Refined multi-time scale optimal scheduling of dynamic integrated energy system based on superposition of energy flow response

Accurately capturing dynamic energy flow in an integrated energy system (IES) is time-consuming and cannot meet the demands of short-time scale dispatching. It also brings other challenges, such as scheduling decision delays caused by transmission inertia and maintaining state continuity between scheduling intervals. However, the neglect of state variables’ dynamic constraints results in the system being overloaded or out of limit, which seriously affects the secure and stable operation of IES. This paper proposes an efficient and accurate multi-time scale dynamic optimal scheduling method that leverages the superposition characteristics of energy flows, considering the network dynamics, topologies, and variable constraints, which have never been factored in short-time scale dynamic optimization for its minute-level time-consuming. First, a linear dynamic energy flow constraint is derived by transforming the energy flow model from the Laplace domain into a discrete time-domain mapping function, which reduces solve time from minutes to seconds without any loss of detail dynamics. Next, a multi-time scale dispatching framework that incorporates a double-layer intra-day rolling optimization is introduced, accounting for the influence of system inertia on scheduling decisions. Finally, the impact of decisions as the base value for subsequent optimizations is superimposed on the variations of decision variables, to ensure consistency in short-time scale scheduling. Comprehensive tests were conducted to validate the proposed dynamic optimization model, which performs 24-hour scheduling with a 100-second interval in just 2.75 s, yielding an error of 6.69e−03%. Compared to steady-state scheduling, the wind curtailment of the proposed multi-time-scale dynamic dispatch method is reduced by 94.6%, while operating costs decrease by 4.03%. Furthermore, the calculation efficiency increases by a factor of 59.1 compared to traditional methods, all while ensuring the system operates safely throughout the day.