Case Study on Boil-Off Gas (BOG) Minimization for LNG Bunkering Vessel Using Energy Storage System (ESS)

Liquefied natural gas (LNG) is recognized as a preferable alternative fuel for ship owners, since it can substantially reduce harmful emissions to comply with stricter environmental regulations. The increasing number of LNG-fueled vessels has driven up the number of LNG bunkering vessels (LNGBVs) as well. A key issue of LNGBVs is boil-off gas (BOG) generation, especially the huge amount of BOG that is generated during loading and unloading (bunkering) processes. This study proposes a hybrid system that combines conventional onboard LNG-fueled generators with an energy storage system (ESS) to solve the BOG issue of LNGBVs. This hybrid system is targeted at an LNGBV with the cargo capacity of 5000 m3. The amount of BOG generation is calculated based on assumed operation modes, and the economic study and the environmental analysis are performed based on the results. By comparing the conventional system to the proposed ones, some benefits can be verified: about 46.2% BOG reduction, 66.0% fuel saving, a 7.6-year payback period, and 4.8 tons of greenhouse gas (GHG) reduction for one voyage in the best case, with some assumptions. This proposed hybrid system using the ESS could be an attractive green solution to LNGBV owners.

Download Full-text

On the Investigation of Energy Efficient Torque Distribution Strategies through a Comprehensive Powertrain Model

Sustainability ◽

10.3390/su13084549 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4549

Author(s):

Sara Salamone ◽

Basilio Lenzo ◽

Giovanni Lutzemberger ◽

Francesco Bucchi ◽

Luca Sani

Keyword(s):

Energy Storage ◽

Electric Vehicles ◽

Energy Efficient ◽

Storage System ◽

Energy Storage System ◽

Torque Distribution ◽

Driving Cycles ◽

Energy Models ◽

Battery State Of Charge

In electric vehicles with multiple motors, the torque at each wheel can be controlled independently, offering significant opportunities for enhancing vehicle dynamics behaviour and system efficiency. This paper investigates energy efficient torque distribution strategies for improving the operational efficiency of electric vehicles with multiple motors. The proposed strategies are based on the minimisation of power losses, considering the powertrain efficiency characteristics, and are easily implementable in real-time. A longitudinal dynamics vehicle model is developed in Simulink/Simscape environment, including energy models for the electrical machines, the converter, and the energy storage system. The energy efficient torque distribution strategies are compared with simple distribution schemes under different standardised driving cycles. The effect of the different strategies on the powertrain elements, such as the electric machine and the energy storage system, are analysed. Simulation results show that the optimal torque distribution strategies provide a reduction in energy consumption of up to 5.5% for the case-study vehicle compared to simple distribution strategies, also benefiting the battery state of charge.

Download Full-text

Optimal Component Sizing for Peak Shaving in Battery Energy Storage System for Industrial Applications

Energies ◽

10.3390/en11082048 ◽

2018 ◽

Vol 11 (8) ◽

pp. 2048 ◽

Cited By ~ 31

Author(s):

Rodrigo Martins ◽

Holger Hesse ◽

Johanna Jungbauer ◽

Thomas Vorbuchner ◽

Petr Musilek

Keyword(s):

Energy Storage ◽

Storage System ◽

Energy Storage System ◽

Optimization Approach ◽

Battery Energy Storage System ◽

Peak Shaving ◽

Battery Energy Storage ◽

Battery Energy ◽

Power Capability

Recent attention to industrial peak shaving applications sparked an increased interest in battery energy storage. Batteries provide a fast and high power capability, making them an ideal solution for this task. This work proposes a general framework for sizing of battery energy storage system (BESS) in peak shaving applications. A cost-optimal sizing of the battery and power electronics is derived using linear programming based on local demand and billing scheme. A case study conducted with real-world industrial profiles shows the applicability of the approach as well as the return on investment dependence on the load profile. At the same time, the power flow optimization reveals the best storage operation patterns considering a trade-off between energy purchase, peak-power tariff, and battery aging. This underlines the need for a general mathematical optimization approach to efficiently tackle the challenge of peak shaving using an energy storage system. The case study also compares the applicability of yearly and monthly billing schemes, where the highest load of the year/month is the base for the price per kW. The results demonstrate that batteries in peak shaving applications can shorten the payback period when used for large industrial loads. They also show the impacts of peak shaving variation on the return of investment and battery aging of the system.

Download Full-text

A new approach of sizing battery energy storage system for smoothing the power fluctuations of a PV/wind hybrid system

International Journal of Energy Research ◽

10.1002/er.3511 ◽

2016 ◽

Vol 40 (9) ◽

pp. 1221-1234 ◽

Cited By ~ 26

Author(s):

Kanwarjit Singh Sandhu ◽

Aeidapu Mahesh

Keyword(s):

Energy Storage ◽

Hybrid System ◽

Storage System ◽

Energy Storage System ◽

Battery Energy Storage System ◽

Battery Energy Storage ◽

New Approach ◽

Power Fluctuations ◽

Battery Energy

Download Full-text

Non-Cooperative Indirect Energy Trading with Energy Storage Systems for Mitigation of Demand Response Participation Uncertainty

Energies ◽

10.3390/en13040883 ◽

2020 ◽

Vol 13 (4) ◽

pp. 883 ◽

Cited By ~ 1

Author(s):

Jeseok Ryu ◽

Jinho Kim

Keyword(s):

Energy Storage ◽

Cooperative Game ◽

Demand Response ◽

Storage System ◽

Analytical Data ◽

Energy Storage System ◽

Gaussian Mixture ◽

Energy Trading ◽

Considerable Uncertainty

This work focuses on the demand response (DR) participation using the energy storage system (ESS). A probabilistic Gaussian mixture model based on market operating results Monte, Carlo Simulation (MCS), is required to respond to an urgent DR signal. However, there is considerable uncertainty in DR forecasting, which occasionally fails to predict DR events. Because this failure is attributable to the intermittency of the DR signals, a non-cooperative game model that is useful for decision-making on DR participation is proposed. The game is conducted with each player holding a surplus of energy but incomplete information. Consequently, each player can share unused electricity during DR events, engaging in indirect energy trading (IET) under a non-cooperative game framework. The results of the game, the Nash equilibrium (N.E.), are verified using a case study with relevant analytical data from the campus of Gwangju Institute of Science and Technology (GIST) in Korea. The results of the case study show that IET is useful in mitigating the uncertainty of the DR program.

Download Full-text