2015-09-30-IRENA-Renewable_Energy_Zones_for_the_Africa_Clean_Energy_Corridor_100页_4mb

Summary of Renewable Energy Zones for the Africa Clean Energy Corridor

Core Content

This document outlines the methodology and findings of a study conducted by the International Renewable Energy Agency (IRENA) and Lawrence Berkeley National Laboratory (LBNL) to identify and evaluate Renewable Energy Zones (REZs) for the Africa Clean Energy Corridor (ACEC). The study uses a Multi-Criteria Decision Analysis (MCDA) approach to assess the suitability of areas for wind, solar photovoltaic (PV), and concentrating solar power (CSP) development, considering both techno-economic and socio-environmental factors.

Main Objectives

The primary objectives of the study were:

• To identify high-quality renewable energy resources in 21 countries within the Eastern Africa Power Pool (EAPP) and Southern Africa Power Pool (SAPP).
• To provide a multi-criteria planning framework that enables stakeholders to prioritize development areas.
• To support low-regret, cost-effective, and environmentally sustainable renewable energy projects.
• To develop interactive tools and maps that facilitate decision-making for large-scale renewable energy development.

Key Methodology

The study followed a five-stage methodology:

1. Resource Assessment: Evaluated wind, solar PV, and CSP resources using geospatial and statistical data.
2. Project Opportunity Area (POA) Creation: Identified areas with high renewable potential and spatial contiguity.
3. Estimation of POA Attributes: Assessed various factors such as distance to transmission lines, load centers, roads, and environmental constraints.
4. Zone Creation: Combined resource quality, proximity, and size to define REZs.
5. Capacity Value Estimation: Determined the capacity value of wind sites using hourly demand and generation profiles.

The Multi-Criteria Analysis (MapRE) was used to evaluate the suitability of areas based on a combination of economic, technical, and environmental criteria. This approach allows stakeholders to assign weights to different factors and calculate a cumulative suitability score for each area.

Key Findings

• The ACEC covers 21 countries with significant renewable energy potential.
• Wind, solar PV, and CSP resources were mapped using geospatial data and statistical models.
• Land use/land cover, human footprint, population density, distance to transmission infrastructure, and resource quality were key factors in the analysis.
• Capacity value and levelized cost of electricity (LCOE) were used to evaluate the economic and operational viability of renewable energy projects.
• Interactive maps and tools were developed to allow stakeholders to visualize and prioritize REZs.
• Sensitivity analyses were conducted to understand the impact of varying assumptions on LCOE and capacity value.
• Data limitations were acknowledged, especially in countries lacking comprehensive resource assessments.

Critical Criteria and Metrics

• Land Use Factor: Installable capacity per unit area (MW/km²).
• Land Use Discount: Percentage of land likely to be developed due to socio-economic, cultural, or physical constraints.
• Capacity Value (ELCC): Additional load that can be supported while maintaining system reliability.
• Solar Multiple: Ratio of actual solar field size to the size required for nominal turbine capacity.
• Human Footprint Score: Reflects the socio-environmental impact of a site.
• Levelized Cost of Electricity (LCOE): Average cost of generating electricity over the lifetime of a project, including capital, operation, fuel, and maintenance costs.

Key Recommendations

• The MapRE methodology provides a robust framework for renewable energy planning that integrates geospatial, economic, and socio-environmental considerations.
• Interactive tools and PDF maps should be used to support stakeholder engagement and decision-making.
• Country-specific adjustments are necessary to reflect local conditions and priorities.
• Further data collection and on-the-ground surveys are recommended to improve the accuracy of resource assessments and land use discounts.

Limitations

• Inadequate geospatial and economic data in many African countries limits the precision of the analysis.
• Static assessments are common in African studies, which lack the flexibility for stakeholders to customize criteria and weights.
• Sensitivity analyses indicate that assumptions about air density, turbine efficiency, and resource quality can significantly affect LCOE and capacity value estimates.

Conclusion

The study provides a comprehensive framework for identifying and prioritizing Renewable Energy Zones in the Africa Clean Energy Corridor. It highlights the importance of multi-criteria planning in ensuring that renewable energy development is cost-effective, environmentally sustainable, and socially equitable. The interactive tools and maps developed in this study can be used by policy makers, project developers, and stakeholders to guide the planning and implementation of large-scale renewable energy projects across the region.