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working cycle anticipated or postponed but not
interrupted (e.g, dishwashers or laundry
machines);
Re-parameterizable: loads that can have their
control parameters re-set (i.e., air conditioners);
Interruptible: loads whose operation can be
interrupted during a certain period of time (e.g.,
electric water heaters).
Non-controllable: loads that cannot be the target
of any type of demand response actions (e.g.,
computers, entertainment);
Based on this end-use load categorization, it is
possible to exploit the load flexibility within each
house. The large-scale deployment of LEB imposes
an essential challenge concerning the coordination
of grid and end-user objectives. I.e., requests from
the grid should be weighed against the flexibility of
end-use loads to be shifted, re-parameterized or
interrupted in a certain period of time. The EBAg
will gather this flexibility from the end-users by
means of each LEB, asking them to adjust their load
profile by offering a remuneration scheme specified
in a contract, which is typically variable along the
day with some relation with market prices.
Thereby, the EBAg is able to sell the flexibility
gathered by making offers to the grid according to
its requests in each time slot (i.e., increase or
decrease a certain amount of load), with the aim of
optimizing its own profits and offering benefits to all
entities involved (increasing retail profits,
decreasing consumption costs). Figure 1 and figure 2
depict the architecture adopted.
Figure 2: EBAg global architecture.
The EBAg sees its associated end-users grouped
into clusters, which contain end-users with similar
characteristics. The normal energy profile of the
cluster, i.e. in the absence of a flexibility request,
can be represented as a baseline load profile curve.
The EBAg knows the baseline load profile of
clusters as well as the possible load profile responses
associated with each request.
The time variable remuneration schemes are
known, i.e. the €/kW value paid by the grid to the
EBAg and by the EBAg to the clusters. The
incentive to remunerate the customer to participate
in DR programs is a significant issue for the success
of this type of programs (Quinn et al., 2010). An
adequate EBAg business model should be designed
to attract and maintain residential end-users under
demand management contracts, in which end-users
receive a reward to offer a certain amount of load
flexibility to the EBAg. Customers sign up for
programs depending on the benefits they derive in
the form of upfront payments, i.e. when demand
discounts and interruption payments exceed their
perceived cost of interruption or consumption
shifting (Fahrioglu and Alvarado, 2000).
The remuneration scheme presently implemented
is based on the electricity price at OMIP (MIBEL -
Iberian EM) (OMIP). The remuneration schemes are
in place when the EBAg requests for consumption
change are satisfied. The EBAg may be an Energy
Service Company (ESCO) that buys and manages
the end-user’s flexible load and sells it to provide
system services to the SO/EM.
Using the information gathered from the grid
(SO/EM) and the individual LEB aggregated in
clusters, a model to maximize the EBAg profits is
developed that is then tackled using a GA approach.
3 OPTIMIZATION MODEL
The model aims at determining the best matching of
the requests by the grid and the flexibility specified
by the LEB. Binary variables denote whether or not
the EBAg is able to gather load flexibility from the
clusters and offer it to the grid, in each time slot.
Continuous variables represent the corresponding
amount of energy managed.
Indices:
c - 1,2, …C. Identifies the cluster, where C is the
number of clusters associated with the EBAg. Each
cluster gathers a set of end-users (LEB).
f - 0,1,… F. Identifies the request that the EBAg
sends to cluster c; f=0 means that no request is sent
by the EBAg. The EBAg congregates the flexibility
offered by each cluster. A response function is
associated with request f.
t = 1,2,…T. Identifies the time slot. A time
resolution of 15 minutes is considered, thus having
T=96 time slots in one day.
Coefficients:
E
t
- Reward paid by the grid to the EBAg for the
load flexibility provided, in each time slot t.
AnOptimizationModelfortheAggregationofEnd-userEnergyManagementSystemsinaResidentialSetting
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