Energy system models for Finland, 2023 and 2030, incl. power and district heating sectors

This dataset is used for modeling of Finnish energy system for reference years 2023 and 2030. The system model includes power and district heating sectors.

Two different modeling frameworks were used: EnergyPLAN version 16.3 and Calliope version 0.6. The dataset contains the model files for both frameworks, distribution files used in the simulation, results files for Calliope and scripts used for post-processing the Calliope results.

The approach is focused on optimisation of the district heat generation with expanding variable renewable electricity generation in the power side through the increase of wind power capacity. Various heat generation technologies are used; main focus is in the role of heat pumps and electric boilers. CHP, boilers, heat recovery and waste CHP are also included in the district heating system.

The dataset contains the reference system model for both Calliope and EnergyPLAN. This is named Reference 2023. The 2030 scenarios are ran several times with varied wind generation capacity: SC0 has the original heat generation capacities. SC1.1 is a scenario where 1800MWth of boilers are replaced from both groups 2 and 3 with heat pumps (HPs). SC1.2 has 1800MWth and 3600MWth of HPs in G2 and G3. SC2.1 and SC2.2 utilise eletric boilers (EBs) instead of HP's, capacities are the same as in SC1.1 and SC1.2. SC3 is a Calliope optimization model, where the heat generation capacities are optimized. Furthermore the dataset contains a sensitivity analysis with varied heat pump COP and investment cost.

The .ipynb-files are used for post processing of the Calliope results data with Python (Jupyterlab).

In the selected approach the wind power capacity was varied - hence the optimization was run 10 times with Calliope, and the serial calculations tool was used in EnergyPLAN.

本数据集用于针对参考年份2023年与2030年的芬兰能源系统建模。该系统模型涵盖电力与区域供热两大领域。
本次建模采用两套不同的框架：EnergyPLAN版本16.3与Calliope版本0.6。数据集包含两套框架对应的模型文件、仿真所用的分布文件、Calliope的仿真结果文件，以及用于后处理Calliope结果的脚本。
本研究的建模思路聚焦于：在电力侧通过提升风电装机容量，扩大可变可再生电力装机规模的同时，优化区域供热发电环节。本次建模采用多种供热发电技术，核心关注热泵（Heat Pumps，HPs）与电锅炉（Electric Boilers，EBs）的角色，同时也涵盖了热电联产（Combined Heat and Power，CHP）、常规锅炉、余热回收以及废弃物热电联产等区域供热系统相关技术。
数据集包含针对Calliope与EnergyPLAN的参考系统模型，命名为"Reference 2023"。2030年情景共开展多轮仿真，通过调整风电装机容量实现参数变化：SC0保留原始供热发电装机容量；SC1.1情景中，将第2、3组共1800MWth的锅炉替换为热泵；SC1.2情景中，第2、3组分别配置1800MWth与3600MWth的热泵；SC2.1与SC2.2情景采用电锅炉替代热泵，装机容量与SC1.1、SC1.2保持一致；SC3为Calliope优化模型，其供热发电装机容量为优化所得。此外，数据集还包含针对热泵性能系数（Coefficient of Performance，COP）与投资成本进行调整的敏感性分析内容。
本数据集附带的.ipynb文件用于依托Python（Jupyterlab）对Calliope的仿真结果数据开展后处理工作。
在本次建模方案中，风电装机容量为可调参数，因此依托Calliope共开展10轮优化仿真，并在EnergyPLAN中使用串行计算工具完成相关运算。