Generator Parameter Calibration by Adaptive Approximate Bayesian Computation With Sequential Monte Carlo Sampler

Khazeiynasab, Seyyed Rashid; Qi, Junjian

doi:10.1109/tsg.2021.3077734

Cited by 19 publications

(11 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be noted that the operational costs are chosen based on the NREL ATB file [3] for the year 2050 assuming a capacity factor of 88%, 12%, and 12% for DG8, DG13, and DG30 respectively. More information regarding the power plant parameter identification can be found in [27] [28]. In addition to the cost related parameters, ramp rate limits as well as fixed no-load costs of DGs were considered.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Hydrogen and Battery Storage Technologies for Low Cost Energy Decarbonization in Distribution Networks

Haggi¹,

Brooker²,

Sun³

et al. 2022

Preprint

View full text Add to dashboard Cite

Deep energy decarbonization cannot be achieved without high penetration of renewables. At higher renewable energy penetrations, the variability and intermittent nature of solar photovoltaic (PV) electricity can cause ramping issues with existing fossil fuel generation, requiring longer term energy storage to increase the reliability of grid operation. A proton exchange membrane electrolyzer can produce H 2 and serves as a utility controllable load. The produced H 2 can then be stored and converted back into electricity, or mixed with natural gas, or used as transportation fuel, or chemical feedstock. This paper considers the perspective of the distribution system operator that operates the distributed energy resources on a standard IEEE 33-node distribution network considering the technical and physical constraints with the goal of minimizing total investment and operation cost. Different case studies, at very high PV penetrations are considered to show the challenges and path to net-zero emission energy production using H 2 energy. Sensitivity of utility PV costs and electrolyzer capital costs on producing H 2 at $1/kg are presented showing that the distribution network could produce 100% renewable electricity and H 2 could be produced at a cost of $1/kg by 2050 with conservative cost estimates and by 2030 with accelerated cost declines.

show abstract

Section: Simulation Results and Analysismentioning

confidence: 99%

Hydrogen and Battery Storage Technologies for Low Cost Energy Decarbonization in Distribution Networks

Haggi¹,

Brooker²,

Sun³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Assuming the prior distribution for parameter x as π(x). Bayes's theorem allows us to write its posterior distribution p(x|y) in terms of the prior distribution and the observation distribution p(y|x) [6]. The form of this posterior is…”

Section: Proposed Cvae Methodsmentioning

confidence: 99%

“…The identification of parameters remains a challenge for academic researchers and industrial practitioners due to the rising diversity of loads and the integration of distributed energy resources (DERs). Many parameter identification approaches use measurementbased approaches by taking voltage and power measurements during fault-induced delayed-voltage-recovery (FIDVR) events to determine the parameters of dynamic load models [5], [6].…”

Section: Introductionmentioning

confidence: 99%

WECC Composite Load Model Parameter Identification Using Deep Learning Approach

Khazeiynasab

Zhao

Duan

2022

2022 IEEE Power &Amp; Energy Society General Meeting (PESGM)

View full text Add to dashboard Cite

The increased integration of distributed energy resources (DERs) has significantly changed the load composition. As compared to traditional static or induction motor loads, these inverter-based resources (IBRs) exhibit different behavior. Then, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. Western Electricity Coordinating Council (WECC) has developed the composite load model with distributed generation (CMPLDWG) to accurately represent static load, induction motors, and IBRs. In this paper, we propose a novel deep learning-based method using a conditional variational autoencoder (CVAE) to estimate the CMPLDWG model's posterior distributions. The effectiveness of the proposed method is validated using an IEEE 39-bus test system. The results show that the proposed approach can accurately and efficiently estimate the parameters' posterior distributions.

show abstract

“…If is sufficiently small, the distribution p (α c |z * ) will be a good approximation of the posterior distribution. Recently, algorithms using Sequential Monte Carlo (SMC) with particle filtering have gained growing attention [17], [18]. ABC SMC samples from a sequence of distributions that increasingly resemble the target posterior.…”

Section: Dc-dc Converters Parameter Calibration By Adaptive Abc Smcmentioning

confidence: 99%

Measurement-Based Parameter Identification of DC-DC Converters with Adaptive Approximate Bayesian Computation

Khazeiynasab¹,

Batarseh²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The recent advances in power plants and energy resources have extended the applications of DC-DC converters in the power systems (especially in the context of DC microgrids). Parameter identification can extract the parameters of the converters and generate accurate discrete simulation models. In this paper, we propose a measurement-based converter parameter calibration method by an adaptive Approximate Bayesian Computation with sequential Monte Carlo sampler (ABC SMC), which estimates the parameters related to passive and parasitic components. At first, we propose to find suitable prior distribution for the parameter which we don't know the prior information about them. With having prior distributions, we can use the ABC SMC to find the exact values of the parameters of the converter. We chose the distance function carefully and based on the simulations we assigned the best method for the threshold sequencing. For improving the computationally of the algorithm, we propose an adaptive weight that helps the algorithm to find the optimal values with fewer simulations. The effectiveness of the proposed method is validated for a DC-DC buck converter. The results show that the proposed approach can accurately and efficiently estimate the posterior distributions of the buck parameters subject to gross errors in the prior distributions of the parameters. The proposed algorithm can also be applied to other parameter identifications and optimization applications such as rectifiers, filters, or power supplies, among others.

show abstract

Generator Parameter Calibration by Adaptive Approximate Bayesian Computation With Sequential Monte Carlo Sampler

Cited by 19 publications

References 49 publications

Hydrogen and Battery Storage Technologies for Low Cost Energy Decarbonization in Distribution Networks

Hydrogen and Battery Storage Technologies for Low Cost Energy Decarbonization in Distribution Networks

WECC Composite Load Model Parameter Identification Using Deep Learning Approach

Measurement-Based Parameter Identification of DC-DC Converters with Adaptive Approximate Bayesian Computation

Contact Info

Product

Resources

About