Multi-stage Sensitivity Analysis of Distributed Energy Systems: A Variance-based Sobol Method

Wei, Shangshang; Li, Yiguo; Gao, Xianhua; Lee, Kwang Y.; Sun, Li-Peng

doi:10.35833/mpce.2020.000134

Cited by 16 publications

(3 citation statements)

References 41 publications

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“…Fitting function and function test. In this paper, quasi monte Carlo method is adopted to improve the Latin hypercube sampling design, establish the mathematical model of target variables for each design variable mapping, based on the variance, 21,22 and sensitivity analysis to get the output target to the sensitivity of the input variables, the first-order sensitivity index, and the main effect index values, for different output target, Design variables choice is different, therefore, will have sensitivity to the different output target of each design variable data set, with the combined for seven design variables, respectively on the battery shell, shell plate, shell plate, after the two relatively according to lug around on size, thickness, as far as possible to avoid the above index dimension and reduce the computation time. In this paper, the seven groups of design variables are divided into two groups according to their influence on different output targets, discarding the variables with too small total effect index, which provides a basis for fitting function and optimization by the pelican algorithm.…”

Section: Pelican Optimization Algorithm Test Response Surface Optimiz...mentioning

confidence: 99%

Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks

Gao

Liu

et al. 2022

Advances in Mechanical Engineering

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With the rapid development of artificial neural networks, more sophisticated network models and more accurate prediction results are provided for solving engineering applications. In this paper, the weights and thresholds of the feedforward neural network model were optimized using the pelican algorithm, and the optimal solution was output by simulating the pelican predation scheme and assigned as the new parameters of the neural network. A POA-BP network model was proposed, and its better prediction was demonstrated by comparing the fitting and prediction performance with 13 neural network models such as random forest, support vector machine, and wavelet basis by evaluating metrics such as RMSE, MSE, and MAE. To further improve the prediction accuracy, different hidden layer topologies of POA-BP were compared, and the Monte Carlo method was used to obtain seven design variables for the lithium battery shell size parameters, and parameter regression prediction was performed for the structure after the variable density topology optimization used the isotropic material interpolation model (SIMP) with the moving asymptote method by invoking the MinGW-w64 compiler, and the 1-3-1 neural network was selected model to predict each dimension of the battery shell structure, the final shell weight reduction ratio was 18.12% and the first-order intrinsic frequency was increased by 14.56%.

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Section: Pelican Optimization Algorithm Test Response Surface Optimiz...mentioning

confidence: 99%

Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks

Gao

Liu

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

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“…With worldwide commitment to the ambitious goal of carbon neutrality, the penetration of renewable energy will inevitably increase [1,2]. However, the electrical grid system, under high penetration of renewable energy, will face many unavoidable challenges due to the strong uncertainty of renewable sources, such as security, stability and reliability [3,4]. In order to alleviate the impact of uncertainties on the grid system, exploiting controllable power generation plants to maintain the quality requirements of grid system is a necessity.…”

Section: Introductionmentioning

confidence: 99%

Flexible Operation of Concentrating Solar Power Plant with Thermal Energy Storage Based on a Coordinated Control Strategy

et al. 2022

Self Cite

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With the ambition of achieving carbon neutrality worldwide, renewable energy is flourishing. However, due to the inherent uncertainties and intermittence, operation flexibility of controllable systems is critical to accommodate renewables. Existing studies mainly focus on improving the flexibility of conventional plants, while no attention has been paid to the flexible operation of concentrating solar power with thermal energy storage (CSP-TES) systems. To this end, the ultimate goal of this work is to investigate the potentiality and realization of CSP-TES systems to flexibly operate in grid system regulation. With this goal, the dynamic characteristics of a 50 MW parabolic trough collector CSP plant with molten-salt-based TES is analyzed, and its dominant control characteristics are concluded to demonstrate the possibility of the ideal. After that, a coordinated control strategy is proposed. Specifically, a disturbance observer-based feedforward–feedback control scheme and a feedforward–feedback controller are designed, respectively, for the solar field and the energy storage subsystems, while the power block subsystem is regulated by a two-input and two-output decoupled controller. Based on the decentralized structure, three simulation cases are, respectively, performed to testify the capacity of the CSP-TES system to wide-range load variation tracking, strong disturbance rejection, or both. The results show that the CSP-TES system can adequately track the grid commands based on the proposed coordinated control strategy, even under strong fluctuation of irradiation, demonstrating the flexibility of CSP-TES participating in grid regulation. In the context of continuous penetration of renewable energy into the grid system, research on the role transition of the CSP-TES system from its own optimization to grid regulator is of great importance.

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“…In recent years, increased global awareness has grown on the comprehensive utilization of multiple energy [1]. The integrated energy systems (IES) with combined cooling, heating, and power (CCHP) [2] can contribute to improve the efficiency of energy production and reduce the emissions; therefore, it has been recognized as a good option for future energy systems [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Variable Performance Parameters Temperature–Flowrate Scheduling Model for Integrated Energy Systems

et al. 2021

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Optimal scheduling strategy of integrated energy systems (IES) with combined cooling, heating and power (CCHP) has become increasingly important. In order to make the scheduling strategy fit to the practical implementation, this paper proposes a variable performance parameters temperature–flowrate scheduling model for IES with CCHP. The novel scheduling model is established by taking flowrate and temperature as decision variables directly. In addition, performance parameters are treated as variables rather than constants in the proposed model. Specifically, the efficiencies of the gas turbine and the waste heating boiler are estimated with the partial load factor, and the coefficient of performance (COP) of the electrical chillers and heat pumps are estimated with the partial load factor and outlet water temperature. Then, to deal with the model nonlinearities caused by considering the variability of COPs, the COP-expansion method is developed by adopting a specific representation of the COP and the expansion of the outlet water temperature. Finally, case studies show that the variable performance parameters’ temperature–flowrate scheduling model can account for the variation of performance parameters, especially the impacts of water temperature and the part load factor on the COP. Therefore, the proposed scheduling model can obtain more adequate and feasible operation strategy, thereby suggesting its applicability in engineering practice.

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Multi-stage Sensitivity Analysis of Distributed Energy Systems: A Variance-based Sobol Method

Cited by 16 publications

References 41 publications

Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks

Prediction of stamping parameters for imitation π-shaped lithium battery shells by building variable weight and threshold pelican-BP neural networks

Flexible Operation of Concentrating Solar Power Plant with Thermal Energy Storage Based on a Coordinated Control Strategy

A Variable Performance Parameters Temperature–Flowrate Scheduling Model for Integrated Energy Systems

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