Respuesta de la demanda de electricidad de una red de área industrial basada en alta incertidumbre
The demand response study (DR) for industries search an efficient and optimal use for the energy using an optimization algorithm who is fed by a prediction mechanism of the demand with historical data, operated by Markov chains and the Monte Carlo method. This algorithm determines the probability of...
| Autor Principal: | Barahona Quelal, Bayardo Julián |
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| Formato: | bachelorThesis |
| Idioma: | spa |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: |
http://dspace.ups.edu.ec/handle/123456789/13540 |
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| Sumario: |
The demand response study (DR) for industries search an efficient and optimal use for the energy using an optimization algorithm who is fed by a prediction mechanism of the demand with historical data, operated by Markov chains and the Monte Carlo method. This algorithm determines the probability of the energy consumption increases, don’t have changes or decreases; once the different probabilities are obtained the transition matrix for Markov chain is constructed and the value for the possible future event will be predicted. Once the consumption curve is obtained and with the data of levels of production, an optimization algorithm for the energy cost is proposed based in an objective function oriented to the reduction energy costs per unit produced, with the respective restrictions obtained from the same process, such as the number of units required, the number workers required, the working time of the sub processes and even the order of the processes; The optimization result will indicate, what time in the day the production will be started because this is the best, another goal is to determinate how many work shifts are required to produce the units that are needed, always aimed at reducing the monthly energy costs of production based on a tariff schedule. |
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