Socio-Technical Complexity in Energy Infrastructures
Conceptual Framework to Study the Impact of Domestic Level
Energy Generation, Storage and Exchange
Michiel Houwing*, Petra Heijnen*, and Ivo Bouwmans*
Abstract—Household level energy conversion, storage and
exchange technologies are assumed to pervade the energy
infrastructure in the future. These novel technologies will
influence the total infrastructure in a bottom-up way; both
technically and socially. Not only the physical networks, but also
the social actor network consisting of households, network
managers, energy suppliers and producers is influenced. This
paper describes and conceptualizes a complex systems approach
towards energy infrastructures based on a large penetration of
decentralized technologies. Households thereby contain an
energy hub; an interface between a number of energy sources
and loads. Households can interact with each other and with
other actors via their hubs. Our approach paves the way for
modelling the socio-technical complexity via Agent-Based
Modelling (ABM) and for subsequent exploratory simulations.
chance of becoming widely present in the energy
infrastructure in the future. DG units can provide power
on-site and feed in power at the low voltage level.
Advantages of DG are mentioned, among others, in [3-5] and
include:
- Environmental benefits (emission reductions via the use of
renewable sources and the efficient use of fossil fuels);
- Reduced investment risks;
- Diversification of energy sources (needed due to the
depletion of fossil fuels);
- Energy autonomy and fuel diversification (less
geo-political dependency on fossil fuel rich countries);
- Energy efficiency increase: less line losses and combined
heat and power (CHP) applications.
Index Terms—Agent-Based Modelling, Complex Systems,
Distributed Generation, Energy Infrastructures.
Large-scale diffusion of DG will probably have a profound
impact on energy infrastructure functioning: it will bring
radical changes to the traditional model of generation and
supply as well as to the business model of the energy industry.
DG will affect actors’ performance and decision making due
to changing technical and social dynamics within the
infrastructure. Besides electricity generation, other novel
(and more sustainable) technologies to convert, exchange and
store electricity and heat at a decentralized level are under
development (e.g. fuel cells, heat pumps, aquifers).
From now on, with the term ‘DG’ we mean generation of
electricity and heat at the domestic level and this term
includes technological options for conversion and storage as
well. In this study, we assume the presence of a large share of
DG at the domestic user level. Household consumers
represent a substantial part of the total consumption and many
DG technologies are designed to be implemented at the
domestic level (e.g. micro-CHP, photovoltaics). The scale of
future DG penetration and the different possibilities for
operation and control regarding DG are uncertain and
therefore the answer to the question of how the infrastructure
will be impacted by domestic level DG introduction is
uncertain as well. Electricity and gas networks, for example,
are impacted (technically and economically) by DG
introduction and network managers can not know this impact
precisely as it depends on the types of installed technologies
and the types of contractual arrangements made between
households and the other actors. So, the consequences for the
operation and management of network managers are
uncertain. The influence on the total infrastructure is complex
as many interacting actors are involved.
T
I. INTRODUCTION
HE domestic sector is responsible for a substantial part of
a society’s energy consumption. In 2000 the domestic
sector accounted for 18% of the final Dutch energy
consumption [1]. Other sectors include transport, industry,
services and agriculture. Households consume final energy in
the form of electricity and heat. In The Netherlands,
household heat (space heating, hot water, cooking) is mainly
produced inside the house from natural gas. In 2003 the
domestic sector was responsible for 25 % of the Dutch
electricity consumption and 24% of the Dutch natural gas
consumption [1, 2]. At present, households in developed
economies use electricity that is generated via a relatively
small number of power plants connected to the high voltage
grid. This electricity is subsequently transported to the end
consumers via lower voltage levels. Additionally, households
have individual appliances for heat supply, using either gas or
electricity. Also district heating systems are sometimes used.
A. Distributed Generation at the Domestic Level
Much literature states that Distributed Generation (DG) of
electricity (e.g. via photovoltaics or wind turbines) has a large
This research is sponsored by the “Next Generation Infrastructures”
research programme (www.nginfra.nl).
*All authors are part of the section of Energy and Industry, Faculty of
Technology, Policy and Management, Delft University of Technology, P.O.
Box 5015, 2600 GA Delft, The Netherlands. Corresponding author: Michiel
Houwing (PhD researcher), e-mail: [email protected].
‘the market’
APX
actor
layer
large
consumers
The energy infrastructure is defined as the total system of
generation, transport, distribution, trade, supply and
consumption of energy. This means not only the physical
network (e.g. power plants, gas pipes, heat delivery stations),
but also the social (economic and institutional) network that
manages and controls the physical system. Together, these
networks form a socio-technical infrastructure system. It is a
complex system; the technological, economic, and
institutional domains are strongly interdependent. The system
is situated in and acted upon by an environment, which
influences system behaviour, but is itself influenced by this
behaviour very little; the environment is external to the
system (see Fig. 1). The Dutch energy infrastructure, for
example, can be regarded as a complex system situated in the
global energy infrastructure. When considering the Dutch
system the process of global oil price formation can be
regarded as being external to the system, i.e. as a part of the
environment.
The focus in literature on DG is mainly on the separate
technology options; the technologies are dealt with
individually (e.g. [3]). Also, many studies focus on only one
of the energy infrastructures, i.e. electricity, gas or heat (See,
for example, [6, 7]). Implementing DG technologies at the
domestic user level on a large scale combines different energy
infrastructures and enables the interaction between different
DG technologies. There is a high level of socio-technical
complexity involved (many interactions between social
actors and technological components; different actors having
conflicting objectives). Viewing energy infrastructures in a
combined manner and relating technological and social
aspects is a novel research area and studying this is important,
because actor behaviour determines for a large part how the
total socio-technical system will perform.
Fig. 2 gives a representation of the current electricity
infrastructure as a socio-technical system. The current
electricity network configuration in developed countries is
that large producers and consumers are connected to the high
voltage (HV) grid and that smaller producers and consumers
are connected to the medium and low voltage distribution
grid. DG technologies are connected to the distribution level
of the network. The institutional environment acts upon this
system. The introduction of DG will affect not only the
physical layer in Fig. 2, but also the connected (social) actor
layer. The situation with DG is depicted in Fig. 3. Electricity
from large generators reaches the loads via the transmission
and distribution networks (a-b-c). Distributed generators
produce directly for loads (d) (in households) and surplus
electricity flows to the distribution network (e) and can even
flow into the transmission network (f). In the economic
subsystem, the dark blue arrows designate the electricity
sales. Large producers sell electricity to the market (1).
Distributed producers can sell electricity to the market (2)
(when being relatively big, when organized in groups or
when being part of a production company), they can deliver
directly to consumers (the households themselves) (5), or to a
supplier (6). Large consumers and suppliers can obtain
electricity from the market (4, 3). Consumers can also obtain
electricity from their supplier (7). Suppliers can also sell
electricity to the market (electricity that has been bought 'too
bilateral
market
producers
balancing
market
import
capacity
auction
small
consumers
II. THE ENERGY INFRASTRUCTURE AS A COMPLEX
SOCIO-TECHNICAL SYSTEM
Fig. 1. An infrastructure as a socio-technical system.
retail companies
B. Objective of Paper
As the proper functioning of the energy infrastructure is
vital to our economies and social welfare in general,
researching the possible impacts of DG on the infrastructure
and its different constituents is important. This paper deals
with the complexity that arises in the energy infrastructure
once DG is present. We do not answer the question if these
technologies will pervade the future infrastructure; we
assume the presence of DG. We elucidate the novelty and the
need of researching the socio-technical complexity in energy
infrastructures incorporating DG. Modelling of the
infrastructure system’s complexity will provide insight for
different actors in the infrastructure on how their operational
activities are impacted in a future where DG is widely
present. This knowledge can help these actors to prepare for
such futures when they become reality. The modelling work
is in progress and is not described in this paper. Here, we
present our conceptual model and research approach.
Section II describes the energy infrastructure as a complex
socio-technical system. In section III, the concept of energy
hubs in households is introduced. Section IV discusses the
complexities of a DG-based infrastructure in more detail.
Agent-Based Modelling (ABM) as a paradigm for modelling
complex systems is the subject of section V. The paper ends
with conclusions and recommendations.
system
operator
transmission
network
managers
distribution
network
managers
TenneT
physical
layer
generation
transmission
network
distribution
networks
Fig. 2. The conventional electricity infrastructure represented as a
socio-technical system, taken from [8].
load
Fig. 3. The socio-technical electricity infrastructure with DG.
much' previously, or electricity from distributed generators
via so-called virtual power plants) (3). What the actor layer
will look like precisely in a future with DG depends on the
interactions between actors in a future with DG. It also
depends on the authorities given to the actors to undergo
interactions and therefore on legislation and regulation.
So, the operation and management of the system and the
performance of actors will be influenced by DG introduction.
An example within the electricity infrastructure would be
ensuring the energy balance. Physical balancing of electricity
is necessary and is achieved via a market. Producers and large
consumers (including retail companies) in The Netherlands
have the responsibility to hand in energy programs to the
system operator in which they state their predicted amounts of
production and consumption for the next day. With their
control and reserve capacity, producers also bid on the
so-called imbalance market. When actual delivery takes
place, there is always imbalance, as the exact demand can
never be predicted. This sometimes results in substantial
imbalance costs for retail companies (and imbalance revenues
for the bidding producers on the imbalance market, in which
the system operator is the single buyer). Once DG is present,
predicting household demand by suppliers will change. How
to deal with households producing, exchanging and possibly
storing energy regarding the energy balance? A possible
future option for minimizing imbalance costs could be to
control households with financial incentives (see [9]). When
a supplier operates a virtual power plant, he could minimize
imbalance by adjusting DG units and when an energy
company consists of both a generation and a supply business,
it has an additional option to internally balance its portfolio
by making use of households with DG (besides the central
power plants it controls). Another issue is regular electricity
trade. Electricity trade options increase, as households will
function as producers from whom retailers could purchase
power, for example.
Presently, different energy flows are relatively
independent (electricity, gas, heat). Increased incorporation
of DG will lead to increased interaction between energy flows
(co- and trigeneration). Energy can be converted between
electrical, thermal and chemical states. So the physical as well
as the social networks of the energy infrastructure become
more entwined. DG introduction will not only influence the
electricity infrastructure, but the gas (and other chemical
energy carriers) and heat infrastructures as well. This
influence results in different decision making on the
operational, tactical and more strategic level.
In our study, we are interested in the changes in operational
level management and decision making. The focus is on
household load patterns, load management and load
forecasting by utilities, and on demand-side management
options. We do not focus on near real-time operations
pertaining to ancillary services as frequency and voltage
control and reactive power management. The time constant of
the studied dynamics of the system will be in the several
minutes to a quarter of an hour range.
Households are considered as having (connected) energy
hubs (section III) that can interact with each other and with
other actors in the infrastructure. There are many interactions
and contractual arrangements conceivable between actors in a
future that incorporates DG in order to increase operational
efficiency. Intelligent metering (facilitated by the use of data
exchange systems as GSM and the internet) will play an
important role in the possible interactions. It enables near
real-time monitoring and control as well as options for
demand-side-management
(therefore
also
metering
companies managing the read data will have an important
role). As stated in [5]: “In a DG-dominated grid, energy,
control information, and money will flow in all directions.”
The influence of large-scale DG introduction on future
decision making, operation and management, and
performance of actors is not precisely known at present. In
our work, DG technology functioning, the possible
interactions between actors (facilitated via contractual
arrangements) and the resulting performance of actors will be
mapped and modelled. Subsequent simulation studies will
serve as experiments to assess the operational performance
with different implemented technology mixes and interaction
options. With the simulation outcomes, different actors can
be advised on how to prepare for DG-based futures.
III. MULTI-SOURCE MULTI-PRODUCT SYSTEMS AND
ENERGY HUBS
The term multi-source multi product system (MSMP)
pertains to an energy system with multiple energy in- and
outputs [10]. With the term MSMP system, the real physical
system is meant. For the purpose of modelling and
optimization it is convenient to introduce the concept of an
energy hub, adopted from [11]. Energy hubs represent an
interface between a number of power sources and loads.
From a system point of view, the energy hub represents a part
or a unit of a mixed energy carrier power system providing
the basic features:
1. input and output
2. conversion, and
3. storage
of different energy carriers. Energy hubs can exchange
electrical, chemical, and thermal power. So, the energy hub is
an input-output model, which describes the essential features
of a connected system of energy conversion devices. The hub
contains converter elements establishing couplings between
different energy carriers. Storage devices affect the power
flows as well. Loads and generation from renewable sources
can be taken into account (e.g. hydro, wind, solar) as well as
connections to other hubs [11]. A MSMP system and an
energy hub can have a certain intelligence, which enable the
sensing and control actions in the MSMP system. Energy
hubs can be modelled mathematically by stating input and
output vectors containing different flows and products,
coupled by a conversion matrix [10].
We conceptualise households as containing an energy hub
or a part of a hub. Fig. 4 illustrates a household with the
maximum of features envisioned by us to fulfil its electricity
and heating (and cooling) load. Households have certain
energy needs, depending on season and time of day. A
conversion technology (‘C’ in Fig. 4) can have chemical
energy carriers (natural gas, hydrogen, methanol), electricity
and heat as inputs as well as outputs. A direct carbon fuel cell
converting heat and carbon into electricity and carbon
monoxide is an example of a conversion technology using
heat as input. Electricity can be obtained externally from
power plants or from other households via an external grid or
it can be generated from renewable sources (wind, solar) or
via a conversion technology (e.g. micro-CHP, fuel cell).
Excess electricity can be delivered back to the grid.
Electricity storage in batteries is also considered. Hot water
can be generated by solar boilers or via a conversion
Fig. 4. A household containing an energy hub: energy carrier flows,
conversion and storage technologies.
technology (e.g. a conventional boiler). The heat stream
leaving ‘C’ can be in the form of hot water as well as hot flue
gas. A cooking stove can be regarded as a conversion
technology as well; it produces hot flue gas. Additionally or
alternatively, heat can be taken from a district heating
network or from a waste heat stream. Heat pumps can
upgrade this heat to required temperature. Waste and district
heat can be used directly in household radiators and as hot
water source, or the heat can be exchanged to a second water
cycle. Another source of relatively hot water can be an
aquifer, which is an underground layer of water-bearing
permeable material (rock, gravel, sand, silt, clay) from which
groundwater can be extracted. Again, the household can use
this water directly or after heat exchange with a second cycle.
An aquifer can be part of the household or it can be shared
with others. Hot water can be stored in a thermal energy
storage. When heat is an unused by-product, it could be
blown off into the environment. Cooling via air conditioning
is included in the electricity load.
Chemical energy (methane, hydrogen) can be stored before
conversion or after having been produced (for example by a
fuel cell or an electrolyser). In the total energy system there is
often a mismatch between supply and demand. If the systems
should be operated as efficiently as possible, energy storage is
inevitable. It might happen that a temporal mismatch between
supply to and demand from a household is (economically)
advantageous and a household might store energy for later
use or for sale to other household or energy companies.
Households can exchange electric, thermal and chemical
energy with each other as well as with the external grid.
Further, intelligent metering is assumed to be present in
households, which allows real-time data transfer on price
levels and energy consumption between households and their
energy supplier to which they are connected.
In reality there are many types of households. They can
vary in energy consciousness, number of members, income,
insulation, et cetera. Households can have different load
demand patterns and differing subsets of the features shown
in Fig. 4. In our model, different households will be defined:
an example of a household using micro-CHP is shown in Fig.
5 on the next page. An important aspect of a household is its
human (social) aspect. In time, there is a fluctuating need of
electric and thermal energy and possibly a fluctuating
comfort valuation. Comfort could be exchanged for financial
remuneration. Depending on, for example, the costs of the
options to fulfil a certain temporal energy demand, their
environmental impact and their speed of delivering the
required load, a household will choose a specific option or set
of options that is available to him. Therefore, there can be
multiple criteria for a household could consider in its choice.
When advanced domotics (the application of computer and
robot technologies to domestic appliances) would be
implemented in a household, the intelligent meter could
optimize which option(s) to choose. For example, when gas is
cheap the intelligent meter in a house with a micro-CHP unit
could choose to generate its own electricity instead of using
centrally generated electricity and to blow of some heat. The
interaction between a micro-CHP equipped household and its
Fig. 5. Household using micro-CHP without heat storage.
energy supplier and options to control households with DG
units are described in detail in [9, 12]. Another possibility
could be that the energy supplier is authorised to operate DG
technologies within certain boundaries, which are specified in
a contractual arrangement between the household and its
supplier (the energy company then operates a so-called
virtual power plant). So, via intelligent metering, different
‘modes of control’ regarding DG operation can be thought of.
Also the microgrid concept, in which a group of households
fulfil their energy needs by exchanging energy only amongst
themselves, is an interesting option. The energy hub concept
described in this section provides a good way for future
modelling of MSMP systems in households.
IV. COMPLEXITY IN A DG-BASED INFRASTRUCTURE
We approach the energy infrastructure as a complex
system as theory and methods from complexity science can
help elucidate and model the complexity arising in the
infrastructure once DG becomes widely applied. Complex
systems have a significant number of the following
characteristics [13, 14]:
• Many components: Many interacting components
constitute the system. They behave in a simple or more
sophisticated way and could strive for different, even
conflicting, objectives. Components in our system are
households, suppliers, producers, network managers, the
physical network, etc.
• Heterogeneity: System components differ in important
characteristics, e.g. different types of households with
different behaviour and DG technologies.
• Nonlinear dynamics: System component characteristics
change over time, as entities adapt to their environment,
learn from their experiences, or experience natural
selection in a regeneration process. The dynamics that
describe how the system changes over time are usually
nonlinear. Fuel cell technology behaviour is nonlinear, for
example. Households could adapt their behaviour to price
changes by operating their technologies differently.
• Feedback: Changes in component characteristics are often
the result of feedback they receive because of their
activities. Decisions made by suppliers will affect decision
making of households, which in turn will affect the
performance of suppliers. See [9].
• Organization: System components are organized into
groups or hierarchies. These organizations are often rather
structured, and these structures influence how the
underlying system evolves over time. Network managers
set tariffs for electricity transport over the distribution and
transmission networks. They help shape the arena in which
households make their decisions.
• Nestedness: The components of a complex system may
themselves be complex systems. Our studied system
consists of clusters of households together with an energy
supplier and a network manager, for example. These
clusters again consist of individual households. We can
also speak of ‘systems of systems’.
• Difficult boundary definition: the observer of the system
makes the decision of what falls inside the system
boundary. We first assume price formation on the power
exchange as taking place outside the system boundary.
This formation process could later be included, however.
• Emergence: The macro-level behaviour of the system
emerges from the actions and interactions of the individual
components. The behaviour of a household cluster (energy
wise) will depend on the sum of the individual household
behaviour.
V. AGENT BASED MODELLING
A modelling paradigm that is very applicable in our study
is Agent-Based Modelling. We first introduce the agent
concept and then elaborate a bit more on what Agent-Based
Models (ABMs) are and why they suit our modelling efforts
so well. Agents are defined as self-contained problem solving
software entities [15]. They are autonomous, goal-driven and
environment sensitive. The agent concept provides a
powerful way to describe a software entity that is capable of
acting with a certain degree of autonomy in order to
accomplish tasks. Viewing households as agents is a very
elegant way to include the diversity of household behaviour
and the installed DG technologies in households. For
example, it can be assumed that households will individually
or collectively optimise their use of energy, but one can also
think of households ‘choosing randomly from the options
they have at their disposal’. They can respond to price
incentives by shedding load or by generating energy for their
suppliers. Demand side management options (via novel
contracts between households and other parties) can be
experimented with in order to manage energy consumption.
Technologies can also be operated in various ways. For
example, in [16], 6 different operating modes of micro-CHP
are mentioned. Other agents than households are, for
example, energy suppliers, network managers and
governments. Multi-agent systems (MAS) and ABMs are
based on the agent concept. The advantage of MAS and ABM
is the inherent bottom-up approach. The overlying concerns
in ABMs are the macro-level behaviours that emerge from the
assumptions about the actions and interactions of the
individual agents. Our ABM development will consist of
several stages; it is an iterative process with a steady
knowledge build-up in cooperation with experts. Sector
experts are regularly consulted to discuss the expected
impacts on their operational activities in a DG-based future.
A relatively rudimentary model is started with. First, we
consider household agents operating only one DG technology
and always fulfilling their energy demand. Later, virtual
power plant and microgrid concepts will be considered, in
which there is more interaction amongst the households and
between households and energy utilities. Different decision
making of households will result in a changing emergent
behaviour. Wide application of DG can result in more
decision-making complexity: decisions made within different
levels (sub-systems) of the energy infrastructure will
influence each other differently and in a more complex
manner than in the situation without these units. This is
mainly because households will be able to generate electricity
themselves. ICT developments and intelligent metering
creates new opportunities for influencing behaviour between
sub-systems of the infrastructure.
Examples of questions to ask our models are: How does a
certain DG-based system perform regarding different criteria
(costs, emissions)? How large are costs and revenues for
agents in the system? How might (the need for) energy
balancing change due to DG introduction? How will
electricity and gas flows through the networks change? In
answering these questions, different technology mixes at the
household level and different possible social interactions
between agents will be experimented with in computer
simulations.
Within the knowledge acquisition community (artificial
intelligence, computer science), the use of ontologies in the
construction of knowledge intensive systems is widespread.
Ontologies are used to facilitate knowledge sharing and
reuse, agent interoperability, and knowledge acquisition [17].
An ontology is a domain description, a language for agents,
as well as a specification of concepts used by agents. An
ontology of a specific domain gives a good overview of what
modellers consider important if models are to be developed of
those domains. An ontology also represents the class
hierarchy for an ABM. An ‘Energy and Industry’1 ontology
has been developed as part of this study and we will use this
ontology to develop our ABM. We will not go into further
detail regarding the ontology here.
VI. CONCLUSIONS AND RECOMMENDATIONS
The energy infrastructure will change bottom-up due to
large-scale DG introduction at the domestic level. Viewing
this infrastructure as a complex, socio-technical system and
modelling its complexity may lead to novel insight in
operational system behaviour and performance regarding
criteria as costs, emissions and reliability. Different options to
operate DG technologies (facilitated by contractual
arrangements) can be experimented with. Households are
hereby conceptualised as containing energy hubs, converting,
exchanging and storing energy. The relevance of developing
ABMs of a DG-based energy infrastructure and obtaining
knowledge on the operational functioning of such systems is
high for all players in the infrastructure.
The concepts presented in this paper pave the way for
modelling the socio-technical complexity via ABMs and for
subsequent exploratory simulations. ABM serves as a
suitable modelling paradigm as it can include the
technological and behavioural diversity of household agents
regarding the fulfilment of their energy needs. We are now
modelling our system in the RepastTM ABM modelling toolkit
and preliminary results are promising.
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