Composite Load Model Research Articles

Squirrel cage induction generator based micro grid voltage assessment with STATCOM using different metaheuristic approaches

Squirrel Cage Induction Generators are the most common machine to operate wind-based power plants. Microgrids are small-scale electrical systems that provide power to a local area, and optimization methods for microgrids aim to ensure that these systems operate in a way that is both efficient and cost-effective. In such applications, STATCOM is used as reactive power provider and It is necessary to use advanced controllers like the Genetic Algorithm (GA), Firefly Algorithm (FA), and Adaptive Neuro Fuzzy Inference System (ANFIS) as the usual approaches for setting gain factors of STATCOM controller which does not perform well in the case of sudden voltage level changes due to disturbances. The firefly algorithm is primarily required to estimate the gain factors of the STATCOM controller which is used in hybrid micro grid with SG and SCIG for controlling the output voltage in the occurrence of large probabilistic ambiguity. The effectiveness of firefly controlled STATCOM controller for voltage-reactive power control in hybrid microgrid is further examined and validated by comparing the results with other GA, ANN and ANFIS tuning techniques. The need for reactive power is accomplished by STATCOM to minimize the time required to control voltage transient response. The main contribution of this paper includes: an assessment of STATCOM performance with step variations in the input wind energy and reactive power load requirement under conditions of severe probabilistic uncertainty, system analyses conducted under dynamic conditions rather than using a composite load model. Additionally, a comparison of reactive power and voltage control by STATCOM is presented, and employing FA techniques to improve, a comparative of STATCOM reactive power and voltage control. Results comparing all tuning techniques shows that utilizing the Integral of Square of Errors (ISE) criterion, sophisticated tuning techniques can maintain excellent performances under a variety of disruptions.

Aggregate data‐driven dynamic modeling of active distribution networks with DERs for voltage stability studies

AbstractElectric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model‐free, data‐driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC‐based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state‐of‐the‐art DER_A model with CMLD. The data‐driven DPM achieved a of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model.

Online modeling method for composite load model including EVs and battery storage based on measurement data

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.

Distribution State Estimation and Its Impact of Load Modeling

To maintain system security and other essential parameters like reliability and quality, continuous monitoring of the system is very important. Considering the distribution network, state estimation (SE) methods can be adopted. The purpose of the method is to identify and estimate unknown variables based on the online measurements of test data. The primary objectives considered in this paper are: To choose the exact SE method and the artificial neural network (back propagation algorithm), which can be used for determination in the islanding mode of distribution network states, the composite load model is considered for the estimation of states and further enhancement. By adopting the system, state variables in terms of error are measured in the 12-bus distribution network with precise measurements and compared with practical values. The SE proposed includes results with the load flow backward-forward sweep method to satisfy the system state variables. Numerical results indicate that the model performs better for error measurement data with states and in the case of state forecasting.

Amortized Bayesian Parameter Estimation Approach for WECC Composite Load Model

Calibrating the composite load model with distributed generation (CMPLDWG) is of a great challenge due to the presence of high-dimension parameters. This paper proposes an amortized Bayesian parameter estimation method for the CMPLDWG model. A prior distribution restrictor is developed via a classifier to filter out invalid simulations because of the unsuitable parameter combinations during the offline training stage. A sparse polynomial chaos expansion (SPCE) enabled global sensitivity analysis is also developed to identify the importance of each parameter's response to the captured system dynamics. This allows us to narrow down the problematic parameters and reduce the problem's complexity. Finally, the multi-fidelity trajectory statistics constructor is proposed to extract the representations from post-disturbance dynamic responses. The representations are further used by conditional masked autoregressive flow (CMAF) to learn the parameter posterior distribution. Comparative results with state-of-the-art approaches on the IEEE 39-bus power system demonstrate the high accuracy and robustness of the proposed method.

Network dynamics in hybrid microgrid and its implications on stability analysis

Abstract Dynamic load is one of the significant factor affecting the stability of hybrid microgrid (MG), owing to their frequency and voltage dependency. As compared with the static load, a small variation in voltage or frequency significantly influences the stability of MG having dynamic load. Therefore, it necessitates considering the dynamics of load for stability analysis of an MG. Moreover, the stability issues become more critical for the islanded operation. This paper investigates the small signal (SS) stability of a hybrid MG utilizing composite load model (CLM) to include the load dynamics. This paper also investigates the critical universal droop control parameters and other internal controller parameters that influence MG stability. Eigenvalue analysis is used to identify the respective stability domain of different control parameters. The effectiveness of the proposed approach is tested by comparing the dynamics of CLM with constant power and impedance load. In addition, a memetic algorithm and SQP-GS is used to find the optimal values of different key parameters. The simulation results confirmed the effectiveness of the proposed SS dynamic model of hybrid MG and the efficacy of the optimization algorithm for tuning control parameters, which improved the dynamic performance of the hybrid MG.

Wide-Area Composite Load Parameter Identification Based on Multi-Residual Deep Neural Network.

Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and dynamic behavior of the load consumption pattern in real-time, simultaneously. In this article, a WAMS-based load modeling method is established based on a multi-residual deep learning structure. To do so, a comprehensive and efficient load model founded on combination of impedance-current-power and induction motor (IM) is constructed at the first step. Then, a deep learning-based framework is developed to understand the time-varying and complex behavior of the composite load model (CLM). To do so, a residual convolutional neural network (ResCNN) is developed to capture the spatial features of the load at different location of the large-scale power system. Then, gated recurrent unit (GRU) is used to fully understand the temporal features from highly variant time-domain signals. It is essential to provide a balance between fast and slow variant parameters. Thus, the designed structure is implemented in a parallel manner to fulfill the balance and moreover, weighted fusion method is used to estimate the parameters, as well. Consequently, an error-based loss function is reformulated to improve the training process as well as robustness in the noisy conditions. The numerical experiments on IEEE 68-bus and Iranian 95-bus systems verify the effectiveness and robustness of the proposed load modeling approach. Furthermore, a comparative study with some relevant methods demonstrates the superiority of the proposed structure. The obtained results in the worst-case scenario show error lower than 0.055% considering noisy condition and at least 50% improvement comparing the several state-of-art methods.

Online correction of the transient voltage security region boundary based on load parameter variations in power systems

The transient voltage security region (TVSR) is an essential part of the dynamic safety region of a power system, which represents the safe operating region of the transient voltage where there is no loss in stability. The power system load significantly affects the transient voltage stability. However, the load model in the existing power systems, which considers the security region of the dynamic process, is too simple to comprehensively characterize the load characteristics of the dynamic process. Furthermore, it neglects the influence of the load model parameters on the dynamic process security region. In this study, a composite load model with distributed photovoltaic power is used as the research object. The main load parameters affecting the voltage stability and the generator nodes that are sensitive to the load parameters are calculated using the trajectory sensitivity method. The TVSR of the system is constructed based on the load and generator’s active powers. The correlations between different load-leading parameter combination scenarios and TVSR boundary points are mined using the CatBoost learning framework. Thus, the TVSR boundary can be promptly corrected online based on the changes in the load parameters, and the system security boundary can be described more accurately. The proposed method is verified using the IEEE39 and IEEE118 node systems. It is observed that the proposed method can correct the TVSR boundary online with high precision corresponding to real-time changes in the load model parameters. This provides a more accurate TVSR boundary for the power system operation scheduler, which helps in guiding it to control the system more accurately.

Adaptive power system frequency support from distributed photovoltaic systems

The integration of converter-based renewable energy resources in a power system leads to reduced inertia, creating a multitude of challenges relating to stability and reliability. On the positive side, the faster response of converter-based renewable energy resources to frequency disturbances, compared to the conventional synchronous-based generators, reduces the required minimum level of inertia in future low-inertia power systems. Accordingly, grid support from distributed photovoltaic (DPV) systems is one of the emerging solutions to overcome the challenges of these systems. This paper demonstrates how adaptive power system frequency support, which modifies the dynamic of frequency support in DPV systems according to the available level of power system inertia, improves overall system operation. In this way, for lower values of power system inertia, a higher amount of power reserve is maintained across DPV systems. Additionally, a faster frequency–Watt response is implemented to provide a larger amount of grid support under frequency disturbances. Simulation results on a composite load model of distribution feeder show that applying for adaptive frequency support from DPV systems improves the frequency nadir during under-frequency events. Experimental results show the effectiveness of the proposed approach in providing a faster dynamic response and higher amount of power support from DPV systems under low inertia conditions.

A composite load model aggregation method and its equivalent error analysis

Although load modeling techniques have been widely applied in practical power system studies, the equivalence of aggregate load models needs to be further illustrated since equivalent load models are commonly adopted to describe the overall characteristics of distributed multi-level power loads in practical scenarios. Driven by two basic aggregation steps, a general composite load model aggregation method is proposed for the first time with load network topology well considered. On this basis, detailed equivalent error analysis of load model aggregation is conducted in order to explain the possible error factors and their influence mechanisms on the equivalent accuracy. Case study results for the modified IEEE 39-bus system have validated the overall feasibility of the proposed model aggregation method and the influence of each error factor under different scenarios. Finally, practical effectiveness of the derived aggregate model is further proved with field measurements in a real-world load system. In this way, the equivalence of adopting aggregate load models can be clearly demonstrated, thus providing necessary foundation for the practical applications of composite load modeling.

General Modelling Method for the Active Distribution Network with Multiple Types of Renewable Distributed Generations

With a proliferation of diverse types of renewable distributed generation (DG) into the distribution network, an equivalent model of an active distribution network (ADN) is extremely important, since the detailed modeling of the whole ADN is much more complex and time consuming. However, different studies developed different model structures of ADNs, which are difficult to be applied in a power system simulation. At the same time, the DG’s low voltage ride through the (LVRT) control was not considered in the existing ADN model, which may lead to a large modelling error. In this paper, a general equivalent model is developed for the ADN with a significant amount of DGs, based on a two-step modelling method. Step one, motivated by the dynamic similarities between the doubly-fed induction generator (DFIG)-based wind turbines, direct drive permanent magnet synchronous generator (DDPMSG)-based wind turbines, and photovoltaic (PV) generation, a general model structure of a renewable DG is initially developed. Then, an aggregation method for the DG’s nonlinear subsystems of the low voltage ride through (LVRT) control and the converter’s current limits are presented. Step two, the ADN model is represented by a general renewable DG model paralleled with a composite load model, and the model is validated, based on an actual distribution network with different renewable DG penetrations and different disturbance degrees. The simulation results show that our model outperforms others with acceptable errors.

A Deep-Learning Approach to Load Modeling in Modern Power Distribution System

Modern Power Distribution Networks (MPDNs) are no longer passive because Distributed Generations (DGs) are integrated with them to enhance system reliability and power quality. For this reason, load modeling has to be updated to capture the new dynamics of active DNs. This paper presents a composite load modeling for a grid-connected photovoltaic (PV) distribution network using the Levenberg-Marquardt algorithm in the deep learning feed-forward neural network approach. Load modeling is constructing a relationship between input excitation(s) and output response(s); it can be used for simulation studies, stability analysis, and control/protection design. A grid-connected PV distribution network was modeled in Matlab/Simulink and generates data for training and model estimation. The estimated model was tested and validated using a laboratory experimental test bed. Results of the model exhibit a good fitness of 99.8% and 97.2% in active and reactive power models respectively during training. While 97.84% and 94.65% respectively were obtained during testing. The estimation errors were found to be 0.0025 and 0.0049 for active and reactive powers respectively with 0.0473 and 0.0701 corresponding errors in testing.

Traction Load Modeling and Parameter Identification Based on Improved Sparrow Search Algorithm

In this paper, a traction load model parameter identification method based on the improved sparrow search algorithm (ISSA) is proposed. According to the load characteristics of the AC traction power supply system under transient disturbance, the model structure of the traction load is equated to the composite load model structure of the static load shunt induction motor’s dynamic load. The traditional sparrow search algorithm is improved to enhance its accuracy and convergence. The generalization ability of the model was tested, and the accuracy of the proposed model was verified. Using the ISSA to determine the load model from the measured data, the results can verify the effectiveness of the ISSA for comprehensive load model parameter identification. Comparing the ISSA with the traditional SSA and PSO algorithms, it shows that the ISSA has better accuracy and convergence.

Online Parameter Identification of a Simplified Composite Load Model by Voltage Sag Events

Online Parameter Identification of a Simplified Composite Load Model by Voltage Sag Events

Control of Distributed Photovoltaic Inverters for Frequency Support and System Recovery

Replacing conventional synchronous generator-based power plants with inverter-based renewable energy resources results in a reduction of the inertia in power systems. To sustain the security and reliability of these low-inertia power systems, frequency support is increasingly required in new standards for grid-connected renewable energy resources, especially distributed photovoltaic (DPV) systems. This article proposes a frequency droop-based control in DPV inverters to improve frequency response in power grids with high penetration of renewable energy resources. A predefined power reserve is kept in the DPV inverter, using flexible power point tracking. The proposed algorithm uses this available power reserve to support the grid frequency. Furthermore, a recovery process is proposed to continue injecting the maximum power after the disturbance, until frequency steady-state conditions are met. The positive inertia using the proposed recovery mode, leads to recovery of the frequency in a shorter period. A composite load model of a distribution feeder, including DPV, is developed to assess the effectiveness of the proposed frequency support algorithm in power systems with high penetration of DPV inverters. The dynamic performance of the proposed frequency support algorithm with over frequency and under frequency events is also evaluated experimentally.

Tensor-Based Parameter Reduction of Dynamic Load Models With Variable Frequency Drive

Accurate load modeling is critical for credible power system stability analysis. Because of the increasing complexity in modern system load, establishing a more comprehensive load model and performing accurate parameter estimation are the two biggest challenges in composite load modeling. In this paper, an induction motor with variable frequency drive (IM-VFD) together with the traditional ZIP+IM model is utilized to describe the dynamic characteristics of the system load. We estimate the time-varying probabilistic distributions of all states and parameters to describe their stochastic characteristics, thereby representing the real-time varying features of an actual load. The Fokker-Planck operator is applied to linearize the updated load model in tensor format. We then use tensor decomposition to reduce the computational and storage costs for calculating load model parameters. A sensitivity indicator is used to analyze the sensitivities of the estimated parameter distributions for subsequent model reduction. Simulations demonstrate that the updated load model has better performance than the traditional ZIP+IM model.

A Fast Parameter Identification Method for Composite Load Model Based on Jumping and Steady-State Points of Measured Data

The classic measurement-based method for load model parameter identification relies on lots of transient simulations and optimization iterations, which is computationally intensive, and is unsuitable to the terminal devices for recording load characteristic to participate in grid edge computing. To solve this difficulty, a fast identification method for load model parameters based on jumping and steady-state points of measured data is proposed, which greatly reduces the calculation time by avoiding the transient simulations and random optimizations of the classic method, and at the same time, well retains the accuracy of identified parameters. Firstly, the method extracts four points of measured data as calculation points, i.e., the point after voltage sag, two points before and after voltage recovery, and the final steady-state point. Then, the method calculates the state variables and powers at the four points through steady-state calculation, implicit trapezoid integration method and Hermite–Simpson method respectively. Finally, according to the measured powers of the four points, the method provides an initial key load model parameters through polynomial approximation method and finds the optimal parameters through Nelder-Mead algorithm. The accuracy and practicality of the method are demonstrated by test and field case studies, and the computation burden is less than 2.5% of that in the classic method without obvious loss of identification accuracy.

Adaptive Wide-Area Control of Power Systems Through Dynamic Load Modulating SVCs

This paper presents an adaptive wide-area control strategy based on energy function. The proposed method enhances the post-fault oscillation damping capability of Static Var Compensators (SVC) by incorporating load dynamics in the wide-area controller design that adapts to load variations. The load dynamics are obtained using Phasor Measurement Units (PMU) through load power and voltage data measurements, which are used to estimate a Moving Average (MA) dynamic load model. The obtained load model is then integrated into the wide-area controller to obtain the required SVC supplementary control action which modulates the load power in response to a disturbance based on inter-area dynamics obtained by employing a nonlinear Kalman filter and PMU measurements. The performance of the proposed strategy is evaluated on the simplified 14 generator model of the southeast Australian power system by using composite load models comprised of induction motor loads of various sizes and combinations. The obtained results show significant improvements in the system stability when the proposed control strategy is applied.

Analysing the impact of optimally allocated solar PV-based DG in harmonics polluted distribution network

The optimal installation of a renewable distributed generation (DG) in a radial distribution network (RDN) provides a sustainable and clean solution for the future smart network. A weighted-sum multi-objective approach is applied to determine the optimal location of solar PV-DG followed by a PSO algorithm to find optimal DG size. The impact of optimal DG allocation in distorted and non-distorted distribution network is efficaciously demonstrated and validated on IEEE 33-bus RDN. The harmonic analysis in the presence of the six-pulse converter non-linear load and non-linear DG is carried out for IEEE 34-bus network. The non-linear loads are modelled as harmonic current source. As composite load model provides lowest voltage drop with decreased capital investment cost, hence, its effect and the effect of constant power load model on energy loss saving, network loss and total harmonic distortion at various load levels is being analysed. The harmonic load flow results obtained considering non-linear and linear loads are compared to other analytical methods and nature-inspired techniques to illustrate the robustness of the proposed strategy for enhancing the performance of the network. The statistical significance of the results is verified by the Wilcoxon test.

Composite load modeling by spatial-temporal deep attention network based on wide-area monitoring systems

With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods.