Lingyu Liang, Huanming Zhang, Wencong Xiao, Xiangyu Zhao, Junbin Chen, Shang Cao, Tao Yu, Hanju Li
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引用次数: 0
Abstract
Extreme events can lead to extensive blackouts, emphasising the crucial significance of power system black-start restoration. In this paper, a two-stage strategy coordinating deep reinforcement learning (DRL) and mixed-integer linear programming (MILP) for power system black-start restoration is proposed. In the first stage, a convolutional neural network-based (CNN-based) invalid action masking assisted proximal policy optimisation (CI-PPO) algorithm is developed to determine component connections. Furthermore, a MILP model for optimal power dispatch is formulated with the objective of minimising generation costs in the second stage. The effectiveness of the proposed method is validated using the New England 10-unit 39-bus power system. The results show strong learning ability and computational efficiency for varying system conditions.
期刊介绍：
IET Renewable Power Generation (RPG) brings together the topics of renewable energy technology, power generation and systems integration, with techno-economic issues. All renewable energy generation technologies are within the scope of the journal.
Specific technology areas covered by the journal include:
Wind power technology and systems
Photovoltaics
Solar thermal power generation
Geothermal energy
Fuel cells
Wave power
Marine current energy
Biomass conversion and power generation
What differentiates RPG from technology specific journals is a concern with power generation and how the characteristics of the different renewable sources affect electrical power conversion, including power electronic design, integration in to power systems, and techno-economic issues. Other technologies that have a direct role in sustainable power generation such as fuel cells and energy storage are also covered, as are system control approaches such as demand side management, which facilitate the integration of renewable sources into power systems, both large and small.
The journal provides a forum for the presentation of new research, development and applications of renewable power generation. Demonstrations and experimentally based research are particularly valued, and modelling studies should as far as possible be validated so as to give confidence that the models are representative of real-world behavior. Research that explores issues where the characteristics of the renewable energy source and their control impact on the power conversion is welcome. Papers covering the wider areas of power system control and operation, including scheduling and protection that are central to the challenge of renewable power integration are particularly encouraged.
The journal is technology focused covering design, demonstration, modelling and analysis, but papers covering techno-economic issues are also of interest. Papers presenting new modelling and theory are welcome but this must be relevant to real power systems and power generation. Most papers are expected to include significant novelty of approach or application that has general applicability, and where appropriate include experimental results. Critical reviews of relevant topics are also invited and these would be expected to be comprehensive and fully referenced.
Current Special Issue. Call for papers:
Power Quality and Protection in Renewable Energy Systems and Microgrids - https://digital-library.theiet.org/files/IET_RPG_CFP_PQPRESM.pdf
Energy and Rail/Road Transportation Integrated Development - https://digital-library.theiet.org/files/IET_RPG_CFP_ERTID.pdf
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