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• The coordinate transformation stability criterion (CTSC) is derived by constructing the SISO model of the GCI system in αβ domain. This approach provides a simple and intuitive form while minimizing conservatism. • The average parameter assessment metric (APAM) is proposed based on CTSC to evaluate the impact of each parameter over a wide range. • The joint parameter assessment metric (JPAM) is proposed to simultaneously adjust multiple parameters on stability. • System stability can be effectively improved by selecting parameter tuning through parameter assessment metrics. The negative resistance of grid-connected inverter (GCI) and the increasing number of GCI in power grid pose great challenges to the stability of GCI. This paper proposes a coordinate transformation stability criterion (CTSC) based on the single-input single-output (SISO) impedance model for GCI system. A low complexity fractional polynomial model of GCI is derived by the Pade approximation and the SISO impedance model in the αβ domain. CTSC is proposed by applying the Nyquist stability criterion, which simplifies the graph trajectory criterion to a numerical criterion. Since the effect of parameter on stability is usually limited to the neighborhood, average parameter assessment metric (APAM) is proposed based on CTSC to evaluate the impact of each parameter over a wide range. Additionally, joint parameter assessment metric (JPAM) is proposed to simultaneously adjust multiple parameters on stability. Finally, compared with the three criteria for conservatism analysis, CTSC possesses the lowest conservatism. The theory proposed in this paper is verified by experiment. [ABSTRACT FROM AUTHOR]

• A failure propagation assessment method that takes into account the electric-driven compressors and electric-driven gas sources is proposed for IESs. • The method can accurately assesses the impact of failure propagation between integrated energy systems through coupling devices. • Markov models of various failure states of natural gas pipelines are established. • The model can clearly assesses the impact of three types of pipeline failures: air leakage from a small hole, air leakage from a large hole, and rupture. With the increasing coupling between different systems, the risk of failure propagation in integrated energy systems (IESs) is consequently increased. At present, the multiple failure states in natural gas pipelines are not considered in most existing failure propagation assessment methods. Therefore, this paper proposes a failure propagation impact assessment method for the IESs that considers the multiple failure states of natural gas pipelines and the dependent characteristics of different systems. First, the multi-state failure model and coupling equipment model of natural gas pipeline are established. Based on the redispatch models of systems, the failure propagation flow of IES system under different coupling levels and different pipeline failure states is analyzed. Then, assesses the impact of these failures using assessment metrics. Finally, the effectiveness of the proposed method is validated in an IES. Numerical results show that compared with using the two-state model, the total load shedding of the IES is decreased by about 50 % by adopting the multi-state model. Also, it is validated that with the consideration of electric-driven compressors and electric-driven gas sources, the failures in power system have more significant impacts on the reliability of natural gas system, which is more correspondent to the actual failure propagation of IESs. [Display omitted] [ABSTRACT FROM AUTHOR]

• An innovative scheduling scheme with an extended decision horizon is proposed for the IEHS, which considers the optimal status of the HST at the end of each dispatch period. • A bi-level hierarchical model integrating an upper-level DRL-based model with a lower-level mathematical programming-based model is developed. • The determination of EHC is formulated as a Markov decision process in the upper-level model and solved through DRL methods. • The beta policy is introduced to the proximal policy optimization algorithm to obtain the optimal DRL policy. The integration of heat storage tank (HST) in the integrated electricity–heat system (IEHS) has been widely adopted to facilitate the accommodation of wind power. However, existing studies generally put a simplified end-of-period heat capacity (EHC) constraints of the HST, which limits the utilization of potential heat supply in subsequent dispatch periods. To addresses the issue, this paper proposes an innovative scheduling scheme with an extended decision horizon for the IEHS, considering the optimal status of the HST at the end of each dispatch period. To implement this scheme, a bi-level hierarchical model is developed, which integrates a high-level planning model based on deep reinforcement learning with a low-level scheduling model based on mathematical programming. The high-level planning model aims to learn the optimal policy for determining a suitable EHC in accordance with the heat release needs of subsequent dispatch periods. Meanwhile, the low-level scheduling model incorporates the EHC as a constraint and formulates a mixed-integer quadratic programming subproblem, wherein the reward is computed and fed back to the high-level model to guide the policy learning. Experimental results conducted on the modified 6-bus test system verified the effectiveness and economic benefits of the proposed scheduling scheme, which significantly enhances the wind power accommodation capacity of the IEHS. [ABSTRACT FROM AUTHOR]

• A precise Rate Of Change Of Frequency (ROCOF) estimation algorithm is proposed through utilizing the least square method with flexible window size and median filler. • An event detector is designed and utilized to analyze the real-world transients captured from the Hawaiian Islands. Three kinds of events are successfully captured and categorized. The parameters in the ROCOF estimation algorithm are optimized through using event based statistic analysis. • The real-world performance of the proposed algorithm is verified through deployment in a low-cost, flexible, and distribution level universal grid analyzer platform. The accuracy of both frequency and ROCOF estimations are verified under steady state and wall sources. • Multiple experiments are conducted both in the laboratory and in the Hawaiian Islands to verify the effectiveness of the proposed algorithm. The precise estimation of Rate Of Change Of Frequencies (ROCOFs) in a generation trip event can be helpful on power system inertia estimation, fast system response, and accurate event capturing. However, the ROCOF estimations from the existing Synchronized Measurement Devices (SMDs) are usually simply calculated by the finite difference between two adjacent frequency measurement points. The ineluctable noises, disturbances, and spikes from real-world frequency measurements can bring large dynamics to the ROCOF estimation and thus can result in an inaccurate estimation of the initial ROCOF. This issue becomes more serious when the target power grid has low inertia and a large amount of distributed energy sources are deployed. To address this issue, a precise ROCOF estimation algorithm is designed based on least square method with flexible window size. In addition, a median filter is also designed and applied on the frequency measurements before using the proposed algorithm. The window size and thresholds in the proposed algorithm are determined with historical event data analysis. The proposed algorithm is deployed in a low cost, flexible, and distribution level universal grid analyzer (UGA) platform. Multiple experiments are conducted in both a laboratory and the Hawaiian Islands to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]