Microgrid Storage Integration Battery modeling and advanced control

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1 Alexandre Oudalov, ABB Switzerland Ltd., 1th Microgrid Symposium, Beijing, November 13-14, 214 Microgrid Storage Integration Battery modeling and advanced control

2 Microgrid Storage Integration Outline Introduction Energy storage technologies and applications LIB aging model Semi-empirical model overview Advanced BESS control strategies Application and SoC control Conclusions LIB BESS SOC Lithium Ion Battery Battery Energy Storage System State of Charge

3 Energy Storage Applications Conventional Central Generation Load leveling for generation utilization 1-1 MW, 1-8h ESS Spinning reserve In case of line loss 1-5 MW,.25-1 h ESS Load Center Variable Renewable Generation 2 kv Integration of renewables 1-1 MW,1-1h ESS 22 kv 22 kv 11 kv Load leveling 22 KV for postponement of grid upgrade 1-1 MW, 1-6h ESS ESS Peak shaving.5-1 MW, 1h 2 kv Micro-grid.1-5 MW, 5 min (stabilization).1-5 MW, 1-1h (selfconsumption of local renewables) 2 kv ring 11 kv 11 kv Industry/ ESS Large Commercial Frequency Regulation.4 kv 1-5 MW,.25-1h ESS Solar PV time shift 1-1 kw, 2-6h ESS Residential/Small Commercial/Nano-grids

4 Energy Power Microgrid Storage Integration Energy vs Power Applications in Microgrids Ramp rate ctrl Ramp rate ctrl Spinning reserve Frequency ctrl Spinning reserve Energy Time Shift Capacity firming Energy Time Shift Line load levelling Energy Time Shift Grid ON Weak grid ON Grid OFF Energy applications Power applications

5 Microgrid Storage Integration Key Technologies Mechanical Electrochemical Thermodynamic Electromagnetic Kinetic Heat Batteries Flow cells Hydrogen Electric Flywheel Gravitation Pumped hydro Thermo electric Pressure Compr. Air (CAES) Lead acid NiCd NaS Lithium Ni-MH Vanadium ZnBr PSBr Electrolyser & Fuel Cell Capacitors Supercaps Magnetic Superconducting (SMES) Pressure Heat Adiabatic CAES

6 Projected Cost Reductions of LIB ($/kwh) Bloomberg New Energy Finance Navigant Research Deutsche Bank IHS (isuppli) *) final prices include margins most probably in the range of 3-4%

7 Battery Aging Model Overview Battery capacity fading is a limiting factor for BESS performance Customers usually expect a certain battery life in years or number of cycles for a given application Source: skywriting-net Battery manufacturers usually overdimension the battery to reduce the risk of earlier system depletion We recommend to include battery aging models in the design of and operation strategies for BESS Source:batteryuniversity.com

8 Battery Aging Model LIB Model We propose a semi-empirical model l remain = p SEI e r SEIl faded + 1 p SEI e l faded Capacity fades due to battery cycling and time elapsed l faded = l cycling + l calendar Cycling aging is fully dependent on battery usage and is modeled as a sum of aging during each cycle l cycling N = i=1 σ DoD DoD i σ SoC SoC i σ I (I i ) σ T (T i ) Calendar aging is independent of battery usage and is modeled as a linear function of time, average SoC and temperature l calendar = k calendar σ SoC N i=1 SoCi N σ T N i=1 Ti N i t

9 Battery Aging Model LIB Test Data vs. Model Reconstruction

10 Storage Converter Sub-Station BESS General system architecture Grid P, Q, status, application Other controllers Status, availability (SoC) on/off V, I, f, P, Q T System level control Local applications and ESS protection on/off Switching commands HVAC control on/off P, Q status Status Modbus TCP/RTU Converter control Switching of power electronics on/off HVAC control speed V, I, T BMS SoC, SoH, Ramp limit, faults Charge and health monitoring Protection ABB Group December 9, 214 Slide 1 Primary side Secondary side Functionality

11 Microgrid Plus System Efficient and reliable power flow management PV plant MGC6-P Wind Turbines MGC6-W M+ Operations Local and remote Distribution feeder MGC6-F Residential Communication Power Micro-Grid Network Diesel Generator MGC6-G Grid Stabilising System MGC6-E Distribution feeder MGC6-F Industrial & commercial Grid connection MGC6-N

12 Advanced Battery Control Strategies Combination of application and SoC control Due to a battery inefficiency and in some applications due to a none zero mean control signal, the BESS can be totally discharged or charged in a short time interval It limits a use of BESS until its SoC will be back into an acceptable range An optimal BESS control strategy must cover both: Application control State of charge control Actual state State of charge control Constraints & setpoints Application control BESS PCS Setpoints Setpoints Consideration of a battery aging model can provide additional information in order to take pro-active measures to fulfill the lifetime targets

13 Advanced Battery Control Strategies Frequency Control in Microgrids State of charge (SoC) control according to one of the following strategies Strategy 1: Active when SoC exceeds adjustable thresholds and frequency is within a regulation dead-band Off-set value is -1% *P nom at any time step (preferably small values) Strategy 2: Is continuously activated and adjusts a power set-point using an average over the previous usage, i.e. ctrl signal is zero-mean Variable off-set value is taken from a secondary reserve Strategy 3: Active when SoC exceeds adjustable thresholds Fixed off-set value is taken via a ramp of fixed slope for a fixed duration from a secondary reserve or an intraday market Power (W) Frequency Deviation (Hz) Power (W) Frequency Deviation (Hz) Power (W) ion (Hz) x modified frequency deviation P offset SoC SoCmin SoCmax x 1 5 P offset SoC SoCmin SoCmax x 1 5 modified frequency deviation P offset SoC modified frequency deviation SoC (-) SoC (-) SoC (-)

14 SoC control strategy 1 Offset and SoC variations Power (W) x 1 5 P offset SoC SoCmin SoCmax SoC (-) Frequency Deviation (Hz) modified frequency deviation For P offset = 5% of rated power, the annual capacity fading 6% For P offset = 1% of rated power, the annual capacity fading 3.7%

15 SoC control strategy 1 Variations of different power signals 4 x 15 3 P AS P BESS P Offset 2 Charge Power (W) Discharge Offset is active when system frequency is within a deadband BESS follows exactly the requested ancillary service power when Δf

16 SoC control strategy 2 Offset and SoC variations x 1 5 Power (W) P offset SoC SoC (-) Frequency Deviation (Hz) modified frequency deviation For an averaging period of 1 hour and a dispatch delay of 15 min, the annual capacity fading 3.2%

17 SoC control strategy 2 Variations of different power signals 4 x 15 3 P AS P BESS P Offset Power (W) Charge -2-3 Discharge Offset mechanism forces the BESS power signal to be zero-mean Deviations between PAS and PBESS

18 SoC control strategy 3 Offset and SoC variations Power (W) x 1 5 P offset SoC SoCmin SoCmax SoC (-) Frequency Deviation (Hz) modified frequency deviation For SoC upper threshold = 53%, SoC lower threshold = 47%, off-set level = 1% of rated power, ramp up/down in 5 min and min offset duration = 15 min, the annual capacity fading 3%

19 SoC control strategy 3 Variations of different power signal 4 x 15 3 P AS P BESS P Offset Power (W) Charge -2-3 Discharge Offset active when SoC hits the thresholds Deviations between PAS and PBESS

20 Comparison of three SoC control strategies SoC evolution according to the three strategies Reduced peak for strategy 3 SoC 1 SoC 2 SoC 3 SoC (-) Continuous adjustment of SoC for strategy 2 (even between thresholds).46 Offset 3 is activated once thresholds are hit Time (sec) Offset 1 depends on Δf frequent threshold violations with limited control Offset 2 depends on past values of PAS forces BESS to reach SoCnom Offset 3 is activated once thresholds are hit less sensitive to SoC variation Less degradation for strategy 3

21 Preferred SoC Control Strategies Need for an adaptive approach Operation mode Reference Advantages Off-grid Strategy 1 Grid-tied Strategy 3 The offset does not directly cancel the control signal Less energy is cycled through the offset than in strategy 2 Less battery capacity fading Depending on the operation mode grid-tied or off-grid we switch between strategies Based on the actual generation mix we can tune the parameters of each strategy

22 Conclusions Battery based energy storage plays an important role in microgrids with a large amount of RES A battery model allows to quantify capacity fading and to take corrective measures in case of deviations from the initially planned lifetime trajectory There are several strategies to control SoC and a preferred strategy depends on: status of the microgrid (grid-tied vs isolated) available options for the off-setting part (available generation mix, accessibility to power markets, etc.) Availability of forecast information (RES, load, scheduled islanding operation, etc.) can help to predict future SoC and parameterize the control system accordingly

23

24 Battery Aging Model Stress Factors 6 DoD Stress Model 2 SoC Stress Model f DoD ( 1-5 ) 4 2 f SoC f C DoD[%] C-rate Stress Model C-rate f T SoC[%] Temperature Stress Model T[C]

25 SoC Control Strategies Model 1 SoC evolution for one month BESS degradation after 1 year: = 3.67 % 1 SoC evolution - Model P offset = 1 % P nom SoC (%) SoC evolution 1 SoC limit violations SoC limits x 1 6 ABB December 9, 214 Slide 25

26 SoC Control Strategies Model 2 SoC evolution for one month BESS degradation after 1 year: = 3.22 % SoC evolution - Model 2 SoC evolution SoC limit violations SoC limits Averaging period: 1 hour SoC (%) 5 4 Dispatch delay: 15 min x 1 6 ABB December 9, 214 Slide 26

27 SoC Control Strategies Model 3 SoC evolution for one month BESS degradation after 1 year: = 3.2 % SoC evolution - Model 3 SoC evolution SoC limit violations SoC limits SoC upper threshold: 53 % SoC (%) x 1 6 SoC lower threshold: 47 % Ramp duration: 5 min Min offset time: 15 min

28 References Ancillary power system services SP AusNet Grid Energy Storage System ABB solution Design, engineering, installation and testing of PowerStore-Battery, transformer and diesel generator Microgrid Plus System for overall system management Based on transportable containerized solution Project name SP AusNet GESS Country Victoria, Australia Customer SP AusNet Completion date Due to be completed in 214 Customer benefits Active and reactive power support during high demand periods Transition into isolated/off-grid operation on command or in emergency cases without supply interruption Delay of power line investments About the project First grid-tied microgrid with Battery Energy Storage for distribution network support in Australia

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