4 research outputs found
Analysis, modelling and state estimation for large scale electric demand response
The need for additional reserves increases alongside the intermittency of generation and whilst rotating (conventional) generation is replaced, the system’s inertia
reduces and balance volatility increases. Conceptually, any regulation measure
from the “generation side” has an equivalent countermeasure from the “demand
side”. One of the emerging technologies to provide such balancing services is
Demand Response (DR). DR is commercially used, mainly via industrial loads
combined with small scale diesel and gas generators. However, there is a lot of potential for DR from residential and commercial loads that remains untapped due
to implementation costs, lack of technology expertise, load pattern complexity
and the need to simultaneously control numerous sources.
The main focus of this thesis is to explore the potential of loads, mainly residential
and small commercial, to provide DR services and develop methods focused on
accuracy for the most challenging services (frequency regulation), whilst aiming
for minimal infrastructure and implementation costs. The main points include
analysis of common residential and commercial loads for DR services, focusing on
thermostatically controlled loads (TCLs). TCLs are thermal loads which operate
via thermostats on a duty cycle (on and off state), between two temperature
settings in order to maintain an average set temperature. They use electricity as
a primary energy source or for their control and pumps.
The next part includes analysis and creation of realistic bottom up models to
study aggregated behaviour of TCLs during DR actions, as well as the effect
of external factors. Afterwards, a distributed State Estimation algorithm is
proposed to increase accuracy of aggregated models and track aggregation models
from limited information. A new aggregation framework is proposed, specifically
designed for heterogeneous populations, whilst being universal for all TCL types.
As such, different TCL types can be aggregated together (e.g. cooling and
heating).
The results of this thesis show that with proper aggregation modelling, state
estimation and dynamic updating in time, accuracy of stochastic aggregated
models is improved compared to existing frameworks without the need for
expensive thermal sensors. This suggests that with relatively limited information
the use of residential and commercial TCLs for DR balancing services, is feasible