Open energy-system models are energy-system models that are open source. These models can also be known as open energy models or open-source energy-system models or some combination thereof. Some may use third-party proprietary software as part of their workflows. These models seek to use open data, which facilitates open science.
Energy-system models are often applied to questions involving energy and climate policy. The models themselves vary widely in terms of their type, design, programming, application, scope, level of detail, and sophistication. For many models, some form of mathematical optimization is used to inform the solution process.
Energy regulators and system operators in Europe and North America began to adopt open energy-system models for planning purposes in the early2020s. Web feature. Open models and open data are increasingly being used by government agencies to guide the develop of netzero public policy as well. Companies and engineering consultancies are likewise adopting open models for analysis.
General considerations
Organization
The open energy modeling projects listed here fall exclusively within the bottom-up paradigm, in which a model is a relatively literal representation of the underlying system.
One factor favoring the development of open models and open data includes increasing interest in the transparency of public policy models, improving their acceptance by policymakers and the public. There is also a desire to leverage the benefits of open data and open software development: reduced duplication of effort, better sharing of ideas and information, improved quality, and broader engagement. Model development is therefore usually a team effort and constituted as either an academic project, a commercial venture, or a genuinely inclusive community initiative.
This article does not cover projects which simply make their source code or spreadsheets available to the public, but which omit a recognized free and open-source software license. The absence of a license agreement creates a state of legal uncertainty in which potential users cannot know which limitations the owner may want to enforce in the future.
A 2017 paper lists the benefits of open data and models and discusses the reasons that many projects nonetheless remain closed. This paper makes a number of recommendations for projects wishing to transition to a more open approach. The authors conclude that, in terms of openness, energy research has lagged behind physics, biotechnology, and medicine.
Growth
Open energy-system modeling came of age in the 2010s. Just two projects were cited in a 2011 paper on the topic: OSeMOSYS and TEMOA. Balmorel was also active at that time, having been made public in 2001. , 31such undertakings are listed here (with an approximately equal number waiting to be added). Chang etal (2021) survey modeling trends and find the open to closed division about even after reviewing 54frameworks although that interpretation is based on project count and not on uptake and use. See figure4 in particular. A2022 model comparison exercise in Germany reported eight from 40modeling projects (20%) were open source, these projects also had active communities behind them.
Transparency, comprehensibility, and reproducibility
The use of open energy-system models and open energy data represents one attempt to improve the transparency, comprehensibility, and reproducibility of energy system models, particularly those used to aid public policy development.
A 2010 paper concerning energy efficiency modeling argues that "an open peer review process can greatly support model verification and validation, which are essential for model development". To further honor the process of peer review, researchers argue, in a 2012 paper, that it is essential to place both the source code and datasets under publicly accessible version control so that third-parties can run, verify, and scrutinize specific models. A 2016 paper contends that model-based energy scenario studies, seeking to influence decision-makers in government and industry, must become more comprehensible and more transparent. To these ends, the paper provides a checklist of transparency criteria that should be completed by modelers. The authors however state that they "consider open source approaches to be an extreme case of transparency that does not automatically facilitate the comprehensibility of studies for policy advice."
A one-page opinion piece from 2017 advances the case for using open energy data and modeling to build public trust in policy analysis. The article also argues that scientific journals have a responsibility to require that data and code be submitted alongside text for peer review. And an academic commentary from 2020 argues that distributed development would facilitate a more diverse contributor base and thus improve model quality a process supported by online platforms and enabled by open data and code.
State projects
State-sponsored open source projects in any domain are a relatively new phenomena.
, the European Commission now supports several open source energy system modeling projects to aid the transition to a low-carbon energy system for Europe. The Dispa-SET project (below) is modeling the European electricity system and hosts its codebase on GitHub. The MEDEAS project, which will design and implement a new open source energy-economy model for Europe, held its kick-off meeting in February 2016. , the project had yet to publish any source code. The established OSeMOSYS project (below) is developing a multi-sector energy model for Europe with Commission funding to support stakeholder outreach. The flagship model however remains closed source. The DOI, ISBN, and ISSN refer to the online version.
The United States NEMS national model is available but nonetheless difficult to use. NEMS does not classify as an open source project in the accepted sense.
A 2021 research call from the European Union Horizon Europe scientific research funding program expressly sought energy system models that are open source. Deadline 26April 2022.
Surveys
A survey completed in 2021 investigated the degree to which open energy-system modeling frameworks support flexibility options, broken down by supply, demand, storage, sector coupled, and network response. Of the frameworks surveyed, none supported all types, which suggests that the soft coupling of complementary frameworks could provide more holistic assessments of flexibility. Even so, most candidates opt for perfect foresight and do not natively admit probabilistic actions or explicit behavioral responses.
Electricity sector models
Open electricity sector models have a temporal resolution of one hour or less. Some models focus on engineering characteristics, such as high-voltage transmission networks and AC power flow. Dispatch models depict electricity spot markets. Other models embed autonomous agents to capture, for instance, bidding decisions using techniques from bounded rationality. The ability to handle variable renewable energy, transmission systems, and grid storage have become important considerations.
+ Open electricity sector models Project Host License Access Coding Documentation Scope/type AMIRIS German Aerospace Center Apache2.0 GitLab Java wiki agentbased electricity market modeling Breakthrough Energy Model Breakthrough Energy Foundation MIT GitHub Python, Julia website, GitHub power sector modeling DIETER DIW Berlin MIT download GAMS publication dispatch and investment Dispa-SET EC Joint Research Centre EUPL1.1 GitHub GAMS, Python website European transmission and dispatch E4ST Resources for the Future GPLv3 GitHub Julia website US and Canada detailed dispatch and investment, regional gas markets, coupling with other sectors EMLab-Generation Delft University of Technology Apache 2.0 GitHub Java manual, website agent-based EMMA Neon Neue Energieökonomik CC BY-SA 3.0 download GAMS website electricity market GENESYS RWTH Aachen University LGPLv2.1 on application C++ website European electricity system NEMO University of New South Wales GPLv3 git repository Python website, list Australian NEM market OnSSET KTH Royal Institute of Technology MIT GitHub Python website, GitHub cost-effective electrification pandapower BSD-new GitHub Python website automated power system analysis PowerMatcher Flexiblepower Alliance Network Apache 2.0 GitHub Java website smart grid Power TAC Apache 2.0 GitHub Java website, forum automated retail electricity trading simulation renpass University of Flensburg GPLv3 by invitation R, MySQL manual renewables pathways sci2grid DLR Institute of Networked Energy Systems Apache 2.0 git repository Python website Grid data models SIREN Sustainable Energy Now AGPLv3 GitHub Python website renewable generation SWITCH University of Hawaiʻi Apache 2.0 GitHub Python website optimal planning URBS Technical University of Munich GPLv3 GitHub Python website distributed energy systems
AMIRIS
Project AMIRIS Host German Aerospace Center Status active Scope/type agentbased electricity markets Code license Apache-2.0 Data license CCBY4.0 Language Java Website Repository Documentation Discussion Datasets Publications
AMIRIS is the open Agent-based Market model for the Investigation of Renewable and Integrated energy Systems. The AMIRIS simulation framework was first developed by the German Aerospace Center (DLR) in 2008 and later released as an open source project in 2021.
AMIRIS enables researchers to address questions regarding future energy markets, their market design, and energy-related policy instruments. In particular, AMIRIS is able to capture market effects that may arise from the integration of renewable energy sources and flexibility options by considering the strategies and behaviors of the various energy market actors present. For instance, those behaviors can be influenced by the prevailing political framework and by external uncertainties. AMIRIS may also uncover complex effects that may emerge from the interdependencies of the energy market participants.
The embedded market clearing algorithm computes electricity prices based on the bids of prototyped market actors. These bids may not only reflect the marginal cost of electricity production but also the limited information available to the actors and related uncertainties. But also the bidding can be strategic as an attempt to game official support instruments or exploit market power opportunities.
Actors in AMIRIS are represented as agents that can be roughly divided into six classes: power plant operators, traders, market operators, policy providers, demand agents, and storage facility operators. In the model, power plant operators provide generation capacities to traders, but do not participate directly in markets. Instead, they supply traders who conduct the marketing and deploy bidding strategies on the operators behalf. Marketplaces serve as trading platforms and calculate market clearing. Policy providers define the regulatory framework which then may impact on the decisions of the other agents. Demand agents request energy directly at the electricity market. Finally, flexibility providers, such as storage operators, use forecasts to determine bidding patterns to match their particular objectives, for instance, projected profit maximization.
AMIRIS is based on the open Framework for distributed Agent-based Modelling of Energy systems or FAME.
AMIRIS can simulate largescale agent systems in acceptable timeframes. For instance, the simulation of one year at hourly resolution may take as little as one minute on a contemporary desktop computer.
Breakthrough Energy Model
Project Breakthrough Energy Model Host Breakthrough Energy Foundation Status active Scope/type power sector modeling Code license MIT Data license CCBY4.0 Language Python, Julia Website Repository Documentation