The Value of Predictive Control in the Future Electric Power System

Transcription

1 The Value of Predictive Control in the Future Electric Power System Center for Advanced Process Decision- making January 13th, 2010 Gabriela Hug Assistant Professor 1

2 Outline Introduction to Electric Power Systems Transition to Future Electric Power System Integration of Renewable Generation Challenges Solution Approaches Storage Hydro Power Conclusions 2

3 Outline Introduction to Electric Power Systems Transition to Future Electric Power System Integration of Renewable Generation Challenges Solution Approaches Storage Hydro Power Conclusions 3

4 Structure t of Electric Power System Carnegie Mellon Generation Transmission Distribution Loads Source: U.S.-Canada Power System Outage Task Force 4

5 Consumption Typical Load Curve Commercial Demand Ontario, September 6 th, 2010 Residential Source: IESO 5

6 Generation of Electric Energy Carnegie Mellon Source: Source: Top-Alternative-Sources Source: The Washington Independent Source: pe - Power & Energy 6

7 US Electric Energy Total Electricity Net Generation by Source 70% is fossil fuel based generation (coal, petroleum, natural gas) 20% nuclear power generation 10% renewable generation (of which 66% is hydro power) Source: U.S. Energy Information Administration / Annual Energy Review

8 Peak load units Generation of Electric Energy Hydro power plants with storage Pumped storage hydro power plants Gas turbine power plants Carnegie Mellon Intermediate load units Fossil fuel thermal power plants Biomass thermal power plants Base load units Run of river hydro power plants Nuclear power plants 8

9 Structure t of Electric Power System Carnegie Mellon Source: U.S.-Canada Power System Outage Task Force High Observability Low Observability 9

10 Outline Introduction to Electric Power Systems Transition to Future Electric Power System Integration of Renewable Generation Challenges Solution Approaches Storage Hydro Power Conclusions 10

11 Future Electric Power System Carnegie Mellon FACTS, HVDC Renewable Energy Storage Demand Response $ PEV Renewable Energy Source: U.S.-Canada Power System Outage Task Force High Observability enabled by increased communication capabilities 11

12 Outline Introduction to Electric Power Systems Transition to Future Electric Power System Integration of Renewable Generation Challenges Solution Approaches Storage Hydro Power Conclusions 12

13 Renewable Generation Renewable Portfolio Standards Carnegie Mellon State policy that requires electricity providers to obtain a minimum percentage of their power from renewable energy resources by a certain date. Currently there are 24 states plus the District of Columbia that have RPS policies in place. Five other states, North Dakota, South Dakota, Utah, Virginia, and Vermont, have nonbinding goals for adoption of renewable energy instead of an RPS. Source: DoE 13

14 Variability of Wind and Solar Generation J. Apt, "The Sprectrum of Power from Wind Turbines." Journal of Power Sources, Vol. 169(2), p , 2007 J. Apt, A. Curtright, Whitepaper: The Spectrum of Power from Utility-Scale Wind Farms and Solar Photovoltaic oltaic Arrays, 14

15 Optimal Locations for Wind Generation Image sources: AEP; NYT;socialistaction.org rel.gov/gis/wind.html;

16 Outline Introduction to Electric Power Systems Transition to Future Electric Power System Integration of Renewable Generation Challenges Solution Approaches Storage Hydro Power Conclusions 16

17 Objective Optimize usage of elements in power system, i.e. balance intermittent energy sources with optimal usage of electric storage devices, conventional energy sources and load/demand control 17

18 Predictive Control Control Concept Use model of plant to be controlled to predict influence of input Choose input sequence which gives best performance over horizon Apply first step and measure new state 18

19 Modeling Storage Limits on Storage size Charging and discharging rate No simultaneous charging and discharging 19

20 Conventional Generation Capacity limit Ramp rate Modeling Intermittent Generation Predictions of output available Allow curtailment Load Predictions of demand available Allow demand control 20

21 Wind dgeneration and dstorage Carnegie Mellon Objectives: Minimize N 1 k 0 0 required capacity of conventional power plants changes in conventional power plant outputs losses resulting from conversion in storage curtailment of renewable generation demand side management cg P cg ( k) 2 dcg P cg ( k) 2 St P loss ( k) ig ( P ig ( k) P max ig ( k)) 2 ( P L il ( k) P ref il ( k)) 2 Constraints Power flow equations Limits on power outputs from plants and storage Line capacities Storage capacity 21

22 Simulation Setup Test System Base Backup Wind (each) Storage 5 p.u. 2 p.u. 15pu 1.5 p.u. 1.5 p.u.h Simulations No storage Storage, N = 1, i.e. no predictions Storage, N = 48 (4 hours) 22

23 Simulation Results Carnegie Mellon Base Backup Wind (each) Storage 5 p.u. 2 p.u. 1.5 p.u. 1.5 p.u.h 23

24 Simulation Results Carnegie Mellon Base Backup Wind (each) Storage 5 p.u. 2 p.u. 1.5 p.u. 1.5 p.u.h 24

25 Outline Introduction to Electric Power Systems Transition to Future Electric Power System Integration of Renewable Generation Challenges Solution Approaches Storage Hydro Power Conclusions 25

26 Coordinating Wind and Hydro Power Types of Hydro Power Run-of River Power Plant Storage Power Plant Pumped Hydro Power Plant 26

27 Coordinating Wind and Hydro Power Retention differentiates a river from a tank Objectives Minimize i i changes in total t power output t (sum of hydro and wind power) Minimize discharge variations Minimize deviations of water levels from reference value (and keep within limits) 27

28 Modeling Saint Venant Equations 28

29 Modeling Linear Model River Flow Discharge to Electric Power 29

30 Model Predictive Control Problem Carnegie Mellon Objective Function minimize discharge changes minimize level deviations Constraints River flow model smoothen wind power Constraints on water level and turbine/weir discharges 30

31 Simulation Results Cascade of four run of river power plants (20km apart) Operating Point: 3000m 3 /s (1200m 3 /s through weirs) Water Level Constraints: ±12cm Weir discharge and inflow constant 10% rms prediction error 2 hours prediction horizon, 5 minute resolution 31

32 Simulation Results 32

33 Simulation Results 33

34 Conclusions Electric power systems field is currently in a major transition Major challenges need to be resolved Predictive control allows for full exploitation of device potentials Storage reduces need for fast-ramping backup generation and required ramp rates if optimally controlled Existing hydro power provides large storage capacity Coordination achieves overall optimal performance Additional information available in future electric power system enables coordination Integration of intermittent renewable generation asks for hybrid solution 34