Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control

Abstract

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.

MPC Framework for the Energy Management of Hybrid Ships with an Energy Storage System

“…Furthermore, a comparison with optimal control policies was not present. Huotari et al [38] takes into account mission-scale predictions and benchmarks the proposed MPC-based controller against an exhaustive solution for a diesel-electric cruise ship power plant case study. This study uses mixed integer linear programming (MILP) as the MPC solver while it also incorporates a 2-stage predictive scheme to provide a mission-scale prediction.…”

“…Enables real-time adaptation to shipboard updates on the mission-scale disturbance estimation while sailing, rather than using offline tuning of parameters such as the equivalency factor in ECMS, predefined power demand sequences [38] or rule-based tuning. 6.…”

“…More specifically, it is demonstrated that the controller yields feasible control policies and is robust regarding the integral constraint on the battery SoC, addressing concerns identified in the literature, such as the insufficient peak demand availability observed in [25]. It is also capable of adapting to real-time updates on the future disturbance estimations, as opposed to the controller proposed in [38]. At the same time, it addresses the problem of instantaneous EMS (ECMS, A-ECMS), identified in [16], where the performance of the controller is poor under off-design power demand sequences, different from those used to tune the controller's equivalency factor(s).…”

Convex Optimisation Model for Ship Speed Profile: Optimisation under Fixed Schedule

“…This approach was selected to study the impact of speed profile optimisation under a fixed schedule specifically, whereas a model that restricts operation under certain conditions would require a flexible schedule. The original motivation for this study stemmed from previous work of the authors in [29], where the power demand profile of a ship was predicted based on the expected speed profile during a voyage. To establish an adequate power demand prediction, the expected speed profile had to be of high resolution and obtained with reasonable computational efficiency.…”

Emission Abatement Technology Selection, Routing and Speed Optimization of Hybrid Ships

“…Although shipping emissions are regulated nowadays, they are nevertheless estimated to account for 250,000 deaths and 6.4 million asthma cases annually [1]. Due to the pressure to reduce emissions, and the high cost of abatement systems, identifying least-cost strategies for meeting reduction targets has received considerable attention [2][3][4][5][6][7][8][9][10][11][12].…”