A chance-constrained optimization framework for transmission congestion management and frequency regulation in the presence of wind farms and energy storage systems

Abstract

• Providing a chance-constrained framework for congestion management and frequency regulation. • Evaluating the performance of the model in both normal and emergency conditions. • Investigating the effect of spinning reserve rate on congestion, LMP and frequency. • Investigating the effect of EES systems on congestion, LMP and frequency. This paper presents a chance-constrained optimization framework for transmission congestion management in the presence of wind turbines and electrical energy storage (EES) systems. In the proposed model, the uncertainties of the wind farms’ output power and electricity demand are considered, and in order to model these uncertainties, the chance-constrained method is used. In order to reduce the computational burden in this problem, a filtering algorithm is proposed that detects sensitive lines and applies security constraints only on them. The results illustrate that applying the proposed algorithm has reduced the solution time by about 83%. In addition, the proposed model is implemented on a modified IEEE 30-bus test system and solved under normal and emergency conditions (N-1). The simulation results demonstrate that considering the chance constraints, by increasing the spinning reserve, leads to a decrease in the amount of energy not supplied (ENS) and consequently operating costs during the contingent events. The numerical results illustrate that considering the spinning reserves, despite a 10% increase in operating costs under normal operating conditions, has reduced the amount of ENS by about 18% during the outage of the thermal unit. In addition, the results demonstrate that EES systems keep the system frequency within the allowable range under emergency conditions due to their fast ramp rate.