Transmission Expansion Planning (TEP) involves determining if and how transmission lines should be added to the power grid so that the operational and investment costs are minimized. TEP is a major issue in smart grid development, where demand response resources affect short-and long-term power system decisions, and these in turn, affect TEP. First, this paper discusses the effects of demand response programs on reducing the final costs of a system in TEP. Then, the TEP problem is solved using a Teaching Learning Based Optimization (TLBO) algorithm taking into consideration power generation costs, power loss, and line construction costs. Simulation results show the optimal effect of demand response programs on postponing the additional cost of investments for supplying peak load.

The Transmission Expansion Planning (TEP) problem involves adding new lines to the existing electrical transmission network in order to meet the electrical demand requirements. Demand Response (DR) plays an important role in solving the TEP problem due to the delay in the investment costs. Researchers usually focus on the linear model of DR, while the focus on nonlinear models including power, exponential and logarithmic of DR is small. In this paper and in order to understand which model gives the realistic results, the linear model of DR is studied simultaneously with nonlinear models including power, exponential and logarithmic of DR. Moreover, the effect of incentive and penalty which has been neglected in the studies, is investigated. The study is investigated based on the viewpoint of different participants of the market including Independent System Operator (ISO), Customers and Utilities. In order to prioritize and select the most effective DR program, five characteristics including Peak Reduction, Energy Consumption, Load Factor, Peak to Valley and Customer’s Total Cost are extracted from the load curve. Then, using the weighting coefficients obtained by Entropy technique and implementing the TOPSIS and AHP technique, different DR programs are prioritized.

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