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PROJECTED CLIMATE CHANGE
IMPACTS ON ENERGY AND
PEAK DEMAND IN MANITOBA
March 31, 2015
LEAD AUTHORS:
Manitoba Hydro: Michael J.F. Vieira, Leo L. Laramee, Adam R. Moore.
CONTRIBUTING AUTHORS AND REVIEWERS:
Manitoba Hydro: Louis Kessler, Kristina A. Koenig, Terry M. Miles, C. Reed Winstone,
Kevin D. Gawne, Lois K. Morrison, Timothy Klaas.
Government of Manitoba: Matthew J. Wiens.
Ouranos Consortium: Isabelle Charron.
Natural Resources Canada: Jennifer Ardiel.
RECOMMENDED CITATION:
Manitoba Hydro, 2015. Projected Climate Change Impacts on Energy and Peak Demand in
Manitoba. Report submitted to Climate Change Impacts and Adaptation Division, Natural
Resources Canada, 24p.
This project was made possible with the support from Natural Resources Canada through the
Adaptation Platform Energy Working Group
For more information on climate change impacts and adaptation, please visit: adaptation.nrcan.gc.ca
EXECUTIVE SUMMARY
In 2013, Natural Resources Canada’s (NRCan) Adaptation Platform initiated a directed call
for proposals for Canadian provinces to assess climate change impacts on energy demand.
Manitoba Hydro, as the primary provider of electrical energy and natural gas in Manitoba,
undertook this study with the support of the Manitoba Government. This report
summarizes current forecasting practice and projections of the future effect of weather on
energy (electric and natural gas) and peak electric demand in Manitoba.
Existing relationships between historic weather and demand are defined and held constant
to assess future projections, in isolation of other social factors. Future temperature
scenarios are combined with these relationships to project changes in future energy and
peak demand. Future temperature scenarios are derived from an ensemble of 27 Global
Climate Model (GCM) simulations.
Annual baseline (1981-2010) climate normals are: 3.1 C with 4470 DDH and 189 DDC.
Climate normals translate into baseline annual weather affected energy normals of: 5,107
GWh of electrical energy for heating, 509 GWh of electrical energy for cooling and
1,058,230 103m3 of natural gas. Baseline electrical system peaks are estimated as 3194
MW in summer and 4743 MW in winter, which are the observed system peaks for the
2013/14 fiscal year.
The GCM ensemble average projects annual weather affected changes in demand for the
2050s (2080s) time periods of -756 GWh (-1047 GWh) in electrical energy for heating,
+486 GWh (+756 GWh) in electrical energy for cooling, -156,655 103m3 (-216,873
103m3) in natural gas, +300 MW (+378 MW) in summer electric peak, and -176 MW (-239
MW) in winter electric peak.
The GCM ensemble average is used to summarize results; however, it is important to
consider uncertainty. Despite variability in the magnitude of projected change, most
simulations agree on the direction of change. Generally, future normal weather affected
electric energy and natural gas demand for heating will decrease, and electric energy for
cooling will increase. Most simulations project weather affected summer peak to increase
and winter peak to decrease.
Results from this study have been identified for potential uses in future adaptation and for
the identification of opportunities and risks. The primary application of these results is in
resource planning to explore sensitivities to climate change and the impact on future
energy and peak demand projections.
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TABLE OF CONTENTS
1 Introduction ........................................................................................................................................................ 1 1.1 Objectives ................................................................................................................................................................................ 1 1.2 Study Area ............................................................................................................................................................................... 2 1.3 Energy and Peak Demand ........................................................................................................................................... 3 1.4 Application of Energy and Peak Demand Forecasts ............................................................................... 3 2 Current Practice .............................................................................................................................................. 4 2.1 Approach and Methodology ...................................................................................................................................... 4 2.1.1 Temporal Window .............................................................................................................................................. 4 2.1.2 The Weather Effect .......................................................................................................................................... 4 2.2 Data Sources ......................................................................................................................................................................... 6 2.2.1 Temperature Data .............................................................................................................................................. 6 2.2.2 Energy and Peak Demand Data ............................................................................................................... 6 2.3 Results ........................................................................................................................................................................................ 7 2.3.1 Temperature and Degree Days ................................................................................................................ 7 2.3.2 Energy Demand .................................................................................................................................................... 8 2.3.3 Peak Demand ......................................................................................................................................................... 9 3 Future Scenarios .......................................................................................................................................... 10 3.1 Approach and Methodology ................................................................................................................................... 10 3.1.1 Climate System and Climate Change ................................................................................................ 10 3.1.2 Climate Scenarios from Global Climate Models ....................................................................... 10 3.1.3 Developing Future Scenarios .................................................................................................................. 12 3.2 Data Sources ...................................................................................................................................................................... 12 3.2.1 Future Climate Projections....................................................................................................................... 12 3.3 Results ..................................................................................................................................................................................... 13 3.3.1 Temperature and Degree Days ............................................................................................................. 13 3.3.2 Energy Demand ................................................................................................................................................. 16 3.3.3 Peak Demand ...................................................................................................................................................... 18 3.3.4 Uncertainty............................................................................................................................................................ 19 4 Regional Assessment .................................................................................................................................. 20 5 Adaptation and Opportunities ............................................................................................................... 21 6 Conclusions ..................................................................................................................................................... 22 Acknowledgements ............................................................................................................................................ 23 Abbreviations ......................................................................................................................................................... 23 References .............................................................................................................................................................. 24 ii
LIST OF TABLES
Table 1 - Normal Temperature and Degree Days (1981-2010 Baseline) ........................................... 7 Table 2 - Normal Weather Affected Energy Demand (1981-2010 Baseline) ................................. 8 Table 3 - Observed Peak Demand (2004/05 – 2013/14)............................................................................... 9 Table 4 - Representative Concentration Pathways (van Vuuren et al., 2011) ................................11 Table 5 - Global Climate Model Information .............................................................................................................13 Table 6 - GCM Average Projected Tmean, DDH and DDC Changes.....................................................14 Table 7 - GCM Average Projected Weather Affected Energy Demand Changes .......................17 Table 8 - GCM Average Projected Weather Affected Peak Demand Changes ............................18 Table 9 - 10th, 50th and 90th Percentile Simulations at Winnipeg (Charron, 2015) .............19 Table 10 - Range in Projected Annual Changes for 2050s ..........................................................................19 Table 11 - Range in Projected Annual Changes for 2080s ..........................................................................19 LIST OF FIGURES
Figure 1 - Map of Manitoba ....................................................................................................................................................... 2 Figure 2 - Manitoba Monthly Energy Demand vs. Winnipeg DDH and DDC .................................... 5 Figure 3 - Normal Temperature and Degree Days (1981-2010 Baseline)......................................... 7 Figure 4 - Normal Weather Affected Energy Demand (1981-2010 Baseline) ............................... 8 Figure 5 - Annual Observed Peak Demand Time Series (2004/05 – 2013/14) ............................ 9 Figure 6 - RCP CO2 Concentrations (left) and Projected Global Surface Warming (right) .11 Figure 7 - Annual Tmean Departure Plot ....................................................................................................................14 Figure 8 - Monthly Normal Temperature ....................................................................................................................14 Figure 9 - Annual DDH Departure Plot ........................................................................................................................15 Figure 10 - Monthly Normal DDH ....................................................................................................................................15 Figure 11 - Annual DDC Departure Plot .....................................................................................................................15 Figure 12 - Monthly Normal DDC ....................................................................................................................................15 Figure 13 - Annual Weather Affected Total Energy Demand Departure Plot ..............................16 Figure 14 - Monthly Normal Weather Affected Total Electrical Energy Demand......................16 Figure 15 - Annual Weather Affected Natural Gas Demand Departure Plot ................................17 Figure 16 - Monthly Normal Weather Affected Natural Gas Demand................................................17 Figure 17 - Weather Affected Peak Demand Departure Plot....................................................................18 Figure 18 - Winter Peak vs. Summer Peak Demand - Weather Affected Departures...........18 Figure 19 - Annual DDH Departure Plot for Winnipeg, Brandon and Thompson......................20 Figure 20 - Annual DDC Departure Plot for Winnipeg, Brandon and Thompson ......................20 iii
1 INTRODUCTION
In 2013, Natural Resources Canada’s (NRCan) Adaptation Platform initiated a directed call for
proposals for Canadian provinces to assess how climate change might impact energy demand.
Manitoba Hydro, as the primary provider of electrical energy and natural gas in the Province of
Manitoba, undertook this study with the support of the Manitoba Government. This report
constitutes Manitoba’s contribution to NRCan’s study.
Temperature changes can impact energy demand patterns. As average temperatures increase,
the demand for energy to heat buildings decreases while the demand for energy to cool
buildings increases. Depending on how extreme temperatures change, peak demand may
increase or decrease. A greater understanding of projected changes, their variability in time
and space and their uncertainties can facilitate future resource planning, development of
adaptation strategies and identification of opportunities. This report summarizes current
practice and future projections of energy and peak demand for future temperature scenarios
in Manitoba, including potential impacts, adaptation strategies and opportunities.
Manitoba Hydro’s current practice for energy and peak demand forecasting is based on
historic weather data and considers climate change through sensitivity analyses. Future
temperature scenarios are applied to the current methodology to assess how future energy
and peak demand is projected to change. A workshop which included executive and senior
management from Manitoba Hydro and the Government of Manitoba was held on October
23, 2014, to present results, brainstorm adaptation options and identify opportunities.
In this report, the term energy represents electric demand over time in gigawatt-hours (GWh),
and natural gas demand over time in thousands of cubic metres (103m3). One thousand cubic
metres of natural gas represents 37.8 gigajoules of energy. The term peak demand represents
the maximum hourly average electric demand measured in megawatts (MW).
1.1 OBJECTIVES
The objectives of this report are to:
Summarize current practice for estimating the weather effect on energy and peak demand.
Present changes to weather affected energy and peak demand resulting from climate
change projections.
Discuss impacts, adaptation strategies and opportunities for Manitoba.
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1.2 STUDY AREA
The majority of weather affected energy demand in Manitoba is concentrated within and
around the city of Winnipeg. As such, Winnipeg is central to this study area. For comparative
purposes, this study also considers other regions of Manitoba to understand spatial variability
in climate change projections. Temperature changes at Brandon (Western Manitoba) and
Thompson (Northern Manitoba) are presented in Section 4. These locations are shown in
Figure 1.
Figure 1 - Map of Manitoba
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1.3 ENERGY AND PEAK DEMAND
Manitoba Hydro is a provider of both electricity and natural gas in Manitoba. In 2013/14,
Manitoba Hydro required 25,625 GWh of electrical energy to serve approximately 550,000
electric customers. The electric peak demand in Manitoba has always occurred in the winter
and set a new record peak of 4,743 MW in January 2014. Natural gas demand in 2013/14
for Manitoba was 2,279,682 103m3 for 270,000 natural gas customers, which use natural gas
primarily for space heating.
Manitoba Hydro prepares a twenty year Electric Load Forecast and a ten year Natural Gas
Forecast annually to assess Manitoba Hydro’s future load requirements for its service area
(Manitoba Hydro 2013a, 2013b, 2014a and 2014b). In each forecast, Manitoba Hydro
forecasts individual sectors which are added together along with expected system
transmission and distribution losses to form the system forecasts.
The forecasts are created using historical usage information that is adjusted to normal
weather and assumes normal weather in every year of the forecast. Normal weather is
defined as a 25 year rolling average, meaning the average of the previous 25 years prior to
the first forecast year of any forecast. Winnipeg is the only weather station used for
conducting the weather adjustment as 73% of all electric weather dependant load and 87% of
all natural gas weather dependant load are located within or around Winnipeg.
The impact of weather on an annual basis produces a significant amount of variability to the
energy demand. The effects of an extreme weather year can cause variations of +/- 4% for
annual electric energy demand and +/- 11% for annual natural gas energy demand.
1.4 APPLICATION OF ENERGY AND PEAK DEMAND FORECASTS
A primary use of the forecast of Manitoba annual electric energy and peak electric demand is
in long-term resource planning evaluations. Planning for a reliable and sufficient supply of
power for Manitoba’s electricity requirements is the underlying driver for long-term resource
planning. The Manitoba electrical energy and peak electrical demand forecasts are key inputs
into the determination of the need for and timing of new resource options.
Electric energy and peak electric demand forecasts are also used by other groups at Manitoba
Hydro. For example, the group responsible for energy operations planning uses the forecasts
to plan for expected loads in the shorter term while system planning uses the peak electric
demand forecast to analyze and assess the reliability of the transmission and distribution
system at various locations throughout the province.
The main application of the natural gas forecast is focused on the short term distribution of
natural gas in Manitoba. Manitoba Hydro is not the producer of natural gas and the natural gas
forecast is mainly used to ensure there is enough supply to serve Manitoba and sufficient
infrastructure to meet the forecast load.
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2 CURRENT PRACTICE
This section presents the methodology used to estimate the weather effect in energy and
peak demand forecasting. Baseline weather affected energy and peak demand is also
presented.
2.1 APPROACH AND METHODOLOGY
Understanding the effect of weather in energy and peak demand forecasting allows removal
of some of the year-to-year variability and helps normalize the comparison of total energy
and peak demand. This process is also referred to as weather normalization and is common
practice for electrical and natural gas utilities (Itron Inc., 2014). Weather normalization can
help identify patterns in load growth and the methodology lends itself well to climate change
studies.
Weather normalization highly depends on the quality and availability of temperature data,
energy consumption data and the suitability of temperature versus energy and peak demand
relationships. Generally, relationships between temperature and energy demand are stronger
than relationships between temperature and peak demand. Peak demand has greater
variability and is subject to influence from a greater number of social and economic factors.
For energy demand, Manitoba Hydro’s model converts temperature data to Degree Days
Heating (DDH) and Degree Days Cooling (DDC) which are then correlated to energy demand.
Peak demand is correlated with hourly temperatures. Other factors such as wind, cloud cover
and humidity can influence demand, however, many utilities use only DDH and DDC (Itron Inc.,
2014).
2.1.1 TEMPORAL WINDOW
The use of a 25-year moving average, updated every year, samples a large range of weather
variability and captures recent patterns, including any observed warming trend.
For this study, it was elected to use a longer, 30-year fixed period to establish baseline
conditions. A fixed 30-year period is consistent with the World Meteorological Organization’s
(WMO) definition of normal climate and is commonly used in Manitoba Hydro’s climate
change studies. 1981-2010 was therefore used to establish baseline conditions.
2.1.2 THE WEATHER EFFECT
This section discusses the techniques and variables used to model the weather effect on
energy and peak energy demand.
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2.1.2.1 WEATHER EFFECT ON ENERGY DEMAND
Degree Days are a measure of conditional, time-integrated, temperature. Degree days are
particularly useful in energy demand analysis where deviation of temperature beyond a
threshold will cause shifts in energy usage due to heating in winter and cooling in summer. For
this study, DDH and DDC at Winnipeg are used to represent the magnitude and temporal
components of the heating and cooling seasons, respectively. DDH are accumulated for mean
daily temperatures below 14 C while DDC are accumulated for mean daily temperatures
above 18 C.
DDH and DDC are further correlated with Manitoba’s electrical energy demand (in GWh)
while DDH is also correlated with Manitoba’s natural gas demand (in 103m3).
The weather effect for a given historical year is determined in various sectors by regressing
the last two years of actual monthly energies against the actual DDH (natural gas and electric)
and DDC (electric only) for the month. This produces the weather effect of energy demand
per DDH and an energy demand per DDC for that sector for that year. Only sectors whose
major variation is attributable to weather can have a weather effect estimated and only the
residential and commercial sectors are weather adjusted. Shown in Figure 2 is the correlation
of electric energy demand with DDH and DDC.
Figure 2 - Manitoba Monthly Energy Demand vs. Winnipeg DDH and DDC
The following relationships which will be used to calculate the effect of climate change and
have been derived from 2012/13 and 2013/14 historic data:
1.1 ∗
2.7 ∗
5
10
236.8 ∗
2.1.2.2 WEATHER EFFECT ON PEAK DEMAND
Electric peak demand (in MW) is correlated with the hourly temperature in which that peak
occurs. A winter peak (heating) typically occurs on a very cold winter weekday either in the
morning (around 8AM) or afternoon (around 5PM). A summer peak (cooling) typically occurs
on a very hot weekday afternoon (around 5PM).
The following relationships have been derived from historic data (Manitoba Hydro, 2013a):
30
30
∗ 40
∗ 120
The weather effect on peak demand for natural gas in Manitoba is not recorded nor analyzed.
Manitoba Hydro is a distributor of natural gas which has several delivery points to distribute
natural gas in Manitoba and does not examine system wide peak values.
2.2 DATA SOURCES
Data sources can be divided into two main categories: temperature data and demand data.
2.2.1 TEMPERATURE DATA
The temperature datasets used in current practice and used in this study are:
Hourly Environment Canada recorded data at the Winnipeg Airport (ID: 5023222)
Daily NRCan gridded data (1950-2012; Hutchinson et al., 2009)
Hourly Environment Canada temperature data forms the base for the NRCan datasets and
were used in the development of relationships between temperature and energy demand.
Daily NRCan gridded data were provided by The Ouranos Consortium (www.Ouranos.ca) for
this project and are used as the primary dataset for this report (Charron, 2015). The dataset is
based on Environment Canada meteorological stations and is spatially interpolated to a 10km
by 10km grid (Hutchinson et al., 2009). The dataset spans the baseline period and includes
daily minimum temperature and daily maximum temperature. The NRCan grid nearest the
Winnipeg Airport was used and correlates well with Environment Canada’s data.
2.2.2 ENERGY AND PEAK DEMAND DATA
The energy and peak demand datasets used in the current practice and in this study come
from several datasets. The summarized dataset by sector (Residential & Commercial) are
registered on a monthly basis from Manitoba Hydro’s billing system for both electric and
natural gas.
For the electric system energy demand, the system energy and peak demand values are
calculated on an hourly basis by totaling all the meters at the Generating Stations, including
6
electrical energy imported into Manitoba and removing the portion of energy not attributable
to Manitoba load.
For the natural gas, the system energy demand only addresses volumetric sales at customers’
natural gas meters and does not consider losses attributable to leakage, discrepancies due to
meter inaccuracies and adjustments.
2.3 RESULTS
This section characterizes baseline conditions for climate (temperature, DDH and DDC),
weather affected energy demand and weather affected peak demand.
2.3.1 TEMPERATURE AND DEGREE DAYS
Table 1 and Figure 3 present monthly and annual normals for baseline mean temperature,
DDH and DDC. Baseline period temperature extremes are also shown in Table 1. Since this
report uses NRCan data, the normals and extremes differ slightly from Environment Canada
published values.
Table 1 - Normal Temperature and Degree Days
(1981-2010 Baseline)
Normals
Extremes
TMean (C) DDH DDC TMin (C) TMax (C)
Jan
-16.2
937
0
-39.6
6.9
Feb
-13.1
766
0
-41.8
8.4
Mar
-5.6
607
0
-35.6
17.9
Apr
4.6
287
0
-21.3
31.0
May
11.6
111
9
-9.6
34.7
Jun
17.0
17
37
-2.3
37.8
Jul
19.7
1
71
3.9
35.7
Aug
18.9
5
62
-1.0
38.6
Sep
13.1
71
10
-5.2
38.1
Oct
5.4
270
0
-18.3
30.3
Nov
-4.4
551
0
-35.4
18.7
Dec
-13.3
846
0
-37.6
8.7
Annual
3.1
4,470 189
-41.8
38.6
Figure 3 - Normal Temperature and Degree
Days (1981-2010 Baseline)
Monthly normal temperatures range from -16.2 C in January to +19.7 C in July which
corresponds to the months with the greatest normal DDH and DDC, respectively. While DDH
is possible in all months, January, February, March, November and December are the primary
heating months where June, July and August are the primary cooling months. Annual normals
show a mean temperature of +3.1 C, with 4,470 DDH and 189 DDC.
Extreme temperatures provide useful information in interpreting peak demand and show a
range from -41.8 C in February to +38.6 C in August. Environment Canada publishes
7
extremes based on the entire period of record which shows temperatures as low as
-45.0 C in February and as high as +40.6 C in August.
2.3.2 ENERGY DEMAND
Table 2 and Figure 4 present monthly and annual normals for baseline weather affected
electrical energy demand and natural gas demand. Weather affected electrical energy demand
is presented separately for energy used for heating and cooling while weather affected natural
gas demand only applies to natural gas used for heating.
Table 2 - Normal Weather Affected Energy
Demand (1981-2010 Baseline)
Electrical Energy
Natural Gas (103m3)
(GWh)
Heating
Cooling
Heating
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Annual
1071
876
694
328
127
20
1
6
81
309
629
966
5107
0
0
0
1
25
98
190
166
28
0
0
0
509
221,955
181,441
143,734
67,913
26,314
4,061
261
1,189
16,812
63,971
130,351
200,228
1,058,230
Figure 4 - Normal Weather Affected Energy
Demand (1981-2010 Baseline)
Monthly weather affected electrical energy demand normals are greatest in January
(1,071 GWh) for heating and in July (190 GWh) for cooling. Monthly weather affected
natural gas demand normals are also greatest in January (221,955 103m3). Total weather
affected electrical energy demand (sum of demand for heating and cooling) normals have local
minima in June and September, representing shoulder months with relatively less weather
affected electrical energy demand. Annual normals are 5,616 GWh and 1,058,230 103m3 of
weather affected total electrical energy and natural gas demand, respectively.
The 2013/14 fiscal year actual Manitoba total electrical energy demand was 25,625 GWh and
actual natural gas demand was 2,279,682 103m3. Using 2013/14 as an example, the normal
annual weather affected total electrical energy demand represents approximately 22% of the
total Manitoba demand. For natural gas, the normal weather affected demand represents
approximately 46% of the total Manitoba demand.
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2.3.3 PEAK DEMAND
Weather effect on peak demand is estimated as a change from the average hourly
temperature at peak for summer (+30 C) and winter (-30 C). In this context, the normal
weather effect is zero for both summer and winter peaks for the baseline period.
For comparison, the observed summer and winter peaks during the last ten fiscal years
(2004/05 - 2013/14) are presented in Table 3 and Figure 5. One should note that these
values are influenced by other sources of load growth including population and industrial loads.
Over the last ten years, observed summer peaks have ranged from 2,893 MW to 3,294 MW
and observed winter peaks have ranged from 4,085 MW to 4,743 MW.
Table 3 - Observed Peak Demand
(2004/05 – 2013/14)
Summer Peak Winter Peak
(MW)
(MW)
2004/05
2893
4201
2005/06
3116
4085
2006/07
3141
4208
2007/08
3294
4304
2008/09
3110
4509
2009/10
3000
4393
2010/11
3163
4286
2011/12
3278
4367
2012/13
3260
4559
2013/14
3194
4743
Figure 5 - Annual Observed Peak Demand Time
Series (2004/05 – 2013/14)
For this study, mean daily temperatures are used to project changes in future peak demand.
Changes in the minimum of daily mean temperature and the maximum of daily mean
temperature in future years were used as proxy to evaluate the effects of weather on the
electric system peak demand.
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3 FUTURE SCENARIOS
This section presents projected future scenarios of energy and peak demand as a result of
projected future climate scenarios.
3.1 APPROACH AND METHODOLOGY
Climate scenarios provide views of how the future might unfold under various greenhouse gas
(GHG) emission scenarios. This approach uses Global Climate Model (GCM) projections of
future temperature to derive future weather affected energy and peak demand. Other
variables such as population growth, Demand Side Management (DSM), new customers, and
air conditioning saturation are held constant between the base case and the future climate
projections, and the weather effect is assessed in isolation. Since Manitoba experiences
extreme temperatures in both summer and winter, the existing system includes high
saturations of installed air conditioning systems and high insulation standards.
3.1.1 CLIMATE SYSTEM AND CLIMATE CHANGE
When studying climate change, it is important to distinguish the difference between weather
and climate. According to the Intergovernmental Panel on Climate Change (IPCC, 2013),
weather refers to the day-to-day state of the atmosphere while climate is usually defined as
the average weather, or more rigorously, as the statistical description in terms of the mean
and variability over a period of time.
Climate change refers to a change in the state of the climate that can be identified by changes
in the mean and/or variability of its properties that persists for decades or longer. The climate
system is complex and can change as a result of natural or anthropogenic changes in a
component of the climate system via external or internal processes. Natural climate variability
can be related to external processes such as shifts in radiative forcing (e.g.: volcanic eruptions)
or internal processes such as the El Niño-Southern Oscillation. The climate may respond
slowly or rapidly and can lead to periods of colder or warmer temperatures (IPCC, 2013).
3.1.2 CLIMATE SCENARIOS FROM GLOBAL CLIMATE MODELS
GCMs are advanced tools that are used to simulate the response of the global climate system
to changes in atmospheric GHG concentrations. GCMs simulate past and present climate and
are used to project future climate change by simulating the interaction of the atmosphere,
ocean and land surface. GCMs solve physical equations that describe the movement of energy,
momentum, conservation of mass and other large scale processes at coarse spatial
resolutions. Despite challenges in climate modeling, including those related to coarse
resolutions, GCMs are able to reproduce observed warming patterns quite well (Knutti, 2008).
3.1.2.1 RADIATIVE FORCING SCENARIOS
Radiative forcing scenarios, such as Representative Concentration Pathways (RCPs), are used
to prescribe the levels of various anthropogenic induced forcing agents (e.g.: GHGs and
10
aerosols) in the atmosphere. RCPs include a number of assumptions about societal evolution
and represent different demographic, social, economic, regulatory, technological, and
environmental developments. RCPs are used in GCMs to simulate the evolution of climate
over time in response to changes in atmospheric forcing agents.
RCPs were developed by the research community, independent of the IPCC and are named
according to their radiative forcing in 2100. An overview of the four RCPs is found in van
Vuuren et al. (2011) and summarized in Table 4. Figure 6 illustrates RCP projections of carbon
dioxide (CO2) concentrations and modeled global surface warming. Sanford, Frumhoff, Luers
and Gulledge (2014) show that emission rates are currently tracking just above RCP8.5.
Table 4 - Representative Concentration Pathways (van Vuuren et al., 2011)
RCP
Description
CO2 eq (ppm)
RCP8.5 Rising radiative forcing pathway leading to 8.5 W/m2 by 2100
~1370 ppm
RCP6.0 Stabilization without overshoot pathway to 6 W/m2 at stabilization after 2100
~850 ppm
RCP4.5 Stabilization without overshoot pathway to 4.5W/m2 at stabilization after 2100
~650 ppm
RCP2.6 Peak radiative forcing at ~3W/m2 before 2100 and then a decline to 2.6 W/m2 by 2100 ~490 ppm
Figure 6 - RCP CO2 Concentrations (left) and Projected Global Surface Warming (right)
3.1.2.2 TEMPORAL AND SPATIAL DOMAIN
The temporal domain spans from 1950-2099 and is summarized for two future periods with
respect to one baseline period. Consistent with Section 2.0, 1981-2010 is used as the
baseline. The two future periods are 2040-2069 (the 2050s) and 2070-2099 (the 2080s).
Annual time series and monthly normals are presented for energy demand. Annual time series
for summer and winter are presented for peak demand. In general, climate change signals in
short-term projections (i.e.: pre-2050s) are often masked by natural climate variability and do
not represent the impact of the RCP, since RCPs are similar during this time. Longer term
projections usually show a more prominent climate change signal and allow for differentiating
the impact of RCPs (Charron, 2015).
The primary spatial domain considers a single grid point from the GCM dataset nearest to the
Winnipeg Airport (49.92°N, -97.23°E) which is consistent with current practice. To study
spatial variability, Section 4.0 considers additional grid points in western Manitoba near
Brandon (49.90°N, -99.95°E) and in northern Manitoba near Thompson (55.80°N, -97.85°E).
11
3.1.3 DEVELOPING FUTURE SCENARIOS
Future scenarios of weather affected energy and peak demand are developed by applying
GCM simulated temperature time series to the methodology described in Section 2.0. This
process is performed individually for each GCM simulation since each simulation produces a
slightly different representation of the baseline climate.
GCM daily minimum and maximum temperatures are averaged to obtain time series of daily
mean temperature which forms the basis for the analysis. Mean temperature time series are
applied to equations presented in Section 2.1.2 to determine the weather effect and its
evolution through time.
To aid comparison and interpretation, results are presented in a simplified manner. Annual
time series are illustrated as departures plots which normalize GCM simulations and show the
change compared to the baseline normal. This procedure accounts for bias between the GCM
simulation and observations over the baseline period, and allows for a closer comparison
between observed and simulated variability over the historic period. For any given GCM, the
equation below demonstrates the calculation of the annual departure:
1950, 1951, 1952 …
WhereErepresentsanannualparameter e.g.:weatheraffectedelectricalenergydemand Departure plots contain several lines representing different information. Light grey lines
illustrate projections from all GCM simulations. A red line shows the observed record from the
NRCan gridded dataset. A thick black line illustrates the ensemble mean projection obtained by
averaging all GCM simulations. Similarly, the dotted green, blue and orange lines illustrate the
ensemble mean projection for each RCP. Tables and text will refer to the ensemble mean
projections, but it is important to note the variability (grey lines).
Monthly normal plots illustrate monthly baseline normals (Figure 3 and Figure 4) and
projected monthly normals for the two future periods. Line colors are similar to departure
plots and also distinguish between the two future periods.
3.2 DATA SOURCES
Data used in development of future scenarios include GCM data (1950-2099) and observed
data (1950-2010). GCM data were produced by the Pacific Climate Impacts Consortium
(PCIC) and Ouranos (Charron, 2015). The daily NRCan gridded data described in Section 2.2.1
are used as the observed record.
3.2.1 FUTURE CLIMATE PROJECTIONS
An ensemble of 27 simulations from the Coupled Model Intercomparison Project Phase 5
(CMIP5) is used. These simulations originate from nine GCMs that were presented in the
IPCC’s Fifth Assessment Report (AR5), where each GCM has been driven by RCP2.6, RCP4.5,
12
and RCP8.5. All simulations provide daily output of minimum and maximum temperature from
1950 to 2099 and have been bias corrected and statistically downscaled to a 10x10km grid
(Charron, 2015). Each simulation (GCM and RCP combination) is considered equally plausible.
GCM details can be found in Table 5.
Table 5 - Global Climate Model Information
Model (Vintage)
Institution Full Name (Abbreviation)
CanESM2 (2010)
Canadian Centre for Climate Modelling and Analysis (CCCma)
CCSM4 (2010)
National Center for Atmospheric Research (NCAR)
CNRM-CM5 (2010)
Centre National de Recherches Meteorologiques (CNRM)
CSIRO-Mk3-6-0
Commonwealth Scientific and Industrial Research Organization in collaboration with
(2009)
Queensland Climate Change Centre of Excellence (CSIRO-QCCCE)
GFDL-ESM2G (2012) NOAA Geophysical Fluid Dynamics Laboratory (GFDL)
HadGEM2-ES (2009) Met Office Hadley Centre (MOHC)
Atmosphere and Ocean Research Institute, National Institute for Environmental
MIROC5 (2010)
Studies, and Japan Agency for Marine-Earth Science and Technology (MIROC)
MPI-ESM-LR (2009)
Max Planck Institute for Meteorology (MPI-M)
MRI-CGCM3 (2011)
Meteorological Research Institute (MRI)
Future energy and peak demand scenarios also rely on historic temperature data, energy
demand data, peak demand data and historic weather effect relationships presented in Section
2. Historic relationships are held constant to isolate the sensitivity to climate change.
3.3 RESULTS
Results are presented separately for climate variables (temperature and degree days), energy
demand and peak demand. Departure plots and monthly normal plots show the projected
evolution of variables through time annually (1950-2099) and seasonally for the 2050s and
2080s. Projected changes for the 2050s are summarized in the text and additional
information is included in the tables.
3.3.1 TEMPERATURE AND DEGREE DAYS
Projected annual departures for are shown in Figure 7, Figure 9 and Figure 11. Projected
monthly normals are shown in Figure 8, Figure 10 and Figure 12. Annual and monthly GCM
ensemble average projected changes are shown in Table 6 for the 2050s and 2080s.
The GCM ensemble average projects annual changes for the 2050s (2080s) of +2.5 C
(+3.6 C) in mean temperature, -661.6 DDH (-916.0 DDH) and +181.1 DDC (+281.6 DDC).
The largest temperature changes are projected to occur in winter months.
The figures show a large spread in GCM simulations regarding the magnitude of change,
however all simulations agree on the direction of annual change. The figures show general
agreement in RCP averages until the 2050s, after which the RCPs diverge from one another.
13
Figure 7 - Annual Tmean Departure Plot
Figure 8 - Monthly Normal Temperature
Table 6 - GCM Average Projected Tmean, DDH and DDC Changes
2050s
2080s
Tmean (C)
DDH
DDC
Tmean (C)
DDH
DDC
Jan
3.6
-110
0
5.0
-154
0
Feb
2.8
-79
0
4.5
-127
0
Mar
2.8
-87
0
3.6
-112
0
Apr
1.8
-50
1
2.7
-76
2
May
1.8
-31
11
2.6
-43
18
Jun
2.0
-9
33
2.9
-11
53
Jul
2.3
-1
60
3.3
-1
91
Aug
2.5
-5
55
3.5
-6*
83
Sep
2.4
-33
19
3.3
-39
31
Oct
2.0
-57
2
3.1
-83
4
Nov
2.6
-77
0
3.7
-110
0
Dec
3.9
-122
0
5.0
-155
0
Annual
2.5
-662
181
3.6
-916
282
*This projection, when combined with Table 1 normals, suggests an unrealistic negative DDH in the
corresponding future month. The physical inconsistency is an artifact of numerical averaging and this projection
should be interpreted as zero normal DDH in the future.
14
Figure 9 - Annual DDH Departure Plot
Figure 10 - Monthly Normal DDH
Figure 11 - Annual DDC Departure Plot
Figure 12 - Monthly Normal DDC
15
3.3.2 ENERGY DEMAND
Projected annual departures are shown in Figure 13 and Figure 15. Projected monthly
normals are shown in Figure 14 and Figure 16. Annual and monthly GCM ensemble average
projected changes are shown in Table 7 for the 2050s and 2080s.
The GCM ensemble average projects annual weather affected changes for the 2050s (2080s)
of -756 GWh (-1047 GWh) in electrical energy demand for heating, +486 GWh
(+756 GWh) in electrical energy demand for cooling and -156,655 103m3 (-216,873 103m3)
in natural gas demand. Departures in total weather affected electrical energy demand are a
combination of decreases in winter spring and fall and increases in summer. For natural gas,
the largest changes occur in winter months.
The figures show a large spread in GCM simulations regarding the magnitude of change.
Annually, all simulations agree that weather affected natural gas and electrical energy for
heating demand will decrease and the electrical energy for cooling demand will increase. Most
simulations agree that total weather affected electrical energy demand will decrease (one
simulation projects an increase of +22.1 GWh for the 2080s). For total electrical energy, the
RCP averages agree for annual totals and for monthly demand in the 2050s. In the 2080s,
larger monthly differences are seen in electrical energy demand among RCPs. For natural gas,
RCP averages agree until the 2050s, after which the RCPs diverge.
Despite the GCM and RCP averages projecting decreases in normal demand, Figure 13
illustrates the potential for large year-to-year variability, with potential for weather affected
total electrical energy demand in the future that is similar to the observed record.
Figure 13 - Annual Weather Affected Total Energy Demand Departure Plot
Figure 14 - Monthly Normal Weather Affected Total Electrical Energy Demand
16
Figure 15 - Annual Weather Affected Natural Gas Demand Departure Plot
Figure 16 - Monthly Normal Weather Affected Natural Gas Demand
Table 7 - GCM Average Projected Weather Affected Energy Demand Changes
2050s
2080s
Electrical Energy (GWh) Natural Gas (10 3m3) Electrical Energy (GWh) Natural Gas (103m3)
Heating
Cooling
Heating
Heating
Cooling
Heating
Jan
-126
0
-26,131
-175
0
-36,365
Feb
-90
0
-18,616
-145
0
-30,052
Mar
-100
0
-20,670
-128
0
-26,546
Apr
-58
2
-11,939
-87
4
-17,963
May
-36
29
-7,384
-50
49
-10,278
Jun
-11
90
-2,201
-13
142
-2,594
Jul
-1
161
-250
-1
244
-269*
Aug
-6
148
-1,160
-6
224
-1,307*
Sep
-37
52
-7,707
-44
83
-9,195
Oct
-65
4
-13,496
-95
9
-19,645
Nov
-88
0
-18,219
-126
0
-26,049
Dec
-139
0
-28,883
-177
0
-36,608
Annual
-756
486
-156,655
-1047
756
-216,873
*These projections, when combined with Table 2 normals, suggest an unrealistic negative weather affected
natural gas demand in the corresponding future months. The physical inconsistency is an artifact of numerical
averaging and these projections should be interpreted as zero normal weather affected natural gas demand in the
future.
17
3.3.3 PEAK DEMAND
Projected departures in summer and winter peak are shown in Figure 17 and Figure 18. GCM
ensemble average projected changes are shown in Table 8 for the 2050s and 2080s.
The GCM ensemble average projects annual weather affected changes for the 2050s (2080s)
of +300 MW (+378 MW) in summer and -176 MW (-239 MW) in winter peak demand.
The figures show a large spread in GCM simulations regarding the magnitude and direction of
change. Most simulations project increasing summer peaks and decreasing winter peaks, one
simulation projects a decreasing summer peak (-67 MW) and four simulations project
increasing winter peaks (8 to 80 MW). RCP averages agree until the 2050s, after which the
RCPs diverge. The difference between winter and summer peaks is greatest for RCP8.5.
Figure 18 illustrates winter peak departures versus summer peak departures for 150 years of
27 GCM simulations (4,050 simulation years). The peak season reversal line (shown in green)
separates simulation years that peak in the winter (above line) or peak in the summer (below
line) based on observed 2013/14 peaks. Thirty-five points fall below the peak season reversal
line, suggesting that a summer peaking year in the future is possible but is not projected to
become a common occurrence.
Figure 17 - Weather Affected Peak Demand Departure Plot
Table 8 - GCM Average Projected
Weather Affected Peak Demand
Changes
2050s
2080s
Summer Peak
+300
+378
(MW)
Winter Peak
-176
-239
(MW)
Figure 18 - Winter Peak vs. Summer Peak Demand - Weather
Affected Departures
18
3.3.4 UNCERTAINTY
Many sources of uncertainty exist in future climate scenarios and must be considered when
interpreting results. This report summarizes future scenarios by presenting the GCM ensemble
average, however, it is important to consider the spread of scenarios as there is no way of
evaluating which simulation best projects the ‘real’ future conditions (Charron, 2015). As
recommended in Charron (2015) this report considers all 27 simulations to assess uncertainty
in future forcing scenario (RCP) and GCM selection.
To help characterize uncertainty, Charron (2015) identified a subset of three simulations
which represent the 10th, 50th and 90th percentile projections (Table 9). These simulations
were selected by cluster analysis based on changes in average winter temperature and
changes in average summer temperature. Table 10 and Table 11 summarize the GCM
ensemble average, the 10th, 50th and 90th percentile projections as well as the overall spread
(GCM minimum and GCM maximum) to show the range in annual projections.
Table 9 - 10th, 50th and 90th Percentile Simulations at Winnipeg (Charron, 2015)
Projection
2050s Simulation
2080s Simulation
th
10 Perc.
MRI-CGCCM3 (RCP4.5)
MPI-ESM-LR (RCP2.6)
50th Perc.
HadGEM2-ES (RCP2.6)
CanESM2 (RCP2.6)
90th Perc.
CSIRO-Mk3-6-0 (RCP8.5)
MIROC5 (RCP8.5)
Table 10 - Range in
Tmean
Projection
(C)
GCM Avg.
2.5
GCM Min.
0.9
GCM Max.
5.0
th
10 Perc.
1.5
50th Perc.
2.6
90th Perc.
3.1
Projected Annual Changes for 2050s
Electrical Energy (GWh)
DDH DDC
Heating
Cooling
-662 181
-756
486
-1300 38
-1485
103
-193 364
-221
979
-434 70
-496
189
-678 176
-775
474
-742 300
-848
806
Natural Gas (103m3)
Heating
-156,655
-307,804
-45,767
-102,711
-160,592
-175,703
Peak (MW)
Winter Summer
-176
300
-465
-67
80
590
-131
119
-170
34
-258
328
Table 11 - Range in
Tmean
Projection
(C)
GCM Avg.
3.6
GCM Min.
0.5
GCM Max.
8.4
10th Perc.
1.6
50th Perc.
2.6
90th Perc.
6.3
Projected Annual Changes for 2080s
Electrical Energy (GWh)
DDH DDC
Heating
Cooling
-916 282
-1047
756
-2074 19
-2370
52
-161 743
-184
1995
-471 74
-594
384
-672 189
-771
451
-1509 575
-1683
1588
Natural Gas (103m3)
Heating
-216,873
-491,087
-38,069
-111,510
-159,226
-357,222
Peak (MW)
Winter Summer
-239
378
-506
14
44
947
17
43
-264
144
-315
732
Table 10 and Table 11 demonstrate the uncertainty in projected annual changes and provide
context to compare the ensemble average to the 27 simulation ensemble. In most cases, the
ensemble average agrees well with the 50th percentile simulation, however, since the 50th
percentile simulation is selected based on changes to winter and summer temperatures, larger
differences are seen for certain variables. These differences underline the importance in
considering the overall spread, where possible.
19
4 REGIONAL ASSESSMENT
Impacts at Winnipeg are most relevant for electrical energy and natural gas demand
forecasting since Winnipeg is central to weather affected demand. However, further
assessment of spatial variability in climate change patterns can provide additional information
that could be important for other purposes. To better understand spatial variability, climate
scenarios are presented for two additional locations representing western (Brandon) and
northern (Thompson) Manitoba.
Departure plots for annual DDH and DDC are presented for Winnipeg, Brandon and
Thompson in Figure 19 and Figure 20. Annual departures follow similar patterns over time
with nearly identical departures for Winnipeg and Brandon. Thompson displays slightly larger
changes in DDH and smaller changes in DDC by the year 2100.
Figure 19 - Annual DDH Departure Plot for Winnipeg, Brandon and Thompson
Figure 20 - Annual DDC Departure Plot for Winnipeg, Brandon and Thompson
The presence of a season-dependent north to south gradient in DDH and DDC changes for
Manitoba is an important finding. Relative reductions in normal energy demand for heating are
projected to be greater in northern Manitoba while relative increases in normal energy
demand for cooling are projected to be greater in southern Manitoba. Charron (2015) also
assessed Canada-wide spatial variability in winter and summer temperature projections and
found a similar north to south gradient.
20
5 ADAPTATION AND OPPORTUNITIES
Results from this study can be used to inform various groups with interest in potential changes
to energy and peak demand in Manitoba. On October 23, 2014 a workshop was held with
Manitoba Hydro in conjunction with the Province of Manitoba to present results, brainstorm
potential applications of this study, discuss adaptation strategies, and identify opportunities and
risks. Twenty participants, including executive and senior managers attended the workshop
and provided guidance to enhance the results presented in this report.
The primary application of this study will be in evaluations related to long term resource
planning. Considering climate change impacts on energy and peak demand forecasts will help
to make resource planning decisions more robust. While small projected changes in annual
electrical energy demand might not impact future resource planning, seasonal changes, or
more pronounced reductions in natural gas volume may have a greater impact.
Several secondary applications of this study were identified at the workshop. These potential
applications are summarized in the following list:
Measure the impact of changes in peak demand and extreme temperatures on existing
transmission and distribution infrastructure.
Incorporate seasonal energy demand shifts and changes in peak demand in energy
operations planning.
Incorporate seasonal energy demand shifts in outage planning for energy infrastructure
maintenance.
Incorporate seasonal energy demand shifts in viability and risk assessments for DSM
programs and potential resource switching programs.
Measure the impact of climate change on the export market.
Further study of future vs. historic variability in weather affected energy demand.
Measure the impact of climate change on energy demand in remote communities where
fuel is used for heating.
Apply similar methodology and datasets to address agriculture related energy demands and
also growing season studies.
Apply similar methodology and datasets to address changes to additional variables
important in energy operations planning that can impact energy demand, such as cold
snaps, heat waves, or impacts on river ice formation and breakup.
21
6 CONCLUSIONS
Manitoba Hydro undertook this study to assess climate change impacts on future electrical
energy and natural gas demand in Manitoba. This study summarizes current weather
adjustment methodology and the projected climate change impacts on weather affected
demand. Results provide base knowledge for adaptation strategies and can serve as input to
further potential applications.
Existing relationships between historic Winnipeg weather and demand are held constant to
assess future projections, in isolation of other factors. Future temperature scenarios are
combined with these relationships to project changes in future weather affected demand.
Annual baseline (1981-2010) climate normals are: 3.1 C with 4470 DDH and 189 DDC.
Climate normals translate into baseline annual weather affected energy normals of:
5,107 GWh of electrical energy for heating, 509 GWh of electrical energy for cooling and
1,058,230 103m3 of natural gas. Baseline peaks are estimated as 3194 MW (summer) and
4743 MW (winter) which are the observed system peaks in the 2013/14 fiscal year.
The GCM ensemble average projects annual weather affected changes in demand for the
2050s (2080s) of -756 GWh (-1047 GWh) in electrical energy for heating, +486 GWh
(+756 GWh) in electrical energy for cooling, -156,655 103m3 (-216,873 103m3) in natural
gas, +300 MW (+378 MW) in summer peak, and -176 MW (-239 MW) in winter peak.
GCM ensemble average projections are used to summarize results. However it is important to
consider uncertainties in GCM selection, uncertainties in future forcing scenario (RCP) and
natural climate variability. Furthermore, it is important to note the variability in the observed
record and how this experienced range compares to future projections. While there is
uncertainty in the projected magnitude of change, most simulations agree on the direction of
change. Generally, future normal weather affected electrical energy and natural gas demand
for heating will decrease, and electrical energy for cooling will increase. Most simulations also
project weather affected summer peak to increase and winter peak to decrease.
A regional assessment shows that northern Manitoba will experience greater warming than
southern Manitoba in the winter. However, southern Manitoba will experience greater
warming than northern Manitoba in the summer.
Results from this study have been identified for potential uses in future adaptation and for the
identification of opportunities and risks. The primary application of these results is in resource
planning to explore sensitivities to climate change and the impact on future energy and peak
demand projections.
22
ACKNOWLEDGEMENTS
The authors gratefully acknowledge support from NRCan, Manitoba Hydro and the Province
of Manitoba. Special thanks are given to: Jennifer Ardiel and Mary-Ann Wilson from NRCan;
Isabelle Charron from Ouranos, Trevor Murdock from PCIC and all the supporting staff at
Ouranos and PCIC for providing the temperature datasets for this project; and members of
NRCan’s Adaptation Platform Energy Working Group and other regional project proponents
for ongoing review and methodology refinement. The World Climate Research Programme's
Working Group on Coupled Modelling is also acknowledged, which is responsible for CMIP,
and thanks is given to the climate modeling groups (listed in Table 5 of this report) for
producing and making available their model output.
ABBREVIATIONS
AR5 ........................................ IPCC’s Fifth Assessment Report
Autumn ............................... The season comprised of months: September, October and November
Avg, mean ......................... Average
CMIP5 .................................. Coupled Model Intercomparison Project (Phase 5)
CO2 ........................................ Carbon Dioxide
C ............................................... Degrees Celsius
DDC ...................................... Degree Days Cooling
DDH ...................................... Degree Days Heating
°................................................. Degrees (e.g.: for latitude, longitude and temperature measurements)
DSM....................................... Demand Side Management
Ens .......................................... Ensemble
ENSO ................................... El Niño-Southern Oscillation
GCM ...................................... Global Climate Model
GHG ...................................... Greenhouse Gas
GS ........................................... Generating Station
IPCC ...................................... Intergovernmental Panel on Climate Change
Max......................................... Maximum
MB .......................................... Manitoba
Min.......................................... Minimum
NRCan ................................. Natural Resources Canada
PCIC ...................................... Pacific Climate Impacts Consortium
Perc ....................................... Percentile
RCP........................................ Representative Concentration Pathway
Spring ................................... The season comprised of months: March, April and May
Summer .............................. The season comprised of months: June, July and August
Tmean, ...................................... Mean Temperature
Winter ................................. The season comprised of months: December, January and February
WMO ................................... World Meteorological Organization
23
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