Dataset of Electric Autonomous Dial-a-Ride Instances with Local Energy Communities and Electricity Tariffs

This dataset provides benchmark instances for the electric autonomous dial-a-ride problem (e-ADARP) integrated with local energy communities (LECs). The instances are derived from real-world taxi trip data from Porto, Portugal, and incorporate energy data from two LECs, each consisting of 10 prosumers with photovoltaic generation and individual load profiles. The dataset contains: 20 instance files across 10 size configurations (2 to 20 vehicles, 20 to 200 requests), with two electricity tariff scenarios per configuration: ToU/ - Time-of-use tariff with mid-market rate pricing (files: r{K}-{N}.txt) Flat/ — Flat tariff with mid-market rate pricing (files: rF{K}-{N}.txt) LEC_results_midPoint.xlsx — Excel workbook with complete prosumer-level energy data for both LECs (19 sheets): member locations and feeder IDs, load consumption profiles, PV generation profiles, battery storage parameters, electricity buy/sell prices under ToU and flat tariffs, and grid connection limits. All time-series data are at 15-minute resolution (96 periods per day). results_LEC.zip — LEC optimization output CSV files used as direct input to the instance generation script. Contains the aggregated LEC balance (overproduction), MMR buy prices, and grid tariff buy prices for both LECs under both tariff scenarios, at 15-minute resolution. generate_instances.jl — Julia script implementing the full instance generation pipeline: reading taxi trajectory data, extracting the Porto road network via OSMnx, computing pairwise shortest-path travel time matrices, loading LEC energy profiles, generating time windows, and writing instance files in the format described in the README. group_prosumers.py — Python script implementing the compatibility-constrained clustering algorithm used to form the two LECs from a pool of 100 residential prosumers, based on geographic distance and electrical feeder assignment constraints. README.txt - A plain-text file describing the instance file format block by block, the repository folder structure, the required dependencies for running the generation scripts, and instructions for reproducing the dataset. This is the recommended starting point for new users of the repository. Each instance file encodes: geographic node coordinates (WGS84), pickup/dropoff time windows, vehicle parameters (battery capacity, energy consumption, passenger capacity, minimum final state-of-charge), charging station locations and rates, LEC energy overproduction profiles, electricity tariffs (grid and mid-market rate) per 15-minute interval, and a full pairwise travel time matrix computed via shortest paths on the OpenStreetMap road network. The instances enable reproducible benchmarking of routing and scheduling algorithms that jointly optimize transportation service quality and charging costs under realistic energy market conditions.本数据集为融合本地能源社区（Local Energy Communities, LECs）的电动自主拼车问题（electric autonomous dial-a-ride problem, e-ADARP）提供基准测试实例。这些实例源自葡萄牙波尔图的真实出租车出行数据，并整合了来自两个本地能源社区的能源数据，每个社区均包含10名配备光伏发电系统与个性化负荷曲线的产消者（prosumer）。 本数据集包含： 10种规模配置下共20个实例文件（车辆数覆盖2至20台，请求数覆盖20至200条），每种配置对应两种电价场景： - ToU/：采用中端市场定价的分时电价（Time-of-use tariff），对应文件命名格式为r{K}-{N}.txt - Flat/：采用中端市场定价的固定电价（Flat tariff），对应文件命名格式为rF{K}-{N}.txt LEC_results_midPoint.xlsx：包含两个本地能源社区完整产消者级能源数据的Excel工作簿，共19个工作表，涵盖成员位置与馈线ID、负荷消耗曲线、光伏发电曲线、电池储能参数、分时电价与固定电价下的电力买卖价格，以及电网连接限额。所有时序数据分辨率均为15分钟（每日包含96个时段）。 results_LEC.zip：作为实例生成脚本直接输入的本地能源社区优化输出CSV文件，包含两种电价场景下两个本地能源社区的聚合能源平衡（过剩发电量）、中端市场买入电价与电网关税买入电价，分辨率为15分钟。 generate_instances.jl：实现完整实例生成流程的Julia脚本，功能包括读取出租车轨迹数据、通过OSMnx提取波尔图道路网络、计算两两最短路径旅行时间矩阵、加载本地能源社区能源曲线、生成时间窗，并按照README中描述的格式写入实例文件。 group_prosumers.py：实现兼容性约束聚类算法的Python脚本，基于地理距离与电气馈线分配约束，从100名住宅产消者池中构建出两个本地能源社区。 README.txt：详细说明Instances.zip中实例文件格式的文档。 每个实例文件包含以下信息：地理节点坐标（WGS84坐标系）、取/送件时间窗、车辆参数（电池容量、能耗、载客量、最低最终荷电状态）、充电站位置与充电费率、本地能源社区能源过剩曲线、每15分钟时段的电价（电网电价与中端市场电价），以及基于OpenStreetMap道路网络最短路径计算得到的两两旅行时间矩阵。 本数据集支持在真实能源市场环境下，对联合优化运输服务质量与充电成本的路径规划与调度算法进行可复现的基准测试。