## 1 Introduction
However, this approach of only utilizing PSS or BESS may not be sufficient to enhance low-frequency stability, especially under current large-scale RES integration. Thus, it is imperative to study more effective solutions, such as the coordination between PSS and BESS controllers. As RES output power varies stochastically, this leads to a change in the operating point of a power system, creating multiple operating conditions. The controllers developed using deterministic techniques may not guarantee satisfactory operating performance under these conditions, as they were developed for single or very few operating points [19; 20]. There are few researchers who have considered controller tuning by directly considering these factors [11; 19; 20; 21; 22]. Coordination between WTG incorporating PODs, and PSSs utilizing PSO, was found to be highly successful in damping low-frequency oscillations under wind uncertainty in Reference [11]. In Reference [19], PSS parameters were tuned using differential evolution techniques considering generation and load uncertainties. The tuned PSSs were found to be effective in improving dynamic stability under multiple operating conditions. In Reference [20], the design of probabilistically robust PSS controllers with wide-area signals to improve dynamic stability under wind-power fluctuations was proposed. The so-designed wide-area-based PSSs were found to be effective in improving SSS under different scenarios. The authors in Reference [21] proposed an optimal tuning method for PSSs and FACTS controllers that greatly improves PSS. However, the authors' technique was based on a Monte Carlo simulation, which has very high computational time. A modified fruit-fly algorithm was used for coordinating PSSs and a static-var compensator (SVC) to improve SSS under wind-power fluctuations in Reference [22]. The proposed strategy is based on a computationally efficient method that optimizesthe controller parameters in considerably less time. However, all the mentioned studies in the literature tuned the controllers by considering the same renewable-energy resources [11; 20; 21; 22] or only load uncertainty [19]. As can be seen from before the mentioned descriptions, different metaheuristic techniques were applied to optimally tune power-system controllers considering power-system uncertainties. We chose a metaheuristic technique known as the firefly algorithm for our study. The firefly algorithm is a swarm-intelligence method based on flashing patterns and the behavior of tropical fireflies. It is simple, flexible, easy to implement, and provides better convergence in less computation time compared to other nature-inspired optimization techniques, such as the genetic algorithm and PSO [23; 24]. Thus, in this paper, we address the research gaps that exist in the current literature, as described in the preceding paragraphs. The major contributions of this paper are:  
1. Proposal of a procedure to compute the continuous probability function of RES and use it to assess PSSS. 2. Development of a probabilistic method to optimally tune power-system controllers, such as PSSs and BESSs, considering power fluctuation due to RES. 3. Development of a detailed model of BESSs in DIGSILENT [25], and the study of the effect of the proposed control strategy of utilizing BESS controllers and PSS in a coordinated manner on probabilistic low-frequency oscillatory stability. 4. Use of the firefly algorithm to coordinate power-system controllers. Thus, the novel contribution of our study is the proposal of a probabilistic method to tune power-system controllers, such as PSSs and BESSs controllers, considering stochastic power fluctuation arising due to two popular RES. The proposed method can be divided into two stages: Stage 1, which computes statistical information about the effect of RES integration on SSS, and Stage 2, which uses this knowledge to coordinate the power-system controllers.