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Arc-gis to geo-dependent-energy-web-service +Linking arc-gis database to geo-dependent energy demand web-service. Allowing to extract heating energy demand for each defined rasters in arcgis. The following script MappingDistrict.cs allows to read your arcgis database in csv format with as arguments the location of the center of your raster in CHLV02 geo referenced system. As output the script writes back a result file with all informations required according to the information available in the WebService database  +


Batch run CPlex models on Hypatia from Matlab +This code manages the simulation of multiple CPlex jobs on Hypatia. It could also be adapted to other programs. The steps are: * Log in via SSH * Delete old files * Copy .mps file locally, rename * Copy to Hypatia using PSCP * Submit to qsub, passing job number to script to be sued in file names * Get array of job numbers * Wait until all jobs complete * Copy back .sol files  +
Batch run EnergyPlus models from Matlab +The file RunDirMulti.bat that ships with EnergyPlus allows all files in a directory to be executed at once. This is most useful for running parallel models on the workstations. The arguments are the weather file name and the number of cores to use. This Matlab code runs all the files in C:\eplus\to_run and waits for them to complete.  +
Bidirectional massflow LTN (IDA ICE) +IDA ICE model to simulate a low-temperature network with bidirectional mass flow and to monitor the exergetic flows over the system borders. The model includes a source, a cooling unit and a heating unit. Needs annual profiles of the ambient temperature, the cooling and heating demands with frequency of an hour.  +


CPlex on Hypatia +CPlex is installed as a module. Add the following to .bash_profile: module load CPLEXStudio/ It can be used to run .mps files (exported from AIMMS), with solutions saved to a .sol file. CPlex requires a script (below, saved to a file called cplex_script) that contains the commands to be run. This script is executed by passing it as an input to CPlex: ./cplex < cplex_script  +
Calliope - A multi-scale energy systems (MUSES) modeling framework +Calliope is a framework to develop energy system models, with a focus on flexibility, high spatial and temporal resolution, the ability to execute many runs based on the same base model, and a clear separation of framework (code) and model (data). A model based on Calliope consists of a collection of text files (in YAML and CSV formats) that define the technologies, locations and resource potentials. Calliope takes these files, constructs an optimization problem, solves it, and reports results in the form of Pandas data structures for easy analysis with Calliope’s built-in tools or the standard Python data analysis stack. Calliope’s main features include: * Generic technology definition allows modeling any mix of production, storage and consumption * Resolved in space: define locations with individual resource potentials * Resolved in time: read time series with arbitrary resolution * Model specification in an easy-to-read and machine-processable YAML format * Able to run on computing clusters * Easily extensible in a modular way: custom constraint generator functions and custom time mask functions * Uses a state-of-the-art Python toolchain based on Pyomo and Pandas * Freely available under the Apache 2.0 license ==== ==== For further information, see: * Pfenninger, Stefan and James Keirstead (2015). Comparing concentrating solar and nuclear power as baseload providers using the example of South Africa. Energy. doi: 10.1016/ * Pfenninger, Stefan and James Keirstead (2015). Renewables, nuclear, or fossil fuels? Comparing scenarios for the Great Britain electricity system. Applied Energy, 152, pp. 83-93. doi: 10.1016/j.apenergy.2015.04.102  +
Curve extraction and fitting of energy conversion systems +Version 0.1 This module can be used to extract and fit efficiency curve data from graphical data. The files heat_pump_data.m and chp_data.m show how to load and fit data from .mat files. The tool grabit can be use to digialize graphical data; The function single_binary_fit.m fits the curve data to a piecewise linear model using only 1 binary variable to deal with the possible non-concavity of the energy conversion process. The .m files heat_pump_data.m and chp_data.m are examples of the fitting process.  +


Demand management of random consumption profiles +Electrical appliances do not have constant electricity consumption patterns. This method enables the demand manager to shift any consumption pattern in time using MILP formulations. The MATLAB script contains an example of demand shift to minimize costs. The cost signal is not constant. The example is simple so the result can be easily checked by hand. Version 0.1  +
District heating network routing +Optimal design and operation of distributed energy systems with district heating and network routing optimisation.  +
Domestic active occupancy model +This Excel Workbook provides an example of a domestic house active-occupancy simulation. The model generates stochastic data sets representing the number of active occupants in a house, over a 24-hour period, at a ten-minute resolution. An active occupant is defined as a person who is in a house, but is not asleep. The model is run by inputting the number of residents who live in a house and whether data for a weekend or weekday is required. The simulation uses data derived from the UK 2000 Time Use Survey, available from the UK Data Archive. The Workbook contains all the necessary data to run the simulation, together with the Visual Basic source code. The operation of the model is discussed in the journal paper: Ian Richardson, Murray Thomson, David Infield, A high-resolution domestic building occupancy model for energy demand simulations, Energy and Buildings, Volume 40, Issue 8, 2008, Pages 1560-1566, ISSN 0378-7788, DOI: 10.1016/j.enbuild.2008.02.006.  +
Dynamic carbon factor tool +This tool calculates a dynamic carbon factor for the Swiss electricity grid, based on data from 2015. The tool uses data from ENTSO-E and Swissgrid to calculate a grid carbon factor for each hour of the year, based on Swiss production and imports/exports from/to neighboring countries.  +


Edit AIMMS model +Edit an AIMMS model by manipulating the .ams file  +
Effects of district size on optimal energy hub configuration considering demand variability +Energy demand may vary significantly between buildings in terms of the magnitude and timing of loads. In the residential sector, this variation is driven by the differing schedules, appliance portfolios, preferences and habits of individuals, as well as the differing physical characteristics of buildings and other factors. When the energy demand of multiple buildings is combined in the context of a district multi-energy system, the energy use patterns of different buildings are aggregated, affecting the sizing and cost of necessary infrastructure. The precise relationship, however, is unclear. This collection is used to address the following question: How does the size of a district energy system - in terms of the number of buildings included - influence the total system costs and the optimal sizing of the infrastructure? To address this question, the collection links three modules of the HUES platform, indicated above  +
Energy conversion technology 1 +Operation and capital cost for a wide array of different conversion technologies  +
Energy conversion technology 2 +Information on lifetime, capacity and OM cost of different technologies  +
Energy hub batch run with minute resolution +This model is a modified version of the energy hub model developed by Omu (2015), modified to operate at one minute resolution and to enable testing of energy hubs with varying numbers of buildings. The model takes the results of a building energy model as input and calculates an optimal operation schedule for energy hubs of different sizes. It is assumed that each building can be outfitted with solar thermal panels, which (via a district heating network) connect to a centralized heat storage. The aim is to identify a system configuration that minimizes total costs (investment + maintenance + energy) given the goal of cutting carbon emissions to 25% of their baseline level. The energy hub model is implemented in the optimization package Aimms as a mixed integer linear program. The outputs of the model include the operation schedule of the energy hub and optimal sizing of the heat storage and solar thermal installations. The calculations account for the investment, maintenance and operational costs of the solar thermal and storage installations, as well as of the grid-connected electric heaters that are assumed to supplement these. A single run of the model represents a timeframe of one week in April at one minute time resolution.  +
Energy hub model for design, sizing and operation of an energy system of a building or an aggregation of buildings +In this repository an AIMMS model for an energy hub model is presented. The model can be used for the optimal design, sizing and operation of an energy system. This energy system could be a single building or an aggregation/cluster of buildings. The loads that are currently covered are heating and cooling loads.  +
Energy hub server +A Docker server that solves a PyEHub model.  +
Energy storage technology 1 +lifetime, lifetime cycles, charge efficiency, min. state of charge, loss rate and capital cost of typical battery types used in combinaton with PV  +
Energy storage technology 2 +Capacity, Lifetime cycles, Capital cost of specific batteries  +
Energy storage technology 3 +Current batteries recommended for use in buildings  +
EnergyPlus batch run module +This module automates the setup and execution of multiple building energy models implemented in EnergyPlus, and consists of a set of Matlab scripts for building, editing and running an EnergyPlus model, and parsing the output. The module works by inserting specified parametric objects into a template EnergyPlus IDF file. * eplus_run: Execute model * eplus_runmulti: Execute multiple models * eplus_build: Specifies object properties. * eplus_edit: Find and replace an entry for an object in an IDF * eplus_readcsv: Read the output data from csv file according to column headings * eplus_add: Add an object to an inital IDF * eplus_buildpara: Generate all combinations of some options and make associated * parametric objects * eplus_paralist: Assign statistical properties and make associated parametric objects * eplus_makelayout: Build a complex layout of many zones  +
EnergyPlus on Hypatia +EnergyPlus is installed as a module. The following lines load the module and set an environmental variable that determines the weather file location, and should be added to the file /home/usr/.bash_profile: module load EnergyPlus/8.1.0 export ENERGYPLUS_WEATHER=/mnt/project/usr/eplus/weather The bash script runenergyplus (similar to RunEPlus.bat on Windows) is used to run simulations. The arguments are the IDF filename and the weather file name (without extensions). To use parametric objects, a separate script (below) must be called to execute the parametric pre-processor. Currently this executes each model in sequence, and can only use one core per process. Since crude parallel execution (adding & to the command line) causes huge clashes due to the way runenergyplus moves input files to a common name and path. For now, for parallel execution, separate jobs must be submitted to the queue.  +
EnergyPlus: Typical Residential Buildings in a Swiss Alpine Village +This module contains EnergyPlus .idf files of typical residential buildings in a Swiss alpine village. The residential building stock was categorised into 5 age and 3 size categories: D = Detached building, 1 housing unit. SD = Semi-Detached building, 2 housing unit. L = Large building, 3 or more housing units. Construction years are divided into the following categories: 1900 = Built before 1900 00-59 = Built between 1900-1959 60-79 = Built between 1960-1979 80-99 = Built between 1980-1999 2000 = Built after 2000  +


Fast Fluid Dynamics +Contains solvers for Fast Fluid Dynamics and interfaces / implementations of the solvers in e.g. Rhinoceros 3D / Grasshopper or Processing.  +
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