Supervisory control system for

distributed energy resources and

microgrids

Team: C. Marnay, C. Milan, D. Baldassari,

G. Cardoso, J. Eto, J. Tjaeder,

M. Stadler, N. DeForest, S. Wang,

T. Brandt, W. Feng

mstadler@lbl.gov

building-microgrid.lbl.gov

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23 Apr 2014

Michael Stadler

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Big Picture

Overview

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Challenge:

• demonstrate the use of day-ahead optimization and real-time control to implement optimal system dispatch

• consider grid connected and islanded operation modes

• distributed systems with multiple on-site energy resources: storage, PV, CHP,..

• use weather data to forecast PV generation

• use historic data to forecast energy loads

• use Operations DER-CAM to produce cost-optimal day-ahead dispatches and feed them to the SCADA system

• emphasis on optimal battery scheduling to ensure PV exports within maximum levels

Current Systems

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• disconnected individual assets

• no load forecasting nor generation planning

• no coordinated generation and load management

• no communication

� lost revenues

� volatile resources

� no reactive / active power control

� no coordinated sharing of resources

� no islanding

Future Systems

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SCADA based

Supervisory

Controller:

SuperController

providing

reactive,

active

power and

voltage

support to

grid at PPC

other Distributed

Energy Resources

DER resources at

cost minimization

enduse loads

enduse loads Integrated controller for

generation planning, dispatch, and

load management (building EMS

integration)� can island and form

real microgrid

forecasted

weather

actual

weather

�

�

information flow

Integrated assets with LBNL’s Supervisory Control system:

• load forecasting and generation planning to minimize costs

• coordinated generation / dispatch and load management

• open to any standard SCADA system and any vendor

• connected DER and DR assets

☺ cost minimization

☺ reactive / active power control (smart inverter)

☺ two possible operation modes

– grid connected

– islanded

☺ coordinated sharing of resources

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Current Work for PV and Storage at Fort

Hunter Liggett (FHL)

(Small CPUC Grant to Start the Work)

Overview FHL

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• large 2 MW PV and battery system 1 MWh

• in the future 8 MW of PV and full microgrid

• no supervisory controller available

Current DER-CAM Model at FHL

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DER-CAMplanning

module

utility export limit

module

SCADA data

exchange

module

SCADAinternet

battery, purchase, and

load drop schedules

weather

forecast

actual load, PV

generation and

SOC

deliver set-points

and also dynamic

lookup table

system statusinstructions

Dispatch Planner at FHL

15min cycle

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Forecasting Load and PV Generation

For real-time optimization, load and PV generation need to be

forecasted for FHL. Both factors are driven by different influences.

• load is driven by base occupancy and shows relatively stable

daily patterns. Influence of outside temperature appears to be

relatively minor.

• PV is driven by solar radiation, which depends on the position of

the sun and various seasonal patterns. Patterns can be very

volatile due to clouds.

• load data is available in 15-minutes intervals as net load,

weather data is available in hourly intervals, PV data varies

between 15 minutes and hourly

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Forecasting Load

• load shows consistent patterns for

each day of the week (holidays are

similar to Sundays)

• to forecast next Tuesday, the past

three Tuesdays are considered and

a Fast Fourier Transformation

(FFT) is used to extract the most

important frequencies

• the resulting curve contains the

main pattern without noise

average error over a day is around 10%

� change in base occupancy is only slowly incorporated by FFT

☺ forecasted values are multiplied with a parameter d that starts out at 1 and

decreases / increases whenever deviations between forecasted and actual

values exceed a certain threshold

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Forecasting Load

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Forecasting PV

Weather forecast data is available hourly and difficult to interpolate (qualitative

data like “cloudy”).

• solar radiation depends on position of sun and seasonal factors

• in the short term, these seasonal factors are fixed and sun altitude becomes

dominant influence of clear-sky PV generation

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ab

ove

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PV

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rati

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in

kW

PV and Sun Altitude

PV Generation Sun Altitude

cloudy also cloudyday

night

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Forecasting PV

• clear-sky PV generation is forecasted using a simple linear

model that relates sun altitude to power generated of the past

30 clear-sky hours

• for fog, haze, and clouds clear-sky PV generation is modified by

a specific factor to get expected generation

• deviation (i.e. (forecast-real)/real) is less than 5% in 80% of the

cases and less than 10% in 90% of the cases

• forecast errors mainly due to inaccuracies in qualitative data

(“03 PM overcast” does not necessarily imply that it is overcast

for the entire hour)

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Forecasting PV

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SCADA interface

early development stages achieved successful feeding of Operations DER-CAM

dispatches in the FHL SCADA system

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SCADA Interface

DER-CAM Output: Example

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End

Thank you!

Questions and comments are very welcome.

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