Showing 25 pages using this property.
|ASAM +||Agent-based model to simulate processes of ancillary services acquisition and electricity markets. ASAM uses the agent-based model framework Mesa and the toolbox for power system analyses PyPSA.|
|AnyMOD +||AnyMOD is a framework to create large scale energy system models with multiple periods of capacity expansion. It pursues a graph-based approach that was developed to address the challenges in modelling high-levels of intermittent generation and sectoral integration.|
|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.|
|Breakthrough Energy Model +||The Breakthrough Energy Model is a production cost model with capacity expansion algorithms and heuristics, originally designed to explore the generation and transmission expansion needs to meet U.S. states’ clean energy goals. The data management occurs within Python, the DCOPF optimization problem is created via Julia, and the preferred solver currently being used is Gurobi, while it is flexible to choose various free or proprietary solvers. A fully integrated capacity expansion model is in development.|
|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!|
|CapacityExpansion +||CapacityExpansion is a julia implementation of an input-data-scaling capacity expansion modeling framework.|
|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.|
|ELTRAMOD +||ELTRAMOD is a fundamental bottom-up electricity market model incorporating the electricity markets of the EU-27 states, Norway, Switzerland, United Kingdom and the Balkan region as well as the Net Transfer Capacities (NTC) between these countries. Each country is treated as one node with country-specific hourly time series of electricity demand and renewable feed-in. The country-specific wind and photovoltaic feed-in is characterised by the installed capacity and an hourly capacity factor. The capacity factors are calculated with the help of publically available time series of wind speed and solar radiation. ELTRAMOD is a linear optimisation model which calculates the cost-minimal generation dispatch and investments in additional transmission lines, storage facilities and other flexibility options. The set of conventional power plants consists of fossil fired, nuclear and hydro plants where different technological characteristics are implemented, such as efficiency, emission factors and availability. Daily prices for CO2 allowances, as well as daily wholesale fuel prices supplemented by country-specific mark-ups are implemented in ELTRAMOD. The country- and technology-specific parameters and the temporal resolution of 8760 hours allow an in-depth analysis of various challenges of the future European electricity system. For example, the trade-off between network extension and storage investment as well as import and export flows of electricity in Europe can be analysed.|
|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.|
|EOLES elec +||The EOLES family of models optimizes the investment and operation of an energy system
in order to minimize the total cost while satisfying energy demand. EOLES_elec is the
electricity version of this family of models. It minimizes the annualized power generation
and storage costs, including the cost of connection to the grid. It includes eight power
generation technologies: offshore and onshore wind power, solar photovoltaics (PV), runof-river and lake-generated hydro-electricity, nuclear power (EPR, i.e. third generation
European pressurized water reactors), open-cycle gas turbines and combined-cycle gas
turbines equipped with post-combustion carbon capture and storage. The latter two
generation technologies burn methane which can come from three sources: fossil natural
gas, biogas from anaerobic digestion and renewable gas from power-to-gas technology
(methanation). EOLES_elec also includes four energy storage technologies: pumped hydro storage (PHS), Li-Ion batteries and two types of methanation (with and without CCS).|
|EOLES elecRES +||EOLES_elecRES is a dispatch and investment model that minimizes the annualized power
generation and storage costs, including the cost of connection to the grid. It includes six
power generation technologies: offshore and onshore wind power, solar photovoltaics
(PV), run-of-river and lake-generated hydro-electricity, and biogas combined with opencycle gas turbines. It also includes three energy storage technologies: pump-hydro
storage (PHS), batteries and methanation combined with open-cycle gas turbines.|
|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 Policy Simulator +||About the Energy Policy Simulator
The Energy Policy Simulator (EPS) is a computer model developed by Energy Innovation LLC as part of its Energy Policy Solutions project, an effort which aims to inform policymakers and regulators about which climate and energy policies will reduce greenhouse gas emissions most effectively and at the lowest cost.
The EPS allows the user to control dozens of different policies that affect energy use and emissions in various sectors of the economy (such as a carbon tax, fuel economy standards for vehicles, reducing methane leakage from industry, and accelerated R&D advancement of various technologies). The model includes every major sector of the economy: transportation, electricity supply, buildings, industry, agriculture, and land use. The model reports outputs at annual intervals and provides numerous outputs, including:
->Emissions of 12 different pollutants (CO2, nitrogen oxides (NOx), sulfur oxides (SOx), fine particulate matter (PM2.5), and eight others), as well as carbon dioxide equivalent (CO2e; a measure of the global warming potential of various pollutants).
->Direct cash flow (cost or savings) impacts on consumers, industry (as a whole), government, and several specific industries
->Human deaths avoided thanks to reduced particulate pollution
The composition and output of the electricity sector (e.g. capacity and generation from coal, natural gas, wind, solar, etc.)
->Vehicle technology market shares and fleet composition (electric vehicles, etc.)
->Energy use by fuel type from various energy-using technologies (specific types of vehicles, building components, etc.)
->Breakdowns of how each policy within a policy package contributes to total abatement and the cost-effectiveness of each policy (e.g. wedge diagrams and cost curves)
The EPS is a system dynamics computer model created in a commercial program called Vensim. Vensim is a tool produced by Ventana Systems for the creation and simulation of system dynamics models. The Energy Policy Simulator has been designed to be used with the free Vensim Model Reader. Directions on how to obtain Vensim Model Reader and the Energy Policy Simulator can be found on the Download and Installation Instructions page.
The model is distributed with a complete set of input data representing the United States, but it has a modular structure that allows it to be adapted to different countries and regions by swapping the input data. The EPS reads in all of its input data from external text files, which are generated by accompanying Excel files. All of these files are included in the model distribution.
The EPS is released under the GNU General Public License version 3 (GPLv3) or any later version and is free and open-source software. For more information, please see the Software License page.
The EPS has benefited from the work of many contributors and reviewers.|
|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.|