IEEE Power & Energy Magazine - May/June 2016 - 37
Successful outcomes require acceptance by business and
policy decision makers as a cost-effective, valid, equitable, and
advantageous revenue/investment recovery mechanism.
all relevant local information to a central point limits the accuracy and scalability properties of the approach. When equipment changes locally, the central system must be updated as
well. if the number of responsive houses, buildings, and installations becomes large, the communication and optimization
times grow nonlinearly. the approach also does not respond
gracefully to communications or central optimizer failures.
the price reaction approach is based on a one-way signaling of a dynamic price to end users. at certain time intervals, a new electricity price or a price profile for the coming hours is sent to an automation system at the premises.
this price profile is displayed for the end user or automation system to adjust equipment operations. Benefits of
this approach include 1) simple one-way communications
leading to low system complexity, 2) no issues regarding privacy or autonomy, and 3) an easily implementable
approach in regions having an electricity wholesale market
due to the availability of a day-ahead or intraday price profile from this market.
using the price signal, the operation of responsive devices
can be optimized economically by a local intelligent controller that is owned by and/or under the control of the consumer.
Such a controller would thus be able to increase the consumer's loads during low-priced periods, and generation during
high-priced periods, while taking the device states and user
preferences into account. the controller has the opportunity
to unleash the full response potential. to bill the customer
according to the prices signaled, a communicating electricity meter needs to measure usage at a resolution appropriate
to track response from the price signal. the recent technology developments in advanced metering are providing solutions to mitigate the privacy risks.
these characteristics have advantages compared to
the central optimization case; however, the reaction of a
demand-response pool to each price reaction signal is difficult to predict without knowing each device's state and end
the transactive control quadrant offers distinct advantages
in integrating flexible devices in the electricity operations.
Here, smart homes, buildings, and industrial sites engage
in automated market trade with others at the distribution
system level and with representation of the bulk system.
communications are based on prices and energy quantities
in a two-way negotiation.
analogous to the price reaction approach, the operation of the flexible devices is optimized economically by
a local intelligent controller (or agent) under the control
of the end user. this controller receives price information and takes the device state and user preferences into
account to operate local demand and supply resources.
this is the same as the price reaction approach except
that, before the price reaction takes place, the local controller communicates the available flexibility combined
with their preferences and conditions to an electronic marketplace through a market transaction (price/quantity bid).
consuming devices communicate their willingness to pay,
while producing devices communicate the price for which
they are willing to produce.
Since all resources participating in the market communicate their intended reaction to a range of price levels, the
pool reaction to a range of price signals is known up front
and the market mechanism can determine the price for an
appropriate balance of supply and demand. from the end
user's or energy consumer's point of view, the local energy
management system agent acts on behalf of the user or
consumer to bid into the market and reacts to the resulting
market price signals. unlike the centralized optimization
approach, no direct outside control is involved here. However, from a system perspective, the participants engage in
coordinated control actions. With this approach, demand
response moves from influencing, with an uncertain overall response, into market-based control with a collaboratively derived dynamic price as a control signal to trigger
a predictable system reaction. this is why this approach is
sometimes referred to as market-based control or transactive control multiagent system (see "distributed intelligence
and Multiagent Systems").
When properly implemented, the market bids sent by the
end users' energy management systems can be aggregated
together. When this is done for two devices, the resulting
bid represents the preferences of the two devices together.
the message size of the aggregated bid curve is a simple
combination of the individual device bid curves. using this
property, a highly scalable system can be obtained when,
in a response cluster, bids are aggregated together. the
processing and communication time then scales with the
height of the aggregation tree instead of with the number
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