Optimizing Joint Bidding and Incentivizing Strategy for Price-Maker Load Aggregators Based on Multi-Task Multi-Agent Deep Reinforcement Learning

Optimizing Joint Bidding and Incentivizing Strategy for Price-Maker Load Aggregators Based on Multi-Task Multi-Agent Deep Reinforcement Learning

The increasing penetration of renewable energy sources poses significant challenges for modern power systems, particularly in supply-demand balance and peak regulation. Load aggregators (LAs) play a crucial role by integrating small to medium-sized loads and coordinating demand response (DR). Howeve...

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Bibliographic Details
Main Authors: Jixiang Lu, Zhangtian Xie, Hongsheng Xu, Junjun Liu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Load aggregator
peak-regulation market
demand response
deep reinforcement learning
strategic bidding
incentive pricing
Online Access:https://ieeexplore.ieee.org/document/10742324/
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Summary:The increasing penetration of renewable energy sources poses significant challenges for modern power systems, particularly in supply-demand balance and peak regulation. Load aggregators (LAs) play a crucial role by integrating small to medium-sized loads and coordinating demand response (DR). However, previous research works ignored the inherent coupling between price-maker LAs’ decision-making of bidding price and quantity in the ancillary service market and decision-making of incentive price in DR. This study introduces a joint bidding and incentivizing model for a price-maker LA participating in a peak-regulation ancillary service market (PRM) and developing an incentive-based demand response (IBDR), where the LA’s objective is to maximize its long-term cumulative payoff. In order to solve this complex joint decision-making optimization problem more effectively and efficiently, a model-free multi-task multi-agent deep reinforcement learning-based (MTMA-DRL-based) method incorporating a shared, centralized prioritized experience replay buffer (PERB) is proposed. Case studies in real-world settings confirm that the proposed model effectively captures the interdependence between bidding price, bidding quantity, and incentive price decisions. The proposed MTMA-DRL-based method is also proven to outperform existing methods.
ISSN:2169-3536

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