Tesis
Automatic tariff generation for electricity markets using reinforcement learning
Autor
JONATHAN SERRANO CUEVAS
Institución
Resumen
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.
Materias
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