Comparing deterministic and stochastic
models for electricity market clearing with
high penetration of wind power
penetration of wind power
Ali Daraeepour - Dalia Patiño-Echeverri
Nicholas School of the Environment - Duke University
CEDM Annual Meeting, May 24, 2017
Motivation:
Classic
market
clearing
can
be
improved
High penetration of wind energy resources and market inefficiencies
Day-ahead market
Long Startup Offer dayahead
time
expected
production
Balancing market
4 hour ahead
Deviations from dayahead wind schedules
are settled
Lower forecast
error
Day-ahead committed
generation can be
Insufficient / inefficient
Costly Adjustments in
the Real-time
Real-time Wind
Curtailment
2
Two possible improvements to mkt clearing
1. Deterministic + Flexibility
Reserve
II. Stochastic Market
Clearing
Wind forecast error
Day-ahead
energy offers
Wind
forecast
Flexibility reserve
requirements
Day-ahead Market
Day-ahead
Wind
energy offers scenarios
Deviation
offers
Stochastic Market Clearing
Deviation offers
Energy and
reserve schedules
Balancing Market
Energy and flexibility
reserve schedules
Balancing Market
3
Objective
To assess the performance of stochastic market clearing, relative to
deterministic + flexibility reserves
Environmental benefits
Wind energy integration
Reduction of air emissions
Economic outcomes
Reduction in costs of fossil fuels
Electricity prices
Market efficiency
Need for uplift payments
Convergence of day-ahead and real-time prices
4
Method for comparing both mkt designs
Simulation of hourly operations of both markets
• over one year
• under two different scenarios of wind penetration
• using a Unit Commitment / Economic Dispatch model
•
•
•
•
•
Production cost based / or assuming perfect competition
Transmission constraints not binding
Wind power and curtailment offered at no cost
Electricity demand is deterministic and inelastic
Real time commitment looks two hour ahead
Day D
Day-ahead
Market
Clearing
UC + EDC
Balancing
Market and
Operation
UC+EDC
Day D+1
Uplift
Calculation
Day-ahead
Market
Clearing
UC + EDC
Balancing
Market and
Operation
UC + EDC
Uplift
Calculation
Ensuring the same reliability in both designs
1. Deterministic + Flexibility
Reserve
Wind forecast error
Day-ahead
energy offers
Wind
forecast
II. Stochastic Market
Clearing
Informed by the same reliability
standard = no load shedding in
one year
Day-ahead
Wind Deviation VOLL
energy offers scenarios offers
Reserves rule
Flexibility reserve
requirements
Day-ahead Market
Stochastic Market Clearing
Deviation offers
Energy and
reserve schedules
Balancing Market
Energy and flexibility
reserve schedules
Balancing Market
6
Method
Making the reserves rule and VOLL consistent
Specify a Reliability
Standard
Estimate the
minimum VOLL
that ensures this
reliability standard
Identify the
dynamic flexibility
reserve
requirement rule
Step 1: Reliability Standard  Maximum annual allowable load-shedding equals zero
Step 2: Minimum VOLL 
Run system operation with stochastic market clearing and different VOLL
Find the minimum VOLL that ensures reliability across all scenarios
Step 3: Identify the dynamic flexibility reserve requirement rule
7
Method
Determining a Dynamic flexibility reserve requirement rule
Flexibility Reserve Requirement (d,t) = α × WPSTD (d,t)
Proportion of Uncertainty
covered by Flexibility
reserves
Wind Production
Standard Deviation
Identify the minimum α that ensures the reliability standard
By trying different values until the minimum requirement for the reliability standard is
specified
8
Method
Inform both market clearing designs with the same uncertainty characterization
SynTiSe
4 years
historical data
on day-ahead
wind power
forecast error

Add to dayahead
forecasts
50 scenarios
for day-ahead
hourly wind
Stochastic :
 Use

MCMC model
30 scenarios
for
day-ahead
50 scenarios
forforecast
day-ahead
errors
forecast
errors
scenarios set directly
Deterministic:
 Use
 Use
expected value of wind production scenarios as a day-ahead forecast of wind
standard deviation of wind power production scenarios to calculate
flexibility reserve requirement
9
Test Grid & Data
Wind
1%
12% scaled version of PJM’s fossil-fired generation mix

heat rate and capacity data from EPA-NEEDS

Installed capacity of thermal resources = 20000 MW

Expected Peak = 17314 MW Reserve margin =15.5%

Fuel prices from Energy Information Administration (EIA)
Technology
Nuclear ST(i)
Coal ST
NGCC (ii)
Oil
CT (iii)
NGCT
Capacity
(MW) & share
(%)
4
19
14
8
22
4616 (23%)
8727 (44%)
2996 (15%)
631 (3%)
3030 (15%)
following
reserve
capability
No
Yes
Yes
Yes
Yes
Oil Combustion
Turbine
3%
Nuclear
21.98
22%

# of
units
Other (Hydro,
Solar, &...)
4%
Coal
42%
Natural Gas
Combustion
Turbine
14%
Natural Gas
Combined
Cycle
14%
Quick
start
No
No
No
No
Yes
 BPA’ synchronous demand and wind data
 Three case studies with different wind
penetration levels
Case 1: 6%
Case 2: 12%
Case 3: 21%
10
Results

Fundamental difference between two models is in their DA scheduling of wind

Deterministic always schedules the expected value (i.e., the forecast)

Stochastic schedules different quantities

Sometimes is less than the expected value

Sometimes is a value between the expected value and the maximum

Sometimes is the maximum value
Wind
penetraion
Depending on the ratio of
expected wind to load
At 21% wind penetration, expected
wind is more than 50% of load,
 Schedules of less than expected
value are more common
21%
12%
When expected wind is
not much compared to
load,
 schedule it all
11
Results
Wind integration
20000.0
Wind Integration
18000.0
16000.0
14000.0
Energy (GWh)
12000.0
10000.0
8000.0
6000.0
4000.0
2000.0
0.0
Stochastic
Deterministic
Case 1 (%6)
Integrated wind energy (GWh)
Stochastic
Deterministic
Case 2 (%12)
Surplus wind energy (GWh)
Stochastic
Deterministic
Case 3 (%21)
Curtailed wind Energy (MWh)
12
Results
Reductions in fossil-fired generation
 12% wind penetration
13
Results
Cost savings achieved by stochastic clearing from less use of fossil-fuels
Cost Saving (%)
Fossil-generation cost saving (%)
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
cost saving (%)
Case 1 (%6)
Case 2 (%12)
Case 3 (%21)
14
Results
Day-ahead energy prices
Average hourly day-ahead price
32
30
28
26
24
22
20
18
16
14
12
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Stoch 6%
Deter 6%
Stoch 12%
Deter 12%
Stoch 21%
Deter 21%
15
Results
Real-time energy prices
Avaerage hourly Balancing price ($/MWh)
32
30
28
26
24
22
20
18
16
14
12
1
2
3
4
Stoch 6%
5
6
7
Deter 6%
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Stoch 12%
Deter 12%
Stoch 21%
Deter 21%
16
Results
Generator’s revenue
Breakdown of supply-side revenue
3,000.00
2,500.00
Revenue (M$)
2,000.00
1,500.00
Total revenue of wind resources
Total revenue of fossil-fired resources
1,000.00
500.00
Stochastic
Deterministic
Case 1 (%6 )
Stochastic
Deterministic
Case 2 (%12)
Axis Title
Stochastic
Deterministic
Case 3 (%21)
17
Conclusions
 1) Stochastic market clearing increases wind integration, lowers emission, and fossil fuel
costs
 Under case 2, annual costs are reduced by 1.36% (i.e. 500 Million USD)
 2) Benefits are mostly due to better day-ahead wind energy schedules
 3) Higher wind integration in the stochastic case lowers the day-ahead prices and fossil-fired
generation revenues
 5) Less flexible resources incur significant losses from implementing the stochastic market
clearing
 6) Revenues to generators are lower under stochastic market clearing
  If not able to recover fixed costs, higher payments from capacity market will be
needed
18