The 5th Greater Pearl River Delta Conference - Smart Management System in Building Facilities for Sustainability of Low Carbon Environment

Transcription

1 The 5th Greater Pearl River Delta Conference - Smart Management System in Building Facilities for Sustainability of Low Carbon Environment Deployment of Advanced Technologies for Carbon Efficiency in Process Cooling Ir Michael Waye, Mr Ming Hoi Ming Li, P Eng. and Ir Eric Sin Parsons Brinckerhoff (Asia) Limited Abstract Manufacturing processes require various ingredients, which sometimes require cooling. For most production, cooling is not the most considered ingredient in terms of quality and value. However, for data centre, the quality and cost of cooling process is one of the major factors to determine its efficiency. From experiences of operators, the operation cost of a data centre can be more than its initial capital cost required for the process cooling, during the life time of the facility. Therefore there is a great opportunity to deploy advanced technology to improve the carbon efficiency in data centre process cooling. Not only it alleviates the effect of global warming, it also pays for itself. Many vendors are pushing different solutions to assist the operators. The authors wish to share some of their insight as they applied the advanced technology in recent projects. Presently, there are many vendors offering different software packages to manage a data centre. It is anticipated that better energy management software will be led by data centre environment and proliferated to general building facility. Keywords: forecast, strategic planning carbon efficiency, computerized maintenance; facility management; operation Page 1 of September 2014

2 1. Introduction As the debate of global warming started, people are more and more concerned with the energy usage in a facility. Conserving energy is not only showing good corporate goal to become a responsible social citizen, but also it makes good business sense when energy bill cost is lowered. Energy in a building is mostly consumed by mechanical systems. From the Hong Kong Industry Energy End-use Data 2013 from Electrical and Mechanical Services Department [1], lighting represents 6% of the total energy usage only. HVAC space cooling represents 19% and the industrial process is 53%. Assuming half the industrial process is to provide process cooling, the total HVAC energy cost Figure Hong Kong Industry Energy End-uses represents about 45% of the total energy Data 2013 from EMSD, HKSAR bill. The HVAC system is considered the first candidate to optimize the energy usage. From the operating and maintenance perspective, the system selection, configuration and design have already been done and constructed in a facility; it will be very difficult to conserve energy without affecting the performance of the system. Most often, energy measures could affect the conditions of the indoor environment that cannot be successful unless there is a very strong user acceptance to manage the comfort zone. The authors studied cases and have been working on projects that are very energy hungry, i.e. data centre buildings. Without lowering the Services Level Agreement (SLA) to the tenants, there are opportunities to minimize the cost of energy bill. This approach has started to be accepted by a lot of end users and could become the next driving force for energy conservation. In a data centre environment, to computerize Data Centre Infrastructure Management (DCIM) will become very popular. Page 2 of September 2014

3 Although the DCIM is not rigidly defined and different vendors could have different interpretations and approaches, the idea of having computer software to control the energy consumption is the common principle behind them. With the same concept in DCIM, it is possible that the entire building environment can be carefully controlled to optimize the energy usage. Intelligent control of building management system (BMS) has been a hot topic for discussion since the adoption of electronic control by direct digital control (DDC). There have been many studies such as the Rule Based Integrated Building Management Systems [3] on the investigation of intelligent systems using a set of rules stored in the BMS system. With the rapid development of DCIM software, it provided a path to have full intelligent control of not only data centre but also later on the entire building management system. 2. Energy costs versus other costs Data centre business is a very competitive environment as the annual operation cost is very high. Therefore there have been many studies conducted by different parties on the Total Cost of Ownership for various data centres. A Total Cost of Ownership (TCO) as defined in data centre business included the capital cost of building, owning and operating a data centre physical facilities. Uptime Institute being one of the forerunners of the data centre standard activists, conducted studies on the TCO. In their study, the energy cost is approximately 53% of the TCO based on 10 year life cycle and 70% based on 20 year life cycle [2]. The cost of energy is much more than the capital cost for an infrastructure life time. As a data centre is operating 24/7 non-stop in her life time, lowering the energy by one or two percent represents a huge saving and improvement of competitiveness of the business. Therefore, it is one of the essential elements to be optimized by the data centre operators. 3. How do we optimize the efficiency Energy efficiency is a very complicated issue and affected by many different factors in a facility. Data centre industry provides a good metric to measure the efficiency called Power Usage Effectiveness (PUE). PUE is defined by the input of energy to the data centre divided by the output of the data centre which can be measured from the IT equipment power. Page 3 of September 2014

4 Power Usage Effectiveness (PUE) = Total IT Facility Equipment Power Power To improve the energy performance of a data centre, the following areas are considered at different stages of the project. i. Planning stage In the planning study, the entire mechanical and electrical process should be providing very efficient transmission and distribution of the energy. The entire mechanical and electrical room configuration should be arranged to optimize the process cooling. In practical term, the power cable copper loss should be minimized by locating the transformers close to the load. The chilled water pipe loss should be minimized by locating the chillers close to the load. If a project is well planned, the losses due to the distribution of energy to the designated area (data hall) will be minimized. ii. Design stage In the design stage, the best mechanical and electrical schematic design suitable for the facilities should be considered. Examples of energy efficient design including variable primary flow chilled water system; variable volume air flow control system etc. Equipment selection can also be selected to have maximum Coefficient of Performance (COP). For example, water cooled chillers have got higher COP than air cooled chillers. By choosing the best design and most efficient equipment, the overall system would be very efficient. iii. Operation stage In the operation stage, the venue to minimize the energy usage is little. However, these are the possible scenarios that the operation can consider: a. The energy for operation can be minimized with less output to the facility. Typically it involves closely managing the comfort level. b. The energy for operation can also be minimized by removing the spare capacity in the system. This means that in case of incident in the facility, the risk of total system shutdown could be higher. Effectively this is to trade off energy with system reliability. c. Ideal case is to operate the facility without affecting the comfort level and reliability. Page 4 of September 2014

5 For a facility in planning and design stages, there are many different approaches to ensure efficiency of the system. In this paper, the ideal case of minimizing the energy without affecting the service level agreement on comfort and reliability is focused. 4. Efficiency available for the operation If a facility needed to be improving the efficiency without changing the comfort level and reliability, a detailed study of the energy consumed in the facility should be carried out. For process cooling in data centre, the following optimization can be considered: i. Air velocity of the conditioned air If the airflow rate, temperature and relative humidity of the conditioned air are adequate, it is not required to bring high velocity air through the Computer Room Air Conditioning (CRAC) units. By saving the small energy required for each fan in the CRAC, the overall data centre efficiency can be improved. To address this issue, most designer selected Electronically Commutated (EC) Fans for CRAC. ii. Air temperature For building comfort cooling, air temperature has to be set within the comfort range, mainly for human, as indicated in ASHRAE standards. However, a well-controlled process control cooling system can have a temperature just matching the demand. It will minimize the air Figure 4.1 ASHRAE Environmental Classes for Data movement if the air temperature can be adjusted with higher temperature difference. When higher operating temperature is set, the chiller / DX system can be more efficient with the same model installed. ASHRAE TC9.9 has refined the envelope in 2011 for various Class A1 to A4 envelops that suits the different latest heat tolerated IT equipment [3]. Page 5 of September 2014

6 iii. Chilled Water Flow Similar to air movement, if the water can be circulated with lower velocity flow to maximize the heat transfer without compromising water temperature, the pump energy can be minimized. It is therefore required to study the chilled water system parameters. iv. Water temperature Instead of using traditional temperature set points, a higher chilled water supply water temperature can allow higher efficient chiller system. In ASHRAE Best Practices for Datacom Facility Energy Efficiency, adjusting the 7 Deg C leaving water temperature to 17 Deg C can bring the COP from 5.7 to 9.2 [4]. There is a 60% increase in efficiency. Figure 4.2 Sample chiller efficiency The efficiency of different elements of the systems might not be aligned at the same loading points. The following curves indicated typical efficiency of equipment against the loading conditions. By considering different loading conditions to the equipment, it is possible to optimize the energy usage of the data centre without affecting the standby capacity. Page 6 of September 2014

7 i. Chiller Operating Curve example from ASHRAE High Density Data Centers Case Studies and Best Practices [5] Figure Chiller efficiency ii. UPS Operating Curve from ASHRAE Best Practices for Datacom Facility Energy Efficiency [6] Figure UPS efficiency as a function of equipment type and load Page 7 of September 2014

8 iii. Power Supply Unit (Power Distribution Unit) Operating Curve from ASHRAE Best Practices for Datacom Facility Energy Efficiency [7] Figure 4.5 AC input PSU efficiency as a function of load In a data centre, short circuit of cold and hot air would waste the energy spent in the fan. If airflow to data racks which is vacant, the conditioned air will not have any heat exchange work done after passing through the data racks. Energy used to push the airflow is wasted. With the temperature sensors in a control system, the cold spot in a data centre hot aisle represents a short circuit happened. Therefore some type of control is needed. 5. Integration for the Collection of System Data To provide good control to a facility, it is important for the DCIM software to collect real time data from the field. Collecting of data could be easy if all equipment and system are provided under the same vendor. For most data centre, different vendor equipment could be found. Therefore the data collection could be very complicated. Different scenarios can be happened as the data collection is planned / designed for different purpose. Page 8 of September 2014

9 Some subsystems might have its own data collection protocol which is not in line with the DCIM data collection hierarchy. If DCIM system needs to communicate with the subsystem, various topologies can be considered. From DCIM system perspective, it might be best to have all subsystem elements be reported to DCIM directly. In practical situation, some elements such as the temperature sensors for CRAC might have to be connected to the CRAC for easy interface. It might be very expensive to obtain data directly from sensor to the DCIM system. There could also be an instance that the CRAC cannot operate efficiently when the sensors are not connected directly. While the CRAC is likely communicated with BMS directly, a second communication port, if feasible, for DCIM could incur unnecessary cost. With the assistance from the very well-structured BACnet standard, it is possible that all data collection by DCIM can be done through BACnet. However, the following elements should be considered when DCIM is trying to make connections at high level through BACnet: i. It is possible that the subsystem can only provide essential parameters through BACnet. The design must consider if more details are required where alternative communication means should be considered. ii. As the subsystem might also be required to connect to other traditional base building system such as Building Management System, the subsystem might not have adequate BACnet interface port with the original installation. Appropriate enhancement might be required. Communication with other system / equipment could be very difficult. There are many different methods for communications as shown below table. However, every one of them has its limitation. In order to have full monitoring and control of all systems, a unified network with good communication is essential. The following table sampled some typical system communication requirements. It can be seen that different system / equipment can accommodate different communication standards. Page 9 of September 2014

10 Equipment BACnet OPC Lonwork Modbus BMS Chiller Control ) System Smart Energy / ) Flow Meter UPS ) Power Distribution Unit ) Note: - very well structured information transfer but with less detail - ( ) loosely structured information transfer, need more customization but could obtain more detail. 6. System Data / Parameter Input Unless there is a very strong library included in the DCIM, it is typically required to have some data be manually inserted into the system. In general, the following would be required: i. Parameters of the equipment ii. Actual installed IT equipment When data are manually inserted, it is possible an operator committed an error and stored in the system for many years. Therefore the following computer verifications are desired: i. Automatic equipment identification through computer network to the actual equipment and verification through the equipment IP number. ii. Equipment data input are verified by the DCIM internal program on its consistency. For instance, the voltage, current and kva input into UPS system should be verified internally and highlighted any discrepancies. iii. The IT equipment parameter input can be verified with the actual performance. E.g. a server rated with 5A and 220VAC could not consume more than 5 A at the circuit breaker. If the measured figure is over the rated equipment parameters, it should be raised as a discrepancy alarm. Page 10 of September 2014

11 7. Control Analytics To provide good control analytic, data collection should be checked before further analysis. It is expected the data should be verified with the following: i. Wrong data manually input ii. Data conflict with actual performance iii. Data range inconsistence The data collected from the field and the system input data will then form a set of good operation data that can be used for real time online optimization or system advisory optimization for the operator. The analytics can be ranging from rule base or decision map to artificial intelligent that can learn new ideas / pattern from the field. Rule base control was suggested by various academies in early nineties such as the thesis from Gary George Clark [8] Rule Based Integrated Building Management Systems in 1993 and by Haris Doukas et al Intelligent Building Energy Management System using Rule Sets in 2006 [9]. Rule base or decision map template is preset decision formula or table. As the field condition is fit into one of the scenarios available in the decision map template, corresponding results will be produced. The beauty of decision map template is the decision time is fast and easy. The disadvantage of a decision map is the input to the map needed to be carefully studies by expert engineers to pre-determine the results. If the decision cannot provide exhaustive selection, some decisions might not be available. Scenario Data meet all parameters in the decision table Data failed to meet all parameters in the decision table Some but not all data meeting the parameters in the decision table Decision (Example) Turn the system on Turn the system off Not sure Remark No decision or making a decision on the conservative side Page 11 of September 2014

12 Artificial intelligent is the ideal way for analytic. If DCIM can pick up the operation modes based on the scenario learnt, the chance of making error will be minimized. When a set of parameters are available, an engineering station should be assigned to simulate the results and provide further What if investigation before implementation. The DCIM should also provide stages of implementation so that abrupt changes to the system will not be occurred. 8. DCIM available in market According to the report from eco - Association of the German Internet Industry, there are more than 55 providers of DCIM tools [10]. As there is no single unified definition of DCIM, there are different definitions as provided by various sources including Gartner, 415 Research, Forrester Research, Searchdatacenter and Wikipedia. The analysis from eco indicated the following features are generally provided. Product Features (no definition given) Provider (= Yes) Asset Management 90% Connectivity Management 90% Resource Management 80% Power Management 85% Inventory Management with Contract and 70% License Management Planning Management 85% Task Management 75% Change Management 85% Workflow Management 80% Business Intelligence 75% Administration of Investment in Equipment 60% Dashboard, Report and Print Management 85% Page 12 of September 2014

13 From the features listed, power management is only one of the elements in DCIM package. It is also less frequently referred by the vendor as compare with Asset Management and Connectivity Management. Although energy saving might not be the initial driving force for DCIM, it can be seen from that the computer software can be further developed into the tool that can provide intelligence for energy saving. In the Module Details of the report, it can be seen that the following product features are provided by various vendors: i. Monitor of the power management ii. Control of power management iii. Optimization of power management iv. Energy management A review of the present leading DCIM vendors revealed that a total energy management with artificial intelligent might not be available by most DCIM package. This could be due to a lack of demand from the subscribers or drive from the DCIM competitors. However, the analytic features requirements appear to be reasonable within the capability of the DCIM software. Some DCIM vendors provided analytic features in their business intelligence and operation already. With the further enhancement of the DCIM features, it is expected sophisticated energy management features will be available soon. 9. Conclusion Data centre and buildings are all require good energy management to provide efficient operation. Smart Management System should be deployed to these facilities for operation and sustainability low carbon environment. DCIM used in Data Centre is now taking a lead in the energy management for HVAC system which used up most of the energy in building environment. As a large portion of Data Centre operation cost is in energy consumption, there is a very strong incentive for the end users to have full monitor and control of the energy usage. Therefore more supplicated energy management software modules will be applied in DCIM. Although DCIM is a multipurpose Infrastructure Management software package for many different operation requirements, it can be seen that DCIM provided good foundation and platform for real time energy management. Page 13 of September 2014

14 References [1] The Energy Efficiency Office, Electrical and Mechanical Services Department, September 2013 Hong Kong Energy End-use Data 2013 Graph 21 [2] Uptime Institute White Paper, A Simple Model for Determining True Cost of Ownership for Data Centers, Table 1. [3] ASHRAE, TC9.9 White Paper, 2011 Thermal Guidelines for Data Processing Environments Expanded Data Center Classes and Usage Guidance, Table 2 & Figure 2. [4] ASHRAE, TC9.9 Mission Critical Facilities, Technology Spaces and Electronic Equipment, 2008, Best Practices for Datacom Facility Energy Efficiency, [5] ASHRAE, TC9.9 Mission Critical Facilities, High Density Data Centers Case Studies and Best Practices, Figure 3.10 [6] ASHRAE, TC9.9 Mission Critical Facilities, Technology Spaces and Electronic Equipment, 2008, Best Practices for Datacom Facility Energy Efficiency, Figure 7.7 [7] ASHRAE, TC9.9 Mission Critical Facilities, Technology Spaces and Electronic Equipment, 2008, Best Practices for Datacom Facility Energy Efficiency, Figure 8.11 [8] Gary George Clark, July 1993, Rule-Based Integrated Building Management System. [9] Haris Doukas, Konstantinos D. Patlitzianas, Konstantinos Iatropoulos, John Psarras, October Intelligent Building Energy Management System using Rule Sets [10] eco Association of the German Internet Industry, Data Center Infrastructure Management Market Overview and Orientation Guide, Page 8. Details Corresponding Author Co-author Co-author Names Ir. Michael Ming Fun WAYE Ming Hoi Ming Li, P Eng. Ir. Eric Kin-Kwong, SIN Position Director Senior Associate Project Director Affiliated Parsons Brinckerhoff Parsons Brinckerhoff Parsons Brinckerhoff Organization Address 7/F One Kowloon, 1 Wang Yuen Street, Kowloon Bay, Hong Kong 7/F One Kowloon, 1 Wang Yuen Street, Kowloon Bay, Hong Kong 7/F One Kowloon, 1 Wang Yuen Street, Kowloon Bay, Hong Kong waye.michael@pbworl d.com li.hoiming@pbworld.c om sin.eric@pbworld.com Page 14 of September 2014