Search

With an increasing demand for electric power, new transmission lines should be constructed with a rational plan in the long run to guarantee a reliable and economic operation. The transmission expansion planning (TEP) is a mixed integer non-linear programming (MINLP) optimization problem in nature, which requires tremendous computational efforts especially when it comes to a large-scale power system. Although a diversity of simplifications and computational techniques has been applied to TEP, it is still challenging to derive an optimal plan within little simulation time. Since equivalencing technique is able to reduce the size of a large-scale power system and help achieve remarkable computational performance, it is possibly effective and efficient to handle the intrinsic complexity of TEP problem. In this thesis, based on a detailed literature review, a mixed integer linear programming (MILP) approach in DC model is firstly formulated for a dynamic TEP problem considering N – 1 security criterion. Then, two traditional power flow based equivalencing techniques that are appropriate for simplifying TEP, REI and WARD, with necessary modifications, are respectively implemented in the initial TEP problem. The proposed algorithms are simulated on IEEE 24-bus reliability test system (RTS) to compare optimal plans between the original and equivalent system. Further assumptions and adjustments are searched and tested to get more accurate optimal plans. The results show that both modified equivalencing techniques can significantly decrease the simulation time. Regarding IEEE 24-bus RTS, the proposed algorithm for modified REI method can achieve relatively precise optimal plan with few errors while modified WARD method is not as good as modified REI method. Therefore, the modified REI method has a potential to be implemented in TEP problem to reduce the complexity and computational effort for large power system without jeopardizing accuracy. Further studies are needed to tune the modified REI method and revise the obtained optimal plan.

With an increasing demand for electric power, new transmission lines should be constructed with a rational plan in the long run to guarantee a reliable and economic operation. The transmission expansion planning (TEP) is a mixed integer non-linear programming (MINLP) optimization problem in nature, which requires tremendous computational efforts especially when it comes to a large-scale power system. Although a diversity of simplifications and computational techniques has been applied to TEP, it is still challenging to derive an optimal plan within little simulation time. Since equivalencing technique is able to reduce the size of a large-scale power system and help achieve remarkable computational performance, it is possibly effective and efficient to handle the intrinsic complexity of TEP problem. In this thesis, based on a detailed literature review, a mixed integer linear programming (MILP) approach in DC model is firstly formulated for a dynamic TEP problem considering N – 1 security criterion. Then, two traditional power flow based equivalencing techniques that are appropriate for simplifying TEP, REI and WARD, with necessary modifications, are respectively implemented in the initial TEP problem. The proposed algorithms are simulated on IEEE 24-bus reliability test system (RTS) to compare optimal plans between the original and equivalent system. Further assumptions and adjustments are searched and tested to get more accurate optimal plans. The results show that both modified equivalencing techniques can significantly decrease the simulation time. Regarding IEEE 24-bus RTS, the proposed algorithm for modified REI method can achieve relatively precise optimal plan with few errors while modified WARD method is not as good as modified REI method. Therefore, the modified REI method has a potential to be implemented in TEP problem to reduce the complexity and computational effort for large power system without jeopardizing accuracy. Further studies are needed to tune the modified REI method and revise the obtained optimal plan.