Multi-Agent Based Simulation for Investigating Electric Vehicle Adoption and Its Impacts on Electricity Distribution Grids and CO2 Emissions

Electric vehicles are expected to significantly contribute to CO2-eq. emissions reduction, but the increasing number of EVs also introduces chal-lenges to the energy system, and to what extent it contributes to achieving cli-mate goals remains unknown. Static modeling and assumption-based simula-tions have been used for such investigation, but they cannot capture the realistic ecosystem dynamics. To fill the gap, this paper investigates the impacts of two adoption curves of private EVs on the electricity distribution grids and national climate goals. This paper develops a multi-agent based simulation with two adoption curves, the Traditional EV charging strategy, various EV models, driv-ing patterns, and CO2-eq. emission data to capture the full ecosystem dynamics during a long-term period from 2020 to 2032. The Danish 2030 climate goal and a Danish distribution network with 126 residential consumers are chosen as the case study. The results show that both EV adoption curves of 1 million and 775k EVs by 2030 will not satisfy the Danish climate goal of reducing transport sector emissions by 30% by 2030. The results also show that the current resi-dential electricity distribution grids cannot handle the load from increasing EVs. The first grid overload will occur in 2031 (around 16 and 24 months later for the 1 million and 775k EVs adopted by 2030) with a 67% share of EVs in the grid

Agent-Based Simulation of Implicit Demand Response Adoption for Water Distribution System Reservoirs

International audienceThe electricity production from intermittent renewable energy sources, such as wind and solar power, has increased significantly, which requires the electricity grid to be gradually restructured through different approaches. Demand Response (DR) is one of the examples which is applicable to a broad variety of electricity consumers, from households to sizable industrial processes. However, there is a barrier to implement DR in that consumers may not be willing to change their behaviour or invest in energy management technologies without gaining enough monetary benefits from doing so. The purpose of this study is to investigate the behaviour of electricity consumers who are offered implicit DR solutions and to investigate which parameters that characterise the consumers who adopt these solutions. The study applies an agent-based simulation model that uses separate and independent modules for the domain logic, the business solution logic and the DR adoption decision logic, respectively. Furthermore, the case study chosen for the simulation is a population of domestic water distribution system water towers with pumps whose operation can be coordinated with the hourly electricity prices from the day-ahead spot market. The simulation results show that tower/pump pairs on water distribution systems with higher water demands adopt the implicit DR solution faster. The pumping rate and tank capacities do not have significant impact on the adoption, at least not if they are beyond a certain size. Meanwhile, the simulation also finds the maximum investment cost for the implicit DR solution to be 71,000 DKK, if half of a water tower population must adopt the solution within a 5-year ROI period