2026-10-11T02:00:49Z https://www.scidb.cn/oai

10.57760/sciencedb.010h8 2026-08-26T15:26:28Z

2026-08-26 Dataset for Architectural Design and Performance Simulation of MW-level Cyclic Continuous and Parallel Multi-track Ramp Gravity Energy Storage Systems doi:10.57760/sciencedb.010h8 en Addressing the limitations of conventional ramp gravity storage—characterized by low efficiency and poor scalability due to "single-car, single-track, reciprocating" operations—this study innovatively proposes a cyclic continuous and parallel multi-track integrated design. The dataset provides a comprehensive representation of the engineering design logic and performance boundaries of this closed-loop cyclic architecture. Through physical modeling, AnyLogic multi-threaded scheduling simulation, and market research, the dataset offers detailed data for 1–3 MW units, including:Architectural Characteristic Parameters: Unlike reciprocating systems, this dataset includes critical spatial-temporal coordination parameters such as "dynamic online mass blocks," "scheduling intervals," and "buffer exit speed limits" for the closed-loop cycle.Performance Adaptation Data: Quantitative results on how the system shares loads via a "dual-motor on a single track" configuration and achieves power multiplication through multi-track parallel operation across various slope heights (200–500m).Modular Scalability Metrics: Design parameters for mass block structures, mechanical response data for carrier cars (based on ANSYS simulations), and detailed cost breakdowns based on modular components.This dataset provides the first benchmark for the technological transition of gravity energy storage from "reciprocating units" to "cyclic clusters." It is suitable for integrating new long-duration energy storage systems, optimizing multi-unit scheduling strategies, and evaluating the economic feasibility of mountain-based energy storage plants. slope gravity energy storage; modular design; economic cost analysis Zhang Fan Chen Julong Mou Xuepeng Ouyang Zhangzhi Jia Wenlin PUBLIC https://creativecommons.org/licenses/by-nc/4.0/ dataset Science Data Bank