## 2 Literature Review
To benchmark the performance and solution quality for any solution technique, it is necessary to have a variety of electrical test systems [11]. Nowadays, we still lack the existence of standardized test systems that can be used to benchmark the performance and solution quality of proposed techniques. Many papers consider different test systems, which make it very difficult to perform a proper comparison between methods that have proposed [12]. [PERSON] et al. [13] have referred that the existing IEEE test systems developed are used for reliability, power flows, and stability analyses but not for economic analysis. Recently, some panels focusing on the development of standard test systems of transmission and distribution systems for economic analysis have emerged. In 2007, the IEEE Working Group (WG) on Test Systems for Economic Analysis was created, sponsored by the IEEE System Economics subcommittee. As a result of this initiative, [PERSON] et al. [14] proposed a version of the IEEE 18-bus test system, a database of an electrical network, with a reconfiguration of three regions of the US Western Interconnection, including renewable energies. A test system includes transmission, generation, load, wind, and solar data. [PERSON] et al. [15] proposed an update of the IEEE reliability test system, which includes the production of electricity, transmission, and consumption needs, but the last update of this test system was carried out in 1996. The modifications include the inclusion of geographic information of Southwestern United States to include temporal space information wind, solar, and load data with forecasts. [PERSON] et al. [16] presented a proposal for a test system based on real data, which has planning information for network expansion, operation, and reliability. This work integrates information on the expansion of the generation by providing detailed information on the architecture of the electricity grid. The presented load modeling includes hourly, daily, weekly, monthly, and seasonal patterns. The existing test systems consider the architecture of an electrical system, in addition to the recent updates to have renewable energies, as well as information on generation and consumption planning. There are few cases where demand information is included; these systems are input for testing the different economic dispatch models to implement. A fundamental characteristic to test the different algorithms for solving the problem of economic dispatch lies in the treatment of the demand that determines the optimal allocation at the lowest cost. Several authors have documented the seasonal nature of the demand for electrical energy, as well as the availability of fuels and renewable energy in the planning of expansion [17; 18; 19; 20; 21]. For the forecast of the demand for electric energy, there are multiple methodologies. These methodologies are based generally on time series models, machine learning, wavelets theory, multi-agent, or hybrid models [22; 23; 24; 25; 26; 27; 28]. The objective of these models is to forecast as accurately as possible the demand for electric power for a period forward based on the available historical information. The drawback of using this forecasted data to test algorithms for solving the economic dispatch problem is that it is a particular data that considers a specific time horizon and is according to the available information. Therefore, the use of a demand simulation method based on the use of alpha stable distributions and generalized additive models is another option for testing and validating algorithms in a development phase. It has been identified that electrical demand presents a greater degree of impulsivity due to the presence of peaks in the series during the hours of the day and seasons of high-energy demand in the year [24; 25; 26; 27; 28]. For this, we use the Chambers-Mallows-Stuck algorithm for simulating alpha stable random variables characterizing demand peaks and generalized additive models (GAMs) to model the non-linear behavior of real electrical systems.