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This paper evaluates the effect of a large-capacity electrical energy storage, e.g., Li-ion battery, on optimal sailing routes, speeds, fuel choice, and emission abatement technology selection. Despite rapid cost reduction and performance improvement, current Li-ion chemistries are infeasible for providing the total energy demand for ocean-crossing ships because the energy density is up to two orders of magnitude less than in liquid hydrocarbon fuels. However, limited distance zero-emission port arrival, mooring, and port departure are attainable. In this context, we formulate two groups of numerical problems. First, the well-known Emission Control Area (ECA) routing problem is extended with battery-powered zero-emission legs. ECAs have incentivized ship operators to choose longer distance routes to avoid using expensive low sulfur fuel required for compliance, resulting in increased greenhouse gas (GHG) emissions. The second problem evaluates the trade-off between battery capacity and speed on battery-powered zero-emission port arrival and departure legs. We develop a mixed-integer quadratically constrained program to investigate the least cost system configuration and operation. We find that the optimal speed is up to 50% slower on battery-powered legs compared to the baseline without zero-emission constraint. The slower speed on the zero-emission legs is compensated by higher speed throughout the rest of the voyage, which may increase the total amount of GHG emissions.
Antti Ritari; Kirsi Spoof-Tuomi; Janne Huotari; Seppo Niemi; Kari Tammi. Emission Abatement Technology Selection, Routing and Speed Optimization of Hybrid Ships. Journal of Marine Science and Engineering 2021, 9, 944 .
AMA StyleAntti Ritari, Kirsi Spoof-Tuomi, Janne Huotari, Seppo Niemi, Kari Tammi. Emission Abatement Technology Selection, Routing and Speed Optimization of Hybrid Ships. Journal of Marine Science and Engineering. 2021; 9 (9):944.
Chicago/Turabian StyleAntti Ritari; Kirsi Spoof-Tuomi; Janne Huotari; Seppo Niemi; Kari Tammi. 2021. "Emission Abatement Technology Selection, Routing and Speed Optimization of Hybrid Ships." Journal of Marine Science and Engineering 9, no. 9: 944.
We present a novel convex optimisation model for ship speed profile optimisation under varying environmental conditions, with a fixed schedule for the journey. To demonstrate the efficacy of the proposed method, a combined speed profile optimisation model was developed that employed an existing dynamic programming approach, along the novel convex optimisation model. The proposed model was tested with 5 different ships for 20 journeys from Houston, Texas to London Gateway, with differing environmental conditions, which were retrieved from actual weather forecasts. As a result, it was shown that the combined model with both dynamic programming and convex optimisation was approximately 22% more effective in developing a fuel saving speed profile compared to dynamic programming alone. Overall, average fuel savings for the studied voyages with speed profile optimisation was approximately 1.1% compared to operation with a fixed speed and 3.5% for voyages where significant variance in environmental conditions was present. Speed profile optimisation was found to be especially beneficial in cases where detrimental environmental conditions could be avoided with minor speed adjustments. Relaxation of the fixed schedule constraint likely leads to larger savings but makes comparison virtually impossible as a lower speed leads to lower propulsion energy needed.
Janne Huotari; Teemu Manderbacka; Antti Ritari; Kari Tammi. Convex Optimisation Model for Ship Speed Profile: Optimisation under Fixed Schedule. Journal of Marine Science and Engineering 2021, 9, 730 .
AMA StyleJanne Huotari, Teemu Manderbacka, Antti Ritari, Kari Tammi. Convex Optimisation Model for Ship Speed Profile: Optimisation under Fixed Schedule. Journal of Marine Science and Engineering. 2021; 9 (7):730.
Chicago/Turabian StyleJanne Huotari; Teemu Manderbacka; Antti Ritari; Kari Tammi. 2021. "Convex Optimisation Model for Ship Speed Profile: Optimisation under Fixed Schedule." Journal of Marine Science and Engineering 9, no. 7: 730.
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.
Janne Huotari; Antti Ritari; Jari Vepsäläinen; Kari Tammi. Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control. Energies 2020, 13, 4748 .
AMA StyleJanne Huotari, Antti Ritari, Jari Vepsäläinen, Kari Tammi. Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control. Energies. 2020; 13 (18):4748.
Chicago/Turabian StyleJanne Huotari; Antti Ritari; Jari Vepsäläinen; Kari Tammi. 2020. "Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control." Energies 13, no. 18: 4748.
Janne Huotari; Antti Ritari; Risto Ojala; Jari Vepsalainen; Kari Tammi. Q-Learning Based Autonomous Control of the Auxiliary Power Network of a Ship. IEEE Access 2019, 7, 152879 -152890.
AMA StyleJanne Huotari, Antti Ritari, Risto Ojala, Jari Vepsalainen, Kari Tammi. Q-Learning Based Autonomous Control of the Auxiliary Power Network of a Ship. IEEE Access. 2019; 7 ():152879-152890.
Chicago/Turabian StyleJanne Huotari; Antti Ritari; Risto Ojala; Jari Vepsalainen; Kari Tammi. 2019. "Q-Learning Based Autonomous Control of the Auxiliary Power Network of a Ship." IEEE Access 7, no. : 152879-152890.