A novel interval-based formulation for optimal scheduling of microgrids with pumped-hydro and battery energy storage under uncertainty

Nowadays, microgrids are emerging as an invaluable framework for the integration of renewable energy sources and demand response programs. In such systems, energy storage facilities are also frequently deployed to properly manage surplus energy from renewable sources on pursuing more efficient management of the system. Hybrid storage systems in which various storage facilities are combined may result in a more effective solution than only considering one storage technology. This way, the good features of the different technologies may be jointly exploited while their drawbacks are minimized. Due to the large-scale integration of renewable energies in this kind of grid, coping with uncertainties becomes a critical issue. Moreover, the operation of microgrids frequently deals with other kinds of uncertainties related to energy pricing from the upscale grid (in the case of grid-connected mode) or local demand. This way, proper modeling of uncertainties is essential for adequately operating these systems. This paper contributes to this pool by developing a novel interval-based formulation, for optimal scheduling of microgrids considering battery and pumped-hydro storage systems. To achieve this goal, the optimal scheduling of a microgrid with pumped-hydro and battery energy storage considering demand response is modeled, firstly. Then, the new interval-based formulation is used to cope with the uncertainties. Finally, the suggested model is verified using simulations in various cases, and the results confirm the effectiveness of the novel interval-based formulation for the optimal scheduling of microgrid with pumped-hydro and battery energy storage under uncertainty.

Automated summary

Microgrids serve as an important framework for integrating renewable energy sources and demand response programs. The deployment of energy storage facilities and the use of hybrid storage systems can lead to more efficient management. Coping with uncertainties and properly modeling them is crucial for the operation of microgrids.