Incentive-based demand response for smart grid with reinforcement learning and deep neural network

By Renzhi Lu, Seung Ho Hong. Applied Energy, 2019

347 citations recorded by the source corpus.

Abstract Balancing electricity generation and consumption is essential for smoothing the power grids. Any mismatch between energy supply and demand would increase costs to both the service provider and customers and may even cripple the entire grid. This paper proposes a novel real-time incentive-based demand response algorithm for smart grid systems with reinforcement learning and deep neural network, aiming to help the service provider to purchase energy resources from its subscribed customers to balance energy fluctuations and enhance grid reliability. In particular, to overcome the future uncertainties, deep neural network is used to predict the unknown prices and energy demands. After that, reinforcement learning is adopted to obtain the optimal incentive rates for different customers considering the profits of both service provider and customers. 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 provider and customers.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/bd7638ddbbe249c3e6070f951b39f961b5e61cb5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Quo vadis artificial intelligence? (2022, background): “The application fields of AI are extensive, covering (but not limited to) speech recognition [4, 5], image processing [6, 7], natural language processing [8, 9], smart robots [10, 11], autonomous vehicles [12, 13], energy systems [14, 15], healthcare [16, 17], Fintech [18], etc.”
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, methodology): “Additionally, Lu and Hong [109] focus on learning the optimal incentive rates for different electricity consumers considering the profitability of both consumers and the service providers (aggregators) in a hierarchical electricity market.”
Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network (2019, methodology): “In this paper, the inputs of ANN are chosen based on correlation analysis, some empirical guidelines described in [28]–[30], and a number of accuracy tests and comparisons in the simulation.”