TASK-BASED LOAD FORECASTING AND ROBUST RESOURCE SCHEDULING IN SMART GRID

In microgrids, the uncertainty of load and renewables and lack of generation capacity will lead to a wide variety of operation problems in both grid-connected mode and islanded mode. This motivates the design of the state-of-art microgrid master controller for microgrid energy management, load forecasting, and demand response. Uncertainty in renewables and load is a great challenge for microgrid operation, especially in islanded mode as the microgrid may be small in size and has limited flexible resources. A multi-timescale, two-stage robust dispatch model is proposed to optimize the microgrid operation. The proposed one uses only one model to combine the hourly and sub-hourly dispatch together, which means the day-ahead hourly dispatch results must also satisfy the sub-hourly conditions. At the same time, the feasibility of the day-ahead dispatch result is verified in the worst-case condition considering the high-level uncertainty in renewable energy output and load consumptions. In addition, battery energy storage system (BESS) and solar PV units are integrated as a combined solar-storage system in the proposed model and the output power of the combined solar-storage system remains unchanged on an hourly basis. Furthermore, both BESS and thermal units provide regulating reserve to manage solar and load uncertainty. The model has been tested in a controller hardware in loop (CHIL) environment for the Bronzeville Community Microgrid system in Chicago. The simulation results show that the proposed model works effectively in managing the uncertainty in solar PV and load and can provide a flexible dispatch in both grid-connected and islanded modes.When the generation capacity of an islanded microgrid is less than the load demand, load curtailment is inevitable. This dissertation proposes a multi-objective optimization model to minimize the load curtailments. Specifically, the proposed model minimizes the generation cost and total load curtailments and also minimizes the maximum load curtailment. Furthermore, the impact of the penalty coefficients of total load curtailment and maximum load curtailment is analyzed, which provides a strategy to choose the value of the two penalty coefficients according to different practical purposes. The proposed model can be used in both microgrid generation scheduling and microgrid planning problems. It was tested in the Bronzeville Community Microgrid system and the results showed that the proposed model can reduce the total load curtailment and maximum load curtailment.Load forecasting is one of the most important and studied topics in modern power systems. However, traditional load forecasting is an open-loop process as it does not consider the end use of the forecasted load. This dissertation proposes a closed-loop task-based day-ahead load forecasting model labeled as LfEdNet that combines two individual layers in one model, including a load forecasting layer based on deep neural network (Lf layer) and a day-ahead stochastic economic dispatch (SED) layer (Ed layer). The training of LfEdNet aims to minimize the cost of the day-ahead SED in the Ed layer by updating the parameters of the Lf layer. Sequential quadratic programming (SQP) is used to solve the day-ahead SED in the Ed layer. The test results demonstrate that the forecasted results produced by LfEdNet can lead to lower cost of day-ahead SED at the expense of slight reduction in forecasting accuracy