Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework
원문 초록
The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid. The coupling between the electric vehicle travel behaviors and charging events is therefore complex. This paper focuses on the demonstration of a scalable computational framework for the electric vehicle charging infrastructure planning over the tightly integrated transportation and electric grid networks. On the transportation side, a charging profile generation strategy is proposed leveraging the EV energy consumption model, trip routing, and charger selection methods. On the grid side, a genetic algorithm is utilized within the optimal power flow program to solve the optimal charger placement problem with integer variables by adaptively evaluating candidate solutions in the current iteration and generating new solutions for the next iterations.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Can LLM-Driven Hard Negative Sampling Empower Collaborative Filtering? Findings and Potentials
A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction
PGTask: Introducing the Task of Profile Generation from Dialogues
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations
LettinGo: Explore User Profile Generation for Recommendation System
토론
이 논문과 연결된 포럼 글이 아직 없습니다.