1
A Dynamic Market Mechanism for
Integration of Renewables and Demand
Response
Anuradha Annaswamy
[email protected]
Panel on Market-based Approaches for Demand Response
IEEE Power and Energy Society General Meeting 2015
Denver, CO
07/28/2015
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)
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
Usual practice:
Generation = Demand
⇒
Practice with renewables:
𝐺𝐺 + 𝐷𝐷 = 0
𝐺𝐺 + 𝐺𝐺𝑟𝑟 + 𝐷𝐷 + 𝐷𝐷 𝑑𝑑 = 0
Renewables
Demand
Response
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
Cost curves ($/MW)
ConCos
Market
Players
ISO
GenCos
ensure grid feasibility
compute optimal
price
minimize
cost
price ($)
power dispatch (MW)
and price ($)
ISO-NE
Forward
Capacity
Markets
CAISO
CAISO
NY-ISO
NY-ISO
PJM
PJM
Forward
Reserve
Markets
Day
Ahead
Markets
Real
Time
Markets
Regulation
Markets
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
• DR real-time market integration
– role in a Dynamic Market Mechanism
• Case study (IEEE-118 bus, Polish grid)
Demand Response: Type of Consumers
• Demand characterization
3%
26%
37%
Residential
Commercial
Industrial
PHEVs
34%
• Industrial
– Food industry – refrigeration
16%
– Chemical sector – metal and
paper
– Data centers – 1.3% of total
energy
• Commercial
• Residential level
– Single household
– Aggregated level
Source: EIA
Demand Response Models
𝑣𝑣(λ, 𝜇𝜇, 𝜃𝜃)
Low
Risk
aversion
𝑟𝑟(𝑣𝑣)
Consumer Decision
Making
Device Constraints
Device characteristics:
• Curtailable → Bucket
𝑟𝑟(𝑣𝑣)
• Interruptible → Battery
High
Value function
𝑣𝑣
Value function 𝑣𝑣(𝜆𝜆, 𝜇𝜇, 𝜃𝜃):
• Economic component 𝜆𝜆
• Comfort component 𝜇𝜇
• Environment component 𝜃𝜃
• Deferrable → Bakery
Adjustable
Demand
Dynamic Characteristics of Loads
• Defer in inherent magnitude, run-time and integral constraints
Bakery
Bucket
Home battery system
HVAC
Refrigerator
Chemical process
Battery
EV battery
Water heater
Swimming pool
filtering
Washer
A New Demand Response Taxonomy
Buckets PDc
•
•
Most flexible type of demand (can consume
or supply power)
Example: energy storage units, HVAC
Batteries PDt
•
•
Have a deadline for achieving a fully charged
state
Example: Plug-in Hybrid Electric Vehicle
Bakeries PDk
•
•
Energy must be consumed in an uninterrupted
stretch
Example: industrial production cycles
The BBB Configuration
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.
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)
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
OPF Formulation (including BBB)
Note:
The index k corresponds with
the market clearing instance
The index K corresponds with
the negotiation iterations
Nodal Power
Balance
Line Capacity
Generation/Deman
d Power Limits
Generation
Rates of
Change
Demand
Energy Limits
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
DMM structure
• Iterative negotiations over a wide area grid
Cost curves
Suggested
bids($/MW)
(MW)
k +1
Market
Market Players
Players
ConCos
GenCos GenCos
ConCos
maximize
minimize
cost
minimize
utility
cost
ISO
ensure grid feasibility
compute optimal
price
power dispatch (MW)
and price ($)
price ($) Suggested
price ($)
k
x = x + ∆x
k
1
λ k +=
λ k + ∆λ k
𝑥𝑥: states of players and ISO
𝜆𝜆: Lagrange multiplier (LMP)
𝑘𝑘
𝑃𝑃𝐺𝐺𝐺𝐺
𝑘𝑘
𝑘𝑘 = 𝑃𝑃𝐺𝐺𝐺𝐺
𝑥𝑥
𝑘𝑘
𝑃𝑃𝐷𝐷𝑟𝑟
𝛿𝛿 𝑘𝑘
• Challenges addressed:
– Computation time
– Most information must be kept private
– Stability
Conventional generation
Renewable generation
Demand response
Voltage angles
20
Collect cost curves
Economic Dispatch Today
Find optimal dispatch
Communicate set-points
ISO
Generation
Periodic with a regular interval.
Single iteration process.
Centralized computation.
Flexible
demand
Inflexible
load
Automatic
generation
control
Generation
set-points
Economic dispatch interval
Time
21
Our Solution: Dynamic Market Mechanism (DMM)
Negotiate and converge to an optimal solution
Most recent information is included.
Individual constraints remain private.
Start negotiations
Sufficiently long
period for
convergence
Implement set-points
Inflexible
load
Automatic
generation
control
Generation
set-points
Economic dispatch interval
Benefits when addressing:
o Fuel uncertainty
• Wind
• Solar
• Natural gas
o Change in operating conditions
of components
• Saturation limits
• Protection tripping
• Emergency conditions
o Dynamic price response
• Lower real-time prices
before dispatching
• Close-loop price control
Time
22
DMM and shorter dispatch interval
Negotiate and converge to an optimal solution
Implement dispatch on shorter
intervals.
Start negotiations
Inflexible
load
Sufficiently long
period for
convergence
Opportunities for addressing:
o Significant and unpredicted
penetration of renewables
o Non-zero mean volatility of
renewable generation
o High regulation requirements
in presence of renewables
Implement set-points
Automatic
generation
control
Generation
set-points
Economic dispatch interval
Time
23
Integrated DMM (economic dispatch + AGC)
Conventional architecture
Energy
Market
Proposed approach
Regulation
Market
Energy
Market
Regulation
Market
Automatic
Generation
Control
Automatic
Generation
Control
Assumption of magnitude and time-scale
separation between OPF and AGC.
Aggregated feedback from AGC
Large penetration of intermittent energy
represents a challenge.
Simultaneous decisions at both markets.
24
Time-scales Introduced by DMM
DMM
Negotiation
s
AGC
Updates
4
DMM
Market
Clearing
OPF Market
Clearing
Existing timescales
New time-scales
25
Feedback from AGC to DMM
𝑡𝑡𝑚𝑚−2
Measurements
𝑡𝑡𝑚𝑚−1
Negotiations
𝑡𝑡𝑚𝑚
Operation
𝑡𝑡𝑚𝑚+1
• 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 .
DMM Iterates Final Form
Approximated Hessian
• Increases rate of convergence
• Preserves privacy
State and price
update equations
Distributed gradient updates
• A single cost/utility bid per iteration
• Preserves privacy
� −1 � 𝛻𝛻𝛻𝛻 𝑥𝑥 𝑘𝑘 + 𝑁𝑁𝜆𝜆̂ 𝑘𝑘+1
𝑥𝑥 𝑘𝑘+1 = 𝑥𝑥 𝑘𝑘 − 𝛼𝛼 � 𝐻𝐻
𝜆𝜆𝑘𝑘+1 = 𝜆𝜆̂ 𝑘𝑘+1 − 𝛼𝛼 � 𝑐𝑐 � ℎ′ 𝑥𝑥 𝑘𝑘
Modified power balance
• Integrates real-time market and AGC
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)
Modified IEEE 118 Bus Test Case
Bus consists of:
• 45 conventional generators
• 9 renewable generators (30% penetration)
• 7 consumers (10% penetration)
• 186 transmission lines
Implications of Our Architecture
Conven Gen.
Renewables
Demand Response Units
TRANSACTIVE
ARCHITECTURE
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DMM
Market Clearings (50 clearings)
2600
30 s
Flexible Demand [MW]
Generation [MW]
2400
275
270
265
260
255
250
245
2200
2000
1800
1600
1400
0
Conventional
Generation
𝑚𝑚
𝑃𝑃𝐺𝐺𝐺𝐺
500
Time [s]
Renewable
Generation
𝑚𝑚
𝑃𝑃𝐺𝐺𝐺𝐺
1000
1500
Flexible
Demand
𝑚𝑚
𝑃𝑃𝐷𝐷𝐷𝐷
36
400
Conventional Generation [MW]
Flexible Consumption [MW]
Negotiations over a single 30 second period
34
32
30
28
26
24
22
1110
1115
1120
1125
Time [s]
1130
1135
1140
𝑷𝑷𝒌𝒌𝑮𝑮𝑮𝑮
𝑘𝑘
𝑃𝑃𝐺𝐺𝐺𝐺
𝑘𝑘
𝑥𝑥 =
𝑷𝑷𝒌𝒌𝑫𝑫𝒓𝒓
𝛿𝛿 𝑘𝑘
350
300
250
200
150
100
50
0
1110
1115
1120
Conventional generation
Renewable generation
Demand response
Voltage angles
1125
Time [s]
1130
1135
1140
Actual Generation and Demand (AGC time-scale)
2600
Flexible Demand [MW]
Generation [MW]
2400
275
270
265
260
255
250
245
2200
2000
1800
1600
1400
0
Conventional
Generation
𝐾𝐾
𝑃𝑃𝐺𝐺𝐺𝐺
500
Time [s]
Renewable
Generation
𝐾𝐾
𝑃𝑃𝐺𝐺𝐺𝐺
1000
1500
Flexible
Demand
𝐾𝐾
𝑃𝑃𝐷𝐷𝐷𝐷
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
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 10MW150MW
• Extension to renewable energy
resources and demand response is
straight forward.
Data source:
Matpower
Figure source:
www-pub.iaea.org
Single DMM Clearing on Polish 3120 System
Transmission line flows
Power generation
Line 59
congestion
Lines 31,32
congestion
𝑡𝑡1
𝑡𝑡2 − 𝑡𝑡1 =30 s
30ms per iteration
Locational marginal prices
𝑡𝑡2
Generation and price increase at bus 3010
once three transmission lines reach their limits.
Number of iterations to convergence
Matpower test cases
Number of iterations does not
increase with decision variables
The convergence time depends on:
• Step size
• Congestion
Demonstrates the scalability of the DMM
• Cost curves
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