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Model Predictive Control in a Power Grid Considering Network Limits

Albracht, Michiel (2018) Model Predictive Control in a Power Grid Considering Network Limits. Research Project, Industrial Engineering and Management.


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With the upcoming market share of renewable energy sources, a problem rises. Where the produc- tion of electrical energy used to be adjusted to the demand of its customers, this is not possible for most sustainable energy sources. This is the orientation of this thesis. Instead of only controlling the generation of electricity, this thesis looks into controlling the demand side with the use of a model predictive controller. The control is performed on the network-level. Hence, the power flow between the different homes is considered. In this thesis, both of the basic principles behind this so-called, optimal power flow (OPF) and model predictive control (MPC) are explained and the OPF converted into a convex, solvable problem using semidefinite programming (SDP). This gives the opportunity to combine the MPC and OPF into one problem. This problem is built into an application to test the effect of different parameters and constraints. Because of the modular nature of the application, it is suitable as basis for future research within the area of smart grid based on model predictive control. This thesis provides a case study with example scenarios which are solved using this application. This case study simulates a small network of households where the heating systems is controlled via a model predictive controller. Some of the households have production facilities in the form of solar panels, and storage possibilities. The controller takes into consideration, the power flow constraints and limits. This case study is used to find the effect of using these constraints. This results in the conclusion that the bus and line constraint have a significant effect on the power flows in the network. Using MPC in a power grid without the bus and line limits, results in a solution that might even be unfeasible in the real network. This gives the optimization a solution that is not suitable to be implemented, and make the results unreliable.

Item Type: Thesis (Research Project)
Supervisor name: Scherpen, J.M.A.
Degree programme: Industrial Engineering and Management
Thesis type: Research Project
Language: English
Date Deposited: 12 Jun 2018
Last Modified: 21 Jun 2018 07:48

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