Optimal Dispatch for Integrated Energy System Considering Data-Driven Dynamic Energy Hubs and Thermal Dynamics of Pipeline Networks

The modeling of dynamics in energy devices and pipeline networks reflects the real states of multi-energy flows, which is significant for realizing accurate optimal dispatch of integrated energy system (IES). In this paper, an approach with data-driven dynamic energy hubs (DDEH) and thermal dynamics of pipeline networks (TDPN) is proposed to describe the energy dynamic response process of IES. Based on the efficiency characteristics of energy conversion and storage devices, data-driven deep neural network (DNN) is adopted to excavate input-output relationship of the variable efficiency devices, which helps establish DDEH in time domain. To address the problem of nonlinear introduced by DNN, the nonlinear activation function in DDEH is equivalently converted into mixed integer linear model. At the same time, the TDPN is developed by bilateral characteristic line method (BCLM), which quantifies the time delay and loss of pipeline networks. TDNP demonstrates the network transportation dynamics in optimal dispatch, and the virtual energy storage effect of pipeline networks are analyzed. Case study from a community IES verifies the proposed approach effectively improve dynamic modeling accuracy of devices and pipeline networks, and have superiority in providing precise, reasonable and highly efficient optimal dispatch scheme.