Abstract
This paper proposes a real-time integrated energy system (IES) management approach which aims at promoting overall energy efficiency, increasing renewable energy penetration, and smoothing load fluctuation. The electric vehicles (EVs) charging scheduling is incorporated into the IES management, where the uncertain arrivals and departures of multiple EVs are considered as a stochastic but flexible load to the IES. Furthermore, towards the carbon neutralization target, a carbon emissions trading mechanism is introduced into the IES management to incentivize the system to operate in an eco-friendlier manner. To tackle the computational complexity induced by the stochastic and intermittent nature of the renewable energy sources and EVs load, the scheduling of the IES is realized in a neural network based real-time manner, driven by a deep reinforcement learning approach that guarantees safe training and operation. The case study verifies the effectiveness of the proposed approach.
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This study is supported in by the National Key R&D Program of China, Grant Number: 2022YFE0198700.
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Li, Y., Wang, B., Zhang, L., Liu, L., Fan, H. (2023). A Neural Approach Towards Real-time Management for Integrated Energy System Incorporating Carbon Trading and Electrical Vehicle Scheduling. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1870. Springer, Singapore. https://doi.org/10.1007/978-981-99-5847-4_40
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DOI: https://doi.org/10.1007/978-981-99-5847-4_40
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