REE6263: Wind and Solar Energy

1. Rationale and Purpose

The global energy landscape is undergoing a fundamental transformation driven by the urgent need to mitigate climate change, reduce dependence on finite fossil fuel resources, and achieve sustainable development goals. Wind and solar energy have emerged as the cornerstones of this transition, with solar PV accounting for nearly 80% of new renewable capacity added globally in 2024, and wind energy continuing its rapid expansion both onshore and offshore.

This module provides a comprehensive, integrated treatment of wind and solar energy systems at the master's level. It bridges the gap between fundamental physical principles and practical engineering applications, incorporating the latest advances in artificial intelligence, machine learning, and data analytics that are transforming the renewable energy sector. Students will develop the theoretical foundations, analytical skills, and computational competencies required to design, analyze, and optimize renewable energy systems for real-world applications.

2. Aims and Learning Objectives

Overall Aims

1. To develop a thorough understanding of the physical principles governing solar and wind energy conversion
2. To equip students with the analytical tools and computational methods for system design and performance prediction
3. To integrate modern AI and data analytics approaches into renewable energy system analysis
4. To enable students to critically evaluate and optimize hybrid renewable energy systems
5. To prepare students for research and professional practice in the renewable energy sector

Learning Outcomes

Upon successful completion of this module, students will be able to:

Knowledge and Understanding:

1. Analyze solar and wind resource data using statistical methods and advanced computational tools

2. Explain the physical principles underlying photovoltaic, solar thermal, and wind turbine technologies

3. Evaluate the performance of renewable energy systems using appropriate analytical frameworks

4. Critically assess the techno-economic and environmental aspects of renewable energy deployment

5. Understand the integration challenges and solutions for grid-connected and off-grid renewable systems

Cognitive and Intellectual Skills:

1. Formulate and solve problems in renewable energy system design using appropriate mathematical and computational methods

2. Apply machine learning and AI techniques to renewable energy forecasting and optimization

3. Design photovoltaic, wind turbine, and solar thermal systems for specific applications

4. Evaluate the performance of hybrid renewable energy systems using techno-economic analysis

5. Critically appraise research literature in the field

Practical Skills:

1. Use Python and specialized software (PVlib, WAsP, SAM, HOMER) for renewable energy system modeling

2. Analyze wind and solar resource data using statistical and machine learning techniques

3. Design and size renewable energy systems using appropriate methodologies

4. Prepare professional technical reports and presentations

5. Work effectively in teams on design projects

Transferable Skills:

1. Critical thinking: Evaluate complex technical problems and propose evidence-based solutions

2. Analytical reasoning: Apply mathematical and computational methods to engineering problems

3. Communication: Present technical information clearly to diverse audiences

4. Project management: Plan and execute technical projects within constraints

5. Professional development: Identify and pursue opportunities for continuous learning

3. Module Content Summary

The module is organized into seven interconnected units that build progressively from fundamentals to advanced applications:

Unit 1: Solar Resource Assessment (3 weeks)

Unit 2: Photovoltaic Systems (3 weeks)

Unit 3: Solar Thermal Systems (2 weeks)

Unit 4: Wind Resource Assessment (2 weeks)

Unit 5: Wind Turbine Design and Performance (2 weeks)

Unit 6: Hybrid Systems and Grid Integration (2 weeks)

Unit 7: AI and Data Analytics in Renewable Energy (2 weeks)