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Operational Impacts of

Responsive Electricity Loads

in Cyprus

Matthew BruchonMIT Technology & Policy Program

March 2013Background Methodology Findings Recommendations

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Project Background

Advisors: Stephen Connors & David Marks

Prior work:

The Energy Box

Daniel Livengood (ESD 11)

Woei Ling Leow (ESD 12)

Green Islands Project

Max Parness (TPP 11)

Pamela DeAmicis (TPP 11)

Karl Critz (SDM 12)

Support:

Cyprus Institute Energy, Environment and Water Research Center

Klegerman and Rabinowitz Funds

Background Methodology Findings Recommendations

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Cyprus Context

EU mandate to triple wind and

solar usage

Recent supply shortages

Drivers of demand:1. A/C

2. Tourism

2013 Installed Capacity

2020 Installed Capacity

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Cyprus Context

EU mandate to triple wind and

solar usage

Recent supply shortages

Drivers of demand:1. A/C

2. Tourism

Background Methodology Findings Recommendations

Vasilikos power plant catastrophe, 2011

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Cyprus Context

EU mandate to triple wind and

solar usage

Recent supply shortages

Drivers of demand:1. A/C

2. Tourism

Background Methodology Findings Recommendations

Time of Day

DayoftheYear

Hourly Demand (MW)

Jan. 1

Dec. 31

00:00 12:00 24:00

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Demand Response

Background Methodology Findings Recommendations

Consumers are paid to shift energy usage when called

Grid-scale impacts:

Lowering peak consumption

Reacting to wind and solar fluctuations

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Research Question

HVAC Dishwasher ElevatorAdvance notice >= 0 hours >= 2 hours 0

Recovery rate 0.5 1

Recovery penalty 200% 100% 0%

Load length < = 30 minutes <

Shift length = Load length

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Household A/C Example

Status quo

A/C usage

(kW)

ShiftedA/C usage

(kW)

Change in

grid demand

(kW)

Demand

response

available from

A/C usage

(kW-hours)

Background Methodology Findings Recommendations

11:00 AM

11:00 AM

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12:00 PM

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12:00 PM

12:00 PM

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Model Structure

ScenarioGenerator

(Python script

Karl Critz, SDM

11)

Robust OutputViewer

(MATLAB

Karl Critz, SDM

11)

Excel GUI&

Fast OutputViewer

WILMAR+DR(GAMS/CPLEX

based on WILMAR model from

Ris National Lab, Denmark )

WILMAR: Wind Integration in Liberalized Markets

DR: Demand Response

Fully linear optimization

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Stochasticity

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WinGaoMW)

Wind Forecasts at 12:00

Forecasts Weighted average

Model re-optimizes a sliding window of 16 scenarios every 3 hours

Scenario generator enables sensitivity analysis of any inputs

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Stochasticity

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WinGaoMW)

Wind Forecasts at 15:00

Forecasts Weighted average Past values

Model re-optimizes a sliding window of 16 scenarios every 3 hours

Scenario generator enables sensitivity analysis of any inputs

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Stochasticity

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WinGaoMW)

Wind Forecasts at 18:00

Forecasts Weighted average Past values

Model re-optimizes a sliding window of 16 scenarios every 3 hours

Scenario generator enables sensitivity analysis of any inputs

Background Methodology Findings Recommendations

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Stochasticity

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WinGaoMW)

Wind Forecasts at 21:00

Forecasts Weighted average Past values

Model re-optimizes a sliding window of 16 scenarios every 3 hours

Scenario generator enables sensitivity analysis of any inputs

Background Methodology Findings Recommendations

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Stochasticity

Model re-optimizes a sliding window of 16 scenarios every 3 hours

Scenario generator enables sensitivity analysis of any inputs

0

50

100

150

200

250

300

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WinGaoMW)

Wind Forecasts at 24:00

Forecasts Weighted average Past values

Background Methodology Findings Recommendations

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Limitations

Does not model transmission & distribution

Only models event-based demand response

Modeling of loads could be even more realistic

Adds nonlinearities Assumes perfect behavior

Should model stochastic distributed decision making

Background Methodology Findings Recommendations

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Model Output

Calls to delay load

Recovery

Status Quo Generation (MW)

Generation with DR (MW)

Demand response available (MWh)

Change in grid demand (MW)

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

DR In Action: raises troughs

flattens peaks

DR inaction:

shutting off expensive turbines

flattening thermal output

Background Methodology Findings Recommendations

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Model Output

Calls to delay load

Recovery

Status Quo Generation (MW)

Generation with DR (MW)

Demand response available (MWh)

Change in grid demand (MW)

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

DR In Action: raises troughs

flattens peaks

DR inaction:

shutting off expensive turbines

flattening thermal output

Background Methodology Findings Recommendations

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Model Output

Calls to delay load

Recovery

Status Quo Generation (MW)

Generation with DR (MW)

Demand response available (MWh)

Change in grid demand (MW)

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

DR In Action: raises troughs

flattens peaks

DR inaction: shuts off expensive turbines

smooths turbine output

Background Methodology Findings Recommendations

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Model Output

Calls to delay load

Recovery

Status Quo Generation (MW)

Generation with DR (MW)

Demand response available (MWh)

Change in grid demand (MW)

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

12PM

Sunday

12PM

Monday

12PM

Tuesday

12PM

Wednesday

DR In Action: raises troughs

flattens peaks

DR inaction: shuts off expensive turbines

smooths turbine output

Background Methodology Findings Recommendations

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Findings

My findings are preliminary. The findings shown here are

borrowed from a recent study:

Background Methodology Findings Recommendations

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Demand response can:

Support the transition to renewables

Increase grid stability

Substitute for new power plants

Reduce fuel usage and emissions

Background Methodology Findings Recommendations

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Demand response can:

Support the transition to renewables

Increase grid stability

Substitute for new power plants

Reduce fuel usage and emissions

Background Methodology Findings Recommendations

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Demand response can:

Support the transition to renewables

Increase grid stability

Substitute for new power plants

Reduce fuel usage and emissions

Background Methodology Findings Recommendations

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Demand response can:

Support the transition to renewables

Increase grid stability

Substitute for new power plants

Reduce fuel usage and emissions

Background Methodology Findings Recommendations

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Policy Recommendations

1. CERA: create a demand response pilot program in Cyprus

2. EAC: implement the program

Begin with largest energy consumers

Diversify load types

3. TSO: Track grid impacts when EAC calls demand response

4. Scale the program up with wind and solar

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Next Steps

Learn which load types are most useful

Assess which sectors to include

Address policy design challenges

How to adjust payment from customer to customer?

Should certain sectors be opt-in or opt-out?

Background Methodology Findings Recommendations