KU-MG1: A Dataset for Hybrid Photovoltaic-Natural Gas Generator Microgrid Model of a Residential Area. (For Nirala residential area, Khulna, Bangladesh)

A renewable energy resource-based sustainable microgrid model for a residential area is designed by HOMER PRO microgrid software. A small-sized residential area of 20 buildings of about 60 families with 219 MWh and an electric vehicle charging station of daily 10 batteries with 18.3MWh annual energy consumption considered for Nirala residential area, Khulna (22°48.0'N, 89°33.4'E) is selected as our case study. Solar panels, natural gas generator, inverter and Li-ion batteries are required for our proposed model. The HOMER PRO microgrid software is used to optimize our designed microgrid model. Data were collected from HOMER PRO for the year 2007. We have compared our daily load demand 650KW with the results varying the load by 10%, 5%, 2.5% more and less to find out the best case according to our demand. We have a total of 7 different datasets for different load conditions where each dataset contains a total of 8760 sets of data having 6 different parameters for each set.
Data file contents:
Data 1:: original_load.csv:
This file contains data for 650KW load demand. The dataset contains a total of 8760 sets of data having 6 different parameters for each set. Data arrangement is given below:
Column 1: Date and time of data recording in the format of MM-DD- YYYY [hh]:[mm]. Time is in 24-hour format.
Column 2: Solar power output in KW unit.
Column 3: Generator power output in KW unit.
Column 4: Total Electrical load served in KW unit.
Column 5: Excess electrical production in KW unit.
Column 6: Li-ion battery energy content in KWh unit.
Column 7: Li-ion battery state of charge in % unit.

Data 2:: 2.5%_more_load.csv:
This file contains data for 677KW load demand. The dataset contains a total of 8760 sets of data having 6 different parameters for each set.
Column information is the same for every dataset.

Data 3:: 2.5%_less_load.csv:
This file contains data for 622KW load demand. The dataset contains a total of 8760 sets of data having 6 different parameters for each set.
Column information is the same for every dataset.

Data 4:: 5%_more_load.csv:
This file contains data for 705KW load demand. The dataset contains a total of 8760 sets of data having 6 different parameters for each set.
Column information is the same for every dataset.
Data 5:: 5%_less_load.csv:
This file contains data for 595KW load demand. The dataset contains a total of 8760 sets of data having 6 different parameters for each set.
Column information is the same for every dataset.
Data 6:: 10%_more_load.csv:
This file contains data for the 760KW load demand. The dataset contains a total of 8760 sets of data having 6 different parameters for each set.
Column information is the same for every dataset.
Data 7:: 10%_less_load.csv:
This file contains data for 540KW load demand. The dataset contains a total of 8760 sets of data having 6 different parameters for each set.
Column information is the same for every dataset.

本研究针对居民区设计了基于可再生能源的可持续微电网模型，采用HOMER PRO微电网软件（HOMER PRO）进行建模。本次研究选取位于库尔纳（Khulna）的尼拉拉居民区（Nirala residential area）作为案例，该居民区为小型住宅小区，包含20栋建筑、约60户家庭，年用电量为219 MWh，配套设有日均为10台电池充电的电动汽车充电站，该充电站年能耗达18.3 MWh，地理坐标为北纬22°48.0′，东经89°33.4′。

所提出的模型需配备太阳能光伏板（Solar panels）、天然气发电机（natural gas generator）、逆变器（inverter）以及锂离子电池（Li-ion batteries）。随后采用HOMER PRO微电网软件对所设计的微电网模型进行优化，并采集2007年的相关数据开展分析。

本研究以650 kW的日负荷需求为基准，通过将负荷分别上下浮动10%、5%、2.5%生成多组工况，以探寻契合研究需求的最优方案。本次研究共包含7组针对不同负荷工况的数据集，每组数据集均包含8760条数据，每条数据包含6项不同参数。

数据文件详情：
1. 数据集1：original_load.csv
该文件对应650 kW负荷需求的相关数据，共包含8760条数据，每条数据包含6项参数，各列数据说明如下：
列1：数据记录的日期与时间，格式为MM-DD-YYYY [hh]:[mm]，时间采用24小时制；
列2：太阳能光伏板发电出力，单位为kW；
列3：天然气发电机发电出力，单位为kW；
列4：总供电负荷，单位为kW；
列5：过剩电能产出，单位为kW；
列6：锂离子电池存储电量，单位为kWh；
列7：锂离子电池荷电状态（State of Charge, SOC），单位为%。

2. 数据集2：2.5%_more_load.csv
该文件对应677 kW负荷需求的相关数据，共包含8760条数据，每条数据包含6项参数，各列信息与上述数据集一致。

3. 数据集3：2.5%_less_load.csv
该文件对应622 kW负荷需求的相关数据，共包含8760条数据，每条数据包含6项参数，各列信息与上述数据集一致。

4. 数据集4：5%_more_load.csv
该文件对应705 kW负荷需求的相关数据，共包含8760条数据，每条数据包含6项参数，各列信息与上述数据集一致。

5. 数据集5：5%_less_load.csv
该文件对应595 kW负荷需求的相关数据，共包含8760条数据，每条数据包含6项参数，各列信息与上述数据集一致。

6. 数据集6：10%_more_load.csv
该文件对应760 kW负荷需求的相关数据，共包含8760条数据，每条数据包含6项参数，各列信息与上述数据集一致。

7. 数据集7：10%_less_load.csv
该文件对应540 kW负荷需求的相关数据，共包含8760条数据，每条数据包含6项参数，各列信息与上述数据集一致。