Unlock the energy flexibility resources of zero-emission vehicles to simultaneously alleviate the negative impact on grid and traffic between remote buildings with predictive controls

To mitigate the climate change caused by carbon emission issues, zero-emission vehicles (ZEVs) and zero-energy buildings (ZEBs) have attracted increasing attention due to the significant proportion of energy-related carbon emissions from the transportation and building sectors. The energy-matching problem of ZEBs between demand and generation is widely noticed by academia, and the energy-sharing method using electric vehicles (EVs) has proved to be an effective approach to improve energy-matching performance. However, the stability issue of the grid-interactive performance caused by the unstable renewable energy generation and the negative impact on the road traffic of ZEVs for energy sharing receive limited attention. This paper proposes instantaneous and predictive control methods for a zero-emission system consisting of two zero-energy buildings using ZEV energy sharing to enhance the building-grid interaction stability and reduce the negative impact of ZEVs on road congestion. A genetic algorithm model is implemented in predictive control. The impacts of different ocean renewable energy types on energy matching, grid stability, and economic benefits are investigated. The results show that the instantaneous control can provide up to 71.0 % better grid-interaction stability performance than basic control. An average of 9.2 % enhancement in the stability performance can be further achieved after implementing genetic predictive control. When considering road impact in predictive control, the annual practical road impact changes from around −0.16 to around 0.13 to 0.15 under different scenarios, while the grid-interaction stability performance remains almost the same with the genetic predictive control that just considers grid stability.