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Mao Tan

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4 papers

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EAAI Journal 2025 Journal Article

Federated Reinforcement Learning for smart and privacy-preserving energy management of residential microgrids clusters

Mao Tan
Jie Zhao
Xiao Liu
Yongxin Su
Ling Wang
Rui Wang
Zhuocen Dai

Real-time energy management optimizes energy utilization and manages electrical loads, which is crucial for improving the operational efficiency of residential microgrids. However, existing management methods suffer from model complexity and slow training speed. To solve this problem, we introduce Federated Reinforcement Learning to manage residential microgrids by training a control strategy in a decentralized and privacy-preserving manner. Specifically, a residential microgrid energy optimization management model is first established based on the Proximal Policy Optimization (PPO) method. Then, we propose a cooperative training strategy for multiple Residential microgrids based on Federated Reinforcement Learning (RFRL). The proposed method improves the training speed of residential microgrid models by sharing parameter information, such as network weights, while protects users’ usage data. Finally, clustering analysis is introduced in the case of heterogeneous residential microgrid data. Extensive experimental evaluation shows that our method outperforms the alternative residential microgrid management methods in terms of cost efficiency.

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EAAI Journal 2023 Journal Article

Bi-level optimization of charging scheduling of a battery swap station based on deep reinforcement learning

Mao Tan
Zhuocen Dai
Yongxin Su
Caixue Chen
Ling Wang
Jie Chen

With the rapid increase of in the number of electric vehicle (EV), battery swapping is becoming a promising idea because of its short service waiting time. However, in the face of the uncertainty of the power grid and EV behavior, it is difficult to achieve a forward-looking and fast-response scheduling in a large scale battery swap station (BSS). A new bi-level scheduling model is proposed to solve this problem, in which the upper level is built on a deep reinforcement learning (DRL) framework to optimally allocate power among the chargers, and the lower level is modeled as a series of MILP subproblems for dispatching power among the batteries in a charger. A prediction module is included in the DRL framework improve the foresight of the algorithm, and a safety module is designed to avoid unsafe actions. Experimental results indicate that the proposed approach has excellent performance in large scale problem solving. It reduces the operating costs of the BSS significantly while satisfying the maximum power demand constraint. This is able to provide more economic benefits for the BSS and help peak shaving and valley filling for the power grid.

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EAAI Journal 2023 Journal Article

Fusing domain knowledge and reinforcement learning for home integrated demand response online optimization

Zhiyao Zhang
Yongxin Su
Mao Tan
Rui Cao

Electricity–gas integrated household energy systems (HESs) expose obvious system uncertainties, requiring their integrated demand response (IDR) programs to be able to adapt automatically and quickly to system changes. Deep Reinforcement Learning (DRL) methods, though having been proven promising to tackle such problems, are typically not efficient in learning from random explorations. This paper proposes a method based on DRL with HESIDR knowledge penetration. By interpreting the domain IDR knowledge as a set of control rules, the DRL agent gains learning samples from knowledge-based exploration in addition to the traditional exploration–exploitation tradeoff. Correspondingly, we develop a cooperation scheme for action selection and replay sampling, which is based on exponential probability functions to balance the penetration of knowledge-based exploration, random exploration and policy exploitation. We conduct case studies in a typical home multi-energy system environment. After determining parameters in the exponential probability functions, the learning and cost reduction performance of the proposed algorithm was tested. The results show that the method proposed in the present study spends 48. 78% less training time than the standard DQN, which enables few-minute optimization on a lightweight PC. Also, the proposed method can further reduce energy bills by 26. 17% compared to the uncontrolled scenario and by 9. 88% to the integrated rule-based controller.

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EAAI Journal 2022 Journal Article

Multi-node load forecasting based on multi-task learning with modal feature extraction

Mao Tan
Chenglin Hu
Jie Chen
Ling Wang
Zhengmao Li

Accurate multi-node load forecasting is the key to the safe, reliable, and economical operation of the power system. However, the dynamic nature of load and the coupling nature of networks are difficult to extract, making consistent and accurate forecasting of node load rather difficult. In this regard, this paper proposes a soft sharing multi-task deep learning method for multi-node load forecasting in the power system. It has the following aspects: (1) Considering the coupling characteristics of the node network, a multi-modal feature module, based on the inception strategy and gated temporal convolutional network (GTCN), is firstly designed to explore the coupling features implied in the node load data. (2) A novel multi-objective neural network model is proposed to achieve simultaneous prediction of multi-node load by integrating the multi-modal feature module and gated recurrent unit (GRU). For sharing the learning information of sub-networks, this paper uses the soft sharing mechanism to capture load features, which can better optimize the prediction task for each node load simultaneously. Load data from the New Zealand distribution network and AEMO are used to compare the proposed model’s performance in various scenarios using regression metrics such as mean absolute percentage error (MAPE), Weighted Mean Accuracy (WMA), root mean squared logarithmic error (RMSLE), and Diebold–Mariano (DM). The simulation results show that the proposed method can explore the spatial–temporal coupling characteristics in multi-node load data. Compared with existing state-of-the-art multi-node load prediction methods, our proposed method’s MAPE decrease 17. 04% and 3. 92% in Non-aggregation and Aggregation situations.

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