A Linear Programming Approach to Expansion Co-Planning in Gas and Electricity Markets

作     者：Qiu, Jing Yang, Hongming Dong, Zhao Yang Zhao, Jun Hua Meng, Ke Luo, Feng Ji Wong, Kit Po 

作者机构：Changsha Univ Sci & Technol Hunan Prov Engn Res Ctr Elect Transportat & Smart Hunan Prov Key Lab Smart Grids Operat & Control Sch Elect Engn & Informat Changsha 410114 Hunan Peoples R China Univ Newcastle CIEN Callaghan NSW 2308 Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Chinese Univ Hong Kong Shenzhen Shenzhen Guangdong Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Nedlands WA 6009 Australia 

出 版 物：《IEEE TRANSACTIONS ON POWER SYSTEMS》 (IEEE Trans Power Syst)

年 卷 期：2016年第31卷第5期

页      面：3594-3606页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：CSIRO China Southern Power Grid Research Grant [WYKJ00000027] National Natural Science Foundation of China [71331001, 71420107027, 71071025] 

主　　题：Co-optimization expansion co-planning natural gas systems linear programming 

摘      要：In a carbon-constrained world, the continuing and rapid growth of gas-fired power generation (GPG) will lead to the increasing demand for natural gas. The reliable and affordable gas supply hence becomes an important factor to consider in power system planning. Meanwhile, the installation of GPG units should take into account not only the fuel supply constraints but also the capability of sending out the generated power. In this paper, a novel expansion co-planning (ECP) model is proposed, aiming to minimize the overall capital and operational costs for the coupled gas and power systems. Moreover, linear formulations are introduced to deal with the nonlinear nature of the objective functions and constraints. Furthermore, the physical and economic interactions between the two systems are simulated by an iterative process. The proposed linear co-planning approach is tested on a simple six-bus power system with a seven-node gas system and a modified IEEE 118-bus system with a 14-node gas system. Numerical results have demonstrated that our co-planning approach can allow systematic investigations on supporting cost-effective operating and planning decisions for power systems.