variable renewable energy generation...
TRANSCRIPT
Variable Renewable Energy Generation Forecasting
PRAMOD JAIN, Ph.D.
Consultant, USAID Power the Future
Astana, March, 29 2018
7/6/2018
Agenda
• What is the impact of VRE on System Operations?
• Why is this important?
• How to study the impact on System Operations?
2
What is Variable Renewable Energy Generation
Forecasting?
• Solar PV, Wind, RoR hydro
• Short-term prediction of future VRE power plant
generation:
– Amount of generation depends on weather
– Timeframes: Week-ahead (WA), Day-ahead (DA)
or intraday (ID)
3
VRE Forecasting
Algorithm
Weather Forecast
Historical data
Available
Capacity
Generation
Forecast
Source [1]: Scale up Renewable Energy, Variable Renewable Energy Forecasting Whitepaper, March 2019. Published for review by USAID. Author: Pramod Jain
Benefits of VRE Forecasting
6
VRE Forecasting
Reduce Reserves
Improve System
Flexibility
Reduce Curtailment
Higher Reliability
Source 1
• Root Mean Square Error (RMSE)
– 𝑅𝑀𝑆𝐸 =σt=1𝑛 (𝑓𝑡−𝑎𝑡)
2
𝑛
• Mean Absolute Deviation (MAD)
– 𝑀𝐴𝐷 =σt=1𝑛 𝑓𝑡−𝑎𝑡
𝑛
• Absolute Percent Error (APE)
– 𝐴𝑃𝐸𝑡 =𝑓𝑡−𝑎𝑡
𝐶𝑡× 100
• Mean Absolute Percent Error (MAPE)
– 𝑀𝐴𝑃𝐸 =σt=1𝑛 𝐴𝑃𝐸𝑡
𝑛× 100
• 𝑡 is a time block, 𝑓𝑡is the forecast for
time block 𝑡, 𝑎𝑡is the actual observation
for time block 𝑡
• 𝐶𝑡 is the available capacity of the VRE
facility for time block 𝑡. Available capacity
= installed capacity – capacity under
maintenance
7
Error Terminology
Source 1
Properties of Net Load
• Net-load = Total demand -Variable power generation
• Net-load is more variable than load itself and variability
increases as VRE production increases
• Net-load is more uncertain than load itself and contribution of
VRE to uncertainty (measured as a percentage of VRE
generation) decreases with geographic diversity
• The System Operator dispatches flexible resources to meet
net-load
9
System Operations With and Without VRE
➢ With out VRE, grid manages load variability and
uncertainty through
• Load forecasting
• Flexible generation
• Reserves of different kinds
➢ VRE adds to both variability and uncertainty of Net Load
➢ System Operations in presence of VRE is managed is
similar manner
10
Impact of Forecasting Error on Unit
Commitment
11
Higher day-ahead forecast error
• Large negative error:
• Commit new generators
• Non-spinning reserves are used in
extreme conditions
• Large positive error: Uncommit
generators or suboptimal dispatch
How is Flexible Capacity Deployed?
13
Assuming perfect forecasts
Faster
Dispatching:
Drop in
Regulation
Reserves
Impact of Forecasting Error on Dispatching
14
With forecast errors
With forecast
error: Higher
Regulation
and Following
Reserves
Wind Energy Forecast Accuracy as Function of
Lead Time
15
Source: http://orbit.dtu.dk/files/115470586/energies_08_09594.pdf
Benchmarking exercise was
organized within the
framework of the European
Action Weather Intelligence
for Renewable Energies
(“WIRE”) for evaluating the
performance of state of the
art models for short-term
renewable energy
forecasting. The exercise
consisted in forecasting the
power output of two wind
farms and two photovoltaic
power plants
Flexible Capacity Requirement for Dispatching
to Net-Load
• Flexible resources are dispatched to meet variability and uncertainty of net-load
• How much flexible capacity is required?
– MANAGING VARIABILITY: CAISO computes monthly three-hour flexible capacity need for ramping. The largest expected up-ward change in net-load for the month when looking across a rolling three-hour evaluation window
16
Source: CAISO
Flexible Capacity Requirement
• Amount of flexible resources needed is shaped by the magnitude of the ramps of Net-Load
• If VRE forecasting methods are not accurate enough to provide sufficient notice to the
operator, a more robust flexible system is needed to address both the forecast uncertainty and
ramps
• VRE forecasting improves effective planning and operations
17
Managing Forecast Accuracy: Deviation
Settlement
18
• How to incentivize higher accuracy:
– Centralized forecasting—VRE Plants pay for the service
– Decentralized forecasting—Deviation settlement Mechanism
1. Pay for balancing: Requires a balancing market, which is different from day-ahead market. Two types:
–Single price imbalance
» Regardless of excess or deficit of production, deviations are settled at balancing market price
–Dual price imbalance
» If imbalance is in opposite direction (generator is helping the grid), then imbalance is settled at day-ahead price
» If imbalance is in same direction, then deviation is priced at balancing market price
2. Penalty for excessive deviation
Cost of Wind Integration
20
Arizona Public Service (Acker et al., 2007)
Typical range for all studies:
$1.5‐$4.5/MWh
Source: Wind Energy Forecasting, Michael Brower, AWS
Truepower
Grid Integration Cost, Details
21
Source: https://www.nrel.gov/docs/fy07osti/41329.pdf
Studies on Wind Integration Costs versus Wind Capacity
Penetration in Various Regions of the United States
22
Balancing costs
23
Source: http://www.synapse-energy.com/sites/default/files/Costs-
of-Integrating-Renewables.pdf
Studies on Solar and Wind Integration Costs in Various
Regions of the United States
Cost of VRE Integration
• Other estimates in the literature suggest that at a 20% share of average electricity demand,
wind energy balancing costs range from $1/MWh to $7/MWh (IEA 2011).
• Recent studies in the US show that the cost of grid integration is even lower. Studies prior to
2008 had estimated integration costs up to $5/MWh. By 2013, ERCOT, which has the highest
penetration of wind, was reporting integration cost of $0.5/MWh, primarily due to operating
reserve requirements. Source: 2015 Wind Vision report (US Department of Energy 2015, chapter 2.7).
https://www.energy.gov/sites/prod/files/wv_chapter2_wind_power_in_the_united_states.pdf
• New transmission line cost is not allocated to a wind project,
– Lines are used by multiple projects and multiple types of power plants
– Instead, usage-based costs (per MWh of energy transported) are paid to transmission
line operators, and these costs are reflected in operation and maintenance cost of a WPP.
24
Managing Forecast Accuracy: Deviation
Settlement
25
• How to incentivize higher accuracy:
1. Pay for balancing: Requires a balancing market, which is different from day-ahead market.
Two types:
–Single price imbalance
» Regardless of excess or deficit of production, deviations are settled at balancing market price
–Dual price imbalance
» If imbalance is in opposite direction (generator is helping the grid), then imbalance is settled at
day-ahead price
» If imbalance is in same direction, then deviation is priced at balancing market price
2. Penalty for excessive deviation
• Penalty for excessive deviation, example
from India
• Example: If the available capacity of a VRE
facility is 1 MW, the forecasted power
generation is 0.62 MW and the actual
power injection is 1 MW, then:
– 𝐴𝑃𝐸 = 100 ∗1−0.62
1= 38%
• Penalty is computed for each time block
• 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 1 = 𝑅𝑠 0.50 ∗ 100𝑘𝑊 ∗15
60= 𝑅𝑠 12.5
• 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 2 = 𝑅𝑠 1.00 ∗ 100𝑘𝑊 ∗15
60= 𝑅𝑠 25
• 𝐷𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 3 = 𝑅𝑠 1.50 ∗ 30𝑘𝑊 ∗15
60= 𝑅𝑠 11.25
• 𝑇𝑜𝑡𝑎𝑙 𝑃𝑒𝑛𝑎𝑙𝑡𝑦 = 12.5 + 25 + 11.25 = Rs 48.75
26
Managing Forecast Accuracy: Deviation
Settlement
APE: Absolute percentage error =
100 ∗𝐴𝑐𝑡𝑢𝑎𝑙 𝑃𝑜𝑤𝑒𝑟 𝐼𝑛𝑗𝑒𝑐𝑡𝑖𝑜𝑛 − 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑒𝑑 𝑃𝑜𝑤𝑒𝑟 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒_𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦
ACCURACY BIN PENALTY (PER KWH)
CERC/POSOCO Andhra Pradesh
APE ≤15% None None
15% <APE ≤ 25% 10% of PPA Rs 0.50
25% <APE ≤ 35% 20% of PPA Rs 1.00
APE >35% 30% of PPA Rs 1.50*
Source 1
Major Components in VRE Forecasting
28
WMS: Weather Monitoring System
SCUC: Security constrained unit commitment
SCED: Security constrained economic dispatchSource 1
Detailed Inputs to VRE Forecasting System
29
Cloud
cover
Meteorology
Station Info
Telemetry
Actuals
NWP
Models
Renewable
Dispatches or
Curtailments
Statistical
Model BlendTerrain
Wind
Profilers
Turbine Info
Outage
Information
Wind/Solar
ForecastSource: CAISO
Forecasting Methods
• VRE Generation Forecasting methods use weather forecasts and historical data (weather and
generation)
- Regression
- Autoregression
- Decision trees
- Time series
- Machine Learning
- Neural networks
30
Types of VRE Forecasting: Centralized and
Decentralized
• Centralized
– System operator generates a forecast for all VRE generation on the system
– In most cases, the forecasting is done for aggregate renewable energy generation at
transmission pooling substations
– Advantages: consistent forecasting methodology, greater accuracy, and economies of scale
–Cost of forecasting is spread across many VRE projects
–Sourcing higher-quality weather data from multiple providers and continuously
improving the forecasting method
–Forecasts are for all VRE plants in the system, hence errors are smoothed out because
of geographic diversity
31
Types of VRE Forecasting: Centralized and
Decentralized
• Decentralized forecasting
– Individual VRE generators are required to submit forecasts to the system operator
– If the incentives are properly set up, then higher accuracy
–More innovation
–More accurate modeling of local weather phenomenon and equipment performance
–Better ensemble forecasts compared to the output of a limited ensemble of a centralized system
– This type of forecasting is likely to incur higher system-wide costs because each VRE plant on the transmission would be required to bear expenses
• Hybrid approach
– Combination of centralized and decentralized forecasting
– Centralized forecast is used to produce schedules/dispatches for each VRE plant. The VRE generator then has the option of using the schedule from SLDC or its own forecast as the generation schedule
32
Data Requirements from VRE Plants
• Master data on the plant
– Total installed capacity
– Capacity of each generator
– Physical and technical properties of each generator (wind turbine or PV module)
– Geographic location, point of interconnection
– Expected annual average energy production
• Near real-time data to system operators
– Power production (active and reactive)
– Renewable energy resource (wind speed and direction or solar radiation and temperature)
– Available capacity, curtailment, and other data, for each time block
– Forecast of available capacity, which may be in the form of start and finish times of scheduled and unscheduled maintenance
VRE plants should be required to provide such data using direct data transfer methods like web-based application programming interfaces
33
Hour vs Day-Ahead: Differences in Forecast
Error
34
Source: Wind Energy Forecasting, Michael Brower, AWS Truepower
Integrating VRE Forecasting with Grid
Operations
• The forecasts may be fine, but will they be used?
• Forecasts should be customized to the real needs of the grid operators
– Confidence levels on routine forecasts
– Focus on critical periods, e.g., times of maximum load or maximum load swing
– Ramp forecasts
– Severe weather forecasts
• Dedicated staff should be assigned to monitor forecasts
• Other steps to make integration more effective: training, visualization tools, plant clustering
35
Recommendations Based on Best Practices
• Develop policy, regulations, and grid code (enhancement specific to VRE) that address in a comprehensive manner VRE forecasting and related activities in system operations
– The grid code should mandate the sharing of data on available capacity, actual production, on-site measured weather data, and other parameters at a frequency and granularity that match the VRE forecast.
• Implement short time block and short lead time forecasting to increase accuracy
– In combination with fast dispatching, this would significantly enhance the flexibility of the grid, resulting in the ability of the grid to absorb larger amounts of VRE.
• Invest in higher-quality weather forecasting services
– This is essential because it is the primary ingredient in VRE generation forecasting.
• Use centralized VRE forecasting with VRE forecasting services/software from experienced vendors
– Centralized forecasting is preferred because it eliminates the need for each VRE plant to purchase software and services for forecasting weather and VRE generation. It should also provide more accurate forecasts because VRE forecasts for multiple plants over larger geographic region reduce variability.
37
Summary for Kazakhstan
• VRE Forecasting is not optional, it is a must
• It must be tightly integrated with System Operations/Dispatching
• Fast Dispatching combined with sub-hourly forecasting reduces cost of integrating VRE
• Investments in weather monitoring is key to improving accuracy of forecasts
• Centralized forecasting with strong collaboration with National Weather Service can yield
higher accuracy
• The value provided by VRE forecasting in grids with small to large penetration of VRE is
significant compared to the cost
38
39
Thank You
USAID Regional Program “Power the
Future”
Pramod Jain, Consultant
President, Innovative Wind Energy, Inc.
+1-904-923-6489
Power the Future
6, Sar y Arka Ave, Office 1430
Astana, Kazakhstan 000010
WWW.PTFCAR.ORG
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