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Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
Optimal Decentralized Protocols for Electric Vehicle Charging
We propose decentralized algorithms for optimally scheduling electric vehicle charging. The algorithms exploit the elasticity and controllability of electric vehicle related loads in order to fill the valleys in electric demand profile. We formulate a global optimization problem whose objective is to impose a generalized notion of valley-filling, study properties of the optimal charging profiles, and give decentralized offline and online algorithms to solve the problem. In each iteration of the proposed algorithms, electric vehicles choose their own charging profiles for the rest horizon according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. The offline algorithms are guaranteed to converge to optimal charging profiles irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles at the expense of a restrictive assumption that all electric vehicles are available for negotiation at the beginning of the planning horizon. The online algorithms relax this assumption by using a scalar prediction of future total charging demand at each time instance and yield near optimal charging profiles. The proposed algorithms need no coordination among the electric vehicles, hence their implementation requires low communication and computation capability. Simulation results are provided to support these results
Optimal Charging of Electric Vehicles in Smart Grid: Characterization and Valley-Filling Algorithms
Electric vehicles (EVs) offer an attractive long-term solution to reduce the
dependence on fossil fuel and greenhouse gas emission. However, a fleet of EVs
with different EV battery charging rate constraints, that is distributed across
a smart power grid network requires a coordinated charging schedule to minimize
the power generation and EV charging costs. In this paper, we study a joint
optimal power flow (OPF) and EV charging problem that augments the OPF problem
with charging EVs over time. While the OPF problem is generally nonconvex and
nonsmooth, it is shown recently that the OPF problem can be solved optimally
for most practical power networks using its convex dual problem. Building on
this zero duality gap result, we study a nested optimization approach to
decompose the joint OPF and EV charging problem. We characterize the optimal
offline EV charging schedule to be a valley-filling profile, which allows us to
develop an optimal offline algorithm with computational complexity that is
significantly lower than centralized interior point solvers. Furthermore, we
propose a decentralized online algorithm that dynamically tracks the
valley-filling profile. Our algorithms are evaluated on the IEEE 14 bus system,
and the simulations show that the online algorithm performs almost near
optimality ( relative difference from the offline optimal solution) under
different settings.Comment: This paper is temporarily withdrawn in preparation for journal
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A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system
The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R
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