Field experience and best practices in managing MW scale Li-ion energy storage systems coupled to large wind and solar plants

Transcription

1 Field experience and best practices in managing MW scale Li-ion energy storage systems coupled to large wind and solar plants Michael Lippert & Jesus Lugaro IRES Düsseldorf 1 March 215

2 Summary 1. Characterization of different operation profiles 2. Battery Sizing System and Revenue Optimization 3. Lessons learnt: factors of successful field operation 4. Conclusion 2

3 Integration of large PV & wind plants High power variability PV more than wind plants in the very short term (3 seconds to 5min) Limited predictability Forecasting errors remain high as per today High penetration levels create voltage issues and congestions in feeders/substations 3 days of PV production (Aerowatt) No inertia (contrary to conventional groups) Particular issue in island grids / poorly interconnected grids 3 Simulation of frequency on island in case of generation loss (EDF R&D)

4 1. Characterization of typical ESS operation profiles for large PV or wind plants 4

5 Support to PV & wind generation Ramp rate control Limit up & down ramp rates to acceptable levels Smoothing Keep production within given forecast window Compensate short term power sags 5

6 Typical characterization for 1 MW power plant Occurrence of cycles per DOD class Example ramp control of PV farm Power 5 MW Energy 1.3 MWh Avge DOD 6% Energy/day 2.5 MWh = 1.9C Example ramp control wind farm Power 2.5 MW Energy.58 MWh Avge DOD 4% Energy/day 1.9 MWh = 3.2 C 6

7 Support to PV & wind generation Shaping Stable power output over several hours Percentage of nominal PV Power Controlled ramping up / down Peak Shaving / curtailment avoidance Provision of peak power to reduce load on grid Absorption of peak generation 7

8 Typical characterization for a 1MW power plant Occurrence of cycles per DOD class Example shaping Power Energy Avge DOD Energy/day of PV power plant 5MW 1 MWh 35% 7 MWh =.7C % of energy throughput per DOD class 8

9 Frequency Regulation Frequency regulation Inject or absorb active power to / from the grid in order to stabilize frequency Primary reserves Seconday reserves 9

10 Typical characterization for 1MW regulation power Occurrence of cycles per power level France Examples Avge Power Energy Avge DOD Energy/day 1 France 17 kw 64 kwh 3-4% 1,4 MWh = 2.2 C USA 31 kw 65 kwh 3-4% 3,7 MWh = 5.7 C USA

11 2. Battery Sizing and System Optimization 11

12 Energy Management System Output requirements - Level of power fluctuation - Max power output - Droop of frequency regulation - voltage control - Inertia Optimization Operation Profile Power Plant Storage System parameters - Energy - Discharge power over SOC - Charge Power over SOC - Calendar ageing - Cycle ageing - Efficiency - EMS Operation Profile Energy Storage System PV / Wind generation High resolution data Site dependent Sizing = Optimizing 12 - Power injected - Power lost - Power consumed -Efficiency -Auxiliaires -Revenues -Penalties -Life time -Capex -Opex

13 Modelling approach for optimal sizing Input profiles needed Energy Management Strategy (EMS) Sizing validates Level of compliance to requirements Thermal behavior Availability Life time: evolution of capacity and IR Economic evaluation Iterative process! 13

14 Matlab models Exactly mimic real battery behavior Electric behavior cell & system Thermal behavior cell & system Ageing behavior Including contactor management Models run same algorithms as battery management systems 14

15 Puerto Rico PV Power plant x 1 PREPA Minimum Requirements PV ramp rate control: 1% per minute Frequency response with 5% droop characteristic, up to 9 minutes P pv P net 2 Power(W) Frequency (Hz) Time(s) x P batt (W) 5 x x Time(s) Time(s) x 1 4

16 Puerto Rico PV Power Plant Optimal sizing The optimum solution 1,3 MWh 5 MW 16

17 La Réunion CRE Tender 9 MW PV PV plant First project out of 16 contracts CRE (5MWp) 9 MWh Li-ion Energy Storage System Consortium Saft, Ingeteam, Corex 9 containers Intensium Max 2+E 5,6 MVA converters in 4 containers Specification Constant power 4% Pmax Primary reserve : 1% Pmax / 15 minutes Voltage support by PCS reactive power Battery Optimization 17 Installation October 214

18 3. Lessons learned and conclusions 18

19 Installations Saft 212/14 19 Li-ion technology, containerized systems

20 Lessons learned and conclusions 2 1. State-of-Charge Management extended life time 2. Accurate State-of-Charge indication system performance 3. Energy Efficiency of Battery & PCS & Auxiliaries performance & TCO 4. Thermal Management efficiency life time

21 Operational February 212 SEPTA Philadelphia Energy Storage, Regen, and Energy Markets an Industry First Intensium Max 2P container 1,5 MW - 4 kwh Partnering with Envitech (ABB) Customer: SEPTA > Brake energy recovery from trains > Injection during train acceleration Grid Services by Viridity to PJM >Participation in frequency regulation markets 21

22 SEPTA Philadelphia : SOC management Daily Energy turnover 1.8 MWh 2-2 Poor SOC management 1 2 Time (s) x x 1 x Time (s) 5 Bat Temp ( C) I battery (A) x SOC State of Charge 22 2 Time(s) Time (s) DOD Depth of Discharge U battery (V) Ageing.13 % per day 99.5 SoC (%) Average DOD 11% 1 Energ. eff. (%) 3 day operation (8/212) Power (kw) 4 2 Time (s) 3 x Time (s) 3 x 1 5

23 SEPTA Philadelphia : SOC management 1 day operation (11/212) Average DOD 4% -2 SOC management implemented 5 Time (s) 1 x 1 5 Time (s) 23 5 Time(s) 1 x 1 4 x 1 5 Time (s) x Bat Temp ( C) I battery (A) SOC State of Charge SoC (%) 75 2 DOD Depth of Discharge U battery (V) Ageing.55% per day Energ. eff. (%) Daily Energy turnover 2.2 MWh Power (kw) Time (s) 1 x Time (s) 1 x 1 4

24 Endesa Gran Canaria: SOC algorithm accuracy 3 IM 2 +E = 3MWh Ingeteam PCS 1 MVA SOC algorithm of BMS corresponds well to integration of energy measured Accurate SOC estimation due to linear voltage-soc relation of NCA and calibration in real time Effective cell balancing Effective use of capacity = revenue Permanent operation at partial SOC without need of maintenance charge 24 Operational October 213

25 ENEL R&D / Endesa Gran Canaria: AC efficiency Lab tests (Enel R&D) DC roundtrip efficiency 97% average 56 kwh battery > 95.5% for any power rate Efficiency stability vs. power rates, SOC, DOD, T (2% difference) Source: ENEL Field test at MV level (Endesa) 83% AC roundtrip efficiency 97% Battery DC efficiency 3 MWh battery Source: ENEL, Utility Week Amsterdam, November

26 La Réunion 9MW PV farm: Thermal management Temperature measurement in operation Low temperature spread among 476 modules in container Temperature is maintained between 25 C and 35 C Homogenous aging of battery modules Below temperature threshold of accelerated calendar ageing Minimum energy for cooling 26

27 Conclusions Operation profiles are complex and multi-functional Battery sizing is a an iterative technico-economic optimization process, involving battery and EMS System performance in scheduled day-ahead or intraday operation patterns rely on consistent, predictable ESS energy and performance. Field performance and life time of large storage systems depend on homogeneity and stability of temperature high and stable efficiency low energy consumption of auxiliaries accuracy of SOC indication 27

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