结合FISCO BCOS与拓扑优化一致性算法的配电网多目标经济调度

doi:10.19678/j.issn.1000-3428.0069274

摘要/Abstract

摘要：

随着分布式能源的高比例渗透、大量储能单元以及柔性负荷的加入，主动配电网的优化调度变得更加具有挑战性。现有经济调度较少考虑柔性负荷和储能单元的接入，收敛速度较慢。结合国家“双碳”目标，提出FISCO BCOS平台下结合通信拓扑优化一致性算法的配电网多目标经济调度策略。该策略综合考虑发电机发电成本、污染气体排放、储能成本和柔性负荷用电效益，利用通信拓扑优化的一致性算法提高系统收敛速度，结合FISCO BCOS联盟链的存储和精简实用拜占庭容错(rPBFT)共识机制优化节点间的信息共享，降低领导节点的中心性，防止部分节点作恶，实现配电网多目标最优功率分配。仿真结果表明，提出的配电网多目标调度经济调度策略收敛速度快，在领导节点切换、不同阶段节点退出与加入及功率交换指令变化、收敛系数变动场景下仍能较快收敛，具有良好的鲁棒性和稳定性，且收敛速度优于快速一致性算法，若目标权重系数选取恰当，经济与环境结果均优于多目标NSGA-Ⅱ算法。

关键词: 主动配电网, 区块链, FISCO BCOS平台, 多目标调度, 通信拓扑优化, 一致性算法

Abstract:

The high penetration of distributed energy, addition of many energy storage units, and flexible loads have hindered the optimization and scheduling of active distribution networks. Existing economic dispatch systems do not consider the integration of flexible loads and energy storage units effectively, resulting in slow convergence speed. Aligning with the national ″dual carbon″ goals, this paper proposes a multi-objective economic dispatch strategy for distribution networks based on the FISCO BCOS platform, combined with the consistency algorithm in communication topology optimization. This strategy comprehensively considers power generation cost, gas emissions, energy storage cost, and the benefits of flexible load electricity consumption. It uses the consistency algorithm in communication topology optimization to improve the system′s convergence speed. Moreover, it combines the storage of the FISCO BCOS alliance chain and the reduced Practical Byzantine Fault Tolerance (rPBFT) consensus mechanism to optimize information sharing between nodes, reduce the centrality of leading nodes, prevent malicious nodes from attacking, and achieve multi-objective optimal power allocation in the distribution network. Simulation results show that the proposed multi-objective scheduling economic dispatch strategy converges quickly despite leader node switching, node exit and addition at different stages, power exchange instruction changes, and convergence coefficient changes. It is robust, stable, and achieves a higher convergence speed than that of the fast-consistency algorithm. It selects appropriate target weight coefficients, yielding economic and environmental outcomes that are better than those provided by the multi-objective NSGA-Ⅱ algorithm.

Key words: active distribution network, blockchain, FISCO BCOS platform, multi-objective scheduling, communication topology optimization, consistency algorithms

王桂兰, 张成, 周国亮. 结合FISCO BCOS与拓扑优化一致性算法的配电网多目标经济调度[J]. 计算机工程, 2025, 51(7): 348-361.

WANG Guilan, ZHANG Cheng, ZHOU Guoliang. Multi-objective Economic Scheduling of Distribution Networks with FISCO BCOS and Topology Optimization Consistency Algorithm[J]. Computer Engineering, 2025, 51(7): 348-361.

https://www.ecice06.com/CN/Y2025/V51/I7/348

图/表 22

图1 rPBFT算法共识流程

Fig.1 Consensus procedure of rPBFT algorithm

图2 主动配电网系统结构

Fig.2 Structure of active distribution network system

图3 FISCO BCOS平台下配电网多目标经济调度框架

Fig.3 Framework of multi-objective economic dispatch for distribution network under FISCO BCOS platform

图4 FISOC BCOS平台结合通信拓扑优化一致性算法的配电网多目标经济调度流程

Fig.4 Multi-objective economic dispatch process for distribution networks using FISOC BCOS platform combined with communication topology optimization consistency algorithm

图5 通信拓扑优化前后一致性变量值的收敛曲线

Fig.5 Convergence curves of consistency variable values before and after communication topology optimization

图6 通信拓扑优化前后各节点功率变化曲线

Fig.6 Power change curve of each node before and after communication topology optimization

图7 领导节点切换情况下一致性变量值收敛曲线

Fig.7 Convergence curve of consistency variable values in case of leader node switching

图8 领导节点切换情况下各节点功率变化曲线

Fig.8 Power change curve of each node in the case of leader node switching

图9 节点退出与加入情况下一致性变量值曲线

Fig.9 Plot of consistency variable values curve in case of node exit and joining

图10 节点退出与加入情况下各节点功率变化曲线

Fig.10 Power variation curves of each node in the case of node exit and joining

图11 功率交换指令变动时一致性变量值曲线

Fig.11 Consistency variable values curve when power switching command is changed

图12 功率交换指令变动时各节点功率分布

Fig.12 Output power of each power supply under network loss fluctuation

图13 收敛系数变动情况下一致性变量值曲线

Fig.13 Consistency variable values curve under variation of convergence coefficient

图14 收敛系数变动情况下各节点功率变化曲线

Fig.14 Power variation curve of each node under variation of convergence coefficient

图15 权重系数不同时一致性变量值曲线

Fig.15 Curve of consistency variables values with different weight coefficients

图16 权重系数不同时各节点功率变化曲线

Fig.16 Power variation curve of each node with different weight coefficients

图17 多目标算法NSGA-Ⅱ得到的解集

Fig.17 The solution set obtained by multi-objective algorithm NSGA-Ⅱ

图18 快速一致性算法下一致性变量值曲线

Fig.18 Curve of consistency variables values under fast consistency algorithm

图19 快速一致性算法下各节点功率变化曲线

Fig.19 Power variation curve of each node under fast consistency algorithm

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