## 2 State of the Art of the Redispatch Models in Real-Time Operation  
Redispatch is optimal when its modeling and implementation allows for supporting the system operator's decisions in real time, becoming a powerful operational and market tool. Redispatch is capable of projecting marginal costs and operating costs when there are deviations in the generation, transmission, and demand system. Its development enables covering various technical and economic restrictions of different types of renewable and conventional generation technologies, and it can even be coupled to the ancillary services market. Most redispatch models limit renewable generation by causing zero-cost energy shedding, to respect n-1 security criteria [12]. There are also economic papers that model gas networks in detail with their pressure and flow capacities for power generation, considering safety and coupling constraints [13]. It should be noted that the economic dispatch problem is the faithful reflection of the merit order list which then migrates to a mixed integer linear programming (MILP) unit commitment model using optimal direct current power flow (DC-OPF) [14; 15]. Other works are devoted to planning redispatch models to mitigate the impact of fault events in gas networks [16]. Redispatch models have succeeded in integrating load flexibility, as is the case of the German electricity system that uses a zonal pricing mechanism [17]. In Central and Western Europe, they use redispatch to manage transmission congestion within critical electricity zones using generation bids [18]. However, the model lacks a systemic dimension, and the aim is to quantify the results of zonal redispatch by testing the developed model in a power system with a larger number of power plants, lines, and busbars [19]. In situations of a total system blackout, there are works that propose two-stage schemes to predict the risk of a blackout in electric power systems. In the first stage, they model the power islands using a mixed integer nonlinear integer programming model that minimizes the cost of redispatch and unsupplied energy, while in the second stage, a data search technique is refined to predict the risk of separation electrical zones from the rest of the system [20; 21]. In South America, the most usual method for redispatch models that help mitigate generation system and demand uncertainty is the merit order list power plant pricing model, as shown in Figure 2. The merit order is a ranking of plants ordered from lowest to highest based on variable generation costs. The merit order list redispatch model uses maximum power prices with demand responding to the price between the market equilibrium for both perfect competition and natural monopoly [22]. There are other advances called more specific redispatch submodels and targeting the market for ancillary services  
Figure 1: Expansion in solar-wind generation and decrease in conventional generation. applied to automatic generation control (AGC) [23]. At present, transmission lines that do not undergo investments in maintenance, capacity increase, or new expansions cause congestion in power flow and increased losses. System losses is a fictitious demand that increases marginal costs and energy prices. This paper proposes a redispatch model that focuses on loss minimization using an objective function that includes the system loss model to minimize plant dispatches [24]. The system parameters that generate uncertainty are calculated based on operational results in previous days and actual incoming data through knowledge delivered by the system operator, resulting in a real-time optimization with acceptable timing and solutions [25]. The simulation of redispatch models has shortcomings in the tuning of technical and economic parameters, caused by the size of the system, high convergence times, and global cost solution far from the optimum value. However, unsupplied energy is one of the most important.