Showing 25 pages using this property.
M | |
Maon + | Maon is a market simulation for fundamental electricity, gas, and emission market analysis. It forecasts the facility-wise hourly dispatch of all supply and demand in whole Europe.
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 exchanges, but also revenues, costs, utilizations, capture rates, price distributions, and thousands of other results. |
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. |
N | |
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. |
O | |
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. |
OpenTUMFlex + | An open-source flexibility estimation model that quantifies all possible flexibilities from the available prosumer devices and prices them. |
P | |
PLEXOS Open EU + | Full Details available at
http://wiki.openmod-initiative.org/wiki/Power_plant_portfolios |
POMATO + | POMATO stands for (POwer MArket TOol) and is an easy to use tool for the comprehensive analysis of the modern electricity market. It comprises the necessary power engineering framework to account for power flow physics, thermal transport constraints and security policies of the underlying transmission infrastructure, depending on the requirements defined by the user. POMATO was specifically designed to realistically model Flow-Based Market-Coupling (FBMC) and is therefore equipped with a fast security constrained optimal power flow algorithm and allows zonal market clearing with endogenously generated flow-based parameters, and redispatch. |
Pandapipes + | An easy to use open source tool for fluid system modeling, analysis and optimization with a high degree of automation. |
Pandapower + | pandapower builds on the data analysis library pandas and the power system analysis toolbox PYPOWER to create an easy to use network calculation program aimed at automation of analysis and optimization in power systems. What started as a convenience wrapper around PYPOWER has evolved into a stand-alone power systems analysis toolbox with extensive power system model library, an improved power flow solver and many other power systems analysis functions. |
PowNet + | PowNet is a least-cost optimization model for simulating the Unit Commitment and Economic Dispatch (UC/ED) of large-scale (regional to country) power systems. In PowNet, a power system is represented by a set of nodes that include power plants, high-voltage substations, and import/export stations (for cross-border systems). The model schedules and dispatches the electricity supply from power plant units to meet hourly electricity demand in substations (at a minimum cost). It considers the techno-economic constraints of both generating units and high-voltage transmission network. The power flow calculation is based on a Direct Current (DC) network (with N-1 criterion), which provides a reasonable balance between modelling accuracy and data and computational requirements. |
PowerMatcher + | "The PowerMatcher is a smart grid coordination mechanism. It balances distributed energy resources (DER) and (flexible) loads ... The PowerMatcher core application provides the market mechanism for the determination of the market equilibrium, while the devices work as actors for demand and/or supply" |
PowerSimulations.jl + | Flexible, modular, and scalable package for power system quasi-static analysis with sequential problem specification capabilities. |
PowerSystems.jl + | The PowerSystems.jl package provides a rigorous data model using Julia structures to enable power systems analysis and modeling. In addition to stand-alone system analysis tools and data model building, the PowerSystems.jl package is used as the foundational data container for the PowerSimulations.jl and PowerSimulationsDynamics.jl packages. PowerSystems.jl supports a limited number of data file formats for parsing. |
Pvlib python + | pvlib python is a community supported tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. |
PyLESA + | PyLESA is an open source tool capable of modelling local energy systems containing both electrical and thermal technologies. It was developed with the aim of aiding the design of local energy systems. The focus of the tool is on modelling systems with heat pumps and thermal storage alongside time-of-use electricity tariffs and predictive control strategies. It is anticipated that the tool provides a framework for future development including electrical battery studies and participation in grid balancing mechanisms.
This tool was developed as part of a PhD, "Modelling and Design of Local Energy Systems Incorporating Heat Pumps, Thermal Storage, Future Tariffs, and Model Predictive Control " by Andrew Lyden. |
PyPSA + | PyPSA is a free software toolbox for simulating and optimising modern energy systems that include features such as variable wind and solar generation, storage units, sector coupling and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series. |
Q | |
QuaSi - GenSim + | GenSim - for "generic building simulation" - is a building simulation software using the EnergyPlus® simulation engine to generate high-resolution heating and cooling demand profiles as well as electricity demand profiles for buildings with various types of use. "Generic" in this context refers to a "generally valid" building model. This means that the software is versatile enough to simulate any type of building in a very flexible and simplified way, enabling users to efficiently adapt the software for any building design.
GenSim was specifically devloped for the use during project pre-planning where detailed simulations of buildings are challenging due to typically constrained time budgets and limited availability of information. Traditional simulation tools require extensive input data, making the process time-consuming. GenSim addresses this by providing presets for multiple building typologies and a streamlined approach for quick, simple, yet accurate building simulations. This is particularly valuable in early planning stages when only rough data about the planned buildings is available. If more detailed information (wall structure, detailed geometry, specific use, ...) is available about the building to be examined, this can be used for more precise results.
More information is available in the documentation: https://quasi-software.readthedocs.io/en/latest/gensim_user_manual/ |
QuaSi - ReSiE + | ReSiE is a software tool that simulates energy supply concepts for buildings, focusing on renewable energy, sector coupling and individual operating strategies. It is part of the QuaSi project that includes additional tools and can be used for individual buildings up to district-level or cities. Unlike many other tools based on systems of equations, ReSiE uses rule-based algorithms, system dynamics and an agent-based approach. This approach enables detailed simulations without linearization, capturing energy flow and system state in each time step. The central mathematical model is based on energy balances and the order of the energy calculations that is determined during preprocessing. In addition, ReSiE is suitable to perform black-box optimizations for optimal component sizing. The model can be easily extended by any energy carriers and additional components or storages of variable complexity. More information is available in the documentation: https://quasi-software.readthedocs.io/en/latest/
Note: ReSiE is currently under development! |
R | |
REopt + | The REopt™ model provides concurrent, multiple technology integration and optimization capabilities to help organizations meet their cost savings and energy performance goals. Formulated as a mixed integer linear program, the REopt model recommends an optimally sized mix of renewable energy, conventional generation, and energy storage technologies; estimates the net present value of implementing those technologies; and provides a dispatch strategy for operating the technology mix at maximum economic efficiency. |