雾计算支持智能电网区块链架构和DRL方法的性能优化

Weijun Zheng, Wenhua Wang, Guoqing Wu, Chenzi Xue, Yifei Wei
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引用次数: 2

摘要

智能电网愿意充分利用分布式清洁能源来缓解能源危机和环境问题。然而,分布式可再生能源对于当前的电力系统来说,往往具有不可见性和不可控性,其发电存在间歇性问题。因此,当分布式能源在电网中达到高渗透率时,如何实现电力平衡,保持安全运行,保证供电的可靠性和质量是一个巨大的挑战。区块链作为研究热点之一,为这一困境带来了新的解决途径。这两个领域在分权、自治、市场化、智能化等方面有许多共同之处。在本文中,我们讨论了集成系统的可行方案,并加入雾计算以降低成本。考虑到系统的实现,我们选择Hyper Fabric作为基本结构,并在共识中加入可验证的随机函数,以提高加密选举的随机性和安全性。同时,为了满足业务需求,设计了一种基于深度Q学习算法的灵活调整方法,实现吞吐量、时延和存储成本的联合优化。该方案具有保密性、灵活性、可扩展性和植入简单等优点。
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Fog Computing enabled Smart Grid Blockchain Architecture and Performance Optimization with DRL Approach
Smart grid is willing to make full advantage of distributed clean energy to alleviate energy crisis and environmental problems. However, distributed renewable energy is usually invisible and uncontrollable for the current power system, and there are intermittent problems in its generation. Therefore, how to achieve the power balance, maintain safe operation, and ensure the reliability and quality of power supply when the distributed energy reaches a high penetration in the grid is a huge challenge. Blockchain as one of the research hotspots brings about new solution approach to the dilemma. The two fields have many commons on decentralization, autonomy, marketization and intelligence. In this paper, we discuss the feasible scheme of the integrated system and add fog computing to reduce costs. Considering the realization of system, we choose Hyper Fabric as the basic structure and add verifiable random function to the consensus aimed to improve randomness and security in the encrypted election. Meanwhile, in order to satisfy the business requirements, a flexible adjustment method based on Deep Q Learning algorithm is designed to realize the joint optimization of throughput, latency and storage cost. The proposed scheme provides the advantages including privacy, flexibility, extensibility and implantation simplicity.
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