基于业务性能偏差感知的电力通信网路由优化策略

doi:10.19595/j.cnki.1000-6753.tces.230660

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Abstract

In the context of new power system construction, the traditional power grid with the main purpose of energy transmission is developing towards the trend of "energy-information" coupling network. Meanwhile, the development of emerging power services such as "source-grid-load-storage" collaborative interaction also puts forward higher demands for the service bearing capacity of power communication network. Due to the multi-timescale energy regulation requirements in the new power system, the bottleneck of network bandwidth and deterioration of service performance caused by frequent information exchange are becoming increasingly prominent. In response to these issues, this paper proposes a service performance deviation awareness-based power communication network routing optimization strategy, which utilizes advanced artificial intelligence methods to achieve deterministic service demand guarantee for power services.
Firstly, an "energy-information" coupling network model is established. The emerging "source-grid-load-storage" collaborative interaction service leads to frequent information exchange in the new power system, where power communication network is one of the three pillars supporting the safe and stable operation of the power system. Therefore, the reliable transmission of information plays a more significant role in the rational allocation of energy. Furthermore, considering the current situation of power communication system construction in China, a power communication network model is established based on software defined network architecture, in which the end-to-end forwarding delay of services is analyzed. Due to the complex characteristics of network topology and forwarding conflicts caused by multiple services concurrent access, a multi hop and long-term power service reliability constraint model is given. Then, this paper set the network utility as the amount of successfully forwarded service data related to the grid node importance and service information priority, and proposes an optimization problem to maximize global network utility through routing selection strategy adjustment.
On account of the difficulty in predicting information about future route nodes, the multi hop and long-term power service reliability constraints are unrealizable to guarantee in single hop routing optimization. So virtual queues are introduced to achieve deviation perception between current services performance and the constraints, thereby ensuring the satisfaction of relevant reliability requirements. Taking the uncertainty of time-varying network and service information into consideration, this paper uses reinforcement learning algorithm to realize the autonomous learning optimization of packet routing optimization strategy for multiple power services in the network. Aiming at the problem of insufficient convergence in traditional single hop optimization algorithms, the improved SARSA(λ) with memory space is adopted for routing optimization. Along with continuous learning, early failed learning strategies will gradually be forgotten to improve algorithm convergence.
The simulation results show that compared with traditional routing optimization algorithms based on Q-learning and SARSA, the proposed algorithm performs better in terms of forwarding delay and packet loss rate. Specifically, the service utility has been improved by 21.01% and 15.92%, the forwarding delay has been reduced by 11.43% and 7.14%, and the packet loss rate has been reduced by 35.32% and 19.66%. Also, the weight coefficient can be adjusted to adapt to the differentiated service demands of different scenarios.

Key words： Power communication network "energy-information" coupling network routing optimization service performance deviation awareness reinforcement learning

Received: 15 May 2023

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TM73

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	Chen Yapeng
	Yang Yang
	Shu Yiling
	Xie Wenzheng
	Zhou Zhenyu

Cite this article:

Chen Yapeng,Yang Yang,Shu Yiling等. Service Performance Deviation Awareness-Based Power Communication Network Routing Optimization Strategy[J]. Transactions of China Electrotechnical Society, 2024, 39(11): 3411-3421.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230660 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I11/3411

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