A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach

By Renzhi Lu, Seung Ho Hong, Xiongfeng Zhang. Applied Energy, 2018

389 citations recorded by the source corpus.

With the modern advanced information and communication technologies in smart grid systems, demand response (DR) has become an effective method for improving grid reliability and reducing energy costs due to the ability to react quickly to supply-demand mismatches by adjusting flexible loads on the demand side. This paper proposes a dynamic pricing DR algorithm for energy management in a hierarchical electricity market that considers both service provider’s (SP) profit and customers’ (CUs) costs. Reinforcement learning (RL) is used to illustrate the hierarchical decision-making framework, in which the dynamic pricing problem is formulated as a discrete finite Markov decision process (MDP), and Q-learning is adopted to solve this decision-making problem. Using RL, the SP can adaptively decide the retail electricity price during the on-line learning process where the uncertainty of CUs’ load demand profiles and the flexibility of wholesale electricity prices are addressed. 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.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/996e12d3bb53ba54be815475e15c32a5d2a12428

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

1. Renzhi Lu
2. Mengmeng Yu
3. Seung Ho Hong
4. Parameswaran Kamalaruban
5. A. Perera
6. David Flynn
7. Xu Xu
8. Zhao Xu

In Electrical and Electronic Engineering

1. Mengmeng Yu
2. Renzhi Lu
3. Seung Ho Hong
4. Parameswaran Kamalaruban
5. A. Perera
6. Xu Xu
7. Zhao Xu
8. Songjian Chai

Citations in the corpus

• Applications of reinforcement learning in energy systems (2021, methodology): “For its branches, Q-learning in the tabular setting is used in all domains – BEMS [78,96], dispatch [83,85], energy markets [129,132], grid [88,138], vehicle energy systems [10,142], and devices [149,151].”
• Incentive-based demand response for smart grid with reinforcement learning and deep neural network (2019, methodology): “The work in [34] proposed a dynamic price-based DR scheme from the SP perspective via RL methodology, where the scheme was modeled as a Markov decision process (MDP), then Q-leaning was adopted to determine the optimal retail prices.”
• A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management (2020, methodology): “In [15], RL is adopted to develop a dynamic pricing DR method based on hierarchical decision-making framework in the electricity market, which considers both profits of the service”
• Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, methodology): “[108] 2018 Q-learning Dynamic pricing DR algorithm between an aggregator and consumers.”
• Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network (2019, methodology): “Most recently, the authors of [20], [21] presented a price-based DR scheme linking the service provider to its customers via RL methodology, where the scheme was modeled as a Markov decision process (MDP), then Q-learning was used to make optimal decisions.”