Demand‐side management for smart grid via diffusion adaptation. Issue 1 (4th February 2020)

• Record Type: Journal Article
• Authors: Latifi, Milad; Khalili, Azam; Rastegarnia, Amir; Bazzi, Wael M.; Sanei, Saeid
• Is Part Of: IET smart grid. Volume 3:Issue 1(2020)
• Page Start: 69
• Page End: 82
• Publication Date: 2020-02-04
• Subjects: power markets -- pricing -- optimisation -- smart power grids -- demand side management -- game theory; demand‐side management -- diffusion adaptation -- fully distributed demand side management framework -- cooperative demand side management framework -- adaptive diffusion strategy -- global information -- customer preferences -- rapidly changing price parameter -- wholesale market -- individual utility functions -- smart grid total payment -- maximisation -- social welfare -- total energy demand; B0240E Game theory -- B0260 Optimisation techniques -- B8110B Power system management, operation and economics; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0271
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : This study presents a novel fully distributed and cooperative demand side management framework based on adaptive diffusion strategy. In this approach, each customer autonomously and without any need for the global information, minimises his incommodity function. The proposed framework has ability to track drifts resulting from the changes in the customer preferences and conditions or any rapidly changing price parameter coming from the wholesale market. In this scenario, the customers aim at maximising their individual utility functions; while the utility company aims at minimising the smart grid total payment (i.e. maximisation of the social welfare). The authors show that there is no need for the utility company to participate in the scheduling program for maximising social welfare. This measurement is maximised adaptively when the customers minimise their incommodity. Moreover, the authors provide a detailed analysis of the robustness of the proposed strategy in the presence of imperfect communication/computation conditions. Numerical results show that the proposed framework performs well, is scale free, and can achieve lower peak‐to‐average ratio of the total energy demand compared with that achieved by the game theoretical methods.