Rolling horizon optimization for real-time operation of thermostatically controlled load aggregator
Rolling horizon optimization for real-time operation of thermostatically controlled load aggregator
Fengji LUO 0 1 2 3
Gianluca RANZI 0 1 2 3
Zhaoyang DONG 0 1 2 3
Fengji LUO 0 1 2 3
Zhaoyang DONG 0 1 2 3
0 School of Electrical and Information Engineering, University of Sydney , Sydney , Australia
1 State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University , Chongqing , China
2 School of Civil Engineering, University of Sydney , Sydney , Australia
3 with the Hong Kong Polytechnic University , Hong Kong, and Transend Networks, Tasmania , Australia. His research interests include power system operation & planning , smart grid, renewable energy systems, computational intelligence, and evolutionary computation
Thermostatically controlled loads (TCLs) have great potentials to participate in the demand response programs due to their flexibility in storing thermal energy. The two-way communication infrastructure of smart grids provides opportunities for the smart buildings/houses equipped with TCLs to be aggregated in their participation in the electricity markets. This paper focuses on the realtime scheduling of TCL aggregators in the power market using the structure of the Nordic electricity markets a case study. An International Organization of Standardization (ISO) thermal comfort model is employed to well control the occupants' thermal comfort, while a rolling horizon optimization (RHO) strategy is proposed for the TCL aggregator to maximize its profit in the regulation market and to mitigate the impacts of system uncertainties. The simulations are performed by means of a metaheuristic optimization algorithm, i.e., natural aggregation algorithm (NAA). A series of simulations are conducted to validate the effectiveness of proposed method.
Thermostatically controlled load; Demand side management; Rolling horizon optimization; Thermal comfort model; Demand response
1 Introduction
Modern power systems have experienced major changes
and enhancements since the first proposal of ‘smart grid’ in
the early 21st century. The integration of renewable energy
sources, participations of demand side and deployment of
advanced sensor infrastructure have further increased the
system complexities and have driven the re-constructions
of grid operations [
1
].
Residential/commercial buildings are large energy
consumers in the distribution systems. For example, in China
the energy consumption of buildings contributes to 33% of
the whole society’s energy consumptions [
2
]. The
thermostatically controlled appliances (e.g., air conditioners,
heaters), widely used in houses and buildings, have great
potentials to participate in demand side management
(DSM) programs due to their thermal storing capabilities.
Extensive effort has been devoted to date at studying the
direct control techniques of thermostatically controlled
loads (TCLs). For example, [
3
] outlined the fundamental
requirements of direct load control (DLC) and presented a
general optimization framework to do the feeder-scale load
reduction, while [
4
] designed a priority based control
scheme for TCLs to participate in grid frequency
regulation. Reference [
5
] proposed a two-stage dispatch method
for TCLs where, in a first stage, a day-ahead scheduling
model is solved to determine the optimal TCL dispatch
and, in a second stage, a real time control model allocates
the desired setpoints to individual TCL. In [
6
], the authors
used the Markov transition matrix to model the populated
TCLs to do the non-disruptive load reduction. Previous
work of the authors made use of an advanced thermal
inertia model to control TCL in smart homes [
7
] and
proposed a model for coordinately dispatch the TCLs and
thermal generation units [
8
]. In above works, the ON/OFF
control actions of TCLs are driven by the thermostats
settings and by a certain pre-set indoor temperature range.
In our previous works [
9, 10
], International Organization of
Standardization (ISO) standard thermal comfort models,
widely adopted to estimate the occupants’ thermal comfort
degree, have been considered in the model representation
for the TCL dispatch. By integrating the thermal comfort
model, the control actions of TCLs are driven by the
calculated comfort level of the occupants instead of
thermostats settings and indoor temperature dead-band.
In the smart grid paradigm, buildings can be aggregated
to place load reduction bids in the day-ahead market. In
this way, the aggregated TCLs act as a virtual power plant
(VPP) [
11
]. It can ‘generate more power’ to the grid by
turning off some TCLs to decrease the load and can
‘generate less power’ by turning some TCLs on. Once the
load reduction contracts are determined for the day-ahead
market, the differences between actual load shedding
amounts and contracted load reduction volumes will be
settled in the regulation market. Therefore, after
determining the day-ahead contracts, t (...truncated)