Uncertainty analysis of the optimal health-conscious operation of a hybrid PEMFC coastal ferry

Dall’Armi, Chiara; Pivetta, Davide; Taccani, Rodolfo

doi:10.1016/j.ijhydene.2021.10.271

Cited by 16 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aiming at the above-mentioned combined explosion problem, a hierarchical modeling method of "combination-reduction" is proposed based on GMM model characteristics and DPHEM algorithm. Only two groups of random variables are combined at a time: f(ξ (1) ) and f(ξ (2) ) Combine f(ξ (1,2) ); f(ξ (3) ) and f(ξ (4) ) Combine f(ξ (3,4) ); …; f(ξ (P−1) ) and f(ξ (P) ) Combine f(ξ (P−1,P) ). After combination f(ξ (1,2) ) 、 f(ξ (3,4) ) 、 …f(ξ (P−1,P) ) It is still a multi-dimensional GMM model and still independent of each other, and the above process can be further repeated.…”

Section: "Combination-reduction" Hierarchical Modeling Of Combination...mentioning

confidence: 99%

“…In the traditional power system, this kind of preset protection control depends on experience and deterministic criteria for setting, and its applicable scenarios are limited. With a large number of wind power, photovoltaic and electric vehicles connected to the power grid, the reform of the power market continues to advance, and the influence of the external environment on the power grid increases, the modern power system has strong uncertainty and complexity, and the system has many operation scenarios and strong coupling of protection and control, which leads to a decrease in the matching degree between the setting scheme and the operation scenarios and a decrease in the adaptability of the traditional methods [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Power System Uncertainty Modeling Based on Gaussian Process

Zhang

2024

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

In order to solve the problem of low accuracy of parameter estimation in expectation maximization algorithm, a modeling method based on Gaussian component number reduction is proposed. Taking the nonparametric kernel density estimation results as the base Gaussian mixture model, the Gaussian mixture model with any number of Gaussian components can be established by reducing the number of Gaussian components by using the density-preserving hierarchical expectation maximization algorithm, which overcomes the problem that the expectation maximization algorithm has low parameter estimation accuracy when there are many Gaussian components. In order to reduce the burden of modeling calculation under large samples, a hierarchical modeling method based on time scale is proposed. In order to solve the problem of Gaussian component number combination explosion of independent random variables, a hierarchical modeling method of “combination-reduction” is proposed. The proposed method is tested by using measured multidimensional wind speed data and load data with complex distribution characteristics. The experimental results show that the Pearson and Spearman correlation coefficients of GMM constructed based on this method are very close to the sample data. The absolute value of Pearson correlation coefficient error is 0.03739, and the root mean square value of error is 0.02388. The absolute value of Spearman correlation coefficient error is 0.11693, and the root mean square error is 0.05797. Conclusion: The accuracy of the proposed method is significantly better than that of Gaussian mixture model and Copula function method based on expectation maximization algorithm.

show abstract

Section: "Combination-reduction" Hierarchical Modeling Of Combination...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Power System Uncertainty Modeling Based on Gaussian Process

Zhang

2024

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

“…For an emission-free target, the fuel cells were sized based on loadleveling, the batteries covered the maximum demand and transient load fluctuations, and the diesel generators were used only for emergency purposes. Dall'Armi et al [78] presented a coupled health-conscious optimization using the MILP algorithm and Monte Carlo analysis, to consider the long-time uncertainties related to fuel cell and battery capital expenses, and hydrogen fuel costs. From the sensitivity analysis it was demonstrated that the hydrogen cost was the most influential parameter in the cost function.…”

Section: Combined Optimal Design-control Studiesmentioning

confidence: 99%

A Comprehensive Review of Modeling and Optimization Methods for Ship Energy Systems

2023

View full text Add to dashboard Cite

This paper presents a comprehensive literature review of the state-of-the art modeling and optimization methods for the power and propulsion systems of ships. Modeling is a tool to investigate the performance of actual systems by running simulations in the virtual world. There are two main approaches in modeling: physics-based and data-driven, which are both covered in detail in this survey paper. The output from the simulations might not be optimal in terms of certain performance criteria such as energy consumption, fuel cost etc. Hence, it is vital to optimize the systems considering the efficient interaction between the components, to yield the optimal performance for the integrated vessel's powertrain. In this paper, the optimization case studies, for the ship energy systems, will be divided in terms of a) optimal design (topology and sizing), b) optimal control and energy management strategies, c) combined optimal design and control. Tables that summarize the literature review outcomes will also be presented at the end of each section. The main outcome is that limited literature is available for optimizations of ship powertrains using datadriven models, especially surrogate models. Surrogate-assisted optimizations for integrated ship energy systems can yield optimal solutions at fast computational speeds, with sufficient accuracy, even for complex, nested, multi-level, multi-objective optimizations.

show abstract

“…Depending on how the operating profile is taken into account, optimization-based EMSs can be divided into two classes: global optimization EMSs and real-time optimization EMSs. The former aim to find the global optimum solution based on the overall operating profile, which needs to be known in advance [22,23,110,[165][166][167][168][169][170][171]. The latter define instantaneous objective functions which can be updated in real time and do not require knowing the operating profile in advance [18,19,[172][173][174].…”

Section: Energy Management Strategymentioning

confidence: 99%

Hybrid PEM Fuel Cell Power Plants Fuelled by Hydrogen for Improving Sustainability in Shipping: State of the Art and Review on Active Projects

2023

View full text Add to dashboard Cite

The interest in hybrid polymer electrolyte membrane fuel cells (PEMFC) fuelled by hydrogen in shipping has seen an unprecedented growth in the last years, as it could allow zero-emission navigation. However, technical, safety, and regulatory barriers in PEMFC ship design and operation are hampering the use of such systems on a large scale. While several studies analyse these aspects, a comprehensive and up-to-date overview on hydrogen PEMFCs for shipping is missing. Starting from the survey of past/ongoing projects on FCs in shipping, this paper presents an extensive review on maritime hydrogen PEMFCs, outlining the state of the art and future trends for hydrogen storage and bunkering, powertrain, and regulations. In addition to the need for a clear regulatory framework, future studies should investigate the development of an efficient fuel supply chain and bunkering facilities ashore. As for the onboard power system, health-conscious energy management, low-temperature heat recovery, and advancements in fuel processing have emerged as hot research topics.

show abstract

Uncertainty analysis of the optimal health-conscious operation of a hybrid PEMFC coastal ferry

Cited by 16 publications

References 35 publications

Power System Uncertainty Modeling Based on Gaussian Process

Power System Uncertainty Modeling Based on Gaussian Process

A Comprehensive Review of Modeling and Optimization Methods for Ship Energy Systems

Hybrid PEM Fuel Cell Power Plants Fuelled by Hydrogen for Improving Sustainability in Shipping: State of the Art and Review on Active Projects

Contact Info

Product

Resources

About