Management and coordination charging of smart park and V2G strategy based on Monte Carlo algorithm

Aryanezhad, Majid; Ostadaghaee, Elahe; Joorabian, Mahmood

doi:10.1109/sgc.2014.7090887

Cited by 19 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

et al. 2017

View full text Add to dashboard Cite

show abstract

“…However, β in the charge/discharge states varies irregularly near a specific value at different temperatures, so their corresponding relationship with SOC is weak. Therefore, the relationship between parameters α, γ , R and SOC can be described by (8).…”

Section: B Sop Prediction Resultsmentioning

confidence: 99%

“…11 (b), the prediction accuracy is much improved, especially at SOC = 0.9, the prediction error is only 7.8%, while the overall prediction errors are around 3%, and the average error is 0.78%. Therefore, using (8) to describe the relationship between model parameters and SOC is very successful.…”

Section: B Sop Prediction Resultsmentioning

confidence: 99%

Novel Polarization Voltage Model: Accurate Voltage and State of Power Prediction

et al. 2020

View full text Add to dashboard Cite

Accurate prediction of battery voltage and state of power (SOP) is a challenge for a battery management system to achieve charge/discharge safety protection and power distribution. An accurate, simple and implementable battery model is key to realizing battery simulation and state estimation/prediction. To establish a battery model that can easily realize battery voltage and SOP prediction, this paper proposes a novel polarization voltage (NPV) model based on current and time by simplifying the equation describing the polarization characteristics in the electrochemical model. The NPV model is implementable for high-precision prediction of battery voltage and SOP at t-second (t > 0) under any constant current (I = 0). Based on the relationship between model parameters and state of charge (SOC), the SOP prediction model with different SOC is successfully realized. The prediction results show that the battery is charged from SOC = 0 to cutoff voltage with 3C, and the high-precision prediction of polarization voltage and terminal voltage can be obtained by using the NPV model. The average errors of the polarization voltage and voltage terminal are only 1.4% and 0.4%, respectively. For the 3C discharge process, the maximum prediction error of terminal voltage is −4.7%, and the error at the end of discharge is only −1.1%, with an average error of 0.6%. The average error of SOP at t-second = 10s predicted by the NPV model is only 0.78%. More importantly, the NPV model parameters are obtained from just small-batch data, and it is timesaving for practical testing with simple structures. Therefore, the NPV model is suitable for battery simulation, state prediction, fast charging security and energy management with broad application prospects. INDEX TERMS Novel polarization voltage model, voltage prediction, state of power, lithium ion battery, electric vehicle. NOMENCLATURE ABBREVIATIONS

show abstract

“…This concept is known as Vehicle to Grid (V2G). For the real implementation of this concept, it is necessary to use power electronics-based devices, as in [6], where a method to analyze the power transaction capabilities of a EVs in V2G operation was proposed. As mentioned, an EV is becoming a key piece in electrical systems that demands or injects energy and can improve their operation.…”

Section: A Motivationmentioning

confidence: 99%

An Enhanced Delivery Route Operational Planning Model for Electric Vehicles

et al. 2020

View full text Add to dashboard Cite

The recent incorporation of Electric Vehicles (EVs) in the worldwide delivery vehicle fleet has increased the importance of their operation in road networks. In particular, the growth of electronic commerce, while promoting the use of non-polluting energy sources, will make EVs play an important role in cities. Thus, an accurate characterization of the technical aspects of EVs and parcel delivery requirements in delivery route operational planning models is required. To that end, this paper presents an enhanced operational planning model for the route and charging of an EV fleet considering technical and economic real-world constraints, such as battery degradation, acceleration-and speed-dependent power consumption, tolls and penalty for delivery delay and non-fulfillment. Moreover, unlike some previous works, the delivery allocation and the number of EVs used are not predetermined, being rather outcomes of the optimization process. Additionally, the Proposed Approach (PA) allows EVs to pass through intersections more than once. The proposed model is formulated in terms of a series of intersection-and path-related decision variables, characterizing the State of Charge (SOC) of batteries and the navigation time. The resulting optimization problem is cast as an instance of Mixed-Integer Linear Programming (MILP). The model is implemented in the mathematical programming language GAMS and solved using the commercial solvers CPLEX and ODH-CPLEX. The model is tested on two intersection maps, including different types of roads, charging rates and delivery points. Results show the effectiveness of the PA over previously reported approaches in terms of the cost, energy and time associated with the resulting operating strategies. INDEX TERMS Battery degradation, delivery allocation, electric vehicle route planning, penalty costs, acceleration-and speed-dependent power consumption. NOMENCLATURE SETS AND INDICES

show abstract

Management and coordination charging of smart park and V2G strategy based on Monte Carlo algorithm

Cited by 19 publications

References 10 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

Novel Polarization Voltage Model: Accurate Voltage and State of Power Prediction

An Enhanced Delivery Route Operational Planning Model for Electric Vehicles

Contact Info

Product

Resources

About