  In order to balance the interests of integrated energy operator (IEO) and
users, a novel Stackelberg game-based optimization framework is proposed for
the optimal scheduling of integrated demand response (IDR)-enabled integrated
energy systems with uncertain renewable generations, where the IEO acts as the
leader who pursues the maximization of his profits by setting energy prices,
while the users are the follower who adjusts energy consumption plans to
minimize their energy costs. Taking into account the inherent uncertainty of
renewable generations, the probabilistic spinning reserve is written in the
form of a chance constraint; in addition, a district heating network model is
built considering the characteristics of time delay and thermal attenuation by
fully exploiting its potential, and the flexible thermal comfort requirements
of users in IDR are considered by introducing a predicted mean vote (PMV)
index. To solve the raised model, sequence operation theory is introduced to
convert the chance constraint into its deterministic equivalent form, and
thereby, the leader-follower Stackelberg game is tackled into a mixed-integer
quadratic programming formulation through Karush-Kuhn-Tucker optimality
conditions and is finally solved by the CPLEX optimizer. The results of two
case studies demonstrate that the proposed Stackelberg game-based approach
manages to achieve the Stackelberg equilibrium between IEO and users by the
coordination of renewable generations and IDR. Furthermore, the study on a real
integrated energy system in China verifies the applicability of the proposed
approach for real-world applications.
