Due to electrical energy restructuring and emerging of the new competitive markets, the traditional system has been transformed and its components have changed to different independent and is some cases conflicting economic sectors. Generating Companies (GENCOs) generate energy in wholesale competition markets Retailers buy their required energy from the markets and sell it to their customers. In operating electricity market, there are some important issues such as, price instability and congestion. So market holders understand that to handle the market, they must include consumers in to it. So they are looking for a way to facilitate the role of customers in the markets. In this research demand side source are simulated and actively are contributed in market. Using Demand Response, is one of the important tools for eliminating problems such as prices instability, congestion in network, available transmission capacity (ATC) increase, reducing losses, units generation costs, power system reliability, and market power.Spinning reserve (SR) is one of the most important resources used by system operators to respond to unforeseen events such as generation outages and sudden load changes. Ideally, the level of security provided through the purchase of ancillary services should be determined through a cost/benefit analysis. A method that can be used to increase the ATC is load management program. The load management program in vertically integrated power systems has been used for many years. Under deregulation, the scope of load management programs has considerably broadened. Demand Response (DR), is the most common part of the load management programs, which has been used recently. In this thesis, the role of demand response programs is determined in the economical and technical operation of restructured power system. For this purpose, we suggest a three stage algorithm for a demand response program. In the first stage, we use an economic load model to represent the demand response. in this model the load is modeled with elasticity coefficients. In the second stage, optimized spinning reserve rate is determined based on the cost and consumer’s loss curve. The optimal amount of SR is the amount that minimizes the sum of the Expected Energy Not Served (EENS) cost and the operating costs. In the last stage, the main problem with respect to three previous functions which are reducing energy cost, minimizing network loss, and increasing ATC.Is determined So the functions are calculated as a Multi-objective function, using Genetic Algorithm (GA) which is a Toolbox is Matlab and also as a single-objective function using Particle Swarm Optimization (PSO) Algorithm. Numerical studies are, Keywords: demand response, congestion management, Expected energy not served , Available transmission capacity , load Modeling.