This paper introduces the full Low-carbon Expansion Generation Optimization (LEGO) model available on Github (https://github.com/wogrin/LEGO). LEGO is a mixed-integer quadratically constrained optimization problem and has been designed to be a multi-purpose tool, like a Swiss army knife, that can be employed to study many different aspects of the energy sector. Ranging from short-term unit commitment to long-term generation and transmission expansion planning. The underlying modeling philosophies are: modularity and flexibility. Its unique temporal structure allows LEGO to function with either chronological hourly data, or all kinds of representative periods. LEGO is also composed of thematic modules that can be added or removed from the model easily via data options depending on the scope of the study. Those modules include: unit commitment constraints; DC- or AC-OPF formulations; battery degradation; rate of change of frequency inertia constraints; demand-side management; or the hydrogen sector. LEGO also provides a plethora of model outputs (both primal and dual), which is the basis for both technical but also economic analyses. To our knowledge, there is no model that combines all of these capabilities, which we hereby make freely available to the scientific community.
翻译:本文介绍了Github(https://github.com/wogrin/LEGO)上现有的全低碳扩大一代优化模型(LEGO),LEGO是一个混合的因果四边限制优化问题,其设计像瑞士的刀子一样,可以作为一种多用途工具,用于研究能源部门的许多不同方面。从短期单位承诺到长期发电和输电扩展规划。基本模型哲学是:模块性和灵活性。它独特的时间结构使LEGO能够使用按时间顺序排列的小时数据或所有具有代表性的时期来运作。LEGO还由专题模块组成,这些模块可以根据研究的范围,通过数据选项很容易地添加或从模型中删除。这些模块包括:单位承诺制约;DC-或AC-OPF配方;电池退化;频率惯性制约变化率率;需求方管理;氢部门。LEGO还提供大量模型产出(包括原始和双重),这是技术数据的基础,但也是我们可自由进行经济分析的基础。