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

Abstract

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.

Eeg Connectivity - Informed Cooperative Adaptive Line Enhancer for Recognition of Brain State

“…Generally, there are no clues about the values of a l k and often for an N node neighbourhood each link weight is considered equal to 1/N [7]. Single-and multi-task cooperative networks have diverse range of applications such as in [8] [9] [10]. In application to EEG, accurate estimation of the combination parameters becomes very crucial as they play the major role in both recognition of brain state and classification of the states eliciting similar EEG waveforms.…”

“…actual and estimated signals overlap duration duration of actual signal (10) From Table 1 it is clear that, although single channel based methods are not always able to detect the full duration of burst waveforms, these particular methods have shown to be very successful in detection of the EEG sleep spindles. On the other hand, if the traditional DA-based cooperative filtering is used in the ALE design, without any prior information about the cooperation weights which quantify the brain connectivity, the system is not sufficiently effective.…”

Discrete event optimization of a vehicle charging station with multiple sockets

“…In (Liu et al 2019) an aggregator of EVs is considered and a predictive transactive control is proposed to manage day-ahead electricity procurement and real-time EVs charging management to minimize its total operating cost, based on a quantification of the charging flexibility. In (Latifi et al 2019), the authors want to maximize grid efficiency, minimize customers' costs, and maximize the capacity for ancillary services. A game-theoretic decentralized approach is proposed for the optimal scheduling of EVs.…”

Hierarchical distributed framework for EV charging scheduling using exchange problem

“…The work is further expanded by Shokri and Kebriaei [21] to consider plug-in hybrid EVs, in which the propulsion provided by gasoline gives more flexibility to EVs for participation in V2G. Latifi et al [22] propose a distributed method where each EV only communicates with its neighbors to reduce the communication overhead, and the optimal charging problem is addressed by the full Nash Folk theorem. Yao et al [23] propose a bi-level programming based hierarchical decomposition EVCS where the objective is to reduce the generation unit cost for DNO.…”

“…It is worthwhile to mention that the convergence rate can be improved by the adaptive penalty term which is not used in this paper. of the EVs are generated as follows: the initial and designated EVs' battery energies are normally distributed over [8,10] kWh and [22,25] kWh, respectively;…”