tool demonstration: demand forecasting · demand forecasting • prediction of future energy demand...
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Tool Demonstration: Demand ForecastingPACE D 2.0 RE Team
PARTNERSHIP TO ADVANCE CLEAN ENERGY
DEPLOYMENT (PACE-D 2.0 RE)
TECHNICAL ASSISTANCE PROGRAM
April 2020
Agenda
• Why Resource Planning
• Demand Forecasting
• Demand Forecasting Tool: Why?
• About the Tool
• Parameters Considered for Demand Forecasting
• Results
• Online DemoSlide No. 2
Importance of Resource Planning
• Significant reduction in power purchase cost for DISCOMs with RE addition in power portfolio,.
• Better matching of demand and supply will reduce the cost of grid integration.
PACE D Program Interventions : Journey so far
• Extensive consultation on existing resource planning practices with 11
DISCOMs (state and private), electricity regulatory commissions and central
agencies and partner states – Assam and Jharkhand.
• Based on discussions catalysed a national dialogue that resulted in a white paper,
“Rethinking DISCOMs Resource Planning in a Renewable-rich Environment”
• Emerged with recommendations that standardized resource planning
methodology and software tool will better position DISCOMs with uptake of
low cost clean and optimize power procurement.
• Progressed with developing an DISCOM Resource
Planning software tool for efficient resource
planning. The 3 tools are
• demand forecasting – Released Today
• generation planning – June 2020
• least-cost power procurement – October 2020
National Broadcast by November 2020
• Partnered at the federal and
state levels to develop model
resource planning
guidelines for state
regulators to adopt in the
renewable rich environment.
Slide No. 4
Demand Forecasting
• Prediction of future energy demand requires an
intuitive and wise judgment
• The forecast needs to be revised at regular
intervals (alternative year) to take care of new
policies and changes in socio-economic trends.
• The demand forecast is used as a basis for system
development, and for determining tariffs for the
future.
• Over-forecasts lead to more generation
resources than is required – Unnecessary capital
expenditure
• Under-forecasts prevent optimal economic tariffs
– Lead to purchase of power form costly units or
high cost power form markets.
Long Term Forecasting:• Plays a fundamental role in
economic planning of new
generating capacity and
transmission networks.
• Spans over 5 to 20 years.
Medium Term Forecasting:• Used mainly for the scheduling
of fuel supplies, maintenance
program, financial planning and
tariff formulation
• Spans over 1 month to 5 years
Slide No. 5
Demand Forecasting Tool: Why?
Slide No. 6
About Tool
• The demand forecasting can
be performed at DISCOM
level for all categories.
• The various consumer
categories, like residential,
commercial, industrial etc.,
can be considered for
forecasting.
The methods that have been provided in the software to arrive at the best forecast values are:
Univariate:• CAGR
• Trend Analysis
Multivariate:• Econometric Method
• ARIMA
• ANN
PEUM:Decomposes the sales of
electricity into its elemental
component of consumption
Slide No. 7
Parameters Considered for Demand Forecasting
• Demand is forecasted under two scenarios:
✓ Business As Usual
✓ Scenario with Drivers
Business As Usual Scenario with Drivers
• Based on the energy sales and econometric
data, the demand is forecasted for all the
consumer categories.
• CAGR, Trend, and Econometric for long
term forecasting
• ARIMA and ANN for medium term
forecasting
• In this, impact of drivers is considered on
BAU scenario to forecast the demand.
• Drivers: Open Access (OA), Captive Power
Plants (CPP), Distributed Energy Sources
(DER), and Electric Vehicles (EVs).
Further, sensitivity and probabilistic analysis is done to study the variation in demand.
Slide No. 8
Results: Long Term Forecasting (JBVNL)
• Long term demand
forecasting: 2020 to 2040
• Based on the average load
factor of previous 3 years, the
peak demand is estimated.
• Energy sales projections for
the 2020 are:
o Tariff Order1: 10388 MU
o Demand Forecast Tool :
9822 MU
o Correction: 5%
1Source: JSERC
0
500
1000
1500
2000
2500
3000
3500
4000
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
PEA
K D
EM
AN
D (
MW
)
JBVNL Area-Peak demand in MW
Slide No. 9
0
5000
10000
15000
20000
25000
30000
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040Net
dem
and (M
U)
JBVNL-Net demand requirement in MU
Note: Net Demand imply ex-bus generation
Results: Long Term Forecasting (APDCL)
0
5000
10000
15000
20000
25000
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
NET
DEM
AN
D (
MU
)
APDCL: Net demand requirement in MU
0
1000
2000
3000
4000
5000
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
PEA
K D
EM
AN
(M
W)
APDCL: Peak demand in MW
• Long term demand
forecasting: 2020 to 2040
• Based on the average load
factor of previous 3 years,
the peak demand is
estimated.
• On an average, % deviation
of demand projections w.r.t
energy sales approved under
AERC MYT Order 2018 is
4.8%2.
Source: BAU Report, average obtained for the Years 2020, 2021 and 2022
Slide No. 10
Results: Medium Term Forecasting (APDCL)
0 200 400 600 800 1000 1200
Mar
Dec
Sep
Jun
Mar
Dec
Sep
Jun
Mar
Dec
Sep
Jun
Mar
Dec
Sep
Jun
Mar
Dec
Sep
Jun
2024
2023
2022
2021
2020
APDCL-Monthly net demand in
MU
0 500 1000 1500 2000 2500 3000
Mar
Dec
Sep
Jun
Mar
Dec
Sep
Jun
Mar
Dec
Sep
Jun
Mar
Dec
Sep
Jun
Mar
Dec
Sep
Jun
2024
2023
2022
2021
2020
APDCL-Monthly peak demand in
MW
Source: BAU Report Slide No. 11
Results: Hourly Load Profiles (JBVNL)
Source: BAU ReportSlide No. 12
Peak demand for each day for each month for FY 2020
Hourly demand for peak day for
each month for FY 2020
Hourly Load Profiles : Unique Proposition
Source: Assam, BAU Report, Demand forecast of 2030, Typical DaySlide No. 13
Provide optics on :
1. Estimated Demand at
hourly level
2. Quantity of Resource
required to meet the
demand
3. Higher uptake and
integration of RE from
multiple sources
Important to help map resources
needed to meet the anticipated
Demand
0
500
1000
1500
2000
2500
3000
0
500
1000
1500
2000
2500
3000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Pe
ak d
em
and
(M
W)
Ge
ne
rati
on
Dis
pat
ch in
MW
Time (Hours)
Solar Wind Thermal Hydro Storage Demand
Probabilistic Analysis (JBVNL)
Probabilistic Energy Sales at Varying Standard Deviation of
Independent Variables for the Year 2030.
For Risk Based Resource Plan IdentificationSource: Probabilistic Analysis ReportSlide No. 14
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
22000
24000
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
To
tal E
nerg
y S
ale
s (M
U)
Year
Tool Highlights: Configuration of DISCOM
• The DISCOM and associated
consumer categories can be
configured as a one-time activity.
• The historical energy sales
observed for each consumer
category can be uploaded into
the tool.
• The SCADA data can be
directly imported into the
tool for capturing the
hourly load profile and the
load factor observed.
Slide No.15
Tool Highlights: Scenario Creation
Several scenarios can be created in the tool to analyse various aspects and carry out
sensitivity studies to understand the impact of various policies and drivers on the total
demand.Slide No. 16
Tool Highlights: Forecast Results The results obtained
for each category by
different forecasting
methods can be
visualized both
graphically and in
tabular form to
identify the most
suitable forecast
results
Results obtained for Domestic category Slide No. 17
Training Videos • Introduction to Tool
• Pre-requisites
• Logging-in and
DISCOM configuration
Getting Started
• Configuration of
• Dependent Variables
• Independent Variables
• Load Profile
• T&D Losses
Data Modeling
• Forecast Methods
• Scenario-specific data configuration
• Execution
Scenario Creation
• View & Analyse Results Summary
• Category-wise Fitted Curve
• Consolidated Results
• Detailed PDF Report
• Probability Analysis
Analysis of results
• Policy Configuration
• Drivers Configuration➢ Distributed Energy
Resources
➢ Open Access
➢ Captive Power Plants
➢ Electric Vehicles
Impact of Policies & Drivers
1 2
3 4 5
Slide No. 18
Impact of COVID-19
0
100
200
300
400
500
600
700
800
Apr May Jun Jul Aug Sep Oct Nov Dec
Energ
y Sa
les
in M
U
Impact of COVID-19 on Medium-term Demand Forecast for
Assam
Energy Sales Energy Sales with Covid-19
✓ Option to model like
COVID-19 impacts
under drivers
✓ Assessment of demand
under dynamic changes
through drivers
✓ Assessment of resource
mix and power
procurement under
these scenarios
Daily demand is about 13
MU/day* in Assam nowadays.
* Assam SLDC website.
Tool has flexibility to capture data and cater to situations such as COVID-19
Brief Demonstration of the Tool
&
Discussion
Slide No 20
Contact:
Sumedh Agarwal | PACE – D 2.0 RE Program
Your Feedback, Questions are
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