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.

With the wide application of distributed energy technology in active distribution networks (ADNs), virtual power plants (VPPs) are introduced as a promising integration and management technology. To coordinate their operation schedule and ensure confidentiality, we establish a coordinated optimal power flow (OPF) model and propose an efficient decentralized algorithm based on multi-parametric quadratic programming and Benders decomposition. In feasible sub-problems, the quadratic exchanging functions are built, which greatly accelerates the convergence process. In infeasible sub-problems, feasible cut sets can be calculated based on benders decomposition. Besides, an effective relaxation method is developed to address the degeneracy problem. To eliminate redundant feasible cut sets, a predetermined feasible region is constructed according to the characteristics of each VPP. The effectiveness of the proposed method is demonstrated via numerical tests using two cases of different scales. The results show that the proposed method converges much faster than some prevailing methods. Furthermore, the coordinated OPF has a better fuel economy than the conventional OPF scheme.

The heat and electricity integrated energy system (HE-IES) has high energy efficiency and bright application prospects. In the HE-IES, the heat transmission in the heating network and the indoor temperature variation in the buildings are governed by (partial) differential equations, which bring the heat dynamics problem in the system operation. In this paper, we propose a comprehensive approach for the operation of the HE-IES to coordinate the electricity and heat dynamics, including modeling, evaluation, and dispatch procedure. Firstly, we formulate a high resolution model for the heating network and buildings to describe the heat dynamics; Two indices are proposed to measure the impact of heat dynamics, which is then used to select the time resolution. Secondly, an optimal dispatch model with high resolution for the heat dynamics is formulated for the HE-IES; A decentralized and parallel solution method is proposed based on the alternating direction method of multipliers (ADMM). Thirdly, a two-stage procedure for the time resolution selection is proposed, which consists of a dispatch decision stage and a time resolution evaluation stage. A small illustrative case is studied to verify the effectiveness of the proposed method. A big case based on a real heating network in Jilin Province, China is also simulated.

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.

The integrated energy system (IES) has bright prospects in engineering applications for its excellent performance in energy efficiency and renewable energy consumption. In the IES, the thermal comfort is affected by the heating power, buildings parameters, and outdoor temperatures simultaneously. Therefore, the uncertainty of outdoor temperature will bring some adverse effects on thermal comfort, which need to be considered in the dispatch decision of the IES. In this paper, we propose a day-ahead adaptive robust dispatch model (ARDM) for the IES to make a dispatch plan under the uncertainties of the net electrical-load and outdoor temperature, with the aim of guaranteeing the safe operation of IES and the thermal comfort of end-users. The thermal dynamic characteristics of the dis-trict heating network and buildings are utilized to provide operational flexibility and improve economic performance. To decrease the conservatism of dispatch results, the multi-interval uncertainty set is introduced to model the uncertainties. The ARDM model is a two-stage robust optimization with a linear recourse problem, and the column-and-constraint generation method is used to solve it. Two cases of different scale are studied to verify the effectiveness and advantages of the pro-posed method.

Electric vehicles (EVs) are expected to play a critical role in future transportation systems. A number of countries have published roadmaps aiming to facilitate the adoption of EVs on the road. It is estimated that existing charging facilities will not be able to satisfy the tremendous charging demands of a dramatically increasing number of EVs. Following the rapid development of artificial intelligence and mobile communication technology, certain charging pricing mechanism is expected to influence the charging behavior of EV drivers. In order to maximize the working efficiency of highway charging facilities and the consumption of the renewable energy near charging facilities, this paper proposes a pricing methodology taking into account the charging facility service ratio, traffic flow and renewable energy generation. To support the adoption of the proposed pricing methodology, forecasts of hour-by-hour traffic flow and renewable generation as well as calculation of the shortest paths to different charging stations (CSs) are investigated. A road network testbed based on the Dublin traffic network is established to evaluate the proposed pricing methodology. It is discovered that for certain wind-rich CSs, the proposed pricing methodology can increase the consumption rate of wind energy by up to 82.97%, with an average improvement of 30.73%; for certain solar-rich CSs, it can improve the level of solar energy consumption by up to 59.50% and an average increase of 29.28% is achieved. The proposed pricing methodology can also reduce traffic jams to some extent at both peak and off-peak times.