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Mingxi Liu - One of the best experts on this subject based on the ideXlab platform.

  • decentralized electric vehicle Charging Control via a novel shrunken primal multi dual subgradient spmds algorithm
    Conference on Decision and Control, 2020
    Co-Authors: Xiang Huo, Mingxi Liu
    Abstract:

    The Charging processes of a large number of electric vehicles (EVs) require coordination and Control for the alleviation of their impacts on the distribution network and for the provision of various grid services. However, the scalability of existing EV Charging Control paradigms are limited by either the EV population size or the distribution network dimension, largely impairing EVs’ aggregate service capability and applicability. To overcome the scalability barrier, this paper, motivated by the optimal EV Charging scheduling problem for the valley-filling service, (1) proposes a novel dimension reduction methodology by grouping EVs (primal decision variables) and establishing voltage (global coupled constraints) updating subsets for each EV group in the distribution network and (2) develops a novel decentralized shrunken primal-multidual subgradient (SPMDS) optimization algorithm to solve this reduced-dimension problem. The efficiency and efficacy of the proposed algorithm are demonstrated through simulations over a modified IEEE 123-bus test feeder.

  • decentralized electric vehicle Charging Control via a novel shrunken primal multi dual subgradient spmds algorithm
    arXiv: Optimization and Control, 2020
    Co-Authors: Xiang Huo, Mingxi Liu
    Abstract:

    The Charging processes of a large number of electric vehicles (EVs) require coordination and Control for the alleviation of their impacts on the distribution network and for the provision of various grid services. However, the scalability of existing EV Charging Control paradigms are limited by either the number of EVs or the distribution network dimension, largely impairing EVs' aggregate service capability and applicability. To overcome the scalability barrier, this paper, motivated by the optimal scheduling problem for the valley-filling service, (1) proposes a novel dimension reduction methodology by grouping EVs (primal decision variables) and establishing voltage (global coupled constraints) updating subsets for each EV group in the distribution network and (2) develops a novel decentralized shrunken primal multi-dual subgradient (SPMDS) optimization algorithm to solve this reduced-dimension problem. The proposed SPMDS-based Control framework requires no communication between EVs, reduces over 43% of the computational cost in the primal subgradient update, and reduces up to 68% of the computational cost in the dual subgradient update. The efficiency and efficacy of the proposed algorithm are demonstrated through simulations over a modified IEEE 13-bus test feeder and a modified IEEE 123-bus test feeder.

  • decentralized Charging Control of electric vehicles in residential distribution networks
    IEEE Transactions on Control Systems and Technology, 2019
    Co-Authors: Mingxi Liu, Phillippe K Phanivong, Yang Shi, Duncan S Callaway
    Abstract:

    Electric vehicle (EV) Charging can negatively impact electric distribution networks by exceeding equipment thermal ratings and causing voltages to drop below standard ranges. In this paper, we develop a decentralized EV Charging Control scheme to achieve “valley-filling” (i.e., flattening demand profile during overnight Charging), meanwhile meeting heterogeneous individual Charging requirements and satisfying distribution network constraints. The formulated problem is an optimization problem with a nonseparable objective function and strongly coupled inequality constraints. We propose a novel shrunken-primal-dual subgradient algorithm to support the decentralized Control scheme, derive conditions guaranteeing its convergence, and verify its efficacy and convergence with a representative distribution network model.

Rui Zhang - One of the best experts on this subject based on the ideXlab platform.

  • distributed Charging Control in broadband wireless power transfer networks
    IEEE Journal on Selected Areas in Communications, 2016
    Co-Authors: Rui Zhang
    Abstract:

    Wireless power transfer (WPT) technology provides a cost-effective solution to achieve a sustainable energy supply in wireless networks, where WPT-enabled energy nodes (ENs) can charge wireless devices (WDs) remotely without interruption to the use. However, in a heterogeneous WPT network with distributed ENs and WDs, some WDs may quickly deplete their batteries due to the lack of timely wireless power supply by the ENs, thus resulting in short network operating lifetime. In this paper, we exploit frequency diversity in a broadband WPT network and study the distributed Charging Control by ENs to maximize network lifetime. In particular, we propose a practical voting-based distributed Charging Control framework, where each WD simply estimates the broadband channel, casts its votes for some strong sub-channels, and sends to the ENs along with its battery state information, based on which the ENs independently allocate their transmit power over the sub-channels without the need of centralized Control. Under this framework, we aim to design lifetime-maximizing power allocation and efficient voting-based feedback methods. Toward this end, we first derive the general expression of the expected lifetime of a WPT network and draw the general design principles for lifetime-maximizing Charging Control. Based on the analysis, we then propose a distributed Charging Control protocol with voting-based feedback, where the power allocated to sub-channels at each EN is a function of the weighted sum vote received from all WDs. Besides, the number of votes cast by a WD and the weight of each vote are related to its current battery state. Simulation results show that the proposed distributed Charging Control protocol could significantly increase the network lifetime under stringent transmit power constraint in a broadband WPT network. Reciprocally, it also consumes lower transmit power to achieve nearly perpetual network operation.

  • distributed Charging Control in broadband wireless power transfer networks
    arXiv: Networking and Internet Architecture, 2016
    Co-Authors: Rui Zhang
    Abstract:

    Wireless power transfer (WPT) technology provides a cost-effective solution to achieve sustainable energy supply in wireless networks, where WPT-enabled energy nodes (ENs) can charge wireless devices (WDs) remotely without interruption to the use. However, in a heterogeneous WPT network with distributed ENs and WDs, some WDs may quickly deplete their batteries due to the lack of timely wireless power supply by the ENs, thus resulting in short network operating lifetime. In this paper, we exploit frequency diversity in a broadband WPT network and study the distributed Charging Control by ENs to maximize network lifetime. In particular, we propose a practical voting-based distributed Charging Control framework where each WD simply estimates the broadband channel, casts its vote(s) for some strong sub-channel(s) and sends to the ENs along with its battery state information, based on which the ENs independently allocate their transmit power over the sub-channels without the need of centralized Control. Under this framework, we aim to design lifetime-maximizing power allocation and efficient voting-based feedback methods. Towards this end, we first derive the general expression of the expected lifetime of a WPT network and draw the general design principles for lifetime-maximizing Charging Control. Based on the analysis, we then propose a distributed Charging Control protocol with voting-based feedback, where the power allocated to sub-channels at each EN is a function of the weighted sum vote received from all WDs. Besides, the number of votes cast by a WD and the weight of each vote are related to its current battery state. Simulation results show that the proposed distributed Charging Control protocol could significantly increase the network lifetime under stringent transmit power constraint in a broadband WPT network.

Duncan S Callaway - One of the best experts on this subject based on the ideXlab platform.

  • decentralized Charging Control of electric vehicles in residential distribution networks
    IEEE Transactions on Control Systems and Technology, 2019
    Co-Authors: Mingxi Liu, Phillippe K Phanivong, Yang Shi, Duncan S Callaway
    Abstract:

    Electric vehicle (EV) Charging can negatively impact electric distribution networks by exceeding equipment thermal ratings and causing voltages to drop below standard ranges. In this paper, we develop a decentralized EV Charging Control scheme to achieve “valley-filling” (i.e., flattening demand profile during overnight Charging), meanwhile meeting heterogeneous individual Charging requirements and satisfying distribution network constraints. The formulated problem is an optimization problem with a nonseparable objective function and strongly coupled inequality constraints. We propose a novel shrunken-primal-dual subgradient algorithm to support the decentralized Control scheme, derive conditions guaranteeing its convergence, and verify its efficacy and convergence with a representative distribution network model.

  • decentralized Charging Control of large populations of plug in electric vehicles
    IEEE Transactions on Control Systems and Technology, 2013
    Co-Authors: Duncan S Callaway, Ian A Hiskens
    Abstract:

    This paper develops a strategy to coordinate the Charging of autonomous plug-in electric vehicles (PEVs) using concepts from non-cooperative games. The foundation of the paper is a model that assumes PEVs are cost-minimizing and weakly coupled via a common electricity price. At a Nash equilibrium, each PEV reacts optimally with respect to a commonly observed Charging trajectory that is the average of all PEV strategies. This average is given by the solution of a fixed point problem in the limit of infinite population size. The ideal solution minimizes electricity generation costs by scheduling PEV demand to fill the overnight non-PEV demand “valley”. The paper's central theoretical result is a proof of the existence of a unique Nash equilibrium that almost satisfies that ideal. This result is accompanied by a decentralized computational algorithm and a proof that the algorithm converges to the Nash equilibrium in the infinite system limit. Several numerical examples are used to illustrate the performance of the solution strategy for finite populations. The examples demonstrate that convergence to the Nash equilibrium occurs very quickly over a broad range of parameters, and suggest this method could be useful in situations where frequent communication with PEVs is not possible. The method is useful in applications where fully centralized Control is not possible, but where optimal or near-optimal Charging patterns are essential to system operation.

  • decentralized Charging Control for large populations of plug in electric vehicles
    Conference on Decision and Control, 2010
    Co-Authors: Duncan S Callaway, Ian A Hiskens
    Abstract:

    The paper develops a novel decentralized Charging Control strategy for large populations of plug-in electric vehicles (PEVs). We consider the situation where PEV agents are rational and weakly coupled via their operation costs. At an established Nash equilibrium, each of the PEV agents reacts optimally with respect to the average Charging strategy of all the PEV agents. Each of the average Charging strategies can be approximated by an infinite population limit which is the solution of a fixed point problem. The Control objective is to minimize electricity generation costs by establishing a PEV Charging schedule that fills the overnight demand valley. The paper shows that under certain mild conditions, there exists a unique Nash equilibrium that almost satisfies that goal. Moreover, the paper establishes a sufficient condition under which the system converges to the unique Nash equilibrium. The theoretical results are illustrated through various numerical examples.

  • decentralized Charging Control for large populations of plug in electric vehicles
    Conference on Decision and Control, 2010
    Co-Authors: Duncan S Callaway, Ian A Hiskens
    Abstract:

    The paper develops a novel decentralized Charging Control strategy for large populations of plug-in electric vehicles (PEVs). We consider the situation where PEV agents are rational and weakly coupled via their operation costs. At an established Nash equilibrium, each of the PEV agents reacts optimally with respect to the average Charging strategy of all the PEV agents. Each of the average Charging strategies can be approximated by an infinite population limit which is the solution of a fixed point problem. The Control objective is to minimize electricity generation costs by establishing a PEV Charging schedule that fills the overnight demand valley. The paper shows that under certain mild conditions, there exists a unique Nash equilibrium that almost satisfies that goal. Moreover, the paper establishes a sufficient condition under which the system converges to the unique Nash equilibrium. The theoretical results are illustrated through various numerical examples.

  • decentralized Charging Control for large populations of plug in electric vehicles application of the nash certainty equivalence principle
    International Conference on Control Applications, 2010
    Co-Authors: Duncan S Callaway, Ian A Hiskens
    Abstract:

    The paper develops and illustrates a novel decentralized Charging Control algorithm for large populations of plug-in electric vehicles (PEVs). The proposed algorithm is an application of the so-called Nash certainty equivalence principle (or mean-field games.) The Control scheme seeks to achieve social optimality by establishing a PEV Charging schedule that fills the overnight demand valley. The paper discusses implementation issues and computational complexity, and illustrates concepts with various numerical examples.

Giorgio Rizzoni - One of the best experts on this subject based on the ideXlab platform.

  • PEV Charging Control Considering Transformer Life and Experimental Validation of a 25 kVA Distribution Transformer
    IEEE Transactions on Smart Grid, 2015
    Co-Authors: Qiuming Gong, Shawn Midlam-mohler, Vincenzo Marano, Emmanuele Serra, Giorgio Rizzoni
    Abstract:

    When considering the characteristics of electric power systems in the U.S., the local distribution is the most likely part to be adversely affected by the unregulated plug-in electric vehicle (PEV) Charging. The increased load that can result from unregulated Charging of PEVs could dramatically accelerate the aging of electrical transformers. In this paper, the Control strategies that can mitigate or eliminate the accelerated aging that could result from load peaks caused by PEV Charging is developed. The aging model makes it possible to develop Charging Control strategies that protect the transformer system while maximizing overall PEV Charging quality. The Charging Control policy makes use of load prediction algorithms using data-driven models that are based on actual electricity consumption data. The experimental tests are done to calibrate the thermal model of a 25 kVA distribution transformer and validate the effectiveness of the Control strategy.

  • PEV Charging Control for a parking lot based on queuing theory
    2013 American Control Conference, 2013
    Co-Authors: Q. Gong, Elisa Serra, Shawn Midlam-mohler, Vincenzo Marano, Giorgio Rizzoni
    Abstract:

    PEV (Plug-in Electric Vehicle) Charging Control is important due to the fact that the unregulated Charging can have a great impact on current power grid system. This paper concerns the PEV Charging Control for a case study of a shopping center parking lot. A queuing model is used to predict the temporal evolution of the number of PEVs in the parking lot. The Charging Control considers the loss of life of a dedicated distribution transformer for the facility. The objective of the Control algorithm is to balance the minimization of the transformer loss of life and the maximization of Charging service quality. The queuing theory is also used to estimate the waiting time under different number of chargers. The simulation results of the proposed smart Charging strategy show the effectiveness of the methodology.

Pochien Hsu - One of the best experts on this subject based on the ideXlab platform.

  • system dynamic model and Charging Control of lead acid battery for stand alone solar pv system
    Solar Energy, 2010
    Co-Authors: B J Huang, Pochien Hsu
    Abstract:

    Abstract The lead-acid battery which is widely used in stand-alone solar system is easily damaged by a poor Charging Control which causes overCharging. The battery Charging Control is thus usually designed to stop Charging after the overcharge point. This will reduce the storage energy capacity and reduce the service time in electricity supply. The design of Charging Control system however requires a good understanding of the system dynamic behaviour of the battery first. In the present study, a first-order system dynamics model of lead-acid battery at different operating points near the overcharge voltage was derived experimentally, from which a Charging Control system based on PI algorithm was developed using PWM Charging technique. The feedback Control system for battery Charging after the overcharge point (14 V) was designed to compromise between the set-point response and the disturbance rejection. The experimental results show that the Control system can suppress the battery voltage overshoot within 0.1 V when the solar irradiation is suddenly changed from 337 to 843 W/m 2 . A long-term outdoor test for a solar LED lighting system shows that the battery voltage never exceeded 14.1 V for the set point 14 V and the Control system can prevent the battery from overCharging. The test result also indicates that the Control system is able to increase the charged energy by 78%, as compared to the case that the Charging stops after the overcharge point (14 V).