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Dynamic load management for a residential customer; Reinforcement Learning approach
, Article Sustainable Cities and Society ; Volume 24 , 2016 , Pages 42-51 ; 22106707 (ISSN) ; Rayati, M ; Ranjbar, A. M ; Sharif University of Technology
Elsevier Ltd
Abstract
United Nation aims to double the global rate of improvement in energy efficiency as one of the sustainable development goals. It means researchers should focus on energy systems to enhance their overall efficiency. One of the effective solution to move from suboptimal energy systems to optimal ones is analyzing energy system in Energy Hub (EH) framework. In EH framework, interactions between different energy carriers are considered in supplying the required loads. The couplings and selecting proper combinations of inputs energy carriers lead to more optimized and intelligent consumption. The appropriate combination is found by solving an optimization problem at each time step. Utilizing...
An integrated two-level demand-side management game applied to smart energy hubs with storage
, Article Energy ; Volume 206 , 2020 ; Sheykhha, S ; Madlener, R ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
Energy hubs, an important component of future energy networks employing distributed demand-side management, can play a key role in enhancing the efficiency and reliability of power grids. In power grids, energy hub operators need to optimally schedule the consumption, conversion, and storage of available resources based on their own utility functions. In sufficiently large networks, scheduling an individual hub can affect the utility of the other energy hubs. In this paper, the interaction between energy hubs is modeled as a congestion game. Each energy hub operator (player) participates in a dynamic energy pricing market and tries to maximize his/her own payoff when satisfying energy...