bachelor assignment
Recently, there have been numerous articles in newspapers about the lack of power grid capacity in the Netherlands. The lack of capacity now is putting a strain on the amount of renewable energy, such as solar panels, that we can integrate in our energy system to reach the climate goals. One solution is better supply and demand matching in both time and location, for which ICT systems form the cornerstone to allow such automated coordination within a Smart Grid.
Load-flow solvers play an essential role in the development of such Smart Grid systems. These solvers are algorithms which are based on physical laws and mathematical concepts to determine the steady state of a power grid. To develop energy management systems and assess their performance, it is imperative to know the state of the grid to prevent overloading and blackouts from happening.
In the Energy Group, we developed an in-house load-flow solver for DEMKit, our open-source software for Smart Grid technologies. However, the development of load-flow solvers has not stood still since then. We now see that new load-flow solver methods emerge that are significantly faster, whilst the complexity of smart grid operations also rapidly increases. This project aims to investigate and compare the performance of these new load-flow algorithms and investigate their accuracy. Ultimately, the goal is to integrate the new solver into DEMKit software. When integrated, the new solver will directly enhance the performance of Smart Grid systems deployed in the real world.
Methodology
a) Compare the performance of our in-house load-flow solver with two other solvers and analyze the reasons for differences in performance.
b) Develop an integrated load-flow and optimization system using the selected solver.
c) Evaluate the accuracy of the selected solver in predicting the steady state of an electrical grid.
d) Produce a conference-paper-like report, including the methodology, results, and conclusions of the study.
Background
Read section 2.4.1, Section 4.3, and Chapter 5 of “A Cyber-Physical Systems Perspective on Decentralized Energy Management” [2].
[1] https://www.utwente.nl/en/eemcs/energy/demkit/
[2] Hoogsteen, G. (2017). A Cyber-Physical Systems Perspective on Decentralized Energy Management (1 ed.). [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036544320
Workload
Theory: 25%
Coding: 35%
Evaluation: 20%
Writing: 20%
Contact
Supervisor (CAES Group): Ivo Varenhorst
Coordinator (CAES Group): Gerwin Hoogsteen
