Stochastic optimal reactive power dispatch at varying time of load demand and renewable energy resources using an efficient modified jellyfish optimizer

Solving the optimal reactive dispatch (ORPD) is a strenuous task to assign the best operating point of the electrical system components to obtain the most secure and stable state of system. This problem became more complex problem due the variation of the load demand or inclusion the renewable energy resources (RERs). The aim of this paper is solving the ORPD problem using a modified jellyfish search optimizer (MJSO) under deterministic and probabilistic states of the load demand and the RERs. The MJSO is based on boosting the exploration and exploitation phases of the standard jellyfish search optimizer (JSO) using two strategies. The first strategy is enhancing the exploration process by using a chaotic mutation while the second strategy is implemented for the exploitation process using a spiral orientation motion of the populations around the sorted jellyfish. Three uncertain parameters are considered including the load demand, the solar irradiance, and the wind speed which are represented using the Weibull, the Beta, and the normal probability density functions, respectively. The Monte Carlo simulation along with scenario-based reduction to generate a set of scenarios for the stochastic ORPD. To verify the effectiveness of the MJSO for solving the ORPD problem, it is tested on IEEE 30-bus system and the obtained results are compared with other well-known optimization techniques. The obtained results and the comparison with other techniques indicate that the proposed MJSO algorithm provides effective and robust high-quality solution when solving the ORPD at deterministic state. In addition of that the expected power loss is decreased considerably with application the proposed technique for solving the ORPD at stochastic state.