Optimal spatio-temporal scheduling for Electric Vehicles and Load Aggregators considering response reliability

Li, Chunyan; Zhao, Rongsheng; Wang, Dong; Cai, Wenyue; Yu, Chang-Qing; Gu, Yingzhong; Zhang, Qian

doi:10.1016/j.epsr.2018.05.007

Cited by 44 publications

(6 citation statements)

References 14 publications

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“…There are two types of users' response behaviours under TOU: one is to transfer the load in the peak period to the valley period, and the other is to reduce the load that cannot be transferred during the peak period. In the response behaviour model of price elasticity matrix [12][13][14], the change in users' load per unit time is related to the original load. In the conventional response behaviour model of consumer psychology [23][24], the response power of users in peak/valley period per unit time is the same.…”

Section: User Response Model Under Time Of Use Pricementioning

confidence: 99%

“…Considering this problem, many types of research focus on the uncertainty modelling of demand response. In [12] and [13], the factors that affect the uncertainty of user response are summarized as economic factors and non‐economic factors. The uncertainty of demand response caused by economic factors is described by using the principles of consumer psychology and price correction coefficient, and the influence of non‐economic factors on‐demand response is expressed by using uncertain parameters and norm constraints, thus describing the uncertainty of price‐based demand response in a more refined way.…”

Section: Introductionmentioning

confidence: 99%

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Response reliability and risk analysis of users under time‐of‐use price

Wang

Ren³

et al. 2021

IET Generation Trans & Dist

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Time‐of‐use (TOU) electricity prices are implemented based on the response characteristics of users. TOU prices guide the electricity consumption behaviour of users and make them actively respond to peak shaving and valley filling. In this paper, the conventional psychology response model of consumers is improved based on the history data of customer response to the TOU price. The paper defines four types of response load curves and proposes the response reliability indices. Additionally, it establishes the probability models of customer response. According to the characteristics of customer response, the sensitivity analysis of influencing factors and the risk analysis of customers are carried out according to the probability model of response. For instance, risk and characteristics analyses of the customer response were performed using the load data of residential areas in Nanjing city. The results indicate that the probability distribution of the customer response is approximate to the trapezoid distribution. The sequence of sensitivity coefficients of customer response factors is as follows: customer income, type of customer, and electrical equipment. The proposed method provides a scientific basis for electricity price implementers to accurately grasp the response behaviour of the user and establish a reasonable demand response strategy.

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Section: User Response Model Under Time Of Use Pricementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Response reliability and risk analysis of users under time‐of‐use price

Wang

Ren³

et al. 2021

IET Generation Trans & Dist

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“…Scheduling the charging and discharging of EVs makes the final load flattening, which is beneficial to voltage stability and consumer revenue. Luo 6 studied the model of the AC/DC hybrid distribution network, simplified the object into a Mixed Integer Quadratic Program (MIQP) problem, and optimized the network loss and user convenience 7 . proposes a scheduling model based on spatiotemporal bi‐layers, formulated a scheduling strategy for upper and lower layers with different objectives, and finally verifies the algorithm through a calculation example, but the energy types considered by the model are not rich enough 8 .…”

Section: Introductionmentioning

confidence: 99%

Spatial optimization of integrated energy system including hydrogen electrolysis center based on mixed integer second‐order cone programming

Xiaoxin

Chen

2023

Energy Science & Engineering

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Hydrogen energy is playing an increasingly important role in integrated energy systems (IES) due to its multiple uses. Green hydrogen can be produced from electricity generated from renewable energy, reducing carbon emissions and environmental pollution. Previous work usually only considers electric vehicles (EVs) as flexible loads, and there are few studies on the production and application process of green hydrogen. In this paper, hydrogen electrolysis centers (HECs) and EVs are simultaneously schedulable loads, integrated into IES, and optimized. The distribution network model is established, and the optimization goal is to minimize network loss while ensuring the safe operation of the power grid. Through the power flow optimization method of mixed‐integer second‐order cone programming, the space optimization scheduling of EVs in the IES is carried out, and the optimal location of the HEC is reasonably planned and set. A simulation instance of IEEE 33 nodes is used to verify the proposed method. The final results show that this method can minimize network loss in the range of safe operation of the system for the spatial scheduling of electrolytic hydrogen centers and EVs. At the same time, it can also provide a reference for HEC addressing.

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“…It signs an agreement with users to obtain the decision-making power of aggregatable load and provide economic incentives and auxiliary services to users [19], and brings the dynamic liquidity and business transfer value of the power market [20]. Multiple load aggregators coexist in the electricity market [21], [22], and participate in the load cluster scheduling of the power market, such as single load scheduling of Copyright c 2021 The Institute of Electronics, Information and Communication Engineers electric vehicles and air conditioning [23], [24], multi equipment load cluster scheduling of water heaters and air conditioning groups [25], and multi energy flow scheduling of heating and power supply [26], [27]. Load aggregator can connect the power grid platform and demand side management platform upward, and connect the IOT platform and big data platform downward.…”

Section: Introductionmentioning

confidence: 99%

A Business Service Model of Smart Home Appliances Participating in the Peak Shaving and Valley Filling Based on Cloud Platform

Zhu

et al. 2021

IEICE Trans. Inf. & Syst.

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Mingrui ZHU †a) , Student Member, Yangjian JI †b) , Wenjun JU † †c) , Xinjian GU †d) , Chao LIU † †e) , and Zhifang XU † †f) , Nonmembers SUMMARY With the development of power market demand response capability, load aggregators play a more important role in the coordination between power grid and users. They have a wealth of user side business data resources related to user demand, load management and equipment operation. By building a business model of business data resource utilization and innovating the content and mode of intelligent power service, it can guide the friendly interaction between power supply, power grid and load, effectively improve the flexibility of power grid regulation, speed up demand response and refine load management. In view of the current situation of insufficient utilization of business resources, low user participation and imperfect business model, this paper analyzes the process of home appliance enterprises participating in peak shaving and valley filling (PSVF) as load aggregators, and expounds the relationship between the participants in the power market; a business service model of smart home appliance participating in PSVF based on cloud platform is put forward; the market value created by home appliance business resources for each participant under the joint action of market-oriented means, information technology and power consumption technology is discussed, and typical business scenarios are listed; taking Haier business resource analysis as an example, the feasibility of the proposed business model in innovating the content and value realization of intelligent power consumption services is proved.

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Optimal spatio-temporal scheduling for Electric Vehicles and Load Aggregators considering response reliability

Cited by 44 publications

References 14 publications

Response reliability and risk analysis of users under time‐of‐use price

Response reliability and risk analysis of users under time‐of‐use price

Spatial optimization of integrated energy system including hydrogen electrolysis center based on mixed integer second‐order cone programming

A Business Service Model of Smart Home Appliances Participating in the Peak Shaving and Valley Filling Based on Cloud Platform

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