Showing 25 pages using this property.
|Backbone +||Backbone represents a highly adaptable energy systems modelling framework, which can be utilised to create models for studying the design and operation of energy systems, both from investment planning and scheduling perspectives. It includes a wide range of features and constraints, such as stochastic parameters, multiple reserve products, energy storage units, controlled and uncontrolled energy transfers, and, most significantly, multiple energy sectors. The formulation is based on mixed-integer programming and takes into account unit commitment decisions for power plants and other energy conversion facilities. Both high-level large-scale systems and fully detailed smaller-scale systems can be appropriately modelled. The framework has been implemented as the open-source Backbone modelling tool using General Algebraic Modeling System (GAMS).|
|Balmorel +||Balmorel is a model for analysing the electricity and combined heat and power sectors in an international perspective. It is highly versatile and may be applied for long range planning as well as shorter time operational analysis.|
|CAPOW +||Python-based multi-zone unit commitment/economic dispatch model of CAISO and Mid-C markets coupled with "stochastic engine" for representing effects of multiple spatiotemporally correlated hydrometeorological processes on demand, hydropower and wind and solar power production.|
|Calliope +||Calliope is a framework to develop energy system models using a modern and open source Python-based toolchain. It is under active development and freely available under the Apache 2.0 license.
Feedback and contributions are very welcome!|
|DESSTinEE +||The DESSTINEE model (Demand for Energy Services, Supply and Transmission in EuropE) a model of the European energy system in 2050, with a focus on the electricity system. The model is designed to test assumptions about the technical requirements for energy transport (particularly for electricity), and the scale of the economic challenge to develop the necessary infrastructure. Forty countries are considered in and around Europe, and 10 forms of primary and secondary energy. The model uses a predictive simulation technique, rather than solving a partial or general equilibrium. Data is therefore specified by the user (exogenously), and the model calculates a set of answers for the given set of assumptions.
The DESSTINEE model is available as a set of standalone Excel spreadsheets which perform three tasks:
1. Project annual energy demands at country-level forwards to 2050;
2. Synthesise hourly profiles for electricity demand in 2010 and 2050;
3. Simulate the least-cost generation and transmission of electricity around the continent.|
|DIETER +||The Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) has initially been developed in the research project StoRES to study the role of power storage and other flexibility options in a greenfield setting with high shares of renewables. Meanwhile, several model extensions have been developed and applied to different research questions. The model determines cost-minimizing combinations of power generation, demand-side management, and storage capacities as well as their respective dispatch in both the wholesale and the reserve markets. DIETER thus captures multiple system values of energy storage and other flexibility options related to arbitrage, firm capacity, and reserves. DIETER is an open source model which may be freely used and modified by anyone. The code is licensed under the MIT license, and input data is licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License. The model is implemented in the General Algebraic Modeling System (GAMS). Running the model thus also requires a GAMS system, an LP solver, and respective licenses.|
|Dispa-SET +||The Dispa-SET model is an open-source unit commitment and dispatch model developed within the “Joint Research Centre” and focused on the balancing and flexibility problems in European grids.|
|DynPP +||Full Scope Dynamic Simulation Models of different thermal power plants|
|EA-PSM Electric Arc Flash +||EA-PSM Arc flash model can be used to calculate arc flash incident energy, flash boundary, both arc and fault currents, safe working distance. Calculations are validated in accordance with IEEE 1584 standard. It is possible to choose from different equipment types and calculate incident energy at any selected distance.|
|EA-PSM Electric Short Circuit +||EA-PSM Electric Short Circuit calculation model allows to get immediate results of three-phase, phase-to-phase, phase-to-isolated neutral and phase-to-grounded neutral short circuit currents. Calculations of the model are verified in accordance with IEC 60909 standard.|
|ELMOD +||The "Electricity Model" (ELMOD) is a deterministic linear or mixed integer dispatch model framework of the German (and European) electricity and co-generation heat sector.|
|EMLab-Generation +||The main purpose is to explore the long-term effects of interacting energy and climate policies by means of a simulation model of power companies investing in generation capacity. With this model, we study the influence of policy on investment in the electricity market in order to explicate possible effects of current and alternative/additional policies on the various sector goals, i.e. renewables targets, CO2 emission targets, security of supply and affordability. The methodology, agent-based modelling, allows for a different set of assumptions different as to the mainstream models for such questions: this model can explore heterogeneity of actors, consequences of imperfect expectations and investment behaviour outside of ideal conditions.|
|ESO-X +||The Electricity Systems Optimisation (ESO) framework contains a suite of power system capacity expansion and unit commitment models at different levels of spatial and temporal resolution and modelling complexity. Available for download is the single-node model with long-term capacity expansion from 2015 to 2050 in 5 yearly time steps and at hourly discretisation including endogenous technology cost learning (ESO-XEL) as perfect foresight and myopic foresight planning option.|
|Energy Transition Model +||Web-based model based on a holistic description of a country's energy system.|
|EnergyNumbers-Balancing +||The model uses historic demand data, and historic (half-)hourly capacity factors for PV and wind, to simulate the extent to which demand could be met by some combination of wind, PV and storage. Please do email me if you'd like to request early access to the source, and mention your github username.|
|EnergyRt +||energyRt is a package for R to develop Reference Energy System (RES) models and analyze energy-technologies. The package includes a standard RES (or "Bottom-Up") linear, cost-minimizing model, which can be solved by GAMS or GLPK. The model has similarities with TIMES/MARKAL, OSeMOSYS, but has its own specifics, f.i. definition of technologies.|
|EnergyScope +||EnergyScope is open-source model for the strategic energy planning
of urban and regional energy systems.
EnergyScope (v2.0) optimises both the investment and operating strategy of an entire energy system (including electricity, heating and mobility). Additionally, its hourly resolution (using typical days) makes the model suitable for the integration of intermittent renewables, and its concise mathematical formulation and computational effciency are appropriate for uncertainty applications.|
|Ficus +||A (mixed integer) linear optimisation model for local energy systems|
|FlexiGIS +||FlexiGIS: an open source GIS-based platform for modelling energy systems and flexibility options in urban areas. It extracts, filters and categorises the geo-referenced urban energy infrastructure, simulates the local electricity consumption and power generation from on-site renewable energy resources, and allocates the required decentralised storage in urban settings using urbs. FlexiGIS investigates systematically different scenarios of self-consumption, it analyses the characteristics and roles of flexibilisation technologies in promoting higher autarky levels in cities. The extracted urban energy infrustructure are based mainly on OpenStreetMap data.|
|GAMAMOD +||The gas market model GAMAMOD is a bottom-up model used to determine and analyse the optimal natural gas supply structure in Europe and to examine the utilization of the natural gas infrastructure. In its basic version, the model includes the EU-28 countries as well as Switzerland, Norway, the Baltic States and the Balkan region. In addition, important suppliers for the European natural gas market are considered (e.g. Russia, Algeria, and Qatar). On the supply side, the model considers different production capacities with respect to the production level. The model enables the transport of natural gas by modelling pipelines and liquefied natural gas (LNG) shipping. The capacity of single pipelines between neighbouring countries are aggregated in the model. In the case of LNG shipping, the model considers regasification and liquefaction capacities in export and import countries. The model includes an exogenously imputed natural gas demand for each respective country. Moreover, seasonal demand patterns in the respective countries are considered.
GAMAMOD enables the analysis of trading capacities between regional markets. Due to restricted transmission capacities, regional incidences of congestions might occur. The model allows for examining supply interruptions and their impact on the European natural gas system. As each country is modelled as a single aggregated node, no congestions occur within a market area. Furthermore, the model considers natural gas storage, which ensures security of supply in the European natural gas market.
Cyprus and Malta are isolated from the integrated European natural gas pipeline grid. Therefore, they are not considered in the model.|
|Genesys +||The GENESYS Simulation tool has the central target so optimise the future European power system (electricity) with a high share of renewable generation. It can find an economic optimal distribution of generators, storage and grid in a 21 region Europe.
The optimisation is based on a covariance matrix adaption evolution strategy (CMA-ES) while the operation is simulated as a hierarchical setup of system elements aiming to balance the load at minimal cost.
GENESYS comes with a set of input time-series and a parameter set for 2050 which can be adjusted by the user.
It was developed as open source within a publicly funded project and its development is currently continued at RWTH Aachen University.|
|GridCal +||GridCal is a research oriented power systems software.
Research oriented? How? Well, it is a fruit of research. It is designed to be modular. As a researcher I found that the available software (not even talking about commercial options) are hard to expand or adapt to achieve complex simulations. GridCal is designed to allow you to build and reuse modules, which eventually will boost your productivity and the possibilities that are at hand.|
|MOCES +||MOCES is a modeling tool that allows a simulative investigation of complex energy systems. It is build on top of the modeling language Modelica. It is not restricted to a specific modeling depth, neither spatial nor temporal. Nevertheless, in the time domain it focuses on dynamics with time constants larger then seconds and in the spatial domain it concentrates on the super ‘entity connected to the grid’ level.|
|Maon +||Maon is an integrated digital platform for electricity market and system analysis. The included electricity market model simulates the annual coupled dispatch of all supply and demand for 8760 hours in every bidding zone in Europe.
Web browsers provide access to the simulation and data management suite that derives electricity price and transmission grid usage forecasts. Runs can be carried out immediately with inputs for historical and future scenarios.
Users get support by processing tool kits, data quality checks and geographical visualizations. Unit commitment and market price results are prepared for applications like social welfare analysis or power-flow simulations.|
|MultiMod +||The energy system and resource market model "MultiMod" is a large-scale representation of the supply and demand of fossil fuels and renewable energy sources. It captures endogenous substitution between fuels, infrastructure constraints and endogenous investment (e.g., pipeline capacity, power generation technologies), as well as market power by producers of fossil fuels in a unified framework.
The mathematical framework of the MultiMod model is a dynamic Generalized Nash Equilibrium (GNE) derived from individual players' profit maximisation problems. The formulation is generic and flexible, so that the supply chain of any number of fossil and renewable fuels can be modelled. The framework includes seasonality and allows for a detailed infrastructure representation and a comprehensive transformation sector. Investment in infrastructure (transportation, seasonal storage, transformation) is determined endogenously in the model according to the respective player’s inter-temporal optimisation problem. Furthermore, substitution between different energy carriers on the final demand side is endogenous. Modelling co-production of fuels (e.g. crude oil and associated gas) is possible, as well as a flexible setup of transformation units (multiple inputs, multiple outputs). By formulating the model as an equilibrium problem derived from non-cooperative game theory, the model can incorporate Cournot market power by individual suppliers as well as distinct discount rates by various players concerning their investment.
The current framework is an open-loop perfect foresight model. A stochastic version of the model is under development at NTNU Trondheim. This will allow for consideration of uncertainty and distinct risk profiles for individual players along the supply chain, including investment by consumers in energy efficiency.
For the model description paper, a database representing the global energy system was compiled and used for scenario analysis (Huppmann & Egging, 2014). New datasets or variations on the initial data base are currently under development within specific research projects:
- Focus on US domestic conventional crude and shale oil infrastructure (lead: Johns Hopkins University)
- Focus on Chinese coal policies (lead: Tsinghua University)
- Focus on the global crude oil market and refinery investment (lead: DIW Berlin/TU Berlin)
The model is formulated and solved as a Mixed Complementarity Problem (MCP) and implemented in GAMS, using MS Access and MS Excel for data processing and output reports. The code package includes a number of auxiliary routines and algorithms that greatly facilitate the compilation of the data set as well as calibration of the model.|