This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
IEEE TRANSACTIONS ON SMART GRID
1
Queuing-Based Energy Consumption Management
for Heterogeneous Residential Demands
in Smart Grid
Yi Liu, Chau Yuen, Senior Member, IEEE, Rong Yu, Member, IEEE, Yan Zhang, Senior Member, IEEE,
and Shengli Xie, Senior Member, IEEE
Abstract—In this paper, an energy consumption management
is considered for households (users) in a residential smart grid
network. In each house, there are two types of demands, essential and flexible demands, where the flexible demands are further
categorized into delay-sensitive and delay-tolerant demands. The
delay-sensitive demands have higher priority to be served than
the delay-tolerant demands. Meanwhile, in order to decrease the
delay of delay-tolerant demands, such demands are allowed to
be upgraded to the high-priority queue (i.e., the same queue that
serves the delay-sensitive demands) with a given probability. An
optimization problem is then formulated to minimize the total
electricity cost and the operation delay of flexible demands by
obtaining the optimal energy management decisions. Based on
adaptive dynamic programming, a centralized algorithm is proposed to solve the optimization problem. In addition, a distributed
algorithm is designed for practical implementation and the neural network is employed to estimate the pricing or demands when
such system information is not known. Simulation results show
that the proposed schemes can provide effective management for
household electricity usage and reduce the operation delay for
the flexible demands.
Index Terms—Energy consumption management, heterogeneous demands, operation delay, smart grid.
I. I NTRODUCTION
S
MART GRID, which uses advanced metering infrastructure and central scheduler, enables two-way flows
Manuscript received November 13, 2014; revised February 24, 2015
and April 21, 2015; accepted May 4, 2015. This work was supported
in part by the Energy Innovation Research Program Singapore under
Grant NRF2012EWT-EIRP002-045; in part by the Programs of Natural
Science Foundation of China under Grant 61403086, Grant 61333013, Grant
61422201, Grant 61370159, Grant U1301255, and Grant U1201253; in
part by the Guangdong Province Natural Science Foundation under Grant
S2011030002886; in part by the High Education Excellent Young Teacher
Program of Guangdong Province under Grant YQ2013057; in part by the
Science and Technology Program of Guangzhou under Grant 2014J2200097
(Zhujiang New Star Program); in part by the Research Council of Norway
under Project 240079/F20; and in part by the European Commission
FP7 Project CROWN under Grant PIRSES-GA-2013-627490. Paper no.
TSG-01118-2014. (Corresponding author: Shengli Xie.)
Y. Liu, R. Yu, and S. Xie are with the School of Automation,
Guangdong University of Technology, Guangzhou 510006, China (e-mail:
yiliu115@gmail.com; yurong@ieee.org; shlxie@gdut.edu.cn).
C. Yuen is with the Singapore University of Technology and Design,
Singapore 487372 (e-mail: yuenchau@sutd.edu.sg).
Y. Zhang is with Simula Research Laboratory, Oslo 1325, Norway (e-mail:
yanzhang@simula.no).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TSG.2015.2432571
of electricity and information between the users and utility companies [1]–[3]. Such two-way exchanging is able
to create an automated and distributed advanced energy
delivery network. The main advantage of this network is
that the utility company can implement demand response
management (DRM) programs to control energy consumption
of the users. To implement effective DRM, the smart meters
collect data on the electricity usage of the houses and communicate with the central scheduler. Considering heterogeneous
demands caused by different appliances, the central scheduler
manages and schedules the electricity consumption of the users
while minimizing their cost as an incentive. Meanwhile, the
users’ preferences of the energy usage are also considered by
the central scheduler in many literatures [4]–[6].
From utility companies’ point of view, DRM can reduce
peak electricity loads and increase the reliability of the power
grid. By using DRM, utilities and system operators can encourage users, through incentives, to individually and voluntarily
manage their loads, e.g., shifting of high-energy loads to
off-peak hours. Koutsopolous and Tassiulas [7] focused on
minimizing the grid operational cost for utility by designing a
power demand task scheduling policy. Tasdighi et al. [8] proposed an optimal scheduling model for a microgrid based on
temperature dependent thermal load modeling. From users’
point of view, the DRM can be used to find the optimal
energy consumption schedule decision to minimize the users’
electricity bill, while reducing the users’ energy usage dissatisfaction. Mohsenian-Rad and Leon-Garcia [9] proposed an
optimal residential load control mechanism which attempts to
minimize both electricity payment and waiting time for the residential appliances. Neely et al. [10] investigated the problem
of allocating energy from renewable sources to flexible users
which are served within a specified delay window, and incurs
a cost of drawing energy from other (possibly nonrenewable)
sources if their own supply is not sufficient to meet the
deadlines.
According to aforementioned literatures, a typical dissatisfaction of users’ electricity usage is the operation delay,
which is caused by the service priority of hierarchical loads in
residential networks. Hence, on top of energy cost minimization, users with DRM also dedicate to minimize the operation
delay by adjusting energy consumption according to the queue
length of different classes of demands. Recently, there have
been several studies detailing DRM approaches to deal with
c 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
1949-3053 See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
2
minimizing energy cost with constrained users’ operation
delay in smart grid [4], [11]–[14]. Chen et al. [4] investigated
the cost minimization problem for an end user, such as a home,
community, or a business, which is equipped with renewable
energy devices when electrical appliances allow different levels of delay tolerance. Zhu et al. [11] proposed a mechanism
to schedule both optimal power and optimal operation time
for power-shiftable appliances and time-shiftable appliances,
respectively, according to the power consumption patterns of
all individual appliances. Guo et al. [12] investigated the
minimization of the total energy cost of multiple residential
households with inelastic and elastic energy loads. For any feasible control decision, the constraint of average delay for the
elastic loads should be satisfied. Gatsis and Giannakis [13]
devoted to minimizing the electricity provider cost plus the
total user dissatisfaction, subject to the individual constraints
of operation period. Zhao et al. [14] introduced a general architecture of energy management system in a home area network
and formulated a joint optimization problem to minimize the
electricity expense for users as well as the operation delay. In
these literatures, the operation delay is treated as a significant
component in the users’ preference concerned DRM schemes.
However, how to ensure the fairness of the low-serving priority
loads is still an open issue.
The focus of this paper is to manage the energy consumption
of residential smart grid by minimizing both electricity cost
and operation delay for users. Each household has essential
and flexible demands. According to different delay requirements, the flexible demands are categorized into two types:
1) delay-sensitive; and 2) delay-tolerant demands. Delaysensitive demands always have priority over delay-tolerant
demands, i.e., delay-tolerant demands can only be served
when there is no delay-sensitive demands in the system.
When the residential grid is highly loaded and a large portion of the demands is delay-sensitive demands, the prioritized
scheduling may cause excessive delay for the delay-tolerant
demands. Inspired by [15], an upgrade-by-probability (UBP)
scheme which is used in wireless communication networks
is developed to decrease such delay in smart grid. In this
scheme, the delay-tolerant demands can promote or jump to
the high-priority queue with probability β. Jumped delaytolerant demands are treated as if they are delay-sensitive, i.e.,
they are served over newly arriving delay-tolerant demands.
In this paper, an energy management strategy is studied for
household appliances under the queuing-based demand framework of the residential smart grid. The main contributions of
this paper are as follows.
1) To ensure the fairness of the low-serving priority
demands, the UBP scheme is used to decrease the delay
of delay-tolerant demands if they are not served for a
long time. An optimization problem is formulated to
minimize both total electricity cost and operation delay
for flexible demands.
2) Centralized algorithm based on adaptive dynamic programming (ADP) is proposed to solve the optimization
problem. A discrete-time policy iteration algorithm of
ADP is developed to obtain the optimal controller for
the residential smart grid systems.
IEEE TRANSACTIONS ON SMART GRID
Fig. 1.
System model.
3) Distributed algorithm is designed for practical implementation and neural network is employed to approximate management decision when the real-time system
information are not known.
In addition, numerical results show that the proposed schemes
can provide effective management for household electricity
usage and reduce the operation delay for the flexible demands.
The rest of this paper is organized as follows. In Section II,
the system models are introduced. The optimization problem to
minimize both energy cost and operation delay is proposed and
solved in centralized formulations in Section III. Section IV
studies the distributed algorithm with neural network method
to solve the proposed problem with historic information.
Section V presents the numerical results of the proposed
algorithms. Finally, this paper is concluded in Section VI.
II. S YSTEM M ODEL
A. Residential Grid Networks
The energy of a residential grid network is supplied by the
power grid and shared by several users (homes) through the
power line, as shown in Fig. 1. Each user is equipped with a
smart meter, which not only distributes the electricity from the
power line to all appliances in each home, but also collects
each user’s demands and preference information. Through the
communication line, the smart meters are capable of reporting
the collected information to the central scheduler. Based on
all the collected information, the central scheduler will optimize the consumption over the day and schedule all appliances
in the residential grid network.
The demands in the residential grid network are categorized into two types: 1) essential; and 2) flexible demands.
For the essential appliances such as TVs, electric stoves, and
lamps, which have a fixed power requirement and operational period, the central scheduler will ensure a continuous
supply of power throughout the optimization period. The
scheduling process would only affect the flexible demands.
For flexible appliances, such as optional lighting (OL), water
heating, and clothes dryers (CD), the central scheduler will
be able to control the switch and provide sufficient electricity
corresponding to the power pattern during the scheduled periods. More specifically, the flexible demands can be buffered,
i.e., the energy request can be delayed, first in a queue
before being served. Considering the different delay requirements, the flexible demands are classified as delay-sensitive
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
LIU et al.: QUEUING-BASED ENERGY CONSUMPTION MANAGEMENT FOR HETEROGENEOUS RESIDENTIAL DEMANDS IN SMART GRID
and delay-tolerant demands. Without loss of generality, the
delay-sensitive demands are generated by the appliances,
such as CD and OL, which are sensitive to the operation delay.
The delay-tolerant demands are generated by the appliances,
e.g., heating, ventilation and air conditioning (HVAC) and
water heating (WH), which are insensitive to the operation
delay.
B. Cost Function for Demands
Let N denote the set of users, where the number of users
is N |N |. For each user n ∈ N , let en,b (t), en,s (t),
and en,r (t) denote the electricity consumed to serve essential,
delay-sensitive and delay-tolerant demands in user n at time t,
respectively. Let ln (t) denotes the total energy consumption
at each time slot t ∈ T {1, . . . , T}, where T is the total
number of all unit time slots. Then
ln (t) = en,b (t) + en,s (t) + en,r (t), t ∈ T .
Based on this definition, the total energy consumption
across
all users at each t ∈ T can be calculated as L(t) n∈N ln (t).
To indicate the energy cost in the residential grid system, an
energy cost function should be carefully selected. The energy
cost under this model can be approximated by the quadratic
function as [5]
f (L(t)) = p(t)L(t)2
(1)
where p(t) > 0 at each time slot t ∈ T .
C. Prioritization of Demands With Upgrade-by-Probability
Without loss of generality, we assume that the delaysensitive demands dn,s (t) enter the high-priority queue, while
the delay-tolerant demands dn,r (t) are buffered in the lowpriority queue. At the beginning of a time slot t, when there
are demands in the high-priority queue, the demands in the
low-priority queue cannot be served. It is obvious that this policy may cause significant delay for the delay-tolerant demands
if the arrival rate of delay-sensitive demands is high. Because
the high-priority queue may always have buffered demands,
hence, the UBP scheme is proposed to allow the demands in
the low-priority queue to upgrade to the high-priority queue
with probability β. This updating is only possible when the
high-priority queue is nonempty at the beginning of a slot.
The UBP scheme is presented as follows.
For any user, let un,H (t) and un,L (t) denote the length of the
high- and low-priority queues of user n at the beginning of t,
respectively. The queuing demands can be described by the
pair (un,H (t), un,L (t)). For different cases of the high- and lowpriority demand queues, we have the following expressions.
1) un,H (t) = 0: In this case, the high-priority queue is
empty at the beginning of time slot t and the new arriving
delay-sensitive demands are queued in the high-priority queue.
The demands in the low-priority queue will be served during
time slot t since the low-priority queue is not empty at the
beginning of t. Then
un,H (t + 1) = dn,s (t)
(2)
un,L (t + 1) = [un,L (t) − en,r (t)]+ + dn,r (t)
where []+ denotes the maximum of the argument and zero.
3
2) un,H (t) > 0 and un,L (t) > 0: In this case, the highpriority queue is nonempty at the beginning of time slot t.
Hence, the high-priority queue is served during time slot t. If
the low-priority queue is nonempty as well, the entire lowpriority queue is upgraded to the high-priority queue with
probability β. This upgrading process happens at the end
of time slot t. The new arriving delay-tolerant demands will
also upgrade to the high-priority queue. Then, we have the
following expressions.
1) With probability β
⎧
⎨ un,H (t + 1) = un,H (t) − en,s (t) + dn,s (t)
+ un,L (t) + dn,r (t)
(3)
⎩
un,L (t + 1) = 0.
2) With probability 1 − β
un,H (t + 1) = un,H (t) − en,s (t) + dn,s (t)
un,L (t + 1) = un,L (t) + dn,r (t).
(4)
3) un,H (t) > 0 and un,L (t) = 0: If the high-priority queue is
not empty and the low-priority queue is empty at the beginning
of t, the new arriving delay-tolerant demands will upgrade
to the high-priority queue with probability β. Hence, we can
obtain the following.
1) With probability β
un,H (t + 1) = un,H (t) − en,s (t) + dn,s (t) + dn,r (t)
un,L (t + 1) = 0.
(5)
2) With probability 1 − β
un,H (t + 1) = un,H (t) − en,s (t) + dn,s (t)
un,L (t + 1) = dn,r (t).
(6)
III. E NERGY C ONSUMPTION O PTIMIZATION
A. Problem Formulation
In this paper, two objectives are jointly minimized: 1) the
total energy cost for all household by consuming the electricity
and 2) the queue length (i.e., the delay) of the delay-sensitive
and delay-tolerant queues in each household. Hence, an optimization problem is given by
N
T
1
lim
min
1 f (L(t)) + 2 (un,H (t) + un,L (t))
en,s (t), T→∞ T
t=1
en,r (t),∀n
n=1
s.t. L(t) ≤ Lmax
T
1
lim
un,H (t) ≤ unH,max
T→∞ T
lim
T→∞
1
T
t=1
T
un,L (t) ≤ unL,max
(7)
(8)
(9)
(10)
t=1
where Lmax is the limit of the total energy consumption
across all users at time slot t. unH,max and unL,max are the
limits of the average high- and low-priority queues’ length
at user n, respectively. 1 and 2 are the weight, which
can be adjusted based on users’ preference. Constraint (8)
ensures that the total energy consumed by all users is limited.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
4
IEEE TRANSACTIONS ON SMART GRID
The constraints (9) and (10) are used to ensure the
average delays of the flexible
demands are finite. Let
limT→∞ 1/T Tt=1 un,H (t) and E[un,L ] E[un,H ] limT→∞ 1/T Tt=1 un,L (t). In the next section, E[un,H ] and
E[un,L ] are calculated based on UBP scheme in priority
queuing model.
B. Priority Virtual Queuing Analysis
The dn,s (t), t ∈ T and dn,r (t), t ∈ T are assumed be independent and identically distributed over time. However, dn,s (t)
and dn,r (t) can be correlated during one time slot. Let λ1 and
λ2 denote the arrival rates of the delay-sensitive and delaytolerant demands, respectively. To guarantee the stability of the
max ≤ emax and d max ≤ emax , where d max
queues, we assume dn,s
n,s
n,r
n,r
n,s
max
and dn,r are the maximum value of the amount of arriving
delay-sensitive and delay-tolerant demands at user n, respecmax
tively. emax
n,s and en,r are the maximum amount of the energy
used to serve delay-sensitive and delay-tolerant demands at
user n, respectively. Hence, the joint probability generation
function (pgf) of dn,s (t) and dn,r (t) is defined as B(z1 , z2 ),
d (t) d (t)
i.e., B(z1 , z2 ) E[z1n,s z2n,r ].
Then, the marginal pgfs of the delay-sensitive and delaytolerant demands arrivals per time slot are given by B1 (z) B(z, 1) and B2 (z) B(1, z), respectively. Furthermore, the
total demand arrivals during time slot t is denoted by dn (t),
where dn (t) = dn,s (t) + dn,r (t). Its pgf is given by Bc (z) B(z, z). The corresponding arrival rates, i.e., the mean number of arrived demands per time slot, are indicated by λc Bc (1) = λ1 + λ2 . Let Ut (z1 , z2 ) denote joint pgf of un,H (t) and
un,L (t). Then, we have
u (t) u (t)
(11)
Ut (z1 , z2 ) E z1n,H z2n,L .
Therefore, the expressions of the mean value of un,H and
un,L are given as follows [15]:
E[un,H ] =
(1 − βλ2 )(U(0, 1) − 1) + λ1 + βE[uc ] − βU (2) (0, 1)
β
(12)
(1 − βλ2 )(1 − U(0, 1)) − λ1 + βU (2) (0, 1)
E[un,L ] =
β
(13)
where E[uc ] is the mean total amount of demands and can be
calculated via U(z, z), U (2) (0, 1) (dU(0, z)/dz)|z=1 .
C. Centralized Algorithm
The original problem (7)–(10) is a nonlinear optimization
problem due to the random market prices p(t), random delaysensitive demands dn,s (t), and delay-tolerant demands dn,r (t).
To solve the problem mathematically, we simplify the optimization problem under the assumption that the demands are
ergodic across time slots, as well as the market prices. This
assumption is usually valid because the power load and market
behaviors statistically recur in some daily or seasonal patterns.
Based on this assumption, the original optimization problem
can be rewritten as
N
E[un,H ] + E[un,L ] (14)
1 E[ f (L(t))] + 2
min
en,s (t),
en,r (t),∀n
n=1
s.t.
L(t) ≤ Lmax
E[un,H ] ≤ unH,max
E[un,L ] ≤ unL,max .
(15)
(16)
(17)
The dynamic programming approach is employed to solve
the problem in (14) subject to the constraints (15)–(17). For
each stage k, k = 1, 2, . . ., let dn,s (k) and dn,r (k) be the delaysensitive and delay-tolerant demands of user n at stage k.
The state vector is defined as x(k) = (ds (k), dr (k), a(k)),
where vectors ds (k) = {d1,s (k), . . . , dN,s (k)}, dr (k) =
{d1,r (k), . . . , dN,r (k)} are the delay-sensitive and delay-tolerant
demands of all N users, respectively, and a(k) is the market
price for buying electricity at stage k. Then, the management
decision is defined as e(k) = (es (k), er (k)), where es (k) =
{e1,s (k), . . . , eN,s (k)}, er (k) = {e1,r (k), . . . , eN,r (k)}. In addition, we define a feasible energy consumption management set
corresponding to nth user as follows:
En = e(k) | L(t) ≤ Lmax , E[un,H ] ≤ unH,max ,
E[un,L ] ≤ unL,max .
In the proposed management method, the scheduler validly
controls user’s energy consumption only if e(k) ∈ E.
According to Bellman’s principle of optimality, the optimal
performance index function F ∗ (x(k)) satisfies the following
Hamilton–Jacobi–Bellman (HJB) equation:
F ∗ (x(k)) = min U(x(k), e(k)) + F ∗ (x(k + 1))
e(k)∈En
n
n
where U(x(k), e(k)) = a(k)L(k)2 + N
n=1 (uH,t + uL,t ) is the
utility function. Define the law of optimal control as
e∗ (x(k)) = arg min U(x(k), e(k)) + F ∗ (x(k + 1)) .
e(k)∈En
Hence, the HJB equation can be written as
F ∗ (x(k)) = U x(k), e∗ (x(k)) + F ∗ (x(k + 1)).
To achieve the optimal management decision e∗ (x(k)),
the optimal performance index function F ∗ (x(k)) should be
obtained. Generally, F ∗ (x(k)) cannot be obtained unless all the
e(k) are considered. If the traditional dynamic programming
method is used to obtain F ∗ (x(k)) at every time step, then the
curse of dimensionality should be considered. Moreover, the
optimal management is discussed in infinite horizon which
means the length of the control sequence is infinite. In this
case, the optimal management is nearly impossible to be
obtained by the HJB equation. To address these problems,
a new iterative algorithm based on ADP [19] should be
developed.
Next, a discrete-time policy iteration algorithm based on
adaptive dynamic programming is developed to obtain the
optimal management for the residential smart grid systems.
The goal of the developed policy iteration algorithm is to
construct an iterative management decision vi (x(k)), which
can make an arbitrary initial state x(0) to the equilibrium.
Meanwhile, this iterative management decision also can make
the iterative performance index function to reach the optimum. In the developed policy iteration algorithm, the performance index function and the management decision are
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
LIU et al.: QUEUING-BASED ENERGY CONSUMPTION MANAGEMENT FOR HETEROGENEOUS RESIDENTIAL DEMANDS IN SMART GRID
Algorithm 1: Centralized ADP Algorithm
Algorithm 2: Distributed ADP Algorithm
Initialization:
01: Select randomly an array of initial state x(0);
02: Select a computation precision ε;
03: Give the initial admissible management decision v0 (x(k));
04: Give the max iteration of computation imax ;
Iteration:
05: Let the iteration index i = 0;
06: Construct the iterative performance index function V0 (x(k)) according to
v0 (x(k)) by (18);
07: Update the iterative management decision by (19);
08: Let i = i + 1. Construct the iterative performance index function Vi (x(k)),
which satisfies the GHJB (20);
09: Update the iterative management decision vi+1 (x(k)) by (21);
10: If Vi−1 (x(k)) − Vi (x(k)) < ε, go to step 12. Else go to step 11;
11: If i < imax , then go to step 08. Else, go to step 12;
12: return vi (x(k)) and Vi (x(k)). The optimal management decision is achieved;
13: return The optimal management decision is not achieved within imax
iterations.
updated by iterations. Let v0 (x(k)) be an arbitrary admissible
management decision. For i = 0, let V0 (x(k)) be the iterative performance index function constructed by v0 (x(k)) that
satisfies the following generalized HJB (GHJB) equation:
V0 (x(k)) = U(x(k), v0 (x(k)) + V0 (x(k + 1)).
(18)
Then, the iterative management decision is computed by
v1 (x(k)) = arg min {U(x(k), e(k)) + V0 (x(k + 1))}.
e(k)∈En
(20)
and the iterative management decision is updated by
vi+1 (x(k)) = arg min U(x(k), e(k)) + Vi (x(k + 1)).
e(k)∈En
(21)
According to (18)–(21), the iterative performance index
function Vi (x(k)) is used to approximate F ∗ (x(k)) and the
iterative management decision vi (x(k)) is used to approximate
e∗ (x(k)). As (21) is generally not the HJB equation, it is necessary to determine whether the algorithm is convergent. In [19],
the convergence proof of Vi (x(k)) and vi (x(k)) that converge
to the optimal ones when i → ∞ is provided. Finally, the
detailed implementation of the iteration algorithm is expressed
in Algorithm 1. The computing complexity is O(imax ) where
is the domain size of the variables en,s and en,r , imax is the
maximum number of the iterations of the algorithm.
IV. D ISTRIBUTED E NERGY C ONSUMPTION M ANAGEMENT
A. Distributed Algorithm
The algorithm described above should be able to run in
a distributed manner in order to be implemented in practice.
Hence, in this section, a distributed algorithm is designed to
solve the optimization problem. For each user, the optimization
problem is
min
en,s (t),
en,r (t),∀n
1 E[ f (Ln (t))] + 2 (E[un,H ] + E[un,L ])
Initialization: β0 (k) arbitrarily to some nonnegative value;
Iteration:
Let the iteration index l = 0;
At user n
01: Select randomly initial state xn (0) and initial admissible management decision
v0 (xn (k));
02: Give computation precision ε and the max iteration of computation lmax ;
03: If βl (k) is received, construct the iterative performance index function
Vl (xn (k)), which satisfies the following GHJB equation
Vl (xn (k)) = U(xn (k), vl (xn (k)) + Vl (xn (k + 1)) + βl (k)(Ln (k));
04: Update the iterative management decision vl+1 (xn (k)) by (27)
05: If Vl−1 (xn (k)) − Vl (xn (k)) < ε, return vl (xn (k)) and Vl (xn (k));
06: The optimal management decision is achieved;
At the centralized scheduler
07: If en,l (k) for all users are received, Then;
08: The centralized scheduler calculates Ll (k), and updates the Lagrange
multiplier βl (k) as follows:
Ll (k) =
argmin
0≤Ll (k)≤Lmax
N
un,H (k) + un,L (k)
f Ll (k) +
n=1
⎧
⎡
⎤⎫+
N
⎨
⎬
(l) ⎦
(l)
⎣
Ln (t)
βl+1 (k) = βl (k) − φ L (k) −
⎩
⎭
n=1
where φ > 0 is a constant step-size;
09: End;
10: set l ← l + 1;
11: End;
12: If l < lmax , then go to step 03. Else, go to step 13;
13: return The optimal management decision is not achieved within lmax
iterations.
(19)
For ∀i = 1, 2, . . . , let Vi (x(k)) be the iterative performance
index function constructed by vi (x(k)), which satisfies the
following GHJB equation:
Vi (x(k)) = U(x(k), vi (x(k))) + Vi (x(k + 1))
5
(22)
s.t.
N
Ln (t) ≤ L(t) ≤ Lmax
(23)
n=1
E[un,H ] ≤ unH,max
E[un,L ] ≤ unL,max .
(24)
(25)
Let xn (k) = (dn,s (k), dn,e (k), an (k)), n = 1, . . . , N. For
iteration index ∀l = 1, 2, . . . , iterative performance index
function Vl (xn (k)) for state xn is constructed by vl (xn (k)),
which satisfies the following GHJB equation:
Vl (xn (k)) = U(xn (k), vl (xn (k))) + Vl (xn (k + 1))
(26)
where U(xn (k), vl (xn (k))) is the utility function for user n. Let
en,l (k) = {eln,s (k), eln,r (k)} denote the control action of user n
at iteration l, the iterative control law is updated by
vl+1 (xn (k)) = arg min U(xn (k), en,l (k)) + Vl (xn (k + 1)) .
en,l (k)∈En
(27)
Then, the distributed algorithm is shown in Algorithm 2,
which iteratively solve problem (22) at users and centralized scheduler, respectively, in a distributed fashion. Let βl (k)
denotes the Lagrangian multiplier at the lth iteration for
stage k. At user n, if βl (k) is received, each user individually
calculates its own version of local energy consumption management vector en,l (k) in line 04. The users locally solve the
management problem according to the new announced βl (k).
Then, each user updates the centralized scheduler with its new
value of en,l (k). At the centralized scheduler, the algorithm
starts with some random initial conditions, i.e., the scheduler
assumes a random lagrange multiplier βl (k). This assumption
is implying that, at the beginning, the scheduler has no prior
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
6
IEEE TRANSACTIONS ON SMART GRID
information about the users. If the management vector en,l (k)
from all users are received, the scheduler updates the βl (k)
and broadcasts this updating multiplier to all users in line 08.
Finally, the loop in lines 01–13 is executed until predefined
stopping criterion is satisfied.
As vl (xn (k + j)) is an admissible management decision,
Vl (xn (k+θ )) → 0 for θ → ∞. Hence, if a large θ is choosing,
then
Vl (xn (k)) =
θ−1
U(xn (k + j), vl (xn (k + j))).
j=0
B. Neural Network-Based Distributed Management
In practice, the message, βl (k) or en,l (k), exchanged
between central scheduler and users might be loss due to a
malfunction at transmitter or the noise at the receiver. Without
knowing such messages, user n is not able to calculate the
performance index function Vl (xn (k)) and admissible management decision vl (xn (k)). In this section, the user is allowed
to estimate Vl (xn (k)) and vl (xn (k)) by the historical information. A three-layer neural network [19], [20] is employed by
user n to approximate the Vl (xn (k)) and compute the optimal
vl (xn (k)).
Let J denotes the number of hidden layer neurons. Let
Y denotes the weight matrix between the input and hidden
layers. Let W denotes the weight matrix between the hidden
and output layers. Then, the output of the three-layer neural
network is
(28)
F̂(X, Y, W) = W T σ Y T X + b
where σ (Y T X) ∈ RJ and [σ (z)]j = (ezj − e−zj /ezj + e−zj ),
j = 1, . . . , J are the activation functions and b is the threshold
value. Then, we will introduce the critic network and action
network which are chosen as three-layer feedforward neural
network.
1) Critic Network: In our algorithm, the critic network is
used to approximate the performance index function Vl (x(k)).
For user n, the output of the critic network can be obtained as
j
jT
V̂l (xn (k)) = Wcl σ (Zc (k))
(29)
where Zc (k) = YcT xn (k) + bc . Then, for the critic network, the
error function is defined as
j
j
j
εcl (k) = V̂l (xn (k)) − Vl (xn (k)).
(30)
The objective function in the critic network training is
1 j 2
j
ε (k) .
(31)
Ecl (k) =
2 cl
Therefore, the gradient-based weight updating rule in the critic
network is as follows:
j+1
j
j
Wcl (k) = Wcl (k) + Wcl (k)
j
j
∂Ecl (k) ∂ V̂l+1 (k)
j
= Wcl (k) − αc
j
j
∂ V̂ (k) ∂Wcl (k)
l+1
j
j
= Wcl (k) − αc εal (k) σ (Zc (k))
θ−1
j=0
j
jT
v̂l (xn (k)) = Wal σ (Za (k))
(33)
where Za (k) = YaT xn (k) + ba . Then, the output error of the
action network is defined as
j
j
j
εal (k) = v̂l (xn (k)) − vl (xn (k)).
(34)
The performance of error measurement can be minimized by
updating the weights of the action network as follows:
1 j T j j
ε (k)
εal (k) .
Eal (k) =
(35)
2 al
The calculation of the weights is similar to the one in the critic
network. By using gradient descent rule, we can obtain
j+1
j
j
Wal (k) = Wal (k) + Wal (k)
j
j
j
∂Eal (k) ∂εal (k) ∂ v̂l+1 (k)
j
= Wal (k) − αa
j
j
j
∂εl+1 (k) ∂ v̂l+1 (k) ∂Wal (k)
T
j
j
= Wal (k) − αa σ (Za (k)) εal (k)
(36)
where αa > 0 is the learning rate of critic network. If
the training precision is achieved, then vl+1 (xn (k)) can be
approximated by the action network. The weights convergence
property of the neural networks is shown in the following
theorem.
Theorem 1: The target performance index function and the
target iterative management decision can be expressed as
Vl+1 (xn (k)) = Wcl∗T σ (Za (k))
vl (xn (k)) =
∗T
Wal
σ (Za (k)).
(37)
(38)
Then, the critic and action networks can be trained by (32)
and (36), respectively. If the learning rates αc and αa are both
small enough, the weights of critic network (Wcl (k)) and the
action network (Wal (k)) will asymptotically converge to the
∗ (k), respectively.
optimal weights Wcl∗ (k) and Wal
Proof: The proof of Theorem 1 is give in the Appendix.
V. N UMERICAL R ESULTS
(32)
where αc > 0 is the learning rate of critic network. By precisely training, the Vl+1 (xn (k)) can be approximated by the
critic network. However, it is not easy to obtain the performance index function Vl (xn (k)) when the critic network is not
trained. Vl (xn (k)) can be obtained as follows:
Vl (xn (k)) =
2) Action Network: In this network, the state vector xn (k)
is used as input to create the iterative management decision
as the output of the action network. The output is
U(xn (k + j), vl (xn (k + j))) + Vl (xn (k + θ )).
In this section, the RELOAD database [24], which provides
hourly load profiles of different practical demands including
HVAC, WH, lighting, clothes drying, freezing, etc., is used
to model different demands. Two hundred users are selected
and each user has three types of appliances: 1) essential
appliances: normal lights, cooking machine, freezing, etc.;
2) delay-sensitive appliances: CD and OL; and 3) delaytolerant appliances: HVAC and WH. The market prices for
purchasing electricity from the grid is p(t) = 0.3 cents
at daytime hours, i.e., from 8:00 A . M . to 12:00 A . M . at
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
LIU et al.: QUEUING-BASED ENERGY CONSUMPTION MANAGEMENT FOR HETEROGENEOUS RESIDENTIAL DEMANDS IN SMART GRID
Fig. 2. Total operation delay in terms of different β by using centralized
algorithm.
7
Fig. 3. Operation delay for delay-sensitive and delay-tolerant demands in
terms of different β by using centralized algorithm.
night and p(t) = 0.2 cents during the night, i.e., from
12:00 A . M . to 8:00 A . M . the next day. Neural networks are
used to implement the distributed algorithm. The critic network and the action network are chosen as three-layer neural
networks. For each iteration step, the critic network and the
action network are trained for 80 steps using the learning
rate of α = 0.02 [19]. In addition, to compare the operation
delay of the proposed management scheme, a baseline scheme
(no-priority scheme) where the delay-sensitive demands and
the delay-tolerant demands randomly served without priority
is carried out [5].
A. Operation Delay
To illustrate the operation delay, log10 (actual demand
queue) is used to measure the operation delay of delaysensitive demands and delay-tolerant demands. For the proposed scheme, the jumping probability β is set as 0, 0.2,
and 0.6, respectively. The weights for the user’s usage preference are set as 1 = 1 and 2 = 1. The performance of
the proposed control scheme will be evaluated under different
combinations of the weights in the next section.
In Fig. 2, we show the total operation delay in terms of different value of β by using centralized algorithm. It is observed
that the proposed energy scheduling scheme can obtain lower
delay than that of the no-priority scheduling scheme. This is
because the proposed scheme can probabilistically assign the
low priority demands to the high-priority queue for early service, which results in the reduction of total delay. Fig. 3 shows
the comparison of operation delay for both delay-sensitive
and delay-tolerant demands by using centralized algorithm.
For delay-tolerant demands, the operation delay caused by the
proposed scheme in β = 0 case is higher than that caused by
no-priority scheme. This is because the delay-tolerant demands
cannot be served unless the delay-sensitive queue is empty.
However, the no-priority scheme serves both demands randomly and can result in lower delay of delay-tolerant demands.
The operation delay caused by the proposed scheme in both
β = 0.2 and β = 0.6 case are close or lower than that in
the no-priority scheme. It is expected that more delay-tolerant
demands can upgrade to the delay-sensitive queue with higher
probability β. For delay-sensitive demands, the opposite trend
can be observed. That is, as β increases, more delay-tolerant
Fig. 4. Operation delay for delay-sensitive and delay-tolerant demands in
terms of different β by using distributed algorithm.
demands will be jumped to high-priority queue, which may
lead to higher delay of delay-sensitive demands.
Fig. 4 compares the operation delay for both delay-sensitive
and delay-tolerant demands by using the distributed algorithm.
It is observed that the operation delay obtained by the distributed algorithms is close but higher than that obtained by
centralized algorithm. That is, the distributed scheme needs
to estimate the loss information to obtain the optimal energy
schedule which may lead to the suboptimal schedule for each
user. In contrast, the centralized scheme is able to achieve
optimal management of energy consumption based on the
knowledge of all information. Still, the trend of the delay for
both demands with different β holds.
In Fig. 5, we respectively show the original profiles of the
delay-sensitive and delay-tolerant demands for 200 users, the
delay-sensitive and delay-tolerant queues while we implement
the proposed scheduling method under different value of β.
For the same reason explained in Fig. 3, as the value of
β increases, the queue length of the delay-sensitive queue
increases and the queue length of the delay-tolerant queue
decreases. Here, we want to emphasize that the value of β
can significantly influence the performance of our scheduling
scheme and should be carefully selected.
B. Energy Cost
Fig. 6 shows the energy cost comparison of no-priority
scheme, proposed centralized and distributed algorithms with
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
8
IEEE TRANSACTIONS ON SMART GRID
(a)
(b)
(c)
(d)
Fig. 5. (a) Original profiles of delay-sensitive and delay-tolerant demands. (b) Queue length of delay-sensitive and delay-tolerant demands when β = 0.
(c) Queue length of delay-sensitive and delay-tolerant demands when β = 0.2. (d) Queue length of delay-sensitive and delay-tolerant demands when β = 0.6.
Fig. 6.
Energy cost for 200 users in terms of different β.
different β for 200 users. It is observed that the energy costs
achieved by the proposed schemes in both β = 0 and β = 0.2
cases are less than that of the no-priority scheme. It is obvious
that there should be cost savings when the proposed energy
management is adopted for appliances. It is also noted that
the energy cost in β = 0.6 case is higher than that in β = 0
and β = 0.2 cases and even in the no-priority case. That is,
the length of the high-priority queue (delay-sensitive queue)
becomes longer when low-priority demands (delay-tolerant
demands) jump to the high-priority queue with large β. To
serve the incremental high-priority queue, more energy should
be consumed and higher energy cost will be caused. Moreover,
with the similar reason explained in Section V-A, it is expected
that the energy costs in the distributed algorithm is close but
higher than that in the centralized algorithm.
C. Impact of Control Weight
In this section, the performance of the proposed energy
management scheme that account for users’ preferences are
evaluated when β = 0.2. The energy cost and operation delay
of the proposed management scheme under centralized manner will be influenced by three weight criteria: 1) {1 = 100,
2 = 1}; 2) {1 = 50, 2 = 1}; and 3) {1 = 1, 2 = 1}.
Fig. 7.
Energy cost for 200 users in terms of different combinations of .
Fig. 8.
Delay for 200 users in terms of different combinations of .
Fig. 7 shows the energy cost in terms of different weight criteria by using centralized algorithm. Note that, given 2 = 1,
the energy cost decreases as 1 increases. This indicates that
the dominant of energy cost in problem (7) is decided by 1 .
Inspired by this observation, users can increase 1 if they
focus on minimizing the energy cost. Moreover, the operation delay in terms of different weight criteria is shown in
Fig. 8. It is observed that the operation delay of both delaysensitive and delay-tolerant demands increase as 1 increases.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
LIU et al.: QUEUING-BASED ENERGY CONSUMPTION MANAGEMENT FOR HETEROGENEOUS RESIDENTIAL DEMANDS IN SMART GRID
This is because when energy cost is the dominant criterion in the optimization problem (7), higher delay will be
caused.
VI. C ONCLUSION
In this paper, a demands queuing-based energy management scheme is presented for the residential smart grid.
By allowing delay-tolerant demands jump to delay-sensitive
demands queue, the central scheduler is able to minimize
the energy cost and operation delay for users. A centralized algorithm was proposed for the central scheduler to
find the optimal consumed energy for both delay-sensitive
and delay-tolerant demands. Moreover, a distributed algorithm was proposed to solve the minimization problem. To
deal with the communication error between users and central
scheduler, the proposed distributed algorithms employed the
neural network to estimate the missing messages. Numerical
results showed that the proposed energy management scheme
is able to balance the tradeoff between the operation delay
and energy consumption in the residential smart grid networks. Meanwhile, the effectiveness of the proposed distributed algorithm with loss message has been verified in this
paper.
A PPENDIX
Proof of Theorem 1: Let W̃cl (k) = Wcl (k) − Wcl∗ (k) and
j
j
∗ (k). From (32) and (36)
W̃al (k) = Wal (k) − Wal
j+1
j
j
W̃cl (k) = W̃cl (k) − αc ecl (k) σ (Zc (k))
j+1
j
j
W̃al (k) = W̃al (k) − αa eal (k) σ (Za (k)).
j
j
Consider the following Lyapunov function candidate:
j
j
jT j
jT j
L W̃cl , W̃al = tr W̃cl W̃cl + W̃al W̃al .
(39)
The difference of the Lyapunov function candidate is given by
j
j
( j+1)T ( j+1)
( j+1)T ( j+1)
L W̃cl , W̃al = tr W̃cl
+ W̃al
W̃cl
W̃al
jT j
jT j
− tr W̃cl W̃cl + W̃al W̃al
j
= αc εcl (k) 2 −2 + αc σ (Zc (k)) 2
j
+ αa εal (k) 2 −2 + αa σ (Za (k)) 2 .
(40)
According to the definition of σ (·) in (28), σ (Zc (k)) 2
and σ (Za (k)) 2 are both finite for ∀Zc (k), Za (k). Thus,
if αc and αa are both small enough that satisfy αc ≤ 2/
σ (Zc (k)) 2 and αa ≤ 2/ σ (Za (k)) 2 , then
j
j
L(W̃cl , W̃al ) < 0. The proof is completed.
R EFERENCES
[1] W. Tushar et al., “Three-party energy management with distributed
energy resources in smart grid,” IEEE Trans. Ind. Electron., vol. 62,
no. 4, pp. 2487–2498, Jun. 2014.
9
[2] H. Liu, H. Ning, Y. Zhang, and M. Guizani, “Battery status-aware
authentication scheme for V2G networks in smart grid,” IEEE Trans.
Smart Grid, vol. 4, no. 1, pp. 99–110, Mar. 2013.
[3] N. U. Hassan, Y. Khalid, C. Yuen, and W. Tushar, “Customer engagement plans for peak load reduction in residential smart grids,” IEEE
Trans. Smart Grid, to be published.
[4] S. Chen, N. B. Shroff, and P. Sinha, “Heterogeneous delay tolerant task
scheduling and energy management in the smart grid with renewable
energy,” IEEE J. Sel. Areas Commun., vol. 31, no. 7, pp. 1258–1267,
Jul. 2013.
[5] Y. Liu et al., “Peak-to-average ratio constrained demand-side management with consumer’s preference in residential smart grid,” IEEE J. Sel.
Topics Signal Process., vol. 8, no. 6, pp. 1084–1097, Dec. 2014.
[6] Y. Liu et al., “Electricity cost minimization for a microgrid with distributed energy resource under different information availability,” IEEE
Trans. Ind. Electron., vol. 62, no. 4, pp. 2571–2583, Apr. 2015.
[7] I. Koutsopolous and L. Tassiulas, “Control and optimization meet the
smart power grid scheduling of power demands for optimal energy
management,” in Proc. ACM 2nd Int. Conf. Energy-Efficient Comput.
Netw. (e-Energy), New York, NY, USA, 2011, pp. 41–50.
[8] M. Tasdighi, H. Ghasemi, and A. Rahimi-Kian, “Residential microgrid scheduling based on smart meters data and temperature dependent
thermal load modelling,” IEEE Trans. Smart Grid, vol. 5, no. 1,
pp. 349–357, Jan. 2014.
[9] A.-H. Mohsenian-Rad and A. Leon-Garcia, “Optimal residential load
control with price prediction in real-time electricity pricing environments,” IEEE Trans. Smart Grid, vol. 1, no. 2, pp. 120–133, Sep. 2010.
[10] M. Neely, A. Tehrani, and A. Dimakis, “Efficient algorithms for renewable energy allocation to delay tolerant consumers,” in Proc. 1st IEEE
Int. Conf. Smart Grid Commun. (SmartGridComm), Washington, DC,
USA, Oct. 2010, pp. 549–554.
[11] Z. Zhu, J. Tang, S. Lambotharan, W. H. Chin, and Z. Fan, “An
integer linear programming and game theory based optimization for
demand-side management in smart grid,” in Proc. IEEE Globecom
Workshops (GC Wkshps), Houston, TX, USA, 2011, pp. 1205–1210.
[12] Y. Guo, M. Pan, Y. Fang, and P. P. Khargonekar, “Decentralized coordination of energy utilization for residential households in the smart grid,”
IEEE Trans. Smart Grid, vol. 4, no. 3, pp. 1341–1350, Sep. 2013.
[13] N. Gatsis and G. B. Giannakis, “Residential load control: Distributed
scheduling and convergence with lost AMI messages,” IEEE Trans.
Smart Grid, vol. 3, no. 2, pp. 770–786, Sep. 2012.
[14] Z. Zhao, W. C. Lee, Y. Shin, and K. B. Song, “An optimal power
scheduling method for demand response in home energy management system,” IEEE Trans. Smart Grid, vol. 4, no. 3, pp. 1391–1400,
Sep. 2013.
[15] T. Maertens, J. Walraevens, and H. Bruneel, “Controlling delay differentiation with priority jumps: Analytical study,” Numer. Algebra Control
Optim., vol. 1, no. 4, pp. 657–673, Dec. 2011.
[16] S. Barmada, A. Musolino, M. Raugi, R. Rizzo, and M. Tucci, “A wavelet
based method for the analysis of impulsive noise due to switch commutations in power line communication (PLC) systems,” IEEE Trans.
Smart Grid, vol. 2, no. 1, pp. 92–101, Mar. 2011.
[17] R. Deng et al., “Sensing-performance tradeoff in cognitive radio enabled
smart grid,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 302–310,
Mar. 2013.
[18] Y. Zhang et al., “Cognitive machine-to-machine communications:
Visions and potentials for the smart grid,” IEEE Netw., vol. 26, no. 3,
pp. 6–13, May/Jun. 2012.
[19] D. Liu and Q. Wei, “Policy iteration adaptive dynamic programming
algorithm for discrete-time nonlinear systems,” IEEE Trans. Neural
Learn. Syst., vol. 25, no. 3, pp. 621–634, Mar. 2014.
[20] F.-Y. Wang, N. Jin, D. Liu, and Q. Wei, “Adaptive dynamic programming
for finite-horizon optimal control of discrete-time nonlinear systems with
ε-error bound,” IEEE Trans. Neural Netw., vol. 22, no. 1, pp. 24–36,
Jan. 2011.
[21] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.:
Cambridge Univ. Press, 2004.
[22] K. Schittkowski, “NLQPL: A FORTRAN-subroutine solving constrained
nonlinear programming problems,” Ann. Oper. Res., vol. 5, pp. 485–500,
1985.
[23] D. P. Bertsekas, Nonlinear Programming, 2nd ed. Belmont, MA, USA:
Athena Scientific, 2008.
[24] N. Hassan, M. A. Pasha, C. Yuen, S. Huang, and X. Wang, “Impact
of scheduling flexibility on demand profile flatness and user inconvenience in residential smart grid,” Energies, vol. 6, no. 12, pp. 6608–6635,
Dec. 2013.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
10
IEEE TRANSACTIONS ON SMART GRID
Yi Liu received the Ph.D. degree in signal and information processing from the South China University
of Technology, Guangzhou, China, in 2011.
He joined the Singapore University of Technology
and Design, Singapore, as a Postdoctorate. In 2014,
he joined the Institute of Intelligent Information
Processing, Guangdong University of Technology,
Guangzhou, where he is an Assistant Professor with
the School of Automation. His current research interests include cognitive radio networks, cooperative
communications, smart grid, and intelligent signal
processing.
Chau Yuen (SM’13) received the B.Eng. degree and
the Ph.D. degree in electrical and electronic engineering from Nanyang Technological University,
Singapore, in 2000 and 2004, respectively.
He was a Postdoctoral Fellow with Lucent
Technologies Bell Laboratories, Murray Hill,
NY, USA, in 2005, and a Visiting Assistant
Professor with Hong Kong Polytechnic University,
Hong Kong, in 2008. From 2006 to 2010, he
was with the Institute for Infocomm Research,
Singapore, as a Senior Research Engineer. He
joined the Singapore University of Technology and Design, Singapore, as
an Assistant Professor in 2010. His current research interests include green
communications, massive multiple input multiple output, Internet-of-things,
machine-to-machine, network coding, and distributed storage. He has
published over 150 research papers in international journals or conferences.
Dr. Yuen serves as an Associate Editor for the IEEE T RANSACTIONS ON
V EHICULAR T ECHNOLOGY.
Rong Yu (S’05–M’08) received the Ph.D. degree
in information and communication engineering from
Tsinghua University, Beijing, China, in 2007.
He was with the School of Electronic and
Information Engineering, South China University of
Technology, Guangzhou, China. In 2010, he joined
the Institute of Intelligent Information Processing,
Guangdong University of Technology, where he
is currently a Full Professor. His current research
interest include wireless communications and networking, such as cognitive radio, wireless sensor
networks, and home networking. He is the co-inventor of ten patents, and has
authored/co-authored over 70 international journal and conference papers.
Yan Zhang (SM’10) received the Ph.D. degree in
electrical and electronic engineering from Nanyang
Technological University, Singapore.
He is with Simula Research Laboratory, Oslo,
Norway, and an Adjunct Associate Professor
with the University of Oslo, Oslo. His current
research interests include resource, mobility, spectrum, energy, and data management in wireless
communications and networking.
Dr. Zhang serves as an Organizing Committee
Chair for many international conferences. He is an
Associate Editor/Guest Editor for a number ofinternational journals.
Shengli Xie (M’01–SM’02) received the M.S.
degree in mathematics from Central China Normal
University, Wuhan, China, in 1992, and the Ph.D.
degree in automatic control from the South China
University of Technology, Guangzhou, China, in
1997.
He was the Vice Dean with the School
of Electronics and Information Engineering,
South China University of Technology, from 2006
to 2010. He is currently the Director with the
Institute of Intelligent Information Processing,
Beijing, China, and the Guangdong Key Laboratory of Information
Technology, Guangzhou, for the Internet-of-things, and a Professor with the
School of Automation, Guangdong University of Technology, Guangzhou.
His current research interests include statistical signal processing and
wireless communications, with an emphasis on blind signal processing and
Internet-of-things. He has authored/co-authored four monographs and over
100 scientific papers published in journals and conference proceedings, and
holds 30 patents.