isenec 2018 Integration of Sustainable Energy Model-based optimization of heat pump operation Simple vs. detailed approach

Transcription

1 isenec 2018 Integration of Sustainable Energy Model-based optimization of heat pump operation Simple vs. detailed approach EnCN / Kurt Fuchs Julian, Buderus Technische Hochschule Nürnberg Georg Simon Ohm

2 Agenda 1. Project SENSIBLE - Storage-enabled sustainable energy for Buildings and Communities 2. Introduction 3. Models and Optimization 4. Test Environment Hardware-in-the-Loop test bench 5. Results 6. Conclusion 2

3 Project SENSIBLE Project partners from six European countries (Finland, France, Germany, Great Britain, Portugal, Spain) Overall project goals: Development, demonstration and evaluation of technologies for the integration of small scale storage into local power grids Three demonstration sites to demonstrate project results (Germany, Great Britain and Portugal) This project has received funding from the European Union's Horizon research and innovation programme under grant agreement no

4 Agenda 1. Project SENSIBLE - Storage-enabled sustainable energy for Buildings and Communities 2. Introduction 3. Models and Optimization 4. Test Environment Hardware-in-the-Loop test bench 5. Results 6. Conclusion 4

5 Heat pumps and renewable energies 27% of the energy consumption in Germany is used for space heating (in private households even 69 %) Electrical heat pumps combined with innovative control strategies are able to support an increasing amount of renewable energies Heat pumps show high potentials High efficiency Thermal energy storage are state-of-the-art Load shifting potentials [1] 5

6 Heat pump control systems Conventional heat pump control system Heat pump control system with model-based optimization Heat-led operation of heat pump Availability of renewable energies is not considered Future events are not taken into account (e.g. Demand forecasts) Operation mode depends on the defined optimization goal Minimizing energy costs Maximizing self-consumption Operation can be aligned to the availability of renewable energies Future events can be considered 6

7 Model-based optimization of heat pump operation Forecasts Models Model-based optimization Optimization Algorithm Cost function Operation schedule Starting conditions 7

8 Agenda 1. Project SENSIBLE - Storage-enabled sustainable energy for Buildings and Communities 2. Introduction 3. Models and Optimization 4. Test Environment Hardware-in-the-Loop test bench 5. Results 6. Conclusion 8

9 Geothermal heat pump Technical Data: Thermal power: 10,5 kw (B0/W45) Coefficient of performance: 3,68 (B0/W45) Simple model: Constant plant parameters at average operation conditions (B10/50): QQ tth = 10,88 kkkk CCCCCC = 3,6 P el,gwp,simple Qth = COP 9

10 Geothermal heat pump Detailed Model Equation 1 utilities specific sets of coefficients derived from measurement data Different evaporation and condensation temperatures can be taken into account Q or P = b + b ϑ + b ϑ + b ϑ ϑ + b ϑ th,hp,detail el,hp,detail 1 2 evap, norm 3 cond, norm 4 evap, norm cond, norm 5 evap, norm + b ϑ 2 6 cond, norm with: 1 and: ϑevap, inlet/ cond, inlet Q th,hp,detail ϑ evap/ cond, norm = + 1 ϑhp, supply = + ϑ, 273,15 m c heating p, water HP return 1 modified from [2] 10

11 Geothermal heat pump [3] Technical Data: Buffer storage with a capacity of 500 liters Max. Temp.: 55 C / Min. Temp.: 45 C Thermal capacity 5,8 kwh Simple model: Simple capacity node Temperatures are not observed Constant heat losses Maximum thermal capacity: ( ) Q = V ρ c ϑ ϑ th, cap storage water p, water max min Current thermal capacity: Q ( t) = Q ( t 1) + ( Q Q Q ) t th, cap th, cap th, load th, unload losses 11

12 Geothermal heat pump Detailed model: Stratified storage model with variable number of layers Temperature of each layer is observed Temperature dependent calculation of heat losses Temperature change in each layer: with: ( ) ϑ = C Q + Q + Q + Q i konv kond mix losses C V k storage = ρwater c p, water 1 modified from [4] 12

13 Optimization algorithms Simple Approach Mixed-integer-linear-programming (MILP) Objective function and constraints have to be linear Fast computing time Reproducible results Detailed Approach Genetic Algorithm (ga) Population of individual solutions is repeatedly modified Algorithm is able to solve nonlinear constraints Long computing time Non reproducible results [5] [5] 13

14 Agenda 1. Project SENSIBLE - Storage-enabled sustainable energy for Buildings and Communities 2. Introduction 3. Models and Optimization 4. Test Environment Hardware-in-the-Loop test bench 5. Results 6. Conclusion 14

15 Test environment Test bench Emulation test bench for thermal generation units Heat sink for thermal demand profiles up to 50 kw EnCN / Kurt Fuchs Heat source for testing of geothermal heat pumps Coupling with software and simulation tools possible (e.g. MATLAB or TRNSYS) EnCN / Kurt Fuchs 15

16 Test environment Optimization concept LabView Fixed optimization horizon Forecasts Matlab Operation mode heat pump (0/1) Floating optimization reduces effects of disturbances Starting conditions Optimization Variable timepstep (e.g. 300 s) Set point thermal demand (kw) Inaccuracies of forecasts Visualization of measurement data Data Logging Variable timestep (e.g. 1 s) Inaccuracies of models Measuring data PLC Control Signal / Set points Measuring data Control Signal / Set points Changing user demand (Sensors) Aggregates (Actuators) 16

17 Test environment Thermal load and price profile Thermal load up to 12 kw Electrical price varies between high and low price tariff Test horizon: 6 h 17

18 Agenda 1. Project SENSIBLE - Storage-enabled sustainable energy for Buildings and Communities 2. Introduction 3. Models and Optimization 4. Test Environment Hardware-in-the-Loop test bench 5. Results 6. Conclusion 18

19 Results Heat generation and electrical power demand Simple optimization approach Detailed optimization approach Preferred operation of heat pump in periods of low electricity prices Changing condenser temperatures lead to changing thermal and electrical power of heat pump 19

20 Results Heat generation and electrical power demand Simple optimization approach Preferred unloading of storage in times of high electricity prices Detailed optimization approach Boundaries of thermal storages are satisfied 20

21 Results Reference vs. Optimization Approaches Reference: Thermal generation on time of demand without heat storage Constant COP of 3,7 Comparability of optimization approaches: Different SOC at the end of test (Simple Approach: + 2,48 kwh) Compensation by adding costs for generation of missing energy to total costs of detailed approach (mean el. price: 0,22 EUR/kWh) Referenc e Simple optimization approach Detailed optimization approach Comparison of operational costs Simple Approach Detailed Approach 2,14 1,33 1,26 0 % - 38 % - 41 % 21

22 Agenda 1. Project SENSIBLE - Storage-enabled sustainable energy for Buildings and Communities 2. Introduction 3. Models and Optimization 4. Test Environment Hardware-in-the-Loop test bench 5. Results 6. Conclusion 22

23 Conclusion General Model-based optimization of heat pump in combination with thermal storage devices enables to Reduce operational costs Shift electrical loads to times of low electricity prices or high availability of electricity Floating optimization compensates model inaccuracies, forecasting errors and changes of demand 23

24 Conclusion Simple vs. detailed approach Simple Approach Detailed Approach Low effort for parameterization of models Low computation time (<1s) Reproducibility of results Models are only usable for constant operating conditions High effort for parameterization of models Higher computation times (> 20 s) Results are not reproducible Varying operating conditions can be handled by detailed models Required modelling depth strongly depends on the steadiness of the operating conditions. 24

25 Thank you for your attention! Contact: Technische Hochschule Nürnberg Georg Simon Ohm M.Eng. Julian Buderus Phone: / This project has received funding from the European Union's Horizon research and innovation programme under grant agreement no

26 Literature [1] BMWi. (2017). Energieeffizienz in Zahlen. Online: blob=publicationfile&v=22 ( ) [2] Wetter, M., Afjei, Th. (1996). TRNSYS TYPE 401 Kompressionswärmepumpe inklusive Frost- und Taktverluste. Zentralschweizerisches Technikum Luzern, Schweiz. [3] Glen Dimplex. Universal-Pufferspeicher 500l. Online: ( ) [4] Becker, R. (2006). Optimierung thermischer Systeme in dezentralen Energieversorgungsanlagen. Ph. D.thesis, TU Dortmund University. [5] Math Works (2015). MATLAB. Version R2015a. 26