This paper proposes a robust cost-optimal scheduling of a battery energy storage system (BESS) integrated with a photovoltaic power plant (PV). A power grid with an incentive policy is considered. Power transactions between the grid and its energy resources are normally charged according to the hourly price. Additional hourly incentive is provided if the day-ahead submitted schedule is maintained. Accurate forecasting and robust scheduling are essential for PV-BESS owners to maximize both revenues. The PV power forecast model, which is based on an RNN, uses a CNN discriminator to decrease the gap between its open-loop training and closed-loop test dynamics. The application of a GAN concept to the model training process ensures a stable day-ahead hourly forecast performance. The robust BESS scheduling model handles the remaining forecast error as a box uncertainty set to consider the cost-optimality and cost-robustness of the resulting control schedule. The scheduling model is formulated as a concise mixed-integer linear programming form to enable fast online optimization with the consideration for both transaction and incentive revenues. The introduction of adversarial learning to the forecast model increased the incentive revenue by 7.33%. Moreover, the online BESS scheduling with the incentive consideration enhanced the overall revenue by 3.73%.

This paper presents the development of the building electric power prediction model with local weather forecast information. Annual electric power usage data of the testbed is analyzed to develop a building electricity prediction model. K-means clustering algorithm is selected as a data mining technique. Silhouette index is applied to validate clustering results. Cluster analysis of total high voltage electric power usage of the testbed is performed. Results show that time parameters such as the season and the day type are important factors to classify the total electric power usage pattern. Further analysis results of the low voltage electric power usage are presented. Correlation analysis of each low voltage usage presents the local weather condition has a huge effect on energy facility power usages. Electric power usages of other systems such as lighting and electronics are affected more by a day type than a weather condition. The building electricity prediction model is developed based on the data mining analysis results. Simplified structure and fast calculation speed of the model help it to be applied in various fields of researches.

Increasing demands of eco-friendly vehicles, various types of hybrid electric vehicle (HEV) have been researched and released. Recently, some research has interest in not only the efficiency of the vehicle but also the durability of battery because the life of battery has influence on the cost of maintenance, stability and performance of the vehicle. In this study, backward simulation based on dynamic programming depending on the type of HEV which is consists of engine and battery or engine, battery and ultra-capacitor was conducted. The developed backward simulation algorithm can calculate the optimal fuel economy according to the driving cycle and other vehicle and components conditions. For the analysis of battery life, a battery capacity fade model was applied to the result of backward simulation. Battery life was estimated with an assumption that the vehicle drives repeatedly to follow the result of backward simulation derived to find the optimal fuel economy. From the simulation results, it is shown that HEV with ultra-capacitor has better fuel economy though it is almost similar with HEV without ultra-capacitor. However, the battery life of HEV with ultra-capacitor was estimated better because of the difference of battery power usage. Consequently, applying the ultra-capacitor to the typical parallel HEV has no large advantage in terms of fuel economy but has significant benefit in terms of battery life.