Test Cases for Hardware In The Loop Testing of Air To Water Heat Pump Systems in A Smart Grid Context

Transcription

1 Test Cases for Hardware In The Loop Testing of Air To Water Heat Pump Systems in A Smart Grid Context David Fischer (*)(**), Thomas Wirtz (*), Kilian Dallmer Zerbe (*),Bernhard Wille-Haussmann (*), Hatef Madani (**) (*) Fraunhofer ISE, Freiburg, Germany (**) KTH Royal Institute of Technology, Stockholm, Sweden David.Fischer@ise.fraunhofer.de ABSTRACT Heat Pumps for heating and cooling purposes could play an important role in the future energy system. Combined with a thermal storage they offer the possibility for demand side management applications in a smart grid. Optimized utilization of local solar resources can be achieved by increasingly sophisticated control strategies. Smart gird integration and the use of solar resources will bring new challenges to heat pump system design and operation. However standardized testing procedures to compare HP-systems and controllers in a dynamic and possibly smart grid environment are currently lacking. In this work a method to extract representative test days from energy data, to be used in a Hardware In The Loop test is presented. The resulting sequence of 12 test days is compared to the one year data set and tested in building simulation. Yearly heat generation and SPF can be reproduced with an accuracy of above 94% using the test days. 1. INTRODUCTION In 2009, 47% of the world final energy consumption was used for heat generation. Over one third of the global energy demand is used for space heating, the preparation of domestic hot water (DHW) and cooling in buildings [1]. Heat pumps can be used in many cases to provide this service with lower use of primary energy. In the future, an increase in unit efficiency and the trend towards a decarbonisation of the electricity generation, make the use of heat pumps increasingly attractive. Heat pumps coupled to thermal storage provide the possibility for flexible operation. This flexibility enables heat pumps to offer additional services to the power system. Smoothing of electric load in the grid [2], increasing use of renewable energy or optimizing the use of the national power plant park are possible tasks that could be supported by heat pumps in a smart grid environment. The integration of heat pumps into a smart grid will change heat pump operation conditions towards a more dynamic operation and requirements to system controllers. In residential applications, the trend towards on-site PV generation and the use of solar thermal collectors will impact heat pump operation. Increasing integration of the residential sector in a smart grid and the increasing installation of renewable energy sources at homes has to be considered at the development of heat pumps and controllers. However, a framework for evaluating and benchmarking heat pump systems and its controllers in a dynamic or a smart grid environment is currently missing. Thus three main challenges arise: 1. How can different heat pump systems be compared taking into account changing operation conditions that arise in the future energy system? 2. How to compare heat pump system controllers and their ability to operate in a smart grid context, while optimizing the use of on-site renewable energy? 3. How to design an economic test procedure that takes points 1 and 2 into account for lab tests? 1.1. A review on heat pump testing procedures Heat pump testing procedures differ according to the tested system, testing scope, testing method, testing technique, test period and the resulting values. Procedures for different systems and purpose are presented in Table 1. The testing procedures, which are briefly described in the following, are primarily focused on returning performance values and functionality indicators. In EN a procedure to determine the performance at given stationary operation points, is defined for HP with auxiliary heater. In EN seasonal performance is calculated based on stationary measurements under different conditions, which are weighted according to the use case. The results of the experimental tests are

2 interpolated to the temperature conditions that occur over the year for a chosen climate. For each temperature the duration is defined. The results are weighted accordingly and used for the calculation of annual values. For both norms the heat source temperature, humidity and the supply circuit temperature are given as testing condition and are kept stationary throughout the testing period. Table 1 Comparison of testing procedures for heating systems with focus on HP systems. Procedure System Scope Method Technique Test period Results EN h for each COP, HP + auxiliary Heating Experimental Operating (Part 1-4) condition nominal heater performance + calculative points (2013) (2-6) power EN (2013) EN (2011) Concise Cycle Test (2002) EN (2012) VDI 4655 (2008) HP + auxiliary heater HP + auxiliary heater + Storage Solar combisystem Controller for solar thermal plants CHP-plants Seasonal Heating performance, HP functionality DHW performance System Functionality, Heating + DHW performance Controller Functionality Heating, DHW and Electric demand Experimental + calculative Experimental + calculative Experimental + simulative Experimental Operating points Test cycle Type day test cycle Operating points 1-3 h for each condition (4-7) 24 h 12 days up to 24 h Calculative Type days - SPF, nominal power, functionality COP DHW, provided DHW, functionality functionality, system performance Sensor accuracy, functionality Reference load profiles EN is a procedure for to determine the performance for DHW operation. In EN and EN a test cycle is used, which is constituted of a sequence of heating, stand-by and discharge periods with fixed durations. The Concise Cycle Test is designed to determine system functionality and performance of solar combisystems. 12 type days are selected from annual reference conditions for climate and DHW load and are combined to a test cycle, which is used for experimental testing. The results are used to estimate parameters for a simulation model to calculate the seasonal system performance. A framework for testing the functionality of controllers can be found in IN EN , focusing on solar thermal plants. VDI 4655 is designed to determine the annual performance of CHP systems using reference load profiles for electric, heating and DHW demand. Type days are defined, corresponding to the criteria season, weekday and cloudiness. For each type day weighting factors are defined for the calculation of yearly load profiles. This procedure builds the foundation for the method presented in Section 2. Except for VDI 4655 all methods are predominantly experimental. In some cases experimental results are used as input for calculations or simulation to derive yearly results For testing of heat pump controllers with smart grid interaction a procedure is needed, that includes heating, DHW, solar energy sources, a smart grid signal as well as electric load profiles. The investigated procedures contain valuable ideas such as test cycles (CCT), classification of days (VDI 4655) which will be extended in this paper to generate a sequence of smart grid ready type days. For further reading also refer to [3] Smart Grid and the need for more complex testing As described in the previous Section, current testing procedures are merely focused on determining unit coefficient of performance COP or seasonal performance factor (SPF) using stationary or quasi stationary procedures. Issues of system integration are only rising recently. As shown in [4], [5] heat pumps can be used for tasks in the power system and the domestic energy system. Thus operation will shift towards more

3 dynamic operation posing an increased challenge to heat pump and system controllers, which will play an increasing role in system performance. During the last decades new control schemes have gained increased interest. Among those are predictive controllers that make use of forecasted data for weather and prices. Model predictive controllers that also use a system model for calculation of the optimal control input [6], [7] are gaining increased popularity. In [8] a simulation based framework for controller benchmarking is presented. If testing of increasingly sophisticated controllers has to be done in a lab environment under realistic conditions, a set of consistent data with realistic statistic characteristics and time profiles is important to correctly evaluate the controllers. A first step for a consistent testing framework for testing heat pumps and heat pump systems in a smart grid environment is introduced in this work. In the following Section of this work a method to generate a sequence of test days is presented. The most important requirements for testing heat pump systems are identified in 2.1 and selected in 2.2. In Section 2.3 classification of days according to VDI 4655 [9] is used to generate groups of typical days according to climate and weekdays. From the resulting groups representative days are selected depending on different distance metrics. The days are first analysed in Section 3 and then used in a building simulation to evaluate the effects of data reduction on the informative value of a test using the derived days, which is discussed in Section 5 and Section METHOD FOR TEST DAY CONSTRUCTION The method presented in the following targets on constructing a set of representative days that can be used for testing of heat pump systems in a Hardware In the Loop (HIL) environment within 12 days. Within these tests it the most common operating conditions that are to be expected and which should be covered. Those depend on the target system, climate conditions and data chosen to account for the smart gird environment Definition of target system and requirements The system considered in this work is a multi-family house, where an air-source heat pump is used for space heating and the preparation of domestic hot water. In such applications solar resources such as PV and a solar thermal collector are options to integrate more renewable energy into the building energy system. The smart grid environment is implemented via a dynamic electricity pricing scheme linked to the day-ahead electricity spot price. For testing of heat pumps and system controllers in such an environment, the chosen test days should reflect typical operation conditions such as heating load, domestic hot water loads, the outdoor air and supply temperatures at which the heat pump is operated. In a smart grid scenario the availability of solar resources and the electricity price are considered. The sequence of testing days should correctly respect dynamics in operation, dependencies between inputs and possibly typical characteristic profiles of inputs over time Choice of selection criteria Table 1 shows the most important factors and their dependencies influencing air source heat pump system operation and performance. Ambient air temperature and global irradiation are considered independent expressing a daily and seasonal pattern. Those two values strongly influence the heat load and the generation of solar electricity and heat. Domestic electricity and hot water demand show strong weekly and daily pattern with high dynamics [10]. For the domestic hot water energy demand a seasonal pattern mostly due to a change in cold water temperature can be observed [11]. Electricity used for the operation of appliances is only slightly depended on the season and mostly affected by the use of electric lighting [12]. The electricity price on the spot market shows a strong daily and weekly profile, with minor dependencies on seasons, temperature and irradiation. In the following procedure the goal is to obtain a set of days that reflect the typical characteristics of each criteria in terms of magnitude, daily, weekly and seasonal profile and dynamics. Characteristic days are extracted from time series data to obtain representative, consistent test days. Time resolution and inter dependency of the important criteria are preserved, since a data sample for a day is used. The most important criteria for system performance and their interdependencies are listed in Table 1 of which three are chosen for the selection of reference days.

4 Table 2: Important criteria for heat pump operation in a smart grid context and their dependency assumed for type day generation: n is independent, d is dependent. Type of Dependency Criteria External Influences Temp. Irradiation. Clouds Weekday Time of Day Independent Irradiation n n d n d Temperature n d d n d Highly Weather Dependent PV Generation d d d n d Slightly Weather Dependent, Characteristic Profile in Time Solar Thermal Generation d d d n d Heat Demand d d d n d Electricity Price at Spot Market d d n d d Domestic Electric Load n n n d d DHW Demand n n n d d 2.3. Construction procedure To obtain a sequence of representative test days a stepwise procedure is applied. 1. The time series of the whole year is split into 12 classes based seasons, clouds and day type according to the classification used in VDI Normalize the data in each class from For each class the distances of each day to the group mean are calculated using the selection criteria and distance measure introduced this Section. 4. The day with the minimum sum of squared distance over all selection criteria is selected as type day for the given group. 5. All type days are aligned to a sequence Split data into classes according to VDI 4655 Based on the typology used in VDI 4655 [9] the dataset is split into 12 classes which are classified based on temperatures (3 classes), cloudiness (2 classes) and the day of the week (weekdays or Sundays). This approach classifies the data into the most important influence factors season, cloudiness and day type. Each class now contains up to 30 days. Those days are normalized and then used for selection Compute distances measure for each day After classifying the data according to temperature, clouds and weekdays, representative days of each class are to be selected. Factors having a high impact on system performance and reflecting smart grid use are chosen as selection criteria c. For each day i in the class (containing D days) and for each criteria c from the list of criteria C the distance d to the class mean is computed. The selection of a representative day is done using the minimum distance of the day to the group mean. In this work three different distance measures are compared: 1) Mean: If the mean value μ of each criteria c is in the focus of the selection, the distance d for each day i to the class mean for all days D and all criteria C is computed according to: C d i = 1 D μ j,c μ i,c c=1 D j=1 2 2) Quartiles: For capturing the intraday dispersion of the values, inter quartile distance ΔQ i,c is chosen as distance measure. ΔQ i,c = Q 0.75,c,i Q 0.25,c,i (2) The distance d of each day i from the class centre, which is defined as the mean value of inter quartile distances, is calculated according to: (1)

5 C d i = 1 D ΔQ j,c ΔQ i,c c=1 D j=1 2 3) Shape: If the focus of type day generation is on the selection of a day that captures the typical profile of each value, correlation is chosen as a measure of similarity. For each day i in one class the correlation ρ of the timeseries to the other members j is computed: ρ i,c = 1 D D (4) ρ j,i,c j=1 The distance is defined as: C d i = 1 ρ i,c 2 c= Select the days, generate a sequence and smooth transitions between days From each class the day of minimum distance is selected, according to the criteria and distance measure of interest. The selected days are sorted to provide a 12 day sequence with smooth development. This is done by sorting by season (summer, changing, winter), clouds (cloudy, sunny) and day type (weekday, Sunday). To avoid jumps between the days, transition between days is smoothed using a 5 hour window around midnight and cubic hermite spline interpolation. 3. RESULTING TEST DAYS The method for test day generation is demonstrated using a dataset for German conditions. Since the electricity price is linked to the climate conditions, test reference years are not applicable for generating smart grid test days. Therefore, the year 2012 was selected for climate and electricity price data. German reference climate data location Potsdam is used. The electricity price at the spot market is obtained from the European Energy Exchange AG. Thermal load of the building, the use of domestic hot water and the electricity consumption were modelled using a stochastic bottom-up approach described in [13]. A comparison of the test day sequence with the entire data set is based on yearly sums of the important values and the weighted sums of the test days. Results are shown in Table 3. Ambient temperature, global irradiation on the horizontal plane and the electricity price are chosen as selection criteria based on Table 2. Domestic hot water is treated as entirely independent and is separately selected from within the classes using the described method and added to the time series. Test day sequences for electricity price and ambient temperature, global irradiation and domestic hot water demand are shown in Figure 1. Each sequence starts in summer and then proceeds from changing season to winter. Each season contains four days of which 2 are workdays and 2 Sundays, each sunny and cloudy. The electricity price curves show a difference between Sundays (Evening peak) and working days (M-shaped profile). A comparison of the different distance criteria which were selected to respect the absolute value (mean distance measure), the fluctuations (quartiles distance measure) and the shape of the daily profiles (correlation based measure) is made. It can be seen that the days selected by the mean criteria show the least deviation, between the days and also within a day. For day eight and nine rather mild days are selected. The type days generated using the inter quartile difference show the highest intra-day fluctuations. Type days generated using the shape distance measure show comparably smooth curves during the day. Whereas it can be seen that the absolute value of the curve can be far off from the mean value. To calculate the yearly values each type day is multiplied by the number of days in the respective class, which leads to the yearly results shown in Table 3. Compared to the summed values of the entire time series the values derived from the test day sequence deviate. The values generated using the mean day criteria show the lowest absolute deviation with an error below 7% for all values except the electricity price. The method based on quartile differences also results in low error. PV electricity generation is dependent on temperature and irradiation, leading to a non proportional behavior when it comes to the yearly energy sums. In Figure 1 the relationship between ambient temperatures and global irradiation for each of the test sequences can be investigated. It shows that a type day selection based on profile similarity ends in favoring rather extreme days with a pronounced profile. This leads to higher temperatures and irradiation in summer and lower in winter, leading to the results shown in Table 1. For domestic hot water which has strong stochastic characteristics the days selected by the mean value criteria result in an error for the yearly energy sum of 4% compared to 19% and 14% respectively. The highest deviation in terms of yearly sums for all methods can be found for the data of the electricity spot market (3) (5)

6 Figure 1 Example of a resulting test day sequence of 12 days for the different distance measures and selection criteria. Beginning with a sunny summer workday and ending with a cloudy winter Sunday. Table 3 Comparison of the yearly mean values for the type day sequence and the time series for a whole year. Inputs Reference Case Sequence of days constructed using distance measure One year data mean quartiles shape Ambient Temperature 9.6 C 9.2 C 9.5 C 9.9 C Electricity Price at EEX 100% 81% 79% 81% Global Irradiation 100% 93% 100% 127% Electricity from PV 100% 102% 109% 124% Heat Demand 100% 107% 98% 102% DHW Demand 100% 96% 81% 86% 4. RESULTS USING THE TEST DAYS IN A ONE YEAR SIMULATION The effects of reducing one year to a sequence of 12 days are investigated for a selected heat pump system. This is done by comparing the results of a one year simulation for the selected system with the results obtained using the reference days Scenario and reference system definition A refurbished German multi-family house (6 residential units, 12 habitants, year of construction before 1979 location Potsdam, year 2012) is chosen as reference case. A variable speed air-source heat pump is used for the supply of thermal energy. The heat pump is connected to a 2000 l stratified storage split into a DHW part and a part for space heating. Radiator heating with a maximum supply temperature of 55 C is chosen for heat supply of the building. The building is equipped with a 10 kwp PV plant. To evaluate the test days a simple control strategy without smart grid functionality is applied such that the heat pump controller is set to keep

7 the necessary storage temperatures. This control will serve as a lower bound benchmark for future smart grid comparisons Results from test days sequence and a one year simulation For the given scenario and system a one year simulation with a time resolution of 1 minute was performed using the building simulation tool COLSIM. For each test day sequence a 12 day simulation was done and results for each day multiplied according to its frequency in the chosen year. Electricity costs, SPF (calculated based on yearly heat produced and the needed electricity for the heat pump unit) and the annual heat that is delivered by the heat pump are chosen as key performance measures. Table 4 Results of yearly mean values for a one year building simulation with a German reference case using the full data set and the selected test days. Outputs Reference Case Sequence of days constructed using distance measure One year data mean quartiles shape Heat Produced by HP 100% 105% 95% 108% SPF 100% 106% 103% 103% Electricity Costs 100% 88% 69% 105% Results for a one year simulation, which are listed in Table 3, show that for all methods the deviation in the heat produced by the heat pump between 5% to 8%. The deviation in SPF lies between 3% to 6%. The high deviation in input data for electricity price at the spot market is reflected in the deviation of annual electricity costs, which shows the highest error. For this criteria the shape based method shows the smallest deviation of all. 5. DISCUSSION The sequence of generated test days expresses differences depending on the construction method chosen. A comparison shows that the reference days can reduce the time series data with an accuracy from 100% to 69% for yearly values, depending on the distance measure applied and the criteria under investigation. All methods show the highest deviation in accurately reproducing the yearly mean spot price. The mean day method produces a day sequence with lower individual deviation over all criteria than the quartile and shape method used. The method, focusing on profile similarity produces the sequence with the most extreme days. However the effects of selecting such days seem to even out during the course of the year to a certain extend. The method based on statistic dispersion shows similar results as the mean method for all characteristics except for selecting a suitable day for domestic hot water, thus indicating problems with stochastic data. Although global irradiation is reproduced well for the whole year, the resulting deviations in PV energy generation for this method are higher than for mean value method. 6. CONCLUSIONS In this work a first step has been done towards standardized frame work for testing of heat pump systems in a smart grid context, which is currently lacking. The presented method is used to generate a series of smart grid test days for HIL tests or simulation. By applying the method it is possible to select a set of representative days from time series data according to the criteria of interest. The extraction conserves the connections between linked characteristics such as global irradiation and spot prices. The method generates a series of 12 days based on VDI 4655 classes, which can be tested in HIL within two working weeks. Three different methods for day selection from seasonal classes were tested. 1) Selection based on the mean values for the days in each class, 2) selection based on statistical dispersion and 3) selection based on characteristic shape using Pearson s correlation coefficient. Each method delivers a sequence of different test days shown in Figure 1. 1) Delivers a sequence of days with comparably low fluctuations between the days and which results in errors between 2% and 12% for yearly energy demand, PV generation and costs. 2) Results in a sequence with errors between 0% and 31% for the same indicators. 3) Is producing a sequence favouring extreme conditions where the effects level out to some extend and the resulting error is between 3% and 25%. Hence a mean based method is suggested when overall deviation is in the focus of tests and the shape based method is suggested when extreme conditions and a representation of the characteristic profile are in focus.

8 The results show that with the introduced method an accuracy for yearly values of mostly over 92% can be reached, using about 3% of the dataset, while still conserving the most important properties of the data. With this it is possible to test heat pumps, heat pump systems and controllers in a HIL within 2 working weeks. The test day sequences also include conditions that will be increasingly important when operating in a smart grid future. The introduced method can be applied to extract smart grid ready type days from energy time series data for simulation and lab test depending on the need of the user with a minimum loss of accuracy. 7. OUTLOOK Further consideration will be done to investigate the possibility of reducing the test sequences down to a minimum without losing accuracy. A combination between the shape conserving distance measure and the mean value distance measure is to be investigated as a way to combine the strength of both methods. Besides that a gradient based selection criteria is currently discussed. As a next step the test day sequence will be used in a HIL test and the results will be compared to those from simulation. A test with smart control strategies and an extension of the framework to benchmark predictive, AI and adaptive MPC technologies is currently under development at Fraunhofer ISE. 8. ACKNOWLEDGEMENT The work presented was developed within the Green Heat Pump Project. This project has received funding from the European Union s Seventh Programme for research, technological development and demonstration under grant agreement No REFERENCES [1] International Energy Agency, Technology Roadmap - Energy efficient building envelopes, Paris, [2] D. Fischer, K. B. Lindberg, S. Mueller, E. Wiemken, and B. Wille-haussmann, Potential for Balancing Wind And Solar Power Using Heat Pump Heating And Cooling Systems, in Solar Integration Workshop, [3] M. Y. Haller, E. Bertram, R. Dott, T. Afjei, F. Ochs, and J. C. Hadorn, Review of component models for the simulation of combined solar and heat pump heating systems, Energy Procedia, vol. 30, pp , [4] S. Müller, R. Tuth, D. Fischer, B. Wille-Haussmann, and C. Wittwer, Balancing Fluctuating Renewable Energy Generation Using Cogeneration and Heat Pump Systems, Energy Technol., vol. 2, no. 1, pp , Jan [5] M. Brunner, S. Tenbohlen, and M. Braun, Heat pumps as important contributors to local demandside management, in IEEE PowerTech Conference in Grenoble, 2013, no. year, pp [6] R. W. Wimmer, Regelung einer Wärmepumpenanlage mit Model Predictive Control, [7] C. Verhelst, F. Logist, J. Van Impe, and L. Helsen, Study of the optimal control problem formulation for modulating air-to-water heat pumps connected to a residential floor heating system, Energy Build., vol. 45, pp , Feb [8] ETH, BACTool Assessment of control algorithms for buildings. [Online]. Available: [Accessed: 20-Feb-2015]. [9] G. Dubielzig, H. Frey, K. Heikrodt, K. Ksinsik, A. Nunn, W.-H. Scholz, and T. Winkelmann, Referenzlastprofile von Ein- und Mehrfamilienhäusern für den Einsatz von KWK-Anlagen. VDI Verlag GmbH, Düsseldorf, [10] D. Fischer, A. Härtl, and B. Wille-Haussmann, Model for electric load profiles with high time resolution for German households, Energy Build., vol. 92, pp , [11] B. M. Y. Haller, R. Dott, F. Ochs, J. Bony, and D. T. Systems, The Reference Framework for System Simulations of the IEA SHC Task 44 / HPP Annex 38 Part A: General Simulation Boundary Conditions, [12] I. Richardson, M. Thomson, D. Infield, and A. Delahunty, Domestic lighting: A high-resolution energy demand model, Energy Build., vol. 41, no. 7, pp , Jul [13] D. Fischer, J. Scherer, A. Haertl, K. B. Lindberg, M. Elci, and B. Wille-haussmann, Stochastic Modelling and Simulation of Energy Flows for Residential Areas, in Proceedings Internationaler ETG-Kongress, 2014.