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Cooperative Scheduling for Integrated Electricity and Natural Gas Systems Considering Gas Flow Transient Characteristics and Source–Load Uncertainties
Author(s) -
Zhang Yachao,
Huang Zhanghao,
Shu Shengwen,
Zheng Feng,
Lin Jiahao
Publication year - 2020
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.201901098
Subject(s) - natural gas , electric power system , linearization , power to gas , mathematical optimization , scheduling (production processes) , electricity , wind power , transient (computer programming) , demand response , computer science , control theory (sociology) , engineering , power (physics) , mathematics , nonlinear system , electrical engineering , electrolysis , chemistry , artificial intelligence , electrolyte , waste management , operating system , control (management) , quantum mechanics , physics , electrode
With the continuous expansion of the installation capacity of wind turbines and gas‐fired units, the interdependency between power systems and natural gas systems has increasingly intensified. To deal with wind power uncertainty, an interval optimization‐based cooperative scheduling model for integrated electricity and natural gas systems is proposed, in which natural gas transient flow characteristics are modeled by partial differential equations and transformed by the Wendroff difference scheme and linearization technique. Moreover, demand response uncertainty for power and residential gas loads is considered. Then the uncertainties of wind power generation and power and gas loads, can be represented by interval numbers, and the corresponding objective function and operation constraints can be transformed to the deterministic forms for solving. Two case studies are performed on a 6‐bus power system coupled with 6‐node gas network and the modified IEEE 118‐bus system with 10‐node gas network to demonstrate the effectiveness of the proposed model. Furthermore, the robust optimization model is implemented for comparison. Simulation results demonstrate that considering natural gas transient flow and demand response uncertainty can significantly improve the economic performance of the solution. In addition, the proposed model can reduce the conservativeness of decision‐making by the robust optimization model.