TemoaProject: Energy Storage Options for North Carolina

This zenodo archive includes the Temoa code and data used to produce Energy Storage Options for North Carolina. Temoa was used to evaluate the use of storage for bulk energy time shifting and peak capacity deferral, as described in Section 6.5.

The data files are split into the capacity expansion and operational versions of the data. To begin, the capacity expansion runs are conducted to estimate the 2030 electricity generation mix in North Carolina. The optimal installed capacity is then fixed in the operational model, and the operational model is run to determine how storage with a fixed installed capacity and duration affects the objective function value. The change in objective function value, corresponding to the change in total system cost, is used to assess the net benefit associated with using storage for bulk energy time shifting and peak capacity deferral. (See report for more details.)

The primary difference between the capacity expansion and operational versions of the model are the number of time slices. In the capacity expansion model, electricity supply and demand is balanced over a representative 24-hour period for each of the four seasons, for a total of 96 time slices per year. The operational model includes 8760 time slices per year, thereby representing every hour of the year, which allows us to better capture the value of storage.

In terms of workflow, the capacity expansion runs were conducted with an input sqlite database file. Here we include the raw text-based sql file. It can be converted to a binary sqlite database file with the freely available sqlite program: https://www.sqlite.org. The results from the capacity expansion runs were automatically stored in the output tables associated with the same sqlite database. To run the operational version, capacity results are extracted from the capacity expansion sqlite file, and added to an input DAT file. These input DAT files are text-based files located in the scenario-specific folders for the operational runs. (Note that Temoa can run with either a sqlite or DAT file specified as input.)

Each scenario folder for both the capacity expansion and operational runs also contain the config file used to run the model. Each model run was executed from the command line with the following syntax:

$ python temoa_model/ --config=temoa_model/

Note that the specific instance of the Temoa source code we ran corresponds to git commit hash 06edf7b883cee44ebd1129803f2145687c46bea0.
We verified the model code works with Pyomo versions 5.3 and 5.5.

本Zenodo存档（Zenodo Archive）包含用于支撑《北卡罗来纳州储能方案》研究的Temoa（Temoa）代码与相关数据。如第6.5节所述，本研究借助Temoa评估了储能用于大规模电能时移与峰值容量延迟的应用效果。

数据文件分为容量扩展与运行建模两类数据集。首先执行容量扩展模拟，以估算北卡罗来纳州2030年的发电组合。随后将最优装机容量固定至运行模型中，运行该模型以分析固定装机容量与持续时长的储能对目标函数值的影响。目标函数值的变化（对应系统总成本的变动）被用于评估储能用于大规模电能时移与峰值容量延迟所带来的净收益。（详见研究报告。）

容量扩展模型与运行模型的核心差异在于时间切片数量。容量扩展模型中，电力供需平衡以四季各一个典型24小时周期为基准，全年共计96个时间切片。运行模型则包含全年8760个时间切片，即覆盖一年中的每一小时，从而能够更精准地捕捉储能的应用价值。

从工作流程来看，容量扩展模拟通过输入SQLite数据库（SQLite）文件执行。本次存档中提供了原始文本格式的SQL文件，可通过开源工具SQLite将其转换为二进制SQLite数据库文件（下载地址：https://www.sqlite.org）。容量扩展模拟的结果会自动存储至该SQLite数据库的对应输出表中。若要运行操作模型，需从容量扩展的SQLite文件中提取容量结果，并将其添加至输入DAT文件（DAT File）中。这些输入DAT文件均为文本格式，存放于各运行场景对应的专属文件夹内。（注：Temoa支持以SQLite文件或DAT文件作为输入源。）

容量扩展与运行模拟的各场景文件夹中，均包含用于运行模型的配置文件。所有模型运行均通过命令行执行，语法如下：
$ python temoa_model/ --config=temoa_model/

本次使用的Temoa源代码对应的Git提交哈希值为06edf7b883cee44ebd1129803f2145687c46bea0。经测试，该模型代码可兼容Pyomo（Pyomo）5.3与5.5版本。