## 1 Introduction  
The problem of economic dispatch has generated a wide development in the optimization algorithms for its solution as well as the adequate planning of the generation and expansion of the electrical networks. Its importance is such that in the literature, multiple approaches have been developed in this regard. However, there is a need to have available information from test systems for such algorithms; in most cases, the information available from real electrical networks is not public, so being able to have a robust method for generating test data efficiently represents a relevant contribution. This paper proposes a conceptual framework based on a hybrid model using Levy alpha stable distributions and generalized additive models for the generation of test cases. One of the essential inputs to test the optimization algorithms are the test cases for the behavior of the demand; without such input, it is impossible to test the efficiency of the implemented algorithms. This work contributes to having standardized test cases that can be used to benchmark the performance of many proposed optimization techniques. One of the main advantages of the proposed method to generate test instances is, once obtained, the model parameters can create as many instances as needed without losing robustness in the estimates. This would be equivalent to evaluating the optimization model in a variety of electrical systems. This represents a great advantage over other techniques. Being able to have unlimited test instances will let to analyze in greater detail the robustness of the optimization algorithms used as well as their stability in obtaining feasible solutions. The study of the problem of economic dispatch has been an area in continuous development, integrating aspects such as renewable energy, distributed generation, and smart grids into the electricity grid. However, there has not been much attention in the generation of test systems for the evaluation of optimization algorithms. They are generally considered historical data, or random instances are generated assuming a Normal probability distribution. Both proposals have some disadvantages, the use of historical data limits the availability of information, especially in recently created systems or with innovative elements, as well as limiting the number of available instances. In the case of the generation of random instances through normal distribution, it implies some limitations, such as the presence of heavy tails in historical data. Additionally, when generating random numbers through the normal distribution, the seasonal behavior of the characteristic demand of electrical systems is lost. Therefore, it is difficult to replicate medium and long-term power generation planning periods. This avoids reflecting the response of the economic dispatch models to extreme values and seasonality, thus limiting the conclusions on the solutions found. The proposed hybrid model of this work seeks to overcome these limitations in test cases by using the alpha stable distribution and using generalized additive models. The proposal is integrated in the following way, fitting of historical data to the stable alpha distribution seeks to identify the presence of impulsivity in the data series. With this, it is possible to simulate random instances that follow the alpha stable distribution. For the integration of the non-linear component, the simulated alpha stable data is integrated into a generalized additive model, which consents to segment the data into additive modules, each fitted to a particular non-linear function (such as the spline or kernel functions). Thus, the generated instances considering extreme scenarios as well as replicating the non-linear seasonal behavior of the electrical system analyzed. The structure of the work is as follows: Section 2 presents a literature review of different aspects in the solution of the problem of economic dispatch as well as state of the art on the generation of test cases.