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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]

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model's ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification. [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]

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]

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]

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]

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]

Power system frequency disturbances are caused by various generation and transmission events including generator trips, load disconnections, line trips, etc. Accurate detections of the events are crucial to bulk power system situation awareness and event investigation. This paper utilizes the recent advances of deep learning to build a convolutional neural network model to detect events in an accurate yet straightforward manner. In this paper, the rate of change of frequency and the relative angle shift are converted to images as the inputs of the proposed model. Finally, this paper uses two convolutional neural networks and classifier fusion to achieve the detection result. Compared with the conventional event detection algorithm and the frequency only deep learning model, the proposed model improves the detection accuracy by over 48%. As a promising tool for bulk power system situation awareness, the proposed model requires a short decision time, which is suitable for practical scenarios. [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 demonstrates the potential of inverter-based loads to support grid reliability during power system transients thereby enabling reliable integration of renewable energy in power systems. Such loads are referred to in this paper as grid-supportive loads (GSLs). A new GSL model is developed that simulates the transient response capabilities that can be programmed in electronic loads. The model’s design enables it to be easily integrated in widely used commercial power system transient analysis software. Theoretical expressions are derived that explain the workings of the GSL model. The performance, numerical stability, and impact of the GSL model is validated on 9-bus and 2000-bus synthetic power system models using generator tripping and bus fault disturbances. Results on the 2000 bus system show that in the absence of frequency support from wind/solar generation resources, just 20% of loads with grid-supportive capabilities can improve frequency response by up to 2000 MW/0.1 Hz and reduce deviation in frequency at nadir by up to 60% compared to the situation when GSLs are absent. Power system reliability also improves under fault events. It is further shown that GSLs can aid in integrating more renewable generation without degrading the overall transient response of the power system. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems have been gaining popularity among distribution network engineers. The integration of these small-scale sources has a variety of benefits for the distribution network. One of the most important benefits is the power loss reduction. However, a high penetration level of PV systems may disrupt the protection coordination of conventional overcurrent relays of the distribution network, reducing the profit of the electricity distribution company (EDC) due to healthy feeder de-energization. This paper determines the optimal penetration level of PV systems in the distribution network considering both power loss reduction profit and protection miscoordination cost to maximize the profit of the EDC. The merits of the proposed methodology are verified through several case studies on a simulation model of the IEEE 33-bus test system in the Electrical Transient Analyzer Program (ETAP) environment considering the reliability data of the IEEE Roy Billinton Test System. [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]

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]

To reduce greenhouse emission and achieve sustainability in the electric power industry, improved efficiency and energy conservation are key to finding a sound solution. Energy conservation through voltage reduction (CVR) is one such program. To implement effective CVR programs, load models are of utmost importance in determining proper feeders and methods to implement the CVR programs. Exponential and polynomial load models stand out as the most widely adopted models. The polynomial load model, often referred to as the ZIP (constant impedance, current and power) model, is especially notable for its ability to represent the relationship between the applied voltage and power consumption. This paper sheds light on the limitations of commonly used ZIP load model estimation methods and highlights the significance of using ap-propriate measurement samples and understanding the true nature of the load. Two distinct ZIP load models, i.e., the regular ZIP (RegZIP) and the Non-traditional ZIP (NTZIP) models, have been used for ZIP parameter estimation. The RegZIP load model consumes equal or more power when voltage increases, while the NTZIP model consumes equal or less power when voltage increases. It is emphasized that the RegZIP is an existing load model and the NTZIP is an artificial load model this research created for comparison purposes. The intention of this paper is not to propose any new load model, but to investigate the implications of choosing the wrong model and the impacts of measurement errors of voltage and power data. The research has shown that the two different models can fit the same measurement data equally well for some measured data. The results have demonstrated that simply using certain methods such as the optimization method for estimating load model parameters using unfiltered real time measurements may yield misleading and unreliable results. Wrong load models will inevitably lead to wrong CVR assessment. Identifying the correct measurement samples, obtained during natural or staged events causing substantial voltage variations, is critically important in calculating load parameters and ensure the soundness of the obtained results. The accurate load parameters will be important in the assessment of CVR program effectiveness and will ensure that power system analysis applications that utilize the load models will provide reliable results. [ABSTRACT FROM AUTHOR]

In Australia, similarly to other countries, the grid was designed and constructed to transport energy from large fossil-fuelled generators to load centers. There has been a very rapid uptake of large renewable generation and their lifecycle costs are continuing to fall. These solar farms are generally located in sparsely populated areas where large packets of land are available. However, the grid infrastructure in these areas has limitations as it was not designed to support large power flows. [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]

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]

Bridges play an important role in transportation infrastructure systems. Intelligent and digital management of bridges group is an essential part of the future intelligent transportation infrastructure system. This paper proposes a digital twin system for bridges group in the regional transportation infrastructure network, which is interconnected by measured traffic loads. In physical space, a full-bridge traffic load monitoring system based on information fusion of weigh-in-motion (WIM) and multi-source heterogeneous machine vision is set up on the target bridge to measure traffic loads, also lightweight sensors are employed on the bridges group for structural response information. Furthermore, by establishing mechanical analysis models in the corresponding digital space and using the measured traffic loads as links, the working condition perception and safety warning of all bridges in the regional transportation network is achieved, forming an important support for further intelligent transportation infrastructure system. The proposed digital twin system has been preliminarily implemented in a bridges group around Shanghai, China, demonstrating the feasibility of the technical framework proposed in this paper and the bright prospects. [ABSTRACT FROM AUTHOR]

Peer-to-peer interconnection of households having on-site batteries, multi-port converters and solar panels to form a multi-port converter-enabled solar DC nano-grid is an emerging approach for providing affordable energy access in rural areas. Battery charge and discharge losses, distribution losses and converter losses are significant problem when operating such nano-grids. This paper presents a centralized control algorithm that can help address the power loss problem. The proposed algorithm uses a new problem formulation where the power loss problem is formulated as a two-stage convex optimization problem. The first stage of the optimization problem is an optimal battery dispatch problem for determining optimal battery charge and discharge currents. The second stage is an optimal current flow problem for determining optimal distribution voltages which corresponds to the optimal battery currents. Simulation results of the nano-grid show that the proposed algorithm can minimize the nano-grid power losses while facilitating the power exchange between the households. The proposed algorithm is suitable for small nano-grids where privacy of households is not a concern. In Part II of this paper we propose a distributed control algorithm that preserves the privacy of the households especially where the size of the nano-grid is large. [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]

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]

Demand response (DR) has been widely recognized as an important approach to balance the power grid and reduce peak load of power systems. In order to better estimate the capability and the expense of peak load reduction through DR, we need to obtain the residential load profile and customers’ attitudes toward DR programs. Based on a large-scale online survey collected among over 1500 customers from New York and Texas in the U.S., this paper investigates the relationships among household appliance activities (e.g., electric water heater and air conditioner), load profiles, and incentive-based DR (IBDR) participation for peak load curtailment through reward payment. The daily load profiles of major home appliances are developed. Additionally, this paper estimates the expense of reducing the yearly peak of the local grid load. Finally, this paper addresses the importance of investigating the multifaceted factors of affecting IBDR participation and provides useful suggestions to utility companies when implementing DR programs. [ABSTRACT FROM AUTHOR]

Building energy management (BEM) systems can shift the heating, ventilation, and air conditioning (HVAC) demand of buildings in summer using pre-cooling. The physical and operational constraints of buildings directly impact the operation of low voltage networks (LVNs). There is a gap in the literature on the extent of the electricity consumption and the voltage profiles of LVNs are influenced by different wall constructions of BEM-enabled buildings. In this paper, a joint electrical and thermodynamic model is used to control residential BEMs, taking into account the constraints of the LVN and the buildings. As a case study, the electricity demand of an IEEE LVN with 55 residential buildings are modeled using four common wall constructions: 1) timber clad; 2) brick veneer; 3) reverse brick veneer; and 4) double brick walls. The results show the buildings with higher access to thermal inertia from inside provide enough thermal storage capacity to shift the entire peak-time cooling demand to off-peak periods. Furthermore, the location of a building in LVN affects its HVAC load shifting, with houses farthest from the transformer starting pre-cooling earlier to avoid exceeding the voltage limit. This paper shows the advantage of the joint electrical and thermodynamic model in capturing both the dynamics of HVAC demand for buildings and the electrical dynamics of LVN. [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]

One important element in the progression of aircraft environmental impact reduction is to reduce their overall weight (without impacting other consumption-oriented performance index, such as drag). In addition to the numerous work conducted in material and structural engineering, from a control viewpoint, this challenge is strongly connected to the need of the development and assessment of dedicated load control strategies in response to gust disturbances. Indeed, the load factors due to gust are considered as sizing criteria during the aircraft conception steps and require specific verification according to the certification process. To this end, a dedicated experimental research program based on wind tunnel (WT) campaigns has been carried out. More specifically, this paper contributions are twofold: 1) to identify the gust load effect using two different versatile frequency-domain techniques, namely, the Loewner interpolation and a modified subspace approach and 2) to design and implement an active closed-loop control to alleviate the gust main effect. The entire procedure is validated in a WT setup, involving a gust generator device and a 2-D aeroelastic airfoil, for varying configuration traveling from sub to transonic airflow and varying angles of attack, emphasizing the effectiveness and robustness of the overall approach. [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]

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]

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]

Accurate load forecasting is the key to economic dispatch and efficient operation of Multi-Energy System (MES). This paper proposes a combined load forecasting method for MES based on Bi-directional Long Short-Term Memory (BiLSTM) multi-task learning. Firstly, this paper investigates the multi-energy interaction mechanism and multi-loads characteristics and analyzes the correlation of multi-loads in different seasons. Then, a combined load forecasting method is proposed, which focuses on making full use of the coupling relationship among multiple loads. In the forecasting model, the different loads are selected combinedly as the input features according to the Maximum Information Coefficient (MIC). The multi-task learning is adopted to construct the cooling, heating and electric combined load forecasting model based on the BiLSTM algorithm, which can effectively share the coupling information among the loads. Finally, case studies verify the effectiveness and superiority of the proposed method in both learning speed and forecasting accuracy. [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]

Random traffic flow load (TFL) simulation is an important analysis method for bridge design and safety assessment, and accurate TFL modelling is a prerequisite for high-quality simulation. The existing TFL modelling methods almost all rely on the load data monitored by the weigh-in-motion system (WIM system). However, the WIM system has natural defects such as unsatisfactory measurement accuracy at low speed and the inability to measure vehicle lengths and transverse positions in the lane, limiting the improvement of TFL simulation accuracy. Regarding this, a TFL monitoring system that integrates the functions of machine vision and WIM system is developed in this paper. In this system, a deep learning method is applied, for the accurate detection of vehicles and wheels in the video, and the extraction of key parameters for TFL modelling based on detection results. According to the long-term monitoring value, statistical distributions of key parameters are determined, and then an intelligent TFL model is derived from the Intelligent Driver Model (IDM), considering the car-following behavior of vehicles. Correspondingly, this paper further suggests a TFL simulation method and achieves an accurate TFL simulation. A cable-stayed bridge is taken as an example to verify the feasibility of the method. The results show that, compared to the modelling and simulation methods that only rely on the WIM system, the proposed method not only reduces the measurement error of vehicle dimensions by nearly 4 times, but also performs higher resolution in time measurement. The proposed method effectively overcomes the shortcomings of existing schemes and has good application potential in engineering. [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]

With the rapid development of microgrid and large-scale grid-connected operation, the detection and location of short-circuit faults in microgrid has become a bottleneck. In this paper, a simulation model of short-circuit fault in low-voltage AC microgrid is built on PSCAD/EMTDC. The characteristics of current wavelet energy spectrum under various short-circuit fault states are studied and the early detection of faults is realized. Based on the relationship between the first peak value of fault current wavelet energy spectrum and the location of short-circuit points, a method for early detection and area location of short-circuit faults in microgrid is proposed. The simulation and experimental results show that this method can detect and locate the short-circuit fault area of microgrid quickly and accurately. [ABSTRACT FROM AUTHOR]

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]

This paper presents an energy scheduling and output smoothing scheme for storage aided utility scale photovoltaic systems. A weighted energy scheduling approach is adopted for the peak load periods, and this ensures enhanced performance with well-fitted supply-demand curve and flat net load variation. A novel smoothing method is proposed by blending double grid search support vector machine power prediction with first-in-first-out robust smoothing. The actual hourly and minute interval data sets for Australia are used for case studies, demonstrating the effectiveness and efficiency of the proposed scheme. [ABSTRACT FROM PUBLISHER]

The dynamometer card (DC) is crucial for the remote monitoring of the sucker rod pumping system (SRPS). However, the DC sensors have poor real-time performance and high maintenance costs leading to difficulties in obtaining datasets of the downhole working states from different oil wells. Moreover, the motor power curve is easy to obtain in real-time and can reflect the changes in pumping unit load. Thus, this paper proposed a novel hybrid model for indirect measurement DC based on measured motor power. Firstly, based on the mechanical model analysis of SRPS, the dataset is constructed by transforming the motor power and geometric parameters of the SRPS into the polished rod torque, the first-derivative of polished rod torque, the second-derivative polished rod torque, and the torque factor. These four parameters are merged as the inputs of the data-driven model. Subsequently, a data-driven model implements the particle swarm (PSO) algorithm to optimize the weights and thresholds of the Back Propagation (BP) neural network model, which was trained to predict DCs of three common working states. Finally, the proposed method is verified experimentally through the measured DC and motor power data. Both experimental and prediction results demonstrate the effectiveness of the proposed method for generating DC. The mean DC area relative error of the hybrid method proposed in this paper is 2.52%. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy, designing the efficient midterm (i.e., annual) scheduling for multi-source power systems is valuable in providing substantial cost reductions and flexibilities while ensuring system security against uncertainties of renewables. This paper presents a risk-averse two-stage stochastic midterm scheduling model for thermal-hydro-wind systems against short-term uncertainties of renewable generation. A network-constrained clustered unit commitment (NC-CUC) model is proposed to effectively incorporate short-term operation constraints into the midterm scheduling model, for improving the solution accuracy while reducing computational burden. Specifically, as the traditional CUC model cannot ensure consistent unit commitment status of generators across all scenarios, a power flow-based unit clustering method is proposed to improve the accuracy of short-term operation results. In addition, the conditional value-at-risk (CVaR) is used to quantify the system operational risk caused by uncertainties of wind generation and natural water inflows. Case studies show that the risk-averse two-stage stochastic midterm scheduling model can ensure operation dynamics and solution efficiency, and the proposed clustering method can achieve a tradeoff between solution accuracy and computational burden for the stochastic network-constrained CUC model. [ABSTRACT FROM AUTHOR]

Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors. [ABSTRACT FROM AUTHOR]

The latest developments in dc transformer technology are enabling larger and more complex dc power distribution systems. However, the literature is scarce regarding the investigation and quantification of its impact on the dc grid. Therefore, this paper analyses the impact of the dc transformer location on the system’s characteristics to provide enough information to predict the system performances for future dc grids. To determine the critical resonant frequencies of the system, the power converters outer control loop dynamic are included to provide a better representation in frequency domain, and enable its evaluation for the addition of new equipment to the power distribution network. The proposed solution has an expansible methodology, allowing its application in large systems and easy impedance response assessment. Moreover, the proposed solution is independent of the ac grid backup solutions, allowing the dc power distribution network planning to deal only with the dc variables. Finally, theoretical developments are validated using simulations, and the steady-state and impedance identification process is described in detail. [ABSTRACT FROM AUTHOR]

Nowadays, micro-mobility is one of the major global trends in cities for the innovation of the transport system. In this context a breakthrough introduction of electric kick scooters (e-scooters) has taken place. Unfortunately, these mobility systems cause several accidents mainly for two reasons: wrong use and inadequate safety requirements. Since e-scooters are quite a new type of vehicle, generally accepted mathematical models are yet to be developed. These models can be useful in understanding the dynamical properties of this type of vehicle thus improving its design to reduce riding accidents. The present paper presents a model for the simulation of the vertical dynamic behavior of e-scooters that accounts also for the mechanical impedance of the driver, thus allowing to estimate the overall driver’s comfort and road holding capabilities providing information on possible speed limitations in case of bad road conditions. Furthermore, the paper shows experimental envelope curves for lumped obstacles obtained with a dedicated test bench on which the e-scooter is fixed and tested under different conditions of vertical load and tire inflation pressure. [ABSTRACT FROM AUTHOR]

In light of a reliable and resilient power system under extreme weather and natural disasters, networked microgrids integrating local renewable resources have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to address with the deterministic energy management framework. The paper proposes a comprehensive distributionally robust joint chance-constrained (DR-JCC) framework that incorporates microgrid island, power flow, distributed batteries and voltage control constraints. All chance constraints are solved jointly and each one is assigned to an optimized violation rate. To highlight, the JCC problem with the optimized violation rates has been recognized as NP-hard and challenging to solve. This paper proposes a novel evolutionary algorithm that successfully solves this problem and reduces the solution conservativeness (i.e., operation cost) by around 50% compared with the baseline Bonferroni Approximation. We construct three data-driven ambiguity sets to model uncertain solar forecast error distributions. The solution is thus robust for any distribution in sets with the shared moments and shape assumptions. The proposed method is validated by robustness tests based on these sets and firmly secures the solution robustness. [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]

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]

This paper is concerned about real-time modeling and identification of dynamically changing loads in power systems. An exponential dynamic load model was proposed earlier and was well accepted by several investigators who worked on this paper. This paper considers this model and identifies its parameters in real-time based on synchronously sampled measurements. An unscented Kalman filter is used to track the unknown parameters of the exponential dynamic load model. This paper first implements and tests the proposed method using simulated measurements. The method is then applied to actual recorded utility measurements to identify and track the bus load of the utility. The results are found to be promising, suggesting viability of tracking dynamic load models for online applications. [ABSTRACT FROM PUBLISHER]

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]

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]