2020
DOI: 10.3390/app10207310
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Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling

Abstract: As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that re… Show more

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Cited by 10 publications
(6 citation statements)
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“…Adjustable loads, on the other hand, can be controlled according to actual needs. Based on the controllability and working characteristics of flexible loads, adjustable loads can be classified into IL, RL, TSL, TFL and DES [4,[16][17].…”
Section: The Model For Aggregating and Controlling Multiple Types Of ...mentioning
confidence: 99%
“…Adjustable loads, on the other hand, can be controlled according to actual needs. Based on the controllability and working characteristics of flexible loads, adjustable loads can be classified into IL, RL, TSL, TFL and DES [4,[16][17].…”
Section: The Model For Aggregating and Controlling Multiple Types Of ...mentioning
confidence: 99%
“…Based on the numeric test-case results, the authors have demonstrated the efficacy of their Nash bargaining-based method in reducing the daily peak-to-average ratio of load profile by up to a maximum of around 9%. Song et al [39] have also studied the optimal day-ahead bidding strategies of DRAs, whilst additionally using fuzzy membership functions to address the uncertainty associated with the heterogeneous willingness of end-users to participate in DR activities. By applying the model to a community housing complex, comprising 3,000 residential dwellings, they have shown the effectiveness of the model in increasing the DRAs' profits by up to ~71%.…”
Section: Game-theoretic Models Applied To Demand-side Management Studiesmentioning
confidence: 99%
“…The centralized controller governs the entire MG by gathering information from all the devices. Therefore, if the size of the data is large then the time taken by the central controller to issue the governing signals gets delayed whereas, in the case of decentralized control, each agent, i.e., customer, DGs, and BESS, defines their own load schedule which employs a multi-agent system (MAS) is presented in [71]. Buildings consume 40% of the total load [72], therefore, Nikos Kampelis et al [73] implemented a genetic algorithm (GA)-based optimization technique for EM in a building and used artificial neural networks (ANNs) prediction model for yielding DA power requirements of the customer.…”
Section: Recap Of Energy Trading Modelsmentioning
confidence: 99%