Full Title: Planning for the Renewable Future. Long-Term Modeling and Tools to Expand Variable Renewable Power in Emerging Economies
Author(s): IRENA
Publisher(s): International Renewable Energy Agency
Publication Date: January 1, 2017
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Description (excerpt):
Ambitious national commitments, international agreements and rapid technological progress have prompted countries around the world to turn increasingly renewable energy to expand their power infrastructure. However, the variability of solar and wind energy – two key sources for renewable power generation – presents new challenges.
Proactive energy planners will address the challenges of variable renewable energy (VRE) integration directly, starting with long-term investment choices. Techno-economic assessments can help to inform policy development and set optimal targets for renewable power uptake. Scenario modelling, meanwhile, has become a critical planning tool for the power sector, with considerable knowledge being acquired in certain markets on how to represent VRE in long-term models.
This report highlights the findings from AVRIL (“Addressing Variable Renewable Energy in Long-term Energy Planning”), a project by the International Renewable Energy Agency (IRENA) that has identified the best practices in long-term planning and modelling to represent high shares of VRE.
The report includes two main parts:
- Part One (Planning the transition to variable renewables) offers guidance to energy decision makers and planners by providing an overview of key long-term issues and concerns around the large-scale integration of variable renewables into the power grid.
- Part Two(Long-term models for energy transition planning) offers guidance to technical practitioners in the field of energy modelling, specifically with a catalogue of practical VRE modelling methodologies for long-term scenario planning.
The solutions presented here, although applicable in many countries, have been adapted to support energy planners and practitioners in developing and emerging economies, where the range of modeling options may be comparatively limited.