TOU Electricity Price Optimization Model for Multi-Customer Considering The PV Ratio Coefficient

Yang, Hanbiao; Wang, Lei; Gong, Zhengpeng; Zhang, Xinyu

doi:10.1109/igbsg.2019.8886230

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…However, TOU price is a method of controlling EV load at CS and is also important for supply power and demand-side management. Moreover, the TOU price method enables EVs to adjust recharging time, reduce cost and fill fewer demand hours reducing the number of peak hours [95][96][97][98].…”

Section: Parking Modementioning

confidence: 99%

Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging

Ahmed

Khalid²,

Ullah³

et al. 2022

Energies

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The rise of the intelligent, local charging facilitation and environmentally friendly aspects of electric vehicles (EVs) has grabbed the attention of many end-users. However, there are still numerous challenges faced by researchers trying to put EVs into competition with internal combustion engine vehicles (ICEVs). The major challenge in EVs is quick recharging and the selection of an optimal charging station. In this paper, we present the most recent research on EV charging management systems and their role in smart cities. EV charging can be done either in parking mode or on-the-move mode. This review work is novel due to many factors, such as that it focuses on discussing centralized and distributed charging management techniques supported by a communication framework for the selection of an appropriate charging station (CS). Similarly, the selection of CS is evaluated on the basis of battery charging as well as battery swapping services. This review also covered plug-in charging technologies including residential, public and ultra-fast charging technologies and also discusses the major components and architecture of EVs involved in charging. In a comprehensive and detailed manner, the applications and challenges in different charging modes, CS selection, and future work have been discussed. This is the first attempt of its kind, we did not find a survey on the charging hierarchy of EVs, their architecture, or their applications in smart cities.

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Section: Parking Modementioning

confidence: 99%

Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging

Ahmed

Khalid²,

Ullah³

et al. 2022

Energies

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“…Through decoupling time division and electricity price optimization, a two-step optimization method was designed to build the comprehensive optimization model of peak-to-valley period and electricity price. Reference [25] presented the optimal operation scheme for household user side micro-grid considering the TOU price and demand response. Reference [26] devised the optimization model for large consumers' direct wind power consumption under TOU price.…”

Section: Residential Demand Responsementioning

confidence: 99%

Residential Demand Response Strategy Based on Deep Deterministic Policy Gradient

Deng

2021

Processes

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With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive strategies for power grid companies and demand response strategies for customers plays a crucial role in maximizing the benefits of different participants. To meet different expectations of multiple agents in the same environment, deep reinforcement learning was adopted. The generative model of residential demand response strategy under different incentive policies can be trained iteratively through real-time interactions with the environmental conditions. In this paper, a novel optimization model of residential demand response strategy, based on a deep deterministic policy gradient (DDPG) algorithm, was proposed. The proposed work was validated with the actual electricity consumption data of a certain area in China. The results showed that the DDPG model could optimize residential demand response strategy under certain incentive policies. In addition, the overall goal of peak load-cutting and valley filling can be achieved, which reflects promising prospects of the electricity market.

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Electric Vehicle Charging Modes, Technologies and Applications in Smart Cities

et al. 2022

View full text Add to dashboard Cite

TOU Electricity Price Optimization Model for Multi-Customer Considering The PV Ratio Coefficient

Cited by 3 publications

References 11 publications

Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging

Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging

Residential Demand Response Strategy Based on Deep Deterministic Policy Gradient

Electric Vehicle Charging Modes, Technologies and Applications in Smart Cities

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