# e4clim 1.0 : The Energy for CLimate Integrated Model: Description and Application to Italy

## TL;DR

e4clim 1.0 is an open-source Python tool designed to evaluate and optimize regional energy mixes with high renewable energy shares, considering climate variability and new technology impacts, demonstrated through an Italian case study.

## Contribution

The paper introduces e4clim 1.0, a flexible, extensible software that estimates renewable energy production from climate data and optimizes energy deployment strategies without full-mix cost minimization.

## Key findings

- Effective climate data integration for renewable energy assessment
- Robustness of energy mix strategies under climate variability
- Potential for optimizing renewable deployment in Italy

## Abstract

We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with high shares of VRE; assessing the impact of new technologies and of climate variability; conducting sensitivity studies. Specifically, to limit the algorithm's complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production-demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time-series and fitted to available observations. We illustrate e4clim's potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate-data sources and strategies and assessing the impact of climate variability and the robustness of the results.