Design and Implementation of Smart Micro-Grid

and Its Digital Replica: First Steps

A. José Calderón Godoy

and Isaías González Pérez

Industrial Engineering School, University of Extremadura, Avenida de Elvas, Badajoz, Spain

Keywords: Smart Micro-Grid, Digital Replica, Modelling, Renewable Energy, Hydrogen, Automation, Supervision.

Abstract: It is evident the digital transformation that is spreading in more and more areas of science and technology in

recent times, as demonstrated by scenarios such as Smart Grids, the Internet of Things, Cyber-Physical

Systems or the Industry 4.0. This article outlines the first steps followed to develop a research project which

aim is to bring this digitalization to the field of renewable energies and intelligent energy generation and

distribution grids, the so-called Smart Grids (SG). The objective of this project is twofold. On the one hand,

all the steps necessary to develop digital replicas of the devices that make up a Smart Micro-Grid will be

covered. On the other hand, an automation and energy management system will be implemented over the

micro-grid to optimize the operation of each of the systems that compose it, while guaranteeing the energy

demand and maximizing the use of solar energy. Additionally, hydrogen is used as mid/long-term energy

storage system (backup).

1 INTRODUCTION

A digital transformation is revolutionizing every area

of science and technology, attested by scenarios such

as the Internet of Things (IoT) and the Cyber-Physical

Systems (CPS) (González et al., 2017). This last

emergent concept is orchestrated around the

communication through the network of devices with

embedded connection capacity, in order to sensorize,

monitor and act on physical elements in the real world

(Monostori et al., 2016).

In summary, CPS derives from the convergence

of the physical and virtual or digital worlds. These

systems bring capabilities that turn them into a

promising solution which provide new frameworks to

produce advances, being able to talk about a whole

digital transformation. The impact of CPS is evident

in many fields. For example, in the industrial scope it

gives rise to the so-called fourth industrial revolution,

also referred to as Industry 4.0 (Industrie 4.0

homepage).

In the energy context, intelligent grids for

generating and distributing energy, called Smart

Grids (SG), are a clear example of the establishment

of CPS. In fact, they are considered as the result of

https://orcid.org/0000-0003-2094-209X

https://orcid.org/0000-0001-5645-3832

the convergence of energy systems and Information

and Communication Technologies (ICTs)

(Camarinha-Matos, 2016). These networks are,

therefore, an ideal environment to apply these

technological currents (González et al., 2017; Bedi et

al., 2018).

As a consequence, in recent times, there has been

a transition from systems based on the

interconnection of physical systems where

transmitted information was used for control

purposes, to systems in which information constitutes

the core of the system (Bradley et al., 2015). That is,

it has gone from granting a preponderant role to the

physical components/hardware to transfer this role to

the digital media/software. The role of information

and software tools has gained greater prominence in

the new technological paradigms. Currently,

considering independently the physical and digital

parts in an advanced scenario (CPS, SG, Industry 4.0,

IoT, etc.) lacks sense.

Within the Industry 4.0 concept, one trend that is

receiving great attention from Industry and Academia

is the digital replica, also called digital twin.

Originated in 2002, this paradigm receives multiple

definitions; there is no exact and generally accepted

Calderón Godoy, A. and Pérez, I.

Design and Implementation of Smart Micro-Grid and Its Digital Replica: First Steps.

DOI: 10.5220/0007923707150721

In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019), pages 715-721

ISBN: 978-989-758-380-3

715

definition. Autiosalo (Autiosalo, 2018) proposes a

concise definition for the aim of this work: A digital

replica is the cyber part of a CPS. It is also referred to

as a simulation environment that accurately

represents the dynamics of the real-world system

(O`Dwyer et al., 2019).

Therefore, nowadays, one of the most challenging

tasks in this philosophy of digital transformation is

the development of digital replicas of physical

processes. Figure 1 illustrates the interplay of the

physical and digital parts, where information flows

are established between both entities. On the one

hand, data about the status of the physical system are

fed to the digital replica. On the other hand, data

generated by the digital counterpart are sent to the

physical facility.

Figure 1: Schematic representation of interplay between

physical system and digital replica.

There is a large and increasing amount of papers

dealing with digital replicas in industrial context

(Kritzinger et al., 2018; Borangiu et al., 2019). Even,

the digital replication of employees is being explored

in order to improve the integration of human

operators in Industry 4.0 and CPS scenarios

(Graessler and Poehler, 2018). On the other hand,

some efforts are oriented towards energy

optimization in industrial processes that are digitally

recreated. For instance, in (Lu et al., 2019) it is noted

that to achieve an energy-efficient manufacturing in

Industry 4.0 environment, energy models must be

considered while developing a digital twin of an

intelligent factory.

However, for energy-related frameworks, this

topic has been scarcely treated. Interesting and recent

publications are found in (González-González et al.,

2018) and (Tao et al., 2018) dealing with digital

replicas of wind turbines; in (Kaewunruen et al.,

2019; O’Dwyer et al., 2019) and (Wang et al., 2019)

where a building is digitally mirrored; and in

(Senthilnathan and Annapoorani, 2019) where a

cyber model is proposed for power electronics in the

context of micro-grids.

Out of the academic arena, important enterprises

are also devoting efforts to digital replication of

power grids, like General Electric (GE) or Siemens

(Siemens).

Given the aforementioned lack of publications, it

is evident the need to develop technologies and

methods to be applied to real systems, namely, SGs,

in order to overcome unsolved challenges.

The present work is framed in a research project

which objective is to bring this digitalization to the

field of renewable energies and SG.

Specifically, this project consists of the design and

implementation of a CPS that incorporates a

representation or digital replica of a Smart Micro-

Grid (SMG) based on renewable energies and

hydrogen. In fact, SMGs can be defined as small-

scale SGs which can be autonomous or grid-tied

(Koohi-Kamali and Rahim, 2017). SMGs integrate

physical elements in the power grid and cyber

elements (sensor networks, communication networks,

and computation core) to make the power grid

operation effective (Yang et al., 2016). SMGs are

excellent candidates for the implementation of these

digital replicas. Thus, the processes involved in the

generation, storage and distribution of energy can be

reproduced virtually.

The aim of this project is twofold. On the one

hand, all the steps necessary to develop digital

replicas of the devices that make up the SMG will be

covered. All this with the purpose of obtaining

models that allow real micro-grids to be simulated, so

that the generation of a digital micro-grid model can

be addressed, enhancing the application of ICTs in the

energy field.

On the other hand, an automation and energy

management system will be implemented to optimize

the operation of each of the systems that make up the

SMG, while guaranteeing the energy demand and

maximizing the use of solar energy. With this SMG

will be achieved the generation of electricity from

renewable energy sources (especially photovoltaic),

using hydrogen as an energy storage system (backup).

The remainder of the rest of the paper is as

follows. The next section covers the challenges that

arise when the development of a digital replica is

addressed. Section 3 provides an overview of the

research project where this work is contextualized

and portrays the steps followed. The elements that

compose the platform are reported in the fourth

section. Finally, the main conclusions of the work are

addressed.

2 CHALLENGES TO FACE

In this section, the main challenges to be solved for

the implementation of digital replicas are addressed.

In this regard, the degree of maturity of the

ICINCO 2019 - 16th International Conference on Informatics in Control, Automation and Robotics

716

technologies involved has not yet reached an

adequate state, which makes it difficult for the

ambitious expectations of digital replicas to bear fruit

in an authentic reality.

2.1 Non-generally Accepted Concept

One of the first difficulties found to design a digital

replica is derived from the aforementioned lack of

generalized concept. To overcome this issue, in this

work, the authors consider a digital replica as a

representation of a physical process/system which

runs in a digital environment.

In addition, a linkage between both the physical

and digital counterparts is required, enabling the

updating and adjustment of the digital side. Under this

perspective, a digital replica can adopt multiple

formats, from an equation-based mathematical

model, to dynamic representations in 3D.

2.2 Massive Data Gathering

To create the digital replica, a great variety of sensors

monitor all sorts of data during the operation of a

process (Stock et al., 2018), and are processed and

stored to design a precise representation of the

physical process. As pointed by Haag and Anderl

(Haag and Anderl, 2018), traditional data collection

and processing methods do not meet the needs of the

digital replica paradigm and need to be rethought.

Consequently, another challenge to face is the

need of massive amount of data about the physical

system in order to be properly and accurately

characterized.

In a common scheme of automating a system, a

number of magnitudes must be sensed and processed,

but the focus is mainly put on those that are

considered as critical for control tasks. In the case of

implementing a digital replica, not only critical

magnitudes must be measured, but many other

variables are required to reach the highest fidelity of

the replica.

Even, the acquired data on generation, operation

and consumption of power plants are called Big Data

of electrical energy, and have an enormous potential

to support the decisions of optimization and

management (Wen et al., 2018).

This fact implies two questions to solve. On the

one hand, the deployment of an infrastructure for

sensing, data acquisition and data communication has

to be tackled. This infrastructure must be able to

handle a large amount of sensors and data acquisition

devices fulfilling features about scalability, accuracy,

reliability and modularity.

On the other hand, the magnitudes to sense need

to be determined in order to be illustrative enough for

the system behaviour and, at the same time, not to

increase the expenses to prohibitive levels.

To overcome this difficulty, in the project low-

cost and open-source devices are being integrated in

order to sense and monitor the physical assets (further

commented in Section 4).

2.3 Software Requirements

Developing a digital replica implies the complex task

of designing various models in order to properly

reproduce the geometries, physical properties and

behaviours of the physical system (Tao et al., 2018).

For instance, the geometries can be represented by

CAD models that reflect the 3D aspect of the real

counterpart (Um et al., 2017; Tao et al., 2018).

Therefore, to implement a digital replica of a SMG,

several software packages/environments are required.

Concerning previous works, commercial software

directly focused in energy-related systems, as

EnergyPlus, are used to implement the digital replica

(O’Dwyer et al., 2019). A different perspective

consists on using a mathematical model through

Matlab (González-González et al., 2018). Other

approaches are based on black-box models or

artificial intelligence tools like Neural Networks

(Rahman et al., 2018). Indeed, a software package

commonly used for industrial instrumentation and

monitoring/supervision can be also applied for digital

replication as reported in (Senthilnathan and

Annapoorani, 2019).

The integration of those models is another

problem to solve. Digital replicas are nowadays

considered as more than just collections of digital

artefacts, but rather, as collections of linked digital

artefacts (Vrabic et al., 2018). To achieve a consistent

digital replica, an effective information exchange

among the models must be performed (Talkhestani et

al., 2018). In fact, there is a scientific deficit about

approaches addressing data exchange for

systematically updating the multi-domain models in

case a physical change occurs (Talkhestani et al.,

2018).

Moreover, an additional issue deals with the

synchronization between the physical system and the

digital replica (Talkhestani et al., 2018). The digital

replica must be kept updated with the current status

of the physical system (Talkhestani et al., 2018).

Obviously, any change in the physical asset like

deletion or addition of a component must be updated

in the digital replica (Talkhestani et al., 2018).

Design and Implementation of Smart Micro-Grid and Its Digital Replica: First Steps

717

In this sense, at the current stage of the project, the

software Matlab/Simulink is being applied for the

modelling of the subsystems of the SMG whereas

LabVIEW is used to collect data from the physical

facility.

2.4 Scheduling

A decision to take concerns the appropriate stage to

elaborate the digital replica. In this sense, some

literature reports the convenience of developing the

digital replica before the real-world deployment in

order to test different control algorithms or to avoid

prohibitive costs (O’Dwyer et al., 2019).

However, other publications point out the need of

acquiring massive amount of data of the physical

system operation to develop data-based models

(Stock et al., 2018; Madni et al., 2019). Indeed, some

authors propose creating the digital replica in parallel

to the physical facility (Talkhestani et al., 2018).

Moreover, in any case the digital replica is iteratively

updated and verified along the life cycle of the

physical system (Schneider et al., 2019).

In the present case, the last option has been

chosen, so data gathered from the characterization

and initial operation of the components (photovoltaic

modules, hydrogen equipment, etc.) is being used to

generate digital replicas in parallel.

3 PROJECT MAIN STEPS

This section is devoted to describe in a brief manner

the steps to implement the SMG and the associated

digital replica.

On the view of the surveyed literature, the

following pillars are considered for the

implementation of a digital replica. Namely, massive

data acquisition through smart sensor networks,

cloud-based storage and processing of the data, and

utilization of such data in expert supervisory

applications.

Consequently, the stages to be covered include the

design of sensorization for the collection and massive

accumulation of data, the organization of

transmission and communication in the information

network, the integration of multiple and

heterogeneous components (hardware/software), the

real-time connection of physical and digital systems,

the validation and implementation of prototype

installations in which to develop and evaluate the

technologies involved. Additionally, concerning SGs,

a necessary premise is the development of a

management strategy taking into account both

technological and energy constraints.

Firstly, the identification and analysis of the

necessary sensorization for the registration of the

magnitudes of interest has been carried out.

Subsequently, the design and development of the

appropriate auxiliary elements is being addressed to

develop a network of intelligent sensors through an

interoperable and scalable architecture.

Next, the energy management strategy will be

defined and implemented for the operation of the

SMG, as well as a system for the monitoring and

supervision, which will allow online remote access.

The development of the digital replica will be

performed from the models of each one of the

elements that compose the SMG. Namely, this

equipment includes photovoltaic generators,

electrochemical accumulators, fuel cells, hydrogen

generators, and hydrogen storage vessels. Likewise,

the necessary elements to compose the control,

monitoring and management system of the SMG will

be deployed. These ones comprise Programmable

Logic Controllers (PLCs), sensors, human-machine

interfaces, and programming software.

The validation of the designed replica will be

carried out with the data obtained from the parallel

operation of the experimental micro-grid at

laboratory scale and its digital replica.

Once these steps are fulfilled, the virtual

illustration of the SMG will be applied for diverse

purposes. It can be applied to simulate the system

behaviour while making reconfigurations and, also, to

improve the system performance and detect any

failure (Talkhestani et al., 2018).

In this sense, this digital representation acts as a

powerful platform on which to test, probe, evaluate,

analyse, etc., as if it were the real physical system,

avoiding the limitations and technical-economic

disadvantages of taking the physical system to certain

states operatives.

Namely, there are many possible applications in

the energetic context. Among these, the following

ones can be highlighted: preventive and predictive

maintenance to increase the life time (Prognostics and

Health Management, PHM); reduction of downtimes

and associated costs; improvement of availability,

reliability, trustworthiness and robustness of assets;

study of behaviour, detection of deviations and

reaction to extreme situations (resilience);

optimization of efficiency and operation from an

economic and energy point of view; as well as making

decisions based on data management (expert

supervision). It must be remarked that the

applications regarding extreme situations cannot be

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covered with the thoroughness desirable in the current

state of maturity of the R&D project

All these potential uses derive from the fact that

the digital replica can be executed offline or in real

time simultaneously to the physical system, so that

both respond to the same stimuli. Therefore, the

obtained conclusions can be extrapolated directly to

the physical system.

4 COMPONENTS OF THE SMG

The SMG integrates solar energy and hydrogen to

achieve an autonomous system. A set of

monocrystalline photovoltaic modules up to 2.4 kW,

as a primary renewable energy generator, compose

the Photovoltaic subsystem (PV). An electrochemical

500 Ah battery system, as a short-term backup

element, hosts the electrical flows, playing the role of

DC Bus.

An energy balance is continuously performed to

determine if there is surplus of solar energy. In such

case, a modular Polymer Electrolyte Membrane

(PEM) Hydrogen Electrolyzer (HE) up to 0.75 kW is

used to generate hydrogen. In the opposite case, a

PEM Fuel Cell (FC) up to 1 kW acts as a renewable

secondary (or reserve) generator of electrical energy.

This way, the hydrogen will be used as a long-

term backup element to feed the FC when the energy

demand in the micro-grid cannot be satisfied by the

PV generator and the battery. The hydrogen is stored

in metal hydride vessels. DC and AC loads close the

system.

In addition, there is a series of automation,

instrumentation and monitoring devices that will

make up the control and supervision system, which in

turn, will serve as a system for the massive

acquisition of data. The automation and management

system will be solved using a PLC and a Supervisory

Control and Data Acquisition (SCADA) system.

Namely, the model of PLC is S7-1500 of Siemens and

LabVIEW is used to implement the supervisory

system.

Telemetry is achieved by means of remote units,

namely to connect the PLC with remote signals, a

decentralized periphery station ET200 (Siemens) is

applied. For massive data collection, an additional

data acquisition device (DAQ) is materialised by the

open-source platform Arduino. This technology

brings benefits like low-cost and easy-to-use due to

the wide support provided by the open-source

community.

For the integration of software/hardware entities,

the industrial protocol Open Platform

Communications (OPC) will be used, which allows to

establish an exchange of data between the

components, abstracting from their specific nature

(González et al., 2019).

The real-time connection of both physical and

digital systems will be based on the same

communication network used to interconnect the

sensorization, automation, management and

supervision systems of the SMG. Thus, both the

physical system and its digital replica share the same

signals. It must be noted that the digital replica is still

being developed, so the achieved results will be

reported in further publications.

A preliminary block diagram of the described

SMG is depicted in Figure 2. Also, the

interconnection of the described elements for

automation and management is schematically

portrayed in the figure.

Figure 2: Block diagram of the SMG.

5 CONCLUSIONS

Digital replication of physical systems is a

challenging task that is receiving increasing attention

in modern trends like SGs or Industry 4.0

frameworks.

The paper has presented the activities that are

being developed in a research project that deals with

the design and implementation of a SMG based on

renewable energies and hydrogen as well as its digital

replica.

The main challenges to address have been

expounded, as well as the steps required to develop

the project. In addition, the components of the

physical counterpart, e.g. the SMG have been

described.

ACKNOWLEDGEMENTS

This research has been funded by the project IB18041

supported by the Junta de Extremadura in the VI Plan

Design and Implementation of Smart Micro-Grid and Its Digital Replica: First Steps

719

Regional de I+D+i (2017-2020), co-financed by the

European Regional Development Funds FEDER

(Programa Operativo FEDER de Extremadura 2014–

2020).

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