Adaptive control for evaluation of flexibility benefits in microgrid systems. (1st December 2015) - Holjevac, Ninoslav; Capuder, Tomislav; Kuzle, Igor
Abstract: Aggregating groups of loads and generators at the same location with centralized control is known as the concept of microgrids. However, if those flexible producers and consumers do not have the ability to balance the variability and uncertainty of RES (renewable energy sources) production within them, from the system perspective they are seen as a source of imbalances and potential problems in maintaining the equilibrium of production and consumption. The papers main goal is to quantify the ability of microgrid components to provide flexibility. This flexibility is analysed from two perspectives, defining two operating principles of each microgrid: independently from the distribution grid and connected, interacting and responding to signals from the upstream system. Following on this, the paper presents two relevant cases. In the first part a deterministic model is developed based on MILP (Mixed Integer Linear programming) simulating the microgrid operation over one year period. This model is used to determine the optimal microgrid configuration with respect to the amount of unused energy, thus defining role and capability of different pieces of equipment and their size (RES (renewable energy sources) wind and solar, HS (heat storage), μCHP (micro combined heat and power plants) and EHP (electric heat pumps)). The second part of this paper further expands the model with MPC (Model Predictive Control) approach in order to capture the behaviour of microgrid interaction with the distribution grid, modelling uncertainties of forecasting RES production by stochastic programming. The model is capable to evaluate both the impact of variable energy production and consumption and the impact of energy balancing tariffs depending on the amount of balancing energy needed for the microgrid operation. Highlights: Integrated MILP (Mixed Integer Linear programming) formulation for optimal operation of developed microgrid model. Determining operational flexibility of different microgrid components. Implementation of model predictive control to reduce the impact of uncertainties. For proposed approach, operational cost savings demonstrated.