To retain intact state information of the district heating network (DHN) as possible in the optimization of heat and electricity integrated energy system (HE-IES), this paper combines the transient heat flow and steady-state electric power flow to formulate the dynamic optimal energy flow (OEF) model of HE-IES. For efficient and standardized solution, the finite difference method is applied to convert the partial differential equation constraint (introduced by the temperature dynamics in DHN) into a set of linear equality constraints. The structure of applicable difference schemes for system optimization is analyzed, based on which, a scheme with balanced performances in stability, convergence, simulation accuracy, and computation burden is developed. Moreover, with the proposed multi-objective optimization based method to select proper spatial and temporal calculation step sizes, a compromise between model precision and solution complexity can be reached. Case studies validate the feasibility and benefits of our proposed dynamic OEF computing method, which can further provide support for making optimal planning and operating strategies.

Wide development of the heat and electricity integrated energy system (HE-IES) has provided flexible multi-energy optimization management, which improves energy efficiency and benefits the environment. It is essential to calculate energy flow quickly, accurately, and reliably as the system scale and complexity increase. To handle this problem, we propose a partitional decoupling method based on a decomposed energy flow model for the large-scale HE-IES. The computation mechanisms are presented considering various system scales and topologies, which consist of decoupling mode to solve the non-convergence caused by hydraulic initialization and the iteration mode to correct the variables at common nodes of decoupled systems. Moreover, a unit conversion method is introduced to solve the non-convergence problem in energy flow calculation under extreme scenarios. Case studies show that the proposed method provides accurate energy flow calculations with higher efficiency regard-less of system scale and topology, especially in looped systems. Numerical simulation further verifies the effectiveness of the proposed method for non-convergence.

Energy management is facing new challenges due to the increasing supply and demand uncertainties, which is caused by the integration of variable generation resources, inaccurate load forecasts and non-linear efficiency curves. To meet these challenges, a robust optimization method incorporating piecewise linear thermal and electrical efficiency curve is proposed to accommodate the uncertainties of cooling, thermal and electrical load, as well as photovoltaic (PV) output power. Case study results demonstrate that the robust optimization model performs better than the deterministic optimization model in terms of the expected operation cost. The fluctuation of net electrical load has greater effect on the dispatching results of the combined cooling, heating and power (CCHP) microgrid than the fluctuation of the cooling and thermal load. The day-ahead schedule is greatly affected by the uncertainty budget of the load demand. The economy of the optimal decision could be achieved by adjusting different uncertainty budget levels according to control the conservatism of the model.