Zero carbon emissions by growing electricity production from renewable energy sources (RESs) and moving the world forward on the path of electrification is fundamental to dealing with climate change and global warming. Increasing penetration of electric vehicles (EVs) in the energy communities plays a major role in climate change actions. It also realizes that uncoordinated EV charging can create problems of power quality, reliability, and grid assets aging. In the past few years, optimal charging has become a popular method to reduce EV impact on the power grid. However, the problem of real-time optimization of high congestions of EVs charging in the communities power system is complex, and there aren’t many realistic solutions available.
Ideally, Artificial Intelligence (AI) with big data such as Machine Learning (ML) would be driven the decision-making and assist in faster optimization that can support a wide variety of applications on a large-scale network and complexity. Motivated by these underlying, our holistic vision of AI would provide an efficient solution to this multifaceted requirement. We plan to explore and investigate the real-time optimization of EV charging schedules associated with ML algorithms used by the residents in energy communities having renewable energy. We believe that can help to strengthen the power system’s resilience and stability. The goal is to strengthen the reliability and sustainability of future power grids with clean energy.
Track#1 - Completed
Abstract— Reducing carbon emissions by evolving the transportation sector on the path of electrification plays a major role in climate change action. However, uncontrolled charging of large-scale electric vehicles (EV) penetration in the power distribution networks can cause voltage quality problems, which would affect the system reliability and even significantly increase the aging of grid assets. Therefore, this work presents a coordination strategy for decentralized real-time optimization for an energy community. To benefit users and support grid operation, electric vehicles are managed to schedule for charging and discharging both active and reactive power together with cooperating with heap pumps. The decentralized optimization problem is formulated as a nonlinear programming problem with the presented penalty parameters and then solved using the primal-dual interior-point method. The coordination scheme, based on AC optimal power flow approach, is used to minimize load shedding when a grid violation event is expected. A modified IEEE European Low Voltage Test Feeder is implemented in PowerFactory while the optimization algorithm is developed in a Python environment. All experiments are performed with actual data taken from local meteorological data to demonstrate the effectiveness of the proposed method. Results from the numerical simulations show that the proposed coordination scheme can maintain grid stability while minimizing the household electricity cost, prolonging the lifetime of the EV battery, and smooth indoor temperature compared to the uncoordinated schemes.
Publication: Chitchai Srithapon and Daniel Månsson, " Optimal Electric Vehicle Charging using Real-Time Coordinated and Decentralized Cooperating Heat Pump in Community Grids," 2022 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). (Published)
Track#2 - In progress
To explore ML methods such as reinforcement learning to assist real-time optimal EV charging schedules co-operating renewable energy sources and controllable loads in the community energy system.