Research on adaptive synthesis dynamic load model based on multiple model ideology

An ANN-based load model for fast transient stability calculations

The behavior of the load model in power system stability studies has been shown by various investigators to be a very important factor in determining the stability of power system elements. Load models which are presently available in typical stability programs make it difficult if not impossible to model the actual dynamics of power system load. Present load models allow easy static representations, but require detailed individual dynamic models such as induction or synchronous motor models. Thus a simple load model that can represent power system composite load dynamics will be a valuable tool in the stability study Presented herein is a second order state space load model which can represent power system composite load dynamics. In addition, the parameters of this state space load model can be identified from actual system load measurements by a weighted least squares parameter identification process. Several authors recognized the significance of load behavior to changes in voltage as long as 25 years ago. However, most of the attention in stability studies has been directed toward improved generator and control system models. Until recently, little modeling has been considered necessary for power system loads since the effects of load models in stability studies were considered only secondary. At present the load representation in typical stability programs is limited to a static load model where the load is represented as constant MVA, constant current, constant impedance, or some combination of the three.

Owing to the characteristics of nonlinearity, asymmetry and stochastic volatility, electrified railway traction loads have arose some hazardous effects to security and stability, reactive power compensation and voltage control, power supply quality of power system. It has practical significance to research on the influence of traction loads to power system for ensuring security and stability of power system. By using Power System Analysis Software Package/User Program Interface (PSASP/UPI) function, the electrified railway traction measurement-based load model (traction load model, TLM) for transient stability simulation are established, and then the TLM is applied to Hunan Electrical Power Network. The dynamic characteristics of TLM and the difference characteristics between TLM and the traditional load model are analyzed through setting different faults. The paper provides a convenient reference for further research on the relay protection setting calculation of power system and safety controlling strategies of traction power supply system.

The time-varying parameter identification of load models has attracted broadly attention when large amounts of intermittent distributed generations (DGs) and stochastic loads are integrated in active distribution network (ADN). However, in traditional time-varying parameter identification approaches, the load model is usually developed with the composite load model (CLM) or synthesis load model (SLM), which are not suitable for representing the dynamics of DGs. Moreover, the plateau phenomenon and the continuous low-quality data further reduce the performance of traditional load models with time-varying parameter. Therefore, to address these issues, a novel time-varying parameter identification approach with extended Kalman filter (EKF) is designed for the load modeling. To represent the behavior of modern loads, an improved load model contains a parallel SLM and voltage source converter (VSC) is developed. Then, the target parameters, whose changes produce larger variations in model outputs, are selected with trajectory sensitivity to avoid plateau phenomenon. Also, the Chi-square test and the proposed weighted suppression strategy are utilized to suppress the continuous low-quality data. The simulation results on a system-level ADN model show that the proposed approach could accurately identify the parameters of time-varying load models.

A methodology for measurement-based load modeling based on integrating dynamic models is presented in this paper. A composite load model (CLM) and an exponential recovery load (ERL) model are combined and used as a model structure for load representation coupled with a heuristic criterion. An improved particle swarm optimization (IPSO) algorithm that considers a speed update modification to enhance its global search capabilities is used to identify the model's parameters. Several simulations are performed in two test systems, using load response measurements after small and severe disturbances, considering noise measurement to simulate monitoring devices. Results verify that the proposed load model has a better performance than the individual load models, and also IPSO algorithm is adequate for parameter identification.

Effect of load models on assessment of energy losses in distributed generation planning

This paper presents a time-varying stochastic technique to assess conservation voltage reduction (CVR) effects based on load modeling. A time-varying exponential load model is developed to represent voltage dependences of loads. The recursive least square (RLS) method is applied to identify model parameters in a recursive way. CVR factors can be calculated using the identified model parameters. The time-varying stochastic model for CVR effects can then be constructed by the Kolmogorov-Smirnov (K-S) test. The proposed CVR assessment method is applied to one-year measurement data from a utility company. The calculated CVR factors are verified by a Euclidian distance-based comparison method. Stochastic models of CVR effects in each time window are constructed. Compared with previous efforts on assessing CVR effects, the proposed method does not require control groups or assumptions of linear relationships between the load and its impact factors. The probabilistic nature of CVR effects is also fully considered.

Distributed generation (DG) planning studies are generally conducted while assuming constant generation and load models. However, in this paper, optimal allocation of photovoltaic (PV) based DG is carried out while considering probabilistic generation and time varying voltage dependent load models. First, a novel probabilistic generation model is proposed for solar irradiance uncertainty modeling to compute the hourly output power produced via PV units. Subsequently, the obtained power values are used to determine the optimum PV allocation in the distribution system through particle swarm optimization to minimize the multi-objective function. The proposed algorithm has been validated on 33-bus and 69-bus distribution test systems. The results demonstrate that the proposed model is suitable for solar irradiance modeling and can be employed in power system planning studies.

With the addition of new distributed energy sources, a new and more accurate load model need to be established. Based on MATLAB/Simulink, this paper researches on the modeling and simulating of photovoltaic power generation system and battery energy storage and their equivalent model. First, a detailed model of the photovoltaic system is built. Boost circuit and MPPT control are selected on the DC side of photovoltaic power generation system. The double closed loop control method of current internal loop and external voltage loop is used for grid inverter control. The model is improved considering the capability of LVRT (low voltage ride through). Based on the above precise models, the model is equaled. The equivalent transfer function is calculated by the grid connected control loop, so the model is simplified. The battery energy storage module establishes a general mathematical model based on the charging and discharging principle. Charge and discharge are controlled through the DC/DC converter. And it is connected to the power grid after inverting and filtering. The common PQ control are selected in the inverter control mode. According to the same principle, the energy storage model is equaled. Finally, based on the traditional load model, the above load model is added to set up a new generalized integrated load model. And the actual model is replaced to build up an equivalent model. The equivalent model is compared with the actual model to verify the equivalent description ability.

This paper presents a new method to construct dynamic models for large industrial and commercial facilities commonly connected to power transmission systems. These facilities typically draw large amounts of power and have complex dynamic responses to power system disturbances. Traditional load modeling approaches such as those based on load composition or site measurement are not adequate to produce dynamic models for such facilities. In this paper, a facility template based load modeling technique along with template scaling/equivalence algorithms is proposed to solve the facility modeling problem. Oil refinery facilities are used as an example to illustrate the proposed modeling technique. The technique requires minimal user input and can be implemented in a database program.

<p>Load modeling is an important task in power system stability analysis and control. Taking this into account, the development of dynamic load models using a measurement-based load modeling strategy and an improved particle swarm optimization algorithm is presented in this paper. To accomplish this objective, a measurement-based parameter estimation method is used for identification of an exponential recovery load model. Measurements are obtained performing dynamic simulation of an IEEE 30-bus test system under several disturbances, and additionally, cross validation technique is applied for an analysis of load model generalization capability. An adequate load modeling improves the comprehension of load behavior and the capability of reproduce transient events on power systems.</p>

Efficient Estimation of Interrupted Energy with Time-Varying Load Models for Distribution Systems Planning Studies

Load model has significant impact on power system simulation. Current load modeling approaches are inadequate on revealing the accuracy and time-variation of load compositions. The application of wireless sensors dispersed in power distribution networks provides further opportunities for load modeling. In this article, a data-driven online aggregated load modeling approach is proposed systematically. First, all the electricity consumers are clustered according to big data of power consumption behaviors. In each cluster, typical users are designated to stand for the characteristics of the cluster, and intrusive measurement is adapted to capture these typical users’ time-varying information by employing wireless intelligent terminals, which can identify the composition of static load and induction motor load online. Second, the load models of other users in each cluster are assumed identical to typical users, including static impedance–current–power models and induction motor models. Finally, the composite load model is achieved by hierarchical aggregation and bottom-to-up stepwise equivalence. Simulations demonstrate that the load model built by proposed approach reflects higher accuracy and adaptability in power system.

In a large-scale transmission network, such as the European power system, the regional characteristics of electrical demand may have an impact on local voltage stability. The dynamic load behaviour depends on the sectoral composition of demand, which is subject to regional and seasonal variation. An aggregate load model is presented, which accounts for the sectoral characteristics of 210 European regions for all hours a year from the perspective of the high, medium, and low voltage levels. The proposed load model is used to better understand the influence of regional characteristics on voltage recovery during a short-circuit event, as well as the static point of voltage collapse. Several countries are considered in terms of their sectoral composition, to represent intermediate regions or such dominated by the industry and households. Moreover, the dynamic load model is benchmarked against two static load models, assuming a constant power (PQ) load or a constant impedance (Z) load. The conducted study demonstrates that regional differences in the composition of demand highly impact local voltage stability.

Not only static load model but dynamic load model is necessary for improving the accuracy of time-domain simulation results because only dynamic load model can express the dynamic response of the load. The measurement based auto-derivation method for estimating load model parameters from PQVF monitoring devices has been developed. The PQVF monitoring device which has already been installed into power system captures disturbance data triggered by voltage deviation and frequency deviation. The transfer-function based load model which consists of voltage characteristics index, frequency characteristics coefficient, transient gain, and time constant was used as the dynamic load model. The simple transfer function-based dynamic load model can express 80% of 182 load responses precisely. Therefore, the transfer function-based dynamic model can have applicability to represent the dynamic behavior of the load response concisely and accurately on condition that the quantity of voltage drop during the disturbance is less than 30%.