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This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system's point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is not required. It is shown that: (1) When comparing Single Agent Reinforcement Learning (SARL) and MARL, training time can be reduced by 70% for a four temperature-zone UFH system, (2) the agent can learn and generalize over seasons, (3) the cost of heating can be reduced by 19% or the equivalent to 750 kWh of electric energy per year for an average Danish domestic house compared to a traditional control method, and (4) oscillations in the room temperature can be reduced by 40% when comparing the RL control methods with a traditional control method. [ABSTRACT FROM AUTHOR]

Inrush current simulation plays an important role in many tasks of the power system, such as power transformer protection. However, the accuracy of the inrush current simulation can hardly be ensured. In this paper, a Jiles-Atherton (J-A) theory based model is proposed to simulate the inrush current of power transformers. The characteristics of the inrush current curve are analyzed and results show that the entire inrush current curve can be well featured by the crest value of the first two cycles. With comprehensive consideration of both of the features of the inrush current curve and the J-A parameters, an automatic J-A parameter estimation algorithm is proposed. The proposed algorithm can obtain more reasonable J-A parameters, which improve the accuracy of simulation. Experimental results have verified the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

An energy storage system (ESS) in a power system facilitates tasks such as renewable integration, peak shaving, and the use of ancillary services. Among the various functions of an ESS, this study focused on frequency regulation (or secondary reserve). This paper presents an optimal scheduling algorithm for frequency regulation by an ESS. This algorithm determines the bidding capacity and base point of an ESS in each operational period to achieve the maximum profit within a stable state-of-charge (SOC) range. However, the charging/discharging efficiency of an ESS causes SOC errors whenever the ESS performs frequency regulation. With an increase in SOC errors, the ESS cannot respond to an automatic generation control (AGC) signal. This situation results in low ESS performance scores, and finally, the ESS is disqualified from performing frequency regulation. This paper also presents a real-time SOC management algorithm aimed at solving the SOC error problem in real-time operations. This algorithm compensates for SOC errors by changing the base point of the ESS. The optimal scheduling algorithm is implemented in MATLAB by using the particle swarm optimization (PSO) method. In addition, changes in the SOC when the ESS performs frequency regulation in a real-time operation are confirmed using the PSCAD/EMTDC tool. The simulation results show that the optimal scheduling algorithm manages the SOC more efficiently than a commonly employed planning method. In addition, the proposed real-time SOC management algorithm is confirmed to be capable of performing SOC recovery. [ABSTRACT FROM AUTHOR]

The digital transformation of the energy sector toward the Smart Grid paradigm, intelligent energy management, and distributed energy integration poses new requirements for computer science. Issues related to the automation of power grid management, multidimensional analysis of data generated in Smart Grids, and optimization of decision-making processes require urgent solutions. The article aims to analyze the use of selected artificial intelligence (AI) algorithms to support the abovementioned issues. In particular, machine learning methods, metaheuristic algorithms, and intelligent fuzzy inference systems were analyzed. Examples of the analyzed algorithms were tested in crucial domains of the energy sector. The study analyzed cybersecurity, Smart Grid management, energy saving, power loss minimization, fault diagnosis, and renewable energy sources. For each domain of the energy sector, specific engineering problems were defined, for which the use of artificial intelligence algorithms was analyzed. Research results indicate that AI algorithms can improve the processes of energy generation, distribution, storage, consumption, and trading. Based on conducted analyses, we defined open research challenges for the practical application of AI algorithms in critical domains of the energy sector. [ABSTRACT FROM AUTHOR]

Aiming at the problem of maximum power point tracking (MPPT) of photovoltaic arrays in photovoltaic power generation systems, a particle swarm optimization (PSO) MPPT method combined with adaptive linear active disturbance rejection control (A-LADRC) algorithm was proposed and designed. In this method, PSO is used to track the maximum power point (MPP), and then the A-LADRC controller was used to track the reference voltage. The controller enhances the anti-interference ability against various external disturbances in the MPPT process and accelerates the response speed of the system. Compared with the perturbation observation method (P&O), traditional PSO and LADRC, the proposed method has good tracking performance and an anti-interference ability under various external disturbances. [ABSTRACT FROM AUTHOR]

A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness. [ABSTRACT FROM AUTHOR]

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The artificial potential field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of artificial potential field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel artificial potential field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot's perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of the multiple load forecasting of a regional integrated energy system, a short-term multiple load forecasting model based on the quantum weighted GRU and multi-task learning framework is proposed in this paper. Firstly, correlation analysis is carried out using a maximum information coefficient to select the input of the model. Then, a multi-task learning architecture is constructed based on the quantum weighted GRU neural network, and the coupling information among multiple loads is learned through the sharing layer in order to improve the prediction accuracy of multiple loads. Finally, the PSO algorithm is used to optimize the parameters of the quantum weighted GRU. The simulation data of a regional integrated energy system in northern China are used to predict the power and cooling loads on summer weekdays and rest days, and the results show that, compared with the LSTM, GRU and single task learning QWGRU models, the proposed model is more effective in the multiple load forecasting of a regional integrated energy system. [ABSTRACT FROM AUTHOR]

Emergency generators are important facilities that supply emergency power to fire-fighting facilities in the event of a power outage. Accordingly, a load test of the emergency generator should be performed by cutting off the power source of the fire-fighting target in order to accurately confirm the performance and condition of the emergency generator in normal circumstances. However, the test has usually been carried out without loads due to serious problems, which are caused by a shutdown of the power source for the load test of emergency generators, such as the shutdown of emergency load, etc. In order to overcome these problems, this paper proposes an operation method and algorithm of a load test device using ESS, which can conform to characteristics of emergency loads installed in fire-fighting targets by interconnecting it with the emergency generator. In addition, this paper performs a modeling of an emergency power system using PSCAD/EMTDC, and then this paper confirms the usefulness of the proposed method and operation algorithm of the load test device using ESS. [ABSTRACT FROM AUTHOR]

This work is focused on the development and validation of a spark advance controller, based on a piston "damage" model and a predictive knock model. The algorithm represents an integrated and innovative way to manage both the knock intensity and combustion phase. It is characterized by a model-based open-loop algorithm with the capability of calculating with high accuracy the spark timing that achieves the desired piston damage in a certain period, for knock-limited engine operating conditions. Otherwise, it targets the maximum efficiency combustion phase. Such controller is primarily thought to be utilized under conditions in which feedback is not needed. In this paper, the main models and the structure of the open-loop controller are described and validated. The controller is implemented in a rapid control prototyping device and validated reproducing real driving maneuvers at the engine test bench. Results of the online validation process are presented at the end of the paper. [ABSTRACT FROM AUTHOR]

This work focuses on the implementation of innovative adaptive strategies and a closed-loop chain in a piston-damage-based combustion controller. In the previous paper (Part 1), implemented models and the open loop algorithm are described and validated by reproducing some vehicle maneuvers at the engine test cell. Such controller is further improved by implementing self-learning algorithms based on the analytical formulations of knock and the combustion model, to update the fuel Research Octane Number (RON) and the relationship between the combustion phase and the spark timing in real-time. These strategies are based on the availability of an on-board indicating system for the estimation of both the knock intensity and the combustion phase index. The equations used to develop the adaptive strategies are described in detail. A closed-loop chain is then added, and the complete controller is finally implemented in a Rapid Control Prototyping (RCP) device. The controller is validated with specific tests defined to verify the robustness and the accuracy of the adaptive strategies. Results of the online validation process are presented in the last part of the paper and the accuracy of the complete controller is finally demonstrated. Indeed, error between the cyclic and the target combustion phase index is within the range ±0.5 Crank Angle degrees (°CA), while the error between the measured and the calculated maximum in-cylinder pressure is included in the range ±5 bar, even when fuel RON or spark advance map is changing. [ABSTRACT FROM AUTHOR]

To mine the battery's health factors more comprehensively and accurately identify the lithium battery's State of Health (SOH), an Improved Douglas–Peucker feature extraction algorithm is proposed, and the LAOS-XGboost model is proposed to be used to predict the SOH of the battery. Firstly, to solve the problem that the traditional Douglas–Peucker algorithm has difficulties extracting curve features in a fixed dimension, the Douglas–Peucker algorithm is improved by de-thresholding. Then, the Wrapper method combined with the Improved Douglas–Peucker algorithm is used to construct the feature engineering of battery life prediction, and the optimal feature subset is obtained. Then, LAOS-XGboost is used to establish a battery SOH prediction model; finally, this model is used to predict the SOH of different batteries and the same battery, and the robustness of the model is analyzed. The experimental results show that the R2 of all XGboost models is higher than 0.97 in the prediction experiments of different batteries. The AE of the LAOS-XGboost model is 0, and the TIC index is less than 3% under 10 dB SNR. In the same battery prediction experiment, the TIC index of the model is less than 0.3%. [ABSTRACT FROM AUTHOR]

The application of energy storage in offshore renewable generation systems allows managing the intrinsic uncertainty of the resources and improving the utilization factor of the electrical network. Optimal storage design algorithms generally have to evaluate the behavior of the whole system thousands times before converging to the optimal solution and the reliability of the results obviously depends on the quality of input data. On the other hand, the utilization of simplified storage models in the design stage can reduce the simulation time drastically, while still providing useful information. The goal of this paper is to evaluate the applicability of a methodology for sizing the energy storage system in a hybrid wind and wave farm, which is based on fluid models. The description and performance of thismodeling approachwill be introduced and compared to standard design procedures based on extensive simulations. Advantages and limitations of each approach will be underlined and the impact of input data quality will be discussed. [ABSTRACT FROM AUTHOR]

This paper proposes the active damping control strategy for position sensorless operation of an interior permanent magnet (IPM) motor. The proposed method is applied to both the current controller and the position estimator to control damping characteristics of the IPM drive system. By actively increasing the damping characteristics of the system with the proposed method, the current control and the position estimation loops become immune to parameter variation of the stator resistance which may degrade the accuracy of the position estimator. To analyze the accuracy of the position estimator with and without the proposed method, a small-signal analysis is carried out for low speed operation where the effect of the parameter variation is relatively large due to a low signal-to-noise ratio (SNR). Additionally, an open-loop voltage to angular velocity transfer function including the electrical and the mechanical parameters is investigated. Since no hardware modifications are necessary, the proposed method can be easily implemented just in software routines. Both the simulations and the experimental validations in which the proposed active damping control strategy is incorporated with the existing extended electromotive force (EMF)- based sensorless algorithm are provided to support the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm to solve the optimal energy dispatch of an ice storage air-conditioning system. Based on a real air-conditioning system, the data, including the return temperature of chilled water, the supply temperature of chilled water, the return temperature of ice storage water, and the supply temperature of ice storage water, are measured. The least-squares regression (LSR) is used to obtain the input-output (I/O) curve for the cooling load and power consumption of chillers and ice storage tank. The objective is to minimize overall cost in a daily schedule while satisfying all constraints, including cooling loading under the time-of-use (TOU) rate. Based on the Radial Basis Function Network (RBFN) and Ant Colony Optimization, an Ant-Based Radial Basis Function Network (ARBFN) is constructed in the searching process. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the economic dispatch of ice storage air-conditioning systems, and offering greater energy efficiency in dispatching chillers. [ABSTRACT FROM AUTHOR]

Integration technology of various distribution systems for improving renewable energy utilization has been receiving attention in the power system industry. The wave-offshore hybrid generation system (HGS), which has a capacity of over 10 MW, was recently developed by adopting several voltage source converters (VSC), while a control method for adopted power conversion systems has not yet been configured in spite of the unique system characteristics of the designated structure. This paper deals with a reactive power assignment method for the developed hybrid system to improve the power transfer efficiency of the entire system. Through the development and application processes for an optimization algorithm utilizing the real-time active power profiles of each generator, a feasibility confirmation of power transmission loss reduction was implemented. To find the practical effect of the proposed control scheme, the real system information regarding the demonstration process was applied from case studies. Also, an evaluation for the loss of the improvement rate was calculated. [ABSTRACT FROM AUTHOR]

In response to challenges such as voltage limit violations, excessive currents, and power imbalances caused by the integration of distributed photovoltaic (distributed PV) systems into the distribution network, this study proposes at two-level optimization configuration method. This method effectively balances the grid capacity and reduces the active power losses, thereby decreasing the operating costs. The upper-level optimization enhances the distribution network's capacity by determining the siting and sizing of distributed PV devices. The lower-level aims to reduce the active power losses, improve the voltage stability margins, and minimize the voltage deviations. The upper-level planning results, which include the siting and sizing of the distributed PV, are used as initial conditions for the lower level. Subsequently, the lower level feeds back its optimization results to further refine the configuration. The model is solved using an improved second-order oscillating chaotic map particle swarm optimization algorithm (SCMPSO) combined with a second-order relaxation method. The simulation experiments on an improved IEEE 33-bus test system show that the SCMPSO algorithm can effectively reduce the voltage deviations, decrease the voltage fluctuations, lower the active power losses in the distribution network, and significantly enhance the power quality. [ABSTRACT FROM AUTHOR]

Transformers are fundamental and among the most expensive electrical devices in any power transmission and distribution system. Therefore, it is essential to implement powerful maintenance methods to monitor and predict their condition. Due to its many advantages—such as early detection, accurate diagnosis, cost reduction, and rapid response time—dissolved gas analysis (DGA) is regarded as one of the most effective ways to assess a transformer's condition. In this contribution, we propose a new probabilistic hierarchical intelligent system consisting of five subnetworks of the radial basis functions (RBF) type. Indeed, hierarchical classification minimizes the complexity of the discrimination task by employing a divide-and-conquer strategy, effectively addressing the issue of unbalanced data (a significant disparity between the categories to be predicted). This approach contributes to a more precise and sophisticated diagnosis of transformers. The first subnetwork detects the presence or absence of defects, separating defective samples from healthy ones. The second subnetwork further classifies the defective samples into three categories: electrical, thermal, and cellulosic decomposition. The samples in these categories are then precisely assigned to their respective subcategories by the third, fourth, and fifth subnetworks. To optimize the hyperparameters of the five models, the Linde–Buzo–Gray algorithm is implemented to reduce the number of centers (radial functions) in each subnetwork. Subsequently, a single-layer perceptron is trained to determine the optimal synaptic weights, which connect the intermediate layer to the output layer. The results obtained with our proposed system surpass those achieved with another implemented alternative (a single RBF), with an average sensitivity percentage as high as 96.85%. This superiority is validated by a Student's t-test, showing a significant difference greater than 5% (p-value < 0.001). These findings demonstrate and highlight the relevance of the proposed hierarchical configuration. [ABSTRACT FROM AUTHOR]

Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base model (with all hyperparameters set to the default values) using the coefficient of variation of root-mean-square error (CvRMSE) and coefficient of determination (R2) based on the Measurement and Verification Guideline evaluation metrics. To confirm the model's industrial applicability, its predictions were compared with values measured at a small-to-medium-sized food factory. The optimized model performed better than the base model, achieving a CvRMSE of 12.30% and an R2 of 0.94, and achieving a predictive accuracy of 91.49%. By predicting energy consumption, these findings are expected to promote the efficient operation and management of energy in the food industry. [ABSTRACT FROM AUTHOR]

Installations of an Energy Storage System (ESS) with various functions such as power stabilization of renewable energy, demand management, and frequency adjustment are increasing. In particular, ESS for demand management is being established for high-voltage customers (300 KVA–1000 KVA) who have placed an Auto Section Switch (ASS) at the connection point within the distribution system. However, a power outage may occur in the Power Receiving System (PRS) when a short-circuit fault due to insulation breakdown occurs at the ESS DC side. The reason for this breakdown is that the fault current is reduced by transformer impedance, and the ASS is opened before the DC power fuse. Therefore, using the Graphic Solution Method (GSM), this paper presents an operation algorithm for protection coordination that isolates the fault section by first operating the DC power fuse with a small fault current. Furthermore, fault analysis modeling for a PRS composed of a switchgear section, a main distribution panel, a Power Conditioning System (PCS), a power fuse, and a battery is performed through PSCAD/EMTDC. From the simulation results, it is confirmed that the fault section is quickly isolated, and power outages for high-voltage customers are prevented because the DC power fuse selected by the proposed operation algorithm of protection coordination is opened before the ASS. [ABSTRACT FROM AUTHOR]

As one of the core technologies of electric vehicles (EVs), the state of charge (SOC) estimation algorithm of lithium-ion batteries is directly related to the performance of the battery management system (BMS). Before EVs are put into the market, the SOC estimation algorithm must be tested and verified to ensure the reliability of the BMS and the safe operation of EVs. Therefore, this paper establishes a lithium-ion batteries' SOC estimation algorithm verification platform for the comprehensive performance evaluation and verification of the new SOC estimation algorithm. In addition, there are two schemes, including real-time SOC estimation verification and offline SOC estimation verification can be selected, which improve the reliability and efficiency of verification. Firstly, the design idea of the verification platform (the research and development purpose, functional requirements, and the overall design scheme) is introduced in detail. Secondly, the modular design idea is used to design the hardware structure of the verification platform, which mainly includes the BMS host module, BMS slave module, battery charger module, and electronic load module. Finally, the software system, including the communication architecture, the SOC reference standard and evaluation indexes of the algorithm, and the upper computer function and implementation is designed to realize the functions of the verification platform. [ABSTRACT FROM AUTHOR]

The increasing share of distributed energy resources poses a challenge to the distribution network operator (DNO) to maintain the current availability of the system while limiting the investment costs. Related to this, there is a clear trend in DNOs trying to better monitor their grid by installing a distribution management system (DMS). This DMS enables the DNOs to remotely switch their network or better localize and solve faults. Moreover, the DMS can be used to centrally control the grid assets. Therefore, in this paper, a control strategy is discussed that can be implemented in the DMS for solving current congestion problems posed by the increasing share of renewables in the grid. This control strategy controls wind turbines in order to avoid congestion while mitigating the required investment costs in order to achieve a global cost-efficient solution. Next to the application and objective of the control, the parameter tuning of the control algorithm is discussed. [ABSTRACT FROM AUTHOR]

In this paper, the Greitzer surge model was systematically analysed with the model compressor duct length Lc as the tuning parameter. The surge phenomenon is known to induce a serious risk to centrifugal compressor operation. The two-dimensional Greitzer model is a well-established way of modelling this dangerous instability, but the determination and changes of the model parameters are still being discussed. In this paper an automated procedure determines the Lc value providing the best fit with the experimental data has been presented. The algorithm was tested on five valve positions and revealed that the best fit was obtained for different Lc values following a linear trend against the mass flow rate. The study has also shown that the Greitzer model has two solutions for a given pressure oscillation amplitude: one similar to the deep surge (low Lc) and one similar to the mild surge (low Lc). This suggests that this model can be used to simulate both types of the phenomenon known from the experimental analyses. The study proposes the dimensionless average pressure as the parameter allowing to distinguish which surge cycle was observed at a given instance. Past papers were analysed to observe the surge type that appeared in different experiments. It was found that most researchers obtained low Lc surge. The results show that both deep and mild surge could be simulated with the Greitzer model. It also revealed that the Lc should not be treated as a constant value for a given machine and that it changes with the mass flow rate. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the real-time implementation and measurements of a reduced switch count in space vector pulse width modulation for three-level neutral point clamped inverters (3L-NPC). We implement space vector pulse width modulation, which uses a prediction algorithm to reduce the number of switches in power transistors (switch count) by up to about 13%. The algorithm applies additional redundant voltage vectors. The method is compute-intensive and was implemented on a dual-core TMS320F28379D digital signal controller. The latest measurements of steady and dynamic states of electric drive, powered by a 3L-NPC inverter using this method, confirmed the possibility of using this method in practical implementation. The implementation and results of the measurements are presented in this paper. [ABSTRACT FROM AUTHOR]

PV hosting capacity (PVHC) analysis on a distribution system is an attractive technique that emerged in recent years for dealing with the planning tasks on high-penetration PV integration. PVHC uses various system performance indices as judgements to find an available amount of PV installation capacity that can be accommodated on existing distribution system infrastructure without causing any violation. Generally, approaches for PVHC assessments are implemented by iterative power flow calculations with stochastic PV deployments so as to observe the operation impacts for PV installation on distribution systems. Determination of the stochastic PV deployments in most of traditional PVHC analysis methods is automatically carried out by the program that is using random selection. However, a repetitive problem that exists in these traditional methods on the selection of the same PV deployment for a calculation was not previously investigated or discussed; further, underestimation of PVHC results may occur. To assess PVHC more effectively, this paper proposes an improved stochastic analysis method that introduces an innovative idea of using repetitiveness check mechanism to overcome the shortcomings of the traditional methods. The proposed mechanism firstly obtains all PV deployment combinations for the determination of all possible PV installation locations. A quick-sorting algorithm is then used to remove repetitive PV deployments that are randomly selected during the solution procedure. Finally, MATLAB and OpenDSS co-simulations implemented on a small distribution feeder are used to validate the performance of the proposed method; in addition, PVHC enhancement by PV inverter control is investigated and simulated in this paper as well. Results show that the proposed method is more effective than traditional methods in PVHC assessments. [ABSTRACT FROM AUTHOR]

In this paper, an attempt at the energy optimization of perfect control systems is performed. The perfect control law is the maximum-speed and maximum-accuracy procedure, which allows us to obtain a reference value on the plant's output just after a time delay. Based on the continuous-time state-space description, the minimum-error strategy is discussed in the context of possible solutions aiming for the minimization of the control energy. The approach presented within this study is focused on the nonunique matrix inverse-originated so-called degrees of freedom being the core of perfect control scenarios. Thus, in order to obtain the desired energy-saving parameters, a genetic algorithm has been employed during the inverse model control synthesis process. Now, the innovative continuous-time procedure can be applied to a wide range of multivariable plants without any stress caused by technological limitations. Simulation examples made in the MATLAB/Simulink environment have proven the usefulness of the new method shown within the paper. In the extreme case, the energy consumption has been reduced by approximately 80% in comparison with the well-known Moore–Penrose inverse. [ABSTRACT FROM AUTHOR]

The IEC 61850 communication standard is getting popular for application in electric power substation automation. This paper focuses on the potential application of the IEC 61850 generic object-oriented substation event (GOOSE) protocol in the AC microgrid for adaptive protection. The focus of the paper is to utilize the existing low-voltage ride through characteristic of distributed generators (DGs) with a reactive power supply during faults and communication between intelligent electronic devices (IEDs) at different locations for adaptive overcurrent protection. The adaptive overcurrent IEDs detect the faults with two different preplanned settings groups: lower settings for the islanded mode and higher settings for the grid-connected mode considering limited fault contributions from the converter-based DGs. Setting groups are changed to lower values quickly using the circuit breaker status signal (XCBR) after loss-of-mains, loss-of-DG or islanding is detected. The methods of fault detection and isolation for two different kinds of communication-based IEDs (adaptive/nonadaptive) are explained for three-phase faults at two different locations. The communication-based IEDs take decisions in a decentralized manner, using information about the circuit breaker status, fault detection and current magnitude comparison signals obtained from other IEDs. However, the developed algorithm can also be implemented with the centralized system. An adaptive overcurrent protection algorithm was evaluated with PSCAD (Power Systems Computer Aided Design) simulations, and results were found to be effective for the considered fault cases. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of the compensation networks of a wireless power transfer system. Optimization is performed by means of a genetic algorithm that looks for the reactances of the elements of the compensation networks that maximize the power transfer efficiency and the power transferred to the load. In addition, the algorithm selects the solutions that are less sensitive to the difference between the theoretical and actual reactances. The last part of the paper describes the prototypal setup used for the tests and supports the theoretical findings by reporting experimental results. [ABSTRACT FROM AUTHOR]

The interface algorithm is critical for accuracy of the real-time integration simulation system of renewable energy and the power grid. To improve the overall performance of the existing interface algorithms, this paper proposes an optimized interface algorithm based on the auxiliary damping impedance method interface current feedback. We explain in detail the implementation principle of the new interface algorithm and the calculation method of impedance matching and also provide a parallel timing control logic. Using the new interface algorithm, we derive equations for voltage and current of the digital simulation system side and the device under test side and also compare it with the naturally coupled system without interface delay. Finally, we verify the accuracy of the new interface algorithm via establishing a complete model of the real-time integration simulation system with a wind turbine and the power grid. The results show that the accuracy can be improved 95% in the digital simulation system side and 17% in the device under test side by using the proposed interface algorithm in this paper. [ABSTRACT FROM AUTHOR]

This paper demonstrates an energy management method using traffic information for commuter hybrid electric vehicles. A control strategy based on stochastic dynamic programming (SDP) is developed, which minimizes on average the equivalent fuel consumption, while satisfying the battery charge-sustaining constraints and the overall vehicle power demand for drivability. First, according to the sample information of the traffic speed profiles, the regular route is divided into several segments and the statistic characteristics in the different segments are constructed from gathered data on the averaged vehicle speeds. And then, the energy management problem is formulated as a stochastic nonlinear and constrained optimal control problem and a modified policy iteration algorithm is utilized to generate a time-invariant state-dependent power split strategy. Finally, simulation results over some driving cycles are presented to demonstrate the effectiveness of the proposed energy management strategy. [ABSTRACT FROM AUTHOR]

One of the most important challenges of developing multi-terminal (MT) high voltage direct current (HVDC) grids is the system performance under fault conditions. It must be highlighted that the operating time of the protection system needs to be shorter than a few milliseconds. Due to this restrictive requirement of speed, local measurement based algorithms are mostly used as primary protection since they present an appropriate operation speed. This paper focuses on the analysis of local measurement based algorithms, specifically overcurrent, undervoltage, rate-of-change-of-current, and rate-of-change-of-voltage algorithms. A review of these fault detection algorithms is presented. Furthermore, these algorithms are applied to a multi-terminal grid, where the influence of fault location and fault resistance is assessed. Then, their performances are compared in terms of detection speed and maximum current interrupted by the HVDC circuit breakers. This analysis aims to enhance the protection systems by facilitating the selection of the most suitable algorithm for primary or backup protection systems. In addition, two new fault type identification algorithms based on the rate-of-change-of-voltage and rate-of-change-of-current are proposed and analyzed. The paper finally includes a comparison between the previously reviewed local measurement based algorithms found in the literature and the simulation results of the present work. [ABSTRACT FROM AUTHOR]

Recently, mobile edge computing (MEC) technology was developed to mitigate the overload problem in networks and cloud systems. An MEC system computes the offloading computation tasks from resource-constrained Internet of Things (IoT) devices. In addition, several convergence technologies with renewable energy resources (RERs) such as photovoltaics have been proposed to improve the survivability of IoT systems. This paper proposes an MEC integrated with RER system, which is referred to as energy-harvesting (EH) MEC. Since the energy supply of RERs is unstable due to various reasons, EH MEC needs to consider the state-of-charge (SoC) of the battery to ensure system stability. Therefore, in this paper, we propose an offloading scheduling algorithm considering the battery of EH MEC as well as the service quality of experience (QoE). The proposed scheduling algorithm consists of a two-stage operation, where the first stage consists of admission control of the offloading requests and the second stage consists of computation frequency scheduling of the MEC server. For the first stage, a non-convex optimization problem is designed considering the computation capability, SoC, and request deadline. To solve the non-convex problem, a greedy algorithm is proposed to obtain approximate optimal solutions. In the second stage, based on Lyapunov optimization, a low-complexity algorithm is proposed, which considers both the workload queue and battery stability. In addition, performance evaluations of the proposed algorithm were conducted via simulation. However, this paper has a limitation in terms of verifying in a real-world scenario. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources, especially wind energy, has been on the rise throughout power systems worldwide. Due to this relatively new introduction, the integration of wind energy is often not optimized. Moreover, owing to the technical constraints and transmission congestions of the power network, most of the wind energy has to be curtailed. Due to various factors that influence the connectivity of wind energy, this paper proposes a well-organized posterior multi-objective (MO) optimization algorithm for maximizing the connections of wind energy. In this regard, the dynamic thermal rating (DTR) system and the static VAR compensator (SVC) have been identified as effective tools for improving the loadability of the network. The propose MO algorithm in this paper aims to minimize: (1) wind energy curtailment, (2) operation cost of the network considering all investments and operations, also known as the total social cost, and (3) SVC operation cost. The proposed MO problem was solved using the non-dominated sorting genetic algorithm (NSGA) II and it was tested on the modified IEEE reliability test system (IEEE-RTS). The results demonstrate the applicability of the proposed algorithm in aiding power system enhancement planning for integrating wind energy. [ABSTRACT FROM AUTHOR]

With the implementation of the carbon neutral policy, the number of electric vehicles (EVs) is increasing. Thus, it is urgently needed to manage the charging and discharging behavior of EVs scientifically. In this paper, EVs are regarded as agents, and a multiagent cooperative optimization scheduling model based on Roth–Erev (RE) algorithm is proposed. The charging and discharging behaviors of EVs will influence each other. The charging and discharging strategy of one EV owner will affect the choice of others. Therefore, the RE algorithm is selected to obtain the optimal charging and discharging strategy of the EV group, with the utility function of the prospect theory proposed to describe EV owners' different risk preferences. The utility function of the prospect theory has superior effectiveness in describing consumers' utility. Finally, in the case of residential electricity, the effectiveness of the proposed method is verified. Compared with that of random charging, this method reduces the total EV group cost of EVs by 52.4%, with the load variance reduced by 26.4%. [ABSTRACT FROM AUTHOR]

This paper focuses on the state-of-the-art of unit commitment (UC) and economic dispatch (ED) algorithms suitable for the Italian electricity market. In view of the spread of renewable energy systems (RES), the desired UC algorithm should be able to properly consider the uncertainty affecting key input variables into the formulation of the problem, as well as the different capabilities of dispatched power plants to provide ancillary services (e.g., voltage regulation). The goal of this paper is to resume the developments in UC and ED algorithms which occurred in the last decades, having a particular focus on alternating current (AC) security constrained (SC) approaches and stochastic ones, highlighting the advantages and weakness of each technique. This review is useful for the Italian TSO (Terna) to investigate what is the best solution to formulate a new algorithm to be potentially adopted in the framework of the Italian Ancillary Service Market, striving for an explicit modelization of stochastic variables and voltage constraints. This review is also useful to all system operators (SOs), independently to the market environment in which they operate, because UC algorithms are widely adopted to ensure real-time security of power systems. In conclusion, an SC-UC algorithm which takes into account both stochastic variables and AC formulation does not exist. [ABSTRACT FROM AUTHOR]

Kalman filter (KF) is often based on two models, which are phase angle vector (PAV) model and orthogonal vector (OV) model, in the application of distorted grid AC signal detection. However, these two models lack rigorous and detailed derivation from the principle of dynamic modeling. This paper presents a phase angle vector dynamic (PAVD) model and an orthogonal vector dynamic (OVD) model, which are combined with Kalman filter for detecting distorted grid AC signal. They reveal that the state noise covariance of the dynamic model−based KF is related to the sampling cycle, and overcome the defect of more detecting error for conventional model−based KF. Experiment and evaluation results show that the proposed KF algorithms are reasonable and effective. Therefore, this paper contributes a guiding significance for the application of KF algorithm in harmonic detection. [ABSTRACT FROM AUTHOR]

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a novel control system for the participation of plug-in electric vehicles (PEVs) in the provisioning of ancillary services for frequency regulation, in a way that is transparent to the driver and harmonized with the smart charging service requirements. Given a power-frequency droop curve, which specifies how the set of PEVs collectively participate to the provisioning of the frequency regulation service (we call this curve a "global" droop curve), we propose an algorithm to compute "local" droop curves (one for each PEV), which are optimized according to the current status of the PEV and the current progress of the smart recharging session. Once aggregated, the local droop curves match the global one (so that the PEVs contribute as expected to the provisioning of the ancillary service). One innovative aspect of the proposed algorithm is that it is specifically designed to be interoperable with the algorithms that control the PEV recharging process; hence, it is transparent to the PEV drivers. Simulation results are presented to validate the proposed solution. [ABSTRACT FROM AUTHOR]

The state estimation of distribution networks has long been considered a challenging task for the reduced availability of real-time measures with respect to the transmission network case. This issue is expected to be improved by the deployment of modern smart meters that can be polled at relatively short time intervals. On the other hand, the management of the information coming from many heterogeneous meters still poses major issues. If low-voltage distribution systems are of interest, a three-phase formulation should be employed for the state estimation due to the typical load imbalance. Moreover, smart meter data may not be perfectly synchronized. This paper presents the implementation of a three-phase state estimation algorithm of a real portion of a low-voltage distribution network with distributed generation equipped with smart meters. The paper compares the typical state estimation algorithm that implements the weighted least squares method with an algorithm based on an iterated Kalman filter. The influence of nonsynchronicity of measurements and of delays in communication and processing is analyzed for both approaches. [ABSTRACT FROM AUTHOR]

This paper designs the integrated charging station of PV and hydrogen storage based on the charging station. The energy storage system includes hydrogen energy storage for hydrogen production, and the charging station can provide services for electric vehicles and hydrogen vehicles at the same time. To improve the independent energy supply capacity of the hybrid charging station and reduce the cost, the components are reasonably configured. To minimize the configuration cost of the integrated charging station and the proportion of power purchase to the demand of the charging station, the energy flow strategy of the integrated charging station is designed, and the optimal configuration model of optical storage capacity is constructed. The NSGA-II algorithm optimizes the non-inferior Pareto solution set, and a fuzzy comprehensive evaluation evaluates the optimal configuration. [ABSTRACT FROM AUTHOR]

Increased use of energy in buildings and HVAC systems requires advanced control schemes like model-based control to improve energy efficiency, which in turn requires accurate thermodynamic models of buildings. The Resistive-Capacitive (RC) method is a popular and versatile approach for thermal modeling of buildings. Despite this, it is not easy to find practical solutions of implementation of the RC method. It is the goal of this paper to clarify the RC method and demonstrate simple implementation of this method, especially for multi-zone buildings, which have more potential for energy savings from use of model-based control. This paper provides two contributions. First is a detailed explanation of the RC method, focusing on its use for developing a structure of a model and first-principles approach for estimation of parameters of a model. Second is a demonstration of an algorithm that enables automatic development of the structure of a model from basic information about a building (layout, construction elements) and its combination with data-based parameter estimation. Use of the algorithm is presented with a case-study on industrial multi-zone building, for which such a grey-box model is developed and analyzed. The resulting model is rapidly developed and used in a simulation with the measured data. The outputs of the model are compared with the measured temperatures and they show good fit. [ABSTRACT FROM AUTHOR]

The integration of large amounts of generation into distribution networks faces some limitations. By deploying reactive power-based voltage control concepts (e.g., volt/var control with distributed generators), the voltage rise caused by generators can be partly mitigated. As a result, the network hosting capacity can be accordingly increased, and costly network reinforcement might be avoided or postponed. This works however only for voltage-constrained feeders (opposed to current-constrained feeders). Due to the low level of monitoring in low voltage networks, it is important to be able to classify feeders according to the expected constraint in order to avoid the overloading risk. The main purpose of this paper is to investigate to which extent it is possible to predict the hosting capacity constraint (voltage or current) of low voltage feeders on the basis of a large network data set. Two machine-learning techniques have been implemented and compared: clustering (unsupervised) and classification (supervised). The results show that the general performance of the classification or clustering algorithms might be considered as rather poor at a first glance, reflecting the diversity of real low voltage feeders. However, a detailed analysis shows that the benefit of the classification is significant. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) can be integrated into any radial distribution network (RDN) to meet the increasing demand for electric power. Instead of integrating a single large size DG, two or more than two small DGs (multi DGs) are usually placed. These DGs must be placed in appropriate buses/nodes with suitable size to ensure better performance of the system. The purpose of this paper is to implement an established hybrid optimization algorithm to place multi DGs with suitable size at each site. The proposed hybrid ABC-CS optimization algorithm combines the behaviour of the artificial bee colony (ABC) and cuckoo search (CS) algorithms. The multi DGs are optimally placed by considering objectives like power loss, penalty function (PE) and voltage profile (VP). The outcomes of two different types of radial distribution networks obtained by the suggested method for placement of multi DGs have been correlated to that obtained by using genetic algorithm (GA), particle swarm optimization (PSO) and GA-PSO. The proposed method has also been correlated with the other published methods to indicate its strength. [ABSTRACT FROM AUTHOR]

In the current smart grid scenario, the evolution of a proficient and robust maximum power point tracking (MPPT) algorithm for a PV subsystem has become imperative due to the fluctuating meteorological conditions. In this paper, an adaptive feedback linearization-based NeuroFuzzy MPPT (AFBLNF-MPPT) algorithm for a photovoltaic (PV) subsystem in a grid-integrated hybrid renewable energy system (HRES) is proposed. The performance of the stated (AFBLNF-MPPT) control strategy is approved through a comprehensive grid-tied HRES test-bed established in MATLAB/Simulink. It outperforms the incremental conductance (IC) based adaptive indirect NeuroFuzzy (IC-AIndir-NF) control scheme, IC-based adaptive direct NeuroFuzzy (IC-ADir-NF) control system, IC-based adaptive proportional-integral-derivative (IC-AdapPID) control scheme, and conventional IC algorithm for a PV subsystem in both transient as well as steady-state modes for varying temperature and irradiance profiles. The comparative analyses were carried out on the basis of performance indexes and efficiency of MPPT. [ABSTRACT FROM AUTHOR]

The efficient use of energy in electrical systems has become a relevant topic due to its environmental impact. Parameter identification in induction motors and capacitor allocation in distribution networks are two representative problems that have strong implications in the massive use of energy. From an optimization perspective, both problems are considered extremely complex due to their non-linearity, discontinuity, and high multi-modality. These characteristics make difficult to solve them by using standard optimization techniques. On the other hand, metaheuristic methods have been widely used as alternative optimization algorithms to solve complex engineering problems. The Crow Search Algorithm (CSA) is a recent metaheuristic method based on the intelligent group behavior of crows. Although CSA presents interesting characteristics, its search strategy presents great difficulties when it faces high multi-modal formulations. In this paper, an improved version of the CSA method is presented to solve complex optimization problems of energy. In the new algorithm, two features of the original CSA are modified: (I) the awareness probability (AP) and (II) the random perturbation. With such adaptations, the new approach preserves solution diversity and improves the convergence to difficult high multi-modal optima. In order to evaluate its performance, the proposed algorithm has been tested in a set of four optimization problems which involve induction motors and distribution networks. The results demonstrate the high performance of the proposed method when it is compared with other popular approaches. [ABSTRACT FROM AUTHOR]

In shale gas hydraulic fracture monitoring or rock acoustic emission experiments, fracture plane identification is always a complex task. Conventional approaches typically use the source locating results derived from the micro-seismic event and then interpret the fracture plane using manual qualitative analysis. Large errors typically occur due to manual operations. On the other hand, the density-based clustering algorithm with spatial constraints is widely used in geographic information science, biological cells science and astronomy. It is an automated algorithm and can achieve good classification results. In this paper, we introduced the above-mentioned clustering algorithm with spatial constraints to fracture identification applications. Moreover, because micro-seismic events are 4D in nature, every micro-seismic event has both time and space information. Hence, we improve the conventional clustering algorithm by incorporating a time constraint. We test the proposed method using rock acoustic emissions data and compare our fracture identification results with CT scan images; the comparison clearly shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a static state observer algorithm based on the static equilibrium position is proposed, which can realize accurate control of electric vehicle height adjustment with existing road excitation. The existence of road excitation can lead to deflection variation of the electronically controlled air suspension (ECAS). The use of only dynamic deflection as the reference for the electric vehicle height adjustment will produce great errors. Therefore, this paper provides an observation algorithm, which can realize the accurate control of vehicle height. Firstly, the static equilibrium position equation of suspension is derived according to the theory of hydrodynamics and characteristics of pneumatic chamber. Secondly, a vehicle dynamics model with seven degrees of freedom (7-DOF) is established and the kinetic equations are discretized. Then, the unscented Kalman filter (UKF) algorithm is used to obtain the static equilibrium position of vehicle. According to the vehicle static equilibrium position obtained by UKF, the height of the vehicle is adjusted by using a fuzzy controller. The simulation and experimental results show that this proposed algorithm can realize the control of vehicle height with an accuracy of over 96%, which ensures the excellent driving performance of vehicles under different road conditions. [ABSTRACT FROM AUTHOR]

The importance of Power Hardware-in-the-Loop (PHIL) experiments is rising more and more over the last decade in the field of power system and components testing. Due to the bidirectional exchange between virtual and physical systems, a true-to-reality interface is essential; however, linking several dynamic systems, stability issues can challenge the experiments, the components under test, and the individuals performing the experiments. Over the time, several interface algorithms (IA) have been developed and analyzed, each having different advantages and disadvantages in view of combining virtual simulations with physical power systems. Finally, IA are very specific to the kind of PHIL experiment. This paper investigates the operational range of several IA for specific PHIL setups by calculations, simulations, and measurements. Therefore, a selection of the mainly used respectively optimized IA is mathematically described. The operational range is verified in a PHIL system testing environment. Furthermore, in order to study the influence of different PHIL setups, according to software and hardware impedance, different tests using linear and switching amplifiers are performed. [ABSTRACT FROM AUTHOR]

Gear train optimization problems (GTOPs) can be very difficult. This paper proposes an enumerative optimization procedure (EOP) for the GTOP of a two-speed dedicated electric transmission (2DET) for electric vehicles (EVs). The EOP combines enumeration with the Min-Max Principle of Optimality (MMPO). First, the requirements of the EV and the requirements of manufacture and operation were checked in a dedicated order to obtain the feasible region of the GTOP. Then, the MMPO was implemented within the feasible region to reveal the global optimum in terms of the performance of the EV, the load capacity of the gears and the size of the gear train (GT). Results demonstrated that the EOP was effective in determining the feasible region and simultaneously and globally optimizing multiple criteria for the GTOP. The idea of combining enumeration with optimization, as the EOP presents, may be helpful to solve other GTOPs and provide global optima that are immediately practical and applicable. [ABSTRACT FROM AUTHOR]

This study introduces an innovative approach for the reconstruction of wind turbine tower states using a tangential recursion algorithm. The primary objective is to enable real-time monitoring of the operational condition of wind turbine towers. The proposed method is rooted in strain–load theory, which enables the accurate identification of tower load states. The tangential recursion algorithm is utilized to translate the strain data acquired from strategically placed sensors into reconstructed point positions. The subsequent refinement of these positions incorporates considerations of torsional loads and geometric deformations, culminating in the comprehensive and precise reconstruction of the tower's deformation behavior. Through the use of the OpenFAST V8 simulation software, a thorough analysis is conducted to investigate the load and deformation characteristics of the NREL 5 MW wind turbine tower across diverse operational scenarios. Furthermore, the load conditions corresponding to rated operating circumstances are applied to a finite element model constructed with the lumped mass method. The identification of tower load states and the comprehensive reconstruction of deformation patterns are realized through the extraction of strain data from critical points in the finite element model. The credibility and accuracy of the proposed method are rigorously evaluated by juxtaposing the identification and reconstruction outcomes with the results derived from the OpenFAST simulations and finite element analyses. Notably, the proposed method circumvents the requirement for external auxiliary calibration equipment for the tower, rendering it adaptable to a broader spectrum of operational contexts and making it consistent with unfolding trajectories in wind power advancement. [ABSTRACT FROM AUTHOR]