Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management

Zhao, Tianqiao; Ding, Zhengtao

doi:10.1109/tii.2017.2685422

Cited by 34 publications

(16 citation statements)

References 43 publications

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“…A limitation of [77] is the required initialization procedure during each EV charge cycle. Later, the authors in [78] extended the work to an initialization-free charge control scheme where EVs start from any charge power allocation. c) Maximize the EV user convenience: EV users often wish to attain a high level of user convenience in their charging operations.…”

Section: ) Ev User's Perspective: A) Provision Of Ancillary Servicesmentioning

confidence: 99%

A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid

Nimalsiri

Mediwaththe

Ratnam

et al. 2020

IEEE Trans. Intell. Transport. Syst.

175

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Electric vehicles (EVs) are an eco-friendly alternative to vehicles with internal combustion engines. Despite their environmental benefits, the massive electricity demand imposed by the anticipated proliferation of EVs could jeopardize the secure and economic operation of the power grid. Hence, proper strategies for charging coordination will be indispensable to the future power grid. Coordinated EV charging schemes can be implemented as centralized, decentralized, and hierarchical systems, with the last two, referred to as distributed charging control systems. This paper reviews the recent literature of distributed charging control schemes, where the computations are distributed across multiple EVs and/or aggregators. First, we categorize optimization problems for EV charging in terms of operational aspects and cost aspects. Then under each category, we provide a comprehensive discussion on algorithms for distributed EV charge scheduling, considering the perspectives of the grid operator, the aggregator, and the EV user. We also discuss how certain algorithms proposed in the literature cope with various uncertainties inherent to distributed EV charging control problems. Finally, we outline several research directions that require further attention.

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Section: ) Ev User's Perspective: A) Provision Of Ancillary Servicesmentioning

confidence: 99%

A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid

Nimalsiri

Mediwaththe

Ratnam

et al. 2020

IEEE Trans. Intell. Transport. Syst.

175

View full text Add to dashboard Cite

show abstract

“…The convergence analysis starts with characterizing the equilibrium point of (17). It can obtain the equilibrium point of (17) by…”

Section: A Convergence Analysismentioning

confidence: 99%

“…where λ 2 is the smallest non-zero eigenvalue of L. Proof: Our analysis starts from transferring the equilibrium point of (17) to the origin such aŝ…”

Section: A Convergence Analysismentioning

confidence: 99%

“…Furthermore, in [16], under the premise of maximizing the social welfare, the energy management problem is solved by a MAS-based distributed algorithm. In [17], the authors propose a continuous-time distributed algorithm for maximizing the satisfaction and welfare of plug-in electric vehicle's users without certain initialization process, which requires the sharing information about consumer's cost function. The authors in [18] introduce a distributed algorithm for economic dispatch problem in smart grids with wind power, which does not verify the algorithm under time-varying renewable generation.…”

Section: Introductionmentioning

confidence: 99%

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Consensus-Based Distributed Optimal Energy Management With Less Communication in a Microgrid

Zhao

Ding

2019

IEEE Trans. Ind. Inf.

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In this paper, to reduce required capacities for information exchanges in microgrids, a novel distributed eventbased algorithm is proposed for optimal energy management in a microgrid. Aiming at optimally scheduling the energy supplier's generation, an objective function is formulated to minimize the total cost of maintaining the supply-demand balance with considering power losses. Regarding each participant as an agent, the proposed algorithm is implemented in a distributed manner based on a multi-agent system framework. Therefore, each agent only exchanges information with its neighbours through a local network. Additionally, comparing with the periodical communication of sampled-data mechanisms, the adopted event-based scheme achieves satisfactory performance by using significantly less communication between participants. As a result, it further facilitates the development of networked microgrids. Furthermore, concerning the privacy of participants, the proposed algorithm is implemented without exposing owners' private preferences. The effectiveness of the proposed distributed algorithm is validated through several simulation studies.

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“…The computational complexity and performance gap between the near optimal solution of the MPC-based approach and the optimal solution for any distributions of exogenous random variables where rigorously analyzed in this paper. An optimal charging problem of the PEVs on demand side management (DSM) was formulated in [13] to meet the power system interests, such as reducing the generation supply cost, while respecting each PEV's charging constraint. A distributed consensus initialization-free algorithm was used in this work to estimate the mismatch between all the PEVs allocated powers and the total available charging power on the directed graph.…”

Section: A Related Workmentioning

confidence: 99%

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

Latifi

Rastegarnia

Khalili

et al. 2019

IEEE Trans. Ind. Electron.

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Abstract-This paper presents a game theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantages of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real time dynamic pricing model, and supports the vehicle-to-grid (V2G) and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short term uncertainties.Index Terms-Plug-in electric vehicles (PEVs), decentralized charging, Nash Folk strategy, regret matching.

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Distributed Initialization-Free Cost-Optimal Charging Control of Plug-In Electric Vehicles for Demand Management

Cited by 34 publications

References 43 publications

A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid

A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid

Consensus-Based Distributed Optimal Energy Management With Less Communication in a Microgrid

Agent-Based Decentralized Optimal Charging Strategy for Plug-in Electric Vehicles

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