Stackelberg Game Approach on Modeling of Supply Demand Behavior Considering BEV Uncertainty

Azimian, Behrouz; Fijani, Ramin Faraji; Ghotbi, Ehsan; Wang, Xin

doi:10.1109/pmaps.2018.8440398

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…Hence, we can maximize charging performance while minimizing deployment cost. Indeed, in the literature, several researches have been proposed to optimally utilize the wireless charging systems in an urban road network [46]. For example, a stationary wireless charging stations deployment scheme for taxicabs that optimizes the idle time and continuous operability is introduced in [47].…”

Section: A Wireless Charging For Electric Vehiclesmentioning

confidence: 99%

Modeling and Analysis of Dynamic Charging for EVs: A Stochastic Geometry Approach

Nguyen

Kishk

Alouini

2021

IEEE Open J. Veh. Technol.

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With the increasing demand for greener and more energy efficient transportation solutions, electric vehicles (EVs) have emerged to be the future of transportation across the globe. However, currently, one of the biggest bottlenecks of EVs is the battery. Small batteries limit the EVs driving range, while big batteries are expensive and not environmentally friendly. One potential solution to this challenge is the deployment of charging roads, i.e., dynamic wireless charging systems installed under the roads that enable EVs to be charged while driving. In this paper, we use tools from stochastic geometry to establish a framework that enables evaluating the performance of charging roads deployment in metropolitan cities. We first present the course of actions that a driver should take when driving from a random source to a random destination in order to maximize dynamic charging during the trip. Next, we analyze the distribution of the distance to the nearest charging road. This distribution is vital for studying multiple performance metrics such as the trip efficiency, which we define as the fraction of the total trip spent on charging roads. Next, we derive the probability that a given trip passes through at least one charging road. The derived probability distributions can be used to assist urban planners and policy makers in designing the deployment plans of dynamic wireless charging systems. In addition, they can also be used by drivers and automobile manufacturers in choosing the best driving routes given the road conditions and level of energy of EV battery.

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Section: A Wireless Charging For Electric Vehiclesmentioning

confidence: 99%

Modeling and Analysis of Dynamic Charging for EVs: A Stochastic Geometry Approach

Nguyen

Kishk

Alouini

2021

IEEE Open J. Veh. Technol.

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“…Game theory approaches have become one of the tools adopted for modeling and analyzing energy consumption, due to its effectiveness to capture complex interactions between multiple players. The Stackelberg game is one of the most used game strategies for demand response problems [3,[9][10][11][12]. Another largely used non-cooperative approach, as a solution for DSM, is the Nash equilibrium strategy [3,[13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Satisfaction-Based Energy Allocation with Energy Constraint Applying Cooperative Game Theory

2021

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There has been an effort for a few decades to keep energy consumption at a minimum or at least within a low-level range. This effort is more meaningful and complex by including a customer’s satisfaction variable to ensure that customers can achieve the best quality of life that could be derived from how energy is used by different devices. We use the concept of Shapley Value from cooperative game theory to solve the multi-objective optimization problem (MOO) to responsibly fulfill user’s satisfaction by maximizing satisfaction while minimizing the power consumption, with energy constrains since highly limited resources scenarios are studied. The novel method uses the concept of a quantifiable user satisfaction, along the concepts of power satisfaction (PS) and energy satisfaction (ES). The model is being validated by representing a single house (with a small PV system) that is connected to the utility grid. The main objectives are to (i) present the innovative energy satisfaction model based on responsible wellbeing, (ii) demonstrate its implementation using a Shapley-value-based algorithm, and (iii) include the impact of a solar photovoltaic (PV) system in the energy satisfaction model. The proposed technique determines in which hours the energy should be allocated to maximize the ES for each scenario, and then it is compared to cases in which devices are usually operated. Through the proposed technique, the energy consumption was reduced 75% and the ES increased 40% under the energy constraints.

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“…The concept of demand-side management (DSM) for electric utilities has been a focus of studies in the past few decades [9,10]. In a traditional power grid, proper metering may decrease demand response non-scheduled loads slightly [11]. However, in the smart grid, advanced metering infrastructures and novel DSM algorithms are more beneficial for suppliers and customers.…”

Section: Introductionmentioning

confidence: 99%

Techno-Economic Analysis and Optimization of a Microgrid Considering Demand-Side Management

Nazemi

Mahani

Ghofrani

et al. 2020

2020 IEEE Texas Power and Energy Conference (TPEC)

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The control and managing of power demand and supply become very crucial because of penetration of renewables in the electricity networks and energy demand increase in residential and commercial sectors. In this paper, a new approach is presented to bridge the gap between Demand-Side Management (DSM) and microgrid portfolio, sizing and placement optimization. Although DSM helps energy consumers to take advantage of recent developments in utilization of Distributed Energy Resources (DERs) especially microgrids, a huge need of connecting DSM results to microgrid optimization is being felt. Consequently, a novel model that integrates the DSM techniques and microgrid modules in a two-layer configuration is proposed. In the first layer, DSM is employed to minimize the electricity demand (e.g. heating and cooling loads) based on zone temperature set-point. Using the optimal load profile obtained from the first layer, all investment and operation costs of a microgrid are then optimized in the second layer. The presented model is based on the existing optimization platform developed by RU-LESS (Rutgers University, Laboratory for Energy Smart Systems) team. As a demonstration, the developed model has been used to study the impact of smart HVAC control on microgrid compared to traditional HVAC control. The results show a noticeable reduction in total annual energy consumption and annual cost of microgrid.

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Stackelberg Game Approach on Modeling of Supply Demand Behavior Considering BEV Uncertainty

Cited by 10 publications

References 25 publications

Modeling and Analysis of Dynamic Charging for EVs: A Stochastic Geometry Approach

Modeling and Analysis of Dynamic Charging for EVs: A Stochastic Geometry Approach

Satisfaction-Based Energy Allocation with Energy Constraint Applying Cooperative Game Theory

Techno-Economic Analysis and Optimization of a Microgrid Considering Demand-Side Management

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