The impact of electric vehicle penetration and charging patterns on the management of energy hub – A multi-agent system simulation

Lin, Haiyang; Liu, Yiling; Sun, Qiang; Xiong, Rui; Li, Hailong; Wennersten, Ronald

doi:10.1016/j.apenergy.2018.08.083

Cited by 79 publications

(38 citation statements)

References 53 publications

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“…The scheduling problems of EVAs have been assessed in a number of recent papers that evaluate the impact of EVAs on various problems, including: the effects of a high EV penetration in the electricity market [16][17][18][19], modeling driving behaviors and fluctuation of electricity prices [20,21], and applying risk-based strategies [22,23].…”

Section: Literature Reviewmentioning

confidence: 99%

Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market

et al. 2020

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Selecting an appropriate contract price between electric vehicle aggregators and electric vehicle owners is an uncertain, multi-criteria decision-making issue. In addition, the results can cause strong conflict due to different aims: the optimal value for increasing electric vehicle aggregator (EVA) profit negatively affects the cost for owners. The value of the contract price can change the optimal scheduling of EVAs in the day-ahead market. Taking into consideration this context, the current paper proposes to solve the multi-objective scheduling problem of an aggregator with a goal programming approach. The presented approach sets a satisfaction level for each goal according to decision-makers' preference. Numerical results illustrate the validity of this approach to balance different performance measures. Furthermore, optimal scheduling of electric vehicle aggregators in the day-ahead market is created.

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Section: Literature Reviewmentioning

confidence: 99%

Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market

et al. 2020

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“…In recent years, many studies in the area of EVs have focused on the scheduling problem of EVs' aggregation from di erent points of view including the impacts of EVs' high penetration in the market [26][27][28], investigation of uncertainty-based models to simulate EV owners' behavior and market prices [29,30], and risk-based approach [31,32]. Carpinelli et al [33] proposed a multi-objective function to optimize the operation of micro grids in the presence of a large number of EVs and renewable resources.…”

Section: Eclectic Vehicles Aggregator (Eva)mentioning

confidence: 99%

“…Note that the proposed IGDT method is generally known as bi-level, which can be solved through the common bi-level models as explained [39]. In addition, in certain circumstances, the IGDT-based models can be divided into two single level problems [27]. In the proposed scheduling problem, decreasing electricity price has a negative impact on the scheduling pro t. In other words, if the market price drops, then the scheduling pro t will increase as well, or vice versa; if the electricity price decreases, the pro t will certainly decrease.…”

Section: Robust Schedulingmentioning

confidence: 99%

Risk-based Cooperative Scheduling of Demand Response and Electric Vehicle Aggregators

2019

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This paper proposes a new cooperative scheduling framework for Demand Response Aggregators (DRAs) and Electric Vehicle Aggregators (EVAs) in a day-ahead market. The proposed model implements the Information-Gap Decision Theory (IGDT) to optimize the scheduling problem of the aggregators, which guarantees to obtain the predetermined pro t by the aggregators. In the proposed model, the driving pattern of electric vehicle owners and the uncertainty of day-ahead prices are simulated via scenariobased and bi-level IGDT-based methods, respectively. The DRA provides DR from two demand-side management programs including Time-Of-Use (TOU) and reward-based DR. Then, the obtained DR is o ered in day-ahead markets. Furthermore, the EVAs not only meet the EV owners' demand economically, but also participate in the day-ahead market while are willing to set DR contracts with the DRA. The objective function is to maximize the total pro t of DR and EV aggregators by pursuing two di erent strategies to deal with price uncertainty, i.e., risk-seeking strategy and risk-averse strategy. The proposed plan is formulated in a risk-based approach and its validity is evaluated with respect to a case study with realistic data of electricity markets.

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“…The energy hub presented in [27] included the heating and cooling demands of a residential community. How the implementation of V2G can impact electricity and cooling prices was shown.…”

Section: Introductionmentioning

confidence: 99%

Vehicle-To-Grid for Peak Shaving to Unlock the Integration of Distributed Heat Pumps in a Swedish Neighborhood

2020

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The city of Stockholm is close to hitting the capacity limits of its power grid. As an additional challenge, electricity has been identified as a key resource to help the city to meet its environmental targets. This has pushed citizens to prefer power-based technologies, like heat pumps and electric vehicles, thus endangering the stability of the grid. The focus of this paper is on the district of Hammarby Sjöstad. Here, plans are set to switch from district heating to heat pumps. A previous study verified that this choice will cause overloadings on the electricity distribution grid. The present paper tackles this problem by proposing a new energy storage option. By considering the increasing share of electric vehicles, the potential of using the electricity stored in their batteries to support the grid is explored through technical performance simulations. The objective was to enable a bi-directional flow and use the electric vehicles' (EVs)' discharging to shave the peak demand caused by the heat pumps. It was found that this solution can eliminate overloadings up to 50%, with a 100% EV penetration. To overcome the mismatch between the availability of EVs and the overloadings' occurrence, the minimum state of charge for discharging should be lower than 70%.

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The impact of electric vehicle penetration and charging patterns on the management of energy hub – A multi-agent system simulation

Cited by 79 publications

References 53 publications

Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market

Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market

Risk-based Cooperative Scheduling of Demand Response and Electric Vehicle Aggregators

Vehicle-To-Grid for Peak Shaving to Unlock the Integration of Distributed Heat Pumps in a Swedish Neighborhood

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