Two-Stage Optimal Dispatch of Electricity-Natural Gas Networks Considering Natural Gas Pipeline Leakage and Linepack
- 19 February 2024
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Smart Grid
- Vol. 15 (4) , 3542-3553
Abstract
The interdependence of electricity and natural gas networks (ENGNs) has led to increased complexity and competing interests among stakeholders. In the event of a leakage failure in a natural gas pipeline, the changes in pipeline parameters pose significant challenges to the optimization and dispatch of natural gas and power grid supply systems. Hence, it would be difficult to simultaneously coordinate the respective interests and reduce the loss of load in the power grid. This paper first presents a natural gas pipeline flow model that accounts for pipeline leakage and linepack, followed by a two-stage dispatch to optimize the benefits for both electricity and natural gas suppliers, where the optimal dispatch with the worst-case scenario is linearized through mixed-integer programming (MIP). Then, a joint solution model for leakage rate and linepack is established based on the macropore leakage model and the dispatch of natural gas suppliers, considering the impact of natural gas flow rate changes on leakage parameters. The actual natural gas supply and the two-stage optimal dispatch are obtained by using the column-and constraint generation (C&CG) method. Simulation results demonstrate that the proposed two-stage optimal dispatch improves the power supply reliability and offers promising economic prospects for ENGNs, where the accuracy of power grid dispatch is significantly improved considering the natural gas pipeline leakage and linepack.Keywords
Funding Information
- National Natural Science Foundation of China (52107097)
- Yunnan Revitalizing Talent Plan (KKRD202204021)
- Yunnan Fundamental Research Projects (202101BE070001-061, 202201AU070111)
- High-Level Platform Construction Project of the Kunming University of Science and Technology (KKZ7202004004)
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