Texas Synthetic Power System Test Case (TX-123BT).zip

The dataset of the synthetic Texas 123-bus backbone transmission (TX-123BT) system. The procedures and details to create TX-123BT system are described in the paper below: Jin Lu, Xingpeng Li et al., “A Synthetic Texas Backbone Power System with Climate-Dependent Spatio-Temporal Correlated Profiles”. If you use this dataset in your work, please cite the paper above. ***Introduction: The TX-123BT system has similar temporal and spatial characteristics as the actual Electric Reliability Council of Texas (ERCOT) system. TX-123BT system has a backbone network consisting of only high-voltage transmission lines distributed in the Texas territory. It includes time series profiles of renewable generation, electrical load, and transmission thermal limits for the entire year of 2019. The North American Land Data Assimilation System (NLDAS) climate data is extracted and used to create the climate-dependent time series profiles mentioned above. Two sets of climate-dependent dynamic line rating (DLR) profiles are created: (i) daily DLR and (ii) hourly DLR. ***Power system configuration data: 'Bus_data.csv': Bus data including bus name and location (longitude & latitude, weather zone). 'Line_data.csv': Line capacity and terminal bus information. 'Generator_data.xlsx': 'Gen_data' sheet: Generator parameters including active/reactive capacity, fuel type, cost and ramping rate. 'Solar Plant Number' sheet: Correspondence between the solar plant number and generator number. 'Wind Plant Number' sheet: Correspondence between the wind plant number and generator number. ***Time series profiles: 'Climate_2019' folder: Include each day's climate data for solar radiation, air temperature, wind speed near surface at 10 meter height. Each file in the folder includes the hourly temperature, longwave & shortwave solar radiation, zonal & Meridional wind speed data of a day in 2019. 'dynamic_rating_2019' folder: Include the hourly dynamic line rating for each day in 2019. Each file includes the hourly line rating (MW) of a line for all hours in 2019. In each file, columns represent hour 1-24 in a day, rows represent day 1-365 in 2019. 'Daily_rating_2019.csv': The daily dynamic line rating (MW) for all lines and all days in 2019. 'solar_2019' folder: Solar production for all the solar farms in the TX-123BT and for all the days in 2019. Each file includes the hourly solar production (MW) of all the solar plants for a day in 2019. In each file, columns represent hour 1-24 in a day, rows represent solar plant 1-72. 'wind_2019' folder: Wind production for all the wind farms in the case and for all the days in 2019. Each file includes the hourly wind production (MW) of all the wind plants for a day in 2019. In each file, columns represent hour 1-24 in a day, rows represent wind plant 1-82. 'load_2019' folder: Include each day's hourly load data on all the buses. Each file includes the hourly nodal loads (MW) of all the buses in a day in 2019. In each file,columns represent bus 1-123, rows represent hour 1-24 in a day. ***Python Codes to run security-constrainted unit commitment (SCUC) for TX-123BT profiles Recommand Python Version: Python 3.11 Required packages: Numpy, pyomo, pypower, pickle Required a solver which can be called by the pyomo to solve the SCUC optimization problem. *'Sample_Codes_SCUC' folder: A standard SCUC model. The load, solar generation, wind generation profiles are provided by 'load_annual','solar_annual', 'wind_annual' folders. The daily line rating profiles are provided by 'Line_annual_Dmin.txt'. 'power_mod.py': define the python class for the power system. 'UC_function.py': define functions to build, solve, and save results for pyomo SCUC model. 'formpyomo_UC': define the function to create the input file for pyomo model. 'Run_SCUC_annual': run this file to perform SCUC simulation on the selected days of the TX-123BT profiles. Steps to run SCUC simulation: 1) Set up the python environment. 2) Set the solver location: 'UC_function.py'=>'solve_UC' function=>UC_solver=SolverFactory('solver_name',executable='solver_location') 3) Set the days you want to run SCUC: 'Run_SCUC_annual.py'=>last row: run_annual_UC(case_inst,start_day,end_day) For example: to run SCUC simulations for 125th-146th days in 2019, the last row of the file is 'run_annual_UC(case_inst,125,146)' You can also run a single day's SCUC simulation by using: 'run_annual_UC(case_inst,single_day,single_day)' * 'Sample_Codes_SCUC_HourlyDLR' folder: The SCUC model consider hourly dynamic line rating (DLR) profiles. The load, solar generation, wind generation profiles are provided by 'load_annual','solar_annual', 'wind_annual' folders. The hourly line rating profiles in 2019 are provided by 'dynamic_rating_result' folder. 'power_mod.py': define the python class for the power system. 'UC_function_DLR.py': define functions to build, solve, and save results for pyomo SCUC model (with hourly DLR). 'formpyomo_UC': define the function to create the input file for pyomo model. 'RunUC_annual_dlr': run this file to perform SCUC simulation (with hourly DLR) on the selected days of the TX-123BT profiles. Steps to run SCUC simulation (with hourly DLR): 1) Set up the python environment. 2) Set the solver location: 'UC_function_DLR.py'=>'solve_UC' function=>UC_solver=SolverFactory('solver_name',executable='solver_location') 3) Set the daily profiles for SCUC simulation: 'RunUC_annual_dlr.py'=>last row: run_annual_UC_dlr(case_inst,start_day,end_day) For example: to run SCUC simulations (with hourly DLR) for 125th-146th days in 2019, the last row of the file is 'run_annual_UC_dlr(case_inst,125,146)' You can also run a single day's SCUC simulation (with hourly DLR) by using: 'run_annual_UC_dlr(case_inst,single_day,single_day)' The SCUC/SCUC with DLR simulation results are saved in the 'UC_results' folders under the corresponding folder. Under 'UC_results' folder: 'UCcase_Opcost.txt': total operational cost ($) 'UCcase_pf.txt': the power flow results (MW). Rows represent lines, columns represent hours. 'UCcase_pfpct.txt': the percentage of the power flow to the line capacity (%). Rows represent lines, columns represent hours. 'UCcase_pgt.txt': the generators output power (MW). Rows represent conventional generators, columns represent hours. 'UCcase_lmp.txt': the locational marginal price ($/MWh). Rows represent buses, columns represent hours. ***Geographic information system (GIS) data: 'Texas_GIS_Data' folder: includes the geographic information systems (GIS) data of the TX-123BT system configurations and ERCOT weather zones. The GIS data can be viewed and edited using GIS software: ArcGIS. The subfolders are: 'Bus' folder: the shapefile of bus data for the TX-123BT system. 'Line' folder: the shapefile of line data for the TX-123BT system. 'Weather Zone' folder: the shapefile of the weather zones in Electric Reliability Council of Texas (ERCOT). *** Maps(Pictures) of the TX-123BT & ERCOT Weather Zone 'Maps_TX123BT_WeatherZone' folder: 1) 'TX123BT_Noted.jpg': The maps (pictures) of the TX-123BT transmission network. Buses are in blue and lines are in green. 2) 'Area_Houston_Noted.jpg', 'Area_Dallas_Noted.jpg', 'Area_Austin_SanAntonio_Noted.jpg':The maps for different areas including Houston, Dallas, and Austin-SanAntonio are also provided. 3) 'Weather_Zone.jpg': The map of ERCOT weather zones. It's ploted by author, may be slightly different from the actual ERCOT weather zones. ***Funding This project is supported by Alfred P. Sloan Foundation. ***License: This work is licensed under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0) license. ***Disclaimer: The author doesn’t make any warranty for the accuracy, completeness, or usefulness of any information disclosed and the author assumes no liability or responsibility for any errors or omissions for the information (data/code/results etc) disclosed. ***Contributions: Jin Lu created this dataset. Xingpeng Li supervised this work. Hongyi Li and Taher Chegini provided the raw historical climate data (extracted from an open-access dataset - NLDAS).

本数据集为合成德克萨斯123节点骨干输电（TX-123BT）系统数据集。TX-123BT系统的构建流程与细节已在以下论文中详述：Jin Lu、Xingpeng Li等人发表的《考虑气候相关时空关联特性的合成德克萨斯骨干电力系统》（A Synthetic Texas Backbone Power System with Climate-Dependent Spatio-Temporal Correlated Profiles）。若您的研究工作中使用了本数据集，请引用上述论文。

**简介：**
TX-123BT系统具备与实际德克萨斯电力可靠性委员会（Electric Reliability Council of Texas, ERCOT）系统相似的时空特性。该系统的骨干网架仅由分布于德克萨斯州境内的高压输电线路构成，包含2019年全年的可再生能源发电、电力负荷及输电热稳定极限的时序曲线。本数据集采用了从北美陆面数据同化系统（North American Land Data Assimilation System, NLDAS）提取的气候数据，以生成上述气候相关的时序曲线。本次数据集共生成两类气候相关的动态线路额定值（dynamic line rating, DLR）曲线：(i) 日度动态线路额定值；(ii) 小时度动态线路额定值。

**电力系统配置数据：**
- 'Bus_data.csv'：包含母线名称、位置（经纬度及气象分区）的母线数据。
- 'Line_data.csv'：包含线路容量与终端母线信息的数据。
- 'Generator_data.xlsx'：包含以下两个工作表：
- 'Gen_data'工作表：包含发电机组有功/无功容量、燃料类型、成本及爬坡速率的参数。
- 'Solar Plant Number'工作表：光伏电站编号与发电机组编号的对应关系。
- 'Wind Plant Number'工作表：风电场编号与发电机组编号的对应关系。

**时序曲线数据：**
1. 'Climate_2019'文件夹：包含2019年每日的气候数据，涵盖太阳辐射、气温、10米高度近地面风速。该文件夹内每个文件对应2019年某一日的逐小时气温、长波与短波太阳辐射、纬向与经向风速数据。
2. 'dynamic_rating_2019'文件夹：包含2019年每日各线路的小时度动态线路额定值。每个文件对应一条线路2019年全时段的逐小时额定传输容量（MW），文件中列代表一日内的1-24小时，行代表2019年的1-365天。
3. 'Daily_rating_2019.csv'：2019年所有线路、所有日期的日度动态线路额定值（MW）。
4. 'solar_2019'文件夹：包含TX-123BT系统内所有光伏电站2019年全年的发电出力数据。每个文件对应2019年某一日所有光伏电站的逐小时发电出力（MW），文件中列代表一日内的1-24小时，行代表1-72号光伏电站。
5. 'wind_2019'文件夹：包含案例中所有风电场2019年全年的发电出力数据。每个文件对应2019年某一日所有风电厂的逐小时发电出力（MW），文件中列代表一日内的1-24小时，行代表1-82号风电厂。
6. 'load_2019'文件夹：包含每日所有母线的逐小时负荷数据。每个文件对应2019年某一日所有母线的逐小时节点负荷（MW），文件中列代表1-123号母线，行代表一日内的1-24小时。

**用于TX-123BT数据集的安全约束机组组合仿真Python代码：**
本部分包含用于基于TX-123BT数据集运行安全约束机组组合（security-constrained unit commitment, SCUC）仿真的Python代码。
- 推荐Python版本：Python 3.11
- 所需依赖包：Numpy、pyomo、pypower、pickle
- 需配备可被pyomo调用以求解SCUC优化问题的求解器。

### 标准SCUC模型代码（'Sample_Codes_SCUC'文件夹）
该文件夹包含标准SCUC模型。其中，负荷、光伏发电、风电出力数据由'load_annual'、'solar_annual'、'wind_annual'文件夹提供；日度线路额定值数据由'Line_annual_Dmin.txt'提供。
- 'power_mod.py'：定义电力系统的Python类。
- 'UC_function.py'：定义用于构建、求解并保存pyomo SCUC模型结果的函数。
- 'formpyomo_UC'：定义用于生成pyomo模型输入文件的函数。
- 'Run_SCUC_annual'：运行该文件以对TX-123BT数据集的指定日期执行SCUC仿真。

运行SCUC仿真的步骤：
1. 配置Python运行环境。
2. 设置求解器路径：在'UC_function.py'的'solve_UC'函数中，设置`UC_solver=SolverFactory('solver_name',executable='solver_location')`。
3. 指定需运行SCUC的日期：在'Run_SCUC_annual.py'的最后一行，调用`run_annual_UC(case_inst,start_day,end_day)`。例如，若需对2019年第125至146日执行SCUC仿真，可将文件最后一行设置为`run_annual_UC(case_inst,125,146)`；若需运行单日SCUC仿真，可使用`run_annual_UC(case_inst,single_day,single_day)`。

### 带小时度DLR的SCUC模型代码（'Sample_Codes_SCUC_HourlyDLR'文件夹）
该文件夹包含考虑小时度动态线路额定值（DLR）的SCUC模型。其中，负荷、光伏发电、风电出力数据由'load_annual'、'solar_annual'、'wind_annual'文件夹提供；2019年小时度线路额定值数据由'dynamic_rating_result'文件夹提供。
- 'power_mod.py'：定义电力系统的Python类。
- 'UC_function_DLR.py'：定义用于构建、求解并保存带小时度DLR的pyomo SCUC模型结果的函数。
- 'formpyomo_UC'：定义用于生成pyomo模型输入文件的函数。
- 'RunUC_annual_dlr'：运行该文件以对TX-123BT数据集的指定日期执行带小时度DLR的SCUC仿真。

运行带小时度DLR的SCUC仿真的步骤：
1. 配置Python运行环境。
2. 设置求解器路径：在'UC_function_DLR.py'的'solve_UC'函数中，设置`UC_solver=SolverFactory('solver_name',executable='solver_location')`。
3. 指定需运行SCUC的日期：在'RunUC_annual_dlr.py'的最后一行，调用`run_annual_UC_dlr(case_inst,start_day,end_day)`。例如，若需对2019年第125至146日执行带小时度DLR的SCUC仿真，可将文件最后一行设置为`run_annual_UC_dlr(case_inst,125,146)`；若需运行单日带小时度DLR的SCUC仿真，可使用`run_annual_UC_dlr(case_inst,single_day,single_day)`。

SCUC及带DLR的SCUC仿真结果将保存于对应文件夹下的'UC_results'文件夹中，该文件夹内包含以下文件：
- 'UCcase_Opcost.txt'：总运营成本（单位：美元）
- 'UCcase_pf.txt'：潮流结果（MW），行代表线路，列代表小时。
- 'UCcase_pfpct.txt'：潮流占线路额定容量的百分比（%），行代表线路，列代表小时。
- 'UCcase_pgt.txt'：发电机组出力（MW），行代表常规发电机组，列代表小时。
- 'UCcase_lmp.txt'：节点边际电价（单位：美元/兆瓦时），行代表母线，列代表小时。

**地理信息系统（GIS）数据：**
'Texas_GIS_Data'文件夹包含TX-123BT系统配置及ERCOT气象分区的GIS数据，可通过ArcGIS等GIS软件查看与编辑。该文件夹包含以下子文件夹：
- 'Bus'文件夹：TX-123BT系统母线数据的形状文件。
- 'Line'文件夹：TX-123BT系统线路数据的形状文件。
- 'Weather Zone'文件夹：ERCOT气象分区的形状文件。

**TX-123BT系统与ERCOT气象分区地图：**
'Maps_TX123BT_WeatherZone'文件夹包含以下地图文件：
1. 'TX123BT_Noted.jpg'：TX-123BT输电网络地图，其中母线以蓝色标注，线路以绿色标注。
2. 'Area_Houston_Noted.jpg'、'Area_Dallas_Noted.jpg'、'Area_Austin_SanAntonio_Noted.jpg'：分别为休斯顿、达拉斯及奥斯汀-圣安东尼奥等不同区域的地图。
3. 'Weather_Zone.jpg'：ERCOT气象分区地图，由作者绘制，与实际ERCOT气象分区可能存在细微差异。

**资助信息：**
本项目由阿尔弗雷德·P·斯隆基金会（Alfred P. Sloan Foundation）资助。

**许可协议：**
本作品采用知识共享署名4.0（Creative Commons Attribution 4.0, CC BY 4.0）许可协议进行许可。

**免责声明：**
作者不对所披露的任何信息的准确性、完整性或实用性作出任何保证，且不对所披露的信息（数据/代码/结果等）中的任何错误或遗漏承担任何责任或义务。

**贡献说明：**
Jin Lu创建了本数据集，Xingpeng Li负责本项目的监督工作。Hongyi Li与Taher Chegini提供了从开放数据集北美陆面数据同化系统（NLDAS）中提取的原始历史气候数据。