Probabilistic optimal power flow analysis for optimal deployment of Unified power flow controller in renewable integrated hydro power systems using marine predators algorithm

The optimal integration of renewable energy sources into power systems is a challenging but extremely important task for cost reduction to achieve environmental sustainability. To address the challenges of probabilistic optimal power flow problems, this study proposes a novel marine predators algorithm as an optimization tool. The proposed method used to solving the probabilistic optimal power flow problem on conjunction with a unified power flow controller, incorporating the behavior of wind, PV, and small hydro generation. The proposed framework models the variability of renewable resources using appropriate probability density functions like weibull distribution for wind speeds, lognormal distribution for solar irradiance, and gumbel distribution for water flow rates. The simulation results highlight the efficiency and robustness of marine predators algorithm in solving single-objective optimal power flow problems. The findings show that 5112.5 ( $ / h ) and 1.6823 (t/h) is the ideal fuel cost & emission when considering thermal generators alone; when incorporating renewable energy with thermal, the total cost is 4808 ( $ / h ) and the emission is 1.5131 (t/h). When RES with UPFC both are included, fuel costs 4785.3 ( $ / h ) and emissions are 1.469 (t/h). For research validation, selected conventional generators in the IEEE 57-bus system are replaced with renewable sources. Furthermore, the proposed method enhances the operational efficiency of renewable sources and small hydro systems when integrated with flexible AC transmission system devices like unified power flow controller. Performance evaluation on the IEEE 57-bus test system shows that the proposed approach delivers highly competitive results compared to other optimization.