Microgrid Energy Management with Flexibility Constraints: A Data-Driven Solution Method

Microgrid energy management is a challenging and important problem in modern power systems. Several deterministic and stochastic models have been proposed in the literature for the microgrid energy management problem. However, more accurate models are required to enhance flexibility of the microgrids when accounting for renewable energy and load uncertainties. This thesis proposes key contributions to solve the energy management problem for smart building (or small-scale microgrid). In Chapter 3, a deterministic energy management model is presented taking into account system flexibility requirements. Energy storage systems are deployed to enhance the grid flexibility and ramping capability. The objective function of the formulated optimization is to minimize the operation cost. Combined heat and power (CHP) units, which interconnect heat and electricity, are modeled. Thus, electricity and thermal generation and load constraints are formulated. To account for uncertainties of load and renewable energy resources (e.g., solar generation), a stochastic energy management model is proposed in Chapter 4. A data-driven chance-constrained optimization is based method is formulated. The proposed model is nonparametric that imposes no assumption on probability distribution functions (PDFs) of the random variables (i.e., load and renewable generation). Adaptive kernel density estimation is deployed to estimate a nonparametric PDF for each random variable. Confidence levels (risk levels) of the chance constraints are modified according to estimation errors. Several cases are simulated to analyze the deterministic and stochastic optimization models. The simulation results show that the proposed data-driven chance-constrained optimization with the flexibility constraints enhance reliability, resiliency, and economics of the microgrid energy systems. Note that these flexibility constraints avoid propagating solar and load fluctuations to the distribution feeder. That is smart building (microgrid) is capable of capturing fluctuations locally

Data-Driven Photovoltaic Power Production Nowcasting and Forecasting for Polygeneration Microgrids

In this paper, we deal with the problem of nowcasting and forecasting the photovoltaic power production (PvPP) on the basis of real data available for the Savona Campus and coming from the energy management systems (EMSs) of the smart polygeneration microgrid that feeds buildings in the University area. In this paper, we show how PvPP nowcast and forecast problems can be solved with the state-of-the-art data-driven techniques, which use the historical data collected by the EMS. In particular, we compare the performance of the kernelized regularized least squares, the extreme learning machines, and the random forests. In the machine learning field, these algorithms are the best choice in three different families of techniques: kernel methods, neural networks, and ensemble methods. Results show that our proposal can improve of almost one order of magnitude to the actual prediction system used in the EMS of the Savona Campus, which is based on the knowledge of the physical problem. Finally, by using the EMS installed at the Savona Campus, it has been possible to quantify the saving in costs and CO 2 emissions due to the new nowcasting and forecasting models