Shipboard power management using constrained nonlinear model predictive control

Stone, Philip; Opila, Daniel F.; Park, Hyeongjun; Sun, Jing; Pekarek, Steve; DeCarlo, Raymond A.; Westervelt, E.R.; Brooks, James D.; Seenumani, Gayathri

doi:10.1109/ests.2015.7157853

Cited by 22 publications

(11 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unit commitment is then optimized again using the observed state as the initial condition, the first control action taken, and the methodology continued in a similar fashion. Such approaches have been employed in [27][28][29][30][31][32]. In [27], the future power demand profile was expected to be given beforehand.…”

Section: Control Of Ship Energy Systemsmentioning

confidence: 99%

Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control

et al. 2020

View full text Add to dashboard Cite

We present a novel methodology for the control of power unit commitment in complex ship energy systems. The usage of this method is demonstrated with a case study, where measured data was used from a cruise ship operating in the Caribbean and the Mediterranean. The ship’s energy system is conceptualized to feature a fuel cell and a battery along standard diesel generating sets for the purpose of reducing local emissions near coasts. The developed method is formulated as a model predictive control (MPC) problem, where a novel 2-stage predictive model is used to predict power demand, and a mixed-integer linear programming (MILP) model is used to solve unit commitment according to the prediction. The performance of the methodology is compared to fully optimal control, which was simulated by optimizing unit commitment for entire measured power demand profiles of trips. As a result, it can be stated that the developed methodology achieves close to optimal unit commitment control for the conceptualized energy system. Furthermore, the predictive model is formulated so that it returns probability estimates of future power demand rather than point estimates. This opens up the possibility for using stochastic or robust optimization methods for unit commitment optimization in future studies.

show abstract

Section: Control Of Ship Energy Systemsmentioning

confidence: 99%

Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control

et al. 2020

View full text Add to dashboard Cite

show abstract

“…By contrast, a forecast is an estimate of future disturbances that the controller cannot change, but may have some knowledge of. For more aggressive and rapidly changing loads, the controller may receive a forecast of future loads to better prepare for fast transients Gonsoulin et al (2017); Vu et al (2017); Stone et al (2015); Park et al (2015). The controller then incorporates this forecast as it makes its predictions for the effects of the control actions.…”

Section: Introductionmentioning

confidence: 99%

The role of future information in control system design for shipboard power systems

Opila

Stevens

Cramer

2018

Proceedings of the International Ship Control Systems Symposium (iSCSS)

View full text Add to dashboard Cite

Both naval and commercial ships are incorporating new power and energy system technologies to improve fuel economy and performance while servicing high power pulsed loads. These assets can be best utilized with load demand forecasting and/or prediction, especially when considering limits on generator ramp rates, distribution lines, and energy storage capacity. Obtaining future load demand data and designing a controller to accommodate it can be challenging, but with potentially large payoff. However, this information is not useful in all cases. This paper develops a method to quantify the potential value of future information depending on the specific power system characteristics. This quantitative approach aids designers in deciding how and when to deploy future forecasting in controller design, and provides insight into the potential benefits of these more complex controllers. To quantify this trade off, two optimization-based control methods are developed. One uses only current information, while the other has an exact forecast of the future. As examples, the method is applied to a notional naval ship and drill platform service vessel with representative power and energy system architectures under indicative operational load demands.

show abstract

“…At every step, the free variables are optimized with respect to the cost function and constraints, and only the variables for the first step are applied to the system. Multiple suggestions using MPC in marine power plants already exist, such as [1], [21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Battery Power Smoothing Control in a Marine Electric Power Plant Using Nonlinear Model Predictive Control

Bø

Johansen

2017

IEEE Trans. Contr. Syst. Technol.

View full text Add to dashboard Cite

Abstract-This paper presents a power variation smoothing method using batteries on a weak ship grid. For some marine vessels, power fluctuations on the ac grid are large. This results in large variations in the electrical frequency of the grid and excessive wear and tear of the power producers. Adding batteries connected to a DC/AC drive to smooth out the power fluctuations has been suggested. However, due to the large amount of fluctuation, batteries can overheat. Therefore, we suggest using a band-pass filter with cutoff frequency parameters optimized by model predictive control based on a power spectral density estimate of power consumption for disturbance prediction.

show abstract

Shipboard power management using constrained nonlinear model predictive control

Cited by 22 publications

References 4 publications

Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control

Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control

The role of future information in control system design for shipboard power systems

Battery Power Smoothing Control in a Marine Electric Power Plant Using Nonlinear Model Predictive Control

Contact Info

Product

Resources

About