A DISTRIBUTED DEMAND RESPONSE
ALGORITHM AND ITS APPLICATIONS
TO PHEV CHARGING IN SMART GRID
Zhong Fan
IEEE Trans. on Smart Grid.
Z. Fan. A Distributed Demand Response Algorithm and Its Applications to PHEV Charging in Smart Grid.
IEEE Trans. on Smart Gird, vol. 3, num. 3, pp. 1280-1290, 2012.
CONTENTS
 Demand
Response Model
 Distributed
PHEV Charging
 Leveraging
Networking Concepts into
Smart Grid Load Leveling
2
I - DEMAND RESPONSE (DR) IN SMART
GRID
 Demand
Response (DR): a mechanism for
achieving energy efficiency through
managing customer consumption of
electricity in response to supply conditions.
 Ex. Reducing customer demand at critical
times (or in response to market price)
 Advanced communication will enhance the
DR capability (E.g., real-time pricing).
 PHEVs require enhanced demand response
mechanism.
3
DR MODEL – CONGESTION PRICING
 Fully
distributed system (only price is
known)
 A principle of congestion control in IP
networks – Proportionally Fair Pricing (PFP)
Each user declares a price he is willing to pay
per unit time.
 The network resource (bandwidth) is shared in
proportion to the prices paid by the users.
 If each user chooses the price that maximizes
his utility, then the total utility of the network
is maximized [1].

[1] F. Kelly, A. Maulloo, and D. Tan, “Rate control for communication networks: Shadow prices, proportional
fairness and stability,” J. Oper. Res. Soc., vol. 49, no. 3, pp. 237–252, 1998.
4
DR MODEL AND USER ADAPTION (1)
A



discrete time slot system
N users
demand of user i at slot n
user i’s willingness to pay (WTP) parameter

Price of energy in slot n:

Utility function of user i:

The users choose demand
to maximize:
5
DR MODEL AND USER ADAPTION (2)
 User
adaption: user i adapts its demand
according to:
 The
convergence of the adaption:
: optimal demand
: equilibrium price

The error of demand estimate:
6
DR MODEL – NUMERICAL RESULTS (1)
8
Basic simulation
The effect of gamma
DR MODEL – NUMERICAL RESULTS (2)
9
Heterogeneous initial demands
Heterogeneous initial demands and adaption rates
DR MODEL – NUMERICAL RESULTS (3)
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II - DISTRIBUTED PHEV CHARGING
 Price
function:
 User
adaption:
 Charging
dynamics:
 Difference:
Finish service (Charging done, y=1)
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DIFFERENTIAL QOS?
 Total
 If
charging cost for PHEV i:
we assume the price remains constant (p)
 Equilibrium
price:
12
DIFFERENTIAL QOS?
 Several
observation
WTPs affect the price of energy.
 WTPs decide the charging time of individual
PHEVs
 PHEVs with same total charging demand and
different WTPs will have almost same total
charging cost.


After some PHEVs finish charging, the price will go
down, which results in slight differences of the
charging cost between PHEVs with different WTPs.
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SIMULATION RESULTS
 Basic
simulation
 Differential QoS and total cost of charging
 Impact of WTPs on system performance
 Maximum charging rate
 Different number of PHEVs
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BASIC SIMULATION
Parameter
Value
Number of PHEVs
100
Unit of demand
100 kW
Unit of time slot
0.01 h
Initial SOC
15%
Charging efficiency
85%
WTP
0.01+i*0.01
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DIFFERENTIAL QOS AND TOTAL COST
OF CHARGING
Parameter
Value
WTP of PHEV50
2, if charging rate <0.2
Uniform [0,1], other
WTP of other PHEV
Uniform [0,1]
Total charging cost:
PHEV1 only 7% less
than PHEV50
16
IMPACT OF WTPS ON SYSTEM
PERFORMANCE
17
MAXIMUM CHARGING RATE
Parameter
Value
Maximum charging rate
10 kW
WTP
Uniform [0,30]
18
MAXIMUM CHARGING RATE
Parameter
Value
Maximum charging rate
10 kW
WTP
Uniform [0,30]
19
DIFFERENT NUMBER OF PHEVS
Parameter
Value
Number of PHEVs
20, 60, 100
WTP
Uniform [0,2]
20
SOME FUTURE WORK
 How
should PHEVs adapt their WTPs according
to the price policy and their own charging
preference?
 In-depth analysis of how maximum charging rate
impacts the performance.
 Game theoretical analysis of the proposed demand
response model (the social optimum is a Nash
bargaining solution[1])
 The impact of PHEVs as energy storage on the SG.
 The introduction of energy service company (like
charging station) will bring about new problems of
optimization, security and social-economic
interactions[2].
21
[2] C.Wang and M. de Groot, “Managing end-user preferences in the smart grid,” in Proc. 1st Int. Conf. EnergyEfficient Comput. Network. (ACM e-Energy), 2010.
III - INCORPORATING NETWORKING IDEAS
AND METHODS INTO THE RESEARCH OF SG
 Load
leveling as a resource usage
optimization problem
 Resource allocation ideas from networking to
the smart grid.
Load admission control
 OFDMA allocation
 Cooperative energy trading

22
S. Gormus, P. Kulkarni, and Z. Fan, “The power of networking: How networking can help power management,”
in Proc. 1st IEEE Int. Conf. Smart Grid Commun., 2010.
LOAD LEVELING AS A RESOURCE USAGE
OPTIMIZATION PROBLEM

Resource allocation:

Optimization goals
Environmental impact – load will be shifted to when the
renewable resources have higher general mix.
 Cheapest resource available – load will be shifted to the
off-peak time when the price is low.


When outage?
Hierarchical priority manner
 Low priority appliances of low priority customer should
be black out first.

23
LOAD ADMISSION CONTROL
 Like

“call admission control”
Customers send request before accessing SG to the
Power Management System (PMS)
Granted
 Rejected
 If the request with high priority

24
OFDMA ALLOCATION
 OFDMA:
deciding which frequencies to
allocate at what times to users
Resource allocation in SG: what loads to allocate at
what times to which users to optimize resource
utilization and hence improve energy efficiency.
 Learn from the OFDMA with the allocation methods

25
COOPERATIVE ENERGY TRADING
 Future
smart grid: micro grids with local
generation plants (solar, wind, etc.) and users
while connected to the macro grid.
 The idea here is a better utilization of the
available power resources by cooperatively
using available generation resources.
 Similar to the cooperative communication
philosophy where the nodes in a wireless
network try to increase the throughput and
network coverage by sharing available
bandwidth and power resources cooperatively.
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THANKS!
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