A Dispatching Optimization Model for Park Power Supply Systems Considering Power-to-Gas and Peak Regulation Compensation

Qin, Yunfu; Lin, Hui; Tan, Zhongfu; Qin, Yan; Li, Li; Yang, Shenbo; De, Gejirifu; Ju, Liu

doi:10.3390/pr7110813

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The user-side battery energy storage system in the industrial park can achieve peak-shaving and valley-filling, and demand-side management of the internal load of the park can reduce the electricity cost of users in the park and promote the consumption of renewable energy in the park. Compared with residential load and commercial load, the peak-to-valley distribution of industrial load is more random, making peak shaving analysis more difficult [5][6][7]. Different from the unitary time-of-use electricity price, industrial users generally implement a diversified electricity price mechanism, and participating in peak shaving will bring multiple benefits [8].…”

Section: Introductionmentioning

confidence: 99%

Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks

Chen

Jiang

et al. 2021

Entropy

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Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.

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Section: Introductionmentioning

confidence: 99%