Fuzzy based generation scheduling of power system with large scale wind farms

Wind power introduces a new challenge to system operators. Unlike conventional power generation sources, wind power generators supply intermittent power because of uncertainty in its resource. In a power system involved large-scale wind power generation scenario, wind intermittency could oblige the system operator to allocate a greater reserve power, in order to compensate the possible mismatch between predicted and the actual wind power output. This would increase the total operation cost. This paper presents a new approach in fuzzy based generation scheduling (GS) problem using mixed integer nonlinear programming (MINLP). While the reserve requirements, load-generation balance and wind power availability constraints are satisfied. Constraint modeling is an important issue in power system scheduling. Since the constraints are fuzzy in nature, crisp treatment of them may lead to over conservative solutions. In this paper, a fuzzy optimization-based method is developed to solve power system GS, considering fuzzy objective and constraints. This at first converted to a crisp optimization problem. Then, this problem has been solved using mixed integer nonlinear programming. The proposed approach is applied to a 12-unit test system (including 10 conventional units and two wind farms). The results are compared with the crisp problem solution. The general Algebraic Modeling System (GAMS) has been used to solve the minimization of this GS model using the BARON optimization program.