Optimal self-scheduling of a real energy hub considering local DG units and demand response under uncertainties

In this paper, a cost-based mathematical optimization is used to evaluate the optimal amount of imported power from the public main grid to a private microgrid, that is the LAMBDA lab Microgrid testbed placed at Sapienza University of Rome. In this regard, this study considers five tests based on using different sources, including a photovoltaic array, an emergency generator set, a fuel cell and the main grid, for load satisfaction. The LAMBDA lab can be considered as a multi-source multi-output energy hub with three optional sources and both electrical and heat demands in output. This paper considers photovoltaic production and load demand as indeterministic parameters and evaluates the problem under uncertainties. As a result, a stochastic programming model is defined, and a powerful optimization function is used to reach the optimal power received from the main grid. In addition, information gap decision theory (IGDT) is used to model the robustness of the problem against uncertainties associated with renewable generation unit (Photovoltaic system) and electricity loads applied on a real case for the first time. In the result section, the contribution of each source in electrical and heat load demands is presented in addition to the cost of each test by evaluating the effect of DR of 15%. Finally, a comparison between the stochastic programming method and IGDT has been accomplished.