PRIVA ECO: BETTER WORKING, SMARTER ENERGY

PRIVA ECO: BETTER WORKING, SMARTER ENERGY

Saving energy (cost) with a flexible and predictive climate system adapting to a volatile energy grid

Frank VisscherTheo Rieswijk

Thermal inertia

How a building can become a battery https://www.linkedin.com/in/frankvisscher/detail/recent-activity/posts/

https://www.linkedin.com/in/frankvisscher/detail/recent-activity/posts/

https://sustainableurbandelta.org/

15 OFFICES

450 COLLEAGUES

>400

PARTNERS>100

COUNTRIES

>70 000 PROJECTSWORLDWIDE

Who is Priva?

OUR DEVELOPED SOLUTIONS

HARDWARE

CLIMATE AND PROCESS CONTROL

SOFTWARE

CONTROL SOFTWARE AND ONLINE SERVICES

SERVICES

CONSULTING, TRAINING AND SUPPORT

HORTICULTURE BUILDINGAUTOMATION

INDOORGROWING

HORTICULTURE INDOORGROWING

Priva ECO is a start-up within Priva

BUILDINGAUTOMATION

BETTERWORKING

SMARTER ENERGY

WHAT CAN YOU EXPECT TODAY?

BETTERWORKING

SMARTER ENERGY

DIGITAL TWIN TECHNOLOGY ‘BETTER WORKING & SMARTER ENERGY’

1. Building automation explained2. Digital twin explained3. ECO digital twin and machine

learning4. ECO digital twin applications

• Energy efficiency & comfort• Design & simulations• Optimization with in a grid

Conventional Predictive

CONVECTIONAL CLIMATE STEERING VERSUS PREDICTIVE CLIMATE STEERING

Climate steering based on heat curves calculating the required inside temperature and actual

outside temperature

Looking at the future taking all relevant influencers into account

Actual and future issues;• Buildings are better isolated so outside temperature is less

relevant• Installations are getting more complex and more variables are

playing a role (solar panels, ATES, dynamic pricing etc.)• Buildings will have to be integrated in it’s energy grid

DIGITAL TWIN TECHNOLOGY

Physical Digital Twin

ExampleOf a Digital Twin

vs.

What is the similarity?

https://www.linkedin.com/pulse/zet-uw-gebouw-schaakmat-met-een-zelflerende-frank-visscher/

We create a 'digital twin' of the Building and its technical installation. From energy purchasing to pipes and

zones, everything goes into it.

Based on the digital twin, simulations take place. Every half hour 24 hours

in advance. And on the basis of those simulations the strategic deployment

is decided.

It is visualized using a so-called 'Sankey diagram'

It starts by defining a zone and the desired comfort temperature for that zone.

Example:20 - 22°C during the day

15 - 30°C night and weekend

In this example, the energy is supplied by radiators and the weather (such as temperature and solar-influence).

(i.e. heat gain through the sun and heat loss through the facade)

Radiator groups

Outside weather

Receives its energy from the Heat

manifold

Outside weather

Manifold Radiator group

Outside weather

Heat pump

Boiler

Buffer Heat

Manifold

Radiator Group

Outside weather

E-connection

Gas contract Gas connection

Heat Pump Buffer

boiler

ManifoldRadiator Group

Weather

Solar panels

Gas contract Gas connection Boiler Manifold

Electricity grid delivery

Heat pump

Electricity connection

Solar Panels

Radiator Group

Electricity contract

Buffer

Solar panels can also be used to power the

heat pump

ECO can decide to deliver the power back into the grid

Like a game of chess, every move or choice made in the installation (e.g. bringing 10 kW through a heat pump to a zone) can be mathematically made and calculated. Priva ECO is like a chess game which calculates all possible steps in advance mathematically, taking all influences into account, and thus finds the most optimal use of the system.

24 hours ahead Priva ECO predicts the complete HVAC strategy

DIGITAL TWIN

ENRICHING THE DIGITAL TWIN WITH MACHINE LEARNING

Zone heating

Buffers

PRIVA ECO, DIGITAL TWIN

Gas boilers

Heatpumps

gas

electr.

District heating

heat

Local energyproductionelectr. weather

Digital Twin

MachineLearning

PRIVA ECO, AI FORECAST

SteeringSetpoints

AI Forecast

BETTERWORKING

SMARTER ENERGY

WHAT CAN WE DO WITH THIS DIGITAL TWIN

BENEFITS

Energy efficiency Indoor climate & comfort

Design & simulate Optimization within grids

(Dynamic)Energy Prices

EnergyConsumption

Weatherforecast

Better working environmentThermal

building behavior

Strategic deployment of the HVAC Installation

futurepresentpast

Better Energy performance

Installation

Live data

Energy efficiency

Indoor climate & comfort

Here ECO is switched on

Here you can see the supply temperature going down

Same as the previous page; Every time when ECO is turned on the temperatures on the manifold is pushed down. In the blue line you can see that the zone temperature stays comfortable on the same level. Lower temperature means saving energy, with the same temperature in the zones

EXAMPLES

Partner: None (Priva direct)Project: Avans, Den Bosch, The NetherlandsDetails: +/- 30.000 m2 over 35 air handling units, radiators, 2 boilers, 1 heatpumpResult: 45% saving on gas

Partner: None (Priva direct)Project: André Dumont Campus (medical centre)Details: Connected to a Siemens BMS with 2 boilers, heat pump, buffer.Result: Improved comfort Result: Energy savings to be calculated

Partner: VitaniProject: PostNord, Silkeborg, Denmark, 4.100 m2Details: Distribution centre with two floors of offices and three main sorting areas; letters, parcels and commercial post. District heating, 3 radiator groups, 4 air handling units. Result: over 14 weeks 43% savings compare to last year

BENEFITS


Design & simulate Optimization within a grid

Design & simulate

Design & simulate

Design-phase: Simulated energy flows

Gas contract Grid connection Gas

AHU 1

Manifold

Radiators Logistics

Radiators Sales

Zone Sales

Air vent Logistics

Weather

Boiler

Underground storage


Grid connection Electricity

AHU 1 - Heater

AHU 1 – air dampers

Radiators storage Zone Back office

Zone Storage

Zone Logistics

Heat BufferHeat Pump

Air vent Storage

Air vent Back office

Air vent Sales

Design scenario:

Do the zones receive enough energy?

Are the radiators big enough?

Is this heater big enough?

How big should the heat pump be?

How much more electricity will I use?

BENEFITS


Design & simulate Optimization within a grid

LIANDER, RESEARCH CONGESTION, OCTOBER 2019

GRID ISSUES

Grid congestion Balancing problems

Market mechanism

Energy trade

Day ahead energy prices

Balancing markets• DSO (congestion)• TSO (congestion + balancing)

25 august 26 august

Source energieopwek.nl

EXAMPLE OF A SCENARIO

90

80

70

60

50

40

30

20

10

0

Sunday20 Oct 2019

4 6 8 10 12 14 16 18 20 Monday21 Oct 2019

4 6 8 10 12 14 16 18 20 4 6 8 10 12 14 16 18 20 240

2,5

5

7,5

10

12,5

15

17,5

22,5

20

Tuesday22 Oct 2019

HeatpumpSolar panels Room Temp

What to do with the free electricity on a “sunny” Sunday?

Start the heat pump and “store” it in the building (building as a battery)

24 h Demand prediction



Electricity

Gas

Heat


Gas contract

Heat contract

n

1

2

Gas

Heat

Electricity

BETTERWORKING

SMARTER ENERGY

WRAP UP- SUMMARY

SUMMARY

Self learning DT

Building physics with

thermal inertia

Machine learning

Comfort settings

Desigered comfort

Domain knowledge

Thank you…Frank Visscher

Priva ECO

[email protected]

www.privaeco.com

Read my blogs about energy , the ‘chess master’ and flexibility in grids on linked in


http://www.privaeco.com/


Documents

PRIVA ECO: BETTER WORKING, SMARTER ENERGY