Geo-distributed self-balancing of renewable power generation and flexible loads based on information gap decision theory

The real-time supply-demand balance has become a great challenge for the power systems with high-penetration of volatile renewable energy (RE) generation. A promising way for addressing the power balancing issues is to exploit the regulation capability of flexible loads (FLs). Under the deregulated power market environment, the RE stations can be coordinately operated with dispersed FLs and big electric power customers (BEPCs) by making mid-or long-term energy contracts. This paper proposes a risk-averse self-balance scheduling (SBS) approach for a geo-distributed energy alliance involving RE, FLs, and BEPCs. The regulation capability of FLs is attained through the optimal energy management of distributed generators, power-to-gas (PtG) facilities, and multi-carrier energy storage. Also, in the view of individual energy alliances, the congestion of power transmission network can be considered and modeled as an uncertain factor, attributed to the variable power injections of multiple dispersed nodes. Particularly, the Information gap decision theory (IGDT) is applied to hedge against the congestion risks. The resulting risk-averse robust formulation provides an SBS scheme with a maximized robustness level, while satisfying the pre-specified pessimistic expectation. Comparative case studies on 30-bus IEEE test system demonstrate the effectiveness and economic benefits of the proposed scheduling method.