1468248
doi
10.5281/zenodo.1468248
oai:zenodo.org:1468248
user-indigo
user-eu
Sokka, Laura
VTT Technical Research Centre of Finland
Pursiheimo, Esa
VTT Technical Research Centre of Finland
Klobut, Krzysztof
VTT Technical Research Centre of Finland
Koponen, Kati
VTT Technical Research Centre of Finland
INDIGO Planning tool - IndPT
Räma, Miika
VTT Technical Research Centre of Finland
info:eu-repo/semantics/openAccess
GNU General Public License v2.0 only
https://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.html
<p>Indigo DC planning tool (IndPT) supports the optimal design of new DC systems, the assessment of existing systems’ potential for performance or efficiency improvement and the comparisons with building specific cooling systems. Consumers, distribution, production and storage of the cooling energy in DC system, are linked together in the planning tool to obtain the available solutions. Life cycle analysis (LCA) framework is used for economic feasibility and climate impact assessment. The tool may also be used to estimate the size of energy storages based on the availability of resources and variation of demand and costs.</p>
<p>The main input parameters are cooling demand, structure of cooling production, distribution network characteristics and available resources. Secondary input includes data on energy commodity prices, investments and specifications for components e.g. local heat and electricity generation. As an output, the tool provides primary energy consumption, greenhouse gas emissions and costs of the system. The defined potential district cooling system can also be compared with building specific cooling systems delivering the similar cooling service.</p>
<p>Indigo DC planning tool requires following python version and packages:</p>
<ul>
<li>Python 3.4</li>
<li>pyside 1.2.4</li>
<li>qt 4.8.7</li>
<li>oemof 0.2.2</li>
<li>matplotlib 2.0.0</li>
</ul>
<p>All files in main directory and in all subdirectories are released under the GPL 3.0 license.</p>
Main changes:
- An option has been added for deleting selected scenarios
- Updating cooling demand time series is possible without redefining each consumer
- Storage cycles are now calculated correctly
- Some corrections have been made to the economic calculations
- Some changes have been made in default input data specially to price levels
Zenodo
2018-10-22
info:eu-repo/semantics/other
1407213
user-indigo
user-eu
1.2
award_title=New generation of Intelligent Efficient District Cooling systems; award_number=696098; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/696098; funder_id=00k4n6c32; funder_name=European Commission;
1610610539.482437
5063139
md5:28b13d464af1eaacd57baddd408c1012
https://zenodo.org/records/1468248/files/IndPT 1.2.zip
public
10.5281/zenodo.1407213
isVersionOf
doi