Energies | |
Bi-Level Optimization for Available Transfer Capability Evaluation in Deregulated Electricity Market | |
Beibei Wang4 Xin Fang2 Xiayang Zhao3 Houhe Chen1 | |
[1] Department of Electrical Engineering, Northeast Dianli University, Jilin 132012, Jilin, ChinaDepartment of Electrical Engineering and Computer Science, the University of Tennessee, Knoxville, TN 37996, USA;State Grid International Development Corp., Xuanwumen Nei Street 108, Xicheng District, Beijing 100120, China;School of Electrical Engineering, Southeast University, Nanjing, Jiangsu 210018, China; | |
关键词: available transfer capability (ATC); deregulated electricity market; bi-level optimization; economic dispatch (ED); mathematic program with equilibrium constraints (MPEC); mixed-integer linear programming (MILP); | |
DOI : 10.3390/en81212370 | |
来源: mdpi | |
【 摘 要 】
Available transfer capability (ATC) is the transfer capability remaining in the physical transmission network for further commercial activity over and above already committed uses which needs to be posted in the electricity market to facilitate competition. ATC evaluation is a complicated task including the determination of total transfer capability (TTC) and existing transfer capability (ETC). In the deregulated electricity market, ETC is decided by the independent system operator’s (ISO’s) economic dispatch (ED). TTC can then be obtained by a continuation power flow (CPF) method or by an optimal power flow (OPF) method, based on the given ED solutions as well as the ETC. In this paper, a bi-level optimization framework for the ATC evaluation is proposed in which ATC results can be obtained simultaneously with the ED and ETC results in the deregulated electricity market. In this bi-level optimization model, ATC evaluation is formulated as the upper level problem and the ISO’s ED is the lower level problem. The bi-level model is first converted to a mathematic program with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush-Kuhn-Tucher (KKT) optimality condition. Then, the MPEC is transformed into a mixed-integer linear programming (MILP) problem, which can be solved with the help of available optimization software. In addition, case studies on PJM 5-bus, IEEE 30-bus, and IEEE 118-bus systems are presented to demonstrate the proposed methodology.
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
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