Showing 25 pages using this property.
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| 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. |
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| 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 oemof-solph. 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. |
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| GAMAMOD + | The gas market model GAMAMOD is a bottom-up model used to determine and analyze the optimal natural gas supply structure in Germany and to examine the utilization of the natural gas infrastructure. In its basic version, the model is a Linear Program with a high spatial resolution and daily time steps. It contains more than 800 nodes and 1200 edges, while also taking into account parallel transmission lines, storage, and changes to demand and the grid as year progress. It's main outputs are optimal flow, imports, storage usage and retrofitting.
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. |
| GAMAMOD-DE + | The gas market model GAMAMOD is a bottom-up model used to determine and analyze the optimal natural gas supply structure in Germany and to examine the utilization of the natural gas infrastructure. In its basic version, the model is a Linear Program with a high spatial resolution and daily time steps. It contains more than 800 nodes and 1200 edges, while also taking into account parallel transmission lines, storage, and changes to demand and the grid as year progress. It's main outputs are optimal flow, imports, storage usage and retrofitting.
In addition, important suppliers for the European natural gas market are considered. 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.
A version of GAMAMOD focused on optimal retrofitting calculations also exists, this one has the form of a Mixed-Integer Linear Program. |
| GENeSYS-MOD + | The Global Energy System Model (GENeSYS‑MOD) is a linear cost-minimizing optimization model being developed at Technische Universität Berlin, Germany. The project was originally based on the OSeMOSYS framework and the first version was released in 2017 using GAMS. The codebase was later translated into Julia. Both versions and a representative dataset are available on GitHub.
GENeSYS‑MOD couples the demand sectors covering electricity, buildings, industry, and transport and finds the cost-optimal investment into conventional and renewable energy generation, storage, and infrastructure. The research focus is on long-term system development and pathway analysis.
The model was first used to analyze decarbonization scenarios at the global level, broken down into ten regions. However, the framework is highly flexible, allowing for calculations at various levels of detail, from individual households to global aggregations, depending on the desired research question and availability of input data. |
| 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. |
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| HighRES + | The model is used to plan least-cost electricity systems for Europe and specifically designed to analyse the effects of high shares of variable renewables and explore integration/flexibility options. It does this by comparing and trading off potential options to integrate renewables into the system including the extension of the transmission grid, interconnection with other countries, building flexible generation (e.g. gas power stations), renewable curtailment and energy storage.
highRES is written in GAMS and its objective is to minimise power system investment and operational costs to meet hourly demand, subject to a number of system constraints. The transmission grid is represented using a linear transport model. To realistically model variable renewable supply, the model uses spatially and temporally-detailed renewable generation time series that are based on weather data.
Currently there is one version for Europe and one for GB. |
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| IRENA FlexTool + | IRENA FlexTool is an energy and power systems model for understanding the role of variable power generation in future energy systems. It performs capacity expansion planning as well as operational planning.
VTT develops the model for IRENA (and receives a lot of feedback from IRENA to improve the model) |
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| Lemlab + | An open-source tool for the agent-based development and testing of local energy market applications. lemlab allows the user to simulate a LEM using a full agent-based modelling (ABM) in either simulation (SIM) or real-time (RTS) modes. This allows the rapid testing of algorithms as well as the real-time integration of hardware and software components. |
| LoadProfileGenerator + | Generates residential profiles for electricity, water, car charging, occupancy and more.
Agentbased simulation using a psychological behavior model. |
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| 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 a market simulation for fundamental electricity wholesale market analysis. It forecasts the facility-level quarter-hourly dispatch of all supply and demand across a continent. Further, it can predict capacities and uncertainties of generators, interconnectors, storages, and consumers.
Web browsers provide access to the data management, simulation, and analysis environment. It enables high-speed, high-resolution, and large-scale foresights. Scenarios can be parameterized by multiple users at the same time, calculated by one click, and collaboratively visually analyzed.
Users get support by work-leveraging parameterization tools, comprehensive quality checks, and interactive visualizations. Maon provides not only results like prices, dispatches, and capacities, but also capture rates, costs, price distributions, revenues, utilizations, and many other parameters. |
| MicroGridsPy + | The MicroGridsPy model main objective is to provide an open-source alternative to the problem of sizing and dispatch of energy in micro-grids in isolated places. It’s written in python(pyomo) and use excel and text files as input and output data handling and visualisation.
Main features:
-Optimal sizing of PV panels, wind turbines, other renewable technologies, back-up genset and electrochemical storage system for least cost electricity supply in rural isolated areas;
-Optimal dispatch from the identified supply systems;
-Possibility to optimize on NPC or operation costs;
-LCOE evaluation for the identified system.
Possible features:
-Two-stage stochastic optimization;
-Multi-year evolving load demand and multi-step capacity expansion;
-Possibility of connecting to the national grid;
-Two-objective optimization (economic and environmental objective functions);
-Brownfield optimization;
-Built-in load archetypes for rural users;
-Endogenous calculation of renewable energy sources production. |
| Mosaik + | Mosaik is a flexible Smart Grid co-simulation framework.
Mosaik allows you to reuse and combine existing simulation models and simulators to create large-scale Smart Grid scenarios – and by large-scale we mean thousands of simulated entities distributed over multiple simulator processes. These scenarios can then serve as test bed for various types of control strategies (e.g., multi-agent systems (MAS) or centralized control).
Mosaik is written in Python and completely open source (LGPL), including some simple simulators, a binding to pandapower and PYPOWER and a demonstration scenario. |
| 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. |
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| NEMO + | NEMO is a chronological dispatch model for testing and optimising different portfolios of conventional and renewable electricity generation technologies. |
| NEMO (SEI) + | NEMO is a high performance, open-source energy system optimization modeling tool developed in Julia. It is intended for users who seek substantial optimization capabilities without the financial burden of proprietary software or the performance bottlenecks of common open-source alternatives. It can be used in stand-alone mode or with the Low Emissions Analysis Platform (LEAP) as a front-end. |
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| OMEGAlpes + | OMEGAlpes stands for Generation of Optimization Models As Linear Programming for Energy Systems. It aims to be an energy systems modelling tool for linear optimisation (LP, MILP). It is currently based on the LP modeler PuLP. |
| OSeMOSYS + | OSeMOSYS has been created by a community of leading institutions and is capable of powerful energy systems analysis and prototyping new energy model formulations. It is typically used for the analysis of energy systems looking over the medium (10-15yrs) and long (50-100yrs) term. It is used by experienced modellers as an exploratory tool, by developing country modellers where data limitations are an issue, and as a teaching tool. |
| Oemof + | oemof is a framework for energy system model development and its application in energy system analysis. Currently, it bases on collaborative work of three institutions. You can clone/fork the code at github.
Containing a linear optimisation problem formulation library, feedin-data generation library and other auxiliary libraries it is meant to be developed further according to interests of user/ developer community. |
| OnSSET + | OnSSET has been designed for identifying least-cost technology options to electrify areas presently unserved by grid-based electricity and to estimate associated investment needs related to electrification. OnSSET uses energy-related data and information on a geographical basis such as settlement sizes and locations, distances from existing and planned transmission network, power plants, economic activity, local renewable energy flows,road network, nighttime light etc. |
| OpenTEPES + | The openTEPES model presents a decision support system for defining the integrated generation, storage, and transmission expansion plan (GEP+SEP+TEP) of a large-scale electric system at a tactical level (i.e., time horizons of 10-20 years), defined as a set of generation, storage, and (electricity, hydrogen, and heat) networks dynamic investment decisions for multiple future years. |
