACT FROM AUTHOR]

The microgrid concept has evolved from the humble origins of simple remote electrification applications in rural environments to complex architectures. Microgrids are key enablers to the integration of higher penetrations of renewables in the energy sector (including electricity, heating, cooling, transport and industry). In addition to the local energy sources, energy storage systems and loads, the modern microgrid encompasses sophisticated energy and power management systems, peer-to-peer energy markets and digital technologies to support this energy transition. The microgrid concept has recently been applied to all energy sectors, in order to develop solutions that address pressing issues related to climate change and the decarbonization of these important sectors. This paper initially reviews novel applications in which the microgrid concept is being applied, from a detailed analysis of recent literature. This consists of a comprehensive analysis of the state of the art in shipboard microgrids, port microgrids, aircraft microgrids, airport microgrids and space microgrids. Future research directions are then presented, based on the authors' perspectives on pushing the boundaries of microgrids further. [ABSTRACT FROM AUTHOR]

Extreme weather conditions and natural disasters (ND) are the main causes of power outages in the electric grid. It is necessary to strengthen the electrical power system's resilience during these catastrophic occurrences, and microgrids may be seen as the best way to achieve this goal. In this paper, two different energy system scenarios were proposed for increasing the resiliency of the electric power system during random outages. In the first scenario, a diesel generator (DG) was used to deliver energy to key loads during grid disruptions, in conjunction with a utility electric grid (UEG) and local electric load (ELL). A grid-connected ad hoc microgrid (MG) with a photovoltaic (PV) system, a battery energy storage (BES) system, and local electric loads made up the second scenario. The PV system and the BES system were used to supply the key loads with electricity during the outage. The major aim of this research was to compare the two resilient-based systems from the perspectives of technology, economics, and the environment. Given that it requires greater resilience than the other loads during severe weather, a hospital load on Indonesia's Lombok Island was chosen as the critical load. The objective function considers the system's predefined constraints to reduce the overall net present cost (NPC) and the cost of energy in order to maximize the system resilience (COE). The Optimization of Multiple Energy Resources (HOMER) Grid simulated a 3-day outage in August 2021, and the results demonstrated that the resiliency enhancement for both scenarios was nearly identical. The first scenario resulted in fewer CO2 emissions; however, the second scenario delivered lower operating costs and COE. The simulation's findings showed that system 1 created an annual emission of 216.902 kg/yr while system 2 only produced an emission of 63.292 kg/yr. This study shows that since RES-based MGs don't burn fossil fuels to generate power, they are more environmentally friendly resources. [ABSTRACT FROM AUTHOR]

Hybrid microgrid optimization, integration, and control are becoming increasingly important. Renewable energy source integrations are being used more often in shipping ports, as well as on short-distance cruises and ferries. Several seaports presently lack cold ironing services, which are shore-based power stations that provide electricity to ships from the main utility grids. Furthermore, diesel engines and diesel generator-based shipboards must be continuously running and on-line when docking to provide additional loads of ships due to the absence of cold-ironing services at many ports. In this research, we analytically presented the robustness of our proposed hierarchical control design for the hybrid shipboard Microgrid system containing multiple DGs and renewable energy resource (RES) integrations. The performance comparison of the conventional proportional integral (PI) vs. Sliding Mode Controller (SMC)-based control design is validated with simulation tests under different static and dynamical load conditions for both AC and DC types of loads. We further considered multi-DGs and RES integrations into our system to validate our design's robustness against noise and unwanted faulty load conditions. The complete system stability analysis and designing of the control law are performed. Mathematical derivations and simulation results prove the robustness of the proposed hierarchical control architecture and compare the performance characteristics of two secondary controllers designed using a MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

The rapid growth of the distributed generators (DGs) integration into the distribution systems (DSs) creates new technical issues; conventional relay settings need to be updated depending on the network topology and operational mode as fault protection a major challenge. This emphasizes the need for new fault protection methods to ensure secure protection and prevent undesirable tripping. Total harmonic distortion (THD) is an important indicator for assessing the quality of the grid. Here, a new protection system based on the THD of the grid voltages is proposed to address fault events in the electrical distribution network. The proposed protection system combines the THD with the estimates of the amplitude voltages and the zero-sequence component for defining an algorithm based on a finite state machine (FSM) for the detection, identification, and isolation of faults in the grid. The algorithm employs communication lines between all the protective devices (PDs) of the system to transmit tripping signals, allowing PDs to be coordinated. A second order generalized integrator (SOGI) and multiple SOGI (MSOGI) are used to obtain the THDs, estimated amplitude voltages, and zero-sequence component, which allows for fast detection with a low computational burden. The protection algorithm performance is evaluated through simulations in MATLAB/Simulink and a comparative study is developed between the proposed protection method and a differential relay (DR) protection system. The proposed method shows its capability to detect and isolate faults during different fault types with different fault resistances in different locations in the proposed network. In all the tested scenarios, the detection time of the faults has been between 7–10 ms. Moreover, this method gave the best solution as it has a higher accuracy and faster response than the conventional DR protection system. [ABSTRACT FROM AUTHOR]

Several issues of individual microgrids (MGs) such as voltage and frequency fluctuations mainly due to the intermittent nature of renewable energy sources' (RESs) power production can be mitigated by interconnecting multiple MGs and forming a multi-microgrid (MMG) system. MMG systems improve the reliability and resiliency of power systems, increase RESs' utilization, and provide cost-efficient power to the consumers. This paper provides a comprehensive review of the conducted studies in the MMG area summarizing different operational goals and constraints proposed in the literature for efficient operation of MMGs. Besides, different MMG architectures in which the MGs can be interconnected to form an MMG system and their characteristics are discussed. This paper also provides a state-of-the-art review on different control strategies and operation management methodologies for the operation and control of MMGs in centralized, decentralized, distributed, and hierarchical structures. A classification of different sources of uncertainties in an MMG system and proposed uncertainty handling strategies are also presented. Finally, the paper is complemented with a discussion of the main open issues and future research directions of MMG systems. [ABSTRACT FROM AUTHOR]

Impedance-based approachis generally studied for stability analysis of the power electronic converter-based system. Considering multiple-frequency coupling effects, in this article, we establish the harmonic transfer function (HTF) based αβ-frame impedance models of droop inverters based islanded microgrid with unbalanced loads. Therein, the HTF-based αβ-impedance modeling methods of unbalanced loads, as well as droop inverters in parallel, are proposed. Afterward, the HTF-based αβ-frame impedance models are equivalent to single-input single-output (SISO) impedances for simpler impedance measurement and stability analysis, preserving the αβ-frame coupling and multiple-frequency coupling characteristics. Finally, the experimental measurement results verify the accuracy of the αβ-frame SISO impedances. Besides, the stability analysis based on the SISO impedance models accurately reveals low-frequency oscillations of the islanded microgrid with unbalanced loads, further verifying the effectiveness of the SISO impedance models. [ABSTRACT FROM AUTHOR]

Discontinuous conduction mode (DCM) for inverters can lower the size of filter inductors and can realize the soft switching for switches. However, the current stress under the DCM is high. Aiming to solve this problem, this article uses a T-type three-level circuit to construct an LCL-type grid-connected inverter, which introduces the trapezium conduction mode (TPCM) with the zero-voltage switching to lower the current stress when the grid current is large. Both the DCM and the TPCM can work at a fixed switching frequency, but it is difficult to design controllers using the conventional average state model. To address this challenge, this article proposes a charge quantity model (CQM) that is applicable to any inverter topology and conduction mode, where the output charge quantity of the bridge is used as a control variable. The proposed model represents a linear two-order model, which can greatly simplify controller design. Based on the CQM, a direct charge control method working at a fixed switching frequency is developed, which combines a PID regulator and a feedforward controller of the grid voltage. The excellent steady-state and dynamic control performances of the proposed model and control method are verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

Natural disasters threat the resilience of the electricity system. However, little literature has investigated the electricity system's recovering process and progress after natural disasters' hit which strongly influence the system operators' planning and quality of the security of supply for the electricity customers. To fill the research gap, this paper applies an unsupervised machine learning method, the k means clustering algorithm, to investigate the normal/abnormal electricity load profiles, identify natural disaster- and electrical fault-impacted electricity load profiles with a case study of the Lombok electricity system, Indonesia, and ½-hourly electricity load data from 2015 until 2021. The results show that electricity consumption in Lombok has increased over the years, which match the installed production capacity of Lombok. The results prove that the disturbance-induced electricity load patterns and especially natural disaster-impacted load profiles can be identified by the k means clustering algorithm. Especially, the pre-, during, and post-natural disaster impacted load patterns can be portrayed. Furthermore, the investigation results regarding the impacts of natural disasters and electrical faults on the performance of the electricity system, show that the natural disaster-induced load reductions and electrical fault-induced load reductions differ from the short and long-term perspectives. Moreover, the results can facilitate the electricity system operators to better understand the load patterns, predict ND strikes' impact on the electricity system and conduct better long-term energy management strategies. [ABSTRACT FROM AUTHOR]

In symmetrical grids, single-input single-output (SISO) analysis/design tools are applicable for the analysis and design of grid-tied inverter systems as their average αβ- or dq-frame models are to a high extent decoupled (in the high-frequency range). In this way, designing the control system could be done in a straightforward manner. However, this is not the case under asymmetrical grids. In this condition, the interaction between different phases makes the average model of the grid-tied inverter a multi-input multioutput (MIMO) system, and therefore, complicated to analyze and design. To cope with this challenge, this article deals with deriving the MIMO control system considering the impact of the asymmetrical grid. Then, the MIMO system turns into two decoupled SISO systems. Thus, a simple yet accurate model is developed which intuitively demonstrates the effect of the grid asymmetry on creating coupling between current control loops. In this way, the stability analysis could be carried out irrespective of the MIMO system difficulties. The experimental results validate the accuracy of the proposed approach in asymmetrical grid conditions. [ABSTRACT FROM AUTHOR]