<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Renewable energy communities (REC) are a valuable mean of combating climate change: they increase participant self-sufficiency, avert blackouts, minimize costs (and/or CO<sub>2</sub> emissions), and improves the resilience of the community. The planning phase of an energy community requires an analysis of its performance and efficiency. Concretely, the calculation of optimized energy flows of each participant of the REC at each timepoint (and hence, the energy costs and/or CO2 emissions) is the objective of the analysis. The quality and accuracy of the analysis depend directly on the period of the analysis from one side, and on the modelling data from another. The most accepted period of the analysis of RECs (as a special case of micro-grids) comprises a whole year, to avoid seasonal effects. The necessary data for the analysis are energy consumption and production, trade prices and used technologies. Nowadays, most grid operators provide the values of energy flows with at least a 15-minute time resolution. It means each variable involved in the analysis will be represented as an array with 35 040 elements. Increasing the number of participants, technologies, and other involved variables, increases the amount of data, and consequently the complexity of the analysis. The main contribution of this paper is the comparison of different data reduction methods to handle this information and the validation of their results.</p>
</abstract>

Renewable energy communities (REC) are a valuable mean of combating climate change: they increase participant self-sufficiency, avert blackouts, minimize costs (and/or CO2 emissions), and improves the resilience of the community. The planning phase of an energy community requires an analysis of its performance and efficiency. Concretely, the calculation of optimized energy flows of each participant of the REC at each timepoint (and hence, the energy costs and/or CO2 emissions) is the objective of the analysis. The quality and accuracy of the analysis depend directly on the period of the analysis from one side, and on the modelling data from another. The most accepted period of the analysis of RECs (as a special case of micro-grids) comprises a whole year, to avoid seasonal effects. The necessary data for the analysis are energy consumption and production, trade prices and used technologies. Nowadays, most grid operators provide the values of energy flows with at least a 15-minute time resolution. It means each variable involved in the analysis will be represented as an array with 35 040 elements. Increasing the number of participants, technologies, and other involved variables, increases the amount of data, and consequently the complexity of the analysis. The main contribution of this paper is the comparison of different data reduction methods to handle this information and the validation of their results.

Influence of Data Reduction Methods on Economic Evaluation of Energy-Communities

University of Applied Sciences Upper Austria

Environmental and Climate Technologies

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Influence of Data Reduction Methods on Economic Evaluation of Energy-Communities"}

Renewable energy communities (REC) are a valuable mean of combating climate change: they increase participant self-sufficiency, avert blackouts, minimize costs...

Influence of Data Reduction Methods on Economic Evaluation of Energy-Communities

Data publikacji: 24 gru 2022

Zakres stron: 1310 - 1322

DOI: https://doi.org/10.2478/rtuect-2022-0099

Słowa kluczowe
Costs minimization, economic modelling, optimization, renewable energy, smart-grids analysis

© 2022 Fernando Carreras et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Influence of Data Reduction Methods on Economic Evaluation of Energy-Communities

Data publikacji: 24 gru 2022

Zakres stron: 1310 - 1322

DOI: https://doi.org/10.2478/rtuect-2022-0099

Słowa kluczoweCosts minimization, economic modelling, optimization, renewable energy, smart-grids analysis

© 2022 Fernando Carreras et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Costs minimization, economic modelling, optimization, renewable energy, smart-grids analysis