The transmission network expansion planning is necessary for supplying the future needs, considering load growth. Furthermore, in restructured environments, transmission lines provide the required infrastructure for creating a competitive environment. In recent years, there has been a signifi cant advancement in storage technologies. This advancement leads to using energy storage systems to postpone the construction or replacement of transmission lines. Therefore, in this paper, the problems of transmission expansion planning and energy storage systems deployment are investigated simultaneously. Considering the presence of storage devices and their effect on network operation cost, in this paper, the operation cost is modeled as an independent objective function along with investment cost. Moreover, the problems of transmission and storage expansion planning are modeled as a tri-objective optimization problem with the objectives of reducing costs and increasing the social welfare index in the power market. The multiobjective shuffl ed frog leaping evolutionary algorithm is used to solve these problems. The presented model for expansion planning is implemented and analyzed on IEEE 24-bus test system in the presence and absence of energy storage systems, and the effect of change in the price of energy storage systems is studied. The results of this research show that as the technology advances and the storage costs decrease, energy storage systems can play a pivotal role in reducing expansion planning costs of the power network and improving market-based indices in the restructured environment.

This paper proposes an optimal transmission expansion planning (TEP) which is based on determining share of each line in merchandizing surplus (MS) of system, determining by Independent System Operator (ISO). The more share of a line in MS of system denotes the priority of a line for expansion. The procedure of determining MS of each line in a power system is based on determining the MS share of each energy exchange between certain generator and certain demand bus in the power system. By analyzing all energy exchange the optimal planning of transmission line is obtained by ISO. The variable revenue of a Transco is related to performing the optimal planning of transmission lines which is obtained through ISO. The proposed method determines the TEP economical resources and the procedure of receiving these resources (MS of system) from generators and customers. By performing proposed method Transco capacity withholding and misusing is prevented spontaneously caused by relating the revenue of Transco to its optimal performance.

In this paper a new framework is presented for multi–objective Transmission Expansion Planning (TEP). This framework is based on a multiple criteria decision making whose fundamental elements are REliability and MARKet (REMARK). Investment cost, congestion cost, Users' Benefit (UB) and Expected Customer Interruption Cost (ECOST) are considered in the optimization as four objectives. The proposed model is a complicated non-linear mixed-integer optimization problem. A hybrid Genetic Algorithm (GA) and Quadratic Programming (QP) is used, followed by a Fuzzy Sets Theory (FST) to obtain the final optimal solution. The planning methodology has been demonstrated on the 6-machine 8-bus test system to show the feasibility and capabilities of the proposed algorithm. Also, in order to compare the historical expansion plan and the expansion plan developed by the proposed methodology, it was applied to the real life system of the northeastern part of Iranian national 400-kV transmission grid.

An integrated technique for solving transmission network expansion planning considering the allocation of reactive power sources using an AC model is presented in this paper.Embedding reactive power management in TNEP is a challenging task since AC load flow may not converge in absence of proper reactive power allocation. Usually transmission expansion planning and reactive power planning are considered as two distinct problems, but in this paper both problems are investigated concurrently. These two problems can be integrated because there is an inherent interaction between transmission line capacity and reactive power sources. In power systems, transferring reactive power may engage line capacity which requires more new transmission lines, while allocating reactive power sources close to the load centers may prevent the addition of new transmission lines. On the other hand, if reactive power sources are not included, real power losses will increase considerably and thereby reduce transmission line capacities. The main objective of this study is to show that both active and reactive power planning can be handled simultaneously. This non-convex combinatorial optimization problem is solved here using a real genetic algorithm (RGA). Significant results through case studies of two test systems show the advantages of the proposed integrated network planning.