Author(s):
Need: Electric power grid is a system that is in constantly evolving. In its ever-changing state, power system operator needs to handle the current and future obstacles efficiently. One of such areas is the forecasting electric vehicle (EV) charging load demand, as deployment of EVs are growing rapidly. For example, as the increase of EV penetration into the power system, uncoordinated charging causes interruptions in the distributions system (e.g., more frequent outages). Accurate forecasting is crucial to determine how much power supply is required to meet the demand of EV charging. The purpose of this research is to tailor our present curriculums and evolve the power and energy engineering courses accordingly to prepare the future engineering workforce. Students will not learn only about traditional power systems subjects but also will enhance their knowledge in data analytics, renewable energies, distributed energy resources and integration into the grid, machine learning/ artificial intelligence techniques, and others. The proposed method is an application of Concept Map Assessment (CMA) to assist in the enhancement of the Power System Operations (PSO) curriculum.
Guiding Questions: Earlier, we created a CMA for electricity load forecasting in relation to the PSO course at the University of Texas at El Paso (UTEP) in coordination with The University of North Carolina at Charlotte (UNCC). In the proposed work, this concept is further expanded to the emerging EV charging load forecasting problem. Several guiding questions in the development of this research include the following:
1. How should the conceptual map assessment be administered to the students? How much time is required?
2. How should the grading rubric be created to determine the progress of each student?
3. What is a list of terms, each curriculum should use to grade the conceptual map assessments?
4. What are the students learning in the class?
5. What should the students consider when creating the concept maps?
6. Depending on the questions – can we determine if there are any holes in the teaching method?
7. Did the students include sub-branches in the concept map?
8. Did the students include feedback loops in the concept map?
9. By considering sub-branches and feedback loops, do students demonstrate higher level of understanding?
Outcomes: During the Spring 2021 semester, a pre-CMA and a post-CMA were administered in the PSO course at UTEP in coordination with UNCC. The pre-CMA results demonstrated the students’ initial knowledge of load forecasting in relation to the PSO course. As time passed by, several assignments and projects were given to the student to enhance their knowledge on the topic. As a result, when the post-CMA was administered, the results showed that students came up with more knowledge on the topic than they initially had. As a result, the curriculum has then been enhanced and another pre-CMA session has been conducted during the beginning of Spring 2022 semester. We anticipate that the post-CMA results (involving a topic of electricity load forecasting as well as a new topic of EV charging load forecasting as proposed in this work) will be promising in the context of students’ learning and knowledge.
Broader Impacts: Some of the major impacts of this research include enhancement in current engineering education with emerging techniques and industry practices via Power System Operations course at UTEP.
Coauthors
Inez Lopez (First Author, UTEP) and Valentina Cecchi (Last Author from The University of North Carolina at Charlotte)
