Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.
Abstract.The microgrids allow the integration of renewable sources of energy such as solar and wind and distributed energy resources such as combined heat and power, energy storage, and demand response. In addition, the use of local sources of energy to serve local loads helps reduce energy losses in transmission and distribution, further increasing efficiency of the electric delivery system. In this paper, the optimization problem of the energy in a microgrid (MG) located in southeastern of Spain, with Energy Storage System (ESS), which exchanges energy with the utility grid is developed using Model Predictive Control techniques. System modelling use the methodology of the Energy Hubs. The MPC techniques allow maximizing the economic benefit of the microgrid and to minimize the degradation of storage system.
En este trabajo se presenta un método de sintonía de un control PI de pitch y un control difuso de pitch para turbinas eólicas flotantes con la finalidad de maximizar la producción de energía y reducir las vibraciones. Debido a la complejidad del sistema, se han aplicado diversas técnicas de control para encontrar soluciones eficientes con el fin de mejorar la productividad de estos dispositivos de energía renovable. El control PI es sintonizado por generación aleatoria de las ganancias KP y KI a través de múltiples simulaciones, y se presenta también un ajuste basado en datos de un control difuso utilizando dos algoritmos de optimización: enjambre de partículas y búsqueda de patrones. Los controladores propuestos se ha aplicado al aerogenerador flotante tipo barcaza de 5 MW de NREL. Ambos controladores se han comparado con el controlador integrado en FAST, un controlador PI de ganancia programada (GS-PI), dando mejores resultados en términos de error de potencia nominal y menor desplazamiento de la torre y de las oscilaciones de la plataforma.
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