TLBO merged with studying effect for Economic Environmental Energy Management in High Voltage AC Networks Hybridized with Multi-Terminal DC Lines

Nowadays, different types of non-linear and multi-modal hybridized alternating current (AC) power grids with multi-terminal high voltage direct current (HVDC) are of great importance. Energy management aims to reduce costs, network power losses, and environmental pollutants in AC-HVDC electricity grids. This paper describes an improved variant of TLBO (teaching–learning-based optimization) entitled teaching–learning studying-based optimizer (TLSBO) that improves TLBO's global optimization performance. Nevertheless, an advanced model of preventive strategy action is presented that offers an appropriate margin in power output and power transfer through transmission lines to plan for possible contingencies. Following that, a contingency study is conducted to guarantee the system's operation. Also, a corrective strategy action via Re-performing the energy management in the high voltage AC–DC network considering the most critical lines that prevents the system from converging. The suggested upgrade is formed by the inclusion of a change initiative to TLBO known as the studying approach, wherein one individual borrows additional data from some other randomized member in improving its situation. The suggested TLSBO is assessed and contrasted to TLBO and various existing approaches on modified IEEE 30-bus, 57-bus, and large-scale 118-bus AC-HVDC systems. Also, the efficiency of the proposed TLSBO is declared versus the top three techniques in CEC2020 Competition. Compared to the standard TLBO, the proposed TLSBO achieves improvements of 0.04, 4.25, 15.65 and 65.84% for the best, mean, worst and standard deviation metrics, respectively. The simulation outcomes show that the developed TLSBO outperforms the others in terms of efficacy and resilience. TLSBO, on the other hand, has convergence speed and superior quality for the ultimate optimal solution, as well as greater power for escape from converging to local optima than basic TLBO.