A Stochastic Market-Based Clearing Approach In Active Distribution Networks By Using Interval Optimization

The uncertainties inherent to distributed generators (DGs) based on renewable energy sources, such as wind and solar radiation, introduces challenges that must be addressed in order develop efficient electricity markets. In this work, the DGs active power generation and reserve scheduling problem in active distribution networks is solved through a proposed co-optimization market-based clearing approach, in which the uncertainties introduced by the volatility of the load demand and DGs with uncertainties (DGUs) generation are considered. Through Benders decomposition, the formulated problem is decomposed into a master problem and an interval feasibility check sub-problem, in which the interval linear programming theory is used to account for a large amount of scenarios generated. In a stage previous to the market clearing algorithm, the lower and upper limits of the interval parameters associated to the load demand and DGUs generation are defined through an enhanced forecasting system, which uses the Markov models theory and the Monte Carlo method. Tests and comparisons with a method using probability distributions were performed in a distribution test system to show the effectiveness of the proposed approach.