Adaptive Load Management
Adaptive Load Management
Jhi‐Young (J.Y.) Joo and Marija Ilić
Electric Energy Systems Group
Electrical and Computer Engineering
Carnegie Mellon University
This work is supported in part by Bosch.
Outline
™Adaptive load management (ALM)
™Adaptive load management (ALM)
ƒ Future electric energy systems DYMONDS and demand response
demand response
ƒ Motivation and main ideas
ƒ Schematics –
Schematics – information flow
information flow
ƒ Cost analysis
ƒ Future work and open questions
Future work and open questions
2
Future electric energy systems
Future electric energy systems
™Distributed optimization / decision‐making
ƒ Flexible & dynamic system components
ƒ Higher uncertainty
Artwork by Andrew Hsu, EESG
3
Why demand response?
Why demand response?
™Change in supply attributes
g
pp y
ƒ Distributed generation resources
™Higher uncertainty, intermittency
™Requires quick response of the system (higher ramping
™Requires quick response of the system (higher ramping rates)
™Higher cost of additional capacity
™Higher cost of additional capacity
ƒ Need for load shifting
™End‐users’ needs
ƒ Energy efficiency and lower energy cost
4
Elastic
demand with wind generation [2]
Elastic demand with wind generation elastic demand
wind
™System setup
™System setup
ƒ Demand responsive to hourly real‐time pricing of the system
Æ demand curves
ƒ Price reflecting the true Price reflecting the true
cost of the supply including cost of ramping rates i
t
Æ look‐ahead dispatch
IEEE 24‐bus Reliability Test System‐1996
5
Elastic demand with wind generation [2]
Elastic demand with wind generation ™If demand can respond p
rapidly to the system price, it can compensate for the
compensate for the high intermittency of renewable resources.
™What do we need in terms of system operation?
6
The main ideas of ALM
The main ideas of ALM
™ System’s perspective: What is the right information required to b l
balance the system with demand response?
th
t
ith d
d
?
ƒ Where do we see the value of DR in the system balancing mechanism?
™ End‐users’
End‐users perspective: Reflecting various end‐users
perspective: Reflecting various end‐users’ needs
needs and and
preferences
ƒ End‐users’ willingness to pay for energy service
™ Load aggregators’ role
ƒ Physical energy mediator between system/market and end‐users
ƒ Information exchange junction/processor between market and end‐
users
ƒ Value of aggregating different resources and risk management
7
Previous demand response scheme: Direct load control
™ One‐way flow of information
ƒ Load management conducted by utilities
ƒ Top‐down control
™ Exclusive contracts between supply and demand
™ Direct load control
ƒ Regardless of end‐users’ preferences
ƒ No access to market No access to market
information for end‐users
ƒ End‐users’ information invisible to system
8
Information flow of ALM
Tertiary layer
Bid function
b(λ)
Market price
Market price
λ
Secondary layer
Load aggregator I
Demand function
b(λI)
Load aggregator II
Load aggregator III
End‐user rate
λI
Primary layer
End‐user
9
Multi‐layered
ALM – end‐users
to LAs
Multi layered ALM end users to LAs
Tertiary level
Bid function
y(λ)
Market price
Market price
λ
Secondary level
Load aggregator I
Demand function
x(λI)
User rate
λI
Primary level
End‐user
Load aggregator II
Load aggregator III
Multi‐layered
Multi
layered ALM ALM – end
end‐users
users to LAs
to LAs
™ Obtaining individual demand function subject to temperature comfort level
∑ [λ
k0 + N
min
{xi }
k =k 0
where
LA
{(
[k ] ⋅ xi [k ] + Ti [k ] − Ti
) + (T [k ] − T ) }]
max 2
min 2
i
i
Ti [k + 1] = AiTi [k ] + Bi xi [k ]
subject to Ti min ≤ Ti [k ] ≤ Ti max
subject to for all k
for all xi [k ]
™ Obtain different optimal energy usage ’s for different temperature setpoint λ [k ]’ss to infer demand different temperature setpoint
to infer demand
functions 11
Demand function
Demand function
™ Mathematical model of price‐responsive loads ƒ Integrated into the system optimization
™ Includes information of eend‐users’ utility (benefit)
d use s u y (be e )
™ Function of end‐users’ willingness‐to‐pay
willingness
to pay ƒ With respect to electricity demand quantity
cost/price
($/MWh)
λ
demand
(function)
supply
λ*
P*
d (PD ) = aPD + b
quantity
(MWh)
Demand function (cont
Demand function (cont’d)
d)
™ How to obtain from individual temperature control
ƒ Calculate optimal energy usage by hours with a given electricity price
ƒ Perturb the given price by a certain percentage (e.g. ±20%) Perturb the given price by a certain percentage (e g ±20%)
and re‐calculate optimal energy usage with new prices
ƒ Curve‐fit price‐demand quantity pairs to identify the parameters of a demand function
parameters of a demand function
Unit price
(cents/kWh) Demand (kW) Cost analysis of ALM
Cost analysis of ALM
™ Problem setup
ob e setup
ƒ 10 end‐users with different temperature preferences
ƒ Optimizing energy usage over 24 O i ii
24
hours
™Hourly‐varying electricity price given (real‐time pricing)
™Outdoor weather temperature given
price (¢/kWh)
p
Hourly real-time pricing rates
14
12
10
8
6
4
2
0
1
3
5
7
9
11
13
hour
15
17
19
21
23
End­
End
user index
Temperature setpoints (⁰F)
1
68
75
2
70
77
3
72
75
4
74
79
5
75
80
6
64
75
7
63
77
8
72
79
9
72
77
10
73
81
14
Cost analysis of ALM (cont
Cost analysis of ALM
(cont’d)
d)
™Demand functions of end‐user
™Demand functions of end
user #1
#1
hour 14
50
40
30
20
10
0
hour 16
WTP (¢/kkWh)
50
40
30
20
10
0
hour 15
WTP (¢/kkWh)
WTP (¢//kWh)
50
40
30
20
10
0
50
40
30
20
10
0
0 0.1 0.2 0.3 0.4
0 0.1 0.2 0.3 0.4
0 0.1 0.2 0.3 0.4
0 0.1 0.2 0.3 0.4
energy usage (kWh)
energy usage (kWh)
energy usage (kWh)
gy
g (
)
energy usage (kWh)
gy
g (
)
hour 17
hour 18
50
40
30
20
10
0
hour 20
WTP (¢/kWh)
50
40
30
20
10
0
hour 19
WTP (¢/kkWh)
50
40
30
20
10
0
WTP (¢/kkWh)
WTP (¢/kkWh)
WTP (¢/kkWh)
hour 13
50
40
30
20
10
0
0 0.1 0.2 0.3 0.4
0 0.1 0.2 0.3 0.4
0 0.1 0.2 0.3 0.4
0 0.1 0.2 0.3 0.4
energy usage (kWh)
energy usage (kWh)
energy usage (kWh)
energy usage (kWh)
(
)
energy usage (kWh)
15
Cost analysis of ALM (cont
Cost analysis of ALM
(cont’d)
d)
™ Cost savings from “price‐
conscious” optimization
End‐users'
End
users energy costs
energy costs
(hourly RTP)
ƒ Current practice
: end‐users little concerned about hourly prices
b th l
i
ƒ Intelligent optimization with ALM
: optimization w r t time‐
: optimization w.r.t. time
varying price saves costs
ƒ 32% of cost savings (as a whole) in this case
temp+price optimization
p p
p
20
Cost ($/mo
onth)
™Introduction of real‐time pricing may end up in energy cost rise for some end‐users
d
temp. only optimization
15
10
5
0
1
2
3
4
5
6
7
8
9
10
End‐user index
16
Cost analysis of ALM (cont
Cost analysis of ALM
(cont’d)
d)
™ Does real‐time pricing p
g
save costs for all end‐
users?
fixed rate 1
fixed rate 2
2.5
2
cost (($/month)
ƒ Not
Not necessarily
necessarily
ƒ Different end‐users’ load profiles have different values
values.
ƒ Need to aggregate different end‐users’ loads in order to distribute cost
in order to distribute cost or benefit properly and maximize profit
Energy cost comparison with RTP
1.5
1
0.5
0
‐0.5
1
2
3
4
5
6
7
8
9
10
‐1
‐1.5
end‐user
end
user index
index
Fixed rate 1: 7.24 ¢/kWh
Fixed rate 2: 7.84 ¢/kWh
17
Open questions
Open questions
™ Who benefits from the cost savings? It depends on
ƒ Load aggregator’s portfolio in various energy markets
d
’
f l
k
™ Long‐term contracts, day‐ahead/real‐time markets, ancillary service markets, etc.
ƒ Load aggregator’s aggregation of available resources
™ Elastic demand, distributed energy resources (e.g. small‐scale renewables, electric vehicles) hi l )
ƒ Load aggregator’s contracts with end‐users
™ Real‐time pricing, time‐of‐use pricing, flat rates, interruptible loads, etc.
™ What is the smartest way to get end‐users’ demand preferences?
Wh t i th
t t
t
t d
’d
d
f
?
ƒ
ƒ
ƒ
ƒ
ƒ
Temperature settings control
Price response analysis Æ hands‐on experiment on end‐users
Typical load profile analysis
yp
p
y
Behavior analysis on energy usage
and so on…
18
References References
1) J.Y. Joo and M. Ilić, A Multi‐Layered Adaptive Load Management (ALM) System, IEEE PES Transmission and Distribution Conference, April 2010, accepted
2) L. Xie and M. Ilić, Model Predictive Economic/Environmental Dispatch of Power Systems with Intermittent Resources, IEEE PES General Meeting, July 2009
3) M.
M. Ilić, L. Xie, and J.Y. Joo, Efficient Coordination of Wind Ilić, L. Xie, and J.Y. Joo, Efficient Coordination of Wind
Power and Price‐Responsive Demand Part I: Theoretical Foundations, Part II: Case Studies, IEEE Transactions on Power Systems, under review,
y
,
, Mar 2010
19
20
Cost analysis of ALM (cont
Cost analysis of ALM
(cont’d)
d)
™ALM applied to a distribution network
app ed to a d st but o et o
Source: Judith Cardell, Control Strategies and Dynamic Pricing g
y
g
for Small Scale Distributed Generation in a Deregulated Market, PhD Thesis, MIT, 1998
™Economic dispatch with demand functions
n
min ∑ {Ci (Pi ) − Bi (Pi )}
P
i
i =1
subject to Fi , j ≤ Fi , j max
0 ≤ Pi ≤ Pi ,max for ∀i, j
21
Cost analysis of ALM (cont
Cost analysis of ALM
(cont’d)
d)
™ED with and without demand functions
t a d t out de a d u ct o s
ƒ Without demand functions
™Inelastic demand
: demand does NOT change w.r.t. price
d
dd
NOT h
t i
ƒ With demand functions
™Elastic demand
: demand changes w.r.t. price
Æ demand likely to be lower when price higher
ƒ Cost
Cost savings estimate in hour 15 (when demand was savings estimate in hour 15 (when demand was
highly elastic)
: for 13,000 end‐users, $65.12 savings with ALM
22