Coordinated energy management strategy in scheme of flexible grid-connected hubs participating in energy and reserve markets

Abstract

This paper uses the coordinated energy management strategy for different sources and storages in the framework of flexible grid-connected energy hubs that participate in the day-ahead (DA) energy and reserve markets. In the base problem, this method maximizes the difference between the expected revenue of hubs gained by selling energy and reserve power in the proposed markets and the expected cost of lost flexibility (COLF). Also, it is subject to linearized optimal power flow (LOPF) equations in the electricity, gas and district heating systems, as well as hub constraints including different sources, storages and reserve models. This problem contains uncertainties of load, market price, reserve requirement, renewable power and hub mobile storages parameters. Therefore, the hybrid stochastic/robust optimization (HSRO) is suitable to model these uncertain parameters and obtain robust capability for the hub to improve the system flexibility. Accordingly, the bounded uncertainty-based robust optimization (BURO) is used in this paper to model the uncertainty of hub mobile storages to achieve the hub robust potential in improving the system flexibility, and other uncertain parameters are modeled according to scenario-based stochastic programming (SBSP). Finally, the proposed strategy is implemented on a standard test system. The obtained numerical results confirm the capability of the suggested scheme in improving the economic status of sources and storages using the coordinated energy management strategy in the form of an energy hub, as well as enhancing economic conditions, operation, and flexibility of energy networks thanks to hubs for having access to optimal scheduling.

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

“…The uncertainties of wind speed and electricity prices are handled by SP and RO respectively, in [44], where the main objective is to minimize the total operation cost of the EH. Reference [56] considers EVs' uncertainty parameters using RO, whereas the uncertainties of demands, market prices, reserve requirements, and renewable power are handled via SP. Among other works, [36], [39], [45], [46] have utilized SP to deal with the uncertainty of wind power (or PV generation), while deploying RO to cope with the worst-case realization of electricity market prices.…”