A scenario-based optimization of Smart Energy Hub operation in a stochastic environment using conditional-value-at-risk

Besides increasing the installation of distributed generation plants, investigation on multi-carrier energy systems leads recent studies to focus on several aspects of Smart Energy Hub (S. E. Hub) systems. An S. E. Hub incorporates several modules which calculation of optimal size and operation of each have already attracted a great deal of research. Uncertainty in the modeling of these modules is an imperative factor that was not paid attention in S. E. Hub models properly. To build up a more precise framework for S. E. Hubs, here we present a stochastic model for real time electricity and natural-gas prices and electricity demands. In this paper, an S. E. Hub operates in order to minimize a weighted sum function consisting energy bill and penalty for emissions. To have more precise model, we use conditional value at risk (CVaR) technique to control the operational risk of an S. E. Hub when electricity and natural gas are converted to electrical, heating, and cooling energy in its output ports. The proposed optimization method is validated by simulating it on a real office building in Tehran, Iran.