European Coordination for Accelerator Research and Development
Seventh Framework Programme, Capacities Specific Programme, Research Infrastructures, Combination of Collaborative Project and Coordination and Support Action
DELIVERABLE REPORT
V IRTUAL P OWER P LANTS AT A CCELERATOR F ACILITIES
D ELIVERABLE : D3.5
Document identifier: tempfile_110.docx5-Final
Due date of deliverable: End of Month 42 (October 2016) Report release date: 21/12/2016
Work package: WP3: EnEfficient
Lead beneficiary: GSI
Document status: Final
Abstract:
We present an overview of results obtained in our task of the WP3. Virtual power plants (VPP) are introduced. Options for using parts of or whole accelerator facilities as VPP are presented and results of a survey and an actual simulation example with the future FAIR accelerator complex at Darmstadt, Germany, are discussed. We conclude that a real life test of VPP technology at a science facility might lead to generic results transferable to general problems for VPP realisation.
EuCARD-2 Consortium, 2016.
Copyright notice:
EuCARD-2 Consortium, 2016.
For more information on EuCARD-2, its partners and contributors please see http://eucard2.web.cern.ch/.
The European Coordination for Accelerator Research and Development (EuCARD-2) is a project co-funded by the European Commission in its 7th Framework Programme under the Grant Agreement no 312453. EuCARD-2 began in May 2013 and will run for 4 years.
The information contained in this document reflects only the author’s views and the Community is not liable for any use that may be made of the information contained therein.
Delivery Slip
Name Partner Date
Authored by J. Stadlmann GSI 24/10/16
Reviewed by M. Seidel PSI 29/11/16
Approved by WP
Coordinator M. Seidel PSI 29/11/16
Approved by Steering
Committee 12/12/16
Approved by Project
coordinator Maurizio Vretenar 12/12/16
TABLE OF CONTENTS
1. EXECUTIVE SUMMARY...4
2. INTRODUCTION...4
3. POTENTIAL FOR VPP APPLICATIONS AT ACCELERATOR FACILITIES...5
4. EXAMPLE FOR A VPP AT THE FUTURE FAIR FACILITY...7
5. WHAT ARE NECESSITIES AND WHAT ARE PROBLEMS FOR REALISATION?...11
6. CONCLUSION AND OUTLOOK...12
7. REFERENCES...14
8. ANNEX: GLOSSARY...14
1. EXECUTIVE SUMMARY
This report summarizes the work of the task “Virtual Power Plant” of WP3 Energy Efficient.
It discusses the viability of VPP application at accelerator science facilities.
2. INTRODUCTION
Virtual power plants are, considering a very simple definition like [1] , a pool of distributed power producers. The objective of pooling different power sources is to increase the overall availability of the combined energy sources towards the public energy market. VPP are aiming to increase the reliability of unstable sustainable sources like wind or photovoltaics by pure statistics. To further increase the availability the fluctuating sources can be combined with storage systems. Yet storage systems are expensive to build, and running costs are high.
Fig. 1 A virtual power plant is created by interconnecting various unreliable energy producers, storage systems and energy users. The latter preferably with interruptible loads. (Picture taken from [2] ).
If a virtual power plant consists not only of energy producers and storage systems, but also of energy users as shown in Fig. 1, the variability of a given facility’s energy demand becomes an interesting property.
A prime example for a switchable load is a cooled food storage facility. In times of low energy demand it is operating normally or even on a slightly lower temperature as required for the inventory. As soon as the energy demand rises or the variable energy producers stop working, the cooling stops and the temperature of the storage rises slowly. As long as the temperature of the food doesn’t rise over allowed thresholds, the cooling compressors can be switched of. This system works with a very low overhead of additional costs and system complexity. It is even simple enough to work alone just reacting to market demand. Two test facilities in Cuxhaven, Germany, saved 8% of electricity costs by this method [3] .
Another example is the joined venture of Daimler and enercity in Hannover, Germany. The car manufacturer has to provide spare batteries for electric and hybrid cars. Those need to be loaded and unloaded on a regular basis anyway as a maintenance measure. By combining the complete inventory of the storage and connecting it to the public grid they are able to provide a storage system with a maximum power output of 15 MW[4] (see Fig. 2).
Fig. 2 Car manufacturers have to store spare batteries for cars. To keep the accumulators healthy they need to be cycled. If the whole storage is combined and connected to the public energy grid it can work as an energy storage. The facility run by enercity and Daimler in Hannover, Germany, can provide 15 MW of electric power during times of high demand [4]
In general every user of electric energy can try to identify systems which can be switched off or on depending on energy demand in the public grid. The higher the overall energy consumption the more impact the measure has on the public grid.
3. POTENTIAL FOR VPP APPLICATIONS AT ACCELERATOR FACILITIES
Modern large scale research accelerator facilities have high operating costs dominated by personnel and energy consumption. With the increasing demands of the science community towards higher intensities and an increased ecological awareness in the general public, power consumption and general sustainability becomes a crucial factor for future large scale science facilities. If a facility can contribute to stabilise the public energy grid by means of VPP technology as described above it will increase the acceptance and ideally help to reduce operating cost.
The high average energy demand intrinsically provides potential for switching loads because even smaller fractions can have an impact on network stability. The research nature of the facilities offers the opportunity to test modern methods as part of R&D programmes which are neglected due to high risks in production environments.
On the downside modern accelerator projects are technologically extremely demanding and are usually designed to fit into very narrow budget windows. The use of non-standard high- tech prohibits using “out-of-the-box” energy management solutions. Science accelerators are in this sense prototypes and already standard operation is demanding without additional requirements of VVP technology.
Even before the start of EuCARD² we started a study of electrical energy consumption of the present GSI accelerator facility including lab- and office spaces [5] . As depicted in Fig. 3, taken from that work, the energy consumption fluctuates between times of accelerator operation and shutdown periods.
Fig. 3 Measured electrical power consumption over the year 2011 at GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt (general grid, excluding pulsed power, which is overall about 10 % of the energy consumption). Note the difference between operation periods (no marking) and shutdown times (red lines). Shutdown power consumption is 2-4 MW, Operation power consumption ranges from 6 to 10 MW.
The difference in power consumption between shutdown and operation is about 3-4 MW.
Even during times of operation there are differences of about 2-3 MW. The GSI accelerator complex offers very flexible possibilities for science experiments and the energy demand is changing with different experimental setups.
A simple means of energy management can be achieved by scheduling the shutdowns during times of high public energy demand. CERN has accomplished this by scheduling a mandatory winter shutdown. This “slow” and planned use of energy management, despite being highly encouraged for all science facilities, is not focus of this work.
We aim to find means of energy management during operation by identifying switchable loads. Ideally the overall science output for the users of the facility should not be harmed.
This is very dependent on the facility. If a facility delivers beam to experiments constantly without any changes in energy consumption to reach highest statistics there is little potential for load management. If the facility requires higher or lower amounts of power at certain times even during regular operation there is potential to identify switchable loads.
Fig. 4 Taken from [6] A survey was undertaken in the frame of the Energy Efficiency Network and completed by several science facilities. Depicted is the distribution of energy consumption on different parts of the facility.
Accelerator, general cooling and cryogenics, laboratories and experiments, and the general buildings and personal facilities. Note that modern facilities seem to spend less on general buildings and office space but have higher energy use in the lab area.
A survey on energy consumption was undertaken to identify the potential for VPP applications in science facilities [6] . The questionnaire was sent to several science facilities.
Questions included energy consumption, distribution of energy use on different subsystems, and especially changes in energy consumption due to shutdown and different operation modes (see Fig. 4).
4. EXAMPLE FOR A VPP AT THE FUTURE FAIR FACILITY
Fig. 5 The layout of the FAIR facility. The existing GSI accelerators (blue) will be the injectors into the main synchrotron SIS100. An additional proton linac (p-linac) will provide intense proton beams in addition to the existing heavy ion accelerator UNILAC. The example cycle and experiment is the fixed target experiment CBM.
The planned FAIR accelerator complex at Darmstadt, Germany, will provide its users intense primary heavy ion beams from the superconducting heavy ion synchrotron SIS100. Like the existing GSI accelerator facility, it is foreseen to have a very diverse experimental programme resulting in very different modes of operation. Beams can be delivered with slow and fast extraction resulting in cycle times of about one second to up to several ten seconds. The layout of the facility is depicted in Fig. 5.
Fig. 6 Sketch of SIS100’s main dipole magnet and the used nuclotron cable. The iron-dominated magnet has a maximum field strength of 1.9 T and a maximum ramp rate of 4 T/s. At highest ramp rate the total cryogenic losses are dominated by dynamic losses. The losses can go up to 45-50 W per magnet during a triangular cycle to maximum field with maximum ramp rate.
The main magnet of SIS100 is an iron-dominated superconducting dipole (“superferric”) with a maximum field strength of 1.9 T and a maximum ramp rate of 4 T/s (see Fig. 6). The mode of operation has a huge influence on the heat load on the cryogenic system. Table 1 summarizes the cryogenic heat load for two typical modes of operation (Cycle A and B) and an extreme cycle where the synchrotron is receiving one injection from the injector, ramping at maximum ramp rate to highest rigidity, and extracting the beam via fast extraction without delay. The field strength of the main dipole plotted over time forms a triangle in Cycle C, so it’s called “triangular cycle”.
Table 1: Total cryogenic power loss of SIS100 for different modes of operation. Cycle A is the proton cycle for antiproton generation, Cycle B is a typical RIB cycle with heavy ions for in-flight RIB production. Cycle C is a somewhat “artificial” triangular cycle which marks the worst case scenario for cryogenic power loss. These cycles already result in in a span from 3.9 MW to 8.5 MW cryogenic power consumption with an estimated cryoplant efficiency of 230 W/W. Cycle C imposes the maximum heat load on the cryogenic system. Cycles with lower heat load than Cycle A and B are possible.
Cycle
A Cycle B Cycle C Static Load at 4.3 K
no current or beam-induced losses [W] 4060 4060 4060 Shield cooling 50 – 80 K [W] 9,550 9,550 9,550
Dynamic loads [W] 5,934 3,817 18,798
Helium pump [W] 1200 1400 -
Heat load at 4.3K [W] 120 120 120 Mass flow from 50 K to 300 K [g/s] 22 22 22 250 A current leads
Heat load at 4.3K [W] 102 102 102
Heat load at 50 K [W] 6,660 6,660 6,660
Sum, 4 K equivalent [W] 13290 11373 24954
Including safety margin of 1.5 [W] 19935 17060 37431
Cycle C is not a planned mode of operation by any experiment so far. It marks a mode of operation with the highest dynamic loss. The losses, which occur at different temperatures and in different devices, are normalized to 4 Kelvin. The cryoplant is foreseen to have a cooling efficiency of 230 W/W at the Kelvin level resulting in an electrical energy consumption ranging from 3.9 MW to 8.5 MW in the given examples.
It is thinkable to adjust the beam-time schedule to run modes of operation with higher energy demand in times of low energy demand on the grid. As an energy management measure this is possible if there are reoccurring patterns of high and low public energy demand. A real VPP application would be to give the “switch” to the network provider and react dynamically. It does not seem feasible to have a low and a high energy-consuming experiment scheduled at the same time for switching. The experimental collaborations are distributed worldwide and they cannot be on stand-by on site for prolonged times. Furthermore some experiments use the same experimental facilities with different setups which need preparation and constructive changes before changing from one to the other.
Magnetic rigidityStandard CBM Cyc le s low CBM Cy cle Both 10 s extraction time.
4 T/s ramp 1 T/s ramp 11.2 s total 13.7 s total dy nam ic lo ss 3. 9 W in di pole
dy nam ic lo ss
2. 5 W in di pole
Fig. 7. Two different machine cycles of SIS100 for the same experiment (CBM collaboration ). The dynamic heat load per dipole is 3.9 W in the standard cycle and only 2.5 W in the slow cycle. The physics output per cycle is identical but overall the slow cycle delivers about 20 % less statistics (13.7 s/11.2 s)
A simple example calculation uses only one main user (CBM ) and two different cycles for this experiment. Extraction time and intensity per cycle is identical. The slower ramps in the
“slow” energy saving cycle are 1 T/s compared to 4 T/s in the regular cycle (see Fig. 7). The physics output per cycle is the same but the total time needed for equal statistics increases in
the slow cycle. In this example we compensate the experiment by adding extra experimental time. If the slow cycle is running for one hour the energy costs go down by about 100€. The extra cycles which have to be given to the experiment at the end of the beam-time block will result in extra energy costs of about 120 €, and the total cost of shift personnel, people on call etc. is estimated to be about 1,000 €. The data for the energy consumption of the future accelerator facilities are gained from a simulation with real machine cycles from the already existing control system and data supply. Energy prices are present values. The total average energy consumption of FAIR was assumed to be three times higher than the present facility.
To estimate the other costs linked to provide beam-time we scaled a cost estimate we use internally for the present GSI facility with the same factor. The cost consists of extra costs for people on shift and on call. For the calculation see Table 2. The total price is estimated with 1400 €/MWh for actual delivered regulation energy. Discussing the economic feasibility is beyond the scope of this work. Besides economic considerations, the potential cost reductions indicate at the same time a better utilization of available energy in an ecological sense.
The simplistic model does not allow to clarify whether the actual power reduction is possible in seconds (primary reserve) or minutes (secondary reserve). The high inertia of the cryo system might prevent to use the VPP as primary reserve.
The switch of one mode to the other is feasible but there are many effects to consider which will be discussed in the following chapter.
Table 2: Calculation of the VVP scenario. The power network provider switches to “low energy consumption mode” for one hour. The experiment gets compensated later for lost cycles by extending the overall running time. The total power consumption increases because the energy spent in the compensation time is higher than the energy saved and, of course, the total facility cost for the extra time has to be calculated. The energy consumption of the future FAIR accelerators is taken from the already existing control system and data supply.
The data basis is very detailed and basis of studies for energy consumption and power grid use. The total cost is just estimated by scaling the facility cost of GSI today with the same factor as the energy uses of GSI versus FAIR (roughly three).The total cost for one MWh of regulating energy would be about 1400€.
Slow Cycle
[MW] Normal Cycle [MW]
SIS18 0,03247 0,03946
SIS100 1,61792 1,81129
Beamlines 3,01028 3,61003
Sum 4,66067 5,46078
Cycles /1h 262 321
Cycles lost 59
add. normal time needed [h] 0,18
Saved during 1 h 96,01 €
Extra time energy cost: 121,16 € PLUS total facility cost for extra time: 988,18 € Sum: 1.109,33 € Cost of 1 MWh regulated energy: 1.386,48 €
5. WHAT ARE NECESSITIES AND WHAT ARE PROBLEMS FOR REALISATION?
The main focus of the example in the previous chapter was the dynamic load on the cryogenic system. The SIS100 magnets shown in Fig. 6 are cooled with two-phase helium. The helium is cooled in the cryo pump and transported as pressurised gas down into the accelerator tunnel. It is expanded in the feed box and liquefied during this process. The pure liquid helium flows through the superconducting cable cooling it. On its way through the string of magnets more and more helium is evaporated while extracting heat from the magnets forming the two- phase mixture, ideally using it up to the last bit of liquid and exiting the last magnet of the string as pure gas. The helium gas is transported back to the cryo plant at the surface. If the accelerator cycle is changed from higher to lower dynamic loads the cryo system has to reduce the mass flow through the accelerator because liquid helium exiting the magnet string cannot be transported back to the surface. Therefore heaters are foreseen to evaporate it during rapid changes of dynamic load. Moreover those heaters will be switched on permanently for very low heat load cycles because otherwise the mass flow might stop in single magnets.
Projecting that on the example above will not result in any change in primary energy consumption at all because the cryo plant will continue with the same cooling power, and the excess helium is just evaporated by heaters.
1 cryoplant 2 cryoplants
Fig. 8 Comparing the efficiency of cryo plants under variable load. One scenario (left) consists of a single big cryo plant, an alternative scenario uses two smaller plants with the same total power. The investment cost of the two-plant option is estimated to be 125% of the single plant version. If the cooling requirements are changing very dynamically the overall efficiency will be higher for the two-plant solution while adding additional redundancy.
The first step to make the FAIR accelerator eligible for VVP applications would be to pump the leftover helium at the end of the magnet string back to the feedbox. The maximum efficiency to pump liquid helium today is only about 30%. Thus only 2/3 of the cooling power is lost compared to a total loss due to the use of the heaters. To be able to regulate the mass flow during the full dynamic range of different modes of accelerator operation without wasting cooling power and efficiency the helium flow needs to be regulated by valves at every single magnet adding more complexity and risk of failure to the whole system. Moreover the cryo plant must be able to operate with high efficiency over the huge range of load. The optimal solution for a realization is a combination of smaller plants as shown in Fig. 8, which leads to higher investment costs.
6. CONCLUSION AND OUTLOOK
At first sight a helium-cooled superconducting accelerator is very appealing for a VPP scenario. The “simple” solution would be producing excess coolant in times of low public energy demand and powering down the cryo plant during times of high demand. The problem is that many accelerators are cooled with supercritical helium which can only be produced for direct use. Even in the example of the SIS100, which is cooled by two-phase helium, the very low efficiency for pumping helium with present helium pumps rules out the use of a barrel of liquid helium as a “cryo battery”. During a workshop within this network Error: Reference source not foundError: Reference source not foundthe cryo experts offered another scenario Error: Reference source not found. Instead of storing energy in liquid helium one could use liquid nitrogen as helper on the primary side of the helium cooling plant during times of high energy demand, and produce liquid nitrogen at times of low demand. The handling of liquid nitrogen is comparably simple and the secondary accelerator side is totally undisturbed by this measure.
The scenario presented in the previous chapters uses active switching of the accelerator operation from high- to low-energy-use mode. This works for normal conducting machines, too, if the change in operation mode can be transformed to lowered primary energy consumption. The switching scenario adds additional complexity to the accelerator’s control system and auxiliary systems which must be capable of working with variable loads. Even a comparably simple water cooling circuit for RF systems or accelerator magnets will only lead to lowered energy consumption if its pumps are regulated. Many existing systems have not foreseen this option to increase reliability. The added complexity needs higher investment and carries additional risk for operation. The overall energy consumption will be higher, if the users are compensated for the loss of performance during the VPP operation.
Machinery is usually optimized for an optimal point of operation. Complex processes tend to work best in a steady-state operation. Switched operations carry intrinsically more complexity and risk of failure.
If our power grid continues to lose stability due to increased contributions of renewable energy sources, all energy users might be required to be able to switch their electrical energy demand. Obstacles to lower and increased production, to dynamically change energy demand of a given industry, will very probably lead to similar problems as faced during accelerator operation. The VPP operation of a science facility could be a testbed to identify and tackle generic obstacles in dynamically operating complex machinery. In this sense the test of VPP scenarios at science facilities might be useful to gain insights to face these generic problems and learn how to operate complex machinery under these conditions.
7. REFERENCES
[1] “Virtual Power Plants”, Wikipedia, (2016), https://en.wikipedia.org/wiki/Virtual_power_plant [2] Bai, H.; Miao, S.; Ran, X.; Ye, C. Optimal Dispatch Strategy of a Virtual Power Plant Contain-
ing Battery Switch Stations in a Unified Electricity Market. Energies 2015, 8, 2268-2289.
[3] “Auf Eis gelegt”, Technology Review, 6/2014, P.95
[4] Daimler and enercity turning spare parts store into battery store. Feb. 2016 http://media.daimler.com/marsMediaSite/en/instance/ko.xhtml?oid=9919108
[5] Analysis of the electrical energy requirements of the GSI facility, C. Ripp (2013), http://cd- s.cern.ch/search?p=CERN-THESIS-2013-414
[6] “Survey on candidates for virtual power plant in accelerator labs”, MS19, https://edms.cern.ch/ui/file/1325135/2/EuCARD2-Mil-MS19-Final.pdf
“Homepage of CBM collaboration”,
http://www.fair-center.eu/for-users/experiments/cbm.html
[7] [8]
[9] [10]
[11] [12]
[13] [14]
[15] [16]
[17] [18]
