A Formal Holon Model for Operating Future Energy Grids during
Blackouts
Siavash Valipour
1
, Florian Volk
1
, Tim Grube
1
, Leon B
¨
ock
1
, Ludwig Karg
2
and Max M
¨
uhlh
¨
auser
1
1
Telecooperation Lab, Technische Universit
¨
at Darmstadt, Hochschulstr. 10, Darmstadt, Germany
2
B.A.U.M Consult GmbH, Gotzinger Str. 48/50, M
¨
unchen, Germany
Keywords:
Smart Grid, Micro Grid, Smart City, Blackout, Islanded Operation, Holon Model, Energy Network, Decen-
tralized Power Sources.
Abstract:
Modern energy grids introduce local energy producers into city networks. Whenever a city network is discon-
nected from the distribution grid, a blackout occurs and local producers are disabled. Micro grids circumvent
blackouts by leveraging these local producers to power a fixed subset of consumers. In this paper, we evolve
micro grids to Holons, which overcome the need for fixed subsets and power as much of the city network as
possible. We contribute a formal model of Holons and investigate the impact of the Holon concept in a simu-
lation with 10,000 randomly generated city networks. These city networks are based on parameters obtained
from a real-world test site in a medium-sized German city. Our results show that the Holon approach can
supply an average fraction of 22.08% of any city network, even when fixed micro grids would fail to power
the city network as a whole.
1 INTRODUCTION
In the event of distributed small energy providers, for
example, photovoltaic systems, new challenges for
energy grids arise.
The classic scheme of combined energy trans-
mission and distribution networks (energy grids) is
given in Figure 1. The power stations that gener-
ate energy are located in and connected via the so-
called transmission grid, a network of high voltage
transmission lines. The power is transported to the
consumer, e.g., households, via the distribution grid
and city networks. Thereby, a city usually consists
of multiple city networks, all of them are indepen-
dently connected to the distribution grid. All grids are
connected with distribution substations that transform
high-voltage energy in lower-voltage energy and vice
versa.
Energy grids are undergoing paradigm changes
in producing, distributing, monitoring, and metering
electricity. Modern, so-called smart grids evolve the
traditional energy grid by the addition of communica-
tion infrastructure in order to optimize the efficiency
of the grid. Thus, producers and consumers can co-
ordinate their production and consumption of energy
with each other.
Especially, local energy producers are introduced
into city networks. For example, photovoltaic sys-
tems (PV systems), combined heat and power plants,
as well as wind power stations supply power to the
energy grid. Therefore, new challenges and opportu-
nities in distributing power arise for energy grid op-
erators. Our paper focuses on continued operation of
parts of city networks during blackouts.
1.1 Blackout Handling
A failing distribution substation usually cuts off the
entire city network behind it from the distribution
grid. As a consequence, the city network experiences
a power outage, a blackout. Even though local en-
ergy producers might exist inside city networks, these
producers are disabled during a blackout and cannot
supply energy on their own, as they conventionally
depend on the synchronization to the energy grid in
order to maintain grid stability. Local emergency en-
ergy sources, as, e.g., batteries and diesel generators
are only available to those households that operate the
respective sources.
1.2 Holon Approach
This paper adopts the Holon approach, as it is be-
ing developed in the project PolyEnergyNet, a project
146
Valipour, S., Volk, F., Grube, T., Böck, L., Karg, L. and Mühlhäuser, M.
A Formal Holon Model for Operating Future Energy Grids during Blackouts.
In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2016), pages 146-153
ISBN: 978-989-758-184-7
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Transmission
Grid
Distribution
Grid
City Network
+
Hospital
PV
System
Figure 1: Combined energy transmission and distribution
network.
concerned with a resilient and secure energy supply of
tomorrow. The adopted model presented here, inves-
tigates a possibility to operate parts of city networks
independently from the distribution grid by leverag-
ing energy producers located inside the city network
(cf. Figure 2). Our aim here is to supply as many
consumers as possible with energy during a blackout.
Moreover, prospectively, Holons are thought to en-
able prioritization of consumers so that in case of
energy scarcity the most critical parts of city net-
works (hospitals and similar) can be kept active while
only consumers of low importance are affected by the
blackout. Furthermore, Holons should prefer renew-
able energy sources over conventional ones.
-
3 kW
Hospital
PV
System
+5 kW
-
1 kW
-
1 kW
(
-
1 kW)
(
-
1 kW)
Figure 2: A disconnected city network is partly supplied by
a local PV system.
1.3 Paper Structure
This paper is structured as follows: work related to
our approach is reviewed in Section 2. In Section 3,
we present a formal model of the Holon approach, in-
troduce the involved stakeholders, and discuss con-
straints. Our approach is evaluated in the subsequent
sections. Therein, Section 4 presents the evaluation
setup, including a real-world test site, which is un-
der construction in a medium-sized town in Germany.
The results of the evaluation are summarized and dis-
cussed in Section 5. Section 6 concludes and presents
our upcoming research.
2 RELATED WORK
Many approaches for evolving the energy grid to
smart grids (Amin and Wollenberg, 2005) exist. Most
of these approaches target energy-efficiency and de-
mand side management to achieve higher energy grid
stability and cost-reduction (Hashmi et al., 2011;
Fang et al., 2012).
This section first reviews work related to our ap-
proach in Section 2.1 and, later in Section 2.2, dis-
cusses the approach of micro grids, which we see as
an evolutionary base for the Holon approach.
2.1 Smart Grids
The term smart grid is, ampong others, well explained
by Amin in (Amin and Wollenberg, 2005). Both
Hashmi et al. and Fang et al. survey smart grid
technologies and approaches in (Hashmi et al., 2011)
and (Fang et al., 2012), respectively.
With regards to the stability of smart grids,
Gu
´
erard et al. propose a grid model based on dy-
namic graphs to detect and handle network errors
within smart grids (Gu
´
erard et al., 2015). Our ap-
proach specifically targets network errors that isolate
city networks and require isolated operation (here: is-
landed operation) to cope with blackouts.
Farhangi criticizes missing alignment of techno-
logical evolution and legal regulations in smart grids
and presents a general road map for smart grid de-
velopment in (Farhangi, 2014). The Holon approach
requires both new technologies and new regulations,
e.g., the permission to operate local energy producers
during a blackout. In terms of technology, many
challenges are to be solved, for example, operating
multiple power producers concurrently requires them
to synchronize their frequencies during a bootstrap
phase.
A Formal Holon Model for Operating Future Energy Grids during Blackouts
147
The German Association of Energy and Water In-
dustries BDEW proposes a so-called “Traffic Light
Concept” for the operation of smart grids (BDEW,
2014). The traffic light concept classifies smart grid
operation into three phases:
During the green phase, the smart grid is fully
functional and stable. Market mechanisms, like
the ones proposed by Saad et al. (Saad et al.,
2011), are in control of production, consumption,
and prices.
When the smart grid is in danger to become un-
stable (amber phase), the network operators to-
gether with the market participants enforce stabil-
ity rules (BDEW, 2015).
In the red phase, in the event of imminent risk of
failure, the smart grid is solely managed and con-
trolled by the network operators. Market mech-
anisms are suspended in order to restore a stable
energy grid.
The Holon model comes into play during the amber
and red phases of the traffic light concept.
In (Bessler et al., 2015), it is explained in detail
how demand side flexibilities can be used to optimize
city networks. Similar to flexibilities, in (Kausika
et al., 2015) the authors investigate the distribution
and potentials of PV systems. This is done in or-
der to achieve optimal saturation of renewable energy
sources within city networks.
2.2 Micro Grids
Micro grids, often also referred to as “cellular grids”,
are very similar to our Holon approach. By isolating
predefined areas of energy grids, these areas become
able to harvest energy from local energy producers
inside the micro grids. The size of micro grid cells
varies on the given scenario. Sometimes, a micro grid
cell might only be one building, e.g., a hospital with
backup batteries, while in other scenarios, a cell can
be a whole city network, as shown in figure 3.
-
3 kW
Hospital
PV
System
+7 kW
-
1 kW
-
1 kW
-
1 kW
-
1 kW
Switch
(open)
Figure 3: A micro grid in islanded operation.
The German cities of Mannheim and Dresden practi-
cally evaluated the concept of micro grids by rolling
out such an energy grid for 1,000 households (MVV
Energie AG, 2012). The main difference of micro
grids and Holons is that micro grids are predefined
areas that can be physically separated from their sur-
roundings. Holons are virtual micro grids inside city
networks that do not require physical separation and,
thus, allow dynamic service composition based on
communication infrastructure.
In Schiffer’s PhD thesis (Schiffer, 2015), he in-
vestigates practical constraints like frequency stabil-
ity and voltage stability that are introduced by oper-
ating energy producers inside city networks. Schif-
fer proposes multiple control concepts to cope with
these constraints. All these constraints also apply to
Holons.
Operating micro grids in islanded mode, i.e., op-
erating them independently from a distribution net-
work, is discussed in (Kroposki et al., 2008) as well as
in (Shafiee et al., 2014). While this paper at hand fo-
cuses on operating Holons in islanded mode, a mixed
operational setting for Holons that are connected to
the distribution network is planned for the future.
Schiller and Fassmann describe IT challenges for
network operators introduced by micro grids (Schiller
and Fassmann, 2010). As Holons depend on control
and communication infrastructures just as micro grids
do, the challenges discussed by Schiller and Fass-
mann apply to Holons as well.
In (Ramesh et al., 2015), the authors present a mi-
cro grid architecture that allows to identify line faults
and to isolate affected areas. They claim that their ap-
proach is able to localize a fault in less than two sec-
onds. As Holons do not require physical separation,
an isolated segment of a micro grid could continue its
operation as a Holon.
3 HOLON MODEL
In this section, we contribute a formalized Holon
model. The term holon was first described by Arthur
Koestler (Koestler, 1967) as a modeling scheme
for autonomous entities that can consist of other,
smaller autonomous entities. Our model represents
autonomous sets of energy producers and consumers
inside city networks. The overall goal is to evolve
smart grids into more resilient energy grids by over-
coming the limitation of only having predefined mi-
cro grids. Hereby, our understanding of resilience is
aimed at a system which provides a high degree of
service availability. Blackout scenarios result into iso-
lated city networks with specific energy production
SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems
148
and consumption configurations. Such a “snapshot”
of a city network is represented by one energy grid
instance as defined below.
3.1 Formal Model
We model an energy grid as graph G = (N, E) of
nodes N with edges E N × N. The whole energy
grid builds one connected component. A node n N
can be classified into one of the following sets: Pro-
ducers P = {p
i
| p
i
N} that produce energy in the
grid, Consumers C = {c
i
| c
i
N} that consume en-
ergy in the grid, or Junctions J = { j
i
| j
i
N} that
split power cables if necessary, e.g., at crossroads.
For simplicity we define PC = PJ = C J =
/
0.
Any object that has to be covered by more than one
class can be (virtually) split into subnodes, so that the
subnodes can be correctly classified as either produc-
ers, consumers or junctions.
The function
power : N R : n
R
+
if n P
0 if n J
R
if n C
returns the production or consumption of a node in an
energy grid. While the producers supply energy to the
grid and have a positive power value, the consumers
draw energy from the grid and have a negative power
value. A junction does not change the overall energy
and therefore, it has a power value of 0.
The function
cap : E N
+
0
: e cap(e)
returns the capacity of a power cable for transmitting
energy. Thereby, the capacity is defined by the break-
ing capacity of the respective fuse that protects the
power cable.
A Holon is a connected subgraph H = (N
H
, E
H
)
with N
H
N and E
H
= {(n1, n2) E : n1, n2 N
H
}
and can be thought of as kind of Peer-to-Peer–Overlay
that does not alter edges. In order to transmit energy,
the Holon has to be connected, i.e., the Holon has to
form a single connected component.
Additionally, the available energy within a Holon
must be transmittable from producers to the respec-
tive consumers by the existing power cables, leading
to a valid Holon.
3.2 Valid Holons
A holon has to fulfill few prerequisites to be able to
supply consumers with energy in a distributed way.
The first important requirement is the connectiv-
ity. As stated before, connectedness is only a nec-
essary but not a sufficient condition for a Holon.
Moreover, the sum of produced and consumed en-
ergy has to be in balance:
p
i
P
power(p
i
) =
c
i
C
power(c
i
)
A third requirement is the sufficient capacity of
power cables. At no time shall an energy flow
surpass the maximum capacity of any edge in
a Holon. Therfore, e E
H
: f low(e) cap(e)
has to hold. This requirement can be reduced to a
max-flow problem (Schroeder et al., 2004).
Max-flow problems can be solved with the Dinic
algorithm (Dinic, 1970) or the Ford-Fulkerson (Ford
and Fulkerson, 1956) algorithm. In order to be able
to compute these algorithms, an additional source
as well as an additional sink are introduced into the
graph. The source is connected to all producers by
edges with their weights equaling the capacities of
the respective producers. Similar, all consumers are
connected to the sink by edges with their respective
consumption values.
4 EVALUATION
In order to evaluate the effects of applying our Holon
approach to city networks, we conducted a large-
scale simulation based on randomly generated city
networks.
These city networks are generated based on pa-
rameters we obtained from measurements and ex-
periences of a German network operator. The city
networks consist of different energy producers, con-
sumers, and power cables, each with specific capabil-
ities.
Our aim is to gather information on how the Holon
approach performs in comparison with predefined,
fixed micro grids. Especially, we are interested in
specifics that distinguish Holons from static micro
grids.
4.1 Real-world Test Site
The Holon approach will be evaluated in the context
of a publicly funded research project PolyEnergyNet
in a real-world test site. The test environment which
served as basis for evaluation is based on the city net-
work of a real-world, medium sized German city with
an industrial quarter with PV systems and consumers.
The setup of the following simulation is based on
time-snapshot measurements taken in the test site and
on know-how of the local network operator.
Consumer households usually draw 2–3 kW from
the city network. PV systems within city networks
A Formal Holon Model for Operating Future Energy Grids during Blackouts
149
generate 5 kW up to 60 kW under optimal conditions.
The two cable types typically used for wiring are pro-
tected by fuses that allow 150 kW or 185 kW to pass
the network. These values are used to generate ran-
dom city networks and to evaluate our approach.
4.2 Simulation Setup
As stated before, our simulation generates random
city networks based on parameters obtained from
measurements in a real-world test site and on know-
how of a network operator. All parameters are shown
in Table 1.
Table 1: Parameter values used for the generation of random
city networks.
Parameter Value
Network Size 10–25 nodes
Producers 10%
Consumers 80%
Junctions 10%
Production Values {5, 20, 30, 40, 60} kW
Consumption Values {10, 15, 20, 30} kW
Cable Capacities {150, 185} kW
4.2.1 Network Size
Every city network was chosen to have about 10–25
nodes in total. Thereby, every consumer node repre-
sents 5–10 single households sharing one power ca-
ble.
4.2.2 Node Class Probabilities
Based on our observations, we modeled the city net-
works to consist of 80% consumers, 10% producers,
and 10% junctions.
4.2.3 Consumption and Production Values
The network operator describes a consumption of 2–
3 kW as typical value for a household. Thus, con-
sumer nodes with 5–10 households consume either
10, 15, 20, or 30 kW. The production values for pro-
ducer nodes are chosen in the same fashion.
4.3 Research Questions
The simulation is used to investigate the following re-
search questions:
1. How many of the generated city networks can op-
erate in complete as micro grids?
2. How many non-functional micro grids can operate
in parts as Holons?
3. How much of a city network can covered by
Holons?
4. How does the size of city networks influence the
amount of nodes covered by Holons?
Figure 4 shows a city network consisting of seven
nodes and one additional junction. As can be seen,
there are three valid holons in the network:
Producer 1, Consumer 3
Producer 3, Consumer 2, and Consumer 3
Producer 2, Consumer 1
Producer-2 (20)
Consumer-1 (-20)
Consumer-4 (-30)
Junction-1 (0)
Consumer-3 (-20)
Producer-1 (20)
Consumer-2 (-30)
Producer-3 (50)
185185
185185
185
185
185
150
185
185
185
150
150
150
Figure 4: An exemplary city network with three Holons and
71.4% coverage.
Because of the overlap of the first two holons, namely
in Consumer 3, the maximum coverage of this city
network with Holons is given by the five green nodes
in Figure 4. A fixed micro grid would not be able
to operate in islanded operation, while the Holon ap-
proach would power 71.4% of the city grid.
5 RESULTS
The following results are based on 10,000 randomly
generated and evaluated city networks. Each city net-
work was generated according to the parameters in
Table 1.
5.1 Comparison with Micro Grids
Out of the 10,000 generated city networks, only 25
(0.25%) expose valid micro grids. Thus, these 25 city
networks also form Holons, which include all nodes
from the generated city network.
6,139 (61.39%) city networks contain valid
Holons, that is, at least one valid Holon configura-
tion is present. The number of Holons within a city
network increases with the size of the city networks.
In larger city networks, more producers and junc-
tions will appear, thus enabling more valid combi-
nations according to our model. On average, 2.46
SMARTGREENS 2016 - 5th International Conference on Smart Cities and Green ICT Systems
150
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
7500
8000
8500
9000
9500
10000
Coverage
Simulation Round
Figure 5: Distribution of city network supply coverage. The rounds on the x-axis are sorted ascendingly with respect to
the city network size. This result shows higher ratios of coverage with growing network sizes on average, as more nodes
contribute to a more coarse-grained distribution and promote the chances of valid Holon setups.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
6000
Coverage
Simulation Round
Figure 6: Distribution of city network coverage, omitting city networks without valid Holons altogether. The rounds on the
x-axis are sorted ascendingly with respect to the city network size. The reverse trend can be explained by higher average
coverage values due to not considering solutions with 0 solutions.
valid holons exist in every city network. 22.08% of
all buildings can be supplied on average by the largest
possible combination of Holons within the same city
network.
5.2 Influence of City Networks Without
Holons
Substantial differences can be observed when distin-
guishing on the analysis of gathered data from city
networks with at least Holon present and complemen-
tary data from city networks allowing also 0-Holon
configurations. In cases where there is at least one
valid Holon, there are three further Holons to find.
This means, that usually you will find on average 4
Holons per network if you neglect networks which do
not meet the Holon criteria at all. The average sup-
ply coverage also increases from 22.08% to 35.73%
in these networks.
Table 2 summarizes the number of found holons
and the supply coverage ordered by the city net-
work size for all investigated city networks. Table 3
presents the same data for city networks with at least
one valid Holon present.
The histogram in Figure 7 shows how city net-
works without valid Holons are distributed over city
network size. As can be seen, with an increasing num-
ber of nodes, the amount of city networks without
0
50
100
150
200
250
300
350
400
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
City networks without holons
Nodes in city network
Figure 7: Distribution of cells without any solution in rela-
tion to cell size.
valid Holons decreases. This observation may con-
tribute to the difference between the results shown in
Tables 2 and 3.
5.3 Coverage Distribution
A look at the distribution of the overall supply cover-
age (cf. Figures 5 and 6) yields three observations:
There appears to be a correlation between small
city network sizes and the probability of finding
either a Holon that covers the whole city network
or finding no Holon at all. However, it is much
A Formal Holon Model for Operating Future Energy Grids during Blackouts
151
Table 2: Evaluation results for all city networks, including
those without valid Holons.
Network Holons Found Supply Coverage
Size avg. med. avg. med.
10 0.70 0 17.62% 0.00%
11 0.78 0 18.17% 0.00%
12 1.06 0 20.70% 0.00%
13 1.07 1 20.19% 15.38%
14 1.37 1 21.11% 15.38%
15 1.64 1 22.02% 15.38%
16 1.87 1 22.76% 18.75%
17 1.93 1 20.43% 17.65%
18 2.23 1 21.74% 17.65%
19 2.94 1 24.76% 21.05%
20 2.80 2 23.23% 20.00%
21 3.30 2 23.64% 21.05%
22 3.42 2 22.69% 19.05%
23 3.96 2 24.22% 22.22%
24 4.71 2 24.34% 21.05%
25 5.75 2 25.52% 20.83%
Table 3: Evaluation results for city networks that include at
least one valid Holon.
Network Holons Found Supply Coverage
Size avg. med. avg. med.
10 1.79 2 45.06% 42.86%
11 1.85 1 43.28% 40.00%
12 2.20 2 42.90% 36.36%
13 2.13 2 40.11% 38.46%
14 2.52 2 38.79% 33.33%
15 2.83 2 38.00% 30.77%
16 3.17 2 38.64% 35.71%
17 3.21 2 34.02% 28.57%
18 3.54 2 34.52% 29.41%
19 4.25 2.5 35.78% 31.25%
20 4.03 3 33.38% 27.78%
21 4.69 3 33.63% 30.00%
22 4.74 3 31.46% 26.32%
23 5.10 3 31.19% 27.27%
24 6.23 3 32.20% 28.57%
25 7.33 3 32.53% 28.00%
more likely to find no Holons in such a setting.
When including all city networks, the overall cov-
erage by Holons increases with the city network
size (cf. trend line in Figure 5). However, leav-
ing out those city networks without any Holons,
the overall coverage decreases (cf. trend line in
Figure 6).
Holons always have a natural number of nodes.
Thus, the coverage values for small city networks
are more coarse-grained.
6 CONCLUSION AND FUTURE
WORK
In this paper, we investigated the adopted concept of
Holons in city energy networks. Holons are regarded
as enablers for decentralized energy producers inside
city networks to supply power to consumers inde-
pendently from external energy sources, like, power
plants connected via the transmission network.
We conducted a large-scale simulation with ran-
dom city networks based on experiences from a real-
world test site to analyze the performance of the
Holon approach. As the evaluation results show,
Holons can power parts of city networks, even when
they are disconnected from the transmission and dis-
tribution network during a blackout.
Our results show that the Holon concept is able to
supply 22.08% of a city network on average. This
coverage raises with increasing city network size
while the probability of total blackouts drops.
We see this increase of coverage as an effect
of higher flexibility when more producers and con-
sumers are available inside the city network. Our fu-
ture work will investigate how the flexibility in city
networks can be increased. We will investigate how
influencing the consumption of energy (and, likewise,
the production of energy) by introducing lower and
upper bounds for energy consumption relates to flex-
ibility. We hope that increased flexibility enables
higher coverage of city networks. Moreover, our in-
vestigation will include prioritization of specific con-
sumers in order to favor critical infrastructures, like,
hospitals, over other consumers in case of energy
scarcity.
ACKNOWLEDGEMENTS
The work in this paper was performed in the context
of the PolyEnergyNet project and partially funded by
the German Federal Ministry for Economic Affairs
and Energy (BMWi) under grant no. “0325737E”.
Additionally, this work has been co-funded by the
DFG as part of project B.2 within the RTG 2050 “Pri-
vacy and Trust for Mobile Users”. The authors as-
sume responsibility for the content.
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