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Economics and environmental incentives, as well as advances in technology, are reshaping the traditional view of industrial systems. The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) into the market brings up many technical problems that are highly related to industrial information technologies within the next ten years. There is a need for an in-depth understanding of the electrification of transportation in the industrial environment. It is important to consolidate the practical and the conceptual knowledge of industrial informatics in order to support the emerging electric vehicle (EV) technologies. This paper presents a comprehensive overview of the electrification of transportation in an industrial environment. In addition, it provides a comprehensive survey of the EVs in the field of industrial informatics systems, namely: 1) charging infrastructure and PHEV/PEV batteries; 2) intelligent energy management; 3) vehicle-to-grid; and 4) communication requirements. Moreover, this paper presents a future perspective of industrial information technologies to accelerate the market introduction and penetration of advanced electric drive vehicles.Index Terms-Battery, charging infrastructure, communication, electric vehicle (EV), energy management, plug-in electric vehicle (PEV), plug-in hybrid electric vehicle (PHEV), smart grid, vehicle-to-grid (V2G).

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This paper addresses a two-stage framework for the economic operation of a microgrid-like electric vehicle (EV) parking deck with on-site renewable energy generation (rooftop photovoltaic panel). This microgrid-like EV parking deck is a localized grouping of distributed generation (solar), energy storage (EV batteries), and load (EV charging load). Although EV parking decks can enable greater adoption of renewable energy sources by scheduling charging loads to coincide with periods of strong sun, the inherent intermittency of renewable energy resources and variable EV parking behaviors complicates the economic operation. In this paper, the proposed first stage of this framework provides the parking deck operators with a stochastic approach for dealing with the uncertainty of solar energy so as to make an optimal price decision (marginal electricity sale price and parking fee rebate) at the day-ahead time scale. The second stage introduces a model predictive control-based operation strategy of EV charging dealing with the uncertainty of parking behaviors within the real-time operation. Case studies demonstrate the better performance of the proposed framework, offering an effective day-ahead marginal electricity price for tomorrow's operation and increasing the microgrid-like EV parking deck's revenue during the real-time operation.Index Terms-Economic operation, electric vehicle (EV), microgrid, model predictive control (MPC), renewable energy. NOMENCLATUREVariableŝ R Expected day-ahead revenue of parking deck in dollars. R parking Expected day-ahead parking revenue in dollars. R charging Expected day-ahead charging revenue in dollars. C operation Daily parking deck operational cost in dollars. t parking,i Estimated total parking periods for ith electric vehicle (EV) during 24 h. r parking Parking fee for customers ($/h). r charging Electricity charging fee for customers ($/kWh). f c (t) Real-time charging revenue at tth time interval in dollars. f p (t) Real-time parking revenue at tth time interval in dollars. f c (t) Real-time forecasted charging revenue at tth time interval in dollars. f p (t)Real-time forecasted parking revenue at tth time interval in dollars. P s.i (t)Day-ahead charging power for ith EV at tth time interval under sth scenario in kW. P i (t)Real-time charging power for ith EV at tth time interval in kW. P i (t)Real-time planned charging power for ith EV at tth time interval in kW. P grid,s (t)Day-ahead power output from utility grid at tth time interval under sth scenario in kW. P grid (t)Real-time power output from utility grid at tth time interval in kW. P grid (t)Real-time power output from utility grid to be utilized at tth time interval in kW. y s (t)Day-ahead buy/sell (1 or 0) status at tth time interval under sth scenario. y(t)Real-time buy/sell (1 or 0) status at tth time interval. y(t)Real-time planned buy/sell (1 or 0) status at tth time interval. c (t) Electricity purchase price at tth time interval from the grid ($/kWh). h(t)Electricity sale price for the EV parking deck selling...

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This paper proposes a novel consensus-based distributed control algorithm for solving the economic dispatch problem of distributed generators. A legacy central controller can be eliminated in order to avoid a single point of failure, relieve computational burden, maintain data privacy, and support plug-and-play functionalities. The optimal economic dispatch is achieved by allowing the iterative coordination of local agents (consumers and distributed generators). As coordination information, the local estimation of power mismatch is shared among distributed generators through communication networks and does not contain any private information, ultimately contributing to a fair electricity market. Additionally, the proposed distributed algorithm is particularly designed for easy implementation and configuration of a large number of agents in which the distributed decision making can be implemented in a simple proportional-integral (PI) or integral (I) controller. In MATLAB/Simulink simulation, the accuracy of the proposed distributed algorithm is demonstrated in a 29node system in comparison with the centralized algorithm. Scalability and a fast convergence rate are also demonstrated in a 1400-node case study. Further, the experimental test demonstrates the practical performance of the proposed distributed algorithm using the VOLTTRON TM platform and a cluster of low-cost credit-card-size single-board PCs.

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