Estimating plug-in electric vehicle demand flexibility through an agent-based simulation model

Bustos-Turu, Gonzalo; Dam, Koen H. van; Acha, Salvador; Shah, Nilay

doi:10.1109/isgteurope.2014.7028889

Cited by 15 publications

(12 citation statements)

References 8 publications

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“…The modelling methodology builds upon probabilistic and stochastic methods previously carried out [23][24][25] along with agent-based modelling [26,27], system dynamics approaches [28,29], linear and non-linear programming [30] and stochastic modelling [31,32]. Of note in the present work is its simulated and empirical data-driven approach, which utilises vehicle usage data, building demand, renewable energy generation and ancillary market data to simulate the economic potential of EVs with V2G.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

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

et al. 2017

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“…The example application used in this paper is the charging of plug-in electric vehicles (PEVs) in an urban area, following the description in [4]. Driving reduces the state of charge (SOC) of the battery which is recharged when the car is plugged into a charging unit.…”

Section: Case Study: Simulating the Electricity Consumption In An Urbmentioning

confidence: 99%

A Methodology for Simulating Synthetic Populations for the Analysis of Socio-technical Infrastructures

Dam

Bustos-Turu

Shah

2017

Advances in Intelligent Systems and Computing

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Abstract:Modelling socio-technical systems in which a population of heterogeneous agents generates demand for infrastructure services requires a synthetic population of agents consistent with aggregate characteristics and distributions. A synthetic population can be created by generating individual agents with properties and rules based on a scenario definition. Simulation results fine-tune this process by comparing system level behaviour with external data, after which the emergent behaviour can be used for analysis and optimisation of planning and operation. An example of electricity demand profiles is used to illustrate the approach.Keywords: agent-based model, socio-technical system, synthetic population IntroductionTo analyse and understand the operation of socio-technical systems, in which physical systems interact with social networks, simulation models need to include the behaviour of the actors in the model [1]. For example, the emergent behaviour of a group of actors, modelled as agents, could generate the demand for services provided by the socio-technical system. Individual characteristics of agents are necessary to include heterogeneity in the simulation model while remaining consistent with aggregate/average values for the population [2] [3]. In this paper a methodology to generate a synthetic population given certain land-use and general population characteristics is proposed. The approach followed uses geographical information system (GIS) data as input which is enriched with land-use data (e.g. population density, floor space of offices) combined with statistics on the population (e.g. employment rates, car ownership). A case study on electricity consumption is used as an example of the proposed methodology and some illustrative results are presented for an area in West London. MethodologyThe core idea is that a synthetic population can be generated during the initialisation of a simulation model using a few basic properties for the area considered, key characteristics for the population and distributions of the activities they engage in which

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“…Finally, represents the energy consumption rate for each PEV depending on its type (mini, small, medium). Details of this classification can be found in [20]. In the ABS model the level of access to different charging point are considered based on local authority plans and access to off-street parking facilities.…”

Section: Electric Vehicle Demand Modelmentioning

confidence: 99%

“…The specific charging rate will depend on the charging point type (i.e., normal, fast). Details of the representation of the charging infrastructure can be found in [20].…”

Section: Electric Vehicle Demand Modelmentioning

confidence: 99%

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Simulating residential electricity and heat demand in urban areas using an agent-based modelling approach

Bustos-Turu

Dam

Acha

et al. 2016

2016 IEEE International Energy Conference (ENERGYCON)

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Abstract-Cities account for around 75% of the global energy demand and are responsible for 60-70% of the global greenhouse gasses emissions. To reduce this environmental impact it is important to design efficient energy infrastructures able to deal with high level of renewable energy resources. A crucial element in this design is the quantitative understanding of the dynamics behind energy demands such as transport, electricity and heat. In this paper an agent-based simulation model is developed to generate residential energy demand profiles in urban areas, influenced by factors such as land use, energy infrastructure and user behaviour. Within this framework, impact assessment of low carbon technologies such as plug-in electric vehicles and heat pumps is performed using London as a case study. The results show that the model can generate important insights as a decision support tool for the design and planning of sustainable urban energy systems.

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Estimating plug-in electric vehicle demand flexibility through an agent-based simulation model

Cited by 15 publications

References 8 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

A Methodology for Simulating Synthetic Populations for the Analysis of Socio-technical Infrastructures

Simulating residential electricity and heat demand in urban areas using an agent-based modelling approach

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