Traditionally, the mismatch between electricity generation
and consumption has been handled by controlling generation.
However, this approach, also known as known as
“load/demand-following” may not always be feasible, economical,
or environmental-friendly. Progress in low-cost and
high-speed communication between consumption, distribution
and generation has enabled the complementary approach
of “supply-following”, wherein a large number of responsive
loads are shifted or curtailed to help handle the mismatch
between production and consumption. This idea crystallizes
into the concept of Demand Response (DR) systems andseveral projects all over the world are implementing such
systems in different forms and in different domains. DR systems
form an important part of the future Smart Grid picture
and one of the aims of deploying advanced metering infrastructure
is essentially to enable DR [2][3][4][5].
Demand response can be achieved through a number of
different mechanisms such as direct-load-control, incentives,
pricing signals, or a combination of these schemes. The design
of a DR system varies depending on a number of factors
such as the type of generation, distribution, consumption,
and demography. For example, certain geographical areas
may have a large number of rivers or wind to power turbines
while other regions may be depending on oil to produce electricity.
In the former case, customers might be given incentives
to temporarily store excess generation, while in the
latter, time of use prices might be used to discourage consumption
when oil prices are high. Additionally, some locations
may have a single electricity distributor while others
may have multiple distributors and this might determine if a
DR system is implemented with the help of an energy aggregator.
The electricity consumers may be industrial companies,
apartment buildings, or individual houses/villas. In the
latter cases, the age and lifestyle of the consumers might
have influence the design of DR system [6][7][8][9].
With different types of DR systems and implementations,
it is important to understand the performance of proposed
and implemented DR solutions. For example, how effective
are different communication mechanisms in terms of influencing
the consumers to avoid peak load? How efficient are
the financial incentives for different stakeholders? How effective
are different incentive schemes for certain types of
consumers (e.g., residential)? Are the predictions of baseline
electricity consumption correct or even possible for certain
consumer types? Additionally, the acquisition and communication
of information, which is part of the DR solution,
brings its own set of problems. The information must be secure,
trustworthy, and tamper-proof. The system must not
allow information to be misused (i.e., privacy of the stakeholder
must be guaranteed). Lastly, the storage of all customer
information must be done intelligently, both for efficiency of storage reasons as well as the processing of the
data.
In this context, this paper presents different candidate
metrics that may be used to evaluate and compare the effectiveness
of DR programs. This work is part of the EU FP7
WATTALYST project [1], which aims to understand how
consumers respond to DR signals by increasing/decreasing
their demands and how their participation is influenced by
external and internal factors. Another goal of the project is
to understand effective methods of conveying the DR signals
to the users. In particular, the project will focus on interface
design; communication means (in-house displays,
SMS messages), message emphasis (environmental, economical)
and customized messages based on gender, age
and profile.