Demand response potential evaluation for residential air conditioning loads

Chen, Xingying; Wang, Jixiang; Xie, Jun; Xu, Shuyang; Yu, Kun; Gan, Lei

doi:10.1049/iet-gtd.2018.5299

Cited by 91 publications

(26 citation statements)

References 31 publications

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“…In each combination, the basic values of α t i−0 and β t i−0 of user i are randomly generated, as shown in Table 2. Among all loads of users, heating, ventilation and air conditioning (HVAC) load accounts for a large proportion [33], which are greatly affected by the external temperature [34]. And according to [35], temperature is one of the most important factors affecting the load behavior of users.…”

Section: Case Studies a Simulation Settingmentioning

confidence: 99%

Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility

Liu

et al. 2020

IEEE Access

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In demand response programs, load service entity (LSE) can aggregate user as an independent entity to participate in the day ahead energy market, and complete the response target through incentive in the next day. In order to reduce the incentive cost of LSE, a differentiated incentive mechanism that consider the differences in user response flexibility is proposed in this paper. Then, a user response behavior model is established by using the long short-term memory (LSTM) network, with aim of accurately predicting users' response. Subsequently, an optimization strategy combining particle swarm (PSO) and LSTM is proposed, so that the response target can be accurately completed with low cost. Simulation experiments verified that the cost of LSE is close to the theoretical minimum, and can be reduced by 20% compared with the optimal result under unified incentive mechanism. Moreover, it also verified that the proposed strategy has high response accuracy and good stability.

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Section: Case Studies a Simulation Settingmentioning

confidence: 99%

Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility

Liu

et al. 2020

IEEE Access

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“…RACs are assumed to have same thermal characteristics and use the same control functions, which can be simplified further by a first-order ETP model [3], [5] to portray the dynamic process of indoor temperature as shown in (5)- (6).…”

Section: B Response Potential Model Of Racs With Comfort Sensitivitymentioning

confidence: 99%

“…On the other hand, it is proved that the intrinsic insensitivity to temperature fluctuations of RACs may be used to control their power consumption, as well as spatial storage capacity, enabling RACs to provide an array of service to the system [3], [4]. Compared with the traditional investment on the supply side, like building a new power plant, the aggregated RACs on the demand side has an advantage on high cost-effective and low carbon emission [5]. Therefore, RACs is an important option for power companies as a flexible demand response (DR) resource [6]- [8], and it is demonstrated in some recent studies that RACs can perform well in DR projects.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Control Strategy of Large-Scale Residential Air Conditionings Considering Comfort Sensitivity of Differentiated Population

Chen

Yang

2019

IEEE Access

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Residential air conditionings (RACs) can be seen as one of the important flexible demand resources controlled to provide balance services for the power system due to their spatial thermal storage feature. However, such control on RACs will bring out the negative impact on the comfort level of residents inevitably, affecting their proactivity in demand response (DR). So, a novel two-stage control model of large-scale RACs is proposed to participate in DR considering different comfort sensitivity of various residents. The comfort sensitivity index is developed in terms of daily operation time and daily power consumption per area of RACs, classified by K-means clustering. Besides, the DR potential of RACs is established with the adjusted temperature setpoint, where the temperature setpoint is determined by the comfort sensitivity index. Moreover, a two-stage control model of large-scale RACs is proposed comprised of load response and recovery, where the corresponding control strategies at each stage are proposed. Simulations of the proposed method have been performed on residential 20,000 RACs to validate its effectiveness in Changzhou, China. The outcome reveals that the strategies could maximize the DR potential of RACs and ensure the stable recovery of RACs under the premise of meeting the comfort demand of differentiated population.

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“…In [4], the equivalent thermal parameter (ETP) model is used to establish an air-conditioning polymerization model and evaluate the response potential. The [5] based on the first-order physical thermal model to establish a aggregated air conditioning load model, and studied the practicality of the aggregation model to provide frequency modulation standby for the grid. The [6,7] constructed a aggregated temperature control load model composed of electric water heater load, and proposed a new serialization control strategy.…”

Section: Introductionmentioning

confidence: 99%

Regulation capability evaluation of individual electric heating load based on Radial basis Function neural network

2019

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As a time-shifting load that is gradually popularized in the northern region, electric heating load has great adjustment potential. Because the electric heating operation characteristics are affected by many non-linear factors, the traditional equivalent thermal parameters model cannot accurately evaluate the regulation capability of individual electric heating load. Aiming at this problem, this paper proposes an evaluation method for the regulation capability of individual electric heating load based on radial basis function neural network. Firstly, electric heating load control experiments were carried out in a typical room of a residential quarter in winter and relevant experimental data were collected. Then, based on the operation data, the radial basis function neural network is used to evaluate the regulation capability of the individual electric heating load. Finally, the evaluation results based on radial basis function neural network are compared with those based on back propagation neural network and equivalent thermal parameters model. The results show that the proposed method has the least evaluation error and can more accurately evaluate the regulation capability of individual electric heating load.

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Demand response potential evaluation for residential air conditioning loads

Cited by 91 publications

References 31 publications

Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility

Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility

A Two-Stage Control Strategy of Large-Scale Residential Air Conditionings Considering Comfort Sensitivity of Differentiated Population

Regulation capability evaluation of individual electric heating load based on Radial basis Function neural network

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