The planning of energy systems with high penetration of renewables is becoming more and more important due to environmental and security issues. On the other hand, high shares of renewables require proper grid integration strategies. In order to overcome these obstacles, the diversification of renewable energy technologies, programmable or not, coupled with different types of storage, daily and seasonal, is recommended. The optimization of the different energy sources is a multi-objective optimization problem because it concerns economical, technical and environmental aspects. The aim of this study is to present the model EPLANopt, developed by Eurac Research, which couples the deterministic simulation model EnergyPLAN developed by Aalborg University with a Multi-Objective Evolutionary Algorithm built on the Python library DEAP. The test case is the energy system of South Tyrol, for which results obtained through this methodology are presented. Particular attention is devoted to the analysis of energy efficiency in buildings. A curve representing the marginal costs of the different energy efficiency strategies versus the annual energy saving is applied to the model through an external Python script. This curve describes the energy efficiency costs for different types of buildings depending on construction period and location.
Multi-objective optimization algorithm coupled to EnergyPLAN software: The EPLANopt model
Pernetti R.;
2018-01-01
Abstract
The planning of energy systems with high penetration of renewables is becoming more and more important due to environmental and security issues. On the other hand, high shares of renewables require proper grid integration strategies. In order to overcome these obstacles, the diversification of renewable energy technologies, programmable or not, coupled with different types of storage, daily and seasonal, is recommended. The optimization of the different energy sources is a multi-objective optimization problem because it concerns economical, technical and environmental aspects. The aim of this study is to present the model EPLANopt, developed by Eurac Research, which couples the deterministic simulation model EnergyPLAN developed by Aalborg University with a Multi-Objective Evolutionary Algorithm built on the Python library DEAP. The test case is the energy system of South Tyrol, for which results obtained through this methodology are presented. Particular attention is devoted to the analysis of energy efficiency in buildings. A curve representing the marginal costs of the different energy efficiency strategies versus the annual energy saving is applied to the model through an external Python script. This curve describes the energy efficiency costs for different types of buildings depending on construction period and location.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
