Abstract
The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This paper develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a UK microgrid with actual energy generation and consumption data. This work can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.
| Original language | English |
|---|---|
| Pages (from-to) | 1-10 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Industrial Informatics |
| Early online date | 20 Jun 2022 |
| DOIs | |
| Publication status | E-pub ahead of print - 20 Jun 2022 |
Keywords
- Batteries
- Electric vehicle
- Job shop scheduling
- Microgrids
- Real-time systems
- Regulators
- Renewable energy sources
- Vehicle-to-grid
- artificial intelligence
- battery aging mitigation
- microgrid
- renewable energy
- vehicle to grid
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering
Access to Document
- acceptedmanuscript.PDF
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