# Data-based optimal estimation of frequency bias: The case of Southwest Power
Pool ††thanks: Partial funding for this work is provided by the US National
Science Foundation EAGER project #2002570

Miroslav Kosanić  Massachusetts Institute of Technology, Cambridge, MA 02139;
<EMAIL_ADDRESS><EMAIL_ADDRESS>Marija Ilić  Massachusetts Institute of
Technology, Cambridge, MA 02139<EMAIL_ADDRESS><EMAIL_ADDRESS>Daniel Baker
Southwest Power Pool, Little Rock, AR 72223;
dbaker@spp.org;hscribner<EMAIL_ADDRESS>Harvey Scribner  Southwest
Power Pool, Little Rock, AR 72223;
dbaker@spp.org;hscribner<EMAIL_ADDRESS>Casey Cathey  Southwest
Power Pool, Little Rock, AR 72223;
dbaker@spp.org;hscribner<EMAIL_ADDRESS>
###### Abstract

In this paper, we introduce a method to optimally estimate time-varying
frequency bias $\beta$. Current industry practice is to assume that $\beta$ is
changing only on annual basis. We suggest that this improved time-dependent
bias estimate can be used to reduce the cost of frequency regulation needed to
meet industry standards requested by the North American Electric Reliability
Corporation (NERC). Optimization of time-varying frequency bias is posed as a
parameter estimation (calibration) problem whose implementation utilizes
online system measurements. It is further shown how this result can be used to
estimate intra-dispatch load deviations. This knowledge is needed to estimate
more accurately regulation reserve needed, and to therefore reduce overall
regulation cost. Methods can be introduced to give incentives to demand
response to participate in frequency regulation. Overall, we show the
importance of incorporating knowledge of physics-based models for data-enabled
parameter estimation of physical systems.

###### Index Terms:

frequency bias estimation, automatic generation control (AGC), frequency
regulation, regulation reserve, demand response.

##  I Introduction

An AC interconnected electric power system is a complex system whose main
function is to provide uninterrupted electricity service. Therefore, the
system must be operated within pre-specified frequency deviations around
nominal frequency by maintaining online balance of net generation and load.
This is accomplished by ensuring that adequate resources are available to
respond to power imbalances as they happen. Fast-responding power plants are
usually used to do this in a feedback manner. The missed opportunity cost
associated with scheduling more expensive polluting power plants to supply
predictable system load has caused the cost of fast power plants which
regulate frequency in response to system load deviations to be historically
high [9].

###  I-A Frequency stabilization and regulation in today’s industry

Frequency stabilization and regulation are inherent functions of today’s
hierarchical control [8]. Primary control of generator-turbine-governor (GTG)
is a fast local control responding to the deviations of frequency $\omega(t)$
from the frequency set point $\omega^{ref}[kTs]$ of their governors. Set
points of power plants participating in secondary control comprise Automatic
Generation Control (AGC) function which in an automated manner adjusts set
points $\omega^{ref}[kTs]$ on their governors so that the Balancing Authority
(BA) level regulates frequency within the pre-specified limits. Each area $I$
comprises several generators $G_{j}^{I}$ participating in the BA frequency
regulation. The AGC function operates in a feedback manner by responding to
the total area imbalance known as the Area Control Error (ACE) on a minute-by-
minute basis and it does not require the system operator to make decisions in
near real-time. However, as the generation mix is rapidly changing, the
natural response of each area $I$ varies over time since, as reviewed later in
the paper, the sensitivity of frequency deviations with respect to power
imbalances depends on the GTG parameters of all power plants participating in
AGC. Also, the ACE variations are becoming more dynamic and higher in
amplitude as they reflect the net load deviations from the predicted system
load, which routinely includes the effects of intermittent power generation
and BA demand deviations from their historic patterns. All these changes have
made it much more challenging to estimate the amount of regulation reserve,
and this affects the overall cost of meeting frequency regulation standards.

Figure 1: Map of North America independent system operators (ISOs)

###  I-B Pricing regulation reserve in electricity markets

Furthermore, in areas of the US interconnection in which generation is
provided competitively through the evolving electricity markets the Federal
Energy Regulatory Commission (FERC), under order 2000 has defined ancillary
services as a means to balance power during both normal and abnormal
conditions, the latter caused by the large equipment failures. By their
definition, ancillary services maintain reliable operations of the
interconnected transmission system. As it is stated on the FERC website [4],
they encompass load following, reactive power-voltage regulation, system
protective services, loss compensation service, system control, load dispatch
services, and energy imbalance services. The cost of balancing power during
normal conditions, well understood in the regulated industry as the AGC cost,
is included as part of the ancillary service cost. In the past, ACG costs were
not considered to have a major effect on the market electricity prices. This
is rapidly changing as this cost is increasing, and, therefore, it is also
very important to provide better estimates of the regulation reserves
typically purchased in day-ahead markets. As a result, market mechanisms for
frequency regulation have taken on a new importance and the problem is
undergoing its renaissance [5].

###  I-C Growing concerns

System operators in all BAs in the US, regulated or market supported, are
concerned with the growing influx of intermittent power and its effects on the
regulation reserves. Shown in Figure 1 is SPP BA whose measurement data we are
using in this paper. SPP has had a huge growth in both wind and utility-scale
power generation. Estimating net load deviations in the SPP area is important
for planning and utilizing regulation reserves as these types of energy
resources are deployed in larger amounts. To reduce the overall cost of
electricity service, it is becoming increasingly important to estimate net
system load deviations around their predicted patterns much more accurately.
It is also becoming more important to provide incentives to demand response as
a means of balancing these minute-by-minute fast deviations [1, 2].

Notably, the SPP net load volatility has increased since 2016 and is expected
to further increase. Penetration of renewables, primarily wind generation,
increased these changes in net load but also transmission congestion (Oklahoma
has one of the single-largest wind farms in North America) as can be seen from
quarterly presentation in 2022 [11]. SPP attempted to address the first issue
as a way of keeping reliability through offering ramp products to
systematically pre-position resources with ramp capability to manage net load
variations and uncertainties [3] and provide transparent price signals to
incentivize resource flexibility and future economic investment. Since then,
there is an energy price increase [11].

###  I-D Paper organization

This paper is motivated by these overall growing concerns about the ability to
regulate frequency at a reasonable cost as described above. The basic premise
is that by systematic data mining, it is possible to estimate both time-
varying natural response of a BA, SPP in particular, to consequently estimate
more accurately minute-by-minute load deviations $\Delta P_{L}[kTs]$ and
assist system operators and markets in purchasing adequate regulation reserve.
Section II sets an overview of AGC and Section III sets the physics-based
model structure essential for effective estimation of natural BA response. The
problem is posed as an inverse optimization problem of BA droop characteristic
deviation from its physics-based model. Data is used to compute these
deviations and to estimate BA droop characteristic, in particular BA frequency
bias. In Section IV SPP data is used to demonstrate the time-varying frequency
bias estimates around the constant SPP value given to us. Estimates of the
potential saving on the amount of regulation reserve needed and the resulting
regulation reserve cost are briefly described. Finally, in Section V we
discuss and conclude with open questions for future research.

##  II Automatic generation control

Automatic Generation Control (AGC) in the US interconnected power system or
Load Frequency Control (LFC) in Europe, have been examples of the most
ingenious large-scale feedback control schemes of complex man-made dynamical
systems. They have worked amazingly well, despite their simplicity, and have
been the key to regulating interconnection frequency. The main objective of
each Control Area (CA) comprising a subsystem within a large-scale
interconnected system, has been to implement frequency regulation by re-
setting the set points of governor controllers $\omega^{ref}[kT_{s}]$ of power
plants participating in AGC so that the ACE is compensated by their
supplemental generation $P^{reg}[kT_{s}]$. This is done automatically in a
feedback manner on a minute-by-minute basis $[kT_{s}]$ around feed-forward
tertiary level generation scheduling in between dispatch times $[kT_{t}]$.
Historically AGC was implemented using mainly fast power plants most suited to
produce power fast and contribute their share of regulating power to cancel
ACE of a CA. More recently, the US Control Areas (CAs) have merged into larger
Balancing Authorities (BAs) shown in Figure 1. NERC BAL-003-1.1 standard
requires that AGC for the interconnected system should regulate frequency
within the pre-specified deviation limits of $\pm 0.036Hz$ around the nominal
$60Hz$ frequency. This is done by each BA in a distributed manner according to
its natural response published by NERC [10]. Electricity markets require that
regulation reserves are price-based and, as such, the frequency regulation
process has become more complex.

###  II-A Physics-based AGC model

For completeness, we briefly review the physics-based model used for
implementing today’s AGC. Tertiary level feed-forward scheduling of generation
$\hat{P}_{G}[kT_{t}]$ is done hourly at times $[kT_{t}]$ to supply predictable
component of system load $\hat{P}_{l}[kT_{t}]$ and the pre-agreed on Net
Scheduled Interchange (NSI) with the neighboring BAs so that

$\displaystyle\hat{P}_{G}[kTt]=NSI[kTt]+\hat{P}_{L}[kTt]$ (1)

Closer to real-time, power plants participating in frequency regulation
$P^{reg}[kT_{s}]$ adjust their governor set points $\omega^{ref}[kT_{s}]$ to
compensate actual load $P_{L}[kT_{s}]$ and Net Actual Interchange
$NAI[kT_{s}]$ so that the sum of generation scheduled in a feed-forward manner
and regulation power balance actual NAI and actual load each $[kT_{s}]$ time
interval, typically minute-by-minute.

$\displaystyle P_{G}[kTt]+P_{G}^{reg}[kTs]=NAI[kTs]+P_{L}[kTs]$ (2)

Shown in Fig. 2 is the mismatch between predicted demand and generated and
predicted generation for the SPP system. This mismatch is generally caused by:

  1. 1.

not knowing the actual load $P_{L}[kT_{s}]$ SPP incorrect
prediction/measurement of the load as can be seen in Fig. 2), violating the
power balance stated in Eqn. (2)

  2. 2.

and/or by the deviations of net tie-line flow interchange $\Delta F[kT_{s}]$
defined as

$\Delta F[kT_{s}]=NAI[kT_{s}]-NSI[kT_{t}]$ (3)

Figure 2: SPP Generation and demand for 2017-10-14

Frequency deviation from nominal is generally affected by the mismatch of
production and generation, as well as by the net interchange deviations of
$NAI[kT_{s}]$ from the net scheduled interchange $NSI[kT_{t}]$. Combining Eqn.
(1) and Eqn. (2) and decomposing power imbalance into:

  1. 1.

imbalance created by the deviations of actual load $P_{L}[kT_{s}]$ from the
predicted load $\hat{P}_{L}[kT_{t}]$ denoted as $ACE_{f}[kT_{s}]$

  2. 2.

power imbalance $\Delta F[kT_{s}]$ between $NSI[kT_{s}]$ and $NAI[kT_{S}]$,
denoted as $ACE_{interchange}[kT_{s}]$, we obtain:

$ACE[kT_{s}]=ACE_{f}[kT_{s}]+\Delta F[kTs]$ (4)

where $ACE_{f}[kT_{s}]$ is the frequency part of the ACE and $\Delta
F[kT_{s}]$ is the interchange part of the ACE.

To compensate for internal load deviations, nonzero generation by the power
plants participating in regulation is provided in a feedback manner by
adjusting the set points of their governors $\omega^{ref}[kT_{s}]$ as

$P_{G}^{reg[kT_{s}]}=-10b\Delta f[kT_{s}]=\beta\Delta f[kT_{s}]$ (5)

Determining by how much to change the set point of governors so that the
$P_{G}^{reg}[kT_{s}]$ is produced for frequency regulation purposes requires
to know $\beta$ of the BA. In what follows we briefly summarize the physical
interpretation of this coefficient known as the BA frequency bias. The actual
amount of $P_{G}^{ref}[kT_{s}]$ that must be compensated by the BA is
determined by the very hard to know $\Delta P_{L}[kT_{s}]$. The amount of
regulation reserve needed to produce this power cannot be known in a feed-
forward way, say day-ahead or hour-ahead. However, by measuring frequency
deviations $\Delta f[kT_{s}]$ and using frequency bias $\beta$ one can
estimate at least its hourly bounds. This is one of the main reasons we seek a
method for estimating time-varying $\beta[kT_{t}]$, so that the regulation
reserve is scheduled ahead of time and used in a feedback manner in between
dispatch time intervals $[kT_{t}]$.

BA loads are much more dynamic and harder and make it difficult to perform
accurate estimation of frequency bias $\beta$ in systems with a variety of
intermittent distributed energy resources (DERs) such as wind and solar power
plants, as well as the price-responsive demand. $ACE_{frequency}$ portion of
$ACE$ is complex to obtain and control under the presently made assumptions.
As AGC input is ACE there is the need for accurate frequency bias $\beta$
which reflects CA droop, namely the sensitivity of the frequency deviations to
deviations in power imbalances, $\beta=10b$, units of $\beta$ in
$\frac{MW}{Hz}$ or units of $b$ in $\frac{MW}{.1Hz}$.

To compute $ACE_{f}$ portion of $ACE$ the knowledge of $\beta$ is quite
critical. Notably, the need for supplemental control is significantly smaller
than it would be if it were not for the natural self-regulation by most of the
loads. The load power consumption is greatly dependent on both voltage and
frequency deviations, but these are not typically modeled [1]. In the changing
industry with price-responsive demand it becomes quite important to account
for this effect, a major open problem. The intermittent power outputs also
require much more dynamic dispatch of units than in the past, and this
directly affects the frequency bias of the CA.

##  III Optimal Frequency Bias Estimation Method

In this section we first establish the basic structure of the BA model needed
to perform data-enabled parameter estimation, and then propose an otpimization
method which draws on this structure.

###  III-A Basic structure of the BA mode

Basic structure of the BA model is obtained by utilizing the droop
characteristics of individual regulating power plants and then aggregating
them without taking into account the transmission grid. This is the most
common assumption made for modeling bulk power system; for more detailed
models, see [6, 7]. To explain, we first summarize structure of a single
generator droop and then derive an aggregate droop comprising all regulating
power plants.

####  III-A1 Droop characteristic of a single regulating power plant

We derive governor-turbine-generator (GTG) power plant droop by combining the
dynamical equations of a generator and turbine, with the governor dynamical
equation [6, 8]:

$\displaystyle\dot{\theta}_{G}$ $\displaystyle=\omega_{0}\omega_{G}$ (6)
$\displaystyle J\dot{\omega}_{G}+D\omega_{G}$
$\displaystyle=P_{T}+e_{T}a-P_{G}$ (7) $\displaystyle T_{u}\dot{P}_{T}$
$\displaystyle=-P_{T}+K_{t}a$ (8) $\displaystyle T_{a}\dot{a}$
$\displaystyle=-ra-\omega_{G}+\omega_{G}^{ref}$ (9)

The mechanical power $P_{T}$ applied to the turbine-rotor shaft is controlled
by the valve position $a$ according to Eqn. (8). Constant $T_{u}$ is the
turbine time constant while $K_{t}$ is the control gain. Valve position $a$
changes in feedback manner by responding to the deviations of measured
frequency at time $t$ from the set point of the governor at time $[kTs]$
$\Delta\omega=(\omega(t)-\omega^{ref}[kT_{s}])$, according to Eqn.(9). The
dynamics of frequency deviation $\omega_{G}$ from nominal frequency
$\omega_{0}$ is determined by the imbalances between the mechanical power
$P_{T}$ and electrical power generated $P_{G}$, and is further damped because
of damping $D$. Different power plant inertia $J$ result in different
frequency dynamics. The governor set point is changed in response to tertiary
level economic dispatch commands each $[kT_{t}]$, and it is re-adjusted on
power plants participating in AGC each $[kT_{s}]$. The primary control of the
governor is tuned so that valve dynamics settles in response to fast small
fluctuations to $\omega_{G}^{ref}[kT_{s}]$. Parameters in this GTG dynamical
model are derived by linearizing more complex nonlinear power plant dynamics
[8]. Important for understanding droops is the definition of parameter $r$ in
Eqn.(9), which determines how well is valve control tuned. If done right, the
fast dynamics evolving at continuous time settles and at times $[kT_{s}]$
results in the well-known droop of the GTG as follows [8]

$\omega_{G}[k]=(1-\sigma D)\omega_{G}^{ref}[k]-\sigma P_{G}[k]$ (10)

where GTG droop constant is defined as

$\sigma=\frac{\delta\omega_{G}[k]}{\delta P_{G}[k]}$ (11)

Understanding this three-way relationship given in Eqn. (10) is critical for
understanding how frequency is controlled in today’s bulk power systems by the
conventional AGC.

###  Frequency bias and regulation of a single BA

It can be seen from Eqn. (10) that the steady-state frequency deviation of
generator $j$ in area $I$ $\omega_{G,j}^{I}=-\sigma_{G,j}^{I}P_{G,j}^{I}$ when
the set point $\omega_{G,j}^{I,ref}$ is not changed. Since the steady-state
frequency is the same in the entire area $\omega^{I}$, it can be derived by
summing the droop characteristics of all regulating units $j\in I$ a single
aggregate area droop characteristic of the form

$\displaystyle\omega^{I}[kTs]$
$\displaystyle=\alpha^{I}[kTs]\omega^{I,ref}[kTs]-\sigma^{I}[kTs]P^{I}[kTs]$
(12)

where the aggregate natural response is the sum of the natural responses of
all units and $\alpha^{I}[kT_{s}]=(1-\sigma^{I}D^{I})[kT_{s}]$

$\displaystyle\beta^{I}[kT_{s}]$ $\displaystyle=\Sigma_{j\in
I}\frac{1}{\sigma_{j}}$ (13)

and $\alpha=$ The aggregate reference frequency $\omega^{I,ref}$ satisfies

$\displaystyle(1-\sigma^{I}D^{I})\omega^{I,ref}$ $\displaystyle=\Sigma_{j\in
I}(1-\sigma_{j}D_{j})\omega_{j}^{ref}$ (14)

### AGC in two interconnected BAs

Consider now an interconnected system comprising multiple BAs, as shown in
Figure 1. Equation (12) can be -written for any area $S$ as

$\displaystyle\omega_{S}[kTs]$
$\displaystyle=\alpha[kTs]\omega_{S}^{ref}[kTs]-\sigma[kTs]P_{S}[kTs]$ (15)

However, in multi-area interconnected system an additional complexity arises
because the net power generation $P^{S}[kTs]$ of any area $S$ must balance
both its own load $P_{L}^{S}[kT_{s}]$ and the actual net interchange with the
neighbouring BAs $NAI^{S}[kT_{s}]$. This leads to the $ACE^{S}[kT_{s}]$ which
can be decomposed into two components, one caused by the internal load
deviations and the other caused by the deviations in net tie line flows from
schedules. As result, for the case of two-control areas one obtains

$ACE^{S}[kT_{s}]=ACE_{f}^{S}[kT_{s}]+ACE_{interchange}^{S}[kT_{s}]$ (16)

where $ACE_{interchange}^{S}[kT_{s}]=\Delta F^{S}[kT_{s}]$, for any $S$
comprising multi-area inteconnected system.

###  III-B Area frequency bias estimation method

Eqn. (15) represents an aggregate model of single BA with a very distinct
structure. We formulate next inverse optimization problem. Using provided SPP
data, natural response of area $\beta^{I}[kT_{t}]$ is estimated, by viewing it
as a slower varying parameter at times $kT_{t}$ given much faster state
frequency state measurements $\omega^{I}[kT_{s}]$ and area generation
$P^{I}_{G}[kT_{s}]$. Frequency is related to angular frequency as $w=2\pi f$.
Eq. ( 15) can be posed as the problem of parameter calibration through
multiple linear regression. Coefficients of interest
$\alpha_{S}[kTs],\sigma_{S}[kTs]$ are obtained through minimization of the
cost function

(17)

This formulation of regression objective function, where optimization is
performed with respect to L2 norm, is called ordinary least squares (OLS)
multiple regression. OLS has a closed form solution

$\displaystyle\begin{bmatrix}\alpha\\\
\sigma\end{bmatrix}=\left(\begin{bmatrix}f_{s}^{ref}[k]\\\
-P_{S}[k]\end{bmatrix}\begin{bmatrix}f_{s}^{ref}[k]\\\
-P_{S}[k]\end{bmatrix}^{T}\right)^{-1}\begin{bmatrix}f_{s}^{ref}[k]\\\
-P_{S}[k]\end{bmatrix}f_{s}[k]$ (18)

and through solving closed form, we obtain coefficients $\sigma$ and $\alpha$.

Decision variables $\alpha$ and $\sigma$ are changing, so minimizing over the
whole day (24h or 1440 minutes) as in (Eq. 17) we use (Eq. 18, and observe
changes of variables of interest, minute by minute in the next section.

##  IV Numerical results using SPP system

In the data provided to us, we were given the value of frequency bias used for
the adjustment of frequency part of the ACE. This value is constant, computed
annually and provided by NERC. Thus, its crude and constant value (for the
year of 2017, it was decided to be $\beta=409MW/0.1Hz$) cannot be used for
estimation of the load $P_{L}[kTs]$ at the timescale of the secondary
frequency control. Results in this section show how frequency bias value
changes through the day. We show incentive for adoption of time dependent
frequency bias and its use for estimation of load deviations. Lastly, we show
influence of interchange portion of ACE on the scheduling of reserves.
Cooperative control and right information exchange between BAs, as we argue,
would thus help in reducing costs of regulation reserves and give us better
understanding of how much storage will be needed.

###  IV-A Data provided by the system operator

SPP provided us with the data summarized in the table I. This data is sampled
on a minute basis.

Summary of available data
---
Data |  Short description
$\Delta T[min]$ |  interchange part of ACE in MW
$ACE_{f}[min]$ |  frequency part of ACE in MW
$f_{ref}[min]$ |  scheduled frequency in Hz
$f[min]$ |  measured frequency in Hz
$P_{G}[min]$ |  actual generation of the system
$NAI[min]$ |  net actual interchange
TABLE I: Provided data

###  IV-B Optimal frequency bias daily values

The droop (Eq. 10) has such a structure that poses itself naturally for
calibration of its unknown parameters through different data methods. For our
purposes, we’ve used OLS multiple linear regression. Optimal value of
frequency bias indicates that this value changes through the day as can be
seen on the (Fig. 3, 4 and 5). Opposite to the constant value of $\beta$ we
were provided by SPP, we observe that during the night, between 0-300 minutes
(midnight-5am) frequency bias has a dip. From 5am onward, there is a rise,
which settles around noon until 8pm, when trend of $\beta$ goes down. Between
(Fig. 3, 4 and 5) we see slight differences (e.g. morning rising slope),
likely related to (Fig. 3 being Saturday, and later two Tuesday and Thursday.

Figure 3:  Optimal bias for 10-14-2017 on a one minute basis  Figure 4:
Optimal bias for 10-24-2017 on a one minute basis  Figure 5:  Optimal bias for
10-26-2017 on a one minute basis

##  V Impact on secondary regulation reserves annual cost

Using the estimate of frequency bias, learned through the knowledge of past
generation and frequency curves, one can obtain estimate of load deviations.
Up to our knowledge this is one and only way.

$\Delta P_{L}[kTs]=-10\beta[kTs]\Delta f[kTs]$ (19)

Figure 6: Regulation reserves boundaries compared to estimated load deviation
on secondary time scale for 2017-10-14 Figure 7: Interchange $\Delta F$ part
of ACE for the 2017-10-14 Figure 8: Regulation reserves boundaries compared to
ACE on secondary time scale for 2017-10-14

Regulation-up and regulation-down have the highest market clearing prices. We
observe on (Fig. 8) how spill of interchange power through $\Delta F$ part of
ACE prevents tighter bounds of the day ahead regulation reserves. If $NAI$
matches $NSI$ (Fig. 7) tighter bounds would then mean decrease of scheduled
reserves by $200W$ on average per minute, which translates to roughly
$\$2.88\cdot 10^{6}$ assuming average cost [12], of the up $\$10$ and down
$\$10$ regulating reserves. As can be seen in state of the market SPP annual
reports trend of average regulation prices has been slightly increasing, but
the the real benefits of the cooperative BAs control is not for the whole
year, but for the spring and autumn months, when the wind production is high
and the load is low. In these months (e.g. March, April, May and September,
October, November), there is $33\%$ increase in regulation up reserves.
Tighter bounds, possible through information exchange between BAs and
decentralized control could potentially land in annual $\$259.2\cdot 10^{6}$
of savings in previously mentioned months.

##  VI Conclusion

Dynamic changes of frequency bias are happening throughout the day. Using the
droop three-way relationship, we formulate optimization problem, which
provides us with the optimal frequency bias value, along with the damping
coefficient of the system. Both of these quantities change with time. Current
practice where droop is constant should be revised, as there are clear
economic incentives for change and participation of demand-response.
Understanding wind penetration influence on regulation price reserves during
the year incentivizes for cooperation between BAs so that tighter bounds on
regulation reserves schedules can be imposed. In other words, depending on
what you monitor, estimate, how information exchange is performed and if BAs
help each other, one can integrate demand-response and in the process gain
value.

Future research would go in the direction of better understanding cooperation,
and information exchange between BAs and how taking into account for
electrical distances inside BAs influences the estimation of the load.

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