EconPapers    
Economics at your fingertips  
 

Day-Ahead and Intra-Day Collaborative Optimized Operation among Multiple Energy Stations

Jingjing Zhai, Xiaobei Wu, Zihao Li, Shaojie Zhu, Bo Yang and Haoming Liu
Additional contact information
Jingjing Zhai: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Xiaobei Wu: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Zihao Li: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Shaojie Zhu: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Bo Yang: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Haoming Liu: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China

Energies, 2021, vol. 14, issue 4, 1-33

Abstract: An integrated energy system (IES) shows great potential in reducing the terminal energy supply cost and improving energy efficiency, but the operation scheduling of an IES, especially integrated with inter-connected multiple energy stations, is rather complex since it is affected by various factors. Toward a comprehensive operation scheduling of multiple energy stations, in this paper, a day-ahead and intra-day collaborative operation model is proposed. The targeted IES consists of electricity, gas, and thermal systems. First, the energy flow and equipment composition of the IES are analyzed, and a detailed operation model of combined equipment and networks is established. Then, with the objective of minimizing the total expected operation cost, a robust optimization of day-ahead and intra-day scheduling for energy stations is constructed subject to equipment operation constraints, network constraints, and so on. The day-ahead operation provides start-up and shut-down scheduling of units, and in the operating day, the intra-day rolling operation optimizes the power output of equipment and demand response with newly evolved forecasting information. The photovoltaic (PV) uncertainty and electric load demand response are also incorporated into the optimization model. Eventually, with the piecewise linearization method, the formulated optimization model is converted to a mixed-integer linear programming model, which can be solved using off-the-shelf solvers. A case study on an IES with five energy stations verifies the effectiveness of the proposed day-ahead and intra-day collaborative robust operation strategy.

Keywords: integrated energy system; day-ahead and intra-day collaborative scheduling; PV power generation; multi-energy network; demand response (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/4/936/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/4/936/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:4:p:936-:d:497186

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-19
Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:936-:d:497186