Transcript
TREN/06/FP6EN/S07.70918/038344
HOLISTIC Holistic Optimisation Leading to Integration of Sustainable Technologies in Communities Deliverable 8b, WP 1.7, Battery Storage
Due date of deliverable: M47 Actual submission date: M48 Start date of project:
1 June 2007
Duration: 60 Months
Organisation name of lead contractor for this deliverable: Dundalk Institute of Technology (DKIT) Revision 1.0 Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006) Dissemination Level PU
Public
PP
Restricted to other programme participants (including the Commission Services)
RE
Restricted to a group specified by the consortium (including the Commission Services)
CO
Confidential, only for members of the consortium (including the Commission Services)
√
TABLE OF CONTENTS: 1.0 SUMMARY ...................................................................................................... 3 2.0 OBJECTIVE OF THE WORK PACKAGE ....................................................................... 4 3.0 APPROACH TO ACHIEVING THE DELIVERABLE ............................................................. 5 3.1 RESULTS ......................................................................................................28 4.0 CONCLUSION ..................................................................................................44
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1.0 EXECUTIVE SUMMARY: This report is the “final report” deliverable of Work Package W.P 1.7 Battery Storage. The primary aim of this work package was to evaluate a flow battery storage system at Dundalk Institute of Technology (DkIT) in conjunction with a large scale wind autoproducer. A flow battery system was installed in the vicinity of the existing 850kW rated wind turbine on the DkIT campus. Electricity metering and monitoring equipment was also installed. The work package involved initially developing a spread sheet model of flow battery storage with the large scale wind turbine and the investigation of preliminary control strategies to minimise electricity bills. Tendering, installation and operational experience with an industrial scale zinc-bromine flow battery system was gained and a preliminary evaluation of the actual performance of the system was carried out. Finally the potential of using this form of electricity storage in partner communities i.e. Neuchâtel and Mödling was considered. The main conclusions drawn were:
It is currently difficult to justify this form of electricity storage with wind autoproduction on economic grounds in commercial applications
In the medium term, electricity storage can be economically beneficial to wind autoproducers such as the DkIT wind turbine when the mass produced cost is significantly lower than current costs and as electricity prices increase
The technology is suitable for energy management applications i.e. time shifting and peak shaving, however short term response times in less than 10 second may be a limitation in certain high speed load following/modulation applications
The zinc-bromine flow battery technology is relatively straight forward to install and does not require a building
The zinc-bromine flow battery technology characteristics is suitable for Solar PV applications
In the Mödling community a single 50kWh flow battery module could possibly be an appropriate match for the 30kWp Solar PV system. There is limited potential for a storage system any larger than the 50kWh module, though it is recommended that a more detailed technical and economic evaluation be carried out to investigate further
There may be potential in Neuchâtel to have a wide range of battery storage sizes with the 700kWp Solar PV system depending on how the electricity is intended to be used, the load profile over the year, and electricity tariffs etc. A more detailed technical and economic evaluation would be recommended to investigate further
In summary is still relatively early days in the application of flow battery technologies of this size. Further testing and R&D will be required to improve and make the technology more cost effective
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2.0 OBJECTIVE OF THE WORK PACKAGE: The primary aim of this work package was to evaluate a flow battery storage system at Dundalk Institute of Technology (DkIT) in conjunction with a large scale wind autoproducer. A flow battery system was installed in the vicinity of the existing 850kW rated wind turbine on the DkIT campus. Electricity metering and monitoring equipment was also installed. The work package involved developing a spread sheet model of flow battery storage with the large scale wind turbine and the investigation of preliminary control strategies to minimise electricity bills. Tendering, installation and operational experience with an industrial scale zinc-bromine flow battery system was gained and a preliminary evaluation of the actual performance of the system was carried out. Finally the potential of using this form of electricity storage in partnered communities i.e. Neuchâtel and Mödling was considered. The goals of the work package were as follows,
Development of a spreadsheet economic model for the wind-plus-storage system and after installation of the flow battery a report on the operating experience at DkIT
Papers presented at a conference
A report on the evaluation of potential of similar electricity storage in Neuchâtel and Mödling
Milestones and expected results
M8. Develop spreadsheet economic model– an Excel spreadsheet integrating a model of the wind turbine, a storage model, and relevant economic parameters
M10. First paper presented– an overview of possible control approaches and projected performance
M12 Installation of metering equipment – functioning monitoring system for flow battery, data acquisition begins
M24. Second paper presented – an evaluation of the actual operating experience and comparison of performance with projections
M43 A preliminary investigation of the ramp rate response of the flow battery
M47 A final report on the flow battery
M54. Evaluation of potential for electricity storage at Neuchâtel and Mödling – report, using the methodologies and experience developed in Dundalk
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3.0 APPROACH TO ACHIEVING THE DELIVERABLE: Period M0-M12 An Excel model to evaluate the economic benefits of adding battery storage to the wind turbine at DkIT was developed. The model takes as inputs annual half hourly energy generated by the wind turbine and annual half hourly load demand data for DkIT. It then uses seasonal and time of day (SToD) electricity tariffs for the 11kV network to evaluate annual cost savings for a given battery rating (kW), capacity (kWh) and efficiency as specified by the user. Options are available to give value to waste heat generated by the battery and ancillary services. Maximum Import Capacity (MIC) charges and penalties are included in the model. The model calculates payback times, NPV, and IRR for a given battery capital investment. Annual energy (kWh) flows e.g. amount of annual energy imported, exported, cycled through the battery and waste heat generated are also outputs of the model. The user is given some ability to control how the battery operated based on electricity prices. E.g. allow grid to charge the battery below a certain purchase price, allow discharging of battery above a certain purchase price, allow turbine only to charge battery when production exceeds demand. There is also an option to evaluate storage on its own at a site with no wind turbine. The results of the model can be viewed as a first pass evaluation of storage at a given site before detailed storage control is investigated. (See results section for the results) A paper titled “The value of adding electricity storage to the wind turbine at Dundalk Institute of Technology” was accepted for the European Wind Energy Conference (EWEC08) in Brussels in April 2008. Electricity Meters ION electricity meters supplied by Premium Power were installed in the electrical services compound at DkIT. The associated ION Enterprise software that allows many electrical parameters to be measured and logged as well as energy import/export was installed. The ION meters are now connected the college computer network and integrated with an existing SCADA system which was upgraded by Measuresoft Developments. A schematic overview of the end goal of the metering installation is show below in Figure 1.
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Grid
Wind Turbine
Control
Meter
Meter
Meter
Battery Storage
To Loads Figure 1: Schematic overview of the the metering installation Installation of metering Real time data logging of power flows generated by the wind turbine, imported from the grid and exported to the grid using the ION meters is shown in Figure 3 and Figure 4 and a SCADA system on an office computer was implemented that logs power data averaged over 15 minutes. The logged data gives a complete and accurate picture of the electrical energy consumed by DkIT, the electricity being generated by the turbine and the electrical energy available for storage and how these vary with time. Other parameters are also being logged such as voltage current, frequency, reactive power. As a result this will enable the battery system to be evaluated for a wide range of industrial and grid applications.
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Figure 2 Wind Turbine at DkIT (Picture taken from outside DkIT electrical compound)
Incomer Meter (Grid) Wind Turbine Meter
Figure 3 – Inside electrical compound. ION 7600 meter ESB incomer (grid) and ION 7600 meter Wind turbine incomer meter can be seen.
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Figure 4 – ION7600 meter Wind turbine incomer meter. MW, MVAR, MVA is shown Negative value correspond energy generated by turbine (i.e. negative power consumption values imply power generation)
Figure 5 – Communication link from meters being installed to connect to college fibre optic network
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Overview of communication link from meter to SCADA computer
Grid
Ethernet Master Meter
Wind Turbine
RS 485 Meter
Meter
Slave Meter
CAT 5 SCADA PC DkIT Computer networks
TX/RX Optic Fibre
Figure 6 – Communication link from meters being installed to connect to college fibre optic network In the meter communication arrangement, the ESB Incomer (grid) has an Ethernet port that communicates via a CAT 5 cable to a transceiver which interfaces the signal, via a transceiver, to an optic fibre link. The optic fibre link is connected to one of the main campus’ network that allows communication with the SCADA computer in the office. The ESB incomer meter has an Ethernet port and acts a master meter which can be “ethergated” to commutate with the wind turbine meter via an RS 485 cable. The wind turbine meter does not have an Ethernet port and is connected as a slave to the ESB master meter from the point of view of data communications. (In fact a master meter with an Ethernet port can be used to communicate with up to 20 slave meters as might be the case, for example, at a wind farm or a site with numerous metered buildings). Apart from the Ethernet port both meters are identical in every other respect. The SCADA system software was installed by Microsoft Developments Ltd, a company located in Dundalk, Ireland. The DkIT IT department was also involved in the installation and setting up of the communication link connections between the meters and the SCADA system computer. Meetings with Industry A meeting with Distribution Services Operator (DSO), ESB networks, to discuss potential ancillary value for storage at an autoproduction site (at the MV distribution level) took place in October 2007. Ancillary services such as operating reserve or black start assistance are the responsibility of the transmission operator Eirgrid. The Maximum Export Capacity (MEC) at a given point on the distribution level network limits the amount of wind turbine generator capacity that can be connected without network reinforcement. Storage in some instances could allow more wind generation capacity to be connected at given site, however as there will be times when the battery is fully charged, keeping below the required MEC value may not always guaranteed. Therefore in the near term, battery storage will not be of real benefit to the distribution services operator. However it is also expected that battery storage will not cause problems at the grid distribution level either and no concerns were
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expressed by the DSO provided the system adhered to the relevant grid codes. There were already in place as a result of the already installed wind turbine. Period M12-24 During M12 to 24 the main focus was acquiring a flow battery system at Dundalk Institute of Technology (DkIT). In June 2008 DkIT was successful in its application to Enterprise Ireland for a grant under its Capital Equipment funding scheme purchase a storage battery. The value of the grant was €575,000. Due to the size and costs the battery system an EU tendering process was necessary (EU tender 243755-2008 (2008/S 184-243755). A battery specification and tender was prepared during July and August 2008 and was published in September 2008. The project went out to tender. The site for the battery installation at DkIT was selected and a battery system taking into account civil and electrical requirements. In parallel with this metering of the wind turbine production and campus imports and exports has been continuing since M25. A second conference paper was presented on the economics of using flow battery storage with wind autoproducers at the International Renewable Energy Storage Conference in November 2008 (IRES08) in Berlin. From September to November 2008 questions from interested tender applicants were received and answered. The deadline for tender applications was 11 November 2008. As there are a limited number of flow battery commercial suppliers for systems of this size in the world we received two tender applications. These were evaluated on how they met certain criteria. These included,
Appropriate system size electrically and physically Auxiliary equipment/services and housing requirements Health and safety implications of given system Ease of installation Backup and maintenance support Running costs History/experience of company Lead time for system to arrive on site Lead time for spare parts if/when required System costs/remaining within budget
The tender was awarded to ZBB Energy for a 125kW, 500kWh zinc bromide flow battery. Our storages models showed that a 125kWh, 750kWh battery would have been the optimum size but budget constraints were the limiting factor. However research control algorithms can be evaluated equally as well with this size of battery. Factory acceptance testing for the 125kW, 500kWh system commenced at end of May 2009.
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Battery System The battery is 125kW, 500kWh consisting of ten 50kWh modules made up of two parallel strings each containing five 50kWh modules. A power conversion system (PCS) converts DC power to grid quality AC power to enable the battery to be connected to the gird at DkIT at the 400V three phase level. The PCS carries numerous other functions such as monitoring the sate of all the modules in the battery. If there is a mal function with a module it can be switched out of the system while the other modules can continue operating normally.
DC 5OkWh
5OkWh
5OkWh
5OkWh
5OkWh
5OkWh
5OkWh
5OkWh
5OkWh
5OkWh
PCS
AC
Figure 7: Simplified overview of battery system A single 50kWh module during manufacture for the DkIT battery is shown below in figure 8
Figure 8: Single 50kWh module
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The 10 modules are housed in a marine grade sea box container which allows the system to be placed in the outdoors. This is shown in Figure 9.
Figure 9: 125kW, 500kWh battery – One string containing five 50kWh modules in marine grade “sea box” container. (The other string of five modules which can’t be seen is at other side of sea box) Factory acceptance testing commenced at end of May 2009 and included the following
Verification of Controls/Software
Local operator interface testing
Normal start-up and shutdown
Emergency shutdown
Full-load loss test, real and reactive power consumption/generation
Functionality tests of all hardware
Figure 10: Battery system control screen during factory acceptance testing
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Site Selection for Battery at DkIT DkIT is connected to the electricity grid at the three phase 10kV level. On site transformers reduce this voltage to 400V (three phase) to supply various load on the campus. Single phase 230V power is taken from the individual phases of the 400V three phase lines. The battery system is designed to connect at the 400V three phase level. As a result existing 10kV to 400V transformers on site were electrically monitored for power, voltage, currents, harmonic distortion and power factor to assess the point to connect the battery. Following this assessment the transformer in the vicinity of the turbine was chosen as the most appropriate point of connection and site for the battery. Figure 11 shows batter site before battery was installed.
Site for battery system
Transformer 10kV/400V Battery system will be connected at 400V level
Figure 11: Site location at DkIT for battery A simplified schematic of how the battery is connected to the electricity network at DkIT is shown in Figure 12.
Grid (10kV 3 phase)
Wind Turbine
690V 3Phase 690V/10kV
Transformer in base of turbine
10kV 3Phase
On site transformer
To other DkIT transformers (10kV/400V)
10kV/400V 400V 3 phase
PCS/Battery
To DkIT Loads
Figure 12: Overview schematic of how battery will be connected at DkIT
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Control approaches to optimise the battery performance will at any given moment depend on DkIT demand, wind production, battery state of charge and electricity prices. Based on these dependencies power may flow via the transformers from a) Turbine to DkIT loads, b) Turbine to battery, c) Turbine to grid, d) Battery to DkIT loads, e) Grid to battery, f) Grid to DkIT loads and g) Battery to grid. Period M24-36 Following successful factory acceptance testing in June 2009, the ZESS500 flow battery system was installed and commissioned at Dundalk Institute of Technology (DkIT) during the M24 to M36 period. This is the only system of its type installed in Europe and the only one of its type operating with a large scale wind auto producer in the world. The SCADA system at DkIT was expanded to include monitoring and control of the battery system. This has enabled rigorous testing to be carried out on the system to evaluate its operation. This will provide useful real world performance information about the system for applications in wind energy, utilities and for the battery manufacturer. Battery Installation Site works
Figure 13 – Battery site civil works
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Figure 14 – Battery site completed civil works Installation of the System As mentioned earlier, the battery is rated at 125kW, 500kWh consisting of ten 50kWh modules made up of two parallel strings each containing five 50kWh modules. A power conversion system (PCS) converts DC power to grid quality AC power to enable the battery to be connected to the gird at DkIT at the 400V three phase level.
DC 5OkWh
5OkWh
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5OkWh
5OkWh
5OkWh
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5OkWh
5OkWh
PCS
AC
Figure 15 –Simplified overview of battery system
Figure 16 – Battery PCS units arriving at site
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Figure 17 – Battery DC section container units arriving at site
Figure 18 – Commissioning a battery module
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Figure 19 – Commissioning a battery module
Figure 20 – Battery PCS Units (i.e. DC to AC conversion and vice versa)
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Figure 21 – Grid Connection panel boxes and chillier unit
Figure 22 – Finished Installation with wind turbine in background
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Wind and Storage System Schematic Overview
Grid (10kV 3 phase)
Wind Turbine
690V 3Phase Circuit Breakers & Metering
10kV 3Phase 690V/10kV
Transformer in base of turbine SCADA PC
On site transformer To other DkIT transformers (10kV/400V)
10kV/400V 400V 3 phase
PCS/Battery
To DkIT Loads
Figure 23 – Overview Schematic of Wind and Storage facility with SCADA system Preliminary Control Approaches Control approaches to optimise the battery performance will at any given moment depend on DkIT load demand, wind production, battery state of charge and electricity prices. Based on these dependencies and referring to Figure 23 power may flow from a) Turbine to DkIT loads b) Turbine to battery c) Turbine to grid d) Battery to DkIT loads e) Grid to battery f) Grid to DkIT loads and g) Battery to grid Electricity costs to an industrial consumer depend on both purchase and sale prices and vary depending on the time of day and time of year. The following “first pass” control approach is for the case of the industrial scale wind turbine autoproducer and storage system at DkIT for minimising the cost of electricity imports. Depending on a number of dependent variables outlined below, the control algorithm charges or discharges the battery in given time period Tn (chosen by the user) to minimise electricity costs. After each Tn period the algorithm loops for subsequent time periods.
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Control Dependent Variables
Tn = time period (e.g. minutes, quarter hours, half hours or hours) being evaluated Tn-1 = previous time period Tn+1 = next time period DkIT Load(Tn) = Average load consumption of DkIT (kW) during Tn WTG(Tn) = Average wind turbine generator output (kW) during Tn Net DkIT Load = DkIT Load (Tn)_ - WTG (Tn) Import Power = Net DkIT Load positive Export Power = Net DkIT Load negative (=WTG Surplus) Electricity Purchase Price(Tn) = Electricity purchase price during Tn Electricity Sales Price(Tn) = Electricity sales price during Tn Lower Set Price(Tn) = determined by user for charge/discharge control Upper Set Price(Tn) = determined by user for charge/discharge control Discharge(Tn) = Discharge of battery during Tn (according to discharge algorithm) Charge(Tn) = Discharge of battery during Tn (according to charge algorithm) SOC(Tn) = Battery state of charge (kWh) at end of Tn Charge Power = Battery charge rate (kW) during Tn Discharge Power = Battery discharge rate during (kW) Tn Battery Max Charge Power = Maximum rate at which battery can be charged (kW) Battery Max Discharge Power = Maximum rate at which battery can be charged (kW) Battery Max Charge Capacity = Maximum battery capacity (kWh) Battery Min Charge Capacity = Minimum SOC allowed kWh (=0 in this case) Eff = battery efficiency Top Level Control Loop The top level control loop is shown below. In each box there are sub-loops outlined in the following sections which are interdependent on each other. The algorithm varies the state of charge (SOC) of the battery, according to the constraints of the dependent variable list, to minimise the cost of electricity to DkIT.
Cost(Tn) = Energy Import(Tn) x Purchase Price(Tn) - Energy Export(Tn) x Sales Price(Tn)
Net DkIT Load(Tn )= DkIT Load(kW)-WTG(Tn)
Electricity Purchase Prices(Tn)
SOC(Tn-1)
(Tn)
Energy Import(Tn)=Net DkIT Load(Tn)Discharge(Tn)+Charge(Tn) x Tn
Electricity Sales Prices(Tn)
(Tn+1)
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Discharge sub-loop
If Purchase Price(Tn) >= Lower Set Price(Tn) AND SOC(Tn) >0 Yes
No
IF Import Power(Tn) > Battery Max Discharge Power Yes
No
SOC/Tn) >= Battery Max Discharge Power Yes Discharge Battery at Max Power(Tn)
Do nothing
SOC/Tn) >= Import Power(Tn)
No
No
Yes
Discharge at available Battery Power(Tn)
Discharge Battery at Import Power(Tn)
(Tn+1)
Wind surplus battery charging sub-loop If Export Power(Tn) > 0 AND SOC < Max Charge Capacity Yes
No
IF WTG Surplus(Tn) > Battery Max Charge Power Yes
No
IF (Max Charge Capacity –SOC)/Tn) >= Battery Max Discharge Power Yes Charge Battery at Max Power(Tn)
Do nothing
No
IF WTG Surplus (Tn) < (Max Charge Capacity –SOC)/Tn) No
Charge Battery at (Max Charge Capacity -SOC)/Tn)
Yes Charge Battery at WTG Surplus(Tn)
(Tn+1)
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Battery charging from grid sub-loop If Purchase Price(Tn) <= Upper Set Price(Tn) AND SOC(Tn) < Max Charge Capacity Yes
No
IF WTG Surplus(Tn) < Battery Max Charge Power Yes
Do nothing No
IF (Max Charge Capacity –SOC)/Tn) >= Battery Max Discharge Power Yes
WT Charge
No
Charge Battery at Max Power - WTG Surplus(Tn)
Charge Battery at (Max Charge Capacity-SOC)/Tn) –WTG Surplus(Tn)
T(n+1)
Battery state of charge (SOC) sub-loop
If [Charge Power (Tn)xEff - Discharge Power(Tn)] xTn +SOC(Tn-1) >= Max Charge Capacity Yes
No
Max Charge Capacity
If [Charge Power (Tn)xEff - Discharge Power(Tn)] xTn +SOC(Tn-1) <= Min Charge Capacity Yes
No
Min Charge Capacity
[Charge Power (Tn)xEff - Discharge Power(Tn)] xTn +SOC(Tn-1)]
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Grid Applications The storage at facility at DkIT allow the evaluation of the system response to variation in grid frequency i.e. speed of response and ramp rate capability. The efficiency of the system can be evaluated for a wide range of ramp rated and frequency response. The system can also generated and consume reactive power and can be evaluated in how it can assist with grid voltage stability (for utilities and power factor correction (for industrial consumers). Storage systems in comparison to conventional power generators receive no value at present for utility application where it may be of value such as in Operating Reserve Reactive Power Generation Black Start Capacity These detailed grid related studies are areas for future research and outside the scope of WP 1.7 Battery Storage Period M36-M48 Charging and discharging cycles have been run on the flow battery systems. Initial assessments of the operational efficiencies, maximum charge/discharge rates and switching speeds of the system from charging to discharging modes were carried out. From this preliminary test the system response for variation in wind turbine output could be assessed. The tests also give an indication of how suitable this from of flow battery storage may be with other wind and/or other renewable energy generators. An outline of the strengths and limitation this type technology is also given. Charge/Discharge Cycles The system was charged and discharged over full cycles and showed to have an average round trip AC to AC efficiency of ~ 60% to 65%. An example of a full charge/discharge cycle is shown in Figure 24. 150
AC Charge/Discharge over a full cycle 100
Power modulation
kW Charging
AC Power (kW)
50
08:26:58 08:37:58 08:48:58 08:59:58 09:10:58 09:21:58 09:32:58 09:43:58 09:54:58 10:05:58 10:16:58 10:27:58 10:38:58 10:49:58 11:00:58 11:11:58 11:22:58 11:33:58 11:44:58 11:55:58 12:06:58 12:17:58 12:28:58 12:39:58 12:50:58 13:01:58 13:18:49 13:29:49 13:40:49 13:51:49 14:02:49 14:13:49 14:24:49 14:35:49 14:46:49 14:57:49 15:08:49 15:19:49 15:30:49 15:41:49 15:52:49 16:03:49 16:14:49 16:25:49 16:36:49 16:47:49 16:58:49 17:09:49 17:20:49
0
-50
Discharging
-100
-150
Figure 24 – AC to AC charge/discharge cycle (positive power is charging, negative power is discharging)
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Energy losses in the system can be attributed to the following factors• Power conversion efficiency of the PCS unit from AC to DC during charging and from DC back to AC during discharging. The one way conversion efficiency is dependent on the power level and can vary from 90% to 95%. As a result round trip conversion efficiency can the vary from ~ 81% to 90% • As the charging process in the battery stack cells involves plating the negative electrode with solid zinc and the discharging process involves dissolving the solid zinc back to in the zinc in the battery stacks there are losses associated with this plating process and is dependent on the state of charge of the cell. Other factors such as small amounts of internal hydraulic self discharge between anolyte and catholyte solutions in the cell stacks can add to losses but the system sis designed to keep these at a mimimun. There is scope for further research in this area to improve further versions of the design • Power consumption of auxiliary components such as electrolyte pumps and chiller unit is variable and can contribute ~ 5% to 10% reduction in overall system efficiency Battery mode switching and ramping When the battery system is started up from a power down mode the are numbers of system self tests that are automatically performed by the controller (i.e. on 10 individual battery modules, PCS unit and the state of the grid voltage and frequency) out before a main power connection is made to the grid via a soft starting control mechanism. This power-up phase takes about 1 to 2 minutes to complete. When the system has started and is connected to the grid, the power of charging or discharging can then be modulated from 0 to 125kW (or 0 to 125kVAr if reactive power control is required in a given application). The rate of power modulation or changing from charging to discharging is depend primarily on the battery controller receiving the signal request either manually from the user control panel or automatically from the SCADA system and implementing the request. When the battery controller receives a requested to charge or discharge it take in the region of a 10 second response time implement the request i.e. the system can switch from 125kW charging to 125kW discharging in less that 10 seconds As SCADA control algorithms are developed to make battery charging and discharging decisions that depend on a number of variable inputs (e.g. turbine output, campus demand, electricity tariffs etc), the decision making computation time and data communication to/from the SCADA system to the battery controller will add some extra delay. There is future research scope in the design of optimised control algorithms to operate the battery as efficiently and as fast as possible.
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Sample Wind turbine output and wind speed 700
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600 10
500 8
400 Power (kW)
6 Wind Speed (m/s) 300
4 200
2 100 Power StdDevPower WindSpeed 0 00:00:00
02:24:00
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0 00:00:00
Time
Figure 25 – A sample of 10 minute average wind power production as standard deviation along with speed over one particular day Figure 25 shows a sample of 10 minute average power output from the wind turbine as well as the 10 minute standard deviation in wind turbine output over a 24 hour period. The wind speed is also shown. The standard deviation gives and indication of how the power production varies about the mean over the 10 minute period. Wind power production can vary rapidly with rapid changes in wind speed. On an instantaneous basis the battery system may not fully be able to respond to large variations in wind power in time frames less than 10 seconds (e.g. gusts) due to the response of the system as outlined earlier. In the case of Dundalk IT the battery system charging and discharging will be controlled based on the difference between wind productions, load demand and electricity prices over variable averaging time periods. At times when the difference between turbine power output and campus power demand is below a certain value then the battery will not require engagement. At times when there is a large difference between wind turbine power production and campus load demand then large changes in wind power production will have less an impact on the charging/discharging control of the battery. As these are only preliminary tests, the optimised control of the system will require further in depth research. There is also scope for extra research into storage integration within wind turbine systems themselves so that the storage can be accessed on a more instantaneous basis with changes in wind turbine power production. Advantages and limitations of the Zinc Bromine flow batter technology Advantages•
Modular design- can be scaled up in units of 50kWh modules
•
Designed for outdoor installation i.e. no need for buildings or major civil works
•
Little or no environmental hazards
•
Can be discharged to 0% without electrolyte degradation
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•
Electrolyte does not degrade and can be reused at the end of system life
•
Low maintenance requirements
• No complicated temperature controls required i.e. operates near optimum efficiency at ~ 27C Limitations• Relatively low projected maximum number of charge/discharge cycles before end of life (~ 2000) in comparison to other flow battery technologies under development, however life of systems in still comparable to commercial deep cycle lead acid systems • As with all flow battery technologies there are a limited number of commercial suppliers globally (e.g. the system in Dundalk is a European first at this scale and a world first with a large scale wind autoproducer) • For many commercial applications today (2011) the economics of flow battery systems is still relatively unjustified but will improve as markets develop and fossil fuels continue to rise Application of this flow battery in other communities Following the installation of the battery system in Dundalk the final phase of Work Package WP 1.7 was to do a preliminary investigation of the potential for using zinc-bromine flow battery storage with the proposed wind turbines that were planned to be installed in Neuchâtel, Switzerland. As of June 2011 the installations of these turbine has not gone ahead. However there are solar PV systems installed in some partner communities and a preliminary investigation on the applicability of the ZBB flow battery system with solar PV was carried out instead. Preliminary investigation of the ZBB flow batter system with Solar PV The battery system in Dundalk consists has 500kWh capacity and consist of 10 modules of 50kWh each. Single 50kWh module systems are available for smaller scale application and larger system can be built in multiple sizes of 50kWh depending on size and operation of the intended application. Single Battery Module Power/Energy Speciation Power rating = 15kW (can be modulated from 0 to 15kW) Storage capacity = 50kWh E.g. single module can supply 15kW of power for ~ 3 hours The solar resource varies with location on earth time of day, season and local climate conditions. The graph show in Figure 26 the solar irradiance varies over the day in Neuchâtel (47N, 6.96E). The graph was produced from the HC15 - HelioClim3v2 model based on 1 minute satellite data. It shows that the solar resource is does not vary at the same rate as the wind resource that was show in Figure 25 in the last section. Of course this graph is just for one day in the year and every day would have its own profile; however solar profiles are much more predictable than the wind profiles.
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The graph represents solar irradiance the longest day of the year in summer, winter days would be substantially less. Solar Irradiance 21st June 2005 (Neuchatel) 1000 900 800
I (W/m2)
700 600 500 Solar Irradiance
400 300 200 100 0 04:48:00
07:12:00
09:36:00
12:00:00
14:24:00
16:48:00
19:12:00
time (GMT)
Figure 26: Solar Irradiance variation over 1 day on 21st June 2005 in Neuchâtel (Source: www.soda-is.com) Generally the ramp rates and response times for storage are less rigorous for a solar energy system compared to a wind energy system. Two examples cases of solar PV systems in Neuchâtel and Mödling are which are at similar latitudes and would receive similar annual solar irradiances (ignoring site specific local effects such as shading etc). From the HelioClim databases (www.soda-is.com) the mean daily number of peak sun hours (PSH) in June is approximately 6 while the mean daily PSH in December is approximately 1. The annual daily mean PSH is approximately 3.5 (See results section for preliminary evaluation storage evaluation)
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3.1 RESULTS Results M0-12 The grid and wind turbine meters were successfully installed and integrated with the SCADA computer where the power data is being logged. The data logging commenced on April 1 2008. The system is logging the electricity generated by the wind turbine, electricity consumed by DkIT, net electricity imported from the grid and the net electricity exported. The complete power flow picture allows the power available for storage to be assesses accurately and the shape of the power flows will allow an accurate estimation of the optimum battery size and capacity to be determined taking electricity prices into account. Some output displays from the SCADA system is shown below.
Figure 27 – Data from the wind turbine displayed In Figure 27 it can be seen that wind speed is 18.8 m/s and the turbine is at rated output of 851.9kW (850kW nom). This was around 17:40 in the evening when the campus demand is low as a result as most people have gone home we would expect to exporting power at this point. This is confirmed in Figure 28 below.
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Figure 28 – A display of the wind turbine incomer (grid meter) The ESB (Grid) incomer meter is displaying and total DkIT consumption if -546.90kW i.e. the negative value means that this is the power being exported! (Therefore the actual consumption of the college at this time ~ 851.9kW – 546.9kW = 305kW is the college demand and is being all supplied by the wind turbine). Results M12-24 Funding was obtained from Enterprise Ireland for the acquisition of a flow battery storage system was secured in December 2008 and battery tender was awarded to ZBB Energy Corporation for a 125kW, 500kWh zinc-bromine system. Manufacturing of the system took place from February 2009 to May 2009 Metering Electricity metering of DkIT electricity consumption, wind turbine production, electricity net import and electricity net export has been continuing since July 2008. A sample of the monthly measured from July 2008 to December 2008 data is shown in Figure 29.
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400,000
DkIT Metered Montly Electricity Data July-December 2008
350,000 300,000
kWh
250,000
DkIT Load Total WT Generation
200,000 Net Import from Grid
150,000
Net Export to Grid
100,000 50,000 0
Figure 29: Sample of metered monthly electricity data from July to December 2008 Conference Papers A 2nd conference paper was presented at the International Renewable Energy Conference (IRES08) in Berlin in November 2008 on the value of storage with industrial scale autoproducers. As the battery was not yet installed at the time the study focused on different sizes of batteries for use with the wind turbine at DkIT and a projected economic evaluation of storage at the site based on current and future electricity prices and battery capital costs. The Addition of Storage Along with the electricity savings already being achieved by the wind turbine on its own there exists opportunities to reduce the DkIT electricity bills even further by adding electricity storage. Excess electricity generated by the wind turbine can be stored instead of being exported. The stored electricity can then be reused at times when electricity purchase prices are high when the power demand at DkIT exceeds the power generated by the wind turbine. During times when the demand is low and the cost of imported electricity is low (e.g. at night and at weekends), if the wind turbine is not producing sufficient electricity to charge the storage it can be assisted by charging from the grid. Using the developed spread sheet model the following example show in Figure 30 shows a case where a 150kW, 1000kWh battery in the model discharges at high purchase prices during the day and is recharged a night.
30
Power Profile over 1 day in Spring 1200 1000 DkIT Consumption 800
WTG Production
kW 600
Battery Discharge
400
Battery Charge
200
WTG + Battery Output
00 :3 0 02 :0 0 03 :3 0 05 :0 0 06 :3 0 08 :0 0 09 :3 0 11 :0 0 12 :3 0 14 :0 0 15 :3 0 17 :0 0 18 :3 0 20 :0 0 21 :3 0 23 :0 0
0
Time
Figure 30 – The use of storage in the daytime when demand exceeds the wind turbine output Electricity Prices SToD prices are seasonal and time of day variable prices. The 2008 SToD prices at the 10kV/20kV level that were used at the time are shown in Table 1. Along with the varying charges per unit of electricity there are also Maximum Import Capacity (MIC) charges for a given MIC (kVA) value at the site. The higher the agreed MIC value the higher the MIC charge. If too low an MIC is set then there is a higher risk of exceeding the MIC for which more severe penalty charges will be incurred on the excess. The addition of storage can help reduce the MIC value by effectively reducing the number of times MIC is exceeded. The model takes MIC charges and penalties into account including any reduction due to the addition of storage. Electricity Prices
Summer (March October) Weekday Weekend Night (0800day (23002300) (08000800) 2300)
Winter (November-February) Weekday Weekday Weekend Off Peak Peak day (0800(1700(08001700, 1900) 2300) 19002300) 0.1667 0.2999 0.1292
ESB 0.1110 0.0943 0.0536 10kV/20kV Incl VAT 0.1260 0.1070 0.0608 0.1892 0.3404 Annual MIC Charge 26.28 euro/kVA MIC Excess Penalty 6.73 euro per max excess kVA in each billing period (two months)
0.1466
Night (23000800)
0.0659 0.0748
Table 1 - ESB SToD electricity price for 10/20kV customers (January 2008)
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Battery Sizing and Control The developed spreadsheet model was used to evaluate the economics of various battery sizes in rating, capacity and efficiency. The operation of the battery is dictated by electricity prices at any given time. Half hourly data of the electricity generated by the wind turbine and electricity consumption at the site are inputs to the model along with battery rating, capacity and efficiency. The corresponding amounts of half hourly electricity imported, exported and cycled through the flow battery are calculated by the model. There are a number of options for the user to choose from such as; 1. Allow battery to be charged by wind turbine only 2. Allow battery to be charged by wind turbine and grid when the electricity purchase prices are below a certain value. 3. Allow discharging of battery when electricity purchase prices are above a certain value. A range of battery sizes in, rating, capacity and efficiency for four scenarios relating to batter efficiency and values given to exports and waste heat were evaluated using the model for minimising the overall annual electricity purchase from the grid by DkIT. The SToD prices outlined previously in Table 1 and control the criteria in Table 2 were used. Summer Mode Price Prohibit battery charging by grid at purchase prices 0.09 greater than Prohibit battery from discharging at purchase prices less 0.10 than Winter Mode Prohibit battery charging by grid at purchase prices 0.13 greater than Prohibit battery from discharging at purchase prices less 0.14 than Table 2 - Example of a simple battery control criteria for summer and winter Present (2008) capital cost of flow battery technology ~ €2000/kW, €400kWh The four scenarios evaluated for battery efficiency and value given to exported electricity and waste heat produced are outline in Table 3. Parameter Scenario 1 Scenario 2 Battery Efficiency 75% 65% Value of Exported 5c/kWh 5c/kWh Electricity Value of waste heat 3c/kWh 3c/kWh produced Table 3 – Scenarios evaluated for battery efficiency waste heat
Scenario 3 75% 0c/kWh
Scenario 4 65% 0c/kWh
0c/kWh
0c/kWh
and value of exported electricity and
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Annual Electricity Cost - 75% Efficient Battery 280,000 275,000 270,000
€
100kW
265,000
150kW
260,000
200kW 250kW
255,000 250,000 245,000 500
700
900
1100
1300
1500
1700
1900
Battery Capacity kWh
Figure 31 – Scenario 1: Annual electricity costs for various batter power ratings and capacities For a given battery power rating (kW) the annual electricity costs decreases with increasing battery capacity and continues to decrease at a diminishing rate up to a point above which adding more capacity become redundant, as expected. However to determine the optimum size, the return on investment must also be considered. This is largely influenced by capital cost of the battery system for given battery size. This is shown in Figure 32 in terms of payback times for the various battery sizes (at capitals costs in 2008).
Simple Payback Time - 75% Efficient Battery 65.0 60.0 55.0
Years
50.0
100kW
45.0
150kW
40.0
200kW
35.0
250kW
30.0 25.0 20.0 15.0 500
700
900
1100
1300
1500
1700
1900
Battery Capacity kWh
Figure 32 – Sceanrio1: Payback times for various batter power ratings and capacities For each battery power rating (kW) it can be seen 7 hours of storage gives the best result. Combining the results of Figures 31 and 32 gives a 150kW power rating and ~1050kWh (7 hours) that provides the optimum solution for this scenario.
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The follow case (Scenario 2) considers a battery efficiency of 65%
Annual Electricity Cost - 65% Efficient Battery 280,000 275,000 270,000
100kW
€ 265,000
150kW
260,000
250kW
200kW
255,000 250,000 500
700
900
1100
1300
1500
1700
1900
Battery Capacity kWh
Figure 33 – Scenario 2: Annual electricity costs for various batter power ratings and capacities
Simple Payback Time - 65% Efficient Battery 75 65
Years
55
100kW 150kW
45
200kW 250kW
35 25 15 500
700
900
1100
1300
1500
1700
1900
Battery Capacity kWh
Figure 34 – Scenario 2: Payback times for various battery power ratings and capacities
In this case for a battery efficiency of 65% a 150kW rated battery with 6 hours gives the optimal solution. In conclusion a 150kW with 6 or 7 hours of storage will provide the optimum economic solution. Scenarios 3 and 4 shown in Figures 35 to 38 for battery efficiencies of 75% and 65% respectively are more specific to DkIT where value for exported electricity is not given and where it is unlikely that the waste heat produced will be utilised.
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Annual Electricity Cost - 75% Efficient Battery 310,000 305,000 300,000
€
100kW
295,000
150kW
290,000
200kW 250kW
285,000 280,000 275,000 500
700
900
1100
1300
1500
1700
1900
Battery Capacity kWh
Figure 35 – Scenario 3: Annual electricity costs for various batter power ratings and capacities
Simple Payback Time - 75% Efficient Battery 60.0 55.0
Years
50.0 45.0
100kW
40.0
150kW
35.0
200kW 250kW
30.0 25.0 20.0 15.0 500
700
900
1100
1300
1500
1700
1900
Battery Capacity kWh
Figure 36 – Sceanrio3: Payback times for various batter power ratings and capacities The results of Figures 36 and 37 show that a 150kW battery power rating and ~1050kWh (7 hours) provides the optimum solution. However the total annual electricity cost for DkIT is higher as there is no value given to exported electricity (e.g. when the battery is charged and turbine is producing excess electricity). This improves the economics of storage (payback) by a small fraction.
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Annual Electricity Cost - 65% Efficient Battery 310,000 305,000 300,000
100kW
€ 295,000
150kW
290,000
250kW
200kW
285,000 280,000 500
700
900
1100
1300
1500
1700
1900
Battery Capacity kWh
Figure 37 - Scenario 4: Annual electricity costs for various batter power ratings and capacities
Simple Payback Time - 65% Efficient Battery 75 65
Years
55
100kW 150kW
45
200kW 250kW
35 25 15 500
700
900
1100
1300
1500
1700
1900
Battery Capacity kWh
Figure 38 - Sceanrio3: Payback times for various batter power ratings and capacities In this case for a battery efficiency of 65% a 150kW rated battery with 6 hours gives the optimal solution. In conclusion a 150kW with 6 or 7 hours of storage would provide the optimum economic solution under the control strategy that was outlined in Table 2. A summary of the optimum results is given in Table 4.
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Rating kW
Capacity kWh
Efficiency
Value given to Total DkIT Exports Annual (5c/kWh) Costs (€) and Heat (3c/kWh) 0* 0 N/A Heat (N/A) 287,289 150 1050 75% Yes (Scenario 1) 262,593 150 900 65% Yes (Scenario 2) 268,191 0* 0 N/A N/A 318,355 150 1050 75% No (Scenario 3) 291,506 150 900 65% No (Scenario 4) 298,331 Table 4 – Summary of optimum results * Wind turbine only (no
Saving due to battery (€)
Payback Time (Years)
N/A 22,051 17,472 N/A 24,215 18,409 storage)
N/A 32.7 37.8 N/A 29.7 35.9
Battery Storage Future Economics At current electricity prices and capital costs of storage the payback times are long. However as electricity prices continue to rise and as flow battery capital costs reduce in mass production, this form of storage will become increasing viable. Next we compare the current economics in more detail with three possible medium term future scenarios i.e. four cases in total and these are for a 150kW,1050kWh 7 hour system with a 75% efficiency. A discount rate of 4% is used and the period is over 20 years. 1. Current electricity prices in Table 1 and current battery storage capital costs of €2000/kW, €400/kWh 2. Electricity prices increasing by 50% with current battery storage capital costs 3. Electricity prices increasing by 100% with current battery storage capital costs 4. Electricity prices increasing by 100% and medium term battery capital costs 50% of current value Value is given to exports and to waste heat produced. These are scaled in each scenario with the electricity prices from the current values of 5c/kWh and 3c/kWh respectively. The results are given in Table 5.
Case
Rating kW
Capacity kWh
1
150
1050
2 3 4 Table
Total Annual Costs (€) 262,593
Saving due to battery (€) 22,051
Payback Time (Years)
NPV (€)
IRR %
32.7
-4% 398,268 150 1050 393,890 33,077 22.9 0% 237,402 150 1050 525,186 44,102 16.3 -76,536 3% 150 1050 525,186 44,102 8.2 283,463 13% 5 – Summary of battery economics at current capital costs and electricity prices
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Results M24-M36 Conference Papers A 3rd conference paper was presented on “The Flow Battery Storage Facility at DkIT” at an SEAI - Electricity Storage Seminar on 4th November 2009 in Dublin, Ireland. Controls operation and SCADA System
Figure 39 – Screen shot of battery control screen
Figure 40 – Screen shot of meters power display on SCADA System
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Figure 41 –Screen shot of one of the SCADA system meter displays for imports/exports
Figure 42 – Screen shot of one of the SCADA system meter displays for wind turbine production
39
Figure 43 – Screen shot of on of the SCADA System Meter displays for flow battery
Figure 44 – Screen shot of one of the SCADA system’s sample of metered data
40
Results of preliminary control approaches for a Tn of half an hour.
Battery Charging and Discharing Operation over 1 day in Spring 140
600
Battery SOC kWh
120
500
WTG Charge kW
400
Grid Charge kW
300 kWh
Battery Charge kW
200
Battery Discharge kW
100 80
kW 60 40
100
20
:0 0
:0 0
:3 0
:0 0
:3 0
23
21
20
18
:3 0
17
:3 0
:0 0
:0 0
15
14
12
11
:3 0
:0 0
09
:3 0
08
:3 0
:0 0
06
05
03
02
00
:0 0
0
:3 0
0
Figure 45 – Power flows to and from battery along with battery state of charge
Monthly Electricty Demand vs Wind Turbine Production (No Storage) 450000 400000 350000 300000 250000 kWh
DkIT consumption with no WTG Total WTG Production DkIT consumption with WTG WTG Exported Energy
200000 150000 100000 50000
De c
No v
O ct
Se p
Au g
Ju ly
ay
Ju ne
M
Ap r
ar M
Fe b
Ja n
0
Month
Figure 46 – Monthly energy demand, wind turbine production, imports and exports
Monthly Data with Battery Storage (125kW, 500kWh) 350000 300000 250000 200000 kWh 150000
Imported Energy (incl' Grid Charging) Exported Energy WTG Charging Grid Charging Battery Discharging
100000 50000 0 Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec Month
Figure 47 – Monthly energy flows to and from battery and with grid exports and imports
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Economics Summaryod System at DkIT Battery capital cost €575,000 Battery round trip efficiency 65%
Some Scenarios
No storage 125kW, 500kWh 125kW, 500kWh 125kW, 500kWh 125kW, 500kWh
Value given to exports (€/kWh) NA 0.00 0.057 0.00 0.057
Value given to waste heat (€/kWh) N/A 0.00 0.00 0.04 0.04
Total DkIT Annual Costs (€) 330,025 322,156 290,620 318,622 287,086
Annual savings due to battery (€) N/A 7,488 3,597 11,022 7,131
Table 6 – Summary of battery system economics at DkIT The results shown are those using the preliminary control approaches outlined earlier. More detailed statistical control algorithms including load and wind forecasting will provide a focus for further research.
Results M36-48 Conference Papers A 4th conference presentation was given on “The Flow Battery Storage Facility at DkIT” Meitheal na Gaoithe Conference, 6th May 2011, Galway, Ireland. Preliminary evaluation of using zinc-bromine energy storage with solar PV systems in the Mödling and Neuchâtel communities a) 30kWp – BiPV in Mödling Assuming the BiPV is grid connected June: Mean daily PSH = 6 Mean daily energy generation in June = 180kWh December: Mean daily PSH = 1 Mean daily energy generation in December = 30 kWh As the single module stores 50kWh, from an energy point of view it will be well oversized during winter months when the mean daily PSH is less then ~ 1.5 even if all the generated energy was stored. This will occur in 15 November to 15 February. During June the battery module can store approximately 30% of the total mean daily energy generated. This mean that 30% of the energy generated can be time shifted to a different/better electricity tariffs if available.
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Preliminary Conclusion: A single 50kWh module could possibly be an appropriate match for the 30kWp Solar PV system for ~ 70% of the year. There is limited potential for a storage system any larger than the 50kWh though it is recommended that a more detailed technical and economic evaluation be carried to investigate further. The power consumption profile of the building and electricity prices, storage capital costs would have to be factored in to the study. Based on experience in Dundalk, economic justification may be difficult. b) 750kWp – Stadium roof in Neuchâtel Assuming the PV system is grid connected June: Mean daily PSH ~ 6 Mean daily energy generation in June = 4500kWh December: Mean daily PSH ~ 1 Mean daily energy generation in December = 750 kWh There may be potential here to have a wide range of battery storage sizes depending on how the electricity is intended to be used, the load profile over the year, and electricity tariffs etc. e.g. If the main electricity demand is lighting for winter sports one possibility would be to store and time shift the daily electricity generated by the PV system during the winter months to the evening and night consumption by the flood lights e.g. a 1000kWh system (20 x 50kWh system or two Dundalk systems) could time shift electricity generated by the PV system from day to evening/night from November to February. Given the physical size of the stadium site and access there would not appear to issues regarding the physical size of the storage system (i.e. two containers (20 feet each) and a PSC units)
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4.0 CONCLUSION This is the final report of the work package WP1.7. The following conclusions can be drawn about the work done:
Flow battery technology technologies have a lot of potential for many applications and gives tem an exciting medium to long term future
It is currently difficult to justify economically this from of flow battery storage with wind autoproduction and wind farm application
In the medium term, electricity storage can be economically beneficial to wind autoproducers such as the DkIT wind turbine when the mass produced cost is significantly lower than current costs and as electricity prices increase
The technology is suitable for energy management i.e. time shifting and peak shaving, however very short term responses less than 10 second may be limitation in certain high speed load following/modulation applications
Detailed optimal control algorithm development accounting for the statistically nature of wind forecasts, power production and load demand
The zinc-bromine flow battery technology is relatively straight forward to install and does not require a building and has low maintenance requirements
Designed for outdoor installation i.e. no need for buildings or major civil works with little or no environmental hazards. There are not complicated temperature control requirements
Can be discharged to 0% without electrolyte degradation and the electrolyte does can be reused at the end of system life
Modular design- can be easily scaled up in units of 50kWh modules to suit various project sizes
Relatively low projected maximum number of charge/discharge cycles before end of life (~ 2000) in comparison to other flow battery technologies under development, however life of systems in still comparable to commercial deep cycle lead acid systems
As with all flow battery technologies there are a limited number of commercial suppliers globally (e.g. the system in Dundalk is a European first at this scale and a world first with a large scale wind autoproducer).
The zinc-bromine flow battery technology characteristics is suitable for Solar PV applications
In the Mödling community a single 50kWh module could possibly be an appropriate match for the 30kWp Solar PV system for ~ 70% of the year. There is limited potential for a storage system any larger than the 50kWh though it is recommended that a more detailed technical and economic evaluation be carried to investigate further.
There may be potential in Neuchâtel to have a wide range of battery storage sizes with the 700kWp system depending on how the electricity is intended to be used, the load profile over the year, and electricity tariffs etc
In summary is still relatively early days in the application of flow battery technologies of this size. Further testing and R&D will be required to improve and make the technology more cost effective.
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