Synchronised Power Scheduling of Widely Distributed Refrigerators

using IoT

M. R. Zavvar Sabegh and C. M. Bingham

School of Engineering, The University of Lincoln, Brayford Pool, Lincoln, U.K.

Keywords: Internet of Things (IoT), IP-based Synchronized Wireless Mesh Network, Model Predictive Control (MPC),

Smart Home.

Abstract: The paper proposes an IoT controlled platform to remotely monitor and control appliances in the residential

sector. An IP-based synchronized wireless mesh network is implemented through IoT hardware (based on a

NodeMCU) and Google Sheets to monitor and schedule the operation of aggregated domestic refrigerators

under a Model Predictive Control (MPC) scheme. Benefits afforded by the proposed technique are

investigated through experimental trials from VonShef 13/291 (50W), iGENIX IG 3920 (55W) and Russell

Hobbs RHCLRF17B (50W) domestic refrigerators sited in three different domestic locations in the city of

Lincoln, UK. Results demonstrate the ability to monitor and control widely distributed networks of

refrigerators and adaptively schedule the appliances to reduce peak operational loads and facilitate Demand

Side Response (DSR). Further widespread expansion of the proposed technique would allow for a rapidly

deployed regional DSR strategy to aid grid stability. Ultimately the underlying principles also could be used

for the co-ordinated scheduling of other distributed appliances and equipment, both domestic and industrial.

1 INTRODUCTION

The emergence of IoT and Thermostatically

Controlled Loads (TCLs) can be utilized to facilitate

the implementation of ancillary services and demand-

side response schemes in power systems. A key factor

for improving the management of energy

consumption in the residential sector is by the remote

real-time supervision and control of domestic

appliances. Consequently, the need for remote

aggregated scheduling of widely distributed networks

of domestic appliances is gaining increasing attention

for smart cities (Talari et al. 2017).

Recently published research shows the potential

of accessing home appliances remotely and

implementing intelligent smart home systems based

on web-based and smartphone applications (Pavithra

and Balakrishnan 2015, Vishwakarma et al. 2019, Li

et al. 2019, Mandula et al. 2015, Govindraj et al.

2017, Lokhande and Mohsin 2017, Al-Ali et al. 2017

and Xiao et al. 2018). Vishwakarma et al. 2019

propose the implementation of IoT connections for

home appliances through Google Assistant (voice

commands) as well as World Wide Web services to

switch on/off devices such as fans and lighting etc.

without requiring any physical interaction, for the

elderly or those with disabilities for instance, whilst

Mandula et al. 2015 proposes an IoT based home

automation system using an Arduino microcontroller

for indoor and outdoor remote control of appliances.

It develops an Android-based application to provide

on/off control of six home electrical appliances

namely lamp, AC, fan, refrigerator, TV and washing

machine. Furthermore Li et al. 2019 uses a

STM32F407 embedded development board for

remote environmental monitoring. It shows how the

integration of the STM32F407, Reduced Media

Independent Interface (RMII), Flexible Static

Memory Controller (FSMC) interface and web

development can provide a real-time remote monitor

function. Govindraj et al. 2017 develops an Android-

based application for a smart home automation

system using ThingSpeak for data acquisition and

visualisation, whilst Al-Ali et al. 2017 propose the

inclusion of Big Data in IoT with a unique IP address

in a mesh wireless network of devices and

recommend users to remotely monitor and control

devices and generate on-line bills via a mobile app.

Xiao et al. 2018 employ an ESP8266 and a MCU

STM32F103 microcontroller to realise a home

appliance control system with a mobile terminal for

remote control, whilst Hanumanthaiah et al. 2019

Sabegh, M. and Bingham, C.

Synchronised Power Scheduling of Widely Distributed Refrigerators using IoT.

DOI: 10.5220/0010401200890093

In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2021), pages 89-93

ISBN: 978-989-758-512-8

develop a low-cost smart switch system based on an

Arduino UNO interface to facilitate remote

controllability and cloud analytics. Finally, home

automation using Message Queuing Telemetry

Transport (MQTT) and Raspberry Pi is proposed by

(Upadhyay et al. 2016) to enable measurements of

temperature and humidity. Such systems enable users

to benefit from the remote monitoring and control of

devices at isolated sites. However, more substantial

benefits can be obtained from the coordinated control

of widely distributed home appliances for aggregated

load management and load shed for Demand Side

Response (DSR) purposes. A suitably widespread

mesh network affords the opportunity to schedule and

monitor the operation of distributed domestic

appliances simultaneously to collectively improve

energy efficiency and reduce peak power

consumption on the electrical grid. Here then, the

paper proposes the implementation of a Model

Predictive Control (MPC) scheme, initially proposed

by Zavvar Sabegh and Bingham 2019, to provide the

co-ordinated scheduling of power to domestic

refrigerators of a number of households around the

City of Lincoln, UK. Measurements are taken from

the refrigerators under the IP-based wireless mesh

network and Google Sheets is used for cloud data

acquisition and monitoring. The remaining sections

of the paper are organised as follows: Section 2, a

synchronized wireless mesh network is proposed to

remotely monitor, and schedule domestic

refrigerators; Section 3 describes the experimental

setup to implement the proposed network on three

widely distributed refrigerators using a

microcontroller and Google Sheets. Section 4

presents the result of the experiments for monitoring

and scheduling modes. Section 5 concludes the work

presented in the paper.

2 IP-BASED SYNCHRONIZED

WIRELESS MESH NETWORK

The proposed network shown in Figure 1 consists of

two main parts: the server and client units. Client

units are widely deployed at different sites. Each

client is assigned a unique public IP address to

provide remote access and are used to collect

measurements (e.g. internal fridge temperature,

ambient temperature and the power consumption of

the fridge), send the measurements to the server via

received HTTP requests, receive control instructions

from the server and apply them to the house

appliances (e.g. fridge on/off commands). The server

can be located anywhere with Wi-Fi access and is

used to send control commands and HTTP requests to

the clients using the ‘port forwarding’ feature of the

routers to collect sensor data simultaneously (the

centralized coordinator among the clients) and send

them to the Google Sheets for data acquisition and

monitoring—see Figure 1.

Figure 1: IP-based synchronized wireless mesh network

structure.

3 DEPLOYMENT AND

EXPERIMENTAL SETUP

The study uses a wireless IoT scheme based on a

synchronized wireless mesh network to remotely

monitor and schedule three domestic refrigerators

under Binary Quadratic MPC control to facilitate

DSR load-shedding events. The location of the

fridges is shown in Figure 2. The server

microcontroller is sited at the University of Lincoln,

UK. The test facility components and hardware setup

are given in Figure 2. Trials are conducted using a

SMARTGREENS 2021 - 10th International Conference on Smart Cities and Green ICT Systems

VonShef 13/291 (50W), iGENIX IG 3920 (55W) and

Russell Hobbs RHCLRF17B (50W) domestic

refrigerators. It is important to note that the

RHCLRF17B uses thermoelectric cooling

technology, so no compressor is employed. Each

refrigerator is instrumented with two DS18B20

waterproof sensors to measure the internal and

ambient temperatures, a NodeMCU microcontroller

to implement the Binary Quadratic MPC algorithm

and a TP-Link Smart Wi-Fi Plug (HS110) to provide

on-off control and measure the real-time power

consumption of the refrigerators. Moreover, another

NodeMCU is located at the University of Lincoln as

a server to request the data from clients

simultaneously and send them to Google Sheets.

(a)

(b)

Figure 2: System setup (a) The location of the refrigerators

in the City of Lincoln, UK (b) hardware are fridges for

measurement and control.

4 RESULTS

Measurements are taken with a fixed sampling period

of 60 seconds to show the performance of the IP-

based synchronized wireless mesh network under

conditions of i) the refrigerators operate in isolation

without any scheduling controller to show the

monitoring feature of the proposed network, and ii)

the custom Binary Quadratic MPC algorithm is

implemented for jointly scheduling the operation of

refrigerators in DSR events.

4.1 Trial A: Refrigerators Operate in

Normal Operating Conditions

without a Scheduling Controller

(Monitoring Mode)

An initial experiment is conducted under normal

operating conditions with no co-ordinated MPC

scheduling applied. This effectively shows how the

proposed network can monitor the domestic fridges

remotely. Figure 3 shows examples of 12-hour

experiment trial intervals for each refrigerator’s

internal temperature, the ambient temperature,

individual power consumption and the total

aggregated power consumption.

4.2 Trial B: Responding to DSR Events

using Power Scheduling

Measurements now show the performance of the

synchronized wireless mesh network to investigate

how the widely distributed domestic refrigerators can

respond to a DSR events using the MPC controller

proposed by Zavvar Sabegh and Bingham 2019. The

iGENIX, VonShef and Russell Hobbs refrigerators

are loaded with 12L, 6L and 3.5L of water,

respectively, and the doors remained closed for the

duration of the trials. The prediction and control

horizon parameters used in the MPC are set to 5

samples. Desired upper and lower temperature

setpoints and minimum non-working (minimum

OFF) and working (minimum ON) times per cycle for

each refrigerator, are shown in Table 1. These are

required in order to limit the ON-OFF demand

frequency so as to not unduly stress the compressor.

Table 1: Data for iGENIX, VonShef and Russell Hobbs.

Name

Upper

Band

(ºC)

Lower

Band (ºC)

Minimum

on time

(sec)

Minimum

off time

(sec)

iGENIX 3 0.5 220 240

VonShef 2.5 0.5 260 200

Russell 8 4 380 100

Synchronised Power Scheduling of Widely Distributed Refrigerators using IoT

Figure 3: Refrigerators’ internal temperatures, ambient temperatures, power consumption and total power for trial A.

Figure 4: Refrigerators’ internal temperatures, ambient temperatures, power consumption and total power for trial B.

SMARTGREENS 2021 - 10th International Conference on Smart Cities and Green ICT Systems

Results are given in Figure 4. In this scenario the

Russell Hobbs unit is given preferential access to

power during the DSR event via the weighting

matrices in the MPC controller. DSR demanded with

the total power usage of 60W occurs at t = 90 mins

and lasts for 45 minutes. As can be seen from the

measurements (Figure 4), the refrigerators are able to

respond (virtually) instantaneously to power

shedding events and the peak power consumption is

limited to 60W. Moreover, all the refrigerators

maintain their temperatures within required bounds

before the DSR event whilst the Russell Hobbs unit

remains within temperature limits due to the

additional power supply priority attributed to it by the

MPC.

5 CONCLUSIONS

In this paper, the IP-based synchronized wireless

mesh network is proposed and implemented for

monitoring and co-ordinated scheduling of widely

distributed domestic refrigerators, and contribute to

DSR events using simple ‘port forwarding’ of

domestic routers. The network uses Google Sheets for

data acquisition and monitoring in the cloud. The

proposed methodology readily lends itself to other

house appliances, for instance, heating ventilation

and air conditioning (HVAC) systems, or other TCLs.

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Synchronised Power Scheduling of Widely Distributed Refrigerators using IoT