Two-Stage Participation of Grid-Connected Energy Hubs in Day-Ahead and Real-Time Energy Markets Based on Robust Modeling of Uncertainties

Optimal management of sources, storage, and responsive loads in the energy network leads to improvement of economic and technical situation of the network. However, note that single-layer energy management requires to provide the network operator with more amounts of information. This results in the increase in the processing speed for the network operator. Also, coordination of sources, storage, and responsive loads in the form of an aggregator such as an energy hub can play an important role for them in promoting the economic situation and energy efficiency. So, this paper presents the participation of grid-connected energy hubs (EHs) in the day-ahead (DA) and real-time (RT) energy markets. Scheme has a two-stage optimization framework. In the first stage, participation of EHs in the hourly DA energy market is executed based on a two-layer energy management system that is modeled as bilevel problem. EH model in DA market is formulated in upper-level problem aimed at maximizing their profit subject to meeting the linearized constraints of the EH's operation. Lower-level problem deals with the operation of energy networks to minimize cost of energy purchase from the upstream network constrained to linearized optimal power flow equations. The second stage of participation of EHs in a 5 min RT market has an objective function to minimize the unbalance between the EHs' profits in the mentioned two markets. This stage is subject to the upper-level constraints and the lower-level formulation in the first stage. Robust optimization models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage to achieve an optimal robust solution. Benders decomposition algorithm solves bilevel formulation in two-stage scheme. Numerical results confirm capabilities of the scheme in enhancing operation status of the networks and economic situation of EHs. Hence, the Benders decomposition algorithm is able to extract the optimal solution in shorter computation time. Two-layer energy management needs shorter computation time than the single-layer management. Simultaneous modeling of two real-time and day-ahead energy markets reduces the unbalance cost between these markets. The proposed scheme can enhance the operating and economic indicators of the network by up to around 18% and 26% compared to power flow studies of energy networks.

Automated summary

This paper discusses the optimal management of energy sources, storage, and responsive loads in the energy network to improve its economic and technical situation. The participation of grid-connected energy hubs in day-ahead and real-time energy markets is presented, using a two-stage optimization framework. The proposed scheme enhances the operation status and economic indicators of the network, outperforming power flow studies of energy networks.