Integrating energy storage systems (ESSs) into the railway power flow controller (RPFC) offers a promising path to enhance the interaction capability and connection compatibility between the railway power systems (RPSs) and the utility. Despite its technical advantages, full consideration must be given to the media-type & configuration of ESS, the rating of RPFC, and the control framework that can compatibly integrate the control & planning for cost-efficiency improvement. For this problem, a novel concept that takes the real-time control and off-line configuration approach in a unified framework is proposed for hybrid ESS-integrated RPFC (HESS-RPFC) in this paper, in which the comprehensive techno-economic performances of the whole system are taken into account. First, a unified analytical power regulation framework is established for HESS-RPFC, in which the functions of energy management (EM) and power quality control are seamlessly integrated. To realize EM, improve RPS’s grid-connection compatibility, and reduce the rating of RPFC, a double-layer power regulation strategy of HESS-RPFC is developed on the proposed framework. Subsequently, a lifetime net benefit-oriented optimization model is formulated and solved, through which, the optimal rating configuration of the whole system and HESS’s mode-triggering thresholds can be obtained. Finally, a comprehensive performance evaluation based on real-measured data is conducted, revealing that HESS-RPFC with the proposed method provides superior economic benefits over single-media ESS schemes, ensures satisfactory grid-connection compatibility for the RPS, and reduces the RPFC’s rating by nearly 46%. Additionally, the real-time execution feasibility of the proposal is demonstrated by hardware-in-the-loop tests.

In-depth understanding of the multi-energy consumption behavior pattern is the essential to improve the management of multi-energy system (MES). This paper proposes a data-driven three-stage adaptive pattern mining approach for multi-energy loads, which addresses the issues of complex multi-dimensional time-series mining, uncommon daily loads discovery, typical load classification and parameter setting requiring user intervention. In the first stage, the relative state changes over time between different energy loads are excavated based on Autoplait, which realizes time pattern discovery, segmentation and match for multi-dimensional loads. In the second stage, adaptive affinity propagation (AAP) considering trend similarity distance (TSD) is proposed to classify loads into common and uncommon clusters, where uncommon loads are eliminated and daily pattern is obtained by taking average of common loads. In the third stage, AAP with windows dynamic time warping (WDTW) identifies various profiles to obtain typical pattern of daily loads. Specifically, pattern mining provides the key information of multi-energy loads, which is significant to the applications for the demand side, such as load scene compression, load forecasting and demand response analysis. A case study uses MES data from Arizona State University to verify the effectiveness and practicality of the proposed approach.