Toward Multi-Layered MPC for Complex Electric Energy Systems

This chapter formalizes a model predictive control (MPC) formulation for complex multi-temporal multi-spatial electric energy systems. It is motivated by the need for data-enabled decision-making in the changing industry in which different entities have the ability to predict uncertainties at different temporal and spatial granularity. It is explained that these needs arise because today’s industry practice relies on software tools which assume specific temporal and spatial decoupling. These no longer hold in the environment with highly intermittent resources and diverse decision makers. To account for temporal and spatial interdependencies, concepts of spatial and temporal lifting are utilized. These are partly motivated by a previously proposed Dynamic Monitoring and Decision Systems (DyMoNDS) framework for operating smart grids. These methods confirm the information exchange protocols required for near-optimal and highly reliable cost-effective and clean electricity services as proposed earlier. The use of DyMonDS tools is illustrated for efficient integration of temporally diverse generation and demand response. It is shown that this can be done while ensuring stable operation with minimal fast expensive storage.