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This paper exploits smart inverters of distributed energy resources (DERs) for the mitigation of fault-induced delayed voltage recovery (FIDVR). According to the IEEE Standard 1547-2018, smart inverters with voltage ride-through and var injection capabilities are promising technologies for mitigating FIDVR. This paper quantitatively investigates the effectiveness of smart inverters for FIDVR from a system-wide perspective. It shows that the penetration of smart inverters and induction motors are important factors affecting mitigation success. In addition, the over-voltages at the end of FIDVR due to load disconnections can be prevented with coordinated control of DERs and automated switching capacitors. The control scheme of smart inverters used for FIDVR is extended from the distributed volt/var droop control to include low- and high-voltage ride-through capabilities. The IEEE 13-bus feeder, the 8500-node test feeder, and the IEEE 390-node low-voltage network test system are used to illustrate the impact of DER penetration. For example, one can determine the residential air conditioner penetration level (for a given system at certain DER penetration level) that makes a system susceptible to FIDVR. The effects of solar intermittency are also studied. [ABSTRACT FROM AUTHOR]

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. In this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. Finally, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption. [ABSTRACT FROM AUTHOR]

Load models have a significant influence on power system simulation. However, current load modeling approaches can hardly satisfy the diversity and time- varying characteristics of loads [including electric vehicles (EVs) and battery storage] in terms of model accuracy and computing efficiency. An online modeling method for composite load models based on measurement information is proposed in this paper. Firstly, the dominant factors in load model output are analyzed based on the active subspace of parameter space. Then the clustering algorithm is applied to cluster the large number of underlying loads based on the characteristics of load daily output curves. Finally, the underlying loads are equivalently aggregated from the low voltage levels to the high voltage levels to construct the composite load model. Simulation results obtained based on PSCAD/EMTDC demonstrate that the load model constructed by the proposed approach can accurately reflect the actual load characteristics of a power system. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system. [ABSTRACT FROM AUTHOR]

Moisture is one of the key variables that must be considered when determining the load profiles that can be safely applied to a transformer. The presence of high amount of water accelerates the aging rate of the transformer's solid insulation, shortening the life expectancy of the equipment. Moreover, the dynamic processes of moisture migration between paper and oil must be considered in order to avoid a potential cause of failure, such as the formation of bubbles in the paper–oil interface, or the moisture saturation and the subsequent formation of liquid water in oil. These processes are linked to the changes in temperature; however, within the transformer, both processes have very different time constants, which complicates the analysis. In this paper, a mathematical model is presented, which allows us to study the moisture dynamics in a transformer. The model considers a multi-physical approach incorporating a thermal module and a moisture dynamic module that makes it possible to analyze the behavior of the moisture for a certain load profile. Some application cases are included in this paper to illustrate the model operation. [ABSTRACT FROM AUTHOR]

This paper is devoted to electric arc furnace (EAF) modeling using a random differential equation based on the power balance equation. The proposed approach broadens and improves the model through the introduction of stochastic processes in place of existing coefficients. The paper presents a method which enables the estimation of EAF model coefficients with the help of measurement data - voltage and current waveforms recorded during the melting stage of an EAF work cycle. The estimation process is conducted with a Monte Carlo method and genetic algorithm, which is applied iteratively to each of the defined frames of the input signal. The estimated coefficients have been analyzed with respect to their time variability as well as the probability distributions of their values and increments. The results have been extensively visualized. Next, the identification of the stochastic processes representing the model coefficients has been carried out. Based on the previous results and autocorrelation functions, the density functions and parameters of discrete-time stochastic processes were identified. The paper presents solutions validated with statistical tests. [ABSTRACT FROM AUTHOR]

Enhancing power electronics (PE) converter reliability is crucial for ensuring a reliable operation of current and future operating Wind Turbines (WTs). Achieving high reliability of variable speed WT PE systems requires careful consideration of their operation, and particularly their thermal cycling. This paper presents a methodology for evaluating and reconsidering operational strategies of WTs with relation to the thermal loading and lifetime consumption of the converter. The methodology is applied to compare control strategies for the WT generator and evaluate their impact on the converter reliability by observation of the thermal cycles and by calculating the resultant lifetime consumption of those stress cycles. The thermal stress on both the Machine Side Converter (MSC) and the Grid Side Converter (GSC) is examined and compared. It is shown that the least reliable of the three evaluated control strategies is the one that tracks the power curve below rated speed most closely. This paper suggests that dynamic transients associated with the WT control largely influence the IGBT module wear-out and their modelling needs to be prioritized for lifetime studies. These dynamic transients are captured by the improved model whose value is confirmed for the comparisons in the case study of the paper. [ABSTRACT FROM AUTHOR]

Load profile classification is very important in load forecast, planning and management. Although customers are generally grouped by utilities into residential, commercial classes and respective subclasses, there is a lack of systematic framework that can be used to characterize different classes with signatures that are both human-readable and machine-readable. The work presented in this paper attempts to formulate the theoretical framework for customer classification using the annual load profiles. This paper demonstrates how to extract characteristic attributes in frequency domain (CAFD) and use these CAFDs to formulate a hierarchy of load profiles that can be used as the systematic framework for customer load classification. As signatures for customer classes and subclasses, the CAFDs are obtained by using a data mining method called CART (classification and regression tree). The paper presents a load profile classification test to establish the efficacy of the proposed approach which is significant improvement over current practices that provide mostly qualitative labeling. [ABSTRACT FROM AUTHOR]

The proliferation of electronic loads has led to a substantial increase in harmonic emissions within low-voltage distribution networks. The accurate estimation of the expected levels of harmonics in a network is a daunting task for network operators. Stochastic-based harmonic estimation models can offer a comprehensive assessment of the expected levels of harmonics in the presence of existing and future loads, including electric vehicles and smart-grid-enabled devices. Such models offer a valuable tool for network operators to assess the potential impact of harmonics on future networks and to create sustainable design solutions to meet the increasing demand for electricity while achieving net zero targets. However, several variables associated with these estimations models involve a level of uncertainty due to their stochastic nature, leading to inaccuracies in the estimations. This paper aims to provide a more realistic estimate of these uncertainties in order to improve the outcomes of harmonic estimation models for the development of sustainable distribution networks. [ABSTRACT FROM AUTHOR]

In this paper, a new modulation strategy based on the carrier-based switching strategy for the non-square direct matrix converters (MC) is proposed to control the active power of distributed generation (DG) units. In this strategy, the active power of DGs is controlled by the central input current control of the non-square direct MC independent from the voltage and frequency. Conventionally, each DG has a converter, and for supplying a load with N number of DGs, N number of converters are needed and each converter has its own modulation switching and control strategy to control the power output of each DG. Needless to say, in a microgrid with N number of DGs, the control strategy of each converter has more complex structure than that of a microgrid with one converter, and surely the former strategy entails more volume and price. Using the proposed converter in this paper, it is possible to supply a load with N number of DGs through one converter. Also, the power outputs of all DGs are controlled by a central control strategy. The proposed central control strategy is described and simulated for a typical microgrid. Experimental and simulation results validate the effectiveness of the proposed converter and the proposed strategy. The results demonstrate the applicability and efficiency of the system and verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

Aggregated loads of induction motors are often used in the study of power system dynamic stability. This paper reports the finding that aggregation of the same or similar induction motors may likely cause the dynamic instability. Theoretical analysis is presented in the paper to show that a group of N same induction motors in parallel connection is equivalent to N dynamic independent subsystems. Oscillation modes of one of the equivalent subsystems are affected by the number of induction motors. Hence, when the number of induction motors increases, it is possible that the oscillation modes of the group of induction motors may move toward the right on the complex plane, leading to the dynamic instability in the worst case. The analysis explains why aggregation of induction motors may possibly bring about the risk of power system dynamic instability. In this paper, case studies of an example power system with a cluster of induction motors are presented. Results of modal computation and simulation demonstrate that when the number of induction motors increases, aggregation of the same or similar induction motors leads to the dynamic voltage instability and growing low-frequency electromechanical power oscillations in the test case. [ABSTRACT FROM AUTHOR]

This paper presents a novel power flow control system for a remote military microgrid with hybrid energy storage. A combination of batteries and supercapacitors (SCs) is managed by the novel control system to increase the battery life by redirecting the higher frequency current that would have to flow in the battery if SCs were not present. This paper offers a practical solution to manage the SC current and ensure that the SCs are never overcharged or commanded to support the system when they are discharged to the lower operating limit chosen. The new controller allows the independent selection of the low-pass filter parameter and the number of SCs. By making the most out of these two degrees of freedom, we investigate different configurations, identifying the one achieving the highest cash flow for the overall system. Modeling, simulations, and experimental verification are presented and linked to the sensitivity analysis of the economics of the military microgrid. [ABSTRACT FROM AUTHOR]

As the inverter-based generation replaces the conventional synchronous generators, it may also need to fill in the missing ancillary service support. One of these ancillary services is dynamic reactive power provision and voltage control. This paper analyzes optimal strategy of reactive and active fault-current support of the inverter-based generation leading to fast voltage recovery of the system. For the purpose of the analysis, new ramping active current controller able to emulate different behavior of active current injection is proposed. By optimizing its parameters for different case studies of the system, the conclusions about optimal behavior of the inverter based generation with respect to system parameters and operating conditions are drawn. It is observed that the optimal combination of active and reactive fault-current is the most sensitive to the dynamic load component penetration levels in the system. With the increasing penetration levels, the significance of active fault-current injection increases. The results show that with higher penetration levels of dynamic load component in the heavy load areas, the ramping down of the inverter-based generation active fault-current results in slower voltage recovery of the system. Following this conclusion, a recommendation on update of current European grid codes is proposed. [ABSTRACT FROM AUTHOR]

Fast and accurate power-flow methods are of great importance, especially in near real-time optimal operation of power systems. This importance will be even more highlighted in the presence of more repetitions of power-flow calculations, which cause more computational complexities in optimization problems. As a solution, in this paper, a novel fast and accurate approach of linear power-flow formulation is proposed. Principles of the proposed approach are based on dividing power-flow calculations into base and variable parts. To this aim, at first, system modeling of base and variable parts are presented. For the base-part modeling, utilizing a nonlinear power-flow, an accurate base power-flow (BPF) is extracted. Afterwards, by linearizing the power system around the BPF, variable-part model which is the result of a linear fitting process, is obtained. Then, it is shown that the variable-part of the operating point is always a function of the obtained base-part and variable-part models. In this paper, by focusing on the stochastic application of the proposed approach, different uncertainties in a distribution system are considered. Finally, numerical results carried out in the Matlab environment, for a IEEE 34-bus standard distribution system and then a 1486-bus case study, verify the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Existing non-intrusive load identification methods face with the difficulty of being applied to industrial users whose operation and ON/OFF status are hard to be acquired. Focusing on the above challenge, this paper proposes a non-intrusive identification method of harmonic source loads for industrial users. Utilizing Clarke transform, an equation with respect to voltage and current is formulated to calculate the integrated equivalent impedance, which is then decomposed with the improved complex local mean decomposition (CLMD) and complex fast independent component analysis (C-FastICA) achieving non-intrusive separation of the equivalent impedance signals of individual branches. The separated impedance signals will be used as the basis for load type identification. The proposed method is validated by numerical simulations as well as actual three-phase voltage and current monitoring data. The case study shows that it can effectively identify the types of harmonic source loads while being resistant to noise and impacts of sampling accuracy and diverse load capacities, which obviously demonstrates its practical applicability. [ABSTRACT FROM AUTHOR]

Analogous to the way a good driver is aware of neighboring cars, electrical loads can coordinate with other loads within a building or section of a distribution grid. This paper develops methods that enable groups of cyclic loads (devices that turn on and off periodically to maintain setpoints) to reduce their peak aggregate power demand. The methods accommodate a wide variety of cyclic loads, including those with nonlinear or unknown dynamics, and can be implemented in a fully distributed fashion. This paper targets settings with a few hundred cyclic loads or fewer, where the methods developed here could reduce demand peaks significantly while maintaining or improving quality of service. This could save ratepayers money on monthly demand charges, decrease fuel use in microgrids, or extend the life of power delivery equipment. [ABSTRACT FROM AUTHOR]

The energy transition is aimed to take advantage of the operational flexibility of hydropower to extend the integration of intermittent renewable sources. Consequently, the hydrogenerators will have to operate in regimes far away from their designed best-point operation. In order to accurately assess the impact, this paper presents a useful approach to determine the overall operating efficiency of synchronous generators under intermittent operation. An accumulated average efficiency (AAE) model is proposed and compared against an alternative approach; the weighted average efficiency (WAE) model. It is found that the WAE approach produces unrealistic low efficiencies when the generator operates in synchronous condenser mode (SCM) for long periods. In general, the AAE supersedes the WAE for all the different load distributions that were investigated. This was further illustrated by a worked example and by constructing more complex load distributions. A load distribution dominated by SCM yields a difference as high as $33.18 \,\%$ , while an even distribution deviates $1.43 \,\%$ in their respective efficiencies. Finally, a yearly on-site measurement of our studied $103 \,\mathrm{MVA}$ generator’s concentrated load distribution revealed a discrepancy of $0.67 \,\%$ , which is a significant deviation considering what the operating regime would mean in terms of economic implications. [ABSTRACT FROM AUTHOR]

Extreme events can cause severe power system damage. Resilience-driven operation of networked microgrids (MGs) has been heavily studied in literature. There is, though, little research considering the influence of resilience on decision making for planning. In this paper, a three-level model is suggested to solve the optimal sizing problem of networked MGs considering both resilience and cost. In the first level, a meta-heuristic technique based on an adaptive genetic algorithm (AGA) is utilized to tackle the normal sizing problem, while a time-coupled AC OPF is utilized to capture stability properties for accurate decision-making. The second and third levels are combined as a defender-attacker-defender model. In the former, the suggested AGA is utilized to generate attacking plans capturing load profile uncertainty and contingencies for load shedding maximization, while a multi-objective optimization problem is suggested for the latter to obtain a trade-off between cost and resilience. Simulations considering meshed networks and load distinction into critical and non-critical are developed to demonstrate algorithm effectiveness on capturing resilience at the planning stage and optimally sizing multiple parameters. The results indicate that higher resilience levels lead to higher investment cost, while sizing networked MGs leads to decreased investment in comparison with standalone MGs sizing. [ABSTRACT FROM AUTHOR]

With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution networks, making this difficult. This paper proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters. In particular, the proposed method infers existing LV network capacity, as well as losses, across scenarios where only a limited number of customers have Smart Meters installed. Typical daily load profiles across customers with Smart Meters are learned using a Dirichlet sampled Gaussian mixture model (GMM). Learned profiles are then applied to all unmetered customers to estimate network parameters. Method accuracy is assessed by comparing estimations with simulated, fully observed, LV network models. The method is also compared to benchmark models for establishing unobserved demand profiles. Overall, results in the paper show that the proposed method outperforms benchmark models in terms of accurately assessing substation headroom, particularly in scenarios where only 10–50% of customers have Smart Meters installed. [ABSTRACT FROM AUTHOR]

This paper presents an elegant method for determining the simplest model of a power system electrical/mechanical load that will suffice for dynamic frequency power system studies and closed-loop simulation work. The strategy behind this technique is to supply the simplest load model possible that gives sufficiently accurate results for the goals of each unique modeling effort. This paper identifies the frequency characteristics of several different load types. It also identifies the level of load model detail required for testing typical power management systems, contingency-based load-shedding systems, frequency-based load-shedding systems, governor control systems, island/grid/unit autosynchronization systems, and exciter control systems. This paper describes how to lump loads without loss of fidelity, when an induction motor needs to be modeled as a single-cage or double-cage motor model, what sort of mechanical load model is appropriate, when we can assume zero inertia for a direct-on-line type of load, and how to verify the turbine/generator inertia, and load inertia from field tests. This paper concludes with a simple reference that engineers can use to specify the level of detail required when modeling industrial power system loads. [ABSTRACT FROM PUBLISHER]

Electric vehicles (EVs) have gained considerable attention in the last decade due to a paradigm shift in the transport sector driven by a higher awareness of environmental issues. While the importance of EVs cannot be overstated in the context of the global climate crisis, it does raise the question of whether certain countries or states are ready for their implementation. It is, therefore, necessary to analyze the impact of EVs in the power grids of these countries and states, considering factors such as line congestion and the eventual degradation of voltage profiles, to determine their hosting capacity and assess eventual expansion options. This paper proposes a representative prototype of Curaçao's electrical system, which is used for assessing the impacts of EVs, allowing us to determine its hosting capacity. Curaçao is an island in the southern Caribbean Sea that uses fuel generators, wind energy, and solar energy to generate electricity. The idea behind this paper is to analyze the effects caused by an increase in EVs on Curaçao's power grid and propose preventive measures to deal with such problems. Eight EV charging stations were considered, one DC super fast-charging station, three normal DC fast-charging stations, and four AC fast-charging stations. In 2022, there were an estimated 82,360 vehicles on the island. Using this information, this paper analyzes how many vehicles can be simultaneously connected to the grid before it no longer operates under acceptable values. The results showed that 3.5% of the total vehicles can be hosted by the grid. Nonetheless, this can be increased up to 4.5% with the reinforcement of a transmission line. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent modeling and optimization method to set up the smart energy hub model for the multiple energy system and optimize the operation of its associated energy hub model automatically. The core idea of the proposed method is to divide the complex energy hub model into several simple energy hub models. In this paper, the proposed computerized algorithm, which is applied to the smart energy hub model division, includes an energy loop intelligent searching method, a real nodes arrangement method, and a virtual nodes insertion method. One case study for the multiple energy system of a residential district in Guangzhou city is performed to validate the effectiveness of the proposed method. Compared with the traditional calculation method, the proposed method guarantees global optimal operation decision for the whole multiple energy system and the computational burden is significantly reduced. [ABSTRACT FROM AUTHOR]

Test generation procedures target a variety of fault models in order to produce test sets that are effective for defect detection. This paper considers the likely scenario where two-cycle skewed-load tests are generated to detect single transition faults, and the test set is complemented with tests for single stuck-at faults that are not detected by the transition fault test set. For this scenario, this paper makes several unique observations that can be utilized to produce a single compact test set that consists only of two-cycle skewed-load tests for both fault models. The first observation is that a single-cycle test for a stuck-at fault can be transformed into a skewed-load test that is guaranteed to detect the stuck-at fault without performing logic or fault simulation. The second observation is that skewed-load tests, which are transformed from single-cycle tests for stuck-at faults, sometimes detect more transition and stuck-at faults than tests that were generated for transition faults. The third observation is that a static test compaction procedure, which is based on the modification and removal of tests, is effective in this context because it allows tests for stuck-at faults to detect more transition faults and vice versa. This paper describes a test compaction procedure based on these observations and presents experimental results for benchmark circuits to demonstrate the effectiveness of the procedure. [ABSTRACT FROM AUTHOR]

Economic dispatch with valve-point effect (EDVPE) considered is presented as a more accurate model of the real problem compared to the conventional economic dispatch model. It is basically a non-convex, non-differentiable, and multi-modal optimization model with many local minima. Part I of the paper focuses on the local minimum analysis of the EDVPE. The analysis indicates that a local minimum consists of the singular points, the small convex regions, and the output of a slack unit that is dispatched to balance the load demand. Two types of local minima are identified and the second type could be ignored. To verify the rationality of the analyses, a traverse search has been performed to solve the EDVPE with and without considering the transmission loss on different test systems. All the simulation results support the analysis given in the paper. To effectively solve the EDVPE on a large-scale power system, based on the analysis presented in this paper, a new method, dimensional steepest decline method, is proposed in Part II of the paper. [ABSTRACT FROM PUBLISHER]

This paper investigates systemwide short-term voltage stability concerns of power systems due to the multiple parametric uncertainties in the dynamic load model. The impact of regional load model uncertainty can be widespread and may mislead the whole system analysis and subsequent measures, if not properly addressed. This research discloses that the systemwide impact is related to the voltage weak areas through voltage stability modal analysis, and suggests that the impact of uncertain parameters needs to be assessed from a systems perspective, which has rarely been done in the existing practices. We thus present a three-step methodology for evaluating the systemwide uncertainty impacts: Firstly, it screens possible trajectories of all buses. The second step verifies whether the screened trajectories comply with the defined criteria, and determines the necessity of the final step. The final detailed analysis is conducted for those selected scenarios. Comprehensive studies for both the IEEE test and real Korea power systems consistently confirm the observations and demonstrate the efficacy and validity of the proposed method. [ABSTRACT FROM AUTHOR]

There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Mechanical elastic wheel (MEW) has the advantage of explosion-proof, which helps to guarantee the safety and maneuverability of the automobile. Aiming to improve the stability of vehicles matching with MEW, this paper proposes an integrated control strategy that combines the active front steering and the active suspension systems. The integrated controller consists of feed-forward and feedback controllers. Firstly, the differential flatness system of the vehicle is constructed concerning the coupling among yaw, lateral, and roll motion. And the flatness based feed-forward control inputs of the desired front wheel angle and active roll moment are derived. At the same time, the backstepping-adaptive feedback controllers for yaw rate and roll angle are designed. Then, the feed-forward and feedback controllers are coordinated and their weight coefficients are adjusted according to the sideslip angle. Furthermore, this paper also adopts a nonlinear disturbance observer to estimate the vertical load of MEW. The influence of the vertical load is fully considered when estimating the equivalent cornering stiffness of each MEW, so as to realize the online adjustment of the controller parameters. Finally, the maneuver tests of step input steering and double lane change are simulated, respectively. The results show that the proposed control strategy yields a better control effect than the fuzzy-PID controller, and the performance of the vehicle matching with MEW has been significantly improved. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This two-part paper considers the coordination of a population of thermostatically controlled loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak energy constraint. The companion paper (Part I) formulates the problem and proposes a load coordination framework using the mechanism design approach. To address the unknown parameters, Part II of this paper presents a joint state and parameter estimation framework based on the expectation maximization algorithm. The overall framework is then validated using real-world weather data and price data, and is compared with other approaches in terms of aggregated load response. Simulation results indicate that our coordination framework can effectively improve the operational efficiency of the distribution system and reduce power congestion at key times. [ABSTRACT FROM AUTHOR]

This paper presents parameter values and ranges of static load models used in a power system analysis for the representation of both individual electrical devices and aggregate system loads. The paper discusses and correlates load models and their parameters following statistical processing of the responses of around 100 transmission system operators and utilities around the world, who participated in a survey initiated by CIGRE Working Group C4.605, with the corresponding information on load models and their parameter values from the existing literature. According to the survey, the most frequently used static load models are voltage dependent, and voltage and frequency dependent exponential load models. The identified typical parameter values and ranges of these static load models, both for low voltage devices and for aggregate loads at higher voltage levels, are results of the analysis of a large number of data. Based on these results, the paper provides recommendations for their further use in power system studies and also introduces a novel method for obtaining the mean values and ranges of parameters of the aggregate system load models. [ABSTRACT FROM AUTHOR]

This paper presents the results of the experiment with an overmassed wire-array $Z$ -pinch load that is powered by a current pulse (amplitude ~4.3 MA and rise time $\sim 0.9~\mu \text{s}$) from a multidisk magnetocumulative generator. Interesting feature of the experimentally recorded soft X-ray (SXR) pulse is short (~10 ns) duration at a microsecond current rise time with total energy of several hundreds of kilojoules. Obtained results are the evidence of high-quality implosion of the current-carrying plasma. The 2-D magnetohydrodynamic code FLUX-rz was used for the numerical simulation and analysis of the obtained results. The calculations indicated that only ~30% of the initial load mass took part in the SXR generation. The main peculiarities of the overmassed $Z$ -pinch implosion are discussed in this paper. [ABSTRACT FROM AUTHOR]

Renewables, such as solar and wind generation, combined with storage are becoming a key part of modern grid. This paper develops probabilistic tools for analysis of grid reliability with such variable generation resources. The developed tools improve speed and accuracy of the reliability analysis compared to usual Monte Carlo methods. This is achieved by using an extension of well known convolution method applicable to interdependent variables. The interdependent distributions are obtained from historical data using Machine Learning of quantile models. The paper presents a novel approach to the analysis of reliability contribution of storage based on these models and related to Information Theory. The developed tools are demonstrated in several example scenarios for ISO-New England service area. [ABSTRACT FROM AUTHOR]

A suitable probabilistic scenario set of load demand and natural characteristics of renewable energy is becoming a crucial issue in power system planning studies. Properly addressing the impact of potentially thousands of residential PV plants on the resilience and reliability needs of substations necessitates the representation of inherent relations between photovoltaics and the load throughout the long-term planning period. The optimal planning of substation expansions is achievable through proper modeling of input parameters which describes the characteristics of the service areas. In this paper, the co-existence of PV plants and the load in a service area under three different states such as daytime with clear-sky and no-fault, daytime with abnormal events, and nighttime are incorporated into the stochastic dynamic optimization problem by using scenario-based approach. The scenario tree of the problem is branched from three different bases simultaneously instead of only one as in conventional approach. This paper also combines the risk-constrained stochastic dynamic SEP problem and Mixed Integer Linear Programming (MILP) framework under one roof. The comparison between integrating inherent characteristics of PV plants with and without considering abnormal events into the optimization is performed to show the impact of suitable probabilistic model on dynamic nature of investment decisions. [ABSTRACT FROM AUTHOR]

The critical nature of vehicular communications requires their extensive testing and evaluation. Analytical models can represent an attractive and cost-effective approach for such evaluation if they can adequately model all underlying effects that impact the performance of vehicular communications. Several analytical models have been proposed to date to model vehicular communications based on the IEEE 802.11p (or DSRC) standard. However, existing models normally model in detail the MAC (Medium Access Control), and generally simplify the propagation and interference effects. This reduces their value as an alternative to evaluate the performance of vehicular communications. This paper addresses this gap, and presents new analytical models that accurately model the performance of vehicle-to-vehicle communications based on the IEEE 802.11p standard. The models jointly account for a detailed modeling of the propagation and interference effects, as well as the impact of the hidden terminal problem. The model quantifies the PDR (Packet Delivery Ratio) as a function of the distance between transmitter and receiver. The paper also presents new analytical models to quantify the probability of the four different types of packet errors in IEEE 802.11p. In addition, the paper presents the first analytical model capable to accurately estimate the Channel Busy Ratio (CBR) metric even under high channel load levels. All the analytical models are validated by means of simulation for a wide range of parameters, including traffic densities, packet transmission frequencies, transmission power levels, data rates and packet sizes. An implementation of the models is provided openly to facilitate their use by the community. [ABSTRACT FROM AUTHOR]

The degradation of the electrical contact in a gas insulated switchgear (GIS) is closely related to overheating at the contact. However, the disconnector contact is one of the hot contacts in GIS. Therefore, in this paper, the finite element method was used to solve the three-dimensional multiphysics coupling temperature rise field and flow field in the Single Phase in One Tank (SPIOT) type GIS disconnector under different operating conditions. From the perspective of temperature rise field and flow field, this paper analyzed the temperature rise characteristics under different working conditions and explored the feasibility of the direct application of air, CO2 and C4F7N/CO2 mixed gas to an electrical device. The disconnector temperature rise experiment platform was then built to verify the simulation. The results showed that the temperature rise field and the flow field distribution were similar in four gas environments. SF6 gas had the smallest temperature rise, followed by C4F7N/CO2 mixed gas, and the temperature rise of CO2 and air was relatively large. Therefore, from the perspective of the temperature rise field and flow field, it was feasible to directly apply C4F7N/CO2 mixed gas to an electrical device in order to meet insulation requirements. [ABSTRACT FROM AUTHOR]

The increasing prevalence of technologies such as advanced metering and controls and continuously variable power electronic devices are enabling a radical shift in the way frequency regulation is performed in the bulk power system. This is a welcome development in light of the increase of unpredictable and variable generation. The idea of active participation of loads in frequency markets is not new, but the rapidly changing landscape of the power grid requires new techniques for successful integration of new types of resources; this paper works towards that end. Previously, it has been shown that residential HVAC systems can be aggregated and used to provide frequency regulation by utilizing their thermal energy capacity and flexibility of energy consumption. The virtual battery model—a first-order linear dynamical model—was analytically shown to be an accurate and simple model to capture the flexibility of residential HVAC systems. This paper presents a technique for creating the same battery-type models for many other types of systems, which can be much more complex. Our technique is based on stress testing detailed software models of physical systems. A realistic case study involving the terminal building of a small airport is presented as evidence of the effectiveness of the proposed techniques. [ABSTRACT FROM PUBLISHER]

This paper presents an analysis based on recent Electric Power Research Institute research in which a comparison is made between the “loss factor” calculated directly from annual simulations and a loss factor estimated variously from established equations and published papers and articles. The analysis provides new insights into the use of loss factors in estimating feeder losses. Sources of possible errors are identified and quantified. [ABSTRACT FROM PUBLISHER]

By empowering consumers and enabling them as active players in the power and energy sector, demand flexibility requires more precise and sophisticated load modeling. In this paper, a laboratory testbed was designed and implemented for surveying the behavior of laboratory loads in different network conditions by using real-time simulation. Power hardware-in-the-loop was used to validate the load models by testing various technical network conditions. Then, in the emulation phase, the real-time simulator controlled a power amplifier and different laboratory equipment to provide a realistic testbed for validating the load models under different voltage and frequency conditions. In the case study, the power amplifier was utilized to supply a resistive load to emulate several consumer load modeling. Through the obtained results, the errors for each load level and the set of all load levels were calculated and compared. Furthermore, a fixed consumption level was considered. The frequency was changed to survey the behavior of the load during the grid's instabilities. In the end, a set of mathematical equations were proposed to calculate power consumption with respect to the actual voltage and frequency variations. [ABSTRACT FROM AUTHOR]

This is the second of a two-paper series, in which the field-data-based analytical method proposed in Part I has been extended to determine the amount of harmonic currents absorbed by the shunt harmonic filters of the iron and steel (I&S) plants from the rest of the system. The nonlinear characteristics of the plants and the utility are modeled as harmonic current sources, whereas the linear parts of the proposed extended model consist of passive circuit elements, with the harmonic filter $RLC$ circuit parameters also integrated into the model. This extended method, which can be also used for harmonic contribution assessments, requires sample-by-sample time-synchronized harmonic current measurements inside the plants and at the point of common coupling (PCC). It eliminates the need for harmonic voltage measurements at the expense of the need for inside-plant measurements. It has been verified both by computer simulations and by application on field data. The results have shown that the proposed method is a successful tool to determine the magnitude of harmonic currents that are absorbed by the harmonic filters of I&S plants connected to the PCC, particularly when measured harmonic currents of I&S plants exceed the actual contributions. [ABSTRACT FROM AUTHOR]

To test electric devices such as household appliances, electric vehicle and other loads, there is need for emulators instead of real devices, which decreases the cost of testing. This paper presents a dynamically programmable load emulator that imitates the current–time characteristic of different types of the loads. The exact mathematical model of various types of electric loads is firstly extracted. These mathematical models are implemented by LabVIEW software. A non-ideal buck converter is used for the hardware part of the emulator, which is controlled by LabVIEW. The proposed emulator can mimic the types of loads given by LabVIEW software with great accuracy and is an example of an actual load simulator with good approximation. Manufacturers or laboratories of electric equipment can use the proposed emulator to perform their tests instead of using the actual device. This reduces the cost of tests and speeds them up so that all types of electric loads can be modeled with an emulator. In addition, because a simple power electronic converter structure is used, the proposed emulator has low complexity and low manufacturing cost. Simulation and laboratory results prove the efficiency of this emulator in making models of different types of loads. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed renewable energy sources drastically alters the dynamic characteristics of distribution networks (DNs). Therefore, several equivalent models have been recently proposed, to analyze more accurately the complex behavior of modern DNs. However, relatively simple models are still commonly used in practice for dynamic power system studies. In addition, dynamic equivalent models for DNs are sensitive to different operating conditions and there is lack of systematic understanding of their performance. Scope of this paper is to propose a methodology for identifying the applicability range in terms of accuracy and generalization capability of several conventional and newly developed equivalent models for the dynamic analysis of modern DNs. A set of metrics is used for the modelling accuracy assessment and a sensitivity analysis framework is introduced to fully quantify the generalization capabilities of DN equivalent models. Based on the above, guidelines and recommendations for the development of robust equivalent models for DN analysis are proposed. [ABSTRACT FROM AUTHOR]

In the conventional sliding mode control, the four-leg distribution static compensator (DSTATCOM) currents are controlled based on a current error in each phase. However, the four currents in a four-wire system can not be independently controlled variables and hence one of the four controllers is redundant in conventional schemes. Further, the current dynamics of a DSTATCOM converter are coupled through converter pole voltages in the natural reference frame. This leads to cross-coupling in the sliding variable equations with respect to four manipulated input variables. Considering the above points, in this paper, the current due to Thevenin’s equivalent load neutral-point voltage is considered as a fourth independent controlled variable and the corresponding system dynamic equations are presented. To get a decoupled feature, a new sliding surface function is structured. The performance of a DSTATCOM with the proposed control scheme under various operating conditions is validated through a detailed simulation study and experimental results, obtained from a laboratory prototype of four-leg DSTATCOM. [ABSTRACT FROM AUTHOR]

Short-term household electricity load forecasting is important for utility companies to ensure reliable power supplies. Traditional methods for load forecasting relied on historical records from one single data source and have limitations with insufficient or missing data. Recently, an emerging family of machine learning algorithms, multitask learning (MTL), has been developed and has the potential for load forecasting. By MTL, the electricity consumption data from multiple communities can be fused to improve forecasting accuracy. However, appropriate modeling of the relatedness to enable the between-community knowledge transfer remains a challenge. This paper proposes an improved MTL algorithm for a Bayesian spatiotemporal Gaussian process model (BSGP) to characterize the relatedness among the different residential communities. It hypothesizes on the similar impacts of environmental and traffic conditions on electricity consumption in improving short-term load forecasting. Furthermore, this paper proposes a low-ranked dirty model along with an iterative algorithm to improve the learning of model parameters under an MTL framework. This paper uses a real-world case study from two residential communities in Tallahassee, Fl, USA, to demonstrate the method effectiveness. The proposed method significantly outperforms state-of-the-art forecasting methods and effectively captures the relatedness to provide between-community knowledge transfer compared with other MTL methods. [ABSTRACT FROM AUTHOR]

This paper develops an improved dynamic reliability model for compressor rotor system, defined as a “unconventional active system” here. First, regarding to the variety of system-component connections, system specific reliability modeling technique is presented, in which the stress and strength are both nonnegative stochastic processes. The new model fully considers the load characteristics of rotor blades, and inherently embodied the effect of the statistical dependence between the failures of the rotor blades. Then, calculated by the new model, the relationship between reliability and time, and that between the hazard rate and time, for a compressor rotor system are discussed, respectively. Comparing to the traditional series system reliability model, the new system reliability model developed in this paper has higher accuracy for compressor rotor system, since the traditional models neglect the difference between compressor rotor system and traditional active system. Moreover, in order to master the cruise characteristics of the aero-plane during reliability analysis of its compressor rotor blade set, a flight cruise is separated into several stages based on the value of Mach number and its change rate, and a subsection solution method is innovatively proposed accordingly. At the end of the paper, a certain cruise of fighter plane is analyzed to verify the accuracy of the new method. The results show that, as to a flight cruise with different stages, the new method is basically reasonable to most of the load histories, especially for the situation with impact loads existing. [ABSTRACT FROM AUTHOR]

First harmonic analysis (FHA) is arguably the most widely used analytical technique for wireless power transfer (WPT) circuits due to its simplicity. Although FHA can provide closed-form solutions, the existence of rectifier diode forward voltage drop and higher order harmonics, especially the second and third harmonics at variable duty cycle operation, can significantly deteriorate its accuracy. This paper presents an accurate and efficient method called enhanced multiple harmonic analysis (eMHA) for the optimal design and optimal control of WPT systems. The eMHA method considers the nature of nonlinear rectification networks under nonsinusoidal current and reexamines the concept of the equivalent load. As a result, the rectified WPT system is transformed into a series of linear systems with complex load impedances. The steady-state electric quantities can be then explicitly calculated. This enables eMHA to seamlessly work with numerical optimization algorithms to facilitate the automated design and optimization of WPT systems. An example of optimal design and optimal control of a 10 W WPT system is demonstrated. The results obtained by eMHA and FHA are also compared. A prototype of the designed circuit was constructed. The accuracy and effectiveness of eMHA are verified by experimental measurements. This paper is accompanied by a MATLAB-based analytical tool with a graphical user interface demonstrating the effects of circuit variables on electrical quantities and waveforms. [ABSTRACT FROM AUTHOR]

Due to the statistical uncertainty of loads and power sources found in smart grids, effective computational tools for probabilistic load flow analysis and planning are now becoming indispensable. In this paper, we describe a unified simulation framework that allows quantifying the probability distributions of a set of observation variables as well as evaluating their sensitivity to potential variations in the power demands. The proposed probabilistic technique relies on the generalized polynomial Chaos algorithm and on a regionwise aggregation/description of the time-varying load profiles. It is shown how detailed statistical distributions of some important figures of merit, which includes voltage unbalance factor in distribution networks, can be calculated with a two orders of magnitude acceleration compared to standard Monte Carlo analysis. In addition, it is highlighted how the associated sensitivity analysis is of guidance for the optimal allocation and planning of new loads. [ABSTRACT FROM AUTHOR]

Power grid resilience, reliability, and sustainability can be improved significantly by decomposing the large grids into networked microgrids (NMGs). However, the optimal energy management problem and preserving the security in NMGs are more complicated and challenging. This paper aims to propose a secured stochastic energy management framework for NMGs based on the modified blockchain approach, utilizing the directed acyclic graph (DAG). Using the decentralized and transparent blockchain technology will help to have higher security and lower risks within the network, thus eliminating the financial fraud and cutting down the total operational cost. In order to address the issues arising in the traditional blockchain models, mainly due to the storage and high complexities of hash address calculations, this paper proposes a new modified blockchain technology based on the DAG method. Also, a novel data restoration technique is developed to provide a way to restore the data with appropriate accuracy. The unscented transform (UT) approach is employed to model the uncertainties of forecast error in hourly load demand, solar power output, and wind turbines power output. Finally, the proposed model is tested on an NMG system with four MGs, including two residential MGs (as the noncrucial loads), a commercial MG (as the intermediate level loads), and a hospital MG (as the crucial loads). [ABSTRACT FROM AUTHOR]

Cold load pick-up (CLPU) has been a critical concern to utilities. Researchers and industry practitioners have underlined the impact of CLPU on distribution system design and service restoration. The recent large-scale deployment of smart meters has provided the industry with a huge amount of data that are highly granular, both temporally and spatially. In this paper, a data-driven framework is proposed for assessing CLPU demand of residential customers using smart meter data. The proposed framework consists of two interconnected layers: 1) At the feeder level, a nonlinear autoregression model is applied to estimate the diversified demand during the system restoration and calculate the CLPU demand ratio. 2) At the customer level, Gaussian mixture models and probabilistic reasoning are used to quantify the CLPU demand increase. The proposed methodology has been verified using real smart meter data and outage cases. [ABSTRACT FROM AUTHOR]

This paper proposes an approach to obtain dynamic versions of static distribution factors, such as power-transfer, line-outage, and outage-transfer distribution factors. With the proposed dynamic distribution factors (DDFs), one can predict line flows over the post-contingency transient period with the same computational effort as obtaining static distribution factors. Our development centers on deriving closed-form expressions that approximate generator outputs through the post-contingency transient period with a reduced-order aggregate dynamical model to recover dynamic generator participation factors. The full suite of DDFs can then be derived by combining these dynamic generator participation factors with injection shift factors, i.e., static linear sensitivities of line active-power flows with respect to nodal active-power injections, computed at the pre-disturbance steady-state operating point. We illustrate the accuracy and computational benefits of the proposed DDFs via numerical case studies involving the New England test system. [ABSTRACT FROM AUTHOR]

This paper presents a robust nonlinear decentralized control scheme for islanded dc microgrids, where the main control objectives are to achieve the desired voltage at the common dc bus and to maintain the power balance. The proposed control scheme uses the partial feedback linearization scheme to simplify the dynamical models of different components in dc microgrids. In this paper, the dc microgrid includes a solar photovoltaic unit, a fuel cell system, and a battery energy storage system along with dc loads. The robustness of the proposed controller against parametric uncertainties is ensured by the parameter, appearing in the control inputs as unknown, which is then estimated through adaptation laws. The inherent noise decoupling capability of the feedback linearization scheme is used to provide robustness against external disturbances in dc microgrids. The performance of the proposed controller is evaluated on a dc microgrid through simulation and experimental studies in order to demonstrate the effectiveness and robustness under different operating conditions, while considering the effects of parametric uncertainties and external disturbances. Simulation results are also compared with an existing proportional-integral controller. [ABSTRACT FROM AUTHOR]