KARA, DENIZ (2015) Implementing Productivity Based Demand Response in Office Buildings Using Building Automation Standards. Doctoral thesis, Durham University.
|PDF - Accepted Version|
Demand response is an effective method that can solve known issues in electrical power systems caused by peak power demand and intermittent supply from renewable sources. Office buildings are good candidates for implementing demand response because they usually incorporate building management systems which are able to control and monitor various electrical devices, from lighting to HVAC, security to power management.
In order to study the feasibility of using an existing office building management system to implement demand response, a simulator for a typical office building has been built which models the energy consumption characteristics of the building. With the help of this simulator, an Indoor Environment Quality based control algorithm is developed whose aim is to minimise reduction in productivity in an office building during a demand response application. This research revealed two key elements of automatic demand response: lighting loads need to be utilised in every demand response scenario along with HVAC, and the control system needs to be able to operate rapidly because of changing conditions. A multi-agent based demand response control algorithm for lighting is then developed and used to test the suitability of two communication protocols currently widely used in office buildings: KNX and LonWorks. The results show that excessive overload of the communication channel and the lag caused by slow communication speeds using these protocols present serious problems for the implementation of real time agent based communication in office buildings. A solution to these problems is proposed.
|Item Type:||Thesis (Doctoral)|
|Award:||Doctor of Philosophy|
|Keywords:||Demand Response, Office Buildings, Building Automation, Multi-Agent Systems, Fieldbus, KNX|
|Faculty and Department:||Faculty of Science > Engineering and Computing Science, School of (2008-2017)|
|Copyright:||Copyright of this thesis is held by the author|
|Deposited On:||27 Jan 2015 12:24|