Introduction¶
What is Hypatia¶
Hypatia is an energy system modelling framework written in the objective oriented Python programming language. Contrary to most of the Python-based open-source energy and power system modelling frameworks that are using Pyomo for solving the optimization problem, Hypatia is based on CVXPY Domain-Specific Language developed by [Diamond2016] . Hypatia can optimize both the hourly dispatch and the annual capacity deployments of the energy system. Its final objective is to minimize the total discounted cost of the system by considering all the required cost components in each of its optimization modes. In summery, Hypatia is designed with the following main goals:
Allow easy interaction with the model code by using excel-based input data
Formulated to cover both operation and dynamic investment decisions
Provide the possibility to consider the investment annuities in its planning mode based on the given economic lifetime and interest rate of each technology
Allow to model various categories of technologies such as supply, conversion, transmission and storage.
Able to consider the synergies among different sectors of the energy system including power, heat, transport, clean fuel (Hydrogen) and the others.
Designed to follow both the single-node and multi-node approach at will by the user. Each node in Hypatia can be representative of a broad spectrum of spatial resolutions starting from small-scale applications to the national and continental applications.
Allow to model the bilateral trade among any pairs of nodes through modelling the inter-regional transmission links for all the represented energy carriers within the Reference Energy System
Able to adopt arbitary resolutions in time for each modelling year, allowing to consider the full hourly variability of both demand and supply sides.
Have a fully transparent and open-source code, flexible to any possible future modification and integration
Why it is developed¶
Hypatia is inspired by the other existing energy system optimization models particulary OSeMOSYS by [Howells2011], Calliope by [Pfenninger-Pickering2018] and TIMES by [Loulou2005]. It is designed to compelete the path of these frameworks by addressing the main challenges of the modern energy system modelling frameworks that are shortly explained in the following:
Dynamic annual investments on the energy system: With the aim of exploring the possible evolution of the energy systems in the transition pathways, the energy modeling frameworks need to cover both the operation and planning modes by simulateneously delivering the required dynamic annual capacity expansions and full hourly dispatch of different technologies within the energy systems. However, most of the existing models with high temporal resolution are falling short of delivering all the required annual investments in the long-term horizons and just follow a snapshot approach for estimating the required new capacities to be installed for the future growths in the final demand.
Resolution in time: On the other hand, most of the planning models are not computationally able to include fine temporal resolutions down to hourly timesteps within each modelling year of the time horizon. Therefore, they may deliver inaccurate results due to missing the full variability of the both demand and supply sides of the energy system.
Resolution in space: The concept of spatial resolution contains not only the ability of representing multiple regions in different dimensions but also the possibility to model the interconnections among various regions by modelling the inter-regional transmission links.
Sector coupling: The interactions and synergies among different sectors of the energy system must be considered in the energy modelling frameworks by following a comprehensive technology definition similar to all the above mentioned models.
Transparency: The concept of transparency and opennes has manifold aspects. The open science approach for an energy model is not only about publishing the governing structures and equations but also following several critieria such as:
Convenient access to source code, data and assumptions
Providing understanble input data structure not only for the experts but also for any potential user
Clear and modular core code
Flexible source code to any possible future modification and integration
Acknowledgement¶
The development of Hypatia was not possible without the kind attention and help of Professor Emanuela Colombo. We are fully grateful for having the chance to work under her supervison and would like to express our gratitude for her unwavering support.
We would also like to acknowledge Steve Dimond for his kind support and guide that allows us to better understand and use CVXPY for this framework
License¶
This work is licensed under Apache 2.0
References¶
Diamond, S., & Boyd, S. (2016). CVXPY: A Python-embedded modeling language for convex optimization. The Journal of Machine Learning Research, 17(1), 2909-2913.
Howells, M., Rogner, H., Strachan, N., Heaps, C., Huntington, H., Kypreos, S., … & Roehrl, A. (2011). OSeMOSYS: the open source energy modeling system: an introduction to its ethos, structure and development. Energy Policy, 39(10), 5850-5870.
Pfenninger, S., & Pickering, B. (2018). Calliope: a multi-scale energy systems modelling framework. Journal of Open Source Software, 3(29), 825.
Loulou, R., Remme, U., Kanudia, A., Lehtila, A., & Goldstein, G. (2005). Documentation for the times model part ii. Energy Technology Systems Analysis Programme.