Journal
APPLIED ENERGY
Volume 220, Issue -, Pages 220-230Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2018.03.072
Keywords
Demand response; Dynamic pricing; Artificial intelligence; Reinforcement learning; Markov decision process; Q-learning
Categories
Funding
- National Research Foundation of Korea [NRF-2016K2A9A2A11938310]
- Ansan-Si hidden champion fostering and supporting project (Development of Industrial Communication-IoT Gateway & Industrial Wireless IoT Sensor) - Ansan city
- Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- Korea Evaluation Institute of Industrial Technology (KEIT) [20174030201780] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [22A20130012806] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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