A Holonic Multi-Agent Architecture For Smart Grids

Ihab Taleb

, Guillaume Guerard

, Fr

eric Fauberteau

and Nga Nguyen

eonard de Vinci P

ole Universitaire, Research Center, 92 916 Paris La D

efense, France

Keywords:

Smart Grid, Holarchy, Holon, Multi Agent System (MAS).

Abstract:

The global warming and the increase of fossil fuel prices make the minimization of energy generation an

important objective. Thus, smart grids are becoming more and more relevant in a context where we want to

regulate the demand according to the available energy. This regulation can be operated thanks to Demand Side

Management (DSM) tools. While different models and architectures have been developed for smart grids,

only few papers used holonic architectures. For this, we propose in this paper a holonic architecture for smart

grids. This type of architectures is relevant to smart grids as it allows the various actors in the grids to work

even in the cases of technical problems. Holons in the proposed model are composed of ﬁve interconnecting

agents that ensure ﬂexibility on the various aspects. This model has been tested and has proven to work on

3 different scenarios. The ﬁrst scenario simulates a grid in its healthy state. The second one simulates a

grid where a region can be disconnected from a blackout for example. The third one simulates a grid with

production mismanagement. Results show how the grid distributes the available energy depending on the

available production, priorities (if any) and the assurance of the distribution across the various requesting

holons.

1 INTRODUCTION

In 2015, 196 countries accepted the Paris Agreement

for limiting global climate change caused by global

warming to less than 2

C, by restricting the use of fos-

sil fuels (UNFCCC, 2021). In this context, the Euro-

pean Union funds projects to develop solutions reduc-

ing the production of greenhouse gases. For example,

the MAESHA project, in which the contributions of

this article are part, aims at decarbonizing the French

island of Mayotte.

In fact, energy production infrastructures are ma-

jor players in climate change. In ﬁrst projects, electri-

cal production based on natural gas has proven to be

not the ideal solution. First, natural gas is a type of

fossil fuel, which means that its energy is still pollut-

ing. Second, as it is not available in all countries its

price can increase dramatically during transportation

problems whether they are caused by accidents or by

political conﬂicts.

Consequently, it is important to ﬁnd other solu-

tions that can be easier to access and to manage. For

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these reasons, transition policies from fossils like coal

have been discussed in (Spencer et al., 2017) by sug-

gesting the increase of the integration of Renewable

Energy Sources (RESs) by encouraging governments

and people to install RES generators like Photovoltaic

(PV) panels. Although RESs are still costly compared

to other energy sources, the study in (Brockway et al.,

2019) estimates that the Return Of Investment (ROI)

will increase with time (more cost effective) and will

reach, in the near future, that of the fossil fuels. On

the other hand, one of the biggest challenges when

talking about RES is that it is difﬁcult to control, as it

highly depends on weather parameters like sun radia-

tions, temperature, wind, etc. This challenge means

that the more RESs we have in the Electrical Grid

(EG) the harder it is to control energy generation.

As another challenge, the number of Electric Ve-

hicles (EVs) is increasing by the day, which means

higher demand to charge their batteries and higher

risks of serious problems in the grids like partial or

total blackouts (Green et al., 2011). However, with

a proper control over these EVs (delaying or advanc-

ing the charging process and discharging if needed),

it is possible to not only avoid blackouts, but to use

these batteries as a storage point to provide energy in

the peak hour. This method is called Vehicle to Grid

(V2G) (Hannan et al., 2022; Liu et al., 2013). Hav-

126

Taleb, I., Guerard, G., Fauberteau, F. and Nguyen, N.

A Holonic Multi-Agent Architecture For Smart Grids.

DOI: 10.5220/0011803300003393

In Proceedings of the 15th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2023) - Volume 1, pages 126-134

ISBN: 978-989-758-623-1; ISSN: 2184-433X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

ing the uncontrollable generation, and the control-

lable EVs charging and discharging, the only parame-

ter that we can manage in this case is the demand, by

a process called Demand Side Management (DSM).

DSM aims to delay, ﬂatten or plan the demand and

use battery storage when the demand is higher than

the available energy (peak hours) (Kanakadhurga and

Prabaharan, 2022), and store the extra energy pro-

duced during high generation hours. Thus, in order

to beneﬁt from DSM, there is a need to upgrade the

traditional EG to a Smart Grid (SG) that can allow for

smarter energy usage and routing.

SG’s concepts have been deﬁned as it is known

nowadays by (Amin and Wollenberg, 2005). It is the

new version that aims to upgrade EGs on the differ-

ent aspects : measurements, predictions, data registry

and analytics, control, and communication. It cov-

ers all grids’ problems and requirements starting from

consumers and producers to energy distribution and

blackouts handling. SGs improve the communica-

tion and the distributed control over the various ac-

tors (i.e., consumers, producers, storage facilities and

EVs) and integrate the new type of actors called pro-

sumers (Espe et al., 2018). Prosumers are consumers

that can produce all or part of their energy demand

using RES. An example of prosumers is a house hav-

ing PV panels where the energy produced can satisfy

or not the demands of this house depending on the

weather (sun radiation), or an EV having relatively

large battery storage where it can charge or discharge

depending on the available energy on both the grid’s

side and the EV’s side.

In order to use RESs, batteries and EVs to their

maximum potential, SGs require energy routing to

be bidirectional, to allow the users to act not only

as consumers, but also as producers whenever they

have sufﬁcient energy production or storage during

peak hours (Ramchurn et al., 2012). However, to

reach the optimal performance for prosumers, it is im-

portant to have measurements and predictions for the

near future. Predictions help SG actors to make their

demands or offers to other actors ahead of time, al-

lowing the energy to be routed more effectively with

less loss and lower transmission costs, or to delay (if

needed) some of the demands before peak hours hap-

pen. Indeed, multiple deep learning methods have

been proposed to predict energy demands in a ﬂexible

or reusable way. (Dudek et al., 2021; Huang et al.,

2022) proposed deep learning methods that can be

used to predict the demand in various regions. While

(Huang et al., 2022; Pallonetto et al., 2022) have pro-

posed deep learning models to predict on different

time ranges. (Taleb et al., 2022) proposed a ﬂexible

deep learning method that ensures the ﬂexibility on

both time ranges and region domains.

Different architectures and models for SGs have

been proposed. However, one architecture that has not

yet been sufﬁciently tested or deﬁned in the domain

of SGs is the holonic architecture. Indeed, the goal of

this paper is to answer the suggestion made by (How-

ell et al., 2017) by proposing a holonic Smart grid

architecture. A holonic architecture is an architec-

ture deﬁned by the aggregation of one universal entity

called holon. A holon is an entity that can work (by

itself) as a whole while at the same time, being part

of a larger entity of the same type (Mella, 2009). In a

holonic SG, a holon can be seen in such architecture

as the aggregation of multiple microgrids, while each

of them is also an aggregation of smaller microgrids

until we reach the level of houses or electric devices.

The proposed model simulates the behavior of holons

among the SG. Holons include various agents which

can be modiﬁed to simulate various scenarios. For

example, our model can include various energy prices

(ﬂat prices, dynamic prices, carbon-based prices, etc.)

and energy management strategies (shifting, peak and

load reduction, peak clipping, valley ﬁlling, etc.) and

any technologies. Scenarios also includes all kinds of

disturbances on the grid, about its structure, its behav-

iors or external factors.

In this paper, a literature review of Holonic Multi-

Agent System (HMAS) and holons is given in Section

2. Section 3 describes the proposed model as both a

single holon model and as a holarchic model, as well

as discusses some of the possible decision and control

methods that can be applied to the proposed architec-

ture. In Section 4, the materials and methods used for

the simulations are discussed, as well as the three test

cases used on the proposed model. Conclusion and

future work are discussed in Section 5.

2 EXISTING MODELS AND GAPS

The idea of holons and holarchy (holons organized in

a hierarchical architecture) has been ﬁrst introduced

with the book ”The Ghost in the Machine” written

by Arthur Koestler in 1967 (Koestler, 1967). (Ger-

ber et al., 1999) introduced the concept of HMASs

where one agent can be the aggregation of multiple

lower domain agents. The concept of HMASs has

then been applied to a diversity of domains like au-

tomation, manufacturing and transportation systems

(Mar

ık et al., 2013).

While different architectures have been deﬁned

for SGs, the most interesting architectures are the

ones that are based on holarchies as they provide

more ﬂexibility to the different actors of the grid (con-

A Holonic Multi-Agent Architecture For Smart Grids

127

sumers, producers, prosumers, storage facilities and

points of distribution) (Negeri et al., 2013), while at

the same time, beneﬁting from both the decentral-

ization of the decision and the top-down hierarchical

organization or surveillance. Indeed, (Ghorbani and

Unland, 2016) has proposed to compose the SG of

two layers: physical layer where all the connections

to all physical devices happen, and aggregation layer

where all holons from the ﬁrst layer merge or aggre-

gate to form the SG. (Ansari et al., 2015) has deﬁned

their SG based on low and medium voltages: a ﬁrst

level designs smart homes and energy resources, than

the higher levels are for low voltage feeders, medium

voltage feeders, medium voltage substations, etc. up

to the highest level that contains the energy manage-

ment system holon that is responsible for managing

the whole system.

Concerning holonic architecture, (Ferreira et al.,

2015) has introduced the concept of single holon

modeling where one type of holon can manage any-

thing from a physical device, to an apartment, build-

ing, to micro-grids. It also proposes the holon to be

multi-threaded, where each holon has a thread for

the negotiation with peers, a thread for the negotia-

tion with children, and a third thread for the local be-

haviours. (Abdel-Fattah et al., 2020) has discussed

the application of holonic SGs for self-healing appli-

cations, as well as the potential, the challenges and the

requirements for SGs in a holonic architecture. (Wal-

lis et al., 2020) has proposed a framework, based on

holonic architectures, that is composed of three parts:

historical data collection, prediction (FRODO, which

stands for Forecasting of Resources for Dynamic Op-

timization) and decision or strategy selection (OLAF,

which stands for Optimal Load and Energy Flow).

In the next sections we will discuss a new pro-

posed single holon model that is composed of multi-

ple agents. The main goal of this model is to provide

the highest possible ﬂexibility in terms of the deﬁni-

tion of the SG architecture, its reuse and blackouts

avoidance.

3 THE PROPOSED MODEL

A holon is the only component of a holonic architec-

ture. Thus, the more holons are ﬂexible and perform-

ing, the better the model is. In this section we pro-

pose a holon that is composed of ﬁve interconnect-

ing main agents, namely: measurement agent, data

agent, prediction agent, control agent and communi-

cation agent. Figure 1 shows the agents of the holon

and their interactions.

3.1 A Holon Of Five Agents

The ﬁve agents of a holon are deﬁned as follows:

Measurement Agent: is the agent responsible for

collecting data from physical devices: smart devices

that are IoT connected, sensors, smart meters. It is

the intermediary between the data agent (and all other

agents) and these devices.

Prediction Agent: is responsible to provide predic-

tions for future demands and/or generations depend-

ing on historical data provided by the data agent. It

implements the hybrid deep learning algorithm de-

scribed in (Taleb et al., 2022), which is able to make

ﬂexible predictions on both time scale and spatial

scale. For the spatial scale, this method can provide

predictions on a whole island scale as well as on the

scale of a small group of buildings without the need of

any modiﬁcations in the method. On the other hand,

this method can also provide predictions on different

time ranges (real-time, daily and weekly predictions)

with minor changes in the preprocessing phase.

Data Agent: is the agent responsible for handling

data, storing these data and sending them to the pre-

diction agent. It is also responsible for storing predic-

tions made by prediction agent and to send them to

control agent depending on its requests.

Control Agent: is responsible for decision making,

it can be as simple as request-response in an Internet

of Energy (IoE) context as well as more sophisticated

algorithms implementing Evolutionary Game Theory

(EGT) or Q-learning. It takes its decision depending

on two ﬂows of informations. The ﬁrst is the predic-

tion data made by prediction agent and stored with

data agent. The second is the ensemble of requests

and/or offers sent from lower holons and the feed-

back received from the upper holon (in a holarchic

architecture).

Communication Agent: is the agent responsible

for the communications with other holons via their

respective communication agents, it uses the Agent

Communication Language (ACL) speciﬁcations for

the communications with other agents. It also ensures

that lower holons are in synchronization with its cur-

rent step.

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128

Measurement

Agent

Prediction

Agent

Control Agent

Communication

Agent

Data Agent

Figure 1: The structure of the proposed holon, composed of

ﬁve interconnecting agents.

3.2 Holarchic Architecture

A holarchy or holarchic architecture is a holonic ar-

chitecture composed of holons organised in a hierar-

chical way. The concept of holarchy is similar to that

of a tree based architecture. However, the main differ-

ence between the two is that in a holarchy, the parts

are autonomous and able to operate independently,

while in a tree-based hierarchy, the parts are more de-

pendent on the whole and may not be able to function

on their own. The holarchic architecture is used in

this paper to provide ﬂexibility in the functional as-

pect, the spatial aspect and the temporal aspect.

Holons should be able to work for any type of

actor in the SG whether it is a physical device, stor-

age facility, EV, or a micro-grid. Measurement agent

takes care of the communication with various types of

devices or smart meters while communication agent

takes care of communicating with other holons that

are either lower holons representing smaller micro-

grids or the upper holon representing the larger micro-

grid. This ensures the ﬂow of data from both sides

(devices and/or other holons) to the control agent.

On the other hand, holons are created and dis-

tributed on the various levels based on the regional

aspect, which means that the super holon on the very

ﬁrst level (the highest level) will represent a whole

country or an island in the case of the simulation of

this paper. On the second level, each holon represents

a region or an actor of equivalent amount of power de-

mand or generation (e.g., a thermal power plant). The

third level represents villages or any equivalent actor

in terms of the amount of power traded (e.g., RES fa-

cility or a storage facility). the architecture can reach

down as much as needed depending on the decision

of the engineers that will apply this architecture until

it reaches the level of simple smart devices like heat-

ing devices. Holons continually check at each time

step to ensure if any physical devices or subholons

are connected to them respectively in order to provide

for them or from them the energy that is needed or

available. Holons also should also be able to provide

predictions on different regional scale whether it is a

large region or a small group of buildings or even a

small device. Holons in this paper are only connected

to their upper holon, to their lower holons, and to their

proper devices. In order to have a simpler and more

practical architecture, Holons do not communicate di-

rectly with other holons on the same level, but instead,

they wait for the feedback of their upper holon which

will have the broader information.

Moreover, holons should be able to work on dif-

ferent time ranges, depending on which level they are

in and with which type of physical actors and holons

they are dealing with. Data agent stores the data that

could be needed in the next steps for both predictions

and decision taking while prediction agent takes care

of providing predictions on multiple time ranges de-

pending on the needs of control agent.

Figure 2 shows an example of how a holarchy

looks like for the SG while Figure 3 shows a sequence

diagram for the ﬁve agents of a holon, with social

agents of its connected holons.

Figure 2: The holarchic architecture. In this image, we can

see that the holarchy is composed of three levels whereas it

can be extended to as many levels as needed.

3.3 Control Methods

In order to ensure an efﬁcient routing and sharing of

energy across the grid, holons should be able to not

only take local decisions, but to negotiate by request-

ing or offering energy. They also should be able to

change their decisions (e.g., by delaying demands, or

stocking their offer for later use, etc.) depending on

the feedback of their upper holons. Indeed, different

methods can be applied to the negotiations and deci-

sion making for the control of the various holons on

the various levels. Also, these methods should follow

two speciﬁc steps in order to ensure the organization

of the communication of the different actors.

Local Decision: Each holon takes its decision

based on energy requests and offers of both its con-

A Holonic Multi-Agent Architecture For Smart Grids

129

Figure 3: The interaction between the various agents of the holon and the social agents of their connected holons (in this

diagram, we considered that the holon is connected to only 2 subholons while this number can be less or more in other cases).

nected actors and its subholons. Requests and of-

fers are decided, ahead of time, based on predictions.

These decisions can be taken using various algorithms

and methods, like IoE, EGT, optimization, etc.

• The IoE method, proposed in this paper, consists

of calculating, at each level, the request/offer ra-

tio. Based on this ratio, a holon decides whether

to demand or offer energy to its upper holon. This

process happens recursively in a bottom-up ap-

proach until it reaches the highest level holon, the

holon that is supposed to send its feedback. In

peak hours, if the generated energy cannot ful-

ﬁll all the demands, upper holons feedback will

consist of the calculated ratio at the highest level,

which it turns will be the percentage to be de-

creased for each holon and at each level. In this

approach, negotiations can be as simple as one it-

eration of demand and response. Section 4 uses

this approach, tested on the proposed architecture,

in order to show the distribution of energy in dif-

ferent test scenarios.

• In EGT, demands and requests can be seen as

strategies, and each strategy is represented by its

own population. Populations evolve depending

on the feedback of the upper holon on multi-

step negotiations. EGT can be combined with Q-

Learning to update the payoff tables for players at

each time-step.

• In Optimization methods, requests and offers can

be seen as variables that can take positive or nega-

tive values. These variables can be updated de-

pending on the feedback of the upper holon at

each time-step. In Particle Swarm Optimization

(PSO), the feedback received can be considered

as the output of ﬁtness function that is responsi-

ble to choose the particle with best ﬁtness (in this

case, the best offer or demand).

Negotiations and Global Decision: In this step,

holons try to achieve a consensus with their upper

and lower holons. It is important to specify a max-

imum number of iterations and to stop whenever op-

timal values are reached. Simulated annealing is an

option to ensure the convergence of the grid.

4 SIMULATION AND RESULTS

The simulation has been made using JAVA as a pro-

gramming language and JAVA Agent DEvelopment

Framework (JADE) for the development of holons

and their composing agents. It exploits the IoE

method proposed in Section 3.3.

4.1 Materials and Methods

The model proposed in this paper has been tested

on the data of the island of Mayotte provided by the

MAESHA project.

Indeed, weather forecasts, holiday data and histor-

ical data of both energy demand and RES production

on 60 minutes granularity has been provided for the

simulation. The island has two thermal power plants,

one Biogas and various RES facilities. The simula-

tion is composed of 3 levels holarchy. The ﬁrst level

consists of the super holon representing the whole is-

land. The second level represents the 17 regions of

the island. While it is possible to be deﬁned in other

ways, in this simulation, second level holons have the

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

130

two thermal power plants, the Biogaz plant, and the

RES facilities each directly attached to the holon rep-

resenting their region. On the third level, holons rep-

resent the villages of each region. Mayotte has a to-

tal of 72 villages which means a total number of 72

holons on the third level. Third level holons start all

the energy demands, and their requests propagate to

second level holons. Second level holons gather the

data from third level holons and verify if their requests

can be satisﬁed in the local network of holons, other-

wise, they send their request to upper level holons un-

til they reach a point where their request is fulﬁlled or

until the request reaches the highest level with no suf-

ﬁcient energy. In this case, the super holon will send

feedback about lowering the demands, this feedback

will then propagate back from higher levels to lower

levels until it reaches the lowest levels that are respon-

sible for the demands. Although it is not included in

this simulation, it is also possible to have storage fa-

cilities that can store energy (in this case they act like

consumers) and then to provide this energy (in this

case, they act like generators) in later times depending

on the need of the SG (depending on the feedback that

arrives from higher level holons). The architecture de-

ﬁned in Section 3 has been tested in three scenarios.

Having the large number of holons in the island, it is

not possible to show the result of the simulation for

each holon. For this, The results of these three sce-

narios are provided for one speciﬁc holon (in Figure

4, 5, 6 and 7), showing the energy received by the

holon representing Handr

ema, which is a village in

the Bandraboua region. The reason for choosing this

village is that it belongs to a region having PV gen-

eration so that in case of a disconnection like in the

second case (Section 4.3), it can still demand energy

from its region’s holon. Any region or a village that

can either produce its own energy or demand energy

from connected holons should give a similar output as

in the second scenario. However, it is worth mention-

ing that if a holon or a group of holons got discon-

nected from the grid with zero-productions, they will

not be able to satisfy their demands as they have no

way for having energy.

4.2 Standard Scenario

The simulation in this scenario is the standard case

where all holons on the three levels, described in Sec-

tion 4.1, are properly connected and thermal produc-

tion is in its optimal production, which means that

all energy demands can be satisﬁed across the whole

grid. Energy requests, that could not be fulﬁlled lo-

cally, propagate from the third level (the lowest level)

to upper levels. When the highest level holon receives

all requests, it then sends its feedback ranging from 0

(no energy available) to 1 (energy demanded can be

fulﬁlled in full). Figure 4 shows that all the energy,

demanded by (Handr

ema), is received.

Figure 4: Energy received by a holon representing a village

on level 3, in the standard scenario where all the holons

across the whole grid are properly connected.

4.3 Disconnected Holon

The second scenario shows what happens to the en-

ergy received and consumed in the case if a discon-

nection happened between a holon in the second level

and its upper holon (the holon representing the whole

island). In fact, disconnection might happen for mul-

tiple reasons, but it is mostly because of technical

problems in transmission cables. In this scenario, re-

quests can propagate from villages only to one higher

level (i.e., only to their directly upper holon), namely

the holon representing the region Bandraboua. Ban-

draboua can no longer demand from the holon rep-

resenting the island because of the disconnection be-

tween them. Thus, the only energy that can be sent

to its lower holons is the energy produced locally in

this region. The upper holon has two choices: the ﬁrst

choice (described in Section 4.3.1) is to give priority

to speciﬁed holons and give the rest of the energy to

the other holons, while the second choice (described

in Section 4.3.2) is to distribute the energy propor-

A Holonic Multi-Agent Architecture For Smart Grids

131

tionally to their demands (which means, as an exam-

ple, that the holon requesting 10% of the total demand

will receive 10% of the available energy).

4.3.1 Holon Prioritization

In this case the disconnected holon representing the

region Bandraboua gives the priority to the holon

representing the village Handr

ema which means that

this holon will receive all energy that it needs and the

other holons will only receive what is left. Figure 5

shows the received energy for Handr

ema on hourly

basis. At the beginning of the simulation at time-step

0, the energy received starts at zero in the ﬁrst hour as

the connection between the holon (Handr

ema) and

its upper holon (Bandraboua) is not yet established

and it is to be established in this time-step. The en-

ergy received then increases and decreases depending

on the PV energy produced, which is directly corre-

lated to the sun radiations. This explains the zeros re-

ceived between hours 15 and 25 and after 39 which

indicate night-time hours. While this seems like a

problem, it can still be a solution to avoid total or

partial blackouts. The energy received between time-

steps 3 and 13 and 28 and 38 are equal to the energy

demand which means that during these time-steps,

energy production has exceeded energy demand (for

Handr

ema, the prioritized village) and thus, the other

villages can share the energy produced that is left. Al-

though this is not included in the current simulation,

it is worth mentioning that the energy received during

day-time can then be stored in batteries in order to

be used depending on predeﬁned priorities through-

out the day.

4.3.2 No Priorities Given

In this case, the disconnected holon will not give any

priority to any holon and will instead give the energy

proportionally to what has been demanded in total.

Figure 6 shows the amount of energy received by the

holon representing Handr

ema. The results in this ﬁg-

ure show that the speciﬁed holon never receives a suf-

ﬁcient amount of energy because the total demand is

higher than the PV generation.

4.4 Disconnected Plant

In 2023, the thermal power station of Badamiers is

scheduled to be at the end of its life. Thus, the is-

land will be then using only one power plant which

means less energy generation. For this, this test sce-

nario consists in using only the other thermal power

plant (located in Koungou), the biogas station, and

the PV generation. In a similar way to above, when-

Figure 5: Energy received by a holon representing a priori-

tized village on level 3, in a scenario where its upper holon

is disconnected from the grid and the only energy available

is the energy produced locally in the region.

ever the produced energy is not sufﬁcient, thermal en-

ergy gets distributed to every village proportionally

to the population of the village. Figure 7 shows the

energy received for the holon of Handr

ema. In this

ﬁgure, the energy received from thermal power plants

is around 280 kW·h all the time. The result shows that

between time-step 2 and 7, and time-step 29 and 32,

the received energy meets the energy demand because

the amount requested is below the available amount

of energy. On the other hand, during the time-steps

between 8-13, 16-17 and 33-38, the energy requested

was completely fulﬁlled because of the energy gen-

erated additionally to the thermal energy. For all the

other time-steps, energy demands were not fully sat-

isﬁed because of the lack of energy.

5 CONCLUSION AND FUTURE

WORK

In this paper, we proposed a holonic smart grid ar-

chitecture following the concept of single holon mod-

elling, where holons represent geographical zones

starting from houses and buildings up to islands or

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

132

Figure 6: Energy received by a holon representing a village

on level 3, in a scenario where its upper holon is discon-

nected from the grid and the only energy available is the en-

ergy produced locally in the region and no village has any

priority.

countries as a whole thanks to its ﬂexibility on the

regional, spatial and functional aspects. We have dis-

cussed the components (agents) of this holon, the in-

teractions between the agents in the same holon, and

between the various connected holons as well as some

of the methods that can be applied to this architecture.

We then applied this architecture to the French island

of Mayotte, forming 3 levels holarchy. The ﬁrst level

consists of the highest holon which represents the is-

land as a whole. The second level represents the 17

regions of the island and the third level (the lowest

level in the holarchy) is composed of 72 holons. Each

of these holons is connected to its respective upper

holon. We then tested this holarchy on three test sce-

narios. The ﬁrst one is a standard scenario where the

energy ﬂow and the connection between holons are as

supposed to be. The second scenario is a disconnec-

tion scenario where a holon is disconnected from the

main grid and it has to deal with the energy that it has

without going into a blackout. This scenario has been

tested on two cases. The ﬁrst case is a priority case

where we give the priority to a speciﬁc holon where

the second case is a no-priority case where all holons

have the same level of priority and have to share the

Figure 7: Energy received by a holon representing a village

on level 3, in a scenario where only one thermal power plant

is operating.

available energy. The third scenario is a test where

a thermal power plant is disconnected from the grid.

The simulations have proven this architecture to be

ﬂexible and effective in both the standard scenario

and the scenario where a micro-grid can get discon-

nected from the main grid. Finally, this paper has fo-

cused mainly on the proposition of this new architec-

ture, its feasibility and its ﬂexibility in all aspects. In

future works, more sophisticated methods and algo-

rithms will be implemented to this architecture, while

introducing the concepts of delays, storage, priorities

in more details, the formation of virtual power plants

and the negotiations with other holons in multiple it-

erations before taking decisions.

ACKNOWLEDGEMENTS

This project has received funding from the European

Union’s Horizon 2020 research and innovation pro-

gramme under grant agreement No. 957843.

A Holonic Multi-Agent Architecture For Smart Grids

133

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