A Chance-constrained Optimal Power Flow Model Based on Second-order Cone

Abstract

The uncertainty of new energy output and load fluctuation has a great impact on the safe operation of the power system. At present, there are few AC optimal power flow studies considering uncertainty. Most of the studies mainly focus on DC power flow. In this paper, a second-order cone optimal power flow model (SOC-ACOPF) is established. Then this paper advances a chance-constrained optimal power flow model (SOC-CC-ACOPF) and gives its solution method. By simplifying the power flow equation and introducing relaxation variables, the optimal power flow problem is transformed into a second-order cone optimization problem, which has better stability and avoids local optimum. In addition, to restrict the error of the second-order cone relaxation, this paper adds an upper limit of relaxation. The uncertainty of node power takes into account the prediction error of new energy and load. The relationship of the uncertainty is derived by the combination of the error, power flow equations, and Taylor's expansion. Then the error distribution of the uncertainty is obtained. Based on the chance-constrained construction method of error distribution, the chance-constrained optimal power flow model is established as a second-order cone optimization problem that can be directly solved. Finally, this paper uses the IEEE 118- bus network to verify the effectiveness of the method.