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• 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]