IEEE Power & Energy Magazine - July/August 2021 - 33

Input
Parameters
Day of Week
Time of Day
Forecasted
Temperature
Last
Week's Load
Load Forecasting Model
figure 4. The BESS dispatch scheduling process.
15-min resolution) was then passed to an internally developed
optimization tool that generates the next-day dispatch
schedule of the battery. The high-level process is represented
in Figure 4. The model used to forecast the load
uses the day of the week, time of day, forecasted temperature,
and last week's same-day load as inputs to generate
the forecasted load.
A simple model was developed to create the day-ahead
schedule of the BESS. The assumptions and variables considered
for the BESS scheduling optimization algorithm include
the forecasted load, capacity of the battery, minimum and
maximum state of the charge limits, and beginning- and endof-the-day
state of charge constraints.
The test area was a segment of a 12-kV distribution circuit
with a peak load of 1,700 kW. The objective was to flatten the
active power seen from a certain point on a distribution feeder
and simultaneously manage the absolute value of the power
factor above 0.9 while the voltage remains in its desirable
range. Due to the distribution system operation considerations,
this supervised test was performed from 8 a.m. to 5 p.m. each
day so that the energy storage dispatch and circuit parameters
could be monitored online by the team.
To demonstrate the results in the following sections, the
load and BESS charging power and capacitive power factor
values are annotated with a negative sign. Accordingly, the
BESS discharging and inductive power factor are annotated
with a positive sign.
Load Flattening
The load flattening algorithm calculates the schedules with
the following process for the day ahead:
1) Determine the forecasted load profile based on the developed
and verified model.
2) Calculate the upper and lower threshold values such that
a) the area between the load profile at peak demand
hours and the upper threshold should support a
load reduction supported through BESS discharging
within the predefined constraints
b) the area between the load profile at low demand
hours and the lower threshold should support a
load increase supported through BESS charging
within the predefined constraints.
july/august 2021
3) Build the battery charge and discharge schedule based
on the above energy requirements.
The schedules will then be communicated with the BESS
controller. This schedule is expected to provide load peak
shaving and valley filling for the duration of the test. Figure 5
illustrates the load-flattening concept with active power support
from a BESS for a sample load profile.
The results are depicted in Figure 6. The goal of load flattening
on a given day was to dispatch the active power of the
energy storage such that the net load (blue line) at the selected
feeder location is close to an ideal flat load (green line). The
battery dispatch is also denoted at the top (yellow line). In this
example, the battery dispatched power was effective in shifting
the load toward the expected flat line.
Power Factor Correction Along
With Load Flattening
The operation of BESSs can support value stacking by providing
both a flatter load and a compensated power factor.
The process for the multiuse of the energy storage generates
both the active and reactive power battery schedules for the
day-ahead operation. The process starts with determining the
forecasted active and reactive power load profiles based on the
developed models. The battery charge and discharge schedules
are calculated based on the mentioned load-flattening
MATLAB Optimizer
Forecasted
15
10
5
Load for a Given Day
Schedule
BESS
Forecasted Load Profile
Upper Threshold
Lower
Threshold
010
20 30 40 50
60 70 80 90
Time (15 min)
figure 5. The peak shaving and valley filling of a sample
load profile with battery support. The blue areas indicate
the BESS discharging, and the yellow areas indicate the
BESS charging.
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IEEE Power & Energy Magazine - July/August 2021

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