540 research outputs found
Bi-directional coordination of plug-in electric vehicles with economic model predictive control
© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-To-vehicle (G2V) and vehicle-To-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%
Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations
Next-generation power grids will likely enable concurrent service for
residences and plug-in electric vehicles (PEVs). While the residence power
demand profile is known and thus can be considered inelastic, the PEVs' power
demand is only known after random PEVs' arrivals. PEV charging scheduling aims
at minimizing the potential impact of the massive integration of PEVs into
power grids to save service costs to customers while power control aims at
minimizing the cost of power generation subject to operating constraints and
meeting demand. The present paper develops a model predictive control (MPC)-
based approach to address the joint PEV charging scheduling and power control
to minimize both PEV charging cost and energy generation cost in meeting both
residence and PEV power demands. Unlike in related works, no assumptions are
made about the probability distribution of PEVs' arrivals, the known PEVs'
future demand, or the unlimited charging capacity of PEVs. The proposed
approach is shown to achieve a globally optimal solution. Numerical results for
IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this
approach
Decentralised Model Predictive Control of Electric Vehicles Charging
This paper presents a decentralised control strategy for the management of simultaneous charging sessions of electric vehicles. The proposed approach is based on the model predictive control methodology and the Lagrangian decomposition of the constrained optimization problem which is solved at each sampling time. This strategy allows the computation of the charging profiles in a decentralised way, with limited information exchange between the electric vehicles. The simulation results show the potential of the proposed approach in relation to the problem of shaving the aggregated power withdrawal from the electricity distribution grid, while still satisfying drivers’ preferences for charging
MPC-based Voltage Control with Reactive Power from High-Power Charging Stations for EVs
The recent development of EVs with high-capacity batteries and high charging power capabilities leads to an increased demand for fast-charging stations (FCS). However, FCS can cause power quality issues such as voltage drops in distribution grids with limited power capacity. Grid reinforcements are a standard solution for solving power quality issues. However, these can be costly. An alternative approach is to install bi-directional chargers at FCS and use this flexibility source to provide voltage support in peak-load periods by injecting reactive power to the grid. This paper proposes a model predictive controller (MPC) to control and coordinate such high-power chargers. The MPC maximizes the charging rate for the EVs while ensuring that the voltage level stays within the allowable limits. The control system has been evaluated through simulations on a realistic grid model, and the results show that both the FCS and the grid can benefit from utilizing the reactive power.acceptedVersio
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Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
Learning-based Predictive Control via Real-time Aggregate Flexibility
Aggregators have emerged as crucial tools for the coordination of
distributed, controllable loads. To be used effectively, an aggregator must be
able to communicate the available flexibility of the loads they control, as
known as the aggregate flexibility to a system operator. However, most of
existing aggregate flexibility measures often are slow-timescale estimations
and much less attention has been paid to real-time coordination between an
aggregator and an operator. In this paper, we consider solving an online
optimization in a closed-loop system and present a design of real-time
aggregate flexibility feedback, termed the maximum entropy feedback (MEF). In
addition to deriving analytic properties of the MEF, combining learning and
control, we show that it can be approximated using reinforcement learning and
used as a penalty term in a novel control algorithm -- the penalized predictive
control (PPC), which modifies vanilla model predictive control (MPC). The
benefits of our scheme are (1). Efficient Communication. An operator running
PPC does not need to know the exact states and constraints of the loads, but
only the MEF. (2). Fast Computation. The PPC often has much less number of
variables than an MPC formulation. (3). Lower Costs. We show that under certain
regularity assumptions, the PPC is optimal. We illustrate the efficacy of the
PPC using a dataset from an adaptive electric vehicle charging network and show
that PPC outperforms classical MPC.Comment: 13 pages, 5 figures, extension of arXiv:2006.1381
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research
ACN-Sim is a data-driven, open-source simulation environment designed to
accelerate research in the field of smart electric vehicle (EV) charging. It
fills the need in this community for a widely available, realistic simulation
environment in which researchers can evaluate algorithms and test assumptions.
ACN-Sim provides a modular, extensible architecture, which models the
complexity of real charging systems, including battery charging behavior and
unbalanced three-phase infrastructure. It also integrates with a broader
ecosystem of research tools. These include ACN-Data, an open dataset of EV
charging sessions, which provides realistic simulation scenarios and ACN-Live,
a framework for field-testing charging algorithms. It also integrates with grid
simulators like MATPOWER, PandaPower and OpenDSS, and OpenAI Gym for training
reinforcement learning agents.Comment: 9 pages, 8 figures. [v2] Update timezone issue with Fig. 8 where
x-axis and background load was shifted by 3 hour
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