Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

Goh, Hui Hwang; Zong, Lian; Zhang, Dongdong; Dai, Wei; Lim, Chee Shen; Kurniawan, Tonni Agustiono; Goh, Kai Chen

doi:10.3390/en15051869

Cited by 36 publications

(14 citation statements)

References 26 publications

(38 reference statements)

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“…Various descriptions for DR are available in the literature. However, one common stream among all is that DR covers the electricity demand which is flexible with respect to real market rates [42][43][44][45]. To distinguish DR from DSM, DSM through market policies drive DR to model the demand numerically.…”

Section: Demand Response (Dr)mentioning

confidence: 99%

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

et al. 2022

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In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smartgrid.

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Section: Demand Response (Dr)mentioning

confidence: 99%

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

et al. 2022

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“…DR means that users can actively change their original electricity consumption behavior according to price signals or incentive mechanisms. The response methods can be divided into price-based DR and incentive-based DR [30]. This paper mainly considers price-based DR based on charging price and interruptible load based on incentives.…”

Section: Ces Demand Response Modelmentioning

confidence: 99%

Optimal Operation Strategy of PV-Charging-Hydrogenation Composite Energy Station Considering Demand Response

Zhu

et al. 2022

Energies

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Traditional charging stations have a single function, which usually does not consider the construction of energy storage facilities, and it is difficult to promote the consumption of new energy. With the gradual increase in the number of new energy vehicles (NEVs), to give full play to the complementary advantages of source-load resources and provide safe, efficient, and economical energy supply services, this paper proposes the optimal operation strategy of a PV-charging-hydrogenation composite energy station (CES) that considers demand response (DR). Firstly, the operation mode of the CES is analyzed, and the CES model, including a photovoltaic power generation system, fuel cell, hydrogen production, hydrogen storage, hydrogenation, and charging, is established. The purpose is to provide energy supply services for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs) at the same time. Secondly, according to the travel law of EVs and HFCVs, the distribution of charging demand and hydrogenation demand at different periods of the day is simulated by the Monte Carlo method. On this basis, the following two demand response models are established: charging load demand response based on the price elasticity matrix and interruptible load demand response based on incentives. Finally, a multi-objective optimal operation model considering DR is proposed to minimize the comprehensive operating cost and load fluctuation of CES, and the maximum–minimum method and analytic hierarchy process (AHP) are used to transform this into a linearly weighted single-objective function, which is solved via an improved moth–flame optimization algorithm (IMFO). Through the simulation examples, operation results in four different scenarios are obtained. Compared with a situation not considering DR, the operation strategy proposed in this paper can reduce the comprehensive operation cost of CES by CNY 1051.5 and reduce the load fluctuation by 17.8%, which verifies the effectiveness of the proposed model. In addition, the impact of solar radiation and energy recharge demand changes on operations was also studied, and the resulting data show that CES operations were more sensitive to energy recharge demand changes.

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“…The user responsiveness of different levels will eventually be reflected in the different parameters: peak-to-valley electricity price difference p, start threshold p f and saturated threshold p s . λ is the level of user demand response and Maximum demand response of users λ max [8].…”

Section: Community Energy System Model 41 Community User Modelmentioning

confidence: 99%

“…where Equation (8) indicates that the user's FL has a certain margin. The user's deliverable flexible load includes shiftable load and energy storage.…”

Section: Peak-to-valley Electricitymentioning

confidence: 99%

“…Studies have shown that users' electricity consumption behavior is usually affected by family information such as age, income, education level, environmental factors, comfort preferences, conformity psychology and other psychological factors [4,5]. For this reason, most of the existing studies incorporate factors such as user education, income, and average age into the problem of uncertainty of demand response [6], or use fuzzy methods and flexible load probability distribution forms to describe the uncertainty of response behavior [7,8]. Such processes take the uncertainty of the overall characteristics of users into account, suitable for dispatching scenarios of large power grids.…”

Section: Introductionmentioning

confidence: 99%

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Community Flexible Load Dispatching Model Based on Herd Mentality

Jiang

Zeng

et al. 2022

Energies

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In the context of smart electricity consumption, demand response is an important way to solve the problem of power supply and demand balance. Users participate in grid dispatching to obtain additional benefits, which realises a win-win situation between the grid and users. However, in actual dispatching, community users’ strong willingness to use energy leads to low enthusiasm of users to participate in demand response. Psychological research shows a direct connection between users’ herd mentality (HM) and their decision-making behavior. An optimal dispatching strategy based on user herd mentality is proposed to give full play to the active response-ability of community flexible load to participate in power grid dispatching. Considering that herd mentality is generated by the information interaction between users, by calling on some users to share the experience of successfully participating in demand response in the community information center and using the Nash social welfare function to model herd mentality to explore the impact of the user. The analysis of an example shows that the proposed strategy gives full play to the potential of community flexible loads to participate in demand response. When users have similar electricity consumption behavior, the herd mentality can effectively improve users’ enthusiasm to participate in demand response, and the user response effect meets managers’ expectations.

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Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

Cited by 36 publications

References 26 publications

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

Optimal Operation Strategy of PV-Charging-Hydrogenation Composite Energy Station Considering Demand Response

Community Flexible Load Dispatching Model Based on Herd Mentality

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