An optimization framework to integrate distributed energy system in the crude oil stabilization process

ABSTRACT

Crude oil stabilization is one of the important processes for crude oil treatment, which can reduce the evaporation loss of crude oil and improve the process safety of crude oil storage and transportation. The difference of operating conditions affects the product benefit and energy consumption of crude oil stabilization. In order to improve the overall economic efficiency, this paper used the data interaction function of MATLAB and HYSYS, and the temperature and pressure of the stabilized tower were optimized by using the particle swarm optimization (PSO) algorithm. Meanwhile, combined with the distributed energy system, the mixed-integer linear programming model was developed, and the design strategy of the energy supply equipment was determined by taking the minimum annual equipment cost and fuel cost as the objective function, so as to reduce energy costs and carbon emissions. The method was validated in an oil field in China. The results showed that the optimal operating conditions after optimization were 0.1MPa and 130°C. The energy supply equipment was selected as an electric heater, a CHP engine, and a waste heat boiler. The energy consumption cost at this time is $3.109\times 10^5$ CNY/a, which is 17.65% less than the actual operating cost of the site. At the same time, carbon emissions fell by 7.8%. This method can guide the operation of the oilfield site to a certain extent.

KEYWORDS:

• Crude oil stabilization
• positive pressure
• distributed energy system
• HYSYS model
• parameter optimization

Highlights

• A mixed-integer linear programming model for distributed energy systems is established.

• Apply distributed energy system to crude oil positive pressure flash stabilization process.

• Using particle swarm algorithm based on MATLAB and HYSYS connection function.

• The validity of the method is verified by the data of an oilfield in China.

• The proposed method reduces energy consumption by 17.65%.

Nomenclature

Sets and indices	=
$i\in I$	=	All facilities: COG, CHP, GB, EH, WHB
$j\in J$	=	Energy types: electricity, oil, gas
Continuous Parameters	=
$e_q$	=	Light hydrocarbon price [CNY/t]
$e_n$	=	Natural gas price [CNY/m3]
$e_o$	=	Crude oil price [CNY/t]
$Q_{ol}$	=	The yield of crude oil when evaporation losses are not considered [t/a]
$\phi$	=	The evaporation loss rate of crude oil [%]
$e_p$	=	Electricity price [CNY/(kWh)]
$c_1$	=	The calorific value of crude oil [J/(kg·℃)]
${\rho }_1$	=	The density of crude oil [kg/m3]
$B_n$	=	The calorific value of natural gas [J/m3]
${\eta }_1$	=	The heating furnace efficiency [%]
${\eta }_2$	=	The cooler efficiency [%]
$E_i$	=	Fixed cost of facility $i$ [CNY]
$F_i$	=	Linear cost of equipment $i$ installation [CNY/kW]
$S_j$	=	Price of energy types $j$ [CNY/(kWh)]
$T$	=	Working hours of the year [hour]
$r$	=	Discount rate [-]
$t_i$	=	The service life of facility $i$ [years]
$n_i$	=	Conversion efficiency of facility $i$ [-]
$D_{elec}$	=	Electricity demand [kW]
$D_{heat}$	=	Heating demand [kW]
$Ca{p}_{max}$	=	Maximum capacity for facilities [kW]
Positive continuous variables	=
$Q_q$	=	Light hydrocarbon production [t/a]
$Q_n$	=	Natural gas production [m3/a]
$Q_o$	=	Stabilised crude oil production [t/a]
$W_1$	=	Lifting pump power [kW]
$W_2$	=	Lifting pump power [kW]
$\mathrm{\Delta }{T}_1$	=	The heating furnace boost temperature [℃]
$\mathrm{\Delta }{T}_2$	=	The cooler lowering temperature [℃]
$Ca{p}_i$	=	Capacity of facility $i$ [kW]
$C{P}_j$	=	Consumption of energy type $j$ [kW]
$M_i$	=	Energy input to the facility $i$ [kW]
$M_{grid}$	=	Grid input power [kW]
Binary variables	=
$B_i$	=	If the facility $i$ is selected $Bi$ = 1. Otherwise, $Bi$ = 0
Acronyms	=
PSO	=	Particle Swarm Optimization
MILP	=	Mixed Integer Linear Programming
DES	=	Distributed Energy System
COG	=	Crude Oil-fired Generator
CHP	=	CHP engine
GB	=	GAS Boiler
EH	=	Electric Heater
WHB	=	Waste Heat Boiler

Acknowledgements

This work was funded by the National Natural Science Foundation of China (52202405)，and Science Foundation of Zhejiang Ocean University (11025092122).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Department of Education of Zhejiang Province [Y202250605]; National Natural Science Foundation of China [52202405]; Zhejiang Ocean University.