day-ahead active and reactive power controls scheduling to

EPRI – Con EdisonDay-ahead Active and Reactive Power Controls Scheduling to Improve Transmission System

Alberto Del Rosso: EPRI Project ManagerMay 19, 2011

2© 2011 Electric Power Research Institute, Inc. All rights reserved.

T&D Efficiency InitiativeTimeline


Strong Executive Leadership Team Formed

Commissioner Jon Wellinghoff, Chairman, FERCArshad Mansoor, VP, EPRI

US Executive Steering Committee Members:Nick Brown, President, & CEO Southwest Power PoolTerry Boston, President & CEO, PJM InterconnectionSteve DeCarlo, Sr. V.P. Transmission, New York Power AuthorityMike Hervey, V.P. T&D Operations, Long Island Power AuthorityMike Heyeck, Sr. V.P. Transmission, American Electric PowerRob Manning, Executive V.P. Power Systems, Tennessee Valley AuthorityYakout Mansour, President & CEO, California ISOPedro Pizarro, Exec V.P. Power Operations, Southern California EdisonJohn McAvoy, Sr. VP Central Operations, Consolidated Edison Rich Mandes, V.P. Transmission, Alabama PowerSteve Whitley, President & CEO, New York Independent System Operator

International Steering Committee Members:Barry MacColl, Technology Strategy & Planning, ESKOMMagdalena Wasiluk-Hassa, Director, Innovation, PSE OperatorIan Welch, R&D Strategy Manager, National Grid


Overview Industry Wide Demonstrations 17 Companies with 33 Proposed Projects

Transmission Efficiency Improving

Opportunity

1. Reduce System Losses

2. Reduce Line/Equipment Losses

3. Increase Line/System Utilization

Technologies to Improve Transmission Efficiency

1A. Coordination Voltage Var Control

1B. Voltage Upgrade/EHV AC/HVDC

1C. Loss Minimization Optimization

2A. Advanced Conductors/ Low Loss Design

2B. Low Loss/LEED SubstationEquipment

3A. Dynamic Rating

3B. Smart Transmission Control

Technologies Identified to Improve Efficiency and Utilization


Collaboration Across Demonstrations


Project: Integrated Active and Reactive Power Control for Con Edison

Transmission System


Background

• Con Edison Transmission System:– In-City densely loaded transmission system consisting of both 138

KV and 345 KV areas that are tied together. Mostly underground cables.

– Generation capacity: 14000 MW; Peak Load: 12,000 MW, 5000 MVAr

– Several Phase Angle Regulators (PARs):• regulate power flows on ties from adjacent utilities• balance real power flows between the 345kV, 138kV and 69kV

portions of the internal transmission system. – Transformers that bridge two active transmission areas (i.e. 138kV

and 345kV) move reactive power between these areas while adjusting voltage


Background

• Con Edison Current Practice:– On summer peak day the system experience about 3 hours of

ramping load on the order of 1000MW per hour.– Operators place almost all of the capacitors (3300 MVARs) in

service during the morning ramp-up period, and to take most of them out of service during the nightly ramp-down period • “get ahead of the load curve” approach.

– Generators, phase angle regulators, and shunt capacitors are adjusted as needed

– High ramping periods require considerable time and attention from the operators for voltage concerns.

– Currently no plan for the coordinated dispatch of reactive powerresources or reserves on a system-wide basis.

– Current approach successful in practice yet not optimal More efficient switching strategy could be implanted


Projects

• Real and Reactive Power Optimization:– Investigate opportunities for a more

effective and utilization of active and reactive power flow controls to improve transmission efficiency

• Reactive Power Forecasting to Assist VAr Planning:– Investigate the key variables in

determining the reactive power demand for Con Edison system

– Develop reactive power forecasting models over a range of time-horizons, at difference system levels

• Day-ahead Active and Reactive Power Controls Scheduling:

• More optimal placed of reactive resources allocation over time

• 24 hour schedule for system operators to support their decision making capacity over the course of day


End Stage – EPRI Forecast and OPF Applications

Active and Reactive Load Forecast for all Substations

Active and Reactive Load Forecast for all Substations

Reactive Power Optimization for 24 HrReactive Power Optimization for 24 Hr

- PI Data- Historic MV & MVAR load data- Weather data- Manual input

- PI Data- Historic MV & MVAR load data- Weather data- Manual input

- 24 Hr unit commitment and generator dispatch - Interface flows

- 24 Hr unit commitment and generator dispatch - Interface flows

Post-processor & Report BuilderPost-processor & Report Builder

OUTPUT24 Hr scheduling of reactive power resources:- Capacitors and Reactors-Selected transformer taps-Phase shifters, FATCS- Voltage setting at selected busesReactive Load & Reactive power reserve

OUTPUT24 Hr scheduling of reactive power resources:- Capacitors and Reactors-Selected transformer taps-Phase shifters, FATCS- Voltage setting at selected busesReactive Load & Reactive power reserve

-Equipment out of Service-Topological changes

-Equipment out of Service-Topological changes

Pre-processor to incorporate data

Pre-processor to incorporate data


OPF Application Module

• Objective Function:– Min: 1 Σ MW losses + 2 Σ MVAr losses

• Control Variables

– Shunt Capacitors and Reactors

– Selected Transformer taps

– Transformer phase shift angles: 17 Phase Shifters– Generators within Con Edison area are allowed to adjust their

scheduled voltages to meet global objective: 50 Generators– Generators are not subject to direct regulation by Con Edison, but are

expected to adhere to their voltage schedules and respond on request to provide additional support up to their stated limitations



• Constraints:– Generator MVAr limitss: QGEN min ≤ QGEN ≤ QGEN max– Transformer tap ranges: TAP min ≤ TAP ≤ TAP max – Bus Voltage Constraint: Maximum (1.05 p.u.) and minimum “normal”

(1.0 p.u.) voltage limits – Branch Flow limits• Operational Constraints:• No more than 1 daily switching cycle (on/off) on tap and shunt

capacitors/reactors Different control strategies:–Optimize every hour–Optimized at selected number of hours per day

• Interface flows within specified limits



• Optimizer: PSSTME OPF V32• A Python script is been developed to optimize OPF process, input data

and post-processing• Solution is based on a scheduled MW dispatch from day�ahead

schedule which is secure in terms of thermal constraints• An accurate AC model of the network is used• The OPF produces reactive schedules to support voltage security,

without compromising MW security• Possible to migrate to other OPF tool in

future implementation


Active and Reactive Load Forecast Module

• Provide 24 hr active and reactive power load forecast for 62 area stations

• Independent Reactive Power Forecast:based on the fact that reactive load

patterns do not necessary follow their associated real power load patterns in a purely proportional manner

• The computational algorithm utilizes historical information in combination with anticipated forecast conditions (temperature, weather) to predict a load curves at area station level

Industrial Customers

CommercialConsumers

Residential Customers


Short-Term Load Forecasting

• Use of artificial neural networks and data mining provides accurate load forecasts

• Correlations among variables might provides more insight• Help system engineer gain insights of power profile and

characteristics• Enable utilization of existing transmission capacity.


Short-Term Load Forecasting

Real load (Hour ahead) 2%

Real load (24 hour ahead) 5-7%

Reactive load (Hour ahead) 5%

Reactive load (24 hour ahead) ~10%

•Expected MAPE for forecasting at each substation level (Not aggregated)


Estimation of Savings and Benefits

• Several 24 hr periods have been analyzed

• Baseline operating conditions set up based on PI historic data: – load, generation, shunt

capacitor/reactor, selected, transformer taps, voltage level at selected buses).

• Potential significant savings in losses• Results indicate significant

differences in reactive resource allocation as load changed.

• To be used as metric by system operators or engineers to support their ongoing control efforts throughout daily operation

020406080100120140160180

1 3 5 7 9 11 13 15 17 19 21 23

I2R Losses [M

W]

Hour

Optimized Base Case

0

1000

2000

3000

4000

5000

6000

1 3 5 7 9 11 13 15 17 19 21 23

X2R Losses [M

AVr]

Hour

Optimized Base Case


Next steps

• Finalize development of input/output interfaces and load forecast

• Implementation and testing in the control room• Improve capability for more flexible operator intervention:

– “what if” analysis– System performance metrics– Input data manipulation

• Develop and implement visualization dashboard• Evaluate need/convenience to use other OPF software


Together…Shaping the Future of Electricity


Background slides


Optimization strategies

0

2000

4000

6000

8000

10000

12000

14000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

MW

Optimized hours

Non fully optimized hours

Example: optimization performs 4 times a days

day-ahead active and reactive power controls scheduling to

Documents