Simulation of alternative regulations in the Colombian electricity market

The deregulation of the Colombian electricity system took place in 1994 and the pool started operations in 1995. The Colombian system adopted a capacity charge mechanism to increase incentives to invest in new capacity. The capacity charge was showing strength, and apparently driving investments during the initial years. However, the mechanism started to exhibit weaknesses in terms of transparency and disincentives, causing a negative effect on investments. Different authors have presented alternative regulatory options to update the system. A non-standard system dynamics approach to evaluate alternative regulation schemes for the Colombian electricity market is proposed. A specific regulation problem is undertaken to illustrate the proposed methodology. It shows how the capacity charge mechanism, which has been used for reliability purposes, might be changed for alternative schemes. The proposed transformations to the actual regime seem to overcome some of its drawbacks. Simulation results indicate these alternatives improve the general system behaviour. In addition, the underlying model has been used afterwards for other energy policy purposes.     

U-Convolutional model for spatio-temporal wind speed forecasting

The increasing penetration of intermittent renewable energy in power systems brings operational challenges. One way of supporting them is by enhancing the predictability of renewables through accurate forecasting. Convolutional Neural Networks (Convnets) provide a successful technique for processing space-structured multi-dimensional data. In our work, we propose the U-Convolutional model to predict hourly wind speeds for a single location using spatio-temporal data with multiple explanatory variables as an input. The U-Convolutional model is composed of a U-Net part, which synthesizes input information, and a Convnet part, which maps the synthesized data into a single-site wind prediction. We compare our approach with advanced Convnets, a fully connected neural network, and univariate models. We use time series from the Climate Forecast System Reanalysis as datasets and select temperature and u- and v-components of wind as explanatory variables. The proposed models are evaluated at multiple locations (totaling 181 target series) and multiple forecasting horizons. The results indicate that our proposal is promising for spatio-temporal wind speed prediction, with results that show competitive performance on both time horizons for all datasets.     

论高校水电管理的实践与创新

当前水电管理改革需要进入更高级的阶段——水电管理商品化。高校的水电消耗和管理是一个很复杂的过程,与各单位的工作性质、环境、条件等诸多因素有关,有时指标量的核定并不能完全反映一个部门水电消耗的客观实际,所以单一地靠指标化管理已不尽完善,随着高校后勤社会化改革的进一步深入,水电管理商品化已成为必然的发展趋势。