IEEE Systems, Man and Cybernetics Magazine - July 2019 - 28

Many different techniques are available that deal with
these uncertainties. These range from stochastic variants
of model-based optimization techniques using game theories to data-driven approaches. If the uncertainty implies
that a control algorithm always considers the worst-case
situation for a guaranteed expected response, then much of
the available flexibility will remain unused. Alternatively, if
an average scenario is considered, there is some probability
that the demand response request will not be fully fulfilled.
The greater the uncertainty on the flexibility, the more conservative the algorithm's performance.
A final challenge deals with the aggregation aspects
related to the heterogeneity of flexibility providers and their
geospatial location. If the flexibility from a homogenous set
of devices is included, some indirect synchronization might

Table 1. The advantages and disadvantages
of centralized versus decentralized
control paradigms.
Centralized

Decentralized

Simpler

More scalable

Single point of control

No single point of failure

Compatible with supervisory
control and data acquisition (requires dedicated communication
architectures; e.g., master/slave)

Compatible with Internet
(fits with many architectures: overlays, peer to peer;
e.g., publish/subscribe)

Control structure per application

Can be interfaced to many
control applications

More compatible with integrated
energy companies with
few actors

More compatible with a
liberalized, open-market
model with many actors

1) Modeling and
Optimization

3) Device Module

2) MQTT

Figure 10. A photograph and illustration showing a

device model and architecture of demand response
proof of concept [15].
28

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Ju ly 2019

appear because of the device usage; e.g., all EVs arrive
home after work and leave again in the morning. This leads
to a rather high simultaneity factor. Increasing the heterogeneity by, for example, combining flexibility from electric
boilers, vehicles, white appliances, and heat pump-heated
buildings, can spread out the available flexibility over time.
Also, the geospatial location of the flexibility provider (e.g.,
feeder to which device is connected, or its phase) might
play a role if technical objectives are pursued. In contrast,
when flexibility is traded at a regional or national market
level, this locality information is not relevant.
Ways Forward
Thanks to the tremendous advantages in information technology and communication infrastructures (e.g., Internet of
Things, big data, and cloud technology), it becomes more
possible to interconnect flexibility providers to controllers,
and to use the flexibility for the different objectives
described. In recent years, these trends have led to many
new protocols and to standardization initiatives that try to
harmonize ways to deal with flexibility. Examples include
the OpenADR Alliance (www.openADR.org), which was created in 2010 to "standardize, automate, and simplify" demand
response "to enable utilities to cost-effectively meet growing
energy demand, and customers to control their energy
future." Another is the Universal Smart Energy Framework
(https://www.usef.energy/), which was established in 2014 as
an "international common standard that ensures smart energy technologies and projects are connectable at lowest cost."
Its aim is to enable "the commoditization and market trading
of flexible energy use and specify all stakeholder roles (new
and existing), how they interact, and how they can benefit by
doing so." Other examples include such standards as the
extensions to the International Electrotechnical Commission
61850 series "Communication Networks and Systems for
Power Utility Automation." However, many proprietary protocols within closed systems continue to exist.
As an example of combining demand response with
innovative communication technology, our team at Katholieke Universiteit Leuven, together with colleagues from
the University of California, Berkeley, developed a proofof-concept controller for demand response in refrigerators
[10]. Based on a device module that works in parallel with
the built-in controller of a refrigerator (to ensure the temperature constraints), the Message Queuing Telemetry
Transport (MQTT) protocol is used to aggregate the flexibility information from all appliances, and a centralized
controller sends out the demand response requests (Figure 10) [15]. MTTQ, a standardized publish/subscribe protocol, is used in many social media applications.
While communication is essential for demand response
to work, the nature of the information being communicated
between devices and controllers is no less important. Historical flexibility profiles of devices and consumption profiles of households capture a huge amount of data, which
can be applied to improve machine learning-based demand


http://www.openADR.org https://www.usef.energy/

IEEE Systems, Man and Cybernetics Magazine - July 2019

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