Risk-limiting Dispatch of Power Systems with Renewable Generation

Transcription

1 Future Trends for Power Systems A Short Course to Honor Prof. David Hill Sydney, Oct 12, 2009 Risk-limiting Dispatch of Power Systems with Renewable Generation Felix Wu Philip Wong Wilson Wong Professor in Electrical Engineering University of Hong Kong

2 Outline Operation of conventional power systems Worst-case dispatch Future grid Drivers Renewables and demand response increase uncertainty Smart grid increases information and control Risk-limiting Dispatch Risk measures: RaR, CRaR Some preliminary results J. Bialek, P. Varaiya, F. F. Wu, J. Zhong, Smart Operation of Smart Grid, Proceedings of the IEEE, 2010.

3 Advanced A p p lic a tio n s Server System Server Dispatchers With Workstations Failover Logic Alternate System Server Alternate Communications Gateway Com m unications Gateway Dual Redundant S ys te m N e tw o rk Real-time Control of Power Systems Generation Transmission Substation Distribution Consumers EMS Control Center Bridge to Corporate LAN Communications Device Power Station Encoder / Decoder State Monitor Control Driver Measuring SCADA Remote Terminal Device Mostly no real-time control and rely instead on manual control

4 Power System Analysis Reliability Economics 1 ms 1 cycle 1 sec 1 min 10 min 1 hr 1 day 1 mo 1 yr Protection Transient stability Mid/long-term dynamics On-line Small disturbance stability Frequency dynamics Off-line Load flow Generation adequacy

5 Operation of Conventional Electric Grid Limited visibility beyond substation Limited visibility in short period (within minute/ second range)

6 Worst-case Dispatch Day-ahead Market Balancing Market Scheduling Adjustment Operating time Emergency Constraints» Power balance» Operating limits» (N-1) Contingencies Objective» Max social welfare Uncertainty» Load demand» Forced outage of equipment Adjustment Emergency» Load shedding

7 Change Has Come An Inconvenient Truth Global climate change Greenhouse gases CO 2 from fossil fuel energy sources CO2 Concentration

8 Europe Renewable Commitment EU Renewable target: 20% by 2020 UK: 10% now Denmark: 21% now 30% by 2020 Germany: 14% now 27% by 2020 Spain: 20% now 30% by 2010

9 DSIRE: March 2009 CA: 20% by 2010 Renewables Portfolio Standards *WA: 15% by 2020 OR: 25% by 2025 (large utilities) 5% - 10% by 2025 (smaller utilities) *NV: 20% by 2015 MT: 15% by 2015 *UT: 20% by 2025 CO: 20% by 2020 (IOUs) *10% by 2020 (co-ops & large munis) AZ: 15% by 2025 NM: 20% by 2020 (IOUs) 10% by 2020 (co-ops) US Renewables MN: 25% by 2025 (Xcel: 30% by 2020) ND: 10% by 2015 SD: 10% by 2015 IA: 105 MW WI: requirement varies by utility; 10% by 2015 goal IL: 25% by 2025 MO: 15% by 2021 VT: (1) RE meets any increase in retail sales by 2012; (2) 20% RE & CHP by 2017 *MI: 10% + 1,100 MW by 2015 OH: 25%** by 2025 NC: 12.5% by 2021 (IOUs) 10% by 2018 (co-ops & munis) ME: 30% by % by new RE NH: 23.8% in 2025 MA: 15% by % annual increase (Class I Renewables) RI: 16% by 2020 CT: 23% by 2020 NY: 24% by 2013 NJ: 22.5% by 2021 PA: 18%** by 2020 MD: 20% by 2022 *DE: 20% by 2019 DC: 20% by 2020 *VA: 12% by 2022 HI: 20% by 2020 TX: 5,880 MW by 2015 Solar hot water eligible Minimum solar or customer-sited RE requirement * Increased credit for solar or customer-sited RE ** Includes separate tier of non-renewable alternative energy resources State RPS State Goal 28 states have an RPS; 5 states have an RE goal

10 Renewable Generation Uncertainty 30,000 24,000 P{g >x} 18,000 12,000 6,000 n = 20 n = 1 thermal Rated capacity = 1500kWx20 Capacity distribution P{ g x} Average capacity = 14,000kW Generator reliable capacity A( p) = max{ x P{ g x} p}» With prob p, the capacity of the generator will be at least A(p) Reliable capacity (reliable capacity ~ thermal) p =3,000kW

11 Stochastic Resources Using conventional worst-case dispatch, an extra reserve requirement of a wind generator is 90% of its installed capacity. Demand response is not considered in reserve calculation. Stochastic resources are not being fairly treated.

12 John Day Malin Summer L Slatt McNary Ashe reactor 500 Grizzly reactor #2 475 Grizzly reactor # Time - seconds Future Grid Application Information Management Communication Infrastructure Data model standardization Distributed data service Search engine Optical fiber/ wireless Communication network protocols Communication network Monitoring and Control PMU Voltage - kv Embedded intelligent sensors Sensor network technology Power system components Wind SolarCHP Storage Smart Home s PHEV

13 Advanced A p p lic a tio n s Server System Server Dispatchers With Workstations Failover Logic Alternate System Server Alternate Communications Gateway Com m unications Gateway Dual Redundant S ys te m N e tw o rk Future Grid Smart Generation Smart Transmission Smart Substation Smart Distribution Smart Home Wind, solar and other renewables Storage EMS EMS Control Center Bridge to Corporate LAN FACTS WAMS Line condition Communications Device monitoring Power Station Encoder / Decoder State Monitor Control Driver Measuring SCADA Remote Terminal Device SCADA DMS Microgrid DA AMI DER AMI Demand response Intelligent appliances

14 Future Grid More accurate information» Smart meters, sensors More refined control» Battery storage Wind power Solar power» Demand response Tighter feedback» Communication Micro-Grid Load Substation Low emission central plant storage Virtual plant A New Operating Paradigm is Needed in the New Environment!

15 Worst-case dispatch Operating risk» Not meeting the constraints Operating constraints» Power balance g( x( t), u ) = 0» Operating limits Operating Risk: Revisit Risk-limiting dispatch Worst-case dispatch results in inefficient utilization of renewable resources and demand response Future smart grid will provide more just-in-time information h( x( t), u) 0 Uncertainty on faults and equipment failure leads to (N-1) A new operating paradigm criterion by limiting the risk of not meeting operating constraints in a consistent manner.

16 Risk-limiting Dispatch Scheduling Operating time t T σ t Scheduling» Decision u σ : Generation» Max objective such that the risk of not meeting operating constraints is less than (1-p*) based on available information at the time of scheduling. max f( x( t), u ) (e.g., social welfare) σ st.. Pr{ g( x( t), u ) = 0, h( x( t), u ) 0 y } p* σ σ t T σ

17 Risk-limiting Dispatch Scheduling Recourse Operating time t T σ t T ρ t Recourse» Decision u ρ : Generation, storage, demand response» Max objective such that the risk of not meeting operating constraints is less than (1-p*) based on available information at the time of recourse. max f( x( t), u ) (e.g., social welfare) ρ st.. Pr{ g( x( t), u ) = 0, h( x( t), u ) 0 y } p* ρ ρ t T ρ

18 Risk-limiting Dispatch Scheduling Recourse Operating Emergency time t T σ t T ρ t T ε t Emergency» Decision : Generation, interruptible load u ε» The operating constraints must be satisfied. Pr{ g( x( t), u ) = 0, h( x( t), u ) 0 y } = 1 ε ε t T ε

19 Optimal Dispatch Scheduling Recourse Operating Emergency time t T σ t T ρ t T ε t The overall optimization problem for system operation: max f( x( t), u, u, u ) σ ρ ε st.. Pr{ g( x( t), u ) = 0, h( x( t), u ) 0 y } p* σ t T Pr{ g( x( t), u ) = 0, h( x( t), u ) 0 y } p* ρ t T Pr{ g( x( t), u ) = 0, h( x( t), u ) 0 y } = 1 ε σ ρ ε σ t T ε ρ Suppose that the costs of generation for different periods (scheduling, recourse, emergency) are known, for a simpler model, the optimal dispatch has been derived in terms of nested conditional probabilities. We believe that the result can be generalized.

20 Summary Current operation paradigm» Based on worst-case dispatch is unfair to renewable sources and demand response and will be hard pressed to realize full potentials of smart grid Future grid» Distributed resources of renewable generation and demand participation» Enabling technologies of Information and communication technology, as well as power electronics Risk-limiting dispatch of renewable resources» Risk-limiting dispatch is based on just-in-time risk assessment

21 Center for Electrical Energy System The University of Hong Kong