College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Key Laboratory of Intelligent Electric Power Grid of Sichuan Province, Sichuan University, Chengdu 610065, China.
Sensors (Basel). 2023 Jun 5;23(11):5350. doi: 10.3390/s23115350.
In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution.
在现代电力系统中，减少碳排放已成为缓解全球变暖影响的重要目标。因此，可再生能源，特别是风力发电，已在系统中得到广泛应用。尽管风力发电具有优势，但由于其不确定性和随机性，给电力系统的安全、稳定和经济运行带来了重大挑战。最近，多微电网系统（MMGS）被认为是一种适合部署风力发电的候选方案。尽管 MMGS 可以有效地利用风力发电，但不确定性和随机性仍然对系统的调度和运行有重大影响。因此，为了解决风力发电不确定性问题并实现 MMGS 的优化调度策略，本文提出了一种基于气象聚类的可调节鲁棒优化（ARO）模型。首先，采用最大相关性最小冗余度（MRMR）方法和 CURE 聚类算法对气象进行分类，以更好地识别风力模式。其次，采用条件生成对抗网络（CGAN）丰富具有不同气象模式的风力发电数据集，从而构建不确定集。最后，ARO 框架采用的不确定集可以从不确定集中推导出用于建立 MMGS 两阶段协同调度模型的不确定性集。此外，引入阶梯式碳交易来控制 MMGS 的碳排放量。最后，采用交替方向乘子法（ADMM）和列与约束生成（C&CG）算法实现 MMGS 调度模型的分布式求解。案例研究表明，所提出的模型在提高风力发电描述精度、提高成本效率和降低系统碳排放量方面具有良好的性能。然而，案例研究也报告称，该方法消耗相对较长的运行时间。因此，在未来的研究中，将进一步改进解决方案算法，以提高解决方案的效率。
