Massive Energy Storage Design for Renewable Electricity Generation Systems

Transcription

1 Massive Energy Storage Design for Renewable Electricity Generation Systems Benjamin Omell and Donald J. Chmielewski,, Chicago, IL

2 Outline Introduction and Objectives Case Study Method: Supervisory Control Scheme and rofit Control Case study Results Summary and Conclusions

3 Outline Introduction and Objectives Case Study Method: Supervisory Control Scheme and rofit Control Case study Results Summary and Conclusions

4 ower Management With Renewable Energy

5 ower Management With Renewable Energy ower roduced Equals ower Consumed

6 ower Management With Renewable Energy ower roduced Equals ower Consumed Wind energy and other renewable sources are not dispatchable

7 L (MW) r (MW) Load (MW) Renewable (MW) Wind ower Generation Comparison with Load Demand Days Days

8 L (MW) r (MW) Load (MW) Renewable (MW) Wind ower Generation Comparison with Load Demand Days Days

9 L (MW) r (MW) Load (MW) Renewable (MW) Wind ower Generation Comparison with Load Demand Days Days

10 ower Management With Renewable Energy Renewable Dispatchable Load

11 MW MW ower Management With Renewable Energy Renewable Dispatchable Load

12 Solutions ower roduced Equals ower Consumed Wind energy and other renewable sources are not dispatchable

13 Solutions ower roduced Equals ower Consumed

14 Forms of Energy Storage Large Scale Battery Compressed Air Flow Batteries Flywheel Thermal Energy Storage umped Hydro-storage

15 MW Energy Storage Solution Renewable Dispatchable Load

16 MW Energy Storage Solution Renewable Dispatchable Load Energy Storage

17 Objectives Implement supervisory control scheme to regulate power production Implement optimization scheme that sizes energy storage device Obtain both simultaneously Find globally optimal solution

18 rofit Control Constraints Backed - off Operating oint (BO) Expected Dynamic Operating Region (EDOR) Optimal Steady- State Operating oint (OSSO)

19 Outline Introduction and Objectives Case Study Method: Supervisory Control Scheme and rofit Control Case study Results Summary and Conclusions

20 Case Study Gas Turbine Demand Coal Fired Grid Storage Renewable

21 Forms of Energy Storage Large Scale Battery Compressed Air Flow Batteries Flywheel Thermal Energy Storage umped Hydro-storage

22 umped-hydro Storage umping water into a high reservoir from a lower one with excess electricity production Releasing the water to the lower reservoir and spinning a turbine, during peak demand and low renewable energy prod5uction Upsides Massive energy storage with minimal cost Downsides Economical implementation can be limited by geography Turbine/ ump

23 Examples of umped-storage roduction Capacity (MW) Hydro in U.S. Hrs of Available Discharge lant GW-hr Bath County (US, Va) Castaic (US,CA) Helms (US,CA) Northfield MT (US,MA) Ludington (US, MI) Blenheim-Gilboa (US,NY) Lewiston Niagra (US,NY) Bad Creek (US,SC) Racoon Mt (US,TN) Average

24 Case Study Gas Turbine Demand Coal Fired Grid Storage Renewable

25 Outline Introduction and Objectives Case Study Method: Supervisory Control Scheme and rofit Control Case study Results Summary and Conclusions

26 Control Scheme Disturbance MV { { ower Load L Renewable ower R Rate of Coal r T Rate of Gas Turbine r c C T E S r r C T S C T R L Coal ower C (erformance Var.) Gas Turbine ower T (erformance Var.) Energy Stored E s (erformance Var.)

27 Control Scheme Disturbance MV { { ower Load L Renewable ower R Rate of Coal r T Rate of Gas Turbine r c C C x Ax Bu Gw T rt E S r z D x D u S x u C T R L Coal ower C (erformance Var.) Gas Turbine ower T (erformance Var.) Energy Stored E s (erformance Var.)

28 Linear State Model Controller rospective w z u Model x x Ax Bu Gw z D x D u x u

29 rofit Control Constraints Backed - off Operating oint (BO) Expected Dynamic Operating Region (EDOR) Optimal Steady- State Operating oint (OSSO)

30 System Constraints ower plants have limits of imum and minimum production. Long start-up times if shut down lants have limits to rate of changes in power production min C C C min C C r r r C min C C C r Coal ower Limitations Coal Rate Change Limitations C 0.05 C Turbine ower Limitations min T T T min T T T Turbine Rate Change Limitations r r r min T T T r T 6 T Energy Storage 0 ES ES ower roduction Limitations min S S S

31 Sorage Size (MWhr) Variable Constraints Days

32 Sorage Size (MWhr) Variable Constraints Days

33 Sorage Size (MWhr) Variable Constraints Days

34 Sorage Size (MWhr) Variable Constraints Days

35 Variable Constraints E 0 min S E S S min S

36 ower Load (GW) r (MW) Disturbance modeling A shaping filter is used to model the demand and renewable energy production from the wind generators White Noise shaping filter Disturbance Forcasted Data Simulated Data Days

37 roblem Formulation min 1Es 2s XY, i i z i 2 i i 0 Ax Bu Gw z D x D u x u C C C 0.8 C C C 0.05 r rc C C 0.05 r rc C C E S S E T T ( A BY ) ( A BY ) G G 0 E E S E x x w i i x u T T x u i D X D X D Y X T T T 0.2 T T T 6 r rt T T 6 r rt T T S S S D Y S S S

38 roblem Formulation min 1Es 2s XY, i i z i 2 i i 0 Ax Bu Gw z D x D u x u C C C 0.8 C C C 0.05 r rc C C 0.05 r rc C C E S S E T T ( A BY ) ( A BY ) G G 0 E E S E x x w i i x u T T x u i D X D X D Y X T T T 0.2 T T T 6 r rt T T 6 r rt T T S S S D Y S S S

39 Control Scheme Disturbance MV { { ower Load L Renewable ower R Rate of Coal r T Rate of Gas Turbine r c C T E S r r C T S C T R L Coal ower C (erformance Var.) Gas Turbine ower T (erformance Var.) Energy Stored E s (erformance Var.)

40 roblem Formulation min 1Es 2s XY, i i z i 2 i i 0 Ax Bu Gw z D x D u x u C C C 0.8 C C C 0.05 r rc C C 0.05 r rc C C E S S E T T ( A BY ) ( A BY ) G G 0 E E S E x x w i i x u T T x u i D X D X D Y X T T T 0.2 T T T 6 r rt T T 6 r rt T T S S S D Y S S S

41 roblem Formulation min 1Es 2s XY, i i z i 2 i i 0 Ax Bu Gw z D x D u x u C C C 0.8 C C C 0.05 r rc C C 0.05 r rc C C E S S E T T ( A BY ) ( A BY ) G G 0 E E S E x x w i i x u T T x u i D X D X D Y X T T T 0.2 T T T 6 r rt T T 6 r rt T T S S S D Y S S S

42 roblem Formulation min 1Es 2s XY, i i z i 2 i i 0 Ax Bu Gw z D x D u x u C C C 0.8 C C C 0.05 r rc C C 0.05 r rc C C E S S E T T ( A BY ) ( A BY ) G G 0 E E S E x x w i i x u T T x u i D X D X D Y X T T T 0.2 T T T 6 r rt T T 6 r rt T T S S S D Y S S S

43 Reverse-Convex Constraints Global solution obtained using branch and bound scheme

44 roblem Formulation min 1Es 2s XY, i i z i 2 i i 0 Ax Bu Gw z D x D u x u C C C 0.8 C C C 0.05 r rc C C 0.05 r rc C C E S S E T T ( A BY ) ( A BY ) G G 0 E E S E x x w i i x u T T x u i D X D X D Y X T T T 0.2 T T T 6 r rt T T 6 r rt T T S S S D Y S S S

45 Outline Introduction Case Study Method: Supervisory Control Scheme and rofit Control Results Summary and Conclusions

46 Cases Case 1: 40% Coal, 40% Gas Turbine, 20% Renewable Case 2: 10% Coal, 40% Gas Turbine, 50% Renewable Case 3: 75% Coal, 5% Gas Turbine, 20% Renewable ower Storage Cost ( ) $55 /kwh ower Rating Cost ( 1 2 ) $1300/kW

47 Coal (MW/hr) Gas Turbine (MW/hr) MW Case 1 Dispatchable ower 1000 Turbine Coal Days Dispatchable ower Rates Days -5000

48 Rate (MW/hr) Rate (MW/hr) Results Case Gas Turbine Coal ower (MW) ower (MW)

49 GWhr MW Storage Result 2000 Storage ower Days Storage Size Days

50 ower (MW) Rate (MW/hr) Rate (MW/hr) Results Case Gas Turbine Coal ower (MW) Storage ower (MW) Energy (MWh)

51 Cases Case 1: 40% Coal, 40% Gas Turbine, 20% Renewable Case 2:10% Coal, 40% Gas Turbine, 50% Renewable Case 3: 75% Coal, 5% Gas Turbine, 20% Renewable ower Storage Cost ( ) $55 /kwh ower Rating Cost ( 1 2 ) $1300/kW

52 ower (MW) Rate (MW/hr) Rate (MW/hr) Results Case 2 Gas Turbine Coal ower (MW) Storage ower (MW) Energy (MWh) x 10 4

53 Cases Case 1: 40% Coal, 40% Gas Turbine, 20% Renewable Case 2: 10% Coal, 40% Gas Turbine, 50% Renewable Case 3: 75% Coal, 5% Gas Turbine, 20% Renewable ower Storage Cost ( ) $55 /kwh ower Rating Cost ( 1 2 ) $1300/kW

54 ower (MW) Rate (MW/hr) Rate (MW/hr) Results Case Gas Turbine Coal ower (MW) Storage ower (MW) Energy (MWh) x 10 4

55 Results Summary Case Coal ower Gas Turbine Renewable Storage Size Storage ower 1 40% 40% 20% 12.9 GWh 948 MW 2 10% 40% 50% 26.8 GWh 1398 MW 3 75% 5% 20% 61.1 GWh 1188 MW

56 Economic Coefficients Sensitivity The capital cost range for energy storage $/kwh min 1Es 2s XY, The capital cost range for power rating $/kw ower Rating Cost $/kw ower rating cost can range from 6 times to 400 times the cost of the energy storage Storage Cost $/kwh Storage ower $1,300 $100 12,674 MWh 956 MW $1,300 $55 12,884 MWh 948 MW $1,300 $26 13,549 MWh 932 MW $1,300 $13 15,638 MWh 905 MW

57 Conclusions Conclusion resented methodology that regulates power production with a optimal controller Developed optimization scheme that designs power storage facilities Solved both problems simultaneously and arrived at a global optimal solution Sensitivity to cost parameters is small Acknowledgements, IIT NSF-CBET