Multistage stochastic programming for the optimal bid of a wind-thermal power production pool with battery storage.
Fri, 07/08/2022 - 11:26 — admin
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | F.-Javier Heredia; Ignasi Mañé; Marlyn Dayana Cuadrado Guevara |
| Conference Name | EURO 2022 |
| Conference Date | 03-06/07/2022 |
| Conference Location | Espoo, Finland. |
| Type of Work | Invited presentation |
| ISBN Number | 978-951-95254-1-9 |
| Key Words | research; multistage stochastic programming; virtual power plants; unit commitment |
| Abstract | In this study we present a multistage stochastic programming model to find the joint optimal bid to electricity markets of a pool of dispatchable (thermal) and non-dispatchable (wind) production units with battery storage facilities. The assumption is that these programming units are operated by the same utility that, previous to the market clearing, has to dispatch some bilateral contracts with the joint production of the production pool. The multistage model mimics the multimarket bidding process in the Iberian Electricity Market (MIBEL). First, the utility has to decide how to cover the energy of the bilateral contracts with the available units. Second, the production capacity of each unit, not allocated to the bilateral contracts, must be offered to the seven consecutives spot markets (day-ahead and six intraday markets) plus the secondary reserve market (the most relevant ancillary services market). The stochasticity of the electricity clearing prices and the hourly generation of the wind-power units is considered. The stochastic process associated to this multistage decision-making process is modelled through multistage scenario trees with thirty-four stages that are built from forecasting models based on real data of the Iberian Electricity Market. The numerical results show the advantage of the joint operation of the pool of production units with an increase of the overall expected profits, mainly due to a strong reduction of the operational costs. |
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