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

Zhou, Lin; Peng, Zhi Wei; Deng, Chuang; Qi, Xuezhong; Li, P Q

doi:10.1186/s41601-018-0105-1

Cited by 5 publications

(4 citation statements)

References 31 publications

(41 reference statements)

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“…(1) Use the improved genetic algorithm to identify the parameters of the selected data samples for model sensitivity analysis. The function of the fitness fi of each solution is the reciprocal of the error function, and the error function is shown in (8).…”

Section: A Step Of Case Studymentioning

confidence: 99%

“…Although the importance of load modeling with abundant research results in dynamic systems is widely known, it is still a very challenging problem needed to be improved due to the continuous complexity of the load component of industrial development. The load model and modeling method continuously improved with the continuous deepening of research [1]- [8]. [8] combined all-vanadium flow batteries with traditional comprehensive load models to obtain a generalized comprehensive load model.…”

Section: Introductionmentioning

confidence: 99%

“…The load model and modeling method continuously improved with the continuous deepening of research [1]- [8]. [8] combined all-vanadium flow batteries with traditional comprehensive load models to obtain a generalized comprehensive load model. A comprehensive load model that takes into account the distribution network voltage regulation is proposed in [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

Tian¹,

Lii²,

Zhao³

et al. 2020

IEEE Access

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Load modeling plays an important role in accessing and enhancing the dynamic stability of power systems. Though the Synthesis Load Model Considering Voltage Regulation of Distribution Network (π model) has high accuracy, its parameters are too many. In order to improve the identification efficiency and reduce the difficulty of identification, a simplified model identification strategy based on parameter sensitivity analysis is proposed. Firstly, based on the global sensitivity analysis, the sensitivity analysis of the model parameters is carried out to obtain the First Order Sensitivity Indices (FSI)and Total Sensitivity Indices (TSI). Secondly, the FSI and TSI of each parameter are analyzed, and the effect on the output of model of each parameter is determined by FSI. For less influential parameters, whether the parameter should be fixed as constant is determined by the value of TSI. The parameter whose TSI equal or approximately equal to zero should be fixed as a constant. Finally, the improved genetic algorithm is used to identify the parameter-simplified model, and the effectiveness of the simplified identification strategy is verified by comparing the fitting effects with the measured curve and the residual and the integrated parameter models.

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Section: A Step Of Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

Tian¹,

Lii²,

Zhao³

et al. 2020

IEEE Access

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show abstract

“…The simulated annealing method was applied to optimize the structure and parameters of the SVM. A hybrid algorithm combining random forest, SVM and BPNN using the smart meter measurement data was proposed to accurately predict the load of air conditioning system [17], [18]. Wang et al, for the first time, conducted a taxonomy research on the current artificial intelligence prediction techniques, network optimizers and prediction models [19].…”

Section: Introductionmentioning

confidence: 99%

Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning

Chen¹,

2020

IEEE Access

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Accurate and reliable prediction of airconditioning load plays a significant role in prosumer energy management system (EMS), because airconditioning load accounts for a large proportion of the building's total energy consumption. This paper proposes a new meta ensemble learning method to realize short-term prediction of airconditioning load for prosumers. This method is a hybrid of meta ensemble learning and stacked auto-encoder (SAE). First, we design multiple different forecasting structures based on SAE to achieve point prediction of airconditioning loads. SAE is used to learn the deep features in airconditioning load data. Second, a new meta ensemble learning prediction model is proposed. Meta ensemble learning is used to learn the nonlinear features and invariant structures in data, and determine the coefficients of each SAE-based point forecaster. Finally, the prediction results of each point forecaster are aggregated and integrated to estimate the final airconditioning load prediction result. Airconditioning load data from a commercial building in Singapore are used to validate the feasibility and effectiveness of the proposed method, demonstrating that the proposed meta ensemble learning method is attractive in prosumer energy management. INDEX TERMS Airconditioning load, load prediction, meta learning, ensemble learning, prosumer energy management system.

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Short-term load prediction of integrated energy system based on neural network

Wang¹,

Li²,

Liang³

et al. 2021

J. Phys.: Conf. Ser.

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Considering the correlation and nonlinear characteristics of multiple types of loads in the integrated energy system, grey relation analysis (GRA) and long short term Memory (LSTM) neural network are selected to establish the short-term load prediction model of the integrated energy system. The model uses GRA method to analyze the coupling between multiple types of loads and the meteorological factors, and then obtains the load forecast results through the LSTM prediction model. Finally, a practical example is given to verify the validity of the model.

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

Cited by 5 publications

References 31 publications

Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning

Short-term load prediction of integrated energy system based on neural network

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