Techno-Economic Analysis and Optimization of Microgrids with EV Charging
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The document discusses a techno-economic analysis and optimization of microgrids integrated with electric vehicle (EV) charging, emphasizing tailored computational approaches for unique energy systems. It highlights the importance of software in supporting the entire technology development cycle and includes case studies demonstrating optimization objectives for renewable energy systems. Key points include efficient problem formulation using optimization tools and leveraging the MathWorks ecosystem for enhanced design capabilities.
Techno-Economic Analysis and Optimization of Microgrids with EV Charging
- 1. 1 © 2024 The MathWorks, Inc. Techno-Economic Analysis and Optimization of Microgrids with EV Charging Graham Dudgeon Consultant Product Manager for Electrical Technology MathWorks Chris Lee Senior Product Manager for Optimization MathWorks
- 2. 2 Agenda Key takeaways Support for all stages of electrical technology development Case study: Microgrid with EV charging Summary
- 3. 3 Key Takeaways Every energy system is unique, and therefore requires a flexible computational environment to answer unique engineering questions. Optimization Toolbox supports efficient and scalable problem formulation and execution. The extended MathWorks ecosystem provides additional analysis and design capability that support overall energy system technology development.
- 4. 4 Support for all stages of electrical technology development Every successful technological system begins with an idea and ends in proven in-service operation. Computational software is an invaluable aid in supporting technology development. The true value of software comes when it reduces the ‘cognitive load’ of performing individual engineering tasks and supports the transfer of rigorous and defensible engineering information throughout the development cycle. Demonstrate Feasibility Fundamental Idea and Investigation System Test and Qualification Technology Development System Deployment and Operation Early-stage feasibility In-service operation
- 5. 5 Renewable Energy Microgrid with EV Charging
- 6. 6 Renewable Energy Microgrid with EV Charging Scenario 1 Load supplied from utility grid, renewable energy and energy storage. EV fleet supplied from renewable energy.
- 7. 7 Renewable Energy Microgrid with EV Charging Scenario 1 Energy storage charged only from renewable energy.
- 8. 8 Renewable Energy Microgrid with EV Charging Scenario 1 EV charging supplied only from renewable energy and energy storage.
- 9. 9 Renewable Energy Microgrid with EV Charging Scenario 2 Load and EV fleet supplied from utility grid, renewable energy and energy storage.
- 10. 10 Optimization Objectives • Determine power rating for wind, solar and storage. • Determine energy rating for storage. • Meet EV charging demand from. appropriate sources for each scenario. • Minimize overall cost over a 20-year period at one-hour time-steps (assume basic cost model).
- 11. 11 Conduct early-stage techno-economic feasibility and planning studies for power systems involving renewable resources with MATLAB. Leverage the MATLAB platform to: – Determine optimal system component sizes using optimization solvers. – Tailor the model and results analysis for your specific requirements. – Analyze result sensitivities to uncertain data. Summary