Progressive time-differentiated peak pricing (PTPP) for aggregated air-conditioning load in demand response programs

Li, Yang; Shen, Yunwei; Zhou, Lei; Li, Fangxing

doi:10.1002/etep.2664

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…In this paper, the consumer psychology [28] - [31] is applied to describe the consumers' behavior. It is assumed that the allowed dispatch capacities of LC and LS contracts are determined by customers, which follow the consumer psychology.…”

Section: B Customers' Welfarementioning

confidence: 99%

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Consumer Psychology Based Optimal Portfolio Design for Demand Response Aggregators

Shen

Zhang

et al. 2021

Journal of Modern Power Systems and Clean Energy

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Demand response (DR) has received much attention for its ability to balance the changing power supply and demand with flexibility. DR aggregators play an important role in aggregating flexible loads that are too small to participate in electricity markets. In this work, a DR operation framework is presented to enable local management of customers to participate in electricity market. A novel optimization model is proposed for the DR aggregator with multiple objectives. On one hand, it attempts to obtain the optimal design of different DR contracts as well as the portfolio management so that the DR aggregator can maximize its profit. On the other hand, the customers'welfare should be maximized to incentivize users to enroll in DR programs which ensure the effective and flexible load control. The consumer psychology is introduced to model the consumers'behavior during contract signing. Several simulation studies are performed to demonstrate the feasibility of the proposed model. The results illustrate that the proposed model can ensure the profit of the DR aggregator whereas the customers'welfare is considered.

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Section: B Customers' Welfarementioning

confidence: 99%

“…As shown in Fig. 2, ρ i is the unit price for load dispatch in the DR contract and q i is the allowed dispatch capacity [31]. There exists a lower limit of the incentive to each customer.…”

Section: B Customers' Welfarementioning

confidence: 99%

Consumer Psychology Based Optimal Portfolio Design for Demand Response Aggregators

Shen

Zhang

et al. 2021

Journal of Modern Power Systems and Clean Energy

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“…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. During the peak periods, a greater power consumption per unit time leads to a more reduced load after response.…”

Section: User Response Model Under Time Of Use Pricementioning

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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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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“…The intent of such scheme is to motivate customers to modify their consumption pattern in order to level the utilities' load demand over time . On the basis of the principles of PBDR schemes, the customer's payment is calculated considering three well‐known tariff rates including “valley,” “off‐peak,” and “peak.” So far, different research works have studied PBDR programs such as TOU pricing, RTP, and CPP schemes, taking advantage of predetermined time period clusters to assess the benefits of DR programs. All of the above‐mentioned studies are conducted on the basis of predetermined time period clusters pertaining to load demand.…”

Section: Introductionmentioning

confidence: 99%

“…So far, different research works have studied PBDR programs such as TOU pricing, [8][9][10] RTP, [11][12][13] and CPP schemes, 14,15 taking advantage of predetermined time period clusters to assess the benefits of DR programs. All of the abovementioned studies are conducted on the basis of predetermined time period clusters pertaining to load demand.…”

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confidence: 99%

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

Samadi

Aghamohamadi

Mahmoudi

2019

Int Trans Electr Energ Syst

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Summary In time‐of‐use (TOU) schemes, most similar clusters to load demand can properly map the consumption pattern. However, the performance of such scheme is affected by the load responsiveness to the obtained clusters. This paper presents a time period clustering approach for TOU schemes to find the optimal clusters, using not only the load patterns but also its modified version obtained by responsiveness of elastic loads. A responsive load model is employed to simulate the load pattern modifications considering the hourly prices, load elasticities, and customers' benefit function. An imperialist competitive algorithm (ICA) has been designed to iteratively solve the problem. The proposed approach maximizes the load factors pertaining to the modified seasonal load patterns that are obtained on the basis of load responsiveness to the allocated clusters in each iteration of ICA. A comprehensive seasonal case study has been conducted to evaluate the performance of the proposed model. Results and comparisons show that the proposed clustering technique can significantly enhance the performance of the existing TOU schemes in terms of load factor improvement and peak reduction as it considers both load patterns and the actual load responsiveness to the obtained clusters.

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Progressive time-differentiated peak pricing (PTPP) for aggregated air-conditioning load in demand response programs

Cited by 11 publications

References 22 publications

Consumer Psychology Based Optimal Portfolio Design for Demand Response Aggregators

Consumer Psychology Based Optimal Portfolio Design for Demand Response Aggregators

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

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

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