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september/october 2013 IEEE power & energy magazine 751540-7977/13/$31.00©2013IEEE
Digital O bject Identif ier 10.1109/MPE.2013.2268815
Date of pu blicat ion: 16 August 2 013
© I S T O C K P H O T O
. C O M / D E R R R E K
T
THE ENERGY SYSTEMS IN OUR BUILDINGS AND BUILD-
ing districts form a tight network of several energy sources, such as
renewables and fossil fuels, and energy flows, such as electricity and
heat. Over the years, the integration and interaction of these sourcesand flows have become more and more interwoven.
To evaluate the results of certain types of energy system integra-
tion (ESI) in buildings or districts, the Electrical Energy, Building
Physics, and Applied Mechanics and Energy Conversion divisions
of the University of Leuven (KU Leuven) have jointly developed
Integrated District Energy Assessment by Simulation (IDEAS),
a Modelica library for the integrated modeling and simulation of
buildings and districts. IDEAS can describe the built environment,
energy consumption and supply, and networks and control in just
one model, giving rise to a more effective analysis and better control
of the energy system under consideration.
In this article, we focus on the advantages of ESI for electricalmodeling and assessments. With IDEAS, we can assess the
Ideas
for Tomorrow
New Tools forIntegrated Buildingand DistrictModeling
By Juan Van Roy,
Bart Verbruggen,and Johan Driesen
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6 IEEE power & energy magazine september/october 2013
integration, interaction, control, and feedback of multidis-
ciplinary energy systems, buildings, and district systems.
IDEAS is able to simulate the electrical grid to which build-
ings, loads, and distributed generation units are connected.
We can therefore take the limitations of the electrical grid
into account, which makes it possible to assess the impact
of all the energy systems on these grids and investigate the
possible interactions among the systems.
Traditionally, the assessment of
building topologies, thermal building
systems, and electrical systems is per-
formed separately, using discrete tools.
We feel, however, that a multidisci-
plinary energy assessment of individual
buildings and the interactions amongbuildings in districts can lead to bet-
ter integration and interactions among
generation, distribution, control, and
storage of the different energy vectors
in buildings and districts (see Figure 1).
IDEAS: A Tool forIntegrated Building andDistrict SimulationsWith the IDEAS library, we can incor-
porate the dynamics of the hydronic,
thermal, and electrical processes andnetworks in buildings and districts into
a single model and solver. We imple-
mented IDEAS in the Modelica mod-
eling language, which is open-source,
object-oriented, and equation-based (it
uses differential and algebraic equa-
tions). It is well suited for physical
modeling and offers an easier integra-
tion of different domains in a single model.
IDEAS and Electrical Assessments:Possibilities for ESI and Scalability The IDEAS library consists of five sublibraries, for climate,
building, occupant, thermal, and electrical modeling (see
Figure 2). All these components can be easily intercon-
nected for model integration (see Figure 3).
Concepts for Energy Storage
Integrate Buildingsand Transport
Flexibility of Energy Consumers
Integration Tools andControl Strategies
Coupled LocalEnergy Networks
Energy Consumption
Patterns
E-Market
figure 1. ESI in buildings and districts (source: KU Leuven/Electa—NB).
IDEAS
Climate Electrical
System
Integrated
Control
Electricity Demand
Thermal
(HVAC) System
Building and
Occupant
• Heat Gains and Losses• Solar Shading• PV Power Production
• Dynamic Multizone Model• Thermal (Heating and Cooling) Comfort Demand• Occupant Behavior• Use of Electric Appliances and Lighting
• Thermal Energy Generation• Heating/Ventilation• Domestic Hot Water• Thermal Storage
• Distributed Generation• Battery Storage (Distributed and Centrally)• Electric Vehicles• In-Home Grid• Distribution Grid
• Thermal Comfort• Peak Shaving• Voltage Regulation• Self-Consumption Local Generation
figure 2. The five IDEAS sublibraries.
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september/october 2013 IEEE power & energy magazine 77
The electrical component library of IDEAS consists
of models to simulate photovoltaic (PV) systems; battery
storage systems, including electric vehicles; and electrical
grids. The grid-modeling capability includes low-voltage
distribution grids and electrical networks in buildings.
The PV system model simulates the power output of one
PV panel. The model uses parameters taken from existing
panels on the market (it uses the five-parameter model,
with a temperature-dependent equivalent diode circuit). It
uses meteo data (irradiance, temperature, and so on) from
Meteonorm to calculate electricity production. In the model,
we can place several PV panels in series and/or in parallel,
and we can define tilt angles, orientations, and so on. These
capabilities allow great flexibility in the use of the model.
We can also simulate the inverter of the PV system so
as to incorporate inverter losses, as well as inverter con-
trol strategies that curtail electricity production in case ofovervoltage and voltage droop mechanisms that regulate
power output.
The battery storage model in IDEAS calculates the state
of charge of the battery using the electricity flows toward the
battery or from the battery to the building or electricity grid.
We can use this model to simulate decentralized or central-
ized storage units. Further, since most electric vehicles (EVs)
use batteries as storage units, we have also implemented an
EV model in IDEAS that can model EV battery storage and
driving and charging behavior.
Electrical distribution grids connect many different
buildings and energy systems (loads and generation units)within districts. Electrical networks in buildings connect
the different electrical loads in the building itself. Control
strategies for energy systems can include grid parameters,
such as voltages and power exchanges, to shift the operation
of these systems in time. These strategies can ensure, for
instance, that technical grid constraints such as over- and
undervoltages and grid capacity are not violated.
As in-building grids, both single-phase and three-phase
low-voltage distribution grids are radial grids with a single
point of common coupling to another grid (see Figure 4).
Both the single- and three-phase distribution grids therefore
use the same models to build up the grid topology. Describ-
ing grid topologies using only the incidence (or connection)
matrix and the impedance matrix makes for a very flexible
and scalable approach to the modeling of electrical grids. In
this way, clusters of buildings in districts, combinations of
districts, and so on can easily be modeled.
The object-oriented approach in Modelica also offers aflexible use of the different models. For instance, we can
first simulate the models in their respective domains before
interconnecting them. This is useful for the development,
testing, and validation of models. Depending on the scale of
the simulation case (only one building or a combination of
districts with multiple buildings), it is possible to use models
with a lower degree of complexity.
Example: Electrical Bottlenecksat the Feeder Level for a Districtwith Zero-Energy Buildings
The following example from our research demonstrates theuse of the IDEAS tool to assess electrical bottlenecks at the
Grid BIPV
dc
ac
Heat Supply
BMS
Building
Occupants
figure 3. A schematic overview of the IDEAS tool (source: Baetens et al.).
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8 IEEE power & energy magazine september/october 2013
feeder level for a district with zero-energy buildings (ZEBs)
using building-integrated PV systems and heat pumps.The case consists of a residential district with 33 ZEBs
using the radial IEEE 34-node test feeder, for which the
parameters are downscaled to represent a typical low-volt-
age feeder (230/400 V). The three different test scenarios
use different cable cross sections to represent a strong, a
moderate, and a weak feeder design. We assumed fully bal-
anced loading, and we have taken the power losses and volt-
age drop in the feeding transformer into account.
All buildings are detached and are based on four
architectural types that are representative of the Belgian
building stock. Each has a heat pump for heating and domes-
tic hot water and an optimally oriented PV system (facing
south and at a 34c inclination) able to satisfy the building’s
annual electricity consumption with its annual production.
Figure 5 shows the annual cover factor, both self-consump-
tion and self-generation, as a function of the net-zero-energy
design level for individual buildings
and for the three different feeder
designs. The cover factors describe
the simultaneity between the demand
and supply of electricity. In Figure 5,
a design level of one (on the x -axis)
denotes a PV sizing that exactly cov-ers yearly electricity consumption.
At the building level, self-con-
sumption is only about 26% with a
zero-energy building design level of
one, as depicted in Figure 5(a). This
is due to seasonal patterns and high
nonsimultaneity between production
and consumption. This low self-con-
sumption shows that a large part of the
electricity produced is injected into the
electricity grid, which in turn affects the grid (in terms of
voltage deviations, peak loads, and so on).Since there is a diversification of consumption, the aggre-
gated consumption profile is more flattened out. When we
look at the district level, overall self-consumption and self-
generation increase, since a part of the electricity production
in one building can be used in another building. Figure 5(a)
shows this for an ideal feeder. This ideal feeder does not
take into account the grid impact of the PV systems and
heat pumps.
The plots in Figure 5(b), (c), and (d) show the impact of
grid limits on self-consumption and self-generation. Volt-
age deviations can curtail the PV systems if overvoltage
occurs. The curtailment happens more for weaker grids.
And because of such curtailment, yearly energy production
is lower. In such cases, we can therefore observe a higher
self-consumption and lower self-generation. The amount
of curtailment of PV systems depends on the location of
PV
(a) (b)
figure 4. Topology comparison between (a) a distribution grid and (b) an in-building grid.
Design Level of Net ZEB, (-)
(a)
Design Level of Net ZEB, (-)
(b)
Design Level of Net ZEB, (-)
(c)
Design Level of Net ZEB, (-)
(d)
With Ideal Feeder IEEE 34 Bus-Al 150.95.50 IEEE 34 Bus-Al 95.50.35 IEEE 34 Bus-Al 50.35.25
1.0
0.8
0.6
0.4
0.2
0.00 1.0 2.0
C o v e r F a c t o r c ,
( -
)
cS cD
1.0
0.8
0.6
0.4
0.2
0.0
C o v e r F a c t o r c ,
( -
)
0 1.0 2.0
cS cD
1.0
0.8
0.6
0.4
0.2
0.0
C o v e r F a c t o r c ,
( -
)
0 1.0 2.0
cS cD
1.0
0.8
0.6
0.4
0.2
0.0
C o v e r F a c t o r c ,
( -
)
0 1.0 2.0
cS cD
figure 5. Annual cover factors as a function of the design level of net zero energy at the building (gray) and aggregated
(black) levels, including feeder limits: (a) ideal feeder, (b) strong feeder, (c) moderate feeder, and (d) weak feeder (source:Baetens et al.).
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september/october 2013 IEEE power & energy magazine 79
the building in the grid. This explains the spread between
self-consumption and self-generation at the individual-
building level.
Despite a design level able to create ZEBs in theory,
the buildings will not all reach this level in reality due to
grid limits (see Figure 6). Equivalence between generation
and consumption on the district level may possibly still beachieved by enlarging the PV systems, but this could lead
to greater impacts on the grid and higher generation losses.
One way to solve these problems is to increase grid
strength. This may not always be possible, or it may not be
the best possible solution. Integrated simulations can find the
best method for integrating demand-side management, elec-
trical and thermal storage, grid planning, and other parame-
ters. They can also optimize single systems, such as building
design, and investigate the impact of such optimizations on
other energy systems.
ZEBs and ESIThe different climate and energy targets that have been
adopted in Europe and globally (see “Building Climate
Impacts and Targets”) are leading to increased integration of
renewable and distributed energy sources in buildings, such
as PV systems, wind power, and combined heat and power
(CHP). On the other hand, new technologies, such as EVs
and heat pumps, are increasing the energy efficiency of the
whole energy system.
Grid Impact of Renewablesand Energy-Efficient TechnologiesRenewable and distributed energy sources in buildings
often have an intermittent electricity production profile.
For residential buildings, the local production of electric-
ity is typically very much noncoincident with the local
consumption of electricity. In case the local consumption
or storage is lower than the local production, the genera-
tion unit injects the surplus of electricity into the grid. On
the other hand, new technologies are often responsible for
higher electricity consumption in buildings.
Both the injection of electricity into the grid (via PV
systems and CHPs, for example) and higher consumption of
electricity have grid impacts. Residential and commercial
buildings are mainly connected to low-voltage grids. The
injection and consumption of electricity can therefore lead
to peak loads, higher resistive losses, voltage deviations,
phase unbalance, and other issues in the distribution grid.
The literature defines various grid impact and load-matching
indicators, as shown in Table 1.
In building simulations, the resulting voltage devia-
tions and possible overload situations in grids are often
not seen as a problem, since the simulations usually see
With Ideal Feeder IEEE 34 Bus-Al 150.95.50 IEEE 34 Bus-Al 95.50.35 IEEE 34 Bus-Al 50.35.25
2.0
1.5
1.0
0.5
0.00 1.0 2.0 0 1.0 2.0 0 1.0 2.0 0 1.0 2.0
Design Level of Net ZEB, (-) Design Level of Net ZEB, (-) Design Level of Net ZEB, (-) Design Level of Net ZEB, (-)
E f f e c t i v e L e v e l o f N e t Z
E B ,
( - )
2.0
1.5
1.0
0.5
0.0 E f f e c t i v e L e v e l o f N e t Z
E B ,
( - )
2.0
1.5
1.0
0.5
0.0 E f f e c t i v e L e v e l o f N e t Z
E B ,
( - )
2.0
1.5
1.0
0.5
0.0 E f f e c t i v e L e v e l o f N e t Z
E B ,
( - )
5 0 k V A
5 0 k V A
5 0 k V A
5 0 k V A
1 0 0 k V A
1 0 0 k V A
1 0 0 k V A
1 6 0 k V A
2 5 0 k V A
(a) (b) (c) (d)
figure 6. Effective level of net zero energy as a function of the design level of net zero energy at the building (gray) andaggregated (black) levels: (a) ideal feeder, (b) strong feeder, (c) moderate feeder, and (d) weak feeder (source: Baetens et al.).
Building Climate Impacts and TargetsWorldwide, residential, and commercial building stock
accounts for approximately 32% of total energy use and
produces about 30% of the total global end-use CO2
emissions.
The 20-20-20 climate and energy targets are part of
binding legislation in the European Union to reduce EU
greenhouse-gas emissions by 20%, increase the use of
renewable resources to 20% of total consumption, and
improve EU energy efficiency by 20% by 2020.
The European Commission released its energy goals
and benchmarks for buildings in its European Directive
2010/31/EU. The directive states that by 2020 all new
buildings or buildings with large renovations must be
nearly ZEBs (nZEBs).
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0 IEEE power & energy magazine september/october 2013
the grid as an idealized network with no limitations. But
the integration of the grid should be investigated, since
grid limits may have an important impact on building
optimizations.
Rising Electrification in BuildingsRegarding the evolution toward smart buildings in smart
grids, the integration of renewable and distributed energy
sources and energy-efficient technologies in buildings causes
increasing electrification in buildings. On the one hand, this
increases the importance of building electrical energy flows.
On the other, the interaction between electrical and thermal
energy flows grows.
Heat pumps are a good example. These loads con-
sume electricity to generate heat. Heat pumps thus have
an impact on the electricity grid. To control the operation
of the heat pump, however, different inputs can be used,
such as instantaneous PV power output to maximize self-
consumption or grid parameters (voltage, frequency, and
so on) to minimize grid impacts, and others. These control
steps, in turn, have an impact on the operational require-
ments of the heat pump for future time periods, the state of
the storage unit, heat losses, and so on.Given this, the different domains in buildings (electrical,
heat transfer, fluid dynamics, lighting, control, and so on)
tend to become more and more integrated and the interaction
between the energy systems and energy flows increases. This
requires new approaches to the analysis of these integrated
systems. Integrated energy system analyses, such as IDEAS,
have the benefit of taking the inputs of other systems into
account and seem to be an excellent solution.
Operational Flexibilities to Limit Grid Impact Some energy systems offer a certain flexibility to shift
their consumption or production in time (see Figure 7).
For instance, EVs can shift their battery charging in time
as long as the charging delay does not interfere with
driving requirements. Other systems, such as heat pumps
and CHPs, can shift heat generation in time by making use
of thermal storage.
These systems can therefore use this flexibility to
meet objectives such as minimizing the grid impact or maxi-
mizing the self-consumption of local generation. The latter
objective meets the challenge of the intermittent character
of renewables and their possible high noncoincidence with
local demand (see Figure 8). This leads to two corollaries.First, coordination strategies can use this flexibility to
meet such objectives. These strategies plan the operation
of the different energy systems. Second, we can use instan-
taneous grid parameters to shift the operation of different
energy systems in time to take technical grid constraints
into account. For instance, if voltages increase beyond the
allowed limits, control strategies can reduce or postpone the
consumption of appliances through the use of methods such
as a grid-stabilizing voltage droop system.
All this indicates the importance of taking into account
the interaction of multiple domains in building and district
simulations to obtain better system design, demand-sidemanagement (DSM), and storage solutions. By making
Upper Bound
PossiblePath
Lower Bound
E n e r g y
Time
figure 7. A flexibility curve represents the possibleoperation paths of an appliance. The upper and lower
bound curves show, respectively, the operation curvewithout any and with maximum delay of operation.
table 1. Overview and definition of various grid impact indicators (source: Verbruggen et al.).
Indicator Definition
Capacity factor Ratio of total energy exchange and the energy exchange in case the connectioncapacity is fully used
Loss-of-load probability Percentage of time that the load exceeds generation
Cover factor Simultaneity between demand and supply of electricity
1% peak power Mean power of the 1% highest peaks
Peaks above limit Percentage of time that power is higher than a certain value
Dimensioning rate Ratio of the peak power and the connection capacity
kVA credit Reduction potential of the grid connection
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september/october 2013 IEEE power & energy magazine 81
use of ESI, we can utilize the unique benefits each system
offers while maintaining comfort and robustness levels and
improving system efficiency levels. Shortcomings of Traditional ToolsThe complexity of ESI in buildings continues to grow.
Traditional simulation tools are therefore of only limiteduse for integrated modeling. Different tools are available to
simulate the various domains. We can distinguish between
the domain simulation scale (building, system, district,
national, and so on) and the time scale, for example. Two
approaches are available for traditional building and district
simulation tools.
In the first approach, models use thermal building phys-
ics and systems as a starting point. The simulations use
a combination of dynamic simulations of the heating and
cooling demand and stochastic occupant behavior. These
simulations, however, do not perform detailed studies of
the electric networks, and they aggregate the loads on alarge time resolution. They thus neglect grid limitations
and other factors that affect the operation of the various
energy systems.
On the other hand, electrical energy systems serve as
the starting point for the second approach. These models
perform physical calculations of electrical generation and
distribution and stochastic calculations of power loads.
Some tools, such as HOMER or DER-CAM, do not take into
account the electrical distribution grid, while grid simulation
tools like OpenDSS and GridLab-D include only simplified
building and heating models or take load curves as input
without offering any grid feedback possibilities.
The increased integration of energy systems in buildings
and districts requires a new approach in analyzing these
systems. Thankfully, more and more tools are being
developed that meet the aforementioned requirements.
The IDEAS tool is one of these new modeling and simula-
tion tools.
Acknowledgment The work of J. Van Roy is funded through a VITO doctoral
scholarship.
For Further ReadingR. Baetens, R. De Coninck, J. Van Roy, B. Verbruggen,
J. Driesen, L. Helsen, and D. Saelens, “Assessing electrical
bottlenecks at feeder lever for residential net zero-energy
buildings by integrated system simulation,” Appl. Energy,
vol. 96, pp. 74–83, Aug. 2012.
P. Fritzson, Principles of Object-Oriented Modeling and
Simulation with Modelica 2.1. Hoboken, NJ: Wiley, 2004.
J. Salom, J. Widén, J. Candanedo, I. Sartori, K. Voss,
and A. Marszal, “Understanding net zero energy buildings:
Evaluation of load matching and grid interaction indicators,”
in Proc. Building Simulations, Sydney, Australia, Sept.
2011, pp. 2514–2521.
J. Tant, F. Geth, D. Six, and J. Driesen, “Multi-objective
battery storage to improve PV integration in residential
distribution grids,” IEEE Trans. Sustain. Energy, vol. 4,
no. 1, pp. 182–191, Jan. 2013.
B. Verbruggen, R. De Coninck, R. Baetens, D. Saelens,
L. Helsen, and J. Driesen, “Grid impact indicators for active
building simulation,” in Proc. IEEE PES Innovative Smart
Grid Technologies (ISGT), Anaheim, CA, Jan. 2011, pp. 1–6.
M. Wetter, “A view on future building system modeling
and simulation,” in Building Performance Simulation for Design and Operation. London, U.K.: Routledge, 2011, ch.
17, pp. 481–504.
M. Wetter, “Modelica-based modeling and simulation to
support research and development in building energy and
control systems,” J. Build. Perform. Simulat., vol. 2, no. 2,
pp. 143–161, May 2009.
Biographies Juan Van Roy is with KU Leuven, Belgium.
Bart Verbruggen is with KU Leuven, Belgium.
Johan Driesen is with KU Leuven, Belgium. p&e
figure 8. (a) Noncoincidence of local demand andproduction in residential buildings and (b) DSM: peakload reduction.
Household Load
PV Power
(a)
(b)
ousehold Load
PV Power