Economic energy managementof networked flexi-renewable energy hubs according to uncertainty modeling by the unscented transformation method.

The utilization of energy hubs (EHs) in the power system and simultaneous energy management of several energy networks is of high importance to increase energy efficiency. Therefore, this paper focuses on the energy management of grid-connected flexible energy hubs. Scheme includes electrical and heating networks, where renewables and storage devices are in connection using the hub as a coordinator. Problem minimizes the expected operating cost of the mentioned networks subject to networks optimal power flow model and the mathematical representation of flexible EHs. Uncertainties related to the amount of demand, price of electrical and heating energy, and renewable power have also been considered in the problem formulation. The unscented transformation method has been adopted to reduce the volume of the problem to obtain the optimal solution in low computation time, along with the precise modeling of flexibility and modeling of the aforementioned uncertainties. Modeling the flexibility of EHs in the presence of renewable sources, modeling bio-waste energy unit for the simultaneous generation of electric and heat energy, and modeling the various uncertainties of load using the unscented transformation are among the contributions of the paper. The obtained numerical results demonstrate the capability of the proposed scheme in improving the economic and operating status of electrical and heating networks, along with improving the flexibility of EHs in such a way that energy storages in renewable hubs can extract 100% flexibility conditions. In these conditions, the hubs are able to reduce energy costs, energy losses, voltage drop, and temperature by about 25%, 24%, 60%, and 59%, respectively, compared with the power flow studies of the networks under study. Also, based on sensitivity analysis results, the increase in load and energy price leads to an increase in the operating cost of the networks, while the increase in the size of renewable resources and storage devices reduces the networks' energy cost. • Utilizing various flexibility sources besides renewables, to establish a FEH by considering flexibility modeling. • Modeling performance of bio-waste unit with the CHP technology, which can generate electrical and thermal energy. • Modeling uncertainties of load, energy price, and renewable power based on the unscented transformation method. • Using unscented transformation method to accurately model the flexibility and reduce the computation time. [ABSTRACT FROM AUTHOR]