Improved Symbiotic Organisms Search Algorithm for Optimal Operation of Active Distribution Systems Incorporating Renewables and Emerging Data-Center Resources,Energy Science & Engineering

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Improved Symbiotic Organisms Search Algorithm for Optimal Operation of Active Distribution Systems Incorporating Renewables and Emerging Data-Center Resources
Energy Science & Engineering ( IF 3.5 ) Pub Date : 2021-08-15 , DOI: 10.1002/ese3.944
Bo Zeng ₁ , Hao Xu ₁ , Wenshi Wang ₁ , Lei Zhu ₁

Affiliation

The optimal operation analysis plays an important role in estimating the expected return of investment for power systems. However, this work could be highly challenging under the context of active distribution network, where a variety of renewable energy sources and emerging active loads (such as Internet data centers) could be penetrated that introduce a high level of nonlinearity and nonconvexity characteristics into the system modeling. To overcome such challenge, this paper presents a new methodological framework based on the improvements of symbiotic organisms search (SOS) algorithm to address the optimal operation problem of active distribution systems containing renewable energy sources and datacenter resources, aiming to provide a practical tool for system analysis, particularly subject to nonconvexity nature of system components. For this purpose, a generic model for the distribution system including wind power, PV, and datacenter resources is first developed, which not only captures the uncertain nature of system components but also accounts for their spatiotemporal flexibility during operation. On this basis, in order to improve the computation efficiency of the problem, the SOS algorithm is improved by designing the selection strategy of random parameters. By using the penalty function method, the concerned problem is expressed as a nonlinear unconstrained optimization problem. The performance of the proposed model and algorithm is examined through comparative studies. It is shown that the proposed method is able to schedule the renewable energy resources and flexible demand of datacenters coordinately to reduce the operation cost of the system significantly in the case study. In addition, the proposed algorithm demonstrates a higher level of accuracy as well as better convergence efficiency compared to other conventional techniques.

中文翻译：

改进的共生生物搜索算法，用于优化包含可再生能源和新兴数据中心资源的主动配电系统的运行

优化运行分析对估计电力系统的预期投资回报具有重要作用。然而，在有源配电网络的背景下，这项工作可能非常具有挑战性，其中可以渗透各种可再生能源和新兴的有源负载（例如互联网数据中心），从而将高度非线性和非凸特性引入系统造型。为了克服这一挑战，本文提出了一种基于共生生物搜索（SOS）算法改进的新方法学框架，以解决包含可再生能源和数据中心资源的主动配电系统的优化运行问题，旨在为系统提供一个实用的工具。分析，特别是受系统组件的非凸性影响。为此，首先开发了包括风电、光伏和数据中心资源在内的配电系统的通用模型，该模型不仅可以捕捉系统组件的不确定性，还可以考虑它们在运行过程中的时空灵活性。在此基础上，为了提高问题的计算效率，通过设计随机参数的选择策略对SOS算法进行改进。通过使用惩罚函数方法，将关注的问题表示为非线性无约束优化问题。通过比较研究来检验所提出的模型和算法的性能。在案例研究中表明，所提出的方法能够协调调度可再生能源和数据中心的灵活需求，从而显着降低系统的运行成本。此外，与其他传统技术相比，所提出的算法具有更高的精度和更好的收敛效率。

更新日期：2021-10-03

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