Optimal sizing and allocation of PV-DG and DSTATCOM in the distribution network with uncertainty in consumption and generation

The use of distributed generation resources and Flexible AC transmission (FACT) devices to improve technical constraints and reduce dependence on the upstream network eliminates the need to build new power plants. Maximizing line capacity utilization is a priority for the electricity industry. The advantages of using FACT devices include increasing line throughput and preventing line and bus congestion, improving bus voltage profiles, reducing line losses, preventing sub synchronous resonance, and so on. This study examined optimal sizing and allocation of photovoltaic distributed generation (PV-DG) and DSTATCOM. To solve the optimization problem, teaching–learning-based optimization (TLBO) was employed. The algorithm was run in the IEEE 33-bus standard test system. Because of the random nature of the consumption load and the random production nature of renewable DG units, the uncertainty of consumption and production was examined through stochastic programming methods. Moreover, to choose the scenario with the highest probability, the Monte Carlo method was employed. The scenarios included load certainty-PV generation uncertainty, load-PV generation uncertainty, and load-PV generation certainty. Injecting reactive and active power of PV-DG and DSTATCOM improved voltage stability index (VSI) to 0.9745 p.u (36.8%) and reduced the amount of power loss to 10.95 kW (94.8%). Furthermore, comparing TLBO’s results with other algorithms verified its accuracy and minimum solution time.