چكيده به لاتين
Prior to the power grid restructuring, people had only one option to supply the electricity they needed. They could procure electricity through an entity that monopolizes the region's electricity supply. But with the restructuring and deregulation on the grid, free access to energy markets was provided. In the wake of this, new actors, called electricity retailers, have emerged who supply demand of consumers under their contract by purchasing bulk energy through energy markets and bilateral contracts with power plants. One of the retailers to meet customers' electricity demand is the pool market. Since the market price of the pool is volatile, the retailer is subject to uncertainty associated with the pool market prices. Another factor that makes the retailer vulnerable to uncertainty is customer consumption. Also, if the retailer uses renewable energy sources to meet customer demand, because of their nature, they do not have a specific power generation capability, which also serves as an uncertainty parameter that the retailer must be considering to make the best planning, considering the circumstances. This thesis attempts to optimize the retailer's profit by taking into account existing uncertainties. Resources available to retailers to supply customers include pool markets, bilateral contracts, renewable energy sources including wind turbines and photovoltaic systems, and distributed generation resources. It should be noted that energy storage systems (ESS) are also available for energy management. In order to model the uncertainties, the probabilistic scenario based method as well as the robust optimization method were used and the results were analyzed. Sale price in smart grid are also determined by the retailer in three different modes: fixed pricing, time of use pricing and real time pricing. The models used in this thesis are linear and mixed integer nonlinear programming that can be solved by the Bonminh and Bonminh solvers under GAMS optimization software.
