Optimal Scheduling of Electric Vehicle Charging in Distribution Networks
The transport sector accounts for a significant proportion of total energy consumption and is to date largely based on fossil fuels. Mitigation of greenhouse gas emissions via the large-scale electrification of road transport will likely deteriorate voltage profiles and overload network equipment in distribution networks. Controlling the charging schedule of electric vehicles in a centralised and coordinated manner provides a potential solution to mitigate the issues and could defer the investment on upgrading the network infrastructures.
In this work, a robust cost-minimising unidirectional day-ahead scheduling routine for charging electric vehicles overnight in residential low voltage distribution networks is presented that observes local network, equipment and charging demand constraints in a stochastic environment. To reduce the computational complexity, a linear power flow approximation is utilised. The modelled environment involves uncertain residential electricity demand, market prices, and the mobility behaviour of electric vehicle owners including stochastic daily trip distances, arrival and departure times. Knowledge about the probability distributions of these parameters is used to hedge risks regarding the cost of charging, network overloadings, voltage violation and charging reliability.
The results provide an insight into the impact of uncertainty and the effectiveness of addressing particular aspects of risk during optimisation. Particularly, consideration of temporally variable household-level demand peaks and planning with more conservative estimates of initial battery charge levels increased the reliability and technical feasibility of optimised schedules. It is further outlined that the introduction of dynamic grid levies, which amplify the effect of variable electricity prices, constitutes a key determinant of cost saving potential by demand side management that could incur only minor fiscal implications.
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setup.py to install all required packages. The file is executeable.
... using conda main environment
Install the requirements with
$ conda install --file requirements.txt
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