Robust optimal operation scheduling method for integrated energy system considering flexible energy storage and mixed hydrogen natural gas

ABSTRACT

At present, the number of electric vehicles is increasing day by day. The full integration of electric vehicles and multi-energy coupled integrated energy systems is the future development trend. This paper introduces a novel form of flexible energy storage in the integrated energy system ;(IES): electric vehicle (EV). And It proposes that mixed natural gas hydrogen be used as the primary energy source for IES, which replaces traditional natural gas. Combined with the above, the IES is scheduled to meet the system’s multi-load for heating, cooling, and electricity demands. Based on the integrated energy system’s structure and energy conversion principles, a robust two-stage optimization model for the IES is proposed to minimize the daily operation scheduling costs. The proposed model considers the uncertainty of distributed renewable generation and flexible energy storage. Uncertain adjustment parameters are introduced into the model, which can flexibly adjust the conservatism of the scheme. Using the column and constraint generation algorithm (C&CG) and the strong duality theory, the model presented in this paper is solved and the optimal scheduling scheme is obtained. The final simulation results validate the effectiveness of the model and algorithm. The addition of flexible energy storage and mixed natural gas hydrogen effectively improves the economy and flexibility of the system while reducing carbon emissions costs. Consideration of uncertainty improves system robustness and safety. In addition, sensitivity analyses of the influence of the proportion of hydrogen mixed with natural gas, the setting of uncertain adjustment parameters, electricity prices, weather, and load fluctuations on system operation. The results show that compared to existing research, electric vehicles as a flexible energy storage device and mixed natural gas hydrogen supply, increase the economic benefits of IES by 19.8% and 23%, respectively.

KEYWORDS:

• Integrated energy system
• flexible energy storage
• mixed hydrogen natural gas
• robust optimization
• column and constraint generation algorithm
• uncertain adjustment parameters

Nomenclature

$P_{\mathit{PVN}}$	=	The rated output power of a PV panel
$f_{\mathit{PV}}$	=	The derating factor
$\mathit{G}\left(\mathit{t}\right)$	=	The solar radiation incident on the PV panel at time t
$G_{\mathit{ref}}$	=	The incident standard radiation
Α	=	Temperature coefficient
$\mathit{T}\left(\mathit{t}\right)$	=	Operating temperature of PV
$T_{\mathit{ref}}$	=	Operating reference temperature of PV panel
$P_{\mathit{GT}}\left(\mathit{t}\right)$	=	The output electric power of GT
$Q_{\mathit{GTD}}\left(\mathit{t}\right)$	=	Waste heat output power of GT
$F_{\mathit{GT}}\left(\mathit{t}\right)$	=	GT consumption of natural gas
$F_{\mathit{GT}\mathrm{\_h}}\left(\mathit{t}\right)$	=	Hydrogen consumption by GT
${\eta }_{\mathit{GT}}$	=	Power generation efficiency of GT
${\eta }_{\mathit{GTD}}$	=	Waste heat collection efficiency of GT
$\mathit{LH}{V}_{\mathit{GT}}$	=	Low calorific value per unit of natural gas consumed by GT
$\mathit{LH}{V}_{H_2}$	=	Low calorific value per unit of hydrogen consumed by GT
$P_{\mathit{GT}}^{\mathit{min}}$/$P_{\mathit{GT}}^{\mathit{max}}$	=	Minimum and maximum output power of GT
$Q_{\mathit{GB}}\left(\mathit{t}\right)$	=	The output thermal power of GB
$F_{\mathit{GB}}\left(\mathit{t}\right)$	=	Natural gas consumption of GB
$F_{\mathit{GB}\mathrm{\_h}}\left(\mathit{t}\right)$	=	Hydrogen consumption of GB
${\eta }_{\mathit{GB}}$	=	The combustion efficiency of GB
$\mathit{LH}{V}_{\mathit{GB}}$	=	Low calorific value per unit of natural gas consumed by GB
$\mathit{LH}{V}_{H_2}$	=	Low calorific value per unit of natural gas consumed by GB
$Q_{\mathit{GB}}^{\mathit{min}}$/$Q_{\mathit{GB}}^{\mathit{max}}$	=	The minimum and maximum output thermal power of GB
$Q_{\mathit{HR}}^{\mathit{out}}\left(\mathit{t}\right)$/$Q_{\mathit{HR}}^{\mathit{in}}\left(\mathit{t}\right)$	=	Output and input heat power of HRSG
${\eta }_{\mathit{HR}}$	=	Heat collection efficiency of HRSG
$Q_{\mathit{HR}}^{\mathit{min}}$/$Q_{\mathit{HR}}^{\mathit{max}}$	=	Minimum and maximum output heat power of HRSG
$Q_{\mathit{AC}}^{\mathit{cool}}\left(\mathit{t}\right)$	=	The outputs cold power of absorption chillers
$\mathit{CO}{P}_{\mathit{AC}}$	=	Energy efficiency coefficient of absorption chillers
$Q_{\mathit{AC}}^{\mathit{min}}$/$Q_{\mathit{AC}}^{\mathit{max}}$	=	The minimum and maximum cold power of absorption chillers
$E_S\left(0\right)$	=	Initial capacity of the STB
$E_S^{\mathit{min}}$/$E_S^{\mathit{max}}$	=	The minimum and maximum capacity of STB
$P_S^{\mathit{max}}$	=	Maximum charge and discharge power of the STB
$U_S\left(\mathit{t}\right)$	=	The charge and discharge state of the STB
$Q_H^{\mathit{ch}}\left(\mathit{t}\right)$/$Q_H^{\mathit{dis}}\left(\mathit{t}\right)$	=	Input/output thermal power of HST
$Q_H\left(0\right)$	=	Initial heat of the HST
$Q_H^{\mathit{min}}$/$Q_H^{\mathit{max}}$	=	Minimum/maximum capacity of HST
$Q_H^{\mathit{max}}$	=	The maximum power of the HST
$U_H\left(\mathit{t}\right)$	=	The charging and discharging heat state of the HST
$P_{\mathit{EW}}^{\mathit{buy}}\left(\mathit{t}\right)$/$P_{\mathit{EW}}^{\mathit{sell}}\left(\mathit{t}\right)$	=	Electricity purchased/sold by IES at time t
$U_{\mathit{EW}}\left(\mathit{t}\right)$	=	Purchase and sale status of IES
$P_{\mathit{EW}}^{\mathit{MAX}}$	=	The maximum exchange power between IES and distribution network
$P_{\mathit{ew}}\left(\mathit{t}\right)$	=	Output electric power of the grid
${\mathit{\lambda }}_{\mathrm{gas}}$	=	Emission factors of natural gas
${\mathit{\lambda }}_{\mathrm{elec}}$	=	Emission factor of electricity
$P_{\mathit{elec}}^{\mathit{load}}\left(\mathit{t}\right)$	=	The electric load power of the IES
$P_{\mathit{EV}}^{\mathit{dis}}\left(\mathit{t}\right)$/$P_{\mathit{EV}}^{\mathit{ch}}\left(\mathit{t}\right)$	=	Discharge/charging power of electric vehicles
$Q_{\mathit{hot}}^{\mathit{load}}\left(\mathit{t}\right)$	=	The heat load power of IES
$Q_{\mathit{AC}}^{\mathit{hot}}\left(\mathit{t}\right)$	=	The input heat power of the absorption chillers
$Q_{\mathit{cool}}^{\mathit{load}}\left(\mathit{t}\right)$	=	The cooling load power of the IES

Disclosure statement

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

Additional information

Notes on contributors

Yinheng Huang

Yinheng Huang is a research scholar in the Department of Control Science and Engineering, School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, China. His research interests include the operation and dispatch of IES.

Lei Pan

Lei Pan received his Ph.D. degree from the Hebei University of Technology, Tianjin, China, in 2014. He is currently a professor at the Tianjin Chengjian University, Tianjin, China. His research interests are power converters, motor drives, wind power, and renewable energy.

Jianwei Chen

Jianwei Chen received her master’s degree from the China Agricultural University, Beijing, China, in 2007. She is currently a lecturer at the Tianjin Chengjian University, Tianjin, China. Her current area of research is Renewable Energy Systems for off-grid and Hydrogen Energy.

Yi Pang

Yi Pang received his Ph.D. degree from Nankai University, Tianjin, China, in 2015. He is currently a lecturer at the Tianjin Chengjian University, Tianjin, China. His research interests include the operation and dispatch of IES.

Fan Shi

Fan Shi is a research scholar in the Department of Control Science and Engineering, School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, China. His current research area is the planning and dispatch of IES.

Liang Chen

Liang Chen is a research scholar in the Department of Control Science and Engineering, School of Control and Mechanical Engineering, Tianjin Chengjian University, Tianjin, China. His current area of research is the electrolysis of renewable energy for hydrogen production.