Variable Renewable Energy Generation Forecasting

PRAMOD JAIN, Ph.D.

Consultant, USAID Power the Future

Almaty University of Power Engineering and Telecommunications

April 1, 2019

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

Purpose of VRE Forecasting

• Support system operators in performing dispatch planning

4

Source 1

Terminology of Forecasting

• Day-ahead

• Intraday

5

Source 1

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

Net Load = Load –VRE Generation

8Source: NREL report 61721

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

Classification of Reserves

12

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

Economics of Grid Integration of Variable Power

19

Managing Forecast Accuracy: Deviation

Settlement

20

• 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

21

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

VRE Forecasting Process and Methodology

22

Major Components in VRE Forecasting

23

WMS: Weather Monitoring System

SCUC: Security constrained unit commitment

SCED: Security constrained economic dispatchSource 1

Detailed Inputs to VRE Forecasting System

24

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

25

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

26

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

27

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

28

Hour vs Day-Ahead: Differences in Forecast

Error

29

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

30

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.

31

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

32

33

Thank You

USAID Regional Program “Power the

Future”

Pramod Jain, Consultant

President, Innovative Wind Energy, Inc.

pramod@i-windenergy.com,

+1-904-923-6489

Power the Future

6, Sar y Arka Ave, Office 1430

Astana, Kazakhstan 000010

WWW.PTFCAR.ORG

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