Journal ArticleDOI
A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach
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TLDR
Simulation results show that this proposed DR algorithm, can promote SP profitability, reduce energy costs for CUs, balance energy supply and demand in the electricity market, and improve the reliability of electric power systems, which can be regarded as a win-win strategy for both SP and CUs.About:
This article is published in Applied Energy.The article was published on 2018-06-15. It has received 312 citations till now. The article focuses on the topics: Demand response & Dynamic pricing.read more
Citations
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Journal ArticleDOI
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Renzhi Lu,Seung Ho Hong +1 more
TL;DR: Simulation results show that this proposed incentive-based demand response algorithm induces demand side participation, promotes service provider and customers profitabilities, and improves system reliability by balancing energy resources, which can be regarded as a win-win strategy for both service providers and customers.
Journal ArticleDOI
Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review
Ioannis Antonopoulos,Valentin Robu,Valentin Robu,Benoit Couraud,Desen Kirli,Sonam Norbu,Aristides Kiprakis,David Flynn,Sergio Elizondo-Gonzalez,Steve Wattam +9 more
TL;DR: An overview of AI methods utilised for DR applications is provided, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects, where AI methods have been used for energy DR.
Journal ArticleDOI
Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network
TL;DR: Experimental results demonstrate that the proposed hour-ahead DR algorithm can handle energy management for multiple appliances, minimize user energy bills, and dissatisfaction costs, and help the user to significantly reduce its electricity cost compared with a benchmark without DR.
Journal ArticleDOI
A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management
TL;DR: A data-driven method based on neural network (NN) and Q -learning algorithm is developed, which achieves superior performance on cost-effective schedules for HEM system, and demonstrates the effectiveness of the newly developed framework.
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Fundamentals and business model for resource aggregator of demand response in electricity markets
TL;DR: In this article, a comprehensive review of recent literature and projects is presented, with particular attention on RAs' roles in electricity markets as well as their difference from other market entities, and the business model for RA is analyzed systematically, involving resource aggregation, basic information prediction, market bidding strategy development, and settlement process.
References
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Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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Technical Note : \cal Q -Learning
Chris Watkins,Peter Dayan +1 more
TL;DR: This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989), showing that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action- values are represented discretely.
Journal ArticleDOI
Demand response and smart grids—A survey
TL;DR: In this article, a survey of demand response potentials and benefits in smart grids is presented, with reference to real industrial case studies and research projects, such as smart meters, energy controllers, communication systems, etc.
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A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms
TL;DR: This paper provides a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program, and presents various optimization models for the optimal control of the DR strategies that have been proposed so far.
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Asynchronous Stochastic Approximation and Q-Learning
TL;DR: The Q-learning algorithm, a reinforcement learning method for solving Markov decision problems, is studied to establish its convergence under conditions more general than previously available.