Desarrollo de una herramienta de modelado con tiempo en alta resolución para simular la demanda energética y la producción de energía fotovoltaica in situ: Aplicaciones en Costa Rica.

Abstract

Nowadays strategies towards more energy-efficient systems imply the integration and application of urban energy analysis tools, in order to support the sustainable energy systems. In intermittent solar power systems, the tools which couple energy modelling to assess the matching energy indexes require especial research into the comprehensive analysis between the demand and the production involving minimal source of error. A significant source of error is attributed to the coarser time step resolutions used in the simulations; consequently, this also affects the matching indexes results. Solar energy production and domestic energy demand minute resolution models were created in order to identify the impact of time-step. The generated PV on-site production model was developed with data from the research group Energy Efficient and Smart Cities (EESC) of the Technische Universität München. Relevant data needed for the model, such as irradiance and incident global radiation, were obtained using the software Meteonorm. The statistic computations of the high-resolution model of domestic electricity demand developed by the Centre for Renewable Energy Systems Technology of Loughborough University were used and modified in order to create the demand model that adequately represents energy demand in Costa Rica. The matching error was more noticeable in partially cloudy sky conditions; the error was 39,18% for the month in study. In one case scenario, it was shown that daily resolution profiles conduce to the assumption that all the PV power produced is used to cover the house demand, although the 1-minute resolution results indicate that only 39% could be used with this purpose. Overall, it was found that the coarser resolutions average the existing demand and generation profiles spikes into much more continuous and flatter profiles, leading to an inaccurate representation of the general demand profile.