Contribution of FACTS Devices in Probabilistic Optimal Power Flow of Conventional, Renewable Energy Sources and Electric Vehicle Using Quasi‐Oppositional Artificial Hummingbird Algorithm

ABSTRACT The days of fossil fuels are rapidly coming to an end. The best course of action is to concentrate on renewable energy sources (RES), which are beginning to make sense as an alternative to the current situation. This article's goal is to utilize and leverage combined RESs, such as solar, wind, and electric vehicle (EV) charging, along with some appropriate FACTS devices, such as Thyristor controlled series compensator (TCSC) and Thyristor controlled phase shifter (TCPS), to solve probabilistic optimal power flow (POPF) problems. The challenges are solved by using quasi‐oppositional‐based learning (Q‐OBL) in conjunction with the optimization strategy known as the artificial hummingbird algorithm (AHA). Furthermore, adding FACTS, specifically TCPS and TCSC, can enhance the outcomes even further. A reliable and effective meta‐heuristic optimization technique for handling challenging power system issues is QOAHA. The research study attempts to reduce the overall cost of generating by satisfying all equality and inequity requirements. The non‐linearity of the problem is attributed to the existence of valve point loading, the thermal unit's limited working zone, and the variations in wind and sun. Three test systems have been used to evaluate the efficacy of the suggested QOAHA approach: test system 1, which includes a conventional POPF with generators at bus 1 (swing), 2, 3, 6, 8, 9, and 12. Second, considering test system 2, which comprises supplementing the existing thermal generators with RESs such as wind, solar PV cells, and EV charging units. Last but not least, implementing test system 3, which keeps the thermal generators in their customary placements while also integrating solar PV, wind power generation, and EV deployment with the addition of FACTS devices to arrive at the best possible global solution. The computed findings clearly demonstrate that QOAHA is still a useful tool for treating POPF issues, even when RES is used in conjunction with conventional techniques. In terms of the best possible solution to the objective functions and the rate of convergence, the proposed method outperforms other optimization techniques. The robustness of the recommended optimization strategy has been evaluated by statistical analysis. An analysis of variance (ANOVA) test is used to conduct this inspection in a thorough way so that the suggested technique's robustness may be assessed more accurately. A comparison with well‐established optimization techniques has been done in order to address the superiority of the desired strategy.