Risk management and participation planning of electric vehicles in smart grids for demand response

Nezamoddini, Nasim; Wang, Yong

doi:10.1016/j.energy.2016.10.002

Cited by 72 publications

(38 citation statements)

References 43 publications

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“…As such, V2G can help provide a means of powering buildings from a portable battery store, which can be re-charged at a different location on a regular basis [5]. Such concerns are of lesser relevance in the UK however, where research indicates V2G uptake will predominantly be related to system economics and CO 2 emissions as opposed to grid security [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage

et al. 2017

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Citation: GOUGH, R. ...et al., 2017. Vehicle-to-grid feasibility: A technoeconomic analysis of EV-based energy storage. Applied Energy, 192, Additional Information:• This paper was accepted for publication in the journal Ap- 1 Vehicle-to-Grid Feasibility: A Techno-economic Analysis of EV-based Energy StorageRebecca Gough*⁺ Abstract:The potential for electric vehicles to obtain income from energy supplied to a commercial building together with revenue accruing from specific ancillary service markets in the UK is evaluated in this work. A hybrid time-series/probabilistic simulation environment using real-world data is described, which is applied in the analysis of electricity trading with vehicle-to-grid to vehicles, buildings and markets. Key parameters are found to be the electric vehicle electricity sale price, battery degradation cost and infrastructure costs. Three vehicle-to-grid scenarios are evaluated using pool vehicle trip data, market pricing index data and half-hourly electricity demand for a commercial building. Results show that provision of energy to the wholesale electricity market with additional income from the capacity market results in the greatest projected return on investment, producing an individual vehicle net present value of ~£8,400. This is over 10 years for a vehicle supplying energy three times per week to the half-hour day-ahead market and includes the cost of installing the vehicle-to-grid infrastructure. The analysis also shows that net income generation is strongly dependent upon battery degradation costs associated with vehicle-to-grid cycling.

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Section: Introductionmentioning

confidence: 99%

Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage

et al. 2017

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“…Therefore, EVs can be used as mobile energy storage devices in a power grid. [10][11][12] At present, some scholars have studied the coordinated scheduling of the charging loads of EVs and renewable energy sources. 6 Furthermore, EVs remain parked for a long time, and their charging times also exhibit randomness.…”

Section: Introductionmentioning

confidence: 99%

“…9 To address this issue, an effective scheduling strategy for EVs can be adapted by means of a vehicle-to-grid (V2G) system to enhance the ability of the power system to accept intermittent energy (wind and PV power) and improve the economy and security of the system operations. [10][11][12] At present, some scholars have studied the coordinated scheduling of the charging loads of EVs and renewable energy sources. A multitime scale coordinated scheduling model for EVs and wind power has been established, which can be used to smooth out the fluctuations in the equivalent load and absorb excess wind power.…”

Section: Introductionmentioning

confidence: 99%

Multitime scale coordinated scheduling for electric vehicles considering photovoltaic/wind/battery generation in microgrid

Liu

Song

Mao

et al. 2019

Int Trans Electr Energ Syst

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Summary The problem of multitime scale coordinated scheduling for electric vehicles (EVs) with vehicle‐to‐grid (V2G) technology is formulated for a microgrid with photovoltaic/wind/battery generation. The life of the energy storage system in the microgrid, and the benefits for EV users are considered as the optimization goals. Reasonable measures of the charging and discharging behaviors of EVs are considered to construct the objective functions. A stratified sequencing method is designed to transform the multiobjective optimization problem into a single‐objective nonlinear optimization problem that can be solved through adaptive particle swarm optimization (APSO). Then an optimal multitime scale coordinated scheduling scheme can be obtained. The effectiveness and feasibility of the methods are verified by simulation results.

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“…The observability effect could be from EVs in the zip code itself, from the zip code's neighbors, and from the whole NYC. The utility is the weighted value of these three components, as described in Equation (2).…”

Section: Agent-based Approachmentioning

confidence: 99%

“…Electric Vehicles (EVs) consist of Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), and both are required to be charged from the power grids [1][2][3]. The overall sales of EVs have been steadily rising [4].…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City

Ahkamiraad¹,

Wang²

2018

Energies

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Current power grids in many countries are not fully prepared for high electric vehicle (EV) penetration, and there is evidence that the construction of additional grid capacity is constantly outpaced by EV diffusion. If this situation continues, then it will compromise grid reliability and cause problems such as system overload, voltage and frequency fluctuations, and power losses. This is especially true for densely populated areas where the grid capacity is already strained with existing old infrastructure. The objective of this research is to identify the zip-code level electricity consumption that is associated with large-scale EV adoption in New York City, one of the most densely populated areas in the United States (U.S.). We fuse the Fisher and Pry diffusion model and Rogers model within the agent-based simulation to forecast zip-code level EV diffusion and the required energy capacity to satisfy the charging demand. The research outcomes will assist policy makers and grid operators in making better planning decisions on the locations and timing of investments during the transition to smarter grids and greener transportation.

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Risk management and participation planning of electric vehicles in smart grids for demand response

Cited by 72 publications

References 43 publications

Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage

Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage

Multitime scale coordinated scheduling for electric vehicles considering photovoltaic/wind/battery generation in microgrid

An Agent-Based Model for Zip-Code Level Diffusion of Electric Vehicles and Electricity Consumption in New York City

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