A Dynamic Market Mechanism for Integration of Renewables

A Dynamic Market Mechanism for Integration of Renewables and Demand

ResponseAnuradha Annaswamy

[email protected]

1

Panel on Market-based Approaches for Demand Response IEEE Power and Energy Society General Meeting 2015

Denver, CO07/28/2015

Outline• Demand Response (DR) – Importance, potential,

and current practice

• An emerging taxonomy based on the constraints of DR-devices– Buckets, Batteries, Bakeries

• DR real-time market integration – role in a Dynamic Market Mechanism

• Case study (IEEE-118 bus, Polish grid)






Importance of Demand Response

Generation = Demand

⇒ 𝐺𝐺 + 𝐷𝐷 = 0

𝐺𝐺 + 𝐺𝐺𝑟𝑟 + 𝐷𝐷 + 𝐷𝐷𝑑𝑑 = 0

Renewables DemandResponse

Usual practice:

Practice with renewables:

Source: “Vision for Smart Grid Control: 2030 and Beyond,” (Eds. M. Amin, A.M. Annaswamy, C. DeMarco, and T. Samad), IEEE Standards Publication, June 2013.

Importance of Demand Response• Demand response refers to changes in electric usage by

demand-side resources from their normal consumption patterns in response to:– Changes in the price of electricity over time, or– Incentive payments designed to induce lower electricity use at times

of high wholesale market prices or when system reliability is jeopardized

• Demand response benefits:– Peak reduction– Lower electricity prices at

the wholesale market– Higher system reliability– Reduced need for reserves

Figure: EIA

DR Program Types and Potentials

Incentive-based Time-based

Source: Federal Energy Regulatory Commission. “2012 Assessment of Demand Response and Advanced Metering”. Staff Report, 2012.

DR in Today’s Wholesale Markets*• ISO-NE:

– On call response if needed settled through Forward Capacity Market (FCM)– Not allowed to bid in Day-Ahead Market (DAM) or Real-Time Market (RTM)

• PJM:– Allowed to bid in DAM and Regulation Market (REGM), not allowed in RTM

• NY-ISO:– On call response if needed settled

through FCM– Allowed to bid in FRM, DAM, REGM,

not allowed in RTM

• CAISO:– Allowed to bid in FRM, DAM, REGM

* Currently in a state of flux due to halt of FERC order 745

Figure source: Monitoring Analytics, “State of the Market Report for PJM”, August 2014

Wholesale Markets and the Role of DR

power dispatch (MW) and price ($)

ISO

ensure grid feasibilitycompute optimal

price

Cost curves ($/MW)

Market Players

GenCosminimize

cost

ConCos

price ($)

Forward Capacity Markets

Forward Reserve Markets

Day Ahead

Markets

Real Time

Markets

ISO-NE

NY-ISO

Regulation Markets

PJM PJM

NY-ISO

CAISO CAISO

Limitations of Today’s Implementations• In RTM: Consumers are price-takers, not price-setters• ISOs are not relying on response of demand to real-time prices

• Much needed are mechanisms to ensure response to real-time price signals

Outline

• Demand Response (DR) – Importance, potential, and current practice

• A taxonomy of DR-devices– Buckets, Batteries, Bakeries



• Residential level– Single household– Aggregated level

Demand Response: Type of Consumers• Industrial

– Food industry – refrigeration 16%

– Chemical sector – metal and paper

– Data centers – 1.3% of total energy

37%

34%

26%

3%

ResidentialCommercialIndustrialPHEVs

• Demand characterization

• Commercial

Source: EIA

Demand Response Models

Consumer Decision Making

Adjustable Demand

Value function 𝑣𝑣(𝜆𝜆, 𝜇𝜇,𝜃𝜃):• Economic component 𝜆𝜆• Comfort component 𝜇𝜇• Environment component 𝜃𝜃

Device Constraints

Risk aversion

Value function

Device characteristics:• Curtailable → Bucket

• Interruptible → Battery

• Deferrable → Bakery

𝑣𝑣

𝑟𝑟(𝑣𝑣)

𝑟𝑟(𝑣𝑣)𝑣𝑣(λ, 𝜇𝜇,𝜃𝜃)

High

Low

Dynamic Characteristics of Loads• Defer in inherent magnitude, run-time and integral constraints

Water heater

HVAC

WasherEV battery

Chemical processRefrigerator

Swimming pool filtering

Home battery system

Battery

BakeryBucket

• Most flexible type of demand (can consume or supply power)

• Example: energy storage units, HVAC

• Have a deadline for achieving a fully charged state

• Example: Plug-in Hybrid Electric Vehicle

• Energy must be consumed in an uninterrupted stretch

• Example: industrial production cycles

Buckets PDc

Batteries PDt

Bakeries PDk

Source: M. K. Petersen, K. Edlund, L. H. Hansen, J. Bendtsen, and J. Stoustrup, “A Taxonomy for Modeling Flexibility and a Computationally Efficient Algorithm for Dispatch in Smart Grids,” 2013 American Control Conference, 2013.

A New Demand Response Taxonomy

The BBB Configuration






Our Approach• A Dynamic Market Mechanism (DMM) for RTM –

consumers are price-setters, not price-takers

• The DMM is an alternative to the current wholesale electricity market clearing process.

• Rather than submitting one-time bids, generators and consumers repeatedly exchange information with each other and with the ISO to negotiate generation, consumption, and prices.

• Allows direction integration of DR (ex. BBB) into the DMM

Nodal Power Balance

Line Capacity

Generation/Demand Power Limits

Generation Rates of ChangeDemand Energy Limits

Note:The index k corresponds with the market clearing instance

Source: J. Knudsen, J. Hansen, and A.M. Annaswamy “A Scaleable Dynamic Market Mechanism for Integration of Renewables and Demand-Side Management,” IEEE Trans. Control Systems Technology ( b itt d) 2014

OPF Formulation (including BBB)

The index K corresponds with the negotiation iterations

DMM structure• Iterative negotiations over a wide area grid

1k k kx x x+ = + ∆1k k kλ λ λ+ = + ∆

𝑥𝑥: states of players and ISO𝜆𝜆: Lagrange multiplier (LMP)

𝑥𝑥𝑘𝑘 =

𝑃𝑃𝐺𝐺𝐺𝐺𝑘𝑘

𝑃𝑃𝐺𝐺𝑟𝑟𝑘𝑘

𝑃𝑃𝐷𝐷𝑟𝑟𝑘𝑘

𝛿𝛿𝑘𝑘

Conventional generation

Renewable generation

Demand response

Voltage angles

ISO


price

Suggested bids (MW)

Market Players

GenCosminimize

cost

ConCosmaximize

utility

Suggested price ($)

power dispatch (MW) and price ($)

ISO


price

Cost curves ($/MW)

Market Players

GenCosminimize

cost

ConCos

price ($)

• Challenges addressed:– Computation time– Most information must be kept private– Stability

Economic Dispatch Today

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Periodic with a regular interval.Single iteration process.Centralized computation.

Economic dispatch interval Time

Inflexible load

Generationset-points

Automaticgenerationcontrol

Collect cost curves Find optimal dispatch Communicate set-points

ISO

Flexibledemand

Generation

Our Solution: Dynamic Market Mechanism (DMM)21

Economic dispatch interval Time


Most recent information is included. Individual constraints remain private.

Benefits when addressing:o Fuel uncertainty

• Wind• Solar• Natural gas

o Change in operating conditionsof components

• Saturation limits• Protection tripping• Emergency conditions

o Dynamic price response• Lower real-time prices

before dispatching• Close-loop price control

Inflexible load

Start negotiations

Negotiate and converge to an optimal solution

Implement set-points


Sufficiently long period for

convergence

DMM and shorter dispatch interval

22

Implement dispatch on shorter intervals.

Opportunities for addressing:o Significant and unpredicted

penetration of renewableso Non-zero mean volatility of

renewable generationo High regulation requirements

in presence of renewables

Time


Economic dispatch interval


Inflexible load

Start negotiations

Negotiate and converge to an optimal solution

Implement set-pointsSufficiently long

period for convergence

Integrated DMM (economic dispatch + AGC)

23

Energy Market

Regulation Market

AutomaticGenerationControl

Assumption of magnitude and time-scale separation between OPF and AGC.

Large penetration of intermittent energy represents a challenge.

Conventional architecture

Energy Market

Regulation Market

AutomaticGenerationControl

Aggregated feedback from AGC

Simultaneous decisions at both markets.

Proposed approach

4

Time-scales Introduced by DMM

24

DMM Negotiation

s

AGCUpdates

DMM Market

Clearing

OPF Market Clearing

Existing time-scales

New time-scales

Feedback from AGC to DMM

25

• Frequency measurements averaged over 𝑡𝑡𝑚𝑚−2, 𝑡𝑡𝑚𝑚−1 are used in negotiations during 𝑡𝑡𝑚𝑚−1, 𝑡𝑡𝑚𝑚 , which take effect during the operating period 𝑡𝑡𝑚𝑚, 𝑡𝑡𝑚𝑚+1 .

Negotiations OperationMeasurements

𝑡𝑡𝑚𝑚−1𝑡𝑡𝑚𝑚−2 𝑡𝑡𝑚𝑚 𝑡𝑡𝑚𝑚+1

DMM Iterates Final FormApproximated Hessian• Increases rate of convergence• Preserves privacy

Distributed gradient updates• A single cost/utility bid per iteration• Preserves privacy

State and price update equations

𝑥𝑥𝑘𝑘+1 = 𝑥𝑥𝑘𝑘 − 𝛼𝛼 � ��𝐻𝐻−1 � 𝛻𝛻𝛻𝛻 𝑥𝑥𝑘𝑘 + 𝑁𝑁�̂�𝜆𝑘𝑘+1

𝜆𝜆𝑘𝑘+1 = �̂�𝜆𝑘𝑘+1 − 𝛼𝛼 � 𝑐𝑐 � ℎ′ 𝑥𝑥𝑘𝑘

Modified power balance• Integrates real-time market and AGC






Modified IEEE 118 Bus Test CaseBus consists of:• 45 conventional generators• 9 renewable generators (30% penetration)• 7 consumers (10% penetration)• 186 transmission lines

Implications of Our Architecture

TRANSACTIVE ARCHITECTURE

Conven Gen. RenewablesDemand Response Units

12. + + + + + +

. . . .

.

. . . .

.

. . . .

.

1. + + + + + +

2. + + + + + +

0 500 1000 15001400

1600

1800

2000

2200

2400

2600

Time [s]

Gen

erat

ion

[MW

]

0 500 1000 1500

245250255260265270275

Flex

ible

Dem

and

[MW

]

DMM Market Clearings (50 clearings)30 s

Conventional Generation

𝑃𝑃𝐺𝐺𝐺𝐺𝑚𝑚

Renewable Generation

𝑃𝑃𝐺𝐺𝑟𝑟𝑚𝑚

Flexible Demand𝑃𝑃𝐷𝐷𝑟𝑟𝑚𝑚

Negotiations over a single 30 second period

1110 1115 1120 1125 1130 1135 114022

24

26

28

30

32

34

36

Time [s]

Flex

ible

Con

sum

ptio

n [M

W]

1110 1115 1120 1125 1130 1135 11400

50

100

150

200

250

300

350

400

Time [s]Co

nven

tiona

l Gen

erat

ion

[MW

]

𝑥𝑥𝑘𝑘 =

𝑷𝑷𝑮𝑮𝑮𝑮𝒌𝒌

𝑃𝑃𝐺𝐺𝑟𝑟𝑘𝑘

𝑷𝑷𝑫𝑫𝒓𝒓𝒌𝒌

𝛿𝛿𝑘𝑘

Conventional generation

Renewable generation

Demand response

Voltage angles

Actual Generation and Demand (AGC time-scale)

0 500 1000 15001400

1600

1800

2000

2200

2400

2600

Time [s]

Gen

erat

ion

[MW

]

0 500 1000 1500

245250255260265270275

Flex

ible

Dem

and

[MW

]

Conventional Generation

𝑃𝑃𝐺𝐺𝐺𝐺𝐾𝐾

Renewable Generation

𝑃𝑃𝐺𝐺𝑟𝑟𝐾𝐾

Flexible Demand𝑃𝑃𝐷𝐷𝑟𝑟𝐾𝐾

Impact on Area Control Error

• Peaks less severe using DMM than OPF• Adding feedback shifts ACE closer to zero

Summary of DMM Benefits

1. Allows flexible consumers to act as price-setters at the real-time market (and not only to respond to price)

2. Admits the most recent weather predictions in market clearing (every 30 seconds)

3. Enables feedback from AGC layer into the market layer, reducing regulation requirements

4. Preserves privacy of market players’ sensitive information– e.g. cost curves, generation/consumption bounds

Is this scaleable?

Polish 3120 Bus Test System

Data source:Matpower

Figure source:www-pub.iaea.org

The system consists of:• 3120 buses • 3693 transmission lines with line

capacities of 250 MW• 505 generators with linear cost curves

and capacities in the range 10MW-150MW

• Extension to renewable energy resources and demand response is straight forward.

Single DMM Clearing on Polish 3120 System

30ms per iteration𝑡𝑡2 − 𝑡𝑡1=30 s

𝑡𝑡1 𝑡𝑡2

Transmission line flows Power generation

Locational marginal prices

Generation and price increase at bus 3010 once three transmission lines reach their limits.

Line 59congestion

Lines 31,32congestion

Number of iterations to convergenceMatpower test cases

Demonstrates the scalability of the DMM

The convergence time depends on:• Step size • Congestion• Cost curves

Number of iterations does not increase with decision variables

Key Open Questions

• Closer to real-time DR integration:– Reliability and security– Communication infrastructure vs. scalability– Direct load control vs. transactive load control– Sensitivity of customers to the price

Documents

A Dynamic Market Mechanism for Integration of Renewables