Javascript must be enabled for the correct page display

Buildings-to-Grid Integration for Demand-Side Flexibility in Power Systems with Uncertain Generation

Badings, Thom (2019) Buildings-to-Grid Integration for Demand-Side Flexibility in Power Systems with Uncertain Generation. Research Project, Industrial Engineering and Management.


Download (3MB) | Preview
[img] Text
Restricted to Registered users only

Download (123kB)


The increasing penetration of Renewable Energy Sources in power systems (RES), such as wind and solar power, leads to uncertain behavior of the grid, which is traditionally compensated by scheduling reserves. Demand-side flexibility (or demand response) is a promising alternative to reserve scheduling, with advantages including less environmental impact, lower costs, and increased building energy efficiency. To study demand-response applications explicitly, this work is concerned with developing a unified framework with integrated grid and building dynamics, called Buildings-to-Grid (BtG) integration. The model integrates TSO, DSO, and buildings with unified control decisions, and finite-horizon Model Predictive Control (MPC) optimization problems are formulated. Uncertain wind power generation is introduced in the BtG framework, and the deployment of demand-side flexibility is formulated explicitly. A method using the so-called randomization technique is employed to provide an approximated reformulation with probabilistic feasibility certificates for the proposed stochastic MPC. From the simulation studies, it is concluded that buildings can be a primary stakeholder in providing ancillary services to the future power grid, and that demand-side flexibility can substitute traditional reserve scheduling, without losing stability of the grid and violating the building thermal comfort of occupants. Furthermore, societal elements can have a significant impact on the integration of RES and deployment of demand-side flexibility. Suggestions for further research include to embrace non-linear, AC grid modeling to increase accuracy, and a distributed MPC problem formulation to improve computational performance.

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: 22 Apr 2019
Last Modified: 23 Apr 2019 13:38

Actions (login required)

View Item View Item