Integrated energy system (IES) represents a coordinated operation of constrained natural gas (NG), electric power, and thermal systems in three different time scales. Microturbines (MTs) are increasingly utilized in IES as suppliers of combined cooling, heating and power (CCHP) units, which optimize the usage and the delivery of interdependent NG, electric power, and thermal energy sectors in a distributed IES. In this paper, a dynamic IES model is derived by considering the dynamic interactions of the three energy systems in three time-scales. The dynamic integrated system represented as boundary layer system (BLS) is analyzed using singular perturbation. Simulation results demonstrate the validity of the proposed dynamic integrated model in three time-scales, highlighting the necessity of incorporating the dynamic characteristics of NG, electric power, and thermal energy in IES dynamic analyses.

The interdependence of electricity and natural gas networks (ENGNs) has led to increased complexity and competing interests among stakeholders. In the event of a leakage failure in a natural gas pipeline, the changes in pipeline parameters pose significant challenges to the optimization and dispatch of natural gas and power grid supply systems. Hence, it would be difficult to simultaneously coordinate the respective interests and reduce the loss of load in the power grid. This paper first presents a natural gas pipeline flow model that accounts for pipeline leakage and linepack, followed by a two-stage dispatch to optimize the benefits for both electricity and natural gas suppliers, where the optimal dispatch with the worst-case scenario is linearized through mixed-integer programming (MIP). Then, a joint solution model for leakage rate and linepack is established based on the macropore leakage model and the dispatch of natural gas suppliers, considering the impact of natural gas flow rate changes on leakage parameters. The actual natural gas supply and the two-stage optimal dispatch are obtained by using the column-and constraint generation (C&CG) method. Simulation results demonstrate that the proposed two-stage optimal dispatch improves the power supply reliability and offers promising economic prospects for ENGNs, where the accuracy of power grid dispatch is significantly improved considering the natural gas pipeline leakage and linepack.

As the persistent proliferation of renewable energy sources (RESs) will have a significant impact on the prediction of power system inertia, a statistically accurate prediction model for the system inertia, like the traditional prediction-then-optimize (PTO) model, might not necessarily lead to higher economics and security in the day-ahead scheduling. This paper proposes a predict-and-optimize (PAO) model for the day-ahead inertia prediction using distributionally robust unit commitment (DRUC). First, the probabilistic prediction of day-ahead inertia is obtained by the kernel density estimation (KDE), and the confidence set is used for the uncertainty quantization of DRUC. Then, the inertia prediction method is embedded into DRUC, combining with the relevant feature and the closed-loop feedback of DRUC cost increment. The cost-based day-ahead inertia prediction method is trained, in which the prediction quality is evaluated by inducing the DRUC cost increment rather than the statistical prediction error. Furthermore, the proposed PAO is trained by rolling updates to efficiently capture the time series characteristics of inertia, reducing the computational burden and enhancing the solution quality of DRUC. The case studies are carried out on the Finnish power system with a public dataset. The results show that the proposed PAO model offers a better prediction accuracy and incurs smaller re-dispatch cost increments as compared with those of the traditional PTO model, providing a higher security and economically efficient tool for transmission system operators (TSOs) in the day-ahead UC scheduling.

To ensure the high-reliability operation of overhead contact lines (OCLs), this paper proposes a data-driven time dependent reliability prediction approach, incorporating the maintenance factor, common cause failure (CCF), and lightning strike into reliability modeling and prediction for multi-component OCLs. Firstly, a new lightning-related failure probability calculation method is developed, including probabilistic lightning modeling, lightning identification, tracking and association analysis (LITAA), lightning movement trajectory prediction, and electric geometric model-based failure rate calculation. Moreover, a Bayesian network-based reliability model is embedded to integrate historical maintenance information, predictive lightning-related failure rate and fault tree model into reliability assessment of OCLs, which effectively captures the dependencies between failure events and depicts the impact of CCF on OCL components. In this context, the reliability of OCL components and OCLs can be predicted, with update of lightning strike data. Finally, the actual maintenance and lightning-related failure records are implemented to validate the effectiveness of the proposed approach. The result analysis demonstrates that our approach can not only illustrate how the reliability can be predicted using the predictive lightning-related failure rate, simultaneously accounting for imperfect maintenance and CCF, but also enhance risk prevention against lightning strike and faulty OCL components.

Area control centers can effectively utilize the dynamic model of wide-area power systems when large power system areas are subjected to major disturbances. However, as the modeling of individual areas could be performed at different time scales, the boundary power flows between areas may not satisfy power system conditions as individual equivalent area models are merged to represent the wide-area system. In this study, individual areas are represented by a generalized discrete-time equivalent model (GDEM). In particular, the paper considers the impact of modeling interfaces (MIs) for linking several GDEMs in which boundary power flows would be considered exclusively when individual area models are merged. The practical use of MI in GDEM is to represent widely-encountered power transfer paths in the Western Electricity Coordinating Council (WECC), including a two-area radial system for the Washington-Montana flow, and a three-area star-connected system for the Pacific AC Intertie. Based on the detailed two-area radial system and three-area wyeconnected system, we propose a generalized framework for an narea connected system. The China Electric Power Research Institute (CEPRI) system is also studied to verify the application of the proposed method which shows the use of MI in GDEM for analyzing the dynamic characteristics of wide-area power systems.