A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid

Tang, Qiang; Xie, Ming-zhong; Yang, Kun; Luo, Yuansheng; Zhou, Dongdai; Song, Yun S.

doi:10.1007/s11036-018-1049-4

Cited by 71 publications

(28 citation statements)

References 17 publications

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“…The integration of health care and blockchain can overcome the security and privacy protection of medical health data [41][42][43]. Blockchains are also used in smart grids [44][45][46], supply chain management [47], and the Internet of Vehicles [48][49][50], and this is enough to see a wide range of applications for blockchain applications.…”

Section: Bcotmentioning

confidence: 99%

Data Query Mechanism Based on Hash Computing Power of Blockchain in Internet of Things

Ren

Zhu

Sharma

et al. 2019

Sensors

116

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In the IoT (Internet of Things) environment, smart homes, smart grids, and telematics constantly generate data with complex attributes. These data have low heterogeneity and poor interoperability, which brings difficulties to data management and value mining. The promising combination of blockchain and the Internet of things as BCoT (blockchain of things) can solve these problems. This paper introduces an innovative method DCOMB (dual combination Bloom filter) to firstly convert the computational power of bitcoin mining into the computational power of query. Furthermore, this article uses the DCOMB method to build blockchain-based IoT data query model. DCOMB can implement queries only through mining hash calculation. This model combines the data stream of the IoT with the timestamp of the blockchain, improving the interoperability of data and the versatility of the IoT database system. The experiment results show that the random reading performance of DCOMB query is higher than that of COMB (combination Bloom filter), and the error rate of DCOMB is lower. Meanwhile, both DCOMB and COMB query performance are better than MySQL (My Structured Query Language).

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Section: Bcotmentioning

confidence: 99%

Data Query Mechanism Based on Hash Computing Power of Blockchain in Internet of Things

Ren

Zhu

Sharma

et al. 2019

Sensors

116

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“…In the future, we plan to extend our work to relay-node selection on 3D road structures, such as overpass structures and parking lot structures, and utilize AI [46][47][48][49][50][51] to optimize the method [52,53] in complex 3D scenarios.…”

Section: Discussionmentioning

confidence: 99%

ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles

Cao

Jiang

Wang

et al. 2020

Sensors

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The proper utilization of road information can improve the performance of relay-node selection methods. However, the existing schemes are only applicable to a specific road structure, and this limits their application in real-world scenarios where mostly more than one road structure exists in the Region of Interest (RoI), even in the communication range of a sender. In this paper, we propose an adaptive relay-node selection (ARNS) method based on the exponential partition to implement message broadcasting in complex scenarios. First, we improved a relay-node selection method in the curved road scenarios through the re-definition of the optimal position considering the distribution of the obstacles. Then, we proposed a criterion of classifying road structures based on their broadcast characteristics. Finally, ARNS is designed to adaptively apply the appropriate relay-node selection method based on the exponential partition in realistic scenarios. Simulation results on a real-world map show that the end-to-end broadcast delay of ARNS is reduced by at least 13.8% compared to the beacon-based relay-node selection method, and at least 14.0% compared to the trinary partitioned black-burst-based broadcast protocol (3P3B)-based relay-node selection method. The broadcast coverage is increased by 3.6–7% in curved road scenarios, with obstacles benefitting from the consideration of the distribution of obstacles. Moreover, ARNS achieves a higher and more stable packet delivery ratio (PDR) than existing methods profiting from the adaptive selection mechanism.

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“…Secondly, there should be a hierarchical relationship in the aspect of priority among interdependent controlled agents, no matter whether this relationship is connatural or given on purpose. For instance, the control of OLTC and elastic loads in [52], smart EV charging in [53], the demand side management of large populations of thermostatically controlled loads [54], and frequency control with energy storage systems in the distribution network [55].…”

Section: Discussionmentioning

confidence: 99%

Distributed Optimization in Low Voltage Distribution Networks via Broadcast Signals †

Wei¹

2019

Energies

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With the development of distributed energy resources, the low voltage distribution network (LVDN) is supposed to be the integrator of small distributed energy sources. This makes the users in LVDNs multifarious, which leads to more complex modeling. Additionally, data acquisition could be tricky due to rising privacy concerns. These impose severe demands on control schemes in LVDNs that the classical centralized control might not be able to fulfill. To tackle this, a model-free control approach with distributed decision-making architecture is proposed in this paper. Employing statistical methods and game theory, individual users in LVDNs achieve local optimum autonomously. Comparing to conventional approaches applied in LVDNs, the proposed approach is able to achieve active control with less communication burden and computational resources. The paper proves the convergence to the Nash Equilibrium (NE) and uses player compatible relations to form the specific equilibrium. A variant of the log-linear trial and error learning process is applied in a novel "suggest-convince" mechanism to implement the proposed approach. In the case study, a 103 nodes test network based on a real Belgian semiurban LVDN is illustrated. The proposed approach is validated and analyzed with practical load profiles on the 103 nodes network. In addition to that, centralized control is implemented as a benchmark to show the performance of the proposed approach by comparing it with the classical optimization result. The results demonstrate that the proposed approach is able to achieve player compatible equilibrium in an expected way, resulting in a good approximation to the local optimum.

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A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid

Cited by 71 publications

References 17 publications

Data Query Mechanism Based on Hash Computing Power of Blockchain in Internet of Things

Data Query Mechanism Based on Hash Computing Power of Blockchain in Internet of Things

ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles

Distributed Optimization in Low Voltage Distribution Networks via Broadcast Signals †

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