Systematic temporal aggregation for rapid modeling to generate alternatives of energy systems

Energy systems modeling plays a key role in long-term strategic planning. However, some complex constraints are often overlooked in these models, such as public acceptance, leading to systems that are difficult to implement. The Modeling to Generate Alternatives (MGA) method addresses this issue and supports decision-making by proposing near-optimal designs instead of a single optimal one. Despite the clear benefits of this method for future energy system planning, it is hindered by its extensive computational complexity. The present research aims to facilitate the application of MGA by suggesting a systematic approach that employs time-series aggregation to reduce computational complexity while providing a prediction of the induced error. This approach, called 'STAR-MGA', is validated with a case study of Japanese power system optimization, demonstrating a significant computational time reduction with accurately predicted error. A novel error measure that better captures the system's configuration is also introduced.