Modeling Tools for

Energy Smart Grids

Centro Interdipartimentale per l’Energia e l’Ambiente – CIDEAUniversità di Parma

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Energy Systems and Smart Grids

� Users (thermal & electrical loads)

� Generation (boilers, CHP, Heat Pumps, PV,

solar thermal, gasifiers, ORC, etc.)

� Storage (PCM, batteries, etc.)

� Distribution links (pipes, AC/DC lines, etc.)

Prosumers

� Design of efficient Smart Grids (thermal & electric)

� Integration of FER & Storage

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Energy Systems and Grid Modelling

1 – Why?

2 – How?

3 – Tools:• Systems Dynamics (Matlab/Simulink®)• Energy Systems and Buildings (TRNSYS®)

4 – Application: the Campus grids

6 – The role of control strategies: Dynamic Programming for Optimized Strategy definition

5 – XiL approach:• system layout definition & design• management strategies design & validation

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ELECTRICGRID

GAS NETWORK

SPACECOOLING

LIGHTING & APPLIANCES

HOT WATER

SPACE HEATING

BOILER

Energyplant

CHILLER

FuelElectricityHeatingCooling

From an old architecture...

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GROUND

ELECTRICGRID

GAS NETWORK

DISTRICT HEATING

SOLAR THERMAL

(SH)

HEAT PUMP(GSHP and

ASHP)

COGENERATOR

(CHP)

ABSORPTION CHILLER

(ABS)

AIRSUN

SPACECOOLING

LIGHTING & APPLIANCES

HOT WATER

SPACE HEATING

AUXILIARY BOILER

(AB)

Energyplant

AUXILIARY CHILLER

(AC)

SOLAR PHOTOVOLTAIC

(PV)

RadiationFuelElectricityHeatingCooling

...to new smartsolutions

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Models can be used in each step of the

process to limit costs & time.

Concepts &Architecture(prosumers)

ComponentsOptions & Sizes

Integration

Detaileddesign

ManagementSystem

Testing &Running

Design, Improve and Manage Energy Grids

In-field Monitoring + Simulation Models allow to shorten development time and costs and improve Management Strategies

x Real Time

Model complexity

<0,1

1

10

100CPU Time

vs.Model

approach3D Models

1D-3D detailedModels

1D fast runningModels

0D fast runningModels

Mean ValueModels

Map Based Models

System or subsystem models with different level

of complexity foreach step.

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UniPR Simulation ToolsIn the last decade several models for the dynamic simulation of In the last decade several models for the dynamic simulation of Energy Energy Systems have been developed and included in Systems have been developed and included in SimulinkSimulink ®® libraries.libraries.

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Real Application: the Campus grids

THERMAL POWER STATION

THERMAL POWER THERMAL POWER STATIONSTATION

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11°°

22°°

33°°

44°°

55°°

66°°77°°

88°°

99°°

1010°°

1111°°

THERMAL POWER

THERMAL POWER

STATION

STATION

South New branch of the Campus grids

�pE

pl1

pl2

pl3

�pE = pin-poutpli = press.losses

Modelling Approach: pipe loop

�TE

Tl1

Tl2

Tl3

�TE = Tin-ToutTli = temp.losses

Mass flow rate is determined from the fluid mass dynamics taking account of pressure losses and available �pE.

Temperature change is determined from the heat flow estimated from pipe characteristics and int&exttemperatures.

� The first pipe loop can be coupled with the two Dampers.

� Further loops can be added linking them to any pair of nodes of the previous loop.

Earth Sciences Building

Supply Pressure 4.4 [bar]

Return Pressure 4.3 [bar]

Flow rate 10.2 [kg/s]

Return TEMPERATURE

Dept.of Chemistry Building

Supply Pressure 3.2 [bar]

Return Pressure 3.1 [bar]

Flow rate 37.3 [kg/s]

Return TEMPERATURE

Results: Steady-state

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Results: Transients

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Mathematical Models for MiL/SiL/HiL applied to Energy Grids:

•Co-simulation with very low CPU time.•Optimization of grids architecture and sizing.•Design & Testing of management strategies.

Systems Models for XiL applications

Data input (from data base or real

“on-road” monitoring

Grids and Systems Models + Management Strategies

Grids & Systems behavior

Costs and emissions

Design costs & timeEnergy consumptionEmissions

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Optimization A modelling approach is necessary in order to concurrently optimize architecture, sizing and management strategy

size optimization algorithm

control optimization algorithm

multi-source energy system model

Project submitted to PRIN 2015: “Development of methodologies and instruments for the optimal design and management of distributedmulti-energy systems connected to district heating and cooling networks”