Probabilistic Microgrid Economic Dispatch via Economic Model Predictive Control Considering Weather and Load Data Uncertainty

The output power of distributed generation (DG), particularly renewable energy sources depend on weather conditions. The inclusion of renewable sources and the uncertainty they induce in generation adds to the complexity of today's power systems and makes the economic dispatch problem more difficult to solve. In this paper, a framework for solving economic dispatch is proposed, which first models the uncertainties in the power system using Fuzzy Monte Carlo simulation. Then, an economic model predictive control (EMPC) algorithm is developed to address the microgrid's resource allocation problem, taking into account the uncertainty in both generation and demand. The goal is to make energy management more cost-effective while tracking a specified reference trajectory. To validate the effectiveness of the proposed approach, a microgrid composed of a photovoltaic (PV) array, a wind turbine, a diesel generator, and an Electrical Storage System (ESS) is developed. It is shown that the proposed EMPC method is a viable alternative to conventional economic dispatch in the presence of uncertainty in the input variables.