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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/196| Automatic tariff generation for electricity markets using reinforcement learning | |
| JONATHAN SERRANO CUEVAS | |
| JOSE ENRIQUE MUÑOZ DE COTE FLORES LUNA | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Markov processes Competitive intelligence Learning (artificial intelligence). | |
| The most widely used energy sources rely on oil and therefore are not going to supply energy to industrial processes and people's lives forever. Most of the electric energy produced nowadays comes from thermoelectrical plants, which are industrial complexes with a furnace at its heart burning oil to generate steam. The large amounts of electrical energy is then transmitted over the electrical grid to factories, offices and houses. This centralized scheme has several disadvantages. The first one is its oil dependency, because the oil's price increases constantly. The second one is the transmission grid by itself. Furthermore, this grid is very old, and because of its nature, a localized problem has the potential to start a snowball effect massive blackout. An alternative to address both the dependency on oil of current energy generation and its centralized nature is a technology called smartgrid. Smartgrid enables an electrical network with communications, sensing, self-healing and digital capabilities. These features transform the old, centralized energy network into a distributed network, which can integrate small producers and consumers (or hybrids that can be both, consumers and producers) on almost any point of the network at any time. With this scheme, large power suppliers will have its place on the network, but also an increasing amount of small green-energy producers, which by aggregation, will supply large amounts of power to consumers, creating a liberalized energy market. The idea of this market is for it to be driven by brokers, which are entities that can buy energy from producers and sell it to consumers, by means of contracts called tariffs, which are regulated by constraints set by a regulator entity. | |
| Instituto Nacional de Astrofísica, Óptica y Electrónica | |
| 2014-09 | |
| Tesis de maestría | |
| Inglés | |
| Estudiantes Investigadores Público en general | |
| Serrano-Cuevas J. | |
| CIENCIA DE LOS ORDENADORES | |
| Versión aceptada | |
| acceptedVersion - Versión aceptada | |
| Aparece en las colecciones: | Maestría en Ciencias Computacionales |
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| Fichero | Descripción | Tamaño | Formato | |
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| SerranoCJ.pdf | 1.17 MB | Adobe PDF | Visualizar/Abrir |