ntroduction to demand...

1Suryanarayana Doolla, IIT Bombay

INTRODUCTION TO DEMAND RESPONSE

8.12.2016

PROF. SURYANARAYANA DOOLLA

DEPARTMENT OF ENERGY SCIENCE AND ENGINEERING

[email protected]


Reduction of utility load primarily during periods of peak demand

Reduction of utility loads, more or less equally, during all or most hours of the day

Increase of utility loads, more or less equally, during all or most hours of the day

Improvement of system load factor by building load in off-peak periods

Options to alter customer energy consumption on an as-needed basis

Reduction of loads during periods of peak demand with building load in off-peak time

Load Shape Objectives


• Great achievement in generation and transmission but

still

– Concerns on reliability of power due to outages

– Cost of power to utility becomes very high for a short time due to

• Transmission Constraints

• Generator failure

• Demand surge due to special events such as festivals or state

elections

• Sudden heat waves etc.

• Besides capacity additions are cost intensive as well as

have longer gestation periods.

• Short-time peak management is a challenge for the

Discoms.

Demand Response – What is the Need

Demand response is a solution which can solve these issues plus help in integrating renewables especially wind


DSM & Demand Response

• DSM consists of two major components: Demand Response (DR)[part of load management] and Energy Efficiency (EE), which is alsoconsidered as conservation

• Definition of Demand response : Changes in electric usage byend‐use customers from their normal consumption patterns inresponse to changes in the price of electricity over time, or toincentive payments designed to induce lower electricity use at timeof high wholesale market prices or when system reliability isjeopardized

(Definition as per FERC– USA)


Demand Response

o Electricity Consumers can

o Voluntarily trim their electricity use during peak period

o Choose to use electricity at a less congested time

o Electricity Utilities provide

o Incentives to reduce consumption during peak periods

(residential, commercial, industrial etc)

o Initiating Demand Response:

o Manual

o Automatic

o Semi-Automatic

5


DR is a very complex option

Technology Intensive

Regulatory Intervention

Close Monitoring

Confidence

Understanding Demand Response


Types of Demand Response

DR Programs

Price Responsive DR

Time of Use

Real time

Pricing

Critical Power Pricing

Incentive Based DR/DispatchbleDR

Reliability DR

DLC I / CAncillary market

Emergency DR

Economic DR

Demand bidding/Bu

y Back


A promising way to manage flexible demand is to communicate dynamic prices to the consumers and incentivize them to change their consumption amount or to shift load demand in time.

Various types of Demand Response Programs•Price based DR Program

─Time of the day tariff, Real time pricing, Critical peak pricing

• Incentive based DR Mechanisms

─Direct Load Control, Interruptible/Curtailable DR, Demand Bidding, Emergency DR services, Capacity Bidding and Ancillary DR services

Understanding Demand Response


Framework for Technology Assessment of DR

• Identify the stakeholders involved and their need-cum-objective in the involvement.

─Three broad divisions – Utilities, Load Aggregators and Customer

─Demand response programs require a certain degree of technological readiness from the perspective of each of the stakeholders

• Technology enable DR Program for a target load category

─ The technological requirement identified can be categorized as hardware component, software component and the communication technology that enables exchange of information across various entities involved.

─ Technological Prerequisite for smooth conduct of a residential DR Program (Cycle explained)

Components of Demand Response


Utility

• Triggers a DR event through various price based and incentive based mechanisms

• Payment to Customers

Customer

Load aggregator

• Small consumers may not qualify

• Penalty associated

Regulator

Stakeholders in Demand Response


Infrastructure/Architecture for Demand Response


Technology Options – DR in Residential/Small

Consumers


Architecture for Residential DR Program


Network requirements for DR Program

Latency for DR programs represents the time interval between trigger and response. This varies between 500ms to several minutes as per the type of DR programs.

Ancillary and emergency DR programs usually have low latency requirements.

Available Communication Options for DR

Network attributes Demand Response

Latency 500ms to several minutes

Bandwidth 14 to 100 Kbps per node

Minimum reliability 99 %

Security High

Power Back up Not required


Coverage range and data rate specifications for various networks used in DR



Technology Review for Wireless (ZigBee, Wi-Fi, WiMaX, RF), Wired (PLC, Ethernet, (DSL) Optical fibre), Cellular (GSM, GPRS, 3G/4G (LTE))

Comprehensive comparison of various communication technologies (Wireless, Wired and Cellular) for DR

• Data Transfer

• Cost

• Power Consumption

• Existing Support Structure

• Security

• Application



DR Programs – Level of Interaction with Grid, Control

and Telemetry Speed requirements

SpiningReserve (Fast DR)

Real-time DR

Day ahead DR (Slow)

Daily peak load management

ToU Tariff

Daily EE measures

Increased interaction with the Grid

Increased level of control and speed


Open Automated Demand Response (OpenADR) protocols provide an open (nonproprietary), standardized and secure way for Utilities and Independent system operators (ISOs) to communicate DR signals with each other and with their customers using a common language over any existing IP-based communications network, such as the Internet.

OpenADR 1.0 standards were designed for simpler devices such as residential thermostats. Many features have been added in new versions of OpenADR 2.0 such as handling the flow of information back from the buildings.

Open ADR Standards


This reporting feature has been made more robust and can handle real-time results from end users.

Real-time energy consumption changes in building loads can be confirmed with same ease as verification of a home or small business thermostat or load-control device having received the DR signal and if they have responded as per the contract.

Pacific Gas & Electric (PG&E), San Diego Gas & Electric and Southern California Edison (SCE) have added and recommended OpenADR 2.0 certified products to implement their dispatch of emergency and price DR resources.

Open ADR Standards


Case Study – TPDCL - Delhi

o Customers enrolled: 144/173 (connected load > 100 kW)

o Total event period: 6 Months

o Event interval: 30 min to 60 min

o No. of Events: 17

o Achieved about 8% of demand reduction in the pilot project

o 5/10 with morning adjustment is used as baseline

21


Tasks of DRAS

o Task 1

oCollects information from Load forecasting tool, SCADA, and MDMS (Meter Data Management Systems)

o Task 2

oSupply-Demand is determined for present and next block.

o Task 3

oBased on the deficit/surplus situation, commands are sent to the consumers.

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Source: TPDDL Delhi


DR Deployment – Utility Consumer

23

Source: TPDDL Delhi


Sub-Systems Connectivity – Auto DR

24

Source: TPDDL Delhi


Measurement and Verification

for DR


Sequence of Events in DSM Cycle

Load Research & Market Potential

Assessment

Strategic Planning

Program DesignProgram

Implementation

Program Evaluation & Measurement


Aim of M&V - evaluating the actual amount of load that is curtailed during an event.

Requirement - Estimate of the load that the customer is going to consume on the event day more precisely during the event period.

Baseline- Predicted amount of the electricity that would have been used up by a customer in the absence of a DR event.

Consumers - Compensated financially for reducing electricity use•Difference between the customer baseline (CBL) and the actual metered usage.

Baseline is challenging aspect as it should represent the exact usage of electricity by the customers in the absence of load curtailment during DR event

Importance of Baseline


Components of Baseline

Data Selection

Estimation

Adjustment


Baseline window is identified

• Precedes a DR event period over which electricity usage data is collected for the purpose of establishing a baseline.

Some of the instances of baseline windows include:

• Last 10 non-holiday weekdays;

• 10 most recent program-eligible non-event days;

• 10 most recent program-eligible days beginning 2 days before the event;

• Last 45 calendar days

• Previous year

Data Selection


All the above baseline windows exclude some of the days from the from the baseline window so as to neutralize the effect of certain exceptionaldays/events on the estimation of baseline.

Some of the common exclusion rules include:

• Excluding days with DR events.

• Excluding days with outages.

• Excluding days with extreme weather.

• Excluding days with the highest or lowest loads.

Data Selection


Baseline Estimation Methodology -

Example

Select 10 most recent non-event weekday Event days are non-denied settlement days

Exclude the following day-types: National and State holidays on which industry is closed

Weekend Days (Saturdays and Sundays)

Strikes or any other special event

Demand Response event days

Replace excluded days with next valid day

Final Weekday CBL Basis Window should contain 10 days

Actual Load data for these 10 days to be used to calculate CBL of consumer

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

1 (D-10) 2 (D-9) 3 (D-8) 4 (D-7) 5 6

7 8 9 (D-6) 10 11 (D-5) 12 13

14 15 (D-4) 16 (D-3) 17 18 19 20

21 22 (D-2) 23 24 25 26 (D-1) 27

28 29 30

Previous Event Days Base Line Days Current Event Day Weekends


Data Selection – Exception Days

50006000700080009000

10000110001200013000

00:0

0

00:5

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02:4

8

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MW

Hours

Exception Day : 14th Jan 2015 ( Kite festival in Gujrat, Makar Sankranti)

GEB Dmd 13-01-15 GEB Dmd 14-01-15 GEB Dmd 15-01-15

26000

28000

30000

32000

34000

36000

38000

40000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

Hours

WR - Load Curve Holi Day 06.03.2015

4TH MARCH 15 5TH MARCH 15

6TH MARCH 15 7TH MARCH 15

3200034000360003800040000420004400046000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

Hours

Diwali Effect on WR Demand met: 23rd Oct 2014

20-10-2014 21-10-2014 22-10-2014

23-10-2014 24-10-2014 25-10-2014

260002800030000320003400036000380004000042000

00:0

0

00:5

6

01:5

2

02:4

8

03:4

4

04:4

0

05:3

6

06:3

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8

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0

MW

Hours

WR Demand on 25th, 26th and 27th Jan 2015

WRDMD 25-01-15 WRDMD 26-01-15 WRDMD 27-01-15


Methods adapted

• Averaging method calculates for each hour/half-hour of the day, the average of the load at that hour over the included days.

• Regression method calculates load using linear regression model from the included days on weather and other variables, usually with separate regression coefficients by hour of the day.

• Maximum value method takes the maximum of the loads in the included period.

• Rolling average method uses the updated unadjusted baseline for an operating day which is equal to 0.9 times the prior unadjusted baseline plus 0.1 times the most recent included day.

Estimation


In averaging or “X of Y” or “X in Y” method, the baseline for each interval of curtailment day is calculated as the simple average, across all the days chosen by the data selection criteria. The most common “X in Y” baselines include:

10 in 10 - Out of 10 selected days, highest 10 day’s data is taken and the baseline is calculated.




Estimation – Averaging Method


Sample – Utilities in US

ISOBaseline

typeAverage of Out of

CAISO,

MISO10 in 10

10 most recent

weekdays

10 most recent

weekdays

ERCOT Mid 8 of 10

10 most recent

weekdays,

dropping highest

and lowest kWh

days

10 most recent

weekdays

NYISO 5-of-105 highest kWh

days

10 most recent

weekdays

PJM 4-of-5 4 highest kWh

days

5 most recent

weekdays

ISO Baseline

type

Average of Out Of

CAISO,

MISO

10-in-10 4 most recent

weekend days

4 most recent

weekend days

ERCOT Mid 8-of-10 10 most recent

weekend days,

dropping highest

and lowest kWh

days

10 most

recent

weekend days

NYISO 5-of-10 2 highest kWh

weekend days

3 most recent

weekend days

PJM 4-of-5 2 highest kWh

weekend days

3 most recent

weekend days

Weekdays

Weekends

ERCOT’s (Electric Reliability Council of Texas)

NYISO’s (New York ISO)

CAISO (California ISO)

MISO (Midcontinent ISO)

Jaipur Vidyut Vitran Nigam Limited

(JVVNL), for its DR project which was from

July 2013 March 2014, used “5 in 10”

method for estimation of baseline.


Regression model consisting of daily energy equation

• Include consumer’s daily consumption (kWh) as dependent variable

• 24 hourly energy fraction equations.

The explanatory variables in model include calendar variables

• Day of the week, holidays

• Weather variables

• Daylight variable

─ Daylight saving time

─ Time of sunrise and sunset

Estimation - Regression Method


The average method is not weather sensitive and does not depends on occupancy level, due to which it requires adjustments in baseline

Adjustment factor is evaluated to align the baseline with observed conditions of the event day.

• Temperature, humidity; calendar data, Sunrise/Sunset time and event day operating conditions.

Adjustment factors are either additive or scalar (multiplicative).

• Additive - Shifts the curve up or down by a constant amount

• Scalar - Scales the shape by a constant amount

Adjustment


TPDDL - Auto-DR project

•5 out of 10 baseline model with morning adjustment baseline.

In “5 in 10” method, the highest 5 days load out of previous 10 days was averaged excluding the weekend, holidays.

• Morning adjustment baseline, additive factor was used.

5 in 10 with morning adjustment was found to be more close to the actual load curve.

Adjustment – Indian Experience


Energy consumption data of various consumer categories in Mumbai was used to evaluate various unadjusted baselines through averaging method.

These baselines were plotted along with one of the actual days from the data sample which was not used for baseline estimation in order to investigate the performance of these baselines in predicting the load on a DR event day.

Comparison of various Baseline

Techniques- Unadjusted; Mumbai Data


Residential Colony

0

200

400

600

800

1000

1200

1400

1600

Lo

ad

(k

Wh

)

Hours

Load Curve for Residential Colony

(Sep - Oct 2012)

ACTUAL 10 IN 10 7 IN 10 5 IN 10 3 IN 10


Hospital

100

150

200

250

300

350

400

450

Lo

ad

(k

Wh

)

Hours

Load Curve for Hospital (Sep- Oct 2012)

ACTUAL 10 IN 10 7 IN 10 5 IN 10 3 IN 10


Hotel

600

800

1000

1200

1400

1600

kW

h

Load Curve for Hotel - A (Sep - Oct 2012)

Actual 10 in 10 7 in 10 5 in 10 3 in 10


Similar analysis carried out for other sectors• IT Sector

• Commercial establishments

• Apartments

• Manufacturing Setups

• Shopping Centre

The analysis reveals that there cannot be one baseline that is a best fit for all customers. Although, “10 in 10” baseline shows best values for accuracy, bias and variability metrics for most of the customer categories, it needs further analysis before declaring these values conclusive as the effects of temperature and other factors have not been taken into account

Analysis for other sectors


Barriers• Cost (varies depending on extent of EM&V)

• Available Personnel

• Access to energy data

• Understanding and organizing energy data: kWh vs. kW Demand;

• Rate structures;

• Meters and corresponding equipment/facilities

EM&V approaches/methods range from simple and direct to complex and indirect• Sometimes combined

• More complex methods generally require more detailed data and higher cost

Summary


Reports are available in http://dsm-india.org

Further Reading

http://dsm-india.org/


Thank You!

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