Optimization of transactive energy systems with demand response: A cyber-physical-social system perspective

With the increasing penetration of renewable energy, a new type of energy system, transactive energy systems (TES), has emerged. This study investigates the challenges of optimally operating a TES distribution system with demand response (DR) from the cyber-physical-social system (CPSS) perspective. A TES optimization framework that integrates artificial systems, computational experiments, and parallel energy theory for modelling DR, via parallel system theory, is introduced. A data-driven artificial DR system is created and modelled using limited data. In the computational experiment, a complete information Stackelberg game model for the distribution network operator and the artificial DR system is built. This simulates the response relationship between the distribution network operator and the electricity consumer under different price conditions. In the parallel energy optimization model, a multi-time scale energy optimization method which considers day-ahead and intraday scenarios, the interaction between the actual TES and the artificial DR system is shown. Finally, empirical data from the Henan province in China is used as a case study to verify the effectiveness of the optimization method proposed in this study.