Enhanced Transmission Expansion Planning With a High Confidence Strategy Considering Fluctuations in Solar PV Generation and Electricity Demand

Transmission Expansion Planning (TEP) is a popular approach to support the electricity demand increase and accommodate the solar photovoltaic systems increase in the transmission system. In state-of-the-art, maximum and minimum demand situations are often used in the TEP problem which is defined as a conventional TEP. However, modern TEP typically considers several uncertain scenarios, especially through Monte Carlo Simulation (MCS). Moreover, the metaheuristic algorithm is mostly applied to solve the TEP problem. These lead to an exponential increase in computational time as additional situations are included. To address this issue, this work proposes an enhanced TEP-based 24-hour situation optimized by an improved Binary Differential Evolution (BDE) algorithm assisted by the logistic map for adjusting the mutation factor and crossover rate. Zhao's Point Estimation Method (PEM) combined with the Nataf Transformation (NT) is used instead of MCS to generate effective uncertain scenarios. Moreover, power system operation constraints are calculated through a probabilistic power flow with a 95% confidence level. The modified IEEE 24-bus system is used to test the proposed method. The simulation results show that Zhao's PEM combined with NT can provide the mean and standard deviation of the desired parameters close to those from the MCS, while reducing computational time by 99.91%. Additionally, the improved BDE outperforms the conventional BDE by providing faster convergence in both conventional and proposed TEP problems. Furthermore, the proposed TEP solved by the improved BDE can achieve an optimal solution without violations for all scenarios, whereas the conventional TEP fails to support all scenarios.