transactive control: a novel technology for smart grids...transactive control: a novel technology...
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Transactive Control: A Novel Technology for Smart Grids
Anuradha Annaswamy Active-adaptive Control Laboratory
Department of Mechanical Engineering
Massachusetts Institute of Technology
1 Depart of Mechanical Engineering Seminar Series, WPI, October 9, 2013
Outline
• A Smart Grid – A Paradigm Shift
• Transactive Control
– Dynamic Market Mechanisms
– Integrated Secondary and Primary Control
• Case Studies
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Paradigm Shift: From Current to Smart Grids
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Main features: • Renewable energy
resources • Demand Response • Storage • Advanced Metering
Infrastructure
Conventional Grid
Utilities
Distribution
Consumers
Distribution
Industrial
Consumers
Transmission
Network
Operations
Network
Operations
Smart Meters
Transmission
Distribution
Smart Devices
DERs
Demand Response
Utilities
Microgrid
Industrial
Consumers Distribution
Consumers
Distributed
Community
Storage
Energy providers
Gen
era
tion
Facilitie
s
Increasing supply-demand gap
Environmental concerns
Aging infrastructures
Anuradha Annaswamy, Transactive Control
• To maintain power balance in the system.
• To ensure that operating limits are maintained
– Generators limit
– Tie-lines limit
• To ensure that the system frequency is constant (at 50 Hz or 60Hz).
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• To achieve the above with renewable energy despite intermittency & uncertainty
• To ensure affordable power
Smart Grid Control
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GRID CONTROL: CURRENT PRACTICE
Anuradha Annaswamy, Transactive Control
TRANSACTIVE CONTROL: The connecting entity
Anuradha Annaswamy, Transactive Control
THE OVERALL VISION
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Vision for Smart Grid Control: 2030 and Beyond:
Reference Model and Roadmap (Eds. M. Amin, A.M.
Annaswamy, C. DeMarco, and T. Samad), IEEE
Standards Publication, November 2013.
Distributed Decision and Control
• Primary control – Immediate (automatic) action to sudden change of load.
– For example, reaction to frequency change.
• Secondary control – Restore system frequency,
– Restore tie-line capacities to the scheduled value, and,
– Make the areas absorb their own load.
• Tertiary control – Make sure that the units are scheduled in the most
economical way.
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Transactive control: An Emerging Paradigm*
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The use of dynamic market mechanism to send an incentive signal and receive a feedback signal within the power system’s node structure
• Incentive Signal: Dynamic Pricing
• Feedback Signal: Adjustable Demand
* Hammerstorm et al., “Standardization of a Hierarchical Transactive Control System”
Anuradha Annaswamy, Transactive Control
Transactive Control: Example
• Pacific Northwest Demonstration Project
• 112 Households participating in 2009
• 60,000 households in an ongoing project (2010-2015)
• Spans several states
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Courtesy of Olympic Peninsula Project, IBM TIS: Transactive Incentive Signal
TFS: Transactive Feedback Signal
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Transactive control:
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The use of dynamic market mechanism to send an incentive signal and receive a feedback signal within the power system’s node structure
Transactive Control Control architecture that coordinates Market Transactions Active Control at the AGC level with Regulation Demand Response
• Incentive Signal: Ex. Dynamic Pricing • Feedback Signal:
• Adjustable Demand (Market Level) • (Price Responsive, and Regulation Responsive)
• Area Control Error (Secondary Level) • Governor Control (Primary Level)
Our Definition
Anuradha Annaswamy, Transactive Control
Transactive Control Framework*
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SECONDARY (FREQUENCY) CONTROL
PRIMARY (POWER) CONTROL
UNIT- LEVEL
AREA-LEVEL
~10secs
~sec
Set-points Area Control Error (ACE)
Set-points
Incentive Signal Feedback Signal
Generation
Demand
TRANSACTIVE CONTROL
Generation
Demand
Generation
Demand
…
~5 mins
Market Transactions
* A. Kiani, A.M. Annaswamy, and T. Samad, “A Hierarchical Transactive Control Architecture for Renewables Integration in Smart Grids.” HYCON Workshop, Brussels, 2012.
Anuradha Annaswamy, Transactive Control
Primary Level
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Anuradha Annaswamy, Transactive Control
zp =PG
PL
é
ë
êê
ù
û
úú
u[k]=wref [k]
PLref [k]
é
ë
êê
ù
û
úú
f : Tie- Line flow
D : uncertainty
0 =Cxpss[k]+Dzpss[k]+fpss[k]+ Dfpss
0 = Axpss[k]+Bzpss[k]+Fu[k]+ D pss
K K+1 k k+1
t
- time scale t
Steady state
xGss
Primary
Secondary
Tertiary
Secondary Level
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Anuradha Annaswamy, Transactive Control
xs[k +1] = xs[k]+Bsus[k]+CsDs[k]
xs[k] :wGssus[k] :u[k+1]-u[k]
Ds[k] :
es = xs - xt
Secondary Level Dynamics Controller xt xses
: Area Control Error (ACE)
Goal: xs ® xt a reference signal set by the tertiary level
Uncertainty in generation, load, and tie-line flow
0 =Cxpss[k]+Dzpss[k]+fpss[k]+ Dfpss
0 = Axpss[k]+Bzpss[k]+Fu[k]+ D pss
ß
Tertiary Level
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Anuradha Annaswamy, Transactive Control
Primary Level Dynamics Gain xt[K] xs[k]es[k]
Tertiary
How do we design the Tertiary Level?
Secondary level dynamics
Gain
Controller
Electricity Market
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• Centralized mechanism that facilitates trading of energy between buyers and sellers.
• The market operator conducts an auction market and schedules generators based on bids received.
• Determines a market clearing price (Locational Marginal Price (LMP)) and provides commitments and schedules based on security-constrained unit commitments
• Day-ahead (DA) Markets
• Real-time Markets (RTM)
Wholesale Market
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Wholesale Market: A Dynamic System
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Operating Day-1 Operating Day
DA Energy
Market
offer and bid
period closes
12
:00
DA Energy
Market
results
published
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:00
1
6:0
0
18
:00
Revising bids
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:00
ISO finalizes operating plan
for the next day
00
:00
00
:10
Power Delivery Time
Real Time
Energy
Market
clears
Real Time
Energy
Market
opens
Clear Day-ahead Market using Unit commitment and
Economic Dispatch
Day ahead reliability unit commitment
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Market Mechanisms - LMP
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ISO
Bids (MW-h, $) LMPi, Schedules
Generation
Demand
Node 1
Generation
Demand
Node 2
Generation
Demand
Node n
…
Nodes in New England, USA
Anuradha Annaswamy, Transactive Control
Real-time Market
Top Layer: A Dynamic Market Mechanism
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Generation (with Renewable Energy
Resources)
Consumers
1. Equilibrium under constant flux.
2. GenCos and ConCos adjust their power level using a recursive process.
3. Price is a Public Signal that guides all entities to adjust efficiently.
Suggested Price Suggested MW Generation
Substations
Suggested MW Consumption
Phase Angle
Suggested Price
Congestion Rent
Anuradha Annaswamy, Transactive Control
That is, if a generator observes a market price above the
marginal cost , will expand production until the
marginal cost of production equals the price.
Modeling of Generating Company • : market price ~ Locational Marginal Price
at market equilibrium.
• The cost function of each generators unit is
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𝜌
Anuradha Annaswamy, Transactive Control
i.e. Demand with a marginal benefit above the marginal
price will lead to an expansion in consumption until equilibrium is attained.
Modeling of Consumers Company
• : marginal benefit of
• Consumer utility function:
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jdP
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• Energy imbalance Ek at time k
• The pricing policy should depend on the degree of energy imbalance
Pricing Strategy
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A Dynamic Market Model
• The market participants need not have global market information.
• Convergence of the dynamic system to the equilibrium condition implies that the market reaches the condition of Nash equilibrium.
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Distributed
Gaming
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Dynamic Market Mechanism (contd.)
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• Quantifies effect of volatility and stability • Can help reduce reserve costs with wind uncertainty
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Xt xt[K]
xt
Interconnections
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Primary Level Dynamics Gain xt[K] xs[k]es[k]
Tertiary
Secondary level dynamics
Gain
Controller
The overall model, including the primary, secondary, and tertiary level dynamics at multiple time-scales
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Transactive Control: Lower Levels
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Transactive Control: Stability*
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Anuradha Annaswamy, Transactive Control
* A. Kiani and A.M. Annaswamy, “A Hierarchical Transactive Control Architecture for Renewables Integration in Smart Grids,“ CDC 2012, Maui, Hawaii.
Transactive control architecture
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The use of dynamic market mechanism to send an incentive signal and receive a feedback signal within the power system’s node structure
Transactive Control Control architecture that coordinates Market Transactions Active Control at the AGC level with Regulation Demand Response
• Incentive Signal: Ex. Dynamic Pricing • Feedback Signal:
• Adjustable Demand (Market Level) • (Price Responsive, and Regulation Responsive)
• Area Control Error (Secondary Level) • Governor Control (Primary Level)
Anuradha Annaswamy, Transactive Control
Simulation Results
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G
W
L1 L2
Area 2 Area 1
G
Pg2 Pg1 Parameters with following values: cg1 = 0.25; cg2 = 0.55; generator cost coefficients bg1 = 40.2; bg2 = 60; generator cost coefficients kg1 = 0.3; kg2 = 0.8; generator time constants cd1 = cd2 = 0.4; consumer utility coefficients bd1 = bd2 = 70; consumer cost coefficients kd1 = kd2 = 0.3; demand time constants k = 0:7; LMP time constant (market time constant)
• 4-bus network with two generator units at node 1 and wind at bus 2 (Pg1: Base-load; Pg2: Reserve)
• L1, L2: DR-Compatible demand
Anuradha Annaswamy, Transactive Control
Market Stability & Volatility
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Volatility: With increased demand-elasticity (kd)
Stability: With increased latency (kr)
Anuradha Annaswamy, Transactive Control
Simulation Results: Market Stability & Volatility
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Volatility: With increased demand-elasticity (kd)
Stability: With increased latency (kr)
Anuradha Annaswamy, Transactive Control
Simulation Results
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Wind Properties: : Actual Wind Power : Mean value of the projected wind. Current Market Practice : ARMA model of the actual wind power. With Transactive Control 5.3 Stability Analysis of Transactive Controller
G
2 1
4 3
Area 2 Area 1
PD1PD2
G G
L1L2
Figure 5.3: 4-bus system example for Transactive control
It follows that the equilibrium of (5.59)-(5.60) is asymptotically stable if and only if the
origin of (5.67)-(5.68) is asymptotically stable, see Theorem 5.1. Defining τ = t / ε , we can
represent (5.68) in the stretched τ -scale as
d yp(τ )
dτ= Dyp(τ ) + εD− 1C(A− BD− 1C) xp(τ )+
εD− 1CByp(τ ) + εφ p(τ ) + εD− 1C∆ p.
(5.69)
In order to evaluate the stability of the dynamics in the stretched time-scale of τ , we let ε
tend to zero in (5.69), which leads to the boundary-layer system, see Theorem 5.1 [ 102]
d yp(τ )
dτ= Dyp(τ ). (5.70)
Since D = − I , (5.70) is asymptotically stable, with y(τ ) tending to zero as τ → ∞ . There-
fore, it suffices to focus on the reduced system
dxp( t )
dt= (A− BD− 1C) xp( t ) + ∆ p. (5.71)
by setting yp( t ) to zero. From Assumption 5.5 and (5.66a), it follows that the origin of (5.71)
is asymptotically stable, see Section A.1.2 [ 102] . This establishes the stability of xp = 0 in
(5.59) and (5.60). It therefore follows that for any bounded u(k) = 0, the solutions of
(5.59)-(5.60) are globally bounded.
Step 2: Stability of the secondary level
Let x t [ K] ≡ 0 and consider the two lower levels defined by Eqs. (5.59)-(5.63). From
(5.26), Eq. (5.62) can be rewritten as
xs[ k + 1] = (As + CsEs) xs[ k] . (5.72)
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Simulation Results: Effect of Wind Uncertainty
Less reserve is required. Hierarchical coordination
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Simulation Results: IEEE 30 bus Case
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Reserve
Summary
• Transactive Control
– Dynamic Market Mechanisms
– Integrated Secondary and Primary Control
• Case Studies
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A 2013 Publication!
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Thank You!
Anuradha Annaswamy, Transactive Control