Expected Meteorological Conditions, the Day-Ahead Electricity
Market Outcome, and the Demand for Utility Supplied
Electricity: Evidence from New York City
Kevin F. Forbes
Visiting Researcher, ESRI (Dublin)
Associate Professor, Catholic University (Washington DC)
Forbes@CUA.edu
2019 Trans-Atlantic Infraday (TAI)
Federal Energy Regulatory Commission (FERC)
Washington, DC
17 October 2019
The Organization of this Talk
1)
2)
3)
4)
5)
6)
Distributed Solar Energy Resources
Possible operational Issues
Load forecasting and electricity market outcomes in NYISO’s NYC Zone.
The accuracy of solar energy forecasting: Evidence from Belgium
Some observations about the autocorrelative nature of solar energy
Some observations about the autocorrelative nature of electricity
consumption
7) A model that exploits the autocorrelative natures of both electricity load
and solar energy.
8) Out of sample results
1) Distributed Solar Resources: An Important
Aspect of Our Energy Future : Evidence from New
York
Source: NYISO
The Technical Potential is Significant
Source: NREL
The Technical
Potential is
Significant
(Continued)
2. Operational Issues
“Tremendous change is taking place in consumers’ adoption of
Distributed Energy Resources (DER) to supply a portion of their energy
needs. DERs displace energy that was traditionally supplied by the bulk
power system, contributing to declining load on the grid, but adding
complexity to operations, market design efforts, and system planning
needs. This complexity arises because shifting load from the bulk
power system to local DERs is not the same as eliminating load. When
those resources are not producing energy, the bulk power system must
still provide energy to those homes and businesses. As a result,
planning for the reliable operation of the power system as a whole
must consider total expected consumption of energy, including energy
provided by the DERs.” (NYISO, 2019, p. 9)
NERC’s View:
“Today, the effect of aggregated DER is
not fully represented in BPS [bulk power
system] models and operating tools. This
could result in unanticipated power flows
and increased demand forecast errors. An
unexpected loss of aggregated DER could
also cause frequency and voltage
instability at sufficient DER penetrations.
Variable output from DER can contribute
to ramping and system balancing
challenges for system operators whom
typically do not have control or
observability of the DER within the BPS.
“(NERC, 2017, p. vi)
The penetration of solar energy has given rise
to a net load curve that looks like a Duck.
The duck curve in California on a spring day
It is believed that there is an
overgeneration risk at the
“belly of the duck”
The “neck” of the duck is also a concern because of
the steepness of the ramp
It may be relevant that the size of the
ramp is uncertain because of
1)uncertainties in forecasting solar
generation directly fed into the high voltage system
2) The uncertainties in net load induced because of
behind-the-meter solar generation
Evidence of the Duck Curve in New England
The System Operator in New York also
expects to confront a duck curve
PV Capacity (in MW) and Modeled Net Load in New York State on a Typical Winter Day
Source: NYISO
The duck curve is also evident in the PJM
System
The increase in BTM solar energy capacity
is especially evident in NJ, MD, and NC
MD
NC
NJ
The imbalance from
solar power and the
total area electricity
imbalance in the
Kyushu area of Japan,
April 2017
Source: Shinkawa,
2018, p 32
Is there a Duck Curve in Ireland’s Energy
Future ?
The data from NREL in the United States
indicates that level of PV generation will be
highly volatile under Ireland’s Climate Plan
A nontrivial Duck Curve can be expected in the
summer
5000
4000
3000
2000
1000
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Electricity Consumption
PV Gen when PV Capacity equals 1.5 GW
PV Gen when PV Capacity equals 3 GW
PV Gen when PV Capacity equals 6 GW
Based on NREL PVWATTs ( https://pvwatts.nrel.gov/pvwatts.php )
hourly model outputs for Dublin and an assumed PV
capacity of 1.5 GW
3. Some Observations about Load Forecasting and
Electricity Market Outcomes in NYC
The Load Forecast is Seemingly Accurate
Grid supplied load and
the day-ahead forecasted
load for New York City, 1
Jan 2017 – 31 Dec 2018.
Real-Time vs DayAhead Prices in
the NYC Zone, 1
Jan 2017 – 31
Dec. 2018
The Incidence of Large Real-Time
Price Spikes is notable.
Actual Load < Forecasted
Actual Load > Forecasted
The error in the
load forecast and
the realtime/day-ahead
price difference
for NYC, 1 Jan
2017 – 31 Dec
2018.
There is a visually clear relationship between Actual Load exceeding
Forecasted load and the price spikes. In short, the load forecasts, while
seemingly accurate, are not accurate enough.
4. The accuracy of solar energy forecasting:
Evidence from Belgium
Forecasted vs. actual
solar energy in
Belgium, 1 Jan 2017 –
31 Dec 2018
The results for Belgium are consistent with
the academic literature
In this case, the normalized
root mean squared error
was about 16.5 %. The
authors report that this
represented a significant
improvement
Huang et al. , 2013
5. Some observations about the
autocorrelative nature of solar energy
Autocorrelations in Hourly
Solar Energy from the Mauna
Loa Observatory in Hawaii,
1 January 2010 – 31 December
2016
The autocorrelations in solar energy
are significant. This suggests a path forward
in terms of modelling.
More observations about the autocorrelative
nature of solar energy
Autocorrelations in Hourly
Solar Energy from the Summit
Observatory in Greenland,
1 September 2013 – 31
December 2016
Simulated Hourly PV energy production for a
typical year in New York City
PV Generation is Seemingly Chaotic
But the autocorrelations are significant
Based on NREL PVWATTs ( https://pvwatts.nrel.gov/pvwatts.php )
hourly model outputs for Laguardia Airport and an assumed PV
capacity of 2 GW
The autocorrelations in quarter-hour PV generation
in Belgium, 1 Jan 2017 – 31 Dec 2018
6. Some observations about the autocorrelations in
the hourly grid demanded load
As with solar energy, the autocorrelations
are significant.
The autocorrelations
in the hourly grid
demanded load
in NYC, 1 Jan 2017
– 31 Dec 2018
The Autocorrelations are Especially Striking in
the Case of Ireland
The autocorrelations
in the forecast errors,
1 Jan 2018
– 31 Dec 2018
The autocorrelations are even significant in areas with large
quantities of behind-the-meter solar
The autocorrelations in the hourly grid demanded
load in the Southern California Edison load zone,
1 Jan 2017 – 31 Dec 2018
The autocorrelations in the hourly grid demanded
load in the San Diego Gas & Electric load zone, 1
Jan 2017 – 31 Dec 2018
The Situation in Belgium, 1 Jan 2015- 31 Dec
2018
There is significant room for
improvement in the load forecasts
Exploiting the autocorrelative nature of
demand appears to have promise
7. An Econometric Model of Grid Supplied Load
• The structural component includes variables representing
• The level of hourly load forecasted by the system operator
• Day-ahead hourly forecasted meteorological conditions (e.g. forecasted cloud cover).
• A measure of the day-ahead market price adjusted for fuel cost. This variable proxies the
expectations of market participants.
•
The time-series component includes
•
•
•
•
16 ARCH terms
27 AR terms
51 MA terms
Two “ARCH in Mean” terms
These terms capture the
autocorrelative nature of grid
supplied load without having to
explicitly model the supply of BTM
solar energy
•
The model does not presume linearity.
•
•
•
•
The model was estimated using hourly data over the period 1 January 2015- 31 December 2016
The full model has an explanatory power equivalent to an R-Squared of 0.9992
The structural model has an explanatory power equivalent to an R-Squared of 0.7700
The model was evaluated using hourly data over the period 1 January 2017- 31 December 2018
The Meteorological Data Employed in this
Study
• The study employs archived day-ahead forecast data for each hour of the
study.
• The data variables include forecasted hourly temperature, forecasted
hourly humidity, forecasted hourly dewpoint, forecasted hourly probability
of precipitation, forecasted hourly visibility, and forecasted/simulated
measures of cloud cover.
• The forecast data were obtained from CustomWeather
(https://customweather.com ), a California based firm that provides
meteorological forecast services for over 80,000 locations worldwide.
8. The time-series model’s out-of-sample period t-1
predictions vs. actual load, 1 Jan 2017- 31 Dec
2018.
The model’s out-of-sample period t-2 predictions
vs. actual load, 1 Jan 2017- 31 Dec 2018.
Conclusion
• While the day-ahead load forecasts for NYC are seemingly accurate, the visually
apparent relationship between the forecast errors and the incidence of the realtime price spikes indicates that there is room for improvement.
• The trend in the installations in roof-top solar, while conveying environmental
benefits, has the potential to make matters even more challenging.
• The results of this analysis indicates that econometric time-series methods might
be able to significantly mitigate the challenge posed by the “Duck Curve” by
making the ramp associated with the “neck of the duck” more predictable.
• The improvement in predictability may have favourable implications for the
management of balancing resources.
• There do not appear to be any barriers to using this approach to model solar
energy generation that is directly injected into the transmission system.
Supplemental analysis using data from PJM
Map of the PJM Service Territory
• The analysis focus on the MidAtlantic Region. This is the
dominant region in terms of
both population and load.
Washington DC, Baltimore, and
Philadelphia are in this region.
• A variant of the NYC model was
estimated using hourly data over
the period 1 Jan. 2015 – 31 Dec.
2017
Out of Sample Results: 1 Jan – 31 Dec 2018.
PJM’s Day-Ahead Forecast and Actual
Load for the Mid-Atlantic Region
Out-of-Sample Hour-Ahead Predicted
and Actual Load