Abstract
The number of electric vehicles (EVs) and the size of smart grid are witnessing rapid expansion in both spatial and temporal dimensions. This requires an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs). Such an allocation strategy should provide acceptable charging services at different deployment stages while meeting financial and technical constraints. As new CSs get allocated, distributed generation (DG) units need to be also dynamically allocated in both space and time to compensate for the increment in the loads due to the EV charging requests. Unfortunately, existing power grid models are not suitable to reflect such spatio-temporal evolution, and hence, new models need to be developed. In this paper, we propose a spatio-temporal expanding power grid model based on stochastic geometry. Using this flexible model, we perform a dynamic joint allocation of EV CSs and DG units based on a constrained Markov decision process. The proposed dynamic allocation strategy accounts for charging coordination mechanism within each CS, which in turn allows for maximal usage of deployed chargers. We validate the proposed stochastic geometry-based power grid model against IEEE 123-bus test system. Then, we present a case study for a 5-year CSs deployment plan.
Highlights
AND MOTIVATIONWith the world shifting towards green solutions in an attempt to reduce carbon emissions and lessen the dependency on crude oil, the number of electric vehicles (EVs) has been witnessing a dramatic increase
In order to compensate for the expected increment in the load due to the dynamic installation of such EV charging stations (CSs), we present function ’calcDG’ in Algorithm 3, where the number of distributed generation (DG) units to install at each stage t on each bus is calculated as the number of CSs that get allocated to the bus minus 1, since initially the buses that can Algorithm 3 Definitions of CSs and DGs Allocation
We carried out calculations to obtain lower-bound expressions on CSs density for acceptable charging service levels using stochastic geometry and queuing analysis
Summary
With the world shifting towards green solutions in an attempt to reduce carbon emissions and lessen the dependency on crude oil, the number of electric vehicles (EVs) has been witnessing a dramatic increase. Our model can be used as a tool for strategic planning, where for a given power grid realization, a joint dynamic tractable CSs and DGs allocation algorithm can be applied in order to obtain the number of CSs and DGs to allocate in each year and their corresponding locations. In [13], the authors proposed a planning method for CSs based on queuing theory using a 24-node distribution grid, taking into consideration the spatio-temporal distribution of EVs. In [14], the authors solved the joint EVs and DGs allocation problem using Genetic algorithm such that deployment and operation costs as well as green house gas emissions are minimized. The accounting of such a coordination mechanism, used during system operation, within the planning framework will help to minimize the number of required chargers to satisfy the expected charging requests
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