A decomposition and coordination algorithm for SVSM interval of integrated transmission and distribution networks considering the uncertainty of renewable energy. (March 2022) - Wang, Qiong; Lin, Shunjiang; Yang, Yuerong; Liu, Mingbo
Highlights: The interval of the SVSM of ITDNs is calculated. A DCC method for SVSM central value based on polynomial approximation is proposed. A DCC method of the affine interval algorithm for SVSM fluctuation is proposed. Results show the accuracy and efficiency of the proposed method. Abstract: The uncertain power output of renewable energy stations (RESs) will result in the uncertain fluctuation of the static voltage stability margin (SVSM) of a power system with centralized and distributed RESs integrated to the transmission network (TN) and distribution networks (DNs), respectively. A decomposition and coordination calculation (DCC) method is proposed to determine the SVSM interval of integrated transmission and distribution networks (ITDNs) using intervals to describe the uncertain RES outputs. First, based on the polynomial approximation expressions of the injected power of the boundary bus with respect to its voltage and the load growth parameter of each DN, the DCC of continuation power flow between TN and DNs is executed to obtain the central value of the SVSM interval of ITDNs. Next, the affine interval algorithm is used to calculate the SVSM fluctuation interval caused by the uncertain RES output intervals. The block bordered-diagonal form and Gaussian elimination method are used to execute the DCC of the first-order sensitivities of SVSM with respect to RES outputs, and alternating iterations between TN and each DN are used to execute the DCC for optimizing the contractive noise symbol intervals. The proposed DCC method can efficiently calculate the SVSM interval of ITDNs in which lots of DNs are integrated with distributed RESs. Test results on two ITDNs, one composed of modified IEEE systems and the other an actual 2285-bus power grid, demonstrate the correctness and efficiency of the proposed method compared with the centralized algorithm and the Monte Carlo method.