Efficient techno-economic modelling of smart parking lots within the electricity distribution networks - Seyed Ashkan Nejati; David Greenwood; Sanchari Deb; Saman Nikkhah (International Journal of Electrical Power & Energy Systems)
The rapid uptake of electric vehicles is transforming public and workplace car parks into large, highly flexible electrical loads whose behaviour must be co-optimised with distribution network operation. Yet explicitly modelling every plugged-in vehicle makes even modest-sized studies computationally prohibitive and masks the economic tension between parking-lot owners and network operators. This paper proposes a cost-driven optimisation framework that converts a smart parking lot into an equivalent energy storage system, retaining hourly accuracy while collapsing thousands of binary charging decisions into a handful of continuous variables, allowing for the co-optimisation of the smart parking lot and the electricity distribution network. In the network, non-linear AC power-flow limits are convexified with second-order cone programming. At the same time, the discrete operating states of the SPL are handled by mixed-integer linear programming. To capture the multi-actor reality, separate objective functions represent (i) the distribution system operator’s operating cost and (ii) the smart parking lot owner’s profit, allowing for realistic price-signal interactions to emerge without resorting to heavyweight bi-level or game-theoretic solvers. The framework is first benchmarked on the classical IEEE 33-bus distribution network, augmented with photovoltaic, wind turbine, and conventional energy storage resources, and then stress-tested on a real 83-bus primary network from the UK, fed with arrival/departure and state-of-charge data for 200 real electric vehicles from the My Electric Avenue project.Numerical results demonstrate that the proposed model confirms that modelling smart parking lots as energy storage system equivalents unlocks their value to modern distribution networks while keeping optimisation complexity manageable.© 2017 Elsevier Inc. All rights reserved.