scholarly journals A Hierarchical Optimisation of a Compressed Natural Gas Station for Energy and Fuelling Efficiency under a Demand Response Program

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2165 ◽  
Author(s):  
Charles Kagiri ◽  
Lijun Zhang ◽  
Xiaohua Xia

Compressed natural gas stations serve customers who have chosen compressed natural gas powered vehicles as an alternative to diesel and petrol based ones, for cost or environmental reasons. The interaction between the compressed natural gas station and electricity grid requires an energy management strategy to minimise a significant component of the operating costs of the station where demand response programs exist. Such a strategy when enhanced through integration with a control strategy for optimising gas delivery can raise the appeal of the compressed natural gas, which is associated with reduced criteria air pollutants. A hierarchical operation optimisation approach adopted in this study seeks to achieve energy cost reduction for a compressed natural gas station in a time-of-use electricity tariff environment as well as increase the vehicle fuelling efficiency. This is achieved by optimally controlling the gas dispenser and priority panel valve function under an optimised schedule of compressor operation. The results show that electricity cost savings of up to 60.08% are achieved in the upper layer optimisation while meeting vehicle gas demand over the control horizon. Further, a reduction in filling times by an average of 16.92 s is achieved through a lower layer model predictive control of the pressure-ratio-dependent fuelling process.

2017 ◽  
Vol 142 ◽  
pp. 2003-2008 ◽  
Author(s):  
Charles Kagiri ◽  
Lijun Zhang ◽  
Xiaohua Xia

2014 ◽  
Vol 660 ◽  
pp. 442-446 ◽  
Author(s):  
Devarajan Ramasamy ◽  
Z.A Zainal ◽  
R.A. Bakar ◽  
K. Kadirgama

Vehicle efficiency relates to pollutants and cost savings in third world countries. In term of subcompact cars, the vehicle characteristics are governed by the engine for alternative fuels. The main focus of this paper was to evaluate a sub compact car engine for its performance and burn rate of gasoline and Compressed Natural Gas (CNG). A bi-fuel sequential system was used to do this evaluation. Measurements of engine speed, torque and fuel were done on an eddy current dynamometer, while measurements or in-cylinder pressure, crank angle and spark were analyzed from results taken by data acquisition system. The emissions readings were also compared from an emission analyzer. The results were analyzed for burn rate based on the first law of thermodynamic. The comparison shows a drop of 18.6% was seen for the power, brake specific fuel consumption (BSFC) loss was 7% and efficiency loss was at 17.3% in average for all engine speed. Pressure analysis shows peak pressure dropped by 16%. Burn rate shows why CNG had a slower burning speed on the small engine. The engine speed of 4000 rpm at Maximum Brake Torque (MBT) produced the most nearest results to gasoline.


2020 ◽  
Vol 13 (3) ◽  
pp. 1491-1525
Author(s):  
Benedict J. Drasch ◽  
Gilbert Fridgen ◽  
Lukas Häfner

AbstractBuilding operation faces great challenges in electricity cost control as prices on electricity markets become increasingly volatile. Simultaneously, building operators could nowadays be empowered with information and communication technology that dynamically integrates relevant information sources, predicts future electricity prices and demand, and uses smart control to enable electricity cost savings. In particular, data-driven decision support systems would allow the utilization of temporal flexibilities in electricity consumption by shifting load to times of lower electricity prices. To contribute to this development, we propose a simple, general, and forward-looking demand response (DR) approach that can be part of future data-driven decision support systems in the domain of building electricity management. For the special use case of building air conditioning systems, our DR approach decides in periodic increments whether to exercise air conditioning in regard to future electricity prices and demand. The decision is made based on an ex-ante estimation by comparing the total expected electricity costs for all possible activation periods. For the prediction of future electricity prices, we draw on existing work and refine a prediction method for our purpose. To determine future electricity demand, we analyze historical data and derive data-driven dependencies. We embed the DR approach into a four-step framework and demonstrate its validity, utility and quality within an evaluation using real-world data from two public buildings in the US. Thereby, we address a real-world business case and find significant cost savings potential when using our DR approach.


2019 ◽  
Vol 158 ◽  
pp. 2853-2858 ◽  
Author(s):  
Charles Kagiri ◽  
Lijun Zhang ◽  
Xiaohua Xia

2013 ◽  
Author(s):  
Michael J. Economides ◽  
Xiuli Wang ◽  
Francesco Colafemmina ◽  
Vanni Neri Tomaselli

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