# Optimal Management of High-Voltage Line Congestions Using Power Source Redispatching
## 2 Literature Review
According to the authors, the obtained results of their research allowed for achieving almost ideal solutions in the scope of limitation in when the method was implemented in large power systems. In [PERSON] and his team's [51] research on the energy system problems used the Evolutionary Particle Swarm Optimization (EPSO) method, which combines both the advantages of Evolutionary Algorithms and the advantages of PSO. In [41], the authors proposed the use of the metaheuristic method Flower Pollination Algorithm (FPA). According to the authors, this method allows for finding the optimal point of generation limitation at a given moment. Metaheuristic optimization methods were also applied in the work [32], where the Ant Lion Optimization (ALO) method was used. The same method was used by [PERSON] and his team [52] in order to optimally solve the problem of power in the system. In [37], the PSO method was used again, while in [53], the teaching-learning based optimization (TLBO) method was used. The authors of these works conducted research on closed large IEEE test networks, and the results they obtained proved that both of these methods can be successfully used in the power system to limit current overloads. One of the available options for balancing the energy system of a given country is international exchange with another country. The issue of the proper balancing of the network in various energy systems was discussed in detail in the work where the authors focused on various methods of maintaining the network balance [54]. In study [55], one can learn about the techniques of balancing the system used when there is a high saturation of wind energy in Germany. The issue of maintaining a proper balance in a system with a high saturation of renewable energy sources was also focused on in [56], where the authors proposed their own proprietary method based on deep reinforcement learning (DRL). The authors' methodology was tested on a small closed IEEE network, and the obtained results were extremely interesting. An extremely interesting solution to the problem of optimal power limitation in the system was proposed by the authors of [31], who used fuzzy linear programming for this purpose. The authors tested the proposed methodology on a closed 118-node IEEE network, and the obtained results prove the effectiveness of their solution. The paper [57] discusses the calculation of power system parameters using the grey wolf optimization (GWO) algorithm. This algorithm was implemented by the author to a dozen test functions, and the total calculation times and costs performed using the GWO algorithm were lower than those obtained using other commonly used optimization algorithms. The algorithm proposed by the author is both fast and reliable for power systems. Optimization techniques were also used in the article [58]. The estimation of system parameters is a key factor in analyzing power flows, planning the extension of an electricity system, or ensuring its stability. The authors of this paper proposed the use of hybrid moth-flame optimization (MFO) with particle swarm optimization to estimate transmission line parameters based on different scenarios and mathematical validations. Information on source scheduling in the power system can be found in articles [59; 60]. For example, in the [60] article, the authors focused on proposing hybrid policy-based reinforcement learning (HPRL) to realize optimal power system operation. There is a lack of research in the literature on identifying the RESs that are responsible for the resulting overloads of power lines in a high-voltage network saturated with distributed sources. Knowledge on this subject will allow network operators to effectively relieve the overloaded lines. Moreover, this will be an optimal action. Therefore, it is necessary to include the optimization task in this process.