A Blockchain-Enabled Demand Management and Control Framework Driven by Deep Reinforcement Learning

doi:10.1109/TIE.2022.3146631

A Blockchain-Enabled Demand Management and Control Framework Driven by Deep Reinforcement Learning

2023-01-01

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DOI：10.1109/TIE.2022.3146631

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The rapid development of Internet-of-Things in smart grid has enabled millions of grid-connected distributed controllable resources (DCR; e.g., electric vehicles, controllable loads) to provide service to the grid, such as frequency regulation and demand response. The integration of these DCRs may become a large virtual power plant network with various characteristics. This poses great challenges from both control and management perspectives, e.g., computation/communication burden, optimization complexity, scalability limitation, prosumer privacy, etc. In this article, we propose an effective autonomous incentive-based DCR control and management framework to integrate a large amount of DCRs to provide grid services, which simultaneously provides accurate active power adjustment to the grid, optimizes DCR allocations, and maximizes the profits for all prosumers and system operators. A model-free deep deterministic policy gradient-based method is designed to find the optimal incentives in a continuous action space to encourage prosumers to adjust their power consumptions. The method is implemented in a consortium open-source blockchain platform, Hyperledger Fabric, which facilitates controls and transaction management. To demonstrate the effectiveness of the framework, extensive experimental studies are conducted using real-world data.

Keywords:

Blockchains

Resource management

Costs

Batteries

Fabrics

Distributed ledger

Reliability

Blockchain

deep reinforcement learning

energy storage

renewable energy

virtual power plant

A Blockchain-Enabled Demand Management and Control Framework Driven by Deep Reinforcement Learning

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