Model predictive control of a heat booster substation for ... · Model predictive control of a heat...

4th International Conference on Smart Energy Systems and 4th Generation District Heating Aalborg, 13-14 November 2018

Model predictive control of a heat booster substation for ultra-low temperature district heating

4th International Conference on Smart Energy Systems and 4th Generation District Heating 2018

#SES4DH2018

Kevin Michael Smith1, Corentin Latou2, Jan Eric Thorsen3, Svend Svendsen1 (1) Technical University of Denmark

(2) Ecole Centrale de Nantes (3) Danfoss Drives A/S

Components Storage tank Heat pump to storage tank Circulation heat pump Heat exchanger for DHW Ultra-low temperature district heating (ULTDH) Supply: 40°C, return: 25°C Domestic water Hot water: 55°C, cold water: 10°C Circulation loop Forward: 55°C, return: 50°C

4th International Conference on Smart Energy Systems and 4th Generation District Heating 2018 #SES4DH2018

Model Predictive Control (MPC) Optimal equipment scheduling

Applied system dynamics

Smooth differential equations (smaller solver tolerance)

Forecasted energy needs

Minimise cost function (e.g. energy cost + discomfort)

Allows constraints (e.g. max. set-points)

Re-initialisation of states


The process of model identification accounts for 70% of the effort for implementing an MPC controller (Henze, 2013).

System model Modelica language in Dymola

GUI for equation-based system modelling

Modelica Buildings Library from LBNL

Open-source component library

(e.g. stratified tank, HP, pumps, heat ex., etc.)

JModelica Open-source simulation and optimisation


Component validation with real data

Input known parameters to component models

Heat exchanger

Stratified tank model

Heat pump model

Test data to validate models

Default parameters for pumps and valves


Tank validation – Upper level

4th International Conference on Smart Energy Systems and 4th Generation District Heating 2018 - #SES4DH2018

0

10

20

30

40

50

60

70

80

0 7200 14400 21600 28800 36000 43200

T [C

]

Time [s]

01-31: Temperature #1

T tank top #1 (S6) #1 #1 #1

Ideally mixed layers may cause numerical diffusion

Tank validation – Lower level


0

10

20

30

40

50

60

70

80

0 7200 14400 21600 28800 36000 43200

T [C

]

Time [s]

01-31: Temperature #5

T tank #5 (S8) #5 #5 #5

More layers improves convergence with actual data

Large heat pump validation

4th International Conference on Smart Energy Systems and 4th Generation District

Heating 2018 - #SES4DH2018

40

50

60

70

80

28800 32400 36000 39600 43200

T [C

]

Time [s]

Simulation results: condenser temperature

T L-HP cond (measure) Condenser (SIM1) Condenser (SIM2)

Error measurement/simulation results (working time) SIM 1 SIM 2

Condenser mean absolute error [C] 0,27 0,86 Evaporator mean absolute error [C] 1,63 0,80

System model Library includes six versions of booster station


System model


System model Modified tank model to include third port (circulation HP)


MPC of a booster station Optimise

When to charge tank

Condenser and evaporator flow rates

Heat source for circulation heat pump

Middle tank

Return

DH Supply (not included yet in model)

Based on energy costs


Cost function

𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐 = � 𝑃𝑃𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 ∗ 𝑝𝑝𝑝𝑝𝑝𝑝𝑐𝑐𝑝𝑝𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑑𝑑𝑐𝑐𝑡𝑡

0+ � 𝑃𝑃𝐷𝐷𝐷𝐷 ∗ 𝑝𝑝𝑝𝑝𝑝𝑝𝑐𝑐𝑝𝑝𝐷𝐷𝐷𝐷 𝑑𝑑𝑐𝑐

𝑡𝑡

0

DH tariffs based on production weighted price from HOFOR


0,00

0,05

0,10

0,15

0,20

0,25

264 266 268 270 272 274 276 278 280 282 284 286 288

[DKK

/kW

h]

[h]

DH production weighted average cost: 12-01-2016

Electricity price


0,000

1,000

2,000

3,000

4,000

264 266 268 270 272 274 276 278 280 282 284 286 288

[DKK

/kW

h]

[h]

Spot/load-based tariff 12-01-2016

Spot/Load-based tariff Spot/Load-based tariff (consumption prognosis)

Average costs 2 [DKK/MWh] Actual energy 810 35,2% System 150 6,5% Taxes 1340 58,3% Total 2300 100,0%

Method from Ulbig & Andersson (2010) ; Data from Nordpool (spot price and load) & Danish energy statistics 2015 (https://ens.dk/en)

https://ens.dk/en

Reference case: DHW Tapping Profile Pseudo-random Python library Shifted load profile for a 14 apartments Profile 1: 1413 L/day Profile 2: 1397 L/day


0

100

200

300

400

500

0 7200 14400 21600 28800 36000 43200 50400 57600 64800 72000 79200 86400

Flow

rate

[L/h

]

Time [s]

DHW pattern

Profile 1 (14 flats) Profile 2 (14 flats)

Simulation/Optimisation Compile as FMU in Jmodelica for optimisation

Issue in JModelica with block of code (Buildings Lib.)

Successful simulation, once removed everywhere


Simulation


Next steps

• Add DH supply as potential circ. HP source • Optimise controls in JModelica • Test MPC with real data (offline) Thank you for your attention.

4th International Conference on Smart Energy Systems and 4th Generation District

Heating 2018 - #SES4DH2018

Model predictive control of a heat booster substation for ... · Model predictive control of a heat...

Documents

Transcript of Model predictive control of a heat booster substation for ... · Model predictive control of a heat...