Bilevel Optimal Economic Dispatch of CNG Main Station Considering Demand Response

Bilevel Optimal Economic Dispatch of CNG Main Station Considering Demand Response

Compressed natural gas (CNG) main stations are critical components of the urban energy infrastructure for CNG distribution. Due to its high electrification and significant power consumption, researching the economic operation of the CNG main station in demand response (DR)-based electricity pricing...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) Vol. 16; no. 7; p. 3080
Main Authors: Liang, Yongliang, Li, Zhiqi, Li, Yuchuan, Leng, Shuwen, Cao, Hongmei, Li, Kejun
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-04-2023
Subjects:
bilevel programming
CNG main station
Compressed gas
Compressed natural gas
Constraint modelling
Costs
Critical components
Dehydration
demand response
Demand side management
Economic aspects
economic dispatch
Electric power demand
Electricity
Electricity pricing
Energy consumption
Energy management
genetic algorithm
Genetic algorithms
integrated energy user (IEU)
Load
Natural gas
Natural gas vehicles
Optimization
Power consumption
Scheduling
Service life
China
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Compressed natural gas (CNG) main stations are critical components of the urban energy infrastructure for CNG distribution. Due to its high electrification and significant power consumption, researching the economic operation of the CNG main station in demand response (DR)-based electricity pricing environments is crucial. In this paper, the dehydration process is considered in the CNG main station energy consumption model to enhance its participation in DR. A bilevel economic dispatch model for the CNG main station is proposed, considering critical peak pricing. The upper-level and lower-level models represent the energy cost minimization problems of the pre-system and rear-system, respectively, with safety operation constraints. The bilevel programming model is solved using a genetic algorithm combined with a bilevel programming method, which has better efficiency and convergence. The proposed optimization scheme has better control performance and stability, reduces the daily electricity cost by approximately 21.04%, and decreases the compressor switching frequency by 50.00% without changing the CNG filling demand, thus significantly extending the compressor’s service life. Moreover, the average comprehensive power cost of processing one unit of CNG reduces 20.62%.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16073080