scholarly journals Optimization Model of EV Charging and Discharging Price Considering Vehicle Owner Response and Power Grid Cost

2019 ◽  
Vol 14 (6) ◽  
pp. 2251-2261 ◽  
Author(s):  
Zhaoyang Qu ◽  
Jiajun Song ◽  
Yuqing Liu ◽  
Hongbo Lv ◽  
Kewei Hu ◽  
...  
2021 ◽  
Vol 2 (2) ◽  
pp. 1-21
Author(s):  
Hossam ElHussini ◽  
Chadi Assi ◽  
Bassam Moussa ◽  
Ribal Atallah ◽  
Ali Ghrayeb

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.


Author(s):  
Alberto Costa ◽  
Dionysios Georgiadis ◽  
Tsan Sheng Ng ◽  
Melvyn Sim

2014 ◽  
Vol 971-973 ◽  
pp. 979-982
Author(s):  
Yan Hong Li ◽  
Zhi Rong Zhang

Automatic voltage control(AVC) is the highest form of current power grid voltage and reactive power control,during the implementation of AVC, the whole network reactive power optimization isthe core and foundation. Thispaper researches and discuses the application of reactive power optimization inpower grid AVC. In the traditional reactive power optimization, the reactivepower limits of synchronous generators are fixed. In this paper, thesynchronous generator PQ operating limits change with external conditions,thus establishes reactive power optimization model in accordance with therequirements of AVC. Thispaper presents reactive power optimization method based on the principle ofpartition. The method decomposes the system to several partitions. Eachpartition separately optimized, thus reduces the system scale.And the convergence of the algorithm, the calculation speed and the discretevariable processing etc. improve. At the same time, this method reflects theclassification, hierarchical, partition, characteristics of coordinated controlof AVC.


Author(s):  
Zhao Jia ◽  
Fang Haifeng ◽  
Zhu Yueyan ◽  
Li Yueke ◽  
Chen Yisong ◽  
...  

Under the goal of carbon peak and carbon neutrality, the carbon emission reduction of the automobile industry has attracted more and more attention in recent years. Electric vehicle has the dual attributes of power load and energy storage unit. With the increase of the number of electric vehicles, reducing carbon emissions through the collaborative interaction between electric vehicle and power network will become an important way to control carbon emissions in the automotive field. In this study, an optimization model of emission reduction benefits based on integrated development of electric vehicle and power grid is proposed, which explores the best technical way of synergy between power grid and electric vehicle, achieves the best carbon reduction effect and provides a model basis for large-scale demonstration application. Numerical simulations based on the real case in Beijing are conducted to validate the effectiveness of the proposed method.


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanhua Yang ◽  
Ligang Yao

The safe and reliable operation of power grid equipment is the basis for ensuring the safe operation of the power system. At present, the traditional periodical maintenance has exposed the abuses such as deficient maintenance and excess maintenance. Based on a multiagent deep reinforcement learning decision-making optimization algorithm, a method for decision-making and optimization of power grid equipment maintenance plans is proposed. In this paper, an optimization model of power grid equipment maintenance plan that takes into account the reliability and economics of power grid operation is constructed with maintenance constraints and power grid safety constraints as its constraints. The deep distributed recurrent Q-networks multiagent deep reinforcement learning is adopted to solve the optimization model. The deep distributed recurrent Q-networks multiagent deep reinforcement learning uses the high-dimensional feature extraction capabilities of deep learning and decision-making capabilities of reinforcement learning to solve the multiobjective decision-making problem of power grid maintenance planning. Through case analysis, the comparative results show that the proposed algorithm has better optimization and decision-making ability, as well as lower maintenance cost. Accordingly, the algorithm can realize the optimal decision of power grid equipment maintenance plan. The expected value of power shortage and maintenance cost obtained by the proposed method is $71.75$ $MW·H$ and $496000$ $yuan$.


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