Fast Primary Frequency 

Response Using Coordinated 

DER and Flexible Loads: 

Framework and ResidentialScale Demonstration 

Preprint

Blake Lundstrom

National Renewable Energy Laboratory 

Sourav Patel, Sandeep Attree, and

Murti V. Salapaka

University of Minnesota 

To be presented at the 2018 IEEE 

Power and Energy Society General Meeting 

Portland, Oregon

August 5–9, 2018

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Fast Primary Frequency Response using

Coordinated DER and Flexible Loads: Framework

and Residential-scale Demonstration

Blake Lundstrom1, 2, Sourav Patel2, Sandeep Attree2, and Murti V. Salapaka2

1National Renewable Energy Laboratory, Golden, CO, USA

2Department of Electrical and Computer Engineering, University of Minnesota-Twin Cities, Minneapolis, MN, USA

Abstract—Methods for supporting grid frequency stabilization

using widespread flexible load and DER are becoming essential

as the portion of synchronous generation decreases. This article

develops a framework for providing fast primary frequency

response that leverages deferrable loads and DER coordinated

using high-speed measurements in real time to ensure that

local objectives are met. The framework is demonstrated in a

residential-scale experiment with four household appliances and

an inverter. The effectiveness of an example load management

algorithm is demonstrated using three underfrequency anomaly

test cases. The experimental data corroborates that in all cases,

the entire coordinated response, from detection and measurement

of a frequency anomaly to completed coordinated net-load

response, is completed within 143 ms (~8.5 cycles).

Index Terms—fast frequency response (FFR), frequency response, primary frequency control, flexible load, demand response, distributed energy resources (DER), coordinated response

I. INTRODUCTION

The integration of renewable generation and DER into the

electric system continues at a fast pace and is posed to be a

permanent trend. These resources are typically coupled to the

grid using power electronics rather than rotating generators.

The reality of more inverter-coupled generation in addition

to related trends is resulting in decreased system inertia and

posing challenges toward stabilizing grid voltage and frequency. As such, methods for stabilization of grid voltage and

frequency will need to incorporate increased participation from

DER. Continuing deployment of information and communication infrastructure is enabling flexible and controllable load to

become effective resources for grid frequency regulation when

aggregated and coordinated together to create virtual power

plants.

There are multiple distinct frequency control levels in power

systems. Primary frequency control is typically implemented

locally at each generator and works autonomously, usually

within 1-3 s after a disturbance, by using droop controllers to

adjust speed or output of the generator. Secondary frequency

control includes Automatic Generation Control, in which a

central controller adjusts the active power output of multiple

generators in an area to restore the frequency and power

interchanges with other control areas to their target values;

this typically occurs within 4 s to 5 min.

The authors thank the U.S. Advanced Research Projects Agency-Energy

(ARPA-E) for supporting this research via grant no. DE-AR000071. This

work was supported by the U.S. Department of Energy under Contract No.

DE-AC36-08-GO28308 with the National Renewable Energy Laboratory.

Demand side resources (flexible load) are already being

used for both primary and secondary frequency control in

power systems. In particular, thermostatically-controlled loads

(TCLs)–including heating, ventillation, and air conditioning

systems, electric water heaters, and refrigerators–and electric

vehicles are often employed because of their inherent energy

storage properties [1]. The use of populations of flexible load

for primary frequency control generally involves uncoordinated, autonomous controllers that measure local frequency

and adjust the demand of the local load, such as those

developed in [2], [3]. Such independent uncoordinated action

in aggregate has been shown to provide primary frequency

response similar to that of a synchronous generator [2]. It

has been shown that system frequency disturbances propagate

through distribution systems much faster than transmission

systems [4]; thus, a collection of devices connected to a

distribution system will observe, and can respond to, frequency

disturbances just as fast (if not faster) than a traditional

frequency response asset (e.g., sync. generator) connected at

the same location would.

A number of approaches exist for coordinating and controlling flexible load for secondary frequency control [5]–

[10]. Centralized methods for leveraging flexible load within

commercial buildings with response at the 2-s [5] and 4-s [6]

timescales have been experimentally demonstrated. Decentralized optimal load control strategies with response times on the

multiple seconds timescale are developed in [7]–[9]. A hierachichal, communication and control platform that classifies

appliances and demonstrates the scheduling and direct load

control of appliances on a five min. interval is given in [10].

This article develops a unique solution in which fast (within

10 ac cycles) frequency reserves (i.e., a step response for

primary frequency control) are provided by a group of flexible

loads and controllable DER that are coordinated together in

real time. In contrast to existing primary frequency control

approaches, individual resources do not act autonomously

based on local frequency measurements and a priori control objectives. Here, individual loads communicate with a

centralized master controller that reaches an optimal dispatch

decision on how to dispatch controllable DER and deferrable

load. The framework utilizes high-speed local measurements

and ensures that local objectives, which could potentially

vary over time, are met. The method offers many of the

same coordination and optimal control advantages achieved

by approaches for secondary frequency control, but does so on

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 1
a much faster timescale that is suited for primary frequency

control. The framework is developed for use at a building or

plant scale where flexibility across multiple building net-load

devices can be leveraged, but is also well suited for inclusion in

hierarchical or aggregated schemes. The developed approach is

experimentally verified using a residential-scale testbed and an

example control objective of minimizing the total load deferred

while meeting the frequency response target. The platform

is flexible in that it is possible to utilize other load/DER

combinations and control objectives.

II. PROBLEM DESCRIPTION

A. Problem Description and Terminology

The problem objective is to use a net-load unit, defined as

DER and load behind a typical electric customer meter, to

provide a rapid (our goal is <10 ac cycles) automated step response to grid frequency disturbances by controlling the unit’s

DER and load in a coordinated and optimal fashion. Thus,

each unit must detect grid frequency; collect measurements

from constituent DER and flexible loads; make an optimal

decision based on measurements, local objectives (e.g., load

priorities and services compensation from the local electric

system operator), and global objectives (e.g., change in grid

power import/export from each unit towards stabilization of

system frequency); and actuate DER and flexible loads, all

within 10 ac cycles.

For an example net-load unit, we consider the gridconnected smart residential building shown in Fig. 1. This unit

includes a variety of typical household loads, grouped into deferrable and non-deferrable categories (as selected by the user),

and an inverter, all connected on a common bus. Let PL(t) =

{PL,i(t)}

NL

i=1 represent the set of power demands from the

NL non-deferrable loads and PLd(t) = {PLd,i(t)}

NLd

i=1 represent the set of time-varying power demands from the NLd

deferrable loads. We assume that deferrable loads are turned

on or off when issued a command q

∗

Ld,i(t) ∈ {0, 1} such that

the effective power is PˆLd,i(t) = q

∗

Ld,i(t)PLd,i(t). The power

supplied by the inverter is PG,inv(t) where PG,inv(t) > 0

when the inverter is supplying power to the AC bus and

PG,inv(t) < 0 when the inverter is absorbing power from

the AC bus. The inverter must be operated within its ratings

P

min

G,inv ≤ PG,inv(t) ≤ P

max

G,inv and could be uni-directional

(P

min

G,inv = 0) or bi-directional (P

min

G,inv < 0). The inverter’s response to a dispatch command P

∗

G,inv is very fast; here, we experimentally show an inverter closed-loop response of ~7 cycles. The power imported from the grid is given by PG,grid(t)

and has capacity limits P

min

G,grid ≤ PG,grid(t) ≤ P

max

G,grid. The

power consumed or supplied at each point and deferrable load

commands are all assumed to be time-varying quantities; the

time dependence (t) is dropped for ease of notation in some

cases. By Kirchoff’s laws, the (ideal) power consumed from

the grid is: PG,grid =

PNL

i PL,i +

PNLd

i PLd,i − PG,inv.

B. Example Control Objective

The framework developed in this paper is designed to be

flexible such that it can be applied to a variety of net-load

Fig. 1. Residential-scale power system with net load control. Deferrable and

Non-deferrable load categories are determined by the user.

unit configurations and control objectives. For purposes of

demonstration, we choose an example control objective of

minimizing the total amount of load that must be deferred

while providing frequency response; such an example represents a common scenario. However, a variety of other control

objectives and implementations (e.g., prioritizing a set of loads

while providing frequency response) can also be supported.

We begin by assuming that a net-load unit is incentivized

by the local system operator or a local aggregator to provide

a net-load response upon detection of a frequency anomaly

event, which is defined to be when f(t) < (fnom − δf,low)

or f(t) > (fnom + δf,high) where fnom is the nominal grid

frequency and δf,low and δf,high are nominal frequency limits

(in Hz) below and above fnom. When frequency outside these

limits is detected, the net-load unit responds such that:

P

∗

G,grid(te +τ ) = (1−η sign(f (te ) − fnom))PG,grid(te) (1)

where te represents the instant in time in which a frequency

anomaly event is detected, τ is the required unit response

time, η ∈ [0.0, 1.0] is a parameter defining what percentage

of a net-load unit’s power output power must be curtailed

or increased, f(te) is the measured grid AC frequency at

t = te, and PG,grid is the measured power transfer at the

grid point of common coupling (PCC) and P

∗

G,grid is the grid

power command. Given an incentive, the net-load unit operator

(e.g., a homeowner) then desires to provide this net-load

response with minimal inconvenience (e.g., avoid curtailment

of loads). Thus, the objective is to provide the desired netload response by prioritizing the dispatch of the inverter where

if the inverter’s response cannot satisfy the overall net-load

response requirement, the minimum possible deferrable load

is curtailed to meet the requirement. This objective can be

formalized as:

max

q

∗

N

XLd

i=1

PˆLd,i(te + τ ) (2)

subject to PG,grid(te + τ ) = P

∗

G,grid(te + τ ) (3)

P

min

G,inv ≤ PG,inv(t) ≤ P

max

G,inv (4)

P

min

G,grid ≤ PG,grid(t) ≤ P

max

G,grid (5)

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 2
Again, the developed framework is flexible so that other

objectives, such as load prioritization by the homeowner, could

be implemented instead.

The parameter η in equation (1) is chosen to be small and

net-load unit operators only commit to η values small enough

to guarantee a feasible solution to (2)-(5) exists. A feasible

solution to the optimization problem indicates that the new

grid power command in equation (1) can be achieved using

the capacity available in the net-load unit’s flexible load and

DER.

III. METHODOLOGY AND IMPLEMENTATION

The developed framework: 1) implements a centralized

master controller for coordinated, optimal dispatch of all netload resources; 2) realizes and employs a high-speed communication network (e.g., local area network) between each

net-load device and the master controller; 3) provides flexible

communication interfaces to the control layer at each netload resource; and 4) implements physical interfaces for highspeed measurement and connection/disconnection of electrical

loads. Each of these aspects of the framework are discussed

in more detail in the following subsections. The sequence of

operations executed in response to each frequency anomaly

event is summarized in Section (III-E).

A. Centralized Master Net-load Controller

The central net-load controller leverages high-speed measurements from each net-load resource and an algorithmic

implementation of the control objective to provide the optimal

and coordinated dispatch of net-load resources for primary

frequency response. The principal requirement of the master

controller is that it be able to communicate to all net-load

resources and solve the algorithm associated with the control

objective efficiently. For the demonstration case shown, a

Raspberry Pi 3 ARM-based controller met these requirements

well, though more powerful local controllers can be leveraged

for larger problems.

In the subsequent demonstration, the optimization problem described by (2)-(5) is solved using Algorithm 1. The

algorithm determines the optimal inverter set point P

∗

G,inv

and the optimal set of deferrable load on/off set points

{q

∗

Ld}

NLd

i=1 that minimize the total amount of load that must

be deferred, while achieving the desired grid import power

P

∗

G,grid(te + τ ) as closely as possible. This algorithm is

designed for use with residential buildings in which deferrable

loads are often appliances that can be shut off quickly (using

load control interfaces), but may not be able to be turned

on and a known operating point achieved quickly. Thus, in

the subsequent demonstration case, both deferrable load and

inverter generation are used for response to underfrequency

events, but only inverter generation is used for response to

overfrequency events. It can be shown (though a proof is

beyond the scope of this paper) that this algorithm will achieve

the optimal solution to (2)-(5) when: 1) the net-load unit

is equipped with a sufficient amount of deferrable load and

inverter-based generation; 2) using a η reasonable for the

net-load resources available (see discussion in Section II-B);

and 3) an underfrequency event occurs. The implemented

algorithm leverages dynamic programming to efficiently and

dynamically determine the cost associated with each possible

load deferrment action from the bottom up. It can be shown

that a greedy strategy will not work here; the three test cases

in Table I provide counter-examples for a few of the possible

greedy choices.

Algorithm 1 Net-Load Management Control Implementation

INPUT: PLd[1..NLd], PG,inv, P min

G,inv, P maxG,inv, PG,grid, η, f(te), fnom

DO:

Calculate PG,grid∆ = η sign(fnom − f (te ))PG,grid

//Dynamically build the array of all feasible load actions:

Let PLdo[1..NLd] be an array of objects with entries PLdo[i].power = PLd[i] and

PLdo[i].idx = i for all i = 1..NLd

Let PLdp[1..NLdp] be an array of all objects in PLdo where PLdo[i].power > 0

sort PLdp in ascending order by PLdp[i].power

Let PLa be an empty array //all possible total load set points

PLa[1] = 0 //corresponds to the case where no loads are curtailed

PLa[2] = PLdp[1].power //case of only the smallest load is curtailed

for i = 1..(NLdp − 1)

for j = 1..2

i

PLa[2i + j] = PLa[j] + PLdp[i + 1].power

if PLa[2i + j] > PG,grid∆

//all feasible load actions needed for this PG,grid∆ have been added

break out of both for loops

Let NLa = |PLa| be the length of PLa

//first, allocate all available inverter capacity

if (P max

G,inv − PG,inv) ≥ PG,grid∆

P

∗

G,inv = PG,inv + PG,grid∆

PG,grid∆ = 0

else

P

∗

G,inv = P max

G,inv

PG,grid∆ = PG,grid∆ − P

∗

G,inv

//if further reduction required, curtail the minimum total amount

//of load that achieves the required grid power reduction

if PG,grid∆ > 0

for i = 1..NLa

if (PLa[i] ≥ PG,grid∆) or i = NLa

//build the set of all q

∗

Ld,i based on the resulting optimal PLa[i]

Let ix[1..Nb] be an array with entries corresponding to the Nb-bit binary

representation of (i − 1) with the LSB in ix[1] //e.g. 4 = [0, 0, 1]

for j = 1..Nb

idxorig = PLdp[j].idx //get the index corresponding to

load PLdp[j] in the original array

q

∗

Ld[idxorig] = (1 − ix[j]) //curtail cmd. format to on/off format

PG,grid∆ = PG,grid∆ − PLa[i]

break

//reduce inverter output by any load curtailment overshoot to

//achieve the minimum deviation from desired grid power

if PG,grid∆ < 0

P

∗

G,inv = P

∗

G,inv + PG,grid∆

if P

∗

G,inv < P min

G,inv

P

∗

G,inv = P minG,inv

OUTPUT: P

∗

G,inv, {q

∗

Ld,i}

NLd

i=1

B. Communication Network

The framework leverages an Internet Protocol communication network for ease of integration and scalability. The

subsequent demonstration used a wired ethernet IP network,

but the framework also tested successfully with wireless

communication. The ZeroMQ distributed messaging platform

[11] over the Transmission Control Protocol (TCP) was used

to communicate information between devices.

C. Flexible Control Layer Communication Interfaces

Each individual load or DER is interfaced to the control

layer through a flexible control layer communication interface

(CLCI). In the subsequent demonstration, Raspberry Pi 3

controllers were used as CLCIs. This interface publishes

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 3
measurements and statuses from each net-load resource onto

the ZeroMQ message bus and subscribes to requests and

commands from the master controller on the same message

bus. In the subsequent demonstration, the inverter’s CLCI and

local controller are interfaced using serial communication and

each deferrable load CLCI is interfaced using analog signals.

D. Physical Deferrable Load Control Interfaces

While many commercial off-the-shelf solutions for controlling loads already exist, none that natively support on/off

control of loads and reporting high-speed measurements at

the required speed (<10 ac cycles) were found; most existing

solutions are designed for slower speeds and in many cases do

not include sensors. Thus, custom physical interfaces for highspeed measurement and connection/disconnection of electrical

loads are developed. An example of such an interface is

pictured in Fig. 2; this interface is designed for single-phase

120V, 15A nominal loads and a second version designed

for North American split-phase 240V, 30A nominal loads is

also developed. These deferrable load control interfaces are

simple to use by connecting in series with the appliance or

load’s normal house wiring connection using standard NEMA

connectors. Each interface includes a solid state relay; fuses

for overcurrent protection; high-bandwidth (~200 kHz) voltage

and current sensors; analog RMS-to-dc converters; and a

CLCI for processing of measurements and interfacing with

the control layer.

Fig. 2. Physical Deferrable Load Interface

E. Sequence of Operations

1) Grid PCC Measurement Controller: Detects grid frequency anomaly and communicates (via its CLCI) f(te)

and PG,grid(te), to the master controller

2) Master Controller: Commands all deferrable load and

DER resources to provide their latest operating power

3) Each Deferrable Load Controller: Sends (via its CLCI)

PLd,i(te) to the master controller

4) Each DER Controller: Sends (via its CLCI) latest operating power (e.g., PG,inv for inverter) and operating

capacity limits (e.g., (−P

max

G,inv, P maxG,inv)) to the master

controller

5) Master Controller: Uses measurement inputs and solves

optimization problem (2)-(5) to determine P

∗

Ld,i for each

deferrable load and P

∗

inv for the inverter. Sends out

dispatch commands

6) Each Deferrable Load Controller and Inverter Controller:

Executes dispatch command received from master

IV. RESIDENTIAL-SCALE DEMONSTRATION

A residential-scale demonstration using a net-load unit

consisting of an inverter and four household appliances in the

Energy Systems Integration Facility at the National Renewable

Energy Laboratory was completed. As the primary aim of the

experiment is to demonstrate the performance of the framework and the particular load management algorithm implemented, the four appliances were all considered as deferrable

loads and no non-deferrable loads were included. The specific

devices used were a 120V combination refrigerator/freezer

(General Electric Profile PSQS6YGY); bank of fifteen 120V

compact fluorescent and incandescent light bulbs; a 120V

plug-in electric heater; a 240V combination Range/Oven

(Maytag MER8674); and a 120V, 1 kW (P

max

G,inv = 1000 W)

inverter (custom). As can be seen from the current traces in

the top plot of Fig. 3, the fridge and bank of light bulbs

both exhibit non-linear loading characteristics, while the plugin heater and oven were both primarily resistive in nature.

We purposely included TCLs and other loads to demonstrate

that both types can be leveraged by the framework. Given

the fact that PG,grid =

P

i PLd,i − PG,inv for this example

demonstration, no Grid PCC Measurement Controller was

needed and instead grid frequency anomaly detection was

implemented using a quadrature phase locked-loop (QPLL)

[12] on the custom inverter (and thus, step #1 of the sequence

of operations was performed by the inverter and steps #1 and

#4 were combined as step #1). Reliabily measuring frequency

during rapid transients and making a control decision within

the desired time limit can be challenging. However, we found

that our QPLL implementation, with a choice of gains that

traded-off overshoot and settling time, provided reliable, settled, measurements within 1-3 ac cycles throughout all rapid

transients we tested. Further background on QPLLs and their

superior performance vs. other frequency detection methods

during grid transients can be found in [12].

As described in Section II-B, the control objective implemented was to minimize the total amount of load deferred

while providing the desired net-load response. The following

test cases examine three scenarios involving the same loads

and inverter, but varying net-load response target (varying η)

and actual load measurements. Each test case began with the

appliances turned on for some time and the inverter exporting

power at ~80% (800 W) of its nominal power rating. A step

in grid frequency from 60 Hz to 59.7 Hz was then initiated.

The inverter detected the frequency anomaly (δf,low = 0.25

Hz was used) as part of step #1 in the sequence of operations

listed in Section III-E. For each test case, the measurements

listed in the “Start” column of Table I were received from each

resource by the master controller. The master controller then

determined final load on/off status and inverter set points as

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 4
Fig. 3. Oscilloscope screenshot showing time series waveform results for Test

Case #3. The top plot shows current meas. for each deferrable load including

the fridge (“I1”, yellow), bank of light bulbs (“I2”, green), plug-in heater

(“I3”, purple), and oven (“I4”, cyan). The bottom plot shows the grid voltage

(pink), calculated grid frequency (green), and inverter current (blue). It can

be seen that the entire net-load response sequence is completed within 142

ms (~8.5 ac cycles).

shown in the “Finish” column based on the results obtained

by executing Algorithm 1.

TABLE I

INPUT CONDITIONS AND FINAL RESULTS FOR TEST CASES

Measured Power (W) at Start and Finish of Case

Test Case #1 Test Case #2 Test Case #3

(η = 0.09) (η = 0.14) (η = 0.17)

Resource Start Finish Start Finish Start Finish

Fridge (I1) 146 146 175 175 147 0

Lights (I2) 411 0 409 0 402 0

Plug-in Heater (I3) 1360 1360 1360 1360 1345 1345

Oven (I4) 2827 2827 2850 2850 2793 2793

Inverter -797 -742 -801 -911 -802 -949

Total 3947 3591 3993 3436 3885 3227

∆PG,grid 9.02% 13.95% 16.94%

Response Time (ms) 142.6 141.8 141.9

In Test Case #1, 9% (355 W) of the total measured net-load

needed to be deferred. This amount was more than the smallest

load and the remaining generation capacity of the inverter

and so the next largest load (411 W) had to be curtailed.

However, once curtailed, constraint (3) determines that the

optimal solution should defer as nearly 9% of the net-load as

possible. Thus, Algorithm 1 results in inverter’s output being

reduced. In Test Case #2, 14% (559 W) of the measured netload was to be curtailed. This required at a minimum that the

second largest load (409 W) be curtailed. However, after that

load was curtailed the smallest load (175 W) could also have

been curtailed, but it was not because the inverter’s remaining

capacity (200 W) was sufficient to meet the desired set point

and the main objective (2) is to minimize the amount of load to

be curtailed when possible. Test Case #3 requires a little larger

net-load curtailment (17%, 660 W) than Test Case #2 that

makes the lights curtailment and inverter output increase no

longer sufficient to meet the net-load curtailment requirement.

Thus, the optimal solution is to curtail both of the two

smallest loads and then meet the remaining net-load reduction

requirement using an increased inverter output. In all three test

cases, the entire sequence of operations, from detection and

measurement of a frequency anomaly to completed actuation

of coordinated net-load response, is completed within 143 ms

(~8.5 cycles). An oscilloscope screenshot showing waveform

results for Test Case #3 is shown in Fig. 3.

V. CONCLUSIONS

This paper developed and demonstrated a framework for

provision of fast primary frequency response using coordinated

deferrable loads and DER. This approach has the unique aspect

of providing a fast response appropriate for primary frequency

control, but doing so using a group of net-load resources

that is coordinated in real-time in order to maximize local

objectives. The framework is flexible such that a variety of

deferrable load and DER devices can be controlled using

configurable CLCIs and a variety of objectives for the optimal

net-load management scheme can be defined. The use of this

approach in aggregation will enable wide-spread flexible load

and DER to provide significant grid ancillary services that

help stabilize the emerging low-inertia grid. Future work will

further investigate the use of this approach on a larger scale

and its coordination with existing synchronous generators

providing ancillary services.

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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 5