Improved Symbiotic Organisms Search Algorithm for Optimal Operation of Active Distribution Systems Incorporating Renewables and Emerging Data‐Center Resources

• Published in: Energy science & engineering Vol. 9; no. 10; pp. 1719 - 1733
• Main Authors: Zeng, Bo, Xu, Hao, Wang, Wenshi, Zhu, Lei
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.10.2021; Wiley
• Subjects: Active distribution system; Algorithms; Alternative energy sources; Batch processing; Comparative studies; Computer centers; Data centers; datacenter resources; distributed generation; Electric power distribution; Energy consumption; Energy distribution; Energy resources; Energy storage; flexibility; Genetic algorithms; Mutualism; Nonlinearity; operation; Optimization; Participation; Penalty function; Photovoltaic cells; Renewable energy sources; Renewable resources; Scheduling; Search algorithms; Servers; Simulation; Systems analysis; Wind power
• ISSN: 2050-0505; 2050-0505
• DOI: 10.1002/ese3.944

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. Improved SOS algorithm provided a faster convergence speed to find a lower optimal solution value for fitness functions than the SOS, ABC, GA, and PSO methods.