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Assignment Title: Development of a Solar Curtailment Algorithm for Optimal Energy Asset Management
Level: Bachelor of Science, 4th Year
Duration: 20 weeks
Location: Zonnegilde B.V. in Kampen
Payment: 500 euros per month
Student supervisor: Jordi Daniëls, head of Data and AI (Post-Master in Artificial Intelligence, Master of Science in Information sciences)
Hybrid work: 1 day per week at home, scaled to 2 days a week if progress is good
Student: Software Engineering or Data Science, Python experience is required
Language: English and/or Dutch
This assignment is designed to deepen your understanding of solar energy data and the management of renewable energy assets. The solar energy market is dynamic and often subjected to fluctuating energy prices, making it essential for energy assets to operate efficiently. You will explore the operational, security, availability, and integrity features of solar energy systems and apply this knowledge to develop a curtailment solution. This solution will need to be algorithmically sound and adaptable for real-world application.
Objective: To develop and integrate an algorithm that can dynamically curtail solar energy production based on market price signals, ensuring the operational costs do not exceed revenues.
Learning Outcomes: Upon completing this assignment, you should be able to:
1. Understand the basic principles of solar energy generation and data acquisition.
2. Analyse and interpret solar energy production and market price data.
3. Understand the concepts of operational integrity, system security, and data availability in the context of energy systems.
4. Design and implement a curtailment algorithm suitable for integration into existing customer systems.
5. Evaluate, together with Zonnegilde, the potential impact of your solution on operational efficiency and revenue protection.
1. Literature Review and System Understanding:
· Conduct a literature review on solar power generation, data management, and current curtailment practices.
· Describe the typical data flow from solar assets to the data management system and identify key features essential for secure and reliable operation.
· Be able to explain the act of curtailment in systems for non-technical people (e.g. through a presentation or a physical and visual poster in large format).
2. Data Analysis:
· Analyse the provided dataset, which includes solar energy production metrics and market price signals over a defined period.
· Identify patterns and thresholds where energy production costs may exceed the revenue from sales.
3. Algorithm Design:
· Propose an algorithm that can decide when to curtail solar energy production based on real-time price signals and production costs.
· Ensure that your algorithm takes into account operational integrity and the potential wear and tear on solar equipment due to frequent switching.
· Create a simulation to demonstrate how your algorithm performs with historical data.
· Analyse the results to estimate potential revenue savings and operational cost reductions.
5. Integration Proposal:
· Outline a plan for integrating your algorithm into existing customer systems, addressing potential challenges and maintenance considerations.
· Discuss how the algorithm can be updated or scaled as market conditions and technology evolve.
· Document your process, findings, algorithm design, simulation results, and integration plan in a formal report.
· Prepare a presentation to summarise your approach and its anticipated benefits for stakeholders.
1. Written Report: A comprehensive document detailing your literature review, data analysis, algorithm design, simulation results, and integration strategy.
2. Algorithm Code: A well-documented codebase for the curtailment algorithm.
3. Simulation Model: Access to the simulation model, along with a guide on how to interpret the results.
4. Working Integration: Access to the model and (supported) integration into a customer system, a working proof of value which can be monitored by Zonnegilde.
5. Final Presentation Slides: A presentation deck summarising the key aspects of your project.
· Understanding of solar data and asset management principles (20%)
· Quality and insightfulness of data analysis (10%)
· Innovation and practicality of the curtailment algorithm (30%)
· Effectiveness of the simulation in demonstrating the algorithm’s potential (15%)
· Clarity, feasibility, and detail of the integration (25%)
· Access to solar production and market price datasets.
· List of relevant literature and resources for initial review.
· Access to a simulation platform for testing the algorithm (if not available, recommendations for suitable software will be provided).
· Regular consultation hours with the course instructor for guidance.
· The final report should be submitted in a PDF format, with the code and simulation model accessible through a shared repository or appendices.
· The presentation slides should be submitted in a compatible format (.pptx or .pdf).
· All submissions must adhere to the ethical guidelines of academic integrity outlined by the institution.
· Ensure proper citations and references with the APA guidelines.
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