ideas for tomorrow

8/12/2019 Ideas for Tomorrow

http://slidepdf.com/reader/full/ideas-for-tomorrow 1/7

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



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.



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.).




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)


(b)


(c)


(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


( -

)

0 1.0 2.0

cS cD

1.0

0.8

0.6

0.4

0.2

0.0


( -

)

0 1.0 2.0

cS cD

1.0

0.8

0.6

0.4

0.2

0.0


( -

)

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.).




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


E B ,

( - )

2.0

1.5

1.0

0.5


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).




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




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

ideas for tomorrow

Documents