Energy optimization services in a Belgian hospital, facts & results

Transcription

1 Energy optimization services in a Belgian hospital, facts & results Frederiek Demeyer 1 1 University college of West Flanders, technical department PIH, Kortrijk (BE) Corresponding frederiek.demeyer@howest.be SUMMARY The presented paper will describe the outcome of a service agreement to optimize the performance of an installed Building Management System (BMS). In an existing building (a polyclinic) with a BMS, many common control and comfort settings have been questioned and adapted resulting in astonishing energy savings. A thermal reduction of 35,7% and an electrical reduction of 15,7% are achieved after a two year optimization period. These figures resulted in a very fast pay back time of 11 months for the common cost of the audit and service agreement. The impact of the figures is even more impressive when one looks at the initial benchmark results. On a heat consumption base the hospital under investigation wasn t performing bad, the fact we could even improve this figure really creates opportunities for efficiency optimization services at customers with a BMS. Towards the electrical consumption one can clearly see the impact of uncontrolled computerization and fully air conditioned buildings. We hope that this paper will help the technical manager of a facility to experience the optimization potential of a well maintained and controlled BMS. INTRODUCTION In the beginning of 2005 we introduced the Building Performance Optimization Services on the Belgian market. As an environmental concerned organization we want to investigate the impact of the energy optimization services potential for a customer whose building is equipped with a BMS. The outcome of this study is important to quantify possible energy- and emission savings in other buildings who are or could be equipped with a BMS. Table 1: Overview of the ASZ-Aalst hospital group The customer who participated in this study is a ASZ - Aalst Hospital group community hospital of the city of Aalst namely ASZ - Aalst m² # beds the ASZ-Aalst. Hospital The ASZ-Aalst represents a group of three Polyclinic hospitals: ASZ-Geraardsbergen Hospital Eldery people clinic ASZ-Wetteren Hospital Polyclinic

2 Figure 1: The polyclinic in front of the main hospital From these three hospitals, only the newly build polyclinic, located on the ASZ-Aalst territory is having a modern BMS. For this reason, all BMS related improvement measures are implemented at the polyclinic. PAPER In 2004 the technical director of the ASZ-Aalst hospital group wants to know the potential impact of energy optimization measures related to the ASZ-Aalst hospital. A basic audit shows the major discovered energy opportunities. Table 2: Energy audit results Besides a major architectural optimization, the replacement of the current window panes, huge opportunities are waiting in regards to the BMS. As the polyclinic is entirely equipped with a BMS, we suggest to primarily optimize the small day clinic and to use the optimization results to plan further investments in the other hospitals of the ASZ- Aalst group where no accurate BMS is currently available. Calculated Measures - Main hospital Advanced cooling & compressor control Standardisation and optimal control of comfort level & BMS Replacement of the existing window panes (bad shape) Total main hospital Calculated measures - Polyclinic Advanced cooling & compressor control Standardisation and optimal control of comfort level & BMS Total polyclinic Investments in a BMS are financially important decisions, so the ability of a technical director to prove the financial (environmental) benefits towards upper management is a very important aspect to defend the associated investments. A first important step in the workout of the paper is the explanation of the task flows and the basic philosophy behind the BPO service model. Figure 2: Integrated approach of the BPO model 1 As the graphical representation of the BPO model shows, we want to help the customer improving the all round performance of his BMS. This results in the short quote: Helping your building work for you. As this paper is focusing on the energy performance optimization of the model a correct alarm handling and online follow up of the installation is very important. Energy (kwh) CO2 (ton) , , , ,4 Energy (kwh) Savings CO2 (ton) , , ,25

3 Figure 3: Task flows behind the AOC 1 In an Advantage Operation Center (AOC) we have a complete online view of the customer. Any process or consumption abnormality is triggered immediately to the responsible. As most of the programming measures are implemented on site, the follow up and proactive tuning can take place from any location which is a major managerial & environmental advantage. Figure 4: The EMC software label Part of the AOC is the EMC (Energy Monitoring & Controlling) tool which gives us the opportunity to check & follow up online the current consumption (= achieved optimization results). A second important step in the optimization process is the implementation of a basic metering structure. A general electricity-, heat- and water meter are installed on site although no separate domestic heat - and electricity for cooling meter are available. As we have an idea on the domestic heat consumption (basic thermal consumption during hot summer periods), we can use the standard HDD (Heating Degree Day) correction functionality of the EMC software tool on the data. Unfortunately towards cooling we can t use the CDD (Cooling Degree Day) correction as there are no reliable means to quantify this consumption, in the near future a separate electricity consumption meter for the cooling machines will be installed. Once the metering structure is up and running we started the implementation of the different FIM s (Facility Improvement Measures). All the measures can be addressed in one of the following main classes: Comfort Heat distribution Cooling production & distribution Lighting Comfort The first thing we analyze in the optimization process is the current comfort level of the building. As all rooms own their personal comfort adjustment (PCA) panel, extreme temperature settings can be encountered. Another important setting is the difference between the SPV (set point value) for heating and cooling. In the polyclinic both settings were extremely mixed up. Comfort temperatures were varying between 19 C and 26 C, a majority of the room settings had a gap of 1 C between the heating and the cooling set point. This resulted in a first implemented measure: the standardization of the comfort temperatures. In the current situation all the rooms have a SPV for heating of 21 C and a SPV for cooling of 24 C. The personal comfort adjustments are reduced from +/- 3 C towards +/- 2 C. Another change towards the PCA panel is the reset of the adjustment at the end of each comfort state. In the past, once you increased the temperature these settings remained the same until someone changed them again. Now, after each working day when the controller changes from comfort to standby mode, the deviation is resetted to 0 C.

4 Besides these programmable changes, the customer invested in IR protection glass foils. These foils diminish the solar influence on room temperature and increase the thermal resistance of the windows. Although the first measure obviously limits the personal comfort influence of the individuals in the room, both measures resulted in a dramatic fall of comfort complaints. Heat distribution The flow temperature of the different heating circuits is automatically adapted towards the outside temperature, reducing the average temperature of these circuits. Each pump is activated by an underlying demand to reduce the overall amount of running hours and heat input into the building. A further step is the implementation of an OSTP (Optimum Start Stop) algorithm. This algorithm is self learning and adapts automatically the start up - and stop time of the heating/cooling installation as a function of outside temperature, - humidity, inside - and comfort temperature by interpolating the current situation to past situations. Cooling production & distribution The cooling compressors in the past were released based on outside temperature and extreme comfort settings. Now the machine will only be released when one of the rooms achieves an inside temperature of 24 C. By running trends throughout the different rooms in the polyclinic we could shift the air conditioning release temperature from 5 C to 15 C outside temperature. Furthermore the flow temperature of the cold water is adapted as a function of outside temperature. While in the past the machine was working on a fixed 7 C, now we work between 8 C and 12 C. Figure 5: Cold water temperature reset As for the heating distribution circuits Cold water temperature the same strict pump release conditions and OSTP settings are applying. Cold water departure T ( C) Outside T ( C) Cold water T ( C) No reset Cold water T ( C) Reset outside T Lighting The light circuits of the hospital are further separated into outside (window) and inside circuits. By means of an outside light intensity measurement we are able to switch of certain circuits. Also during the night there is a control on the minimum amount of lights to guide the employees through the building. For each room an overwork timer function is integrated to allow a general switch off of the lights at 7:00pm. In case someone is still working, by pushing the light button once he is able to work for another hour before the lights will be switched off again.

5 RESULTS We will spread this section of the paper over three topics Presentation of the impact of the measures on the energy consumption (heat, electricity) Presentation of environmental impact & performance of the polyclinic in Aalst, in comparison with other hospitals Presentation of the financial return on investment Impact of the optimization measure on the heat consumption Figure 6: Evolution of the thermal consumption (HDD corrected) The first graph shows a clear diminution of the thermal consumption throughout 3 consecutive years. A thermal HDD corrected profit of 35,7% is reached over a two year period. Table 3: Evolution of the thermal consumption (HDD corrected) Fuel report - HDD corrected Year kwh kwh kwh Consumption , , ,3 cumulative saving % profit comp. To , ,7 23,1% 35,7% Impact of the optimization measures on the energy signature The energy signature is showing the thermal consumption as a function of outside temperature. Three aspects are important when we analyze the energy signature: The signature itself should be as low as possible The correlation coefficient of the temperature depending points (for the polyclinic this was the interval between 18 C and -10 C) should be as close as possible to 1. The rest heat above 18 C (domestic heat) should be as low as possible. While analyzing the graph we can immediately see that the curve has lowered entirely by more than kwh ( ; 3.68 C kwh ; 3.91 C 52096kWh). Also the correlation coefficient (calculated in excel) between consumption and temperature for temperatures below 18 C has increased (2004: 0, : 0,99). On a monthly basis we see an almost linear controlling algorithm.

6 Figure 7: Energy signatures (Before - After optimizations) Energy signature comparison ,00 Energy consumption (kwh) , , , , , , ,00 0, Outside T ( C) Towards the rest consumption for temperatures higher than 18 C, we can also see a dramatic diminution by more than kwh ( ; 19,63 C kwh ; 19, kwh). A lower heat consumption during summer and mid season will automatically affect the cooling load on the building. Impact of the optimization measures on the electrical consumption Figure 8: Evolution of the electrical consumption Table 4: Evolution of the electrical consumption Electricity report Year kwh kwh kwh Consumption , , ,0 cumulative saving , ,0 % profit comp. To ,9% 15,8% On the graph and table we can see the impact of the optimization measures on the electrical consumption. In contradiction with the heat consumption we don t see a three year

7 consecutive diminution of the consumption. The answer to this statement can be found in the temperature shift of 2005 in comparison with The temperature during the typical cooling months (May October) raised on average with 1,14 C (or 6.8%). As we have no separate meter registering the electricity consumption for cooling so we can t present a CDD corrected electrical consumption. In the final year comparison we can see a yearly electrical consumption increment of 5.1% for 2006 in comparison with On average we have lowered the electricity consumption by 18% in comparison to After analyzing the thermal and electrical consumption one can see that the audit savings were underestimated. Impact of the optimization measures on the CO 2 emission Figure 9: Evolution of the CO2 emission Table 5: Evolution of the CO2 emission CO2 report Year kg kg kg Consumption , , ,0 cumulative saving , ,5 % profit comp. To ,5% 22,0% An immediate effect of an overall diminution of the energy consumption is a reduction in CO 2 emissions. Over a two year period we reached a CO 2 emission drop of 22% representing 320,15 tons of CO 2. Presentation of the environmental performance of the ASZ-Aalst in comparison to other hospitals. Table 6: Senter Novem benchmark figures 2 Senter Novem consumption limits Specific fuel consumption (m³gas/m²)specific electricity consumption (kwh/m 20% 50% 80% 20% 50% 80%

8 Table 7: Specific consumption figures Polyclinic related to Senter Novem benchmark figures Specific fuel report - HDD-corrected Specific electricity report m³gas/m² m³gas/m² m³gas/m² kwh/m² kwh/m² kwh/m² 11,5 8,9 7,4 144,4 115,6 121,5 Conclusions In Belgium no official benchmark figures for hospitals are available. So we worked with the official Dutch figures supplied by Senter Novem. Looking at the thermal consumption figures one can clearly see that the polyclinic was and is still performing well as we have a lower consumption than 20% of all interrogated hospitals. Towards the electrical consumption we get another picture. The consumption is going towards the 80% limit of all interrogated hospitals. A possible explanation of these figures can be found in the fact that there are no residing patients in the polyclinic. No patients mean less thermal consumption for domestic purposes. On the other hand the polyclinic has a denser electrical load because of all the electronic equipment installed for clinical research purposes (scanners, PC etc ), in comparison with an average hospital. Also the fact that the hospital is fully air conditioned is affecting the electrical performance. Financial returns on investment Table 8: Financial analysis of the BPO project Polyclinic ASZ-Aalst Thermal profit ( ) Electrical profit ( ) Investments ( ) , ,1 Audit Pay back time (years) , ,8 Services ,9 Conclusion We have calculated the savings towards the base year The BPO services were ended last December and the customer now only pays a fixed cost to supervise his installation from the AOC. The overall payback time for this 2 year optimization project is refunded in less than one year. Besides the on site optimizations the AOC proved it s value in maintaining and optimizing the BMS by means of a web application (see the results of 2006). Equations Equation 1: HDD corrected year comparison formula used by EMC software 3 January: kwh HDD corrected Jan = kwh actual Jan * (HGT Ref Jan / HGT Actual Jan ) October: kwh HDD corrected Oct = (( kwh Actual Jan + + Oct ) * (A / B)) - kwh bhdd corrected Jan + +Nov. A / B = ( HGT Ref Jan + + Dec / HGT Actual Jan + + Dec) The formula above is used by the EMC software to calculate the HDD corrected heat consumption. The reference year we use is the average Belgian climate at Ukkel over a 30 year period ( ). REFERENCES 1. Morelli, JP Sales Manuel Operational Services. 2. Senter Novem 2005, official Dutch benchmark figures 3. Maier, A Weather adjustment manual EMC