Load Modeling Method Research Articles

Parameter identification method of load modeling based on improved dung beetle optimizer algorithm

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.

An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.

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.

Dynamic load modeling for bulk load-using synchrophasors with wide area measurement system for smart grid real-time load monitoring and optimization

Bulk data modeling in a smart grid dynamic network has been performed using an automated load modeling tool (ALMT), an on-load tap changer, and exponential dynamic load modeling. However, studies have observed that a small parameter variation may lead to considerable variations in measuring grid big data. Therefore, this study presents dynamic real-time load modeling, monitoring, and optimization method for the bulk load. The case study was conducted on Sarawak Energy Berhad (SEB), Malaysia. The grid system's real-time data and load modeling achieved the objectives. Dynamic load model was achieved by using load response in MATLAB Simulink environment. This paper also includes new parameter estimations of the load composition at the selected bus. The simulation results of load models were compared with the recorded data by applying an event of bus tripping time interval. The Least Square Error Method was used to converge the estimated parameter values on load composition and compared with the actual recorded data until optimized load models were achieved. This work is a precious and significant contribution to utility research to identify, monitor, and optimize the most appropriate representation of system loads.

Research on Load Modeling Method for Typical Low Carbon Energy Consumption Scenarios in Border and Cross border Regions Considering Seasonal Migration Characteristics

With the process of urbanization and the ‘the Belt and Road’ initiative, the cross-border energy demand in southwest China has grown rapidly, driving the development of the energy system. The accuracy of load forecasting directly affects the application of energy systems, so it is crucial to conduct research on load forecasting for energy terminals in border and cross-border areas. However, there is a seasonal shift in the diverse energy consumption loads in border and cross-border regions, and currently, research on load forecasting and simulation of typical low-carbon energy consumption scenarios under this feature is basically in a blank state. Based on existing problems, this article conducts research on load modeling methods under the significant ‘seasonal migration’ characteristics of border and cross-border loads, conducts research on characteristic industries in border and cross-border areas, establishes typical low-carbon energy consumption scenarios and simulation models in border and cross-border areas, and uses sensitivity analysis method of dynamic simulation to analyze the impact of different influencing factors on the load of various building types, The Monte Carlo simulation prediction method is used to obtain the sensitivity probability distribution of various influencing characteristic factors, and the typical energy consumption building load model is modified. Finally, by comparing the energy consumption simulation results with statistical results, the accuracy of simulation energy consumption prediction is verified to be higher than 90%.

Deduction of Strategic Planning Guidelines for Urban Medium Voltage Grids with Consideration of Electromobility and Heat Pumps

With the evolution of electromobility and heat pumps in urban areas, distribution system operators find themselves facing new challenges in reinforcing their grids. With this evolution, the power demand is developing rapidly and grid reinforcement is urgently needed. The electromobility and heat pump loads are introduced by giving the assumed development scenarios in Germany and their corresponding nominal power assumptions. Furthermore, a method for load modeling in grid planning is explained. Subsequently, several grid planning approaches are presented while dividing them into conventional and innovative planning strategies. Among the investigated innovative planning strategies are three variants of load management that regulate different load types. By analyzing several urban medium voltage grids, this contribution deduces a solid basis for distribution system operators in the form of planning guidelines. The implemented grid planning method leading to the planning guidelines is presented in detail along the contribution.

Adaptive Fuzzy Logic Strong Tracking Based Load Modeling

The randomness and time variability of power load are the main problems in the load modeling, in which the model error and measurement noise greatly affect the modeling accuracy. In this paper, an adaptive fuzzy logic strong tracking based load modeling method is proposed. The problem of load characteristics and parameter identification of load model is transformed into a real-time tracking of load. The optimal model set is first selected from the initial load model set, and according to the mixing probability, the state estimation and error covariance are mixed as the input of the filter. In order to reduce the model error in the parallel filtering, the strong tracking filter (STF) is constructed based on the orthogonality principle. The adaptive fuzzy logic strong tracking filter is constructed by combining the fuzzy logic with the STF to adjust the covariance matrix of the STF according to the change of measurement noise. Moreover, the weights of each sub model are updated in real-time by the parallel filtering, and the sub models are fused to get a comprehensive load model which can reflect the actual load characteristics. The effectiveness of the method is verified by the simulation of IEEE 39-bus system.

A Load Modeling Method Based on Machine Learning

More and more high-power impact loads in the power grid are put into use. When the power is impacted, these loads will cause the power grid bus voltage fluctuations and reduce the power quality. Therefore, in order to accurately analyze the changes in the power system, it is particularly important to establish a reasonable impact load model. This paper takes the impact load data generated by the CT machine during exposure as an example to analyze the characteristics of the impact load. Based on the active power characteristics of the impact load, machine learning methods (such as support vector machines and long and short-term memory networks) are introduced into the impact load modeling, and the power waveform change law of the impact load is accurately established by this method. Finally, the effectiveness of the method is verified by simulation.

Research on the model of smart grid wide-area measurement cyber-physical fusion system based on data fusion

Due to price and technical reasons, it is currently impossible to install phasor measurement units in the entire network. How to use the minimum number of phasor measurement units to maximize the observation of the state or dynamic behavior of the power grid is the first consideration for the wide-area measurement system at this stage. This paper proposes a method to optimize the configuration of the phasor measurement unit while ensuring that the power grid is fully observable and can capture the dynamic behavior of the power grid under transient conditions. This paper proposes a load modeling method based on wide-area measurement. Aiming at the problems of traditional large-area power grid load modeling based on load characteristic measurement devices and the characteristics of the current wide-area measurement system, a load modeling method based on local PMU observations is proposed. This method combines particle swarm optimization algorithm and time. Domain simulation calculation can identify the load models at all load nodes of the system at the same time. This paper proposes a CPPS information flow transmission model based on graph theory and information flow dynamics. This model considers the coupling effect of CPPS, the processing capacity of communication equipment and the transmission mode of CPPS information flow, and constructs a CPPS dynamics model from the perspective of communication transmission. In the analysis of the calculation example, by applying the information flow transmission model to CPPS topologies generated by different scales and different modeling methods, the influence of network scale, key nodes and network topology structure on the efficiency of CPPS information transmission was explored, and the effectiveness of the model was verified.

Value of information-based risk and fatigue management for offshore structures

ABSTRACT This paper proposes a framework for the planning of the structural integrity and risk management (SIRM) of offshore structures by utilizing the value of information and action (VoIA) analysis. Multiple structural health information sources, i.e. inspections, structural health monitoring (SHM) and damage detection systems (DDS), are modeled in dependency of the information acquirement state. In the probabilistic modeling, it is distinguished whether the information are predicted or already obtained. Two deteriorating structural system modeling approaches are illustrated and the pre-posterior and posterior updating of the structural system performance is formulated. To enhance the efficiency of the analysis of complex structural systems, a response surface method for load modeling is derived and the model uncertainty of the response surface is explicitly modeled. The formulated approaches are applied to a generic structural system and a typical deepwater jacket platform analyzing four SIRM scenarios. The optimal SIRM strategy is identified by maximizing the VoIA. The results show that the implementation of SIRM can reduce the failure risks and the expected total costs over the service life compared to a scenario without SIRM. It is also observed that information from SHM and DDS can reduce future inspection efforts and enhance the value of SIRM.

Input interaction and output fusion of adaptive interacting multiple load modeling

Accurate load modeling with fluctuating loads has been one of the most challenging problems in power system. Input interaction and output fusion of the adaptive interacting multiple load modeling (AIMLM) method is introduced in this paper. Different load models switch through a Markov probability transfer matrix and filter in parallel through the extended Kalman filters (EKF). After the state estimation, model probabilities are updated according to the evaluated errors. Finally, load models are fused and output based on the probabilities, thus generating a synthetic load model. Simulation results show that interaction and output fusion of the AIMLM method are of high accuracy and can effectively solve the time-varying problem of load characteristics.

Robust Time-Varying Synthesis Load Modeling in Distribution Networks Considering Voltage Disturbances

Uncertain power sources are increasingly integrated into distribution networks and causes more challenges for the traditional load modeling. A variety of distributed load components present dynamic characteristics with time-varying parameters. Toward the end, this paper proposes a robust time-varying parameter identification (TVPI) method for synthesis load modeling in distribution networks, including time-varying ZIP, induction motor, and equivalent impedance models. The nonlinear optimization model is developed and solved by the nonlinear least square (NLS) to find the minimum error between estimated outputs and measurements. To cope with TVPI deteriorated by voltage disturbances, dynamic programming is first used to detect the disturbance. Then, a robust TVPI engine is designed to constrain the estimated time-varying parameters within a stable range. Furthermore, advanced tolerance thresholds are also required during iterations of NLS. Numerical simulations on the 9- and 129-bus distribution systems verify the effectiveness and robustness of the proposed TVPI method. Also, this method can be robust to the ambient noise of measurements.

Individual Load Model Parameter Estimation in Distribution Systems Using Load Switching Events

There currently exists a mature literature on modeling the aggregate load of a distribution feeder by making use of measurements at its feeder-head at substation. The primary application of such feeder-aggregated load models is in sub-transmission or transmission system analysis. However, there is a growing need in practice also to model each individual load across the feeder. If available, such individual load models have applications in power distribution system analysis, e.g., to better integrate distributed energy resources or to improve power quality and reliability. Motivated by this observation, in this paper, we propose a new method for individual load modeling in power distribution systems. It works by using the measurements only at the feeder-head. It takes an innovative approach to analyzing the load switching events across the distribution feeder itself, instead of or in addition to relying on upstream voltage events that are commonly used in feeder-aggregated load modeling. By tracking the downstream load switching events, the proposed method can make a robust estimation of the ZIP load model parameters for all individual loads. The proposed method is examined on small illustrative test-feeders as well as the IEEE 33-bus test system under various operating scenarios. The adverse impact of errors in measurements and system parameters are also investigated on the performance of the developed load modeling method.

Scaled sequential threshold least-squares (S2TLS) algorithm for sparse regression modeling and flight load prediction

This paper presents a Scaled Sequential Thresholded Least Squares (S2TLS) algorithm to construct sparse regression models for flight load prediction. The combined use of a sparsification parameter λ and a magnification factor χ is proposed to tune both the model complexity and the regressor complexity. A bumpiness function is introduced to preferentially select simple regressors to improve model prediction. A cost function J consisting of the estimation residual and the bumpiness is then proposed to determine parameters (χ, λ) satisfying balanced performance. Parametric analysis is undertaken to investigate the effect of χ and λ on sparse regression performance. It is found that the optimal solution is hidden within a trench-like region of the (χ, λ) domain. Two methods using different optimization variables and algorithms are then presented to search for optimal combinations of λ and χ. Case studies are performed, and model results are compared against the test and CFD data of flight loads. Excellent agreement is observed, and the data (even with significant complications) is well bounded by the 95% confidence interval. Importantly, the underlying load-driving factors can be successfully identified. The new S2TLS algorithm represents a robust, efficient, and accurate method for flight load modeling and prediction.

Recursive Bayesian-Based Approach for Online Automatic Identification of Generalized Electric Load Models in a Multi-Model Framework

Electric loads are essential for power system dynamic simulation. However, load modeling is one of the most challenging topics due to the diversity and time-varying behavior of the load. When considering the intervention of rapidly developing distributed generation (DG), load modeling becomes more difficult. In this paper, a new solution for determining the unknown generalized load model is proposed. The radial basis function (RBF) neural network-based sub-models of generalized load are stored in the form of a sub-model bank. A recursive Bayesian approach is used to identify the sub-models and then merge them into one generalized load model according to their probabilities. The proposed method can be implemented online and adapt to describing the diversity and time-varying behavior of the generalized load. Numerical studies are carried out using both simulation data and actual measurements. The comparisons with other load modeling methods verify the advantages of the proposed method.

A generalized synthesis load model considering network parameters and all-vanadium redox flow battery

The simulation precision of the classic load model (CLM) is affected by the increasing proportion of installed energy storage capacity in the grid. This paper studies the all-vanadium redox flow battery (VRB) and proposes an equivalent model based on the measurement-based load modeling method, which can simulate the maximum output of the VRB energy storage system and fit the external characteristic of the system precisely in the occurrence of large disturbance and continuous small disturbance. The equivalent model is connected to CLM to form a generalized synthesis load model (GSLM), which considers the parameters of distribution network and reactive power compensation. Compared with CLM, GSLM has better structures and can describe the load characteristics of distribution network with energy storage system more precisely. Simulation results validate the effectiveness and good parameter stability of GSLM, and show that the higher the proportion of energy storage in the grid is the better description ability GSLM has.

A New Method for Power System Load Modeling Using a Nonlinear System Identification Estimator

This paper proposes a new method for measurement-based modeling of nonlinear loads in power systems. The proposed method includes a combination of a binary tree algorithm with nonlinear autoregressive with exogenous input (NARX) identification. This paper demonstrates that the new method performs well without any prior knowledge of the system structure. In contrast to other load modeling methods, which are typically aimed for particular studies or load types, the proposed method can be used with any load type and for any study. Accurate load modeling is particularly important for studies of industrial networks and grids. In the study described, a field data set was collected at a mine site from a large electrical rope shovel. This data set has been used to develop a model of the rope shovel based on the proposed binary tree—NARX algorithm. When compared to other known methods, such as wavelet and sigmoid networks, the proposed method has shown the fastest training time and the highest accuracy. Finally, the modeling results have been verified against another set of field measurements from an existing network and have shown a very good agreement.

A Novel Optimized Fuzzy Approach Based on Monte Carlo Method for System Load, Wind Turbine and Photovoltaic Unit Uncertainty Modeling in Unit Commitment

Developing usage of renewable resources, such as wind turbine and photovoltaic units, and their output uncertainty beside the system total load forecasting imprecision leads to the uncertainty of unit commitment solution. This article presents a new fuzzy approach for modeling of these uncertainty sources behavior in a power system including wind turbine, photovoltaic, and thermal units. For the first time, based on Monte Carlo simulation, a new concept called Monte Carlo mismatch index is proposed as a criterion for uncertainty modeling precision, which is used as a fitness function for optimization of the proposed fuzzy system parameters by genetic algorithm. Then this stochastic-optimized fuzzy system is applied for uncertainty modeling. The fuzzy system output is that part of the power system total load, which should be distributed among thermal units. Then, this output is fed to a crisp unit commitment program based on particle swarm optimization. Finally, optimal thermal units generation cost and scheduling is presented and the proposed method performance is compared with other uncertainty modeling methods.

A probability load modeling method for the charging demand of large-scale PEVs accounting users’ charging willingness

This paper presents a new strategy in order to model the charging power demand due to large-scale plug-in electric vehicles (PEVs) as realistic a fashion as possible and analyze their impact on the residential power distribution system. The strategy takes the charging willingness of PEV users into consideration, and accounts for the difference in charging frequencies among users. A detailed classification, derived from the historical data on users’ driving patterns, on PEV users is conducted in order to ensure that users in the same user set have the same charging properties. Seven probability load models for PEV charging are established for these user sets, and each model accounts the inherent randomness in the usages and recharges of PEVs. After the consideration of charging willingness, the charging demand differs among weekdays. The aggregated charging demand from a user set on each weekday is calculated based on the Law of Large Numbers, and the total charging demand from all PEVs on each weekday can be obtained by accumulating the aggregated charging demand of the user sets with charging willingness. The strategy can ensure a high utilization of the battery capacity, and the aggregated charging demand resulted is more rational and credible. The proposed charging load modeling strategy is finally applied on the electric load profile on a winter day in Manitoba.

Load Modeling Under Unbalanced Disturbances

The majority of the disturbances in power systems are unbalanced in nature whereas most of the existing load modeling methods are applicable for balanced disturbances. This letter presents a measurement-based methodology for modeling the loads under unbalanced disturbances.