Share a story, thought, etc. from your internship (Sharing challenges as a research intern)
When I was younger, I believed that research was asking people questions and writing numbers onto a clipboard. After several weeks as a Mcubed student researcher, however, I have learned that I couldn’t have been more wrong. My project is called “Socio-Cyber-Physical Framework of Future Retail Electricity Market”, and I focus on developing an economic experimental platform to predict business behaviors in the market. As a relatively new idea, I have experienced the struggles of the beginning stages of a research project and all its challenges along the way.
The biggest challenge for any research is the requirement to learn all kinds of new material throughout the project. You realize that although you have understanding in one topic, you still need to keep learning new information for another stage of the project. The research experience is just a continuous learning curriculum. In the beginning weeks, I was cramming PowerPoints, books, and journal papers just to learn the background information of the retail electricity market. Before the start of implementing the experimental economic platform, I had to learn a new coding language along with a new computer platform called Z-Tree: a computer application that specialized in constructing economic experiments.
While learning new computer syntax and coding techniques only took a matter of time to get familiarized, I suspect numerous more obstacles as my team work towards the completion of the project. These challenges might not be as simple as reading a manual or getting some practice. After talking to my mentor, Dr. Abdollah Kavoursi-Fard, I have learned that the project has brought on many challenges that the team didn’t expect. As a project focused on market behavior and real world use, many struggles of predicting business decisions have shown themselves. Complex mathematical and economic algorithms are needed to establish a non-significant error value for real electricity market scenarios. A major problem that we are still facing is the unpredictable behaviors of suppliers and consumers and how to create equations that produce accurate results. This provided for a major setback in the implementation of complex economic algorithms, such as market clearing equations, into the economic platform because each scenario requires revision to account for the unpredictability. The issue has shed light on the limitations of software and simple planning errors in the beginning stages of the project. While other people might’ve simply given up, researchers, such as myself, have to adapt and overcome. Solving problems don’t just take a day and sometimes multiple minds are needed just to devise a possible solution. Retracing steps backwards, identifying possible mistakes, and learning new methods to overcome the obstacles are all in the problem solving toolkit of a researcher. With the aid of my mentor and help from the internet, I hope to develop a comprehensive strategy that will fix the issues and improve features in the experimental economic platform, so we can begin testing in real world markets.