Table 6 Anchor paper bibliography and analysis

Deilami et al. (2011)

Uncontrolled and random EV charging can cause increased power losses, overloads and voltage fluctuations, which are all detrimental to the reliability and security of newly developing smart grids

Simulation

A novel real-time smart load management control strategy is proposed to coordinate the charging of multiple EVs in a smart grid

Tan et al. (2014)

Can costs be reduced by integrating plug-in electric vehicles and renewable distributed generators?

Simulation

In a market in which users can sell back the energy generated by their distributed generators or the energy stored in their plug-in electric vehicles, numerical examples show that the demand curve is flattened by the new pricing model of demand-response management for the future smart grid that integrates plug-in electric vehicles and renewable distributed generators, even though the model includes uncertainties, thus reducing the utility company’s costs

Veldman and Verzijlbergh (2015)

In this paper, we assess the financial impact of various EV charging strategies on distribution grids

Simulation

Using a strategy that minimizes network peak loads (from a network operator’s perspective) with a strategy to minimize charging costs (from the commercial party’s perspective), a large difference in network impacts between the price-based and network-based charging strategies was only observed with a high wind penetration. Therefore, we additionally study the effect of wind energy on electricity prices and, consequently, on the resulting EV load and network impacts

Mukherjee and Gupta (2015)

What work has been done recently in the area of scheduling algorithms for charging EVs in smart grid?

Literature review

The works are first classified into two broad classes of unidirectional versus bidirectional charging. Then each class is further sorted based on whether the scheduling is centralized or distributed and whether any mobility aspects are considered

Kennel et al. (2013)

Can hierarchical model predictive control (HiMPC) improve energy management system for smart grids with electric vehicles?

Simulation

The aggregator in particular provides predictions to the HiMPC on the availability of electric vehicles for LFC based on the current mobility demand and the statistical mobility behavior of the vehicle users. The main component is the HiMPC, which allows covering different time scales, regarding constraints (e.g. power ratings) and predictions (e.g. on renewable generation), as well as rejecting disturbances (e.g. due to fluctuating renewable generation) based on a systematic model- and optimization-based design

Rigas et al. (2015)

How can EVs and the systems that manage EV collectives be made smarter?

Literature review

Artificial intelligence techniques can render EVs and the systems that manage EV collectives smarter. A survey of the literature identifies the commonalities and key differences in the approaches

Kim et al. (2013)

Can novel electricity load scheduling algorithms improve a power system with an aggregator and multiple customers with EVs?

Model

Collaborative and noncollaborative approaches are proposed. In the collaborative approach, an optimal distributed load-scheduling algorithm maximizes the power system’s social welfare. In the noncollaborative approach, the energy scheduling problem is modeled as a noncooperative game among self-interested customers, where each customer determines its own load scheduling and energy trading to maximize its own profit. A tiered billing scheme that can control the electricity rates for customers according to their different energy consumption levels to resolve the unfairness between heavy and light customers in the noncollaborative approach. Both approaches also consider uncertainty in load demands, with which customers’ actual energy consumption varying from the schedule

Sojoudi and Low (2011)

Scheduling the charging of PHEV batteries

Physical simulation

A solution to this highly nonconvex problem optimizes the network performance by minimizing the generation and charging costs while satisfying the network, physical, and inelastic-load constraints. A global optimum to the joint OPF-charging optimization can be found efficiently in polynomial time by solving its convex dual problem whenever its duality gap is zero

Rassaei et al. (2015)

Assigning real-world randomness to EVs’ availability in households and their charging requirements, how can EVs’ demand response (DR) help minimize the peak power demand and, in general, shape the system’s aggregated demand profile?

Simulation

A general demand-shaping problem applicable for limit-order bids to a day-ahead (DA) energy market. We propose an algorithm for distributed DR of the EVs to shape the daily demand profile or to minimize the peak demand. Additionally, we put these problems in a game framework

Xing et al. (2016)

Can utilities gain load-shifting service by optimally scheduling the charging and discharging of EVs in a decentralized fashion?

Algorithm load-shifting service proposal

We propose a solvable approximation of the MDP problem by exploiting the shape feature of the base demand curve during the night, and develop a decentralized algorithm based on iterative water-filling. Our algorithm is decentralized in the sense that the EVs compute locally and communicate with an aggregator

Rostami et al. (2015)

Can grid reliability and energy cost be improved by managing PHEV charging?

Simulation

A novel optimal stochastic reconfiguration methodology to moderate the charging effect of PHEVs by changing grid topology using remote-controlled switches. Uncertainties associated with network demand, energy price, and PHEV charging behavior in different charging frameworks are handled with Monte Carlo simulation and the proposed stochastic problem is solved with krill herd optimization algorithm

Kong and Karagiannidis (2016)

What is the state of the art of existing PHEV battery-charging schemes?

Analysis

For uncontrolled charging, existing studies focus on evaluating the impact of adding variable charging load on the smart grid. Various indirectly controlled charging schemes have been proposed to control energy prices and indirectly influence charging operations. Smart charging schemes can directly control a rich set of charging parameters to achieve various performance objectives, such as minimizing power loss, maximizing operator’s profit, ensuring fairness, and so on. Finally, bidirectional charging allows a PHEV to discharge energy into the smart grid, such that the vehicle can act as a mobile energy source to further stabilize a grid partially supplied by intermittent renewable-energy sources

Martinez et al. (2017)

This paper presents a comprehensive analysis of EMS evolution toward blended mode and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms

Analysis, survey

This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems, traffic information, and cloud computing can provide to enhance PHEV energy management. Nevertheless, it has been evidenced that the EMS cannot be fully optimized without detailed information about the future route

Hu et al. (2017)

How do three important control tasks interact in PHEVs: charging, on-road power management, and battery-degradation mitigation?

Analysis

A new convex-programming (CP)-based cost-optimal control framework to minimize the daily PHEV operational expense seamlessly integrates the three tasks’ costs with a very close precision to DP while running approximately 200 times faster. Sensitivity analysis suggests that the PHEV evolves toward a pure electric vehicle, with increased gasoline price and reduced battery price. The optimization does not allow V2G activities, because V2G-induced battery aging cost outweighs the added V2G revenue

Yao et al. (2017)

How can EV charging and DR programs be coordinated in parking stations?

Convex relaxation, simulation

Extensive simulation results show that the proposed work is able to satisfy EV charging demand while accommodating both types of DR programs in the parking station. The proposed work is also able to simultaneously maximize the number of EVs for charging and minimize expenses

Ahmadi et al. (2017)

What are the potential technological, economic and environmental opportunities for improving energy systems and material efficiency from lithium-ion battery recovery from end-of-life electric vehicles?

Analysis

Results indicate that the manufacturing phase of the Li-ion battery will still dominate environmental impacts across the extended life cycle of the pack (first use in a vehicle, then reuse in a stationary application). For most impact categories, the cascaded use system appears significantly beneficial compared to the conventional system. Consuming clean energy sources for both use and reuse supports global and local environmental stress reductions. Greenhouse gas advantages of vehicle electrification can be doubled by extending the life of the EV batteries and enabling better use of off-peak low-cost clean electricity or intermittent renewable capacity

Shafie-khah et al. (2016)

What is a EV parking lot’s the optimal behavior in the energy and reserve markets?

Analysis

The results indicated that a parking lot, because of its charging-station nature, is similar to a large demand in the system. Consequently, participation in different DRPs significantly affects its operational behavior. Therefore, the pattern of EV charging and discharging, trading energy with the grid, and participation in the reserve market are meaningfully influenced by the DRP type

Yu et al. (2016)

Can an exploration of EV mobility impact DRM in V2G systems in a smart grid?

Simulation with real-world data

Based on simulation with real world data, the districts’ DRM dynamics are coupled with each other through EV fleets. A complex network-synchronization method analyzes the dynamic behavior in V2G mobile-energy networks. Numerical results show that mobility of a symmetrical EV fleet is synchronously stable and power demand is balance among different districts

van der Kam and van Sark (2015)

How can self-consumption of photovoltaic power by smart charging EVs and V2G technology be increased?

Model simulations

Self-consumption increases from 49 to 62–87% and demand peaks decrease by 27–67%. These results clearly demonstrate the benefits of smart charging EVs with PV power. Furthermore, our results give insight into the effect of different charging strategies and microgrid compositions

Deng et al. (2015)

How do four major aspects (programs, issues, approaches, and future extensions of demand response) of incentive-based programs affect utilities’ demand response

Survey, analysis

Reviewing DLC, interruptible/curtailable load, demand bidding and buyback, emergency demand reduction, ToU pricing, CPP, RTP, and IBR, the demand-response issues include mathematic models and problems. Commonly used utility and cost functions model the demand-response activities. Based on the models, most of the existing works aim at utility maximization, cost minimization, price prediction, renewable energy, and energy-storage problems

Jian et al. (2015)

Can a novel event-triggered scheduling scheme for V2G operation based on the scenario of stochastic EV connection to smart grid address power-load fluctuation?

Analysis

Statistical analysis results demonstrate that the proposed V2G scheduling scheme can dramatically smooth out fluctuations in power-load profiles

Erdinc et al. (2015)

Can a collaborative evaluation of dynamic pricing and a bi-directional use possibility for EV and energy storage system improve peak-power-limiting-based DR strategies?

Simulation

The proposed strategy provided a more efficient operation by means of up to 65% relectricity-cost reduction. Adding more smart technologies to a HEM system offers a more economically efficient use of electricity

Mou et al. (2015)

Can DSM for PHEV charging at low-voltage transformers flatten their load curve while satisfying each consumer’s requirement for timely PHEV charged?

Simulations, algorithm

Simulation results show that the proposed algorithm can efficiently fulfill the task of flattening the power demand curve and avoid transformer overloading

Kisacikoglu et al. (2015)

Can a single-phase on-board bidirectional EV charger provide reactive power support to the utility grid in addition to charging the vehicle battery?

Simulation

The proposed unified system controller receives charging active power and reactive power inputs from the utility grid and adjusts the line current and battery current without exceeding THD limits. It provides a fast dynamic response, along with a good steady-state performance

Sbordone et al. (2015)

How do different types of EV charging stations, in reference to the present international European standards, and storage technologies integrate in a smart grid?

Prototyping

The results of the experimental tests show that the system has a good performance in the implementation of peak-shaving functions, making the prototype a nearly zero-impact system

Lopez et al. (2015)

Can an optimization-based model perform load shifting in the context of smart grids?

Simulations

The deviations in the final-load mean curve can be decreased more than 70% with a significant reduction of the difference between the hourly maximum and minimum demand values

Verzijlbergh et al. (2014)

Do we need congestion management in the distribution grid?

Mathematical formulation of the EV optimization problem

The constraint that limited network capacity puts on EV charging has a low associated cost, but shifting demand peaks through an optimal congestion-management mechanism only marginally increases EV-charging costs. Ex-ante fixed tariffs, based on historic network load profiles, do not solve congestion efficiently and may not be effective at all. They influence the economic signal of the wholesale electricity price, leading to unnecessarily high EV-charging costs because that network capacity is only a constraint during a limited number of hours per year, while these tariffs force continuous changes in EV charging

Heymans et al. (2014)

Can we use a MATLAB simulation to analyze the feasibility of and cost savings from repurposing an EV battery unit for peak shifting?

MATLAB simulation

Using repurposed EV batteries for energy storage and peak shaving can save costs to residential users while shifting power from peak to off-peak times, thus reducing strains on the electric grid. However, the approach has marginal economic feasibility without government intervention and moderate economic feasibility with intervention. Thus, governments should subidize this green technology. Such subsidies can be justified through reduced strains on the electricity grid and support of smart-grid objectives for cleaner power generation and energy security

Su et al. (2014)

Can a stochastic problem aid microgrid energy scheduling?

Simulation

The proposed problem formulation minimizes the expected operational cost of the microgrid and power losses while accommodating the intermittent nature of renewable-energy resources. Case studies on a modified IEEE 37-bus test feeder demonstrate the effectiveness and accuracy of the proposed stochastic microgrid energy-scheduling model

Zhang and Chen (2014)

What is a strategy for energy management and optimized operation of EVs considering the impact of EVs’ deep electric-grid penetration?

Simulation

Regional management of EVs based on the microgrid not only averts the adverse effects of uncoordinated charging, but also reduce the difference between the load peak and valley. Besides peak loading, the system economics are improved by encouraging EVs, BSS, and ILs to provide regulation service and reserve capacity, facilitating the integration of DERs and further promoting the renewable sources and reliability of the power supply

Donadee and Ilie (2014)

Can stochastic dynamic programming optimize charging and frequency-regulation capacity bids of an EV in a smart electric grid?

Stochastic dynamic programming

This paper presents an MDP with three sources of uncertainty for EV charging and an approximate SDP algorithm to optimize an EV’s charging and frequency-regulation bids over a continuous space of decisions. The methods developed minimize an approximation of expected future costs and lowers average EV charging costs than deterministic MPC. Although the relative improvement in mean charging cost is large, the absolute improvement very small

Vachirasricirikul and Ngamroo (2014)

Is coordinated V2G control and conventional frequency controller for robust LFC in the smart grid with large wind farms feasible?

Simulation

The V2G power output can be controlled effectively considering the proposed optimized battery SOC deviation control. The PSO based on the fixed-structure mixed-control technique helps concurrently tune the LFC’s PI control parameters. Simulation results demonstrate the robustness and coordinated-control effects of the proposed V2G control and LFC PI controllers against the changed system parameters and various operating conditions

Wang et al. (2013)

Can a multiobjective EV-charging planning method ensure charging service while reducing distribution-system power losses and voltage deviations?

Analysis, simulation

Optimal EV charging-station sizing and locating can be achieved to minimize the power losses and voltage deviations as well as EV travel distances

Jin et al. (2013)

How can EV charging be efficiently scheduled from an electricity-market perspective with joint consideration for the aggregator energy trading in the day-ahead and real-time markets?

Simulation

“It has been shown by extensive simulation results based on real electricity price and load data that compared to a baseline method without charging regulation, the aggregator’s revenue can be improved by 80.1% using optimal charging scheduling and can be further improved by 7.8% with the aid of ES on average. The proposed heuristic algorithm yields close-to-optimal solutions. Moreover, we investigated how several key parameters (including the number of EVs, the number of ES units, the penalty factor, and the prediction accuracy) affect the performance of the proposed approach.”

Tushar et al. (2012)

Can the grid-to-vehicle energy exchange between a smart grid and EVGs be optimized using a noncooperative Stackelberg game?

Distributed algorithm, simulation

The proposed approach yields improved performance in terms of the average utility per EVG, compared to a particle-swarm optimization and an equal-distribution scheme

He et al. (2012)

Is a globally and locally optimal scheduling scheme for EV charging and discharging feasible?

Simulation

The independently developed distributed locally optimal scheduling scheme is not only scalable to a large EV population, but also resilient to dynamic EV arrivals. Such a scheme can achieve performance close to the globally optimal scheduling scheme

Su and Chow (2012b)

Can a suite of computational intelligence-based algorithms (distribution-estimation algorithm, particle-swarm optimization) for optimally manage a large number of PHEVs/EVs charging at a municipal parking station?

MATLAB simulation

The proposed energy-management program can handle the energy management at a large-scale PHEV/EV parking deck. By grouping the vehicle fleet, the proposed energy-management program has the potential to solve a larger energy-management problem with little additional cost

Su and Chow (2012a)

Can an algorithm for optimally managing a large number of PHEVs (e.g., 3,000) charging at a municipal parking station help solve the problem of a large number of PHEVs simultaneously connecting to the grid?

MATLAB simulation

The estimation of distribution algorithm (EDA) effectively solved energy management at a municipal PHEV/EV parking deck. Since an EDA explicitly extract global statistical information from promising solutions, it is immune to the potential local minimum and the nonlinear nature of the problem. The algorithm converged to a better solution than some of the more traditional methods

Wu et al. (2012)

Can a game-theoretic model help understand EV–aggregator interactions in a V2G market where EVs help frequency regulation on the grid?

Simulation

The proposed pricing model and design mechanism work well and can benefit both EVs (additional income) and the grid (frequency regulation)

Pang et al. (2012)

What are the potential benefits of EVs and PHEVs as dynamically configurable dispersed energy storage acting in a vehicle-to-building operating mode? What are the implementation issues of DSM and OM in the smart distribution grid?

Case studies

“The use of BEVs/PHEVs battery as dispersed energy storage should meet requirements for the charging/discharging infrastructure leading to the practical data necessary for V2B operation. For demand side management, the peak load shifting strategy using BEVs/PHEVs can reduce on-peak load demand and energy consumption, which in turn will reduce the electricity purchase cost for the customer and vehicle owner. For outage management, the outage restoration for buildings using BEVs/PHEVs to generate power during faults in the main grid is envisioned by solving a optimization problem of merit-order scheduling of BEV/PHEVs under operating constraints.”

Ota et al. (2012)

How can an autonomous distributed V2G control scheme help energy storage smooth their natural intermittency and ensure grid-wide frequency stability?

Simulation

The proposed V2G control is effective for a distributed spinning reserve without system-wide information exchange and without interfering in the conventional thermal power generation. The proposed smart charging control satisfies the scheduled charging by the vehicle user. The combined control scheme of the V2G and smart charging contribute to a move toward low carbon energy systems through the large-scale integration of intermittent renewable energy sources

Singh et al. (2012)

How can a city be modeled to demonstrate V2G capabilities such as meeting peak demand and voltage-sag reduction?

Simulation

“Simulation results reveal that charging and discharging of EVs can be easily controlled using an FLC. Power leveling and peak saving can be achieved by charging of EVs during off-peak hours and discharging the EVs energy during peak hours.”

Su et al. (2012)

What is the state of electrification of transportation in an industrial environment?

Literature review

Without sophisticated energy management at the charging infrastructure, large numbers of PHEVs and EVs have the potential to threaten the stability of the existing industrial system. Discussing the conceptual and practical knowledge of V2G allows for EVs to feed power back directly to the grid. In addition, V2G can allow easier integration of renewable resources and support grid stability through ancillary services. The successful rollout of EVs also relies on advanced communication technologies and industrial-informatics systems. This paper presents an overview of the appropriate information exchange architectures and framework to facilitate the effective integration of PHEVs and EVs

Boulanger et al. (2011)

Which areas must be addressed to achieve widespread adoption of vehicle electrification?

Systems approach

Policies that reduce the EV and PHEV total cost of ownership, compared to conventional internal-combustion-engine vehicles, will lead to faster market penetration. Greater access to charging infrastructure will also accelerate public adoption. Smart-grid technology will optimize the vehicle integration with the grid, allowing intelligent and efficient use of energy

Peterson et al. (2010)

What are potential economic implications of using vehicle batteries to store grid electricity generated at off-peak hours for off-vehicle use during peak hours?

Simulation, analysis

It appears unlikely that these profits alone will provide sufficient incentive to the vehicle owner to use the battery pack for electricity storage and later off-vehicle use