IEEE TRANSACTIONS ON POWER SYSTEMS
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Conceptual Design of A Multi-Agent System
for Interconnected Power Systems Restoration
Fenghui Ren, Minjie Zhang, Danny Soetanto, and XiaoDong Su
Index Terms—Dynamic team forming, multi-agent system,
power system, restoration.
I. INTRODUCTION
O
caused the August 10, 1996 WSCC system outage [3]. Due
to the complexities and expanding structures of present-day
power systems, conventional centralized and regulated control systems tend to be inadequate because of deficiencies in
robustness, openness, and flexibility.
In order to manage and control power systems more efficiently, multi-agent systems (MASs) have been employed to
solve the challenges in power engineering, and are being developed for a range of applications including fault diagnostics,
system monitoring, system restoration, system simulation, and
system control [2]. In general, two kinds of coordination strategies are considered to manage and control agents in MASs,
which are the centralized coordination strategy (CCS) and the
decentralized coordination strategy (DCS).
By using CCS, agents are hierarchically organized, and a
system coordinator is employed to control almost all activities
of the system. Several papers have introduced studies on CCS.
For example, Nagata et al. proposed an MAS for power system
restoration [4]. In such a system, individual bus agents are controlled by one facilitator agent. When faults occur, the facilitator agent interacts with and manages bus agents in order to decide a suitable configuration to restore the power system. Coury
et al. employed agents to solve the distance relay problem for
multi-terminal lines [5]. In their approach, three coordination
agents are employed for three terminals, and control behaviors
of information collection, decision making, and communication. Finally, coordination agents at each terminal will decide
the correct relay characteristics. Srivastava et al. proposed an
MAS to restore a naval battle shipboard power system [6]. In
their system, a coordinating agent controls both the fault detector agent and the geographic information system agent to detect abnormal conditions and to determine de-energized loads so
as to enhance survivability of naval ships. However, disadvantages of these types of designs, such as deficiencies in robustness, openness, and flexibility, prevent them from being used extensively in open and large scale power systems, such as power
grid networks with heterogeneous and distributed resources.
In order to overcome the above shortcomings of CCS, DCS
were proposed by researchers and employed in many power
management systems. Nordman and Lehtonen presented their
agent concepts for managing electrical distribution networks by
using decentralized functionality [7], [8]. In their approach, the
secondary substation objects within the primary substation area
are intelligent, and distribution automation applications are not
executed in a control center, but executed by local substation
controllers through collaboration with neighboring substations.
Solanki et al. presented a decentralized solution for distribution power system restoration [1]. Agents have the abilities to
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Abstract—Outages and faults in interconnected power systems
may cause cascading sequences of events, and catastrophic failures
of power systems. How to efficiently manage power systems and
restore the systems from faults is a challenging research issue in
power engineering. Multi-agent systems are employed to address
such a challenge in recent years. A centralized coordination
strategy was firstly introduced to manage agents in a power
system. Such a strategy usually adopts a single central coordinator
to control the whole system for system management, maintenance, and restoration purposes. However, disadvantages such as
deficiencies in robustness, openness, and flexibility prevent this
strategy from extensive online applications. Consequently, a decentralized coordination strategy was proposed to overcome such
limitations. But the decentralized coordination strategy cannot
efficiently provide a global solution when serious faults spread
out in a power system. In this paper, a conceptual multi-agent
system design is introduced to express our proposal in power
system modeling. A novel dynamic team forming mechanism is
proposed to dynamically manage agents in power system with a
flexible coordination structure, so as to balance the effectiveness
and efficiency of the introduced multi-agent system. The results
from simulations of case studies indicate the performance of the
proposed multi-agent model.
UTAGES and faults will cause serious problems and/or
failures in interconnected power systems. Unfortunately,
most system outages and faults are inevitable [1]. Even though
technologies can be employed to estimate future possible faults
in power systems [2], sources of vulnerability such as human
faults, control system failures, missing information in decision
making, and communication network failures will also threaten
the system safety. Without considering effective countermeasures for faults and failures, small faults will also generate
catastrophic failures and cascading sequences of events. For
example, the incorrect settings of protective devices and control
strategies could not successfully interrupt cascading events,
Manuscript received January 16, 2011; revised January 22, 2011, May 11,
2011, and August 29, 2011; accepted October 31, 2011. This work was supported by the Australian Research Council (ARC) Linkage Scheme LP0991428
and Transgrid Australia. Paper no. TPWRS-00008-2011.
F. Ren and M. Zhang are with the School of Computer Science and Software
Engineering, University of Wollongong, Wollongong 2500, Australia.
D. Soetanto and X. Su are with the School of Electrical, Computer and
Telecom Engineering, University of Wollongong, Wollongong 2500, Australia.
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/TPWRS.2011.2177866
0885-8950/$26.00 © 2011 IEEE
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Fig. 1. Multiagent system architecture.
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communicate and collaborate with other agents to perform individual tasks or to restore the power system. Nagata et al. extended their power restoration system [4] by using DCS [9],
[10]. In their new design, local agents are controlled by a number
of coordinator agents. Each coordinator agent and its controlled
local agents form a local group, where the coordinator agent acts
as a local group leader. Different groups can communicate, cooperate, and work together to restore the power system. Kodama
et al. proposed an autonomous restoration approach for power
distribution network by using the contract net protocol [11]. An
agent in the outage area will broadcast a connection task in the
network, and all valid agents in the generation area will bid for
that. The agent in the outage area will decide which generation
area the agent should fulfill the task based on itself’s interests.
By comparison with CCS, DCS is more robust and flexible, and
more suitable to be used in modern open, distributed, dynamic
power systems. However, DCS also has some disadvantages,
which may limit the performance of some applications. One of
the biggest issues is that by using DCS, agents may focus on
neighboring communications and information exchanges, and
their decision-making process might be locally optimized, and
it is hard to provide a global optimized solution efficiently .
How to balance the effectiveness and efficiency in a power
system restoration becomes an important research issue. In
this paper, a Dynamic Team Forming Mechanism (DTFM)
is introduced to reach such a balance, so as to improve the
performance of MAS-based power management systems. By
comparison with CCS and DCS, the major differences between
the proposed DTFM and existing works [9], [12] are that: 1)
our MAS structure is not fixed, but changeable in-between the
distributed structure and the hierarchical structure according
to complexity of faults; 2) system keeps a minimal number
of agents during day-to-day normal operations when no fault
is detected. However, when faults are detected in the system,
a reasonable number of agents will be generated to solve the
faults. When the problems are solved, the extra agents will be
destroyed; and 3) system can provide reasonable solutions for
faults at different complexity levels. Local groups are firstly
generated to provide the local optimized solution. The group
size will be gradually increased according to the complexity of
faults. Finally, the global optimized solution can be provided to
solve fatal faults and catastrophic failures when necessary.
The rest of this paper is organized as follows. Section II
introduces the principle of the proposed multi-agent system.
Section III introduces Bus Agent (BA), Coordination Agent
(CA), and DTFM in detail. Section IV tests the proposed
system, and compares the simulation results with other restoration approaches. Section V discusses some related works,
and Section VI concludes this paper and indicates our future
research directions.
Fig. 2. System time frame.
The Proactive Layer is the lowest layer, and is in charge of
day-to-day system operation according to a Day-to-Day Plan
(Phase I, see Fig. 2). The conceptual input, i.e., voltage and reactive power, are collected by the Interface, and forwarded to
the Proactive Layer; BAs work on this layer and monitor these
inputs. If any fault is detected, the Reactive Layer (middle layer)
will be activated. BAs will work on the Reactive Layer and try
to restore the physical bus alone according to their Countermeasure Plans (Phase II). A BA’s Countermeasure Plan contains the actions which the BA can perform to restore the system
from faults [12]. If the faults are serious and the bus cannot be
restored by the BA alone, then the Social Layer will be activated (Phase III). A CA firstly constructs a group and tries to restore the bus locally. The CA will control the decision-making
process and the task allocation process. The group size is dynamically modified according to the complexity of the faults.
Each group member’s Countermeasure Plan will be considered
by the group leader. The conflicts, such as interest conflicts, belief conflicts, and behavior conflicts, between group members
are solved through agent negotiations [13]. The group leader
will consider each member’s ability and allocate execution proposals. The execution proposal will be forwarded to the Reactive Layer to adapt each BA’s individual behavior (Phase IV).
After the system is restored, the group will be destroyed and
all BAs returns to the Proactive Layer (Phase V). Phases II, III,
and IV will be repeated when the CA fails to get a solution in its
group and a bigger group is needed. In the extreme case, when
the faults are fatal, the group size will be enlarged to the whole
power system, and a global solution will be generated.
II. PRINCIPAL SYSTEM CONCEPT
In Fig. 1, the architecture of the proposed MAS is displayed.
In general, the proposed MAS contains three layers, i.e., Proactive Layer, Reactive Layer, and Social Layer, and two types of
agents, i.e., BA and CA. In Fig. 2, the time frame for both BAs
and CAs in each layers are displayed.
III. MULTI-AGENT SYSTEM
A. Bus Agents
A BA is a static agent, and is permanently associated with
a physical bus. A BA will be eliminated only when its corresponding physical bus is removed from the power system. A BA
REN et al.: CONCEPTUAL DESIGN OF A MULTI-AGENT SYSTEM
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Fig. 4. Bus agent.
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Fig. 3. IEEE 30-bus power system.
monitors the status of the physical bus, and controls the behaviors of the physical bus. For example, in Fig. 3, we display the
IEEE 30-bus power system [14], and each physical bus is allocated a BA. BA1 is associated with GLEN LYN Generator, and
can control the generator’s output power.
In order to effectively implement BAs, we use the Belief-Desire-Intention (BDI) agent model. The BDI agent model is a
software model for intelligent agents, and provides a mechanism for agents to select plans through deliberation. Beliefs represent the informational state of the agent and the world. Desires
represent the motivational state of the agent. They represent objectives or situations that the agent would like to accomplish
or bring about. Intentions represent the deliberative state of an
agent—what the agent has chosen to do. Intentions are desires
to which the agent has to do some extent committed.
is illustrated. Firstly, the
In Fig. 4, the procedure of a
Sensors component transfers the signal inputs from the power
system to the conceptual input of the Belief Revision Function
will construct the Belief component to
component. The
represent the existing status of the physical bus at time (i.e.,
the reactive power
and the voltage ) and the Desire component to indicate the theoretical expectation on Bus . Secemploys the Filtering function to choose a practical
ondly,
status that Bus should be by comparing its beliefs and desires,
and the Intentions component is generated (containing intenand voltage ). Then we employ the
tion reactive power
case-based reasoning (CBR) as the internal logic to perform the
action selection function, and the selected actions will be exe’s intentions. If faults are detected, the Reaccuted to fulfill
tive Layer will be triggered and countermeasure actions will be
selected from Plans to restore the bus. Let
indicate all cases in the Plan component, and each case in the li.A
brary is defined as
records a fact that
ever executed an action
in
case
milliseconds after a fault (i.e., fault reactive power is
and voltage is
) was detected, and the action successfully
and voltage to
. Suprestore Bus ’s reactive power to
pose a fault
is detected by
at time ,
in order to select a reasonable countermeasure from its Plans,
firstly retrieve its Plans and evaluate each case in the Plans
according to (1). The purpose of Equation 1 is to calculate the
by considering
difference between the fault and the case
the time, the magnitude of the fault, and the effect of the action:
(1)
where is the time when the evaluation is performed and
is the normalization function.
Suppose case
is most similar one as the fault
, then its action
is selected and executed by
, and the consequent results [i.e., reactive power
) and voltage (
)] are recorded. A new case
(
is added
’s Plans. If the results satisfy
’s Intentions,
into
will turn itself back to the Proactive Layer. Otherwise, if the
’s Intentions,
will ask help from
results do not satisfy
a CA and the Social Layer will be activated.
B. Coordination Agents
A CA works on the Social Layer, and is not fixed to any
physical bus. A CA does not permanently exist, but is created
under a request and is eliminated when its tasks are completed.
Our proposed CA has three features which make its differences
from related work [10], [12]. 1) It can reduce the system cost.
A CA will be created only when a BA cannot solve the problem
caused by a system fault alone. After the problem is solved, the
CA will be eliminated. In Fig. 3, when BA23 detects a fault but
cannot solve the problem, CA1 is created under the request of
BA23. CA1 controls BA23 and BA23’s neighbors (i.e., BA15 and
BA24), and tries to solve the problem within this group. After
the problem is solved, CA1 will release all controlled agents
and CA1 will be eliminated as well. 2) More than one CA can
work concurrently on multiple heterogeneous buses for solving
different problems. In Fig. 3, when CA1 is solving a problem
through coordinating BA15, BA23, and BA24, CA2 is working
on another issue by managing BA1, BA2, and BA3. These BAs
and CAs work on different facilities and have different functionalities. 3) The proposed CAs are robust and scalable for solving
problems in different complexities. In Fig. 3, when CA2 cannot
solve a problem within the existing group (contains BA1, BA2,
and BA3), CA3 is created under the request of CA2. Then CA3
will coordinate CA2, (but CA2 still controls its original group
members), and also coordinate all neighbors of CA2’s group
members. CA3 will lead the new group to solve the problem.
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Fig. 5. Coordination agent.
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In the extreme case, a top CA will be created to control all other
agents, and the global solution will be provided.
In Fig. 5, the coordination procedure of CAs is illustrated.
Firstly, under the request of a Servant Agent (SA) (a BA or a
CA), a CA will be created and performs a team forming process
to generate a group (the detail about team forming will be introduced in Section III-C), and the CA becomes a Master Agent
(MA) to control all group members. Then the CA will collect
information from its SAs. By combining all SAs’ plans and possible behaviors, the Plan Set and the Action Set can be created.
Thirdly, the CA will perform a Practical Reasoning process to
analyze the fault reported by its SA and to find a countermeasure based on the collected information. We still employ the
case-based reasoning for this purpose. Let
be the Plan Set, where
includes plans from
. Let
be the Action Set, where
indicates accan fulfill. Suppose
fails to solve the problem
tions
cased by a fault alone, and request a help from
.
will
, and its counterreport the original fault
measures and consequent results [i.e.,
] to
. Then
will forward time
to its SAs, and request each SA to provide a norperiod
to indicate how it is affected by the fault
malized value
reported and
’s actions.
is calculated by considering
is directly impacted by
, and defined in (2):
how
(2)
where
at time
[
,
, refer to (1) and Section III-A
for details], respectively. Because different SAs may have different knowledge and action plans, conflicts may happen during
will perform as a
their cooperations. In such a case, the
mediator to exchange intentions between the selected SAs, and
adapt their intentions gradually until the conflicts are resolved.
Such process is named as Negotiation. Generally, in order to
will order
reach an agreement during the negotiation, the
SAs to concede their intentions between the most expected bus
status and the existing bus status according to the time past and
the bus’ priority. Because of page limitation, the principle of
agent negotiation can be gotten in the literatures [13], [15] or
our previous work [16], [17]. Lastly, the Task Allocation process
allocates tasks to SAs according to the action selection results
and the negotiation outcomes (if applicable). After SAs fulfill
their tasks, the feedback from the physical bus will indicate the
will be elimiperformance. If the problem is solved, the
will request
nated and all SAs will be released. Otherwise,
helps from another Coordination Agent and a bigger group will
be created (see Section III-C for details). Such a process is repeated until the problem is solved or the group size reaches the
maximum number, i.e., including all BAs.
and
and
, respectively.
are defined as follows:
indicate
,
’s status
, and
(3)
otherwise.
(4)
ranks the SAs according to the
values, and
Then
values are smaller than a predefined
picks up the SAs whose
threshold. After that, each selected
will perform an action selection process based on the result of
C. Dynamic Team Forming Mechanism
Let Agent be an agent in the MAS, if Agent is a BA, set
NP will include all agents which have a direct connection between Agent ; and if Agent is a CA, set NP will include all
agents which have a direct connection with any agent controlled
by Agent . Suppose that a fault happens and is detected by
Agent , Agent will try to solve the problem without helps
from any other agents. However, if Agent fails to solve the
problem alone, a Team Forming Algorithm (TFA) will be per. Then
formed. Firstly, Agent asks help from a CA, i.e.,
will create a group (i.e., Group G), and add Agent as a
. Secondly,
will contact Agent
group member, i.e.,
’s neighbors, i.e., all agents in set NP, for team forming purpose, and wait for their responses. If a neighbor of Agent is not
’s
controlled by any other agents, this neighbor will reply to
request, and becomes a member of Group G. In the case that a
neighbor of Agent is already controlled by another agent, this
’s request to its controller, and let the
neighbor will forward
controller decide whether they will join Group G. Lastly, when
gets all responses from all Agent ’s neighbors, Group G
is finalized. Then
will behave as a MA and controls all
agents in group G. The result of TFA is a hierarchical MAS
is the controller. By comparison with simply
structure, and
adding more BAs to the same group and control the group using
just one CA, the proposed TFA has the following merits. 1) The
proposed TFA generates a hierarchical structured MAS, which
can effectively share work loads among CAs according to fault
complexities, so as to improve the whole system’s efficiency. 2)
The proposed TFA provides the system with the ability to solve
parallel problems, which is difficult to be fulfilled in single CA
structure. 3) The proposed TFA is more robust in the case when
a CA fails, and can prevent the failure of the whole system (see
Team Reforming Algorithm for details 2). In Algorithm 1, we
formally define the TFA.
REN et al.: CONCEPTUAL DESIGN OF A MULTI-AGENT SYSTEM
Algorithm 1: Team forming algorithm
1: Input: Agent
(
),
2: Output: A new CA
and its controlled Group G
3: create a new CA
4: set
to
, G to , and
to
5:
6: for each neighbor agent (Agent
8:
if
then
set
to
9:
10:
else
11:
12:
while
13:
set
do
to
14:
end while
15:
set
16:
17:
do
end if
18: end for
19: return CA
interconnections in a large scale power system will impact the
performance of the proposed TFA. If a power system contains
too many interconnections, each BA will have a large number
of directly connected agents, so as the agent number in each
group will be large and the group controller will spend a long
time to plan the countermeasures and organize each group
members (i.e., similar as the disadvantages in CCS). On the
other hand, if the interconnections in a power system are few
and BAs have very limited directly linked neighbors, many
CAs will be created before an effective solution is generated.
This is because few new agents will be added to an existing
group. Each time a new CA is created, the new CA may not
have significant improvement on its knowledge for the problem
solving, and hence will keep on creating new CAs until an
effective solution is proposed. According to our experience,
if a bus has only one connection with its neighbor, the team
forming process may become time-consuming; and if a bus
has more than 6 connections, the decision-making process may
become time-consuming. Solving such a problem will be one
of our focuses in the further research.
However, during the TFA process, an agent may fail and become unreachable. In order to solve such an issue, a Team Reforming Algorithm (TRA) is proposed. Generally, if a controller
agent notices that a controlled agent is unreachable, the controller agent will eliminate the controlled agent from its group
straightway. If the controlled agent is the one who originally
detects the fault (a BA) or forwards the fault (a CA) to the controller agent, then the controller agent will destroy the group.
The controlled agent will perform the TFA process to request
a help from another CA to solve the problem. If a controlled
agent finds that its controller agent becomes unreachable, the
controlled agent will leave the controller agent’s group, and turn
itself back to an MA. Then the controlled agent will perform the
TFA process again if necessary. The TRA is shown in Algorithm 2.
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7:
) of
5
to
and Group G
In order to check the dominance relationship of an agent ,
a query with response requirement is sent to Agent . Agent
will reply ’MA’ if it is a controller, or ’ ’ if it is controlled
by Agent , then another query will be further sent to Agent
. Such a process is repeated until an agent replies ’MA’, and
the dominance relationship is got by recording the sequence
of replies. For example, suppose we are going to check the
dominance of BA3 in Fig. 3, the following steps are performed:
,
;
1)
2)
,
;
,
; 4)
3)
(where
indicates Agent
return
is controlled by Agent ). Theoretically, a CA can control
any number of agents. A CA’s problem solving ability will be
improved as its group member increases. That is because with
more group members, a CA can collect more information about
a power system, and organize more complex operations within
the group. For instance, CCS can be considered as an extreme
case that a CA dominates a group containing all agents in a
power system. With a large group members, CCS can produce
the global optimal solution, but will spend much longer time.
So getting the balance between effectiveness and efficiency is
very important. Therefore, usually the agent number in a group
is small at the beginning, and gradually increased. Generally,
the proposed method is scalable. However the complexity of
Algorithm 2: Team reforming algorithm (TRA)
1: Input: CA , ’s controlled group G, and ’s controller CA
2: Output: The CA
3: for each agent
4:
and its Group G will be reformed
in Group G do
if
then
5:
6:
set
7:
if
to
then
8:
9:
if
then
10:
11:
else
12:
if
13:
is CA then
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14:
end if
15:
end if
16:
17:
simulated by using the MATPOWER package [19] on Matlab,
and all agents are implemented by using the Jack Agent Software which can allow many agents to be simulated and includes
interagent communication. During the simulation, the data generated from the Matlab on each physical bus are firstly recorded
by Microsoft Excel files, and read by our agents automatically.
After agents make decisions on the following actions, the adjustments on each physical bus will be delivered to the corresponding Excel files. Then the Matlab updates the settings on
each physical bus according to the Excel files, and re-generates
new simulation results. Finally, by recording these simulation
results continuously, the simulation curve for each physical bus
can be generated. In this section, three cases are investigated.
The first case mimics a situation where only one bus has a fault
and the load shedding operation is not necessary to restore the
fault. The second case mimics a situation where a generator bus
has a fatal fault and the load shedding operation is necessary.
The third case mimics a situation where two buses have faults
concurrently. Through the case studies, the dynamic managing
ability of the proposed MAS is demonstrated in the aspect of
restoring a power system.
end if
end if
18: end for
19: if
then
20:
set
21:
if
to
22:
23:
else
24:
25:
end if
26: end if
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then
After a group of agents (Group G) successfully solves a
system problem, the group will be eliminated to reduce the
be the group leader, the TDA is
system operation cost. Let
defined in Algorithm 3. Generally, TDA is a recursive algorithm
solves all allocated problems, and is
and is activated when
will release all agents
not controlled by any agent. Firstly,
in Group G, and then be eliminated. Secondly, if a member
agent of Group G is a CA, the above process will be applied
again on this member agent. Such a recursive procedure will be
continued until all agents in the hierarchical structure controlled
are released or a CA’s tasks are not complete.
by
Algorithm 3: Team dismissing algorithm (TDA)
1: Input: A CA
and its controlled group G
2: Output: The CA
3: if
4:
5:
and
for each agent
set
if
10:
end if
end for
11:
12: end if
in Group G do
is CA then
8:
9:
then
to
6:
7:
and Group G will be eliminated
IV. CASE STUDIES
The IEEE 30-bus power system [14] (Fig. 3) is used to test
the proposed MAS, and the restoration results with the countermeasures introduced in [18] are compared. The 30-bus system is
A. Case I
Case I mimics the situation that only one bus has a fault, and
the load shedding operation is not necessary for restoring the
system. We assume that Bus 3 is broken and the countermeasures introduced in [18] is employed to restore the system. The
countermeasures are chosen to avoid voltage collapse due to the
restoration in generation reactive power output which is around
50 s. Typical countermeasures against voltage drop such as sc
voltage increase, generation and load tap change operation are
carried out in the first 30 s and if the countermeasure has not
caused the voltage to recover, load shedding is actuated at 30 s
giving a manager of 20 s before the reactive power of the first
generator is rescheduled (which can lead to fast voltage collapse region). Such countermeasures are centralized controls,
and must have global view of the power system. The countermeasures are displayed in Table I. In Step 1, the generator
voltage in Bus11 is increased. In Steps 2–3, the transformer ratios in Bus4 and Bus6 are modified. In Steps 4–5, Bus30 and
Bus29 perform load shedding operation. We monitor the voltage
of Bus30 and the power of Bus11 in Fig. 6. It can be seen that
without countermeasures, the power system will collapse, i.e.,
Bus30’s voltage will drop to zero, and Bus11’s generator will
be damaged, and with the traditional protection introduced in
[18], Bus30’s voltage is kept around 1.02 pu, and Bus11’s reactive power is kept around 20 Mvar. Even though the traditional countermeasures can solve the system faults, and restore
the system from vulnerable state, several components (i.e., generators and transformers) are impacted, and the load shedding
operation needs to be performed.
In Table II, the proposed MAS countermeasures are displayed. Firstly, BA3 detects the fault, and activates the Reactive
Layer and Social Layer. Then CA1 is created to solve the
, but it fails. Immediately, a higher
problem within Group
.
level CA2 is created to solve the problem within Group
Finally, CA2 orders BA5 to modify its generator’s voltage,
. After the power
and the problem is solved within Group
REN et al.: CONCEPTUAL DESIGN OF A MULTI-AGENT SYSTEM
Fig. 6. Restoration results comparison (Case I).
are displayed in Fig. 7. It can be seen that with such a protection, the power system is saved. Bus30’s voltage is increased to
0.9 pu, and Bus11’s reactive power is kept round 18 Mvar.
The proposed MAS countermeasures are displayed in
Table III. BA2 firstly detected the fault and asked help from
CA1. CA1 ordered BA1 and BA5 to increase their generators’
voltage and BA4 and BA6 to decrease their transformers’ ratio
in Step 4. However, such a rescue failed and hence CA2 was
generated. In Step 6, CA2 ordered BA13 to increase its generator’s voltage and BA28 to decrease its transformer’s ratio, but
it cannot stop the system collapse as well. Under the request of
CA2, CA3 controlled more BAs and ordered BA11 to increase
its voltage. Such an operation failed as well, and CA4 cannot
perform any new operation to stop the system collapse. Finally,
CA5 was created to control all BAs and CAs in the system.
Because CA5 had the global view of the system, so it ordered
BA29 and BA30 to perform the load shedding operation. The
system was saved and prevented from collapse. It can be seen in
Fig. 7 that before the load shedding operation was performed,
even though several operations was performed to rescue the
power system, BA11’s power dropped dramatically. After the
load shedding operation was performed by CA5, BA11’s power
was recovered and BA30’s voltage became stable. Our proposed
MAS spent similar time as the traditional countermeasures to
solve the fault in Case II. The simulation results indicates
that our proposed MAS approach can also perform the load
shedding operation when it is needed.
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TABLE I
TRADITIONAL COUNTERMEASURES
7
TABLE II
PROPOSED MAS COUNTERMEASURES (CASE I)
C. Case III
Fig. 7. Restoration results comparison (Case II).
system become stable, CA1 and CA2 are eliminated, and all
BAs are back to the Proactive Layer. The restoration results are
compared with the traditional approach’s results in Fig. 6. It
can be seen that by using the proposed MAS, Bus30’s voltage
is kept around 0.99 pu, and Bus11’s reactive power is kept
around 17 Mvar. Although Bus30’s voltage is not restored to
the same level, the proposed MAS can also prevent the power
system from collapse. Furthermore, the proposed MAS only
modifies the generator voltage of Bus5, and does not perform
a load shedding operation, and hence minimizes the impact to
the system users. Also, the traditional countermeasures spent
30 s to restore the system, while our proposed MAS only spent
7 s to solve the fault in a local group.
B. Case II
Case II simulates a situation that a generator bus (i.e., Bus2)
has a problem and the generator is disconnected. In such a case,
the load shedding operation is necessary in order to prevent
system collapse. Firstly, we employ the traditional countermeasures (see Table I) to restore the power system, and the results
Case III mimics a more complex situation, namely that two
faults occur at the same time in a power system. The connection between Bus2 and Bus6 and the connection between Bus24
and Bus25 are broken. Initially, we still employ the traditional
countermeasures (see Table I) to restore the power system, and
the results are displayed in Fig. 8. It can be seen that with such
a protection, the power system is saved. Bus30’s voltage is increased to 1 pu, and Bus11’s reactive power is kept round 20
Mvar. However, as the problem in Case I, the traditional countermeasures require a centralized controller and a global system
view, so its efficiency is decreased. Also, the traditional countermeasures will significantly impact system users because of the
load shedding operation.
In Table IV, the proposed MAS countermeasures are listed.
When faults happen in Bus6 and Bus24, BA6 and BA24 detect
faults, respectively. Firstly, both of them try to solve the problem
alone, but both fail. Then they ask for help from CAs. CA1 con, and solves its problem by ordering BA2 to introls Group
crease generator voltage in Step 4. At the same time, because
CA2 cannot solve its problem, and it requests help from CA3.
is released and CA3 orders both BA10 and
In Step 5, Group
BA27 to increase their transformer ratios, and the problem is
solved. Finally, CA3 and CA2 are eliminated in sequence. The
restoration results are illustrated in Fig. 8. It can be seen that our
MAS also solves the two concurrent system faults using only
local system views, and keeps Bus30’s voltage around 1 pu and
Bus11’s reactive power around 17 Mvar. Obviously, the restoration efficiency is increased, and the impact on system users is decreased. Also, our proposed MAS only spent 13 s to restore the
8
IEEE TRANSACTIONS ON POWER SYSTEMS
fulfill self-healing strategies, i.e., the reactive layer, the coordination layer, and the deliberative layer. Even though SPID
employs distributed agents, the architecture of SPID is static.
Nagata et al. proposed a multi-agent approach for decentralized
power system restoration in a distribution system network [10].
The load agent collects information about the power system,
and the feeder agent controls the entire restoration process in accordance with the priority in the restoration strategy. However,
their approach has a difficulty to provide a global solution for a
catastrophic failure. Solanki et al. proposed a distributed multiagent system to analyze faults in a power system, and to restore
the system after faults [1]. When a fault is detected by a load
agent, an alert message is sent to generator agents. Then generator agents try to restore the fault by sending their remaining
available transfer capacity to the problem load agent by passing
through switch agents. However, their system only considers to
control generators’ actions, but does not involve other facilities.
Lin et al. proposed a centralized multi-agent system to model
the power distribution between substations and end-users[21].
The distribution feeders and substation transformers are modeled by FCB agent and MTR agent, respectively. When a fault
happens in an end-user, the FCB agent firstly allocates the fault
by checking both the upstream and downstream switches’ current readings. Then restoration requirements are forwarded to
the MTR agent. The MTR agent will perform a heuristic reasoning process to decide which loads should be restored or shed
based on capacities of transformers and feeders, and the priorities of each load. Finally, the power will be transferred to the
problem loads through valid feeders, and the distribution system
can be restored.
By comparison with the above work, our proposed
multi-agent system has three merits. 1) Our proposed
multi-agent system consists of three layers and manages
power systems from day-to-day operations to dynamical faults
restoration. 2) Our proposed multi-agent system uses DTFM to
dynamically modify the system architecture. Distributed architectures are generated to perform normal operations and/or to
restore the system by using individual agents, and hierarchical
architectures are created to provide global solutions for faults.
3) Through dynamically adjusting the system architecture and
the number of agents, our proposed multi-agent system can
trade-off the system cost and the system efficiency.
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TABLE III
PROPOSED MAS COUNTERMEASURES (CASE II)
Fig. 8. Restoration results comparison (Case III).
TABLE IV
PROPOSED MAS COUNTERMEASURES (CASE III)
VI. CONCLUSION AND FUTURE WORK
system by using two local groups, while the traditional countermeasures spent 30 s and the system global view was also needed.
V. RELATED WORK
In this section, some related work is discussed and compared
with our approach. Liu et al. [20] proposed a Strategic Power
Infrastructure Defense (SPID) system to prevent catastrophic
failures in power systems. The principle of SPID is to provide
self-healing and adaptive reconfiguration capabilities for power
grids based on wide-area system vulnerability assessment. A
three-layer hybrid multi-agent system model was proposed to
In this paper, a conceptual MAS design is proposed for autonomous power systems management and restoration. The Bus
Agent was introduced to control each individual, physical bus
in a power system, and the Coordination Agent was introduced
to manage behaviors of Bus Agents. A dynamic team forming
mechanism was proposed for agent coordination purposes. The
architecture of the proposed MAS is changeable, and is dynamically and automatically modified according to power system
status. The simulation results demonstrated the feasibility of the
proposed MAS.
Our future work on this research will focus on implementation of the MAS in a real-world application and testing the
system in more complex scenarios.
REN et al.: CONCEPTUAL DESIGN OF A MULTI-AGENT SYSTEM
REFERENCES
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[15] S. Kraus, Strategic Negotiation in Multiagent Environments. Cambridge, MA: MIT Press, 2001.
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[18] W. Lachs and D. Sutanto, “Voltage instability in interconnected power
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[20] C. Liu, J. Jung, G. Heydt, V. Vittal, and A. Phadke, “The strategic
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[3] J. Jung, C. Liu, S. Tanimoto, and V. Vittal, “Adaptation in load shedding under vulnerable operating conditions,” IEEE Trans. Power Syst.,
vol. 17, no. 4, pp. 1199–1205, Nov. 2002.
[4] T. Nagata, H. Watanabe, M. Ohno, and H. Sasaki, “A multi-agent approach to power system restoration,” in Proc. Int. Conf. Power System
Technology, 2000, vol. 3, pp. 1551–1556.
[5] D. Coury, J. Thorp, and K. Hopkinson, “Agent technology applied to
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[6] S. Srivastava, H. Xiao, and K. Butler-Purry, “Multi-agent system for
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[7] M. Nordman and M. Lehtonen, “An agent concept for managing electrical distribution networks,” IEEE Trans. Power Del., vol. 20, no. 2,
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[8] M. Nordman and M. Lehtonen, “Distributed agent-based state estimation for electrical distribution networks,” IEEE Trans. Power Syst., vol.
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[9] T. Nagata, H. Fujita, and H. Sasaki, “Decentralized approach to normal
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[10] T. Nagata, Y. Tao, H. Sasaki, and H. Fujita, “A multiagent approach to
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9
Fenghui Ren, photo and biography unavailable at time of publication.
Minjie Zhang, photo and biography unavailable at time of publication.
Danny Soetanto, photo and biography unavailable at time of publication.
XiaoDong Su, photo and biography unavailable at time of publication.
IEEE TRANSACTIONS ON POWER SYSTEMS
1
Conceptual Design of A Multi-Agent System
for Interconnected Power Systems Restoration
Fenghui Ren, Minjie Zhang, Danny Soetanto, and XiaoDong Su
Index Terms—Dynamic team forming, multi-agent system,
power system, restoration.
I. INTRODUCTION
O
caused the August 10, 1996 WSCC system outage [3]. Due
to the complexities and expanding structures of present-day
power systems, conventional centralized and regulated control systems tend to be inadequate because of deficiencies in
robustness, openness, and flexibility.
In order to manage and control power systems more efficiently, multi-agent systems (MASs) have been employed to
solve the challenges in power engineering, and are being developed for a range of applications including fault diagnostics,
system monitoring, system restoration, system simulation, and
system control [2]. In general, two kinds of coordination strategies are considered to manage and control agents in MASs,
which are the centralized coordination strategy (CCS) and the
decentralized coordination strategy (DCS).
By using CCS, agents are hierarchically organized, and a
system coordinator is employed to control almost all activities
of the system. Several papers have introduced studies on CCS.
For example, Nagata et al. proposed an MAS for power system
restoration [4]. In such a system, individual bus agents are controlled by one facilitator agent. When faults occur, the facilitator agent interacts with and manages bus agents in order to decide a suitable configuration to restore the power system. Coury
et al. employed agents to solve the distance relay problem for
multi-terminal lines [5]. In their approach, three coordination
agents are employed for three terminals, and control behaviors
of information collection, decision making, and communication. Finally, coordination agents at each terminal will decide
the correct relay characteristics. Srivastava et al. proposed an
MAS to restore a naval battle shipboard power system [6]. In
their system, a coordinating agent controls both the fault detector agent and the geographic information system agent to detect abnormal conditions and to determine de-energized loads so
as to enhance survivability of naval ships. However, disadvantages of these types of designs, such as deficiencies in robustness, openness, and flexibility, prevent them from being used extensively in open and large scale power systems, such as power
grid networks with heterogeneous and distributed resources.
In order to overcome the above shortcomings of CCS, DCS
were proposed by researchers and employed in many power
management systems. Nordman and Lehtonen presented their
agent concepts for managing electrical distribution networks by
using decentralized functionality [7], [8]. In their approach, the
secondary substation objects within the primary substation area
are intelligent, and distribution automation applications are not
executed in a control center, but executed by local substation
controllers through collaboration with neighboring substations.
Solanki et al. presented a decentralized solution for distribution power system restoration [1]. Agents have the abilities to
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Abstract—Outages and faults in interconnected power systems
may cause cascading sequences of events, and catastrophic failures
of power systems. How to efficiently manage power systems and
restore the systems from faults is a challenging research issue in
power engineering. Multi-agent systems are employed to address
such a challenge in recent years. A centralized coordination
strategy was firstly introduced to manage agents in a power
system. Such a strategy usually adopts a single central coordinator
to control the whole system for system management, maintenance, and restoration purposes. However, disadvantages such as
deficiencies in robustness, openness, and flexibility prevent this
strategy from extensive online applications. Consequently, a decentralized coordination strategy was proposed to overcome such
limitations. But the decentralized coordination strategy cannot
efficiently provide a global solution when serious faults spread
out in a power system. In this paper, a conceptual multi-agent
system design is introduced to express our proposal in power
system modeling. A novel dynamic team forming mechanism is
proposed to dynamically manage agents in power system with a
flexible coordination structure, so as to balance the effectiveness
and efficiency of the introduced multi-agent system. The results
from simulations of case studies indicate the performance of the
proposed multi-agent model.
UTAGES and faults will cause serious problems and/or
failures in interconnected power systems. Unfortunately,
most system outages and faults are inevitable [1]. Even though
technologies can be employed to estimate future possible faults
in power systems [2], sources of vulnerability such as human
faults, control system failures, missing information in decision
making, and communication network failures will also threaten
the system safety. Without considering effective countermeasures for faults and failures, small faults will also generate
catastrophic failures and cascading sequences of events. For
example, the incorrect settings of protective devices and control
strategies could not successfully interrupt cascading events,
Manuscript received January 16, 2011; revised January 22, 2011, May 11,
2011, and August 29, 2011; accepted October 31, 2011. This work was supported by the Australian Research Council (ARC) Linkage Scheme LP0991428
and Transgrid Australia. Paper no. TPWRS-00008-2011.
F. Ren and M. Zhang are with the School of Computer Science and Software
Engineering, University of Wollongong, Wollongong 2500, Australia.
D. Soetanto and X. Su are with the School of Electrical, Computer and
Telecom Engineering, University of Wollongong, Wollongong 2500, Australia.
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/TPWRS.2011.2177866
0885-8950/$26.00 © 2011 IEEE
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Fig. 1. Multiagent system architecture.
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communicate and collaborate with other agents to perform individual tasks or to restore the power system. Nagata et al. extended their power restoration system [4] by using DCS [9],
[10]. In their new design, local agents are controlled by a number
of coordinator agents. Each coordinator agent and its controlled
local agents form a local group, where the coordinator agent acts
as a local group leader. Different groups can communicate, cooperate, and work together to restore the power system. Kodama
et al. proposed an autonomous restoration approach for power
distribution network by using the contract net protocol [11]. An
agent in the outage area will broadcast a connection task in the
network, and all valid agents in the generation area will bid for
that. The agent in the outage area will decide which generation
area the agent should fulfill the task based on itself’s interests.
By comparison with CCS, DCS is more robust and flexible, and
more suitable to be used in modern open, distributed, dynamic
power systems. However, DCS also has some disadvantages,
which may limit the performance of some applications. One of
the biggest issues is that by using DCS, agents may focus on
neighboring communications and information exchanges, and
their decision-making process might be locally optimized, and
it is hard to provide a global optimized solution efficiently .
How to balance the effectiveness and efficiency in a power
system restoration becomes an important research issue. In
this paper, a Dynamic Team Forming Mechanism (DTFM)
is introduced to reach such a balance, so as to improve the
performance of MAS-based power management systems. By
comparison with CCS and DCS, the major differences between
the proposed DTFM and existing works [9], [12] are that: 1)
our MAS structure is not fixed, but changeable in-between the
distributed structure and the hierarchical structure according
to complexity of faults; 2) system keeps a minimal number
of agents during day-to-day normal operations when no fault
is detected. However, when faults are detected in the system,
a reasonable number of agents will be generated to solve the
faults. When the problems are solved, the extra agents will be
destroyed; and 3) system can provide reasonable solutions for
faults at different complexity levels. Local groups are firstly
generated to provide the local optimized solution. The group
size will be gradually increased according to the complexity of
faults. Finally, the global optimized solution can be provided to
solve fatal faults and catastrophic failures when necessary.
The rest of this paper is organized as follows. Section II
introduces the principle of the proposed multi-agent system.
Section III introduces Bus Agent (BA), Coordination Agent
(CA), and DTFM in detail. Section IV tests the proposed
system, and compares the simulation results with other restoration approaches. Section V discusses some related works,
and Section VI concludes this paper and indicates our future
research directions.
Fig. 2. System time frame.
The Proactive Layer is the lowest layer, and is in charge of
day-to-day system operation according to a Day-to-Day Plan
(Phase I, see Fig. 2). The conceptual input, i.e., voltage and reactive power, are collected by the Interface, and forwarded to
the Proactive Layer; BAs work on this layer and monitor these
inputs. If any fault is detected, the Reactive Layer (middle layer)
will be activated. BAs will work on the Reactive Layer and try
to restore the physical bus alone according to their Countermeasure Plans (Phase II). A BA’s Countermeasure Plan contains the actions which the BA can perform to restore the system
from faults [12]. If the faults are serious and the bus cannot be
restored by the BA alone, then the Social Layer will be activated (Phase III). A CA firstly constructs a group and tries to restore the bus locally. The CA will control the decision-making
process and the task allocation process. The group size is dynamically modified according to the complexity of the faults.
Each group member’s Countermeasure Plan will be considered
by the group leader. The conflicts, such as interest conflicts, belief conflicts, and behavior conflicts, between group members
are solved through agent negotiations [13]. The group leader
will consider each member’s ability and allocate execution proposals. The execution proposal will be forwarded to the Reactive Layer to adapt each BA’s individual behavior (Phase IV).
After the system is restored, the group will be destroyed and
all BAs returns to the Proactive Layer (Phase V). Phases II, III,
and IV will be repeated when the CA fails to get a solution in its
group and a bigger group is needed. In the extreme case, when
the faults are fatal, the group size will be enlarged to the whole
power system, and a global solution will be generated.
II. PRINCIPAL SYSTEM CONCEPT
In Fig. 1, the architecture of the proposed MAS is displayed.
In general, the proposed MAS contains three layers, i.e., Proactive Layer, Reactive Layer, and Social Layer, and two types of
agents, i.e., BA and CA. In Fig. 2, the time frame for both BAs
and CAs in each layers are displayed.
III. MULTI-AGENT SYSTEM
A. Bus Agents
A BA is a static agent, and is permanently associated with
a physical bus. A BA will be eliminated only when its corresponding physical bus is removed from the power system. A BA
REN et al.: CONCEPTUAL DESIGN OF A MULTI-AGENT SYSTEM
3
Fig. 4. Bus agent.
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Fig. 3. IEEE 30-bus power system.
monitors the status of the physical bus, and controls the behaviors of the physical bus. For example, in Fig. 3, we display the
IEEE 30-bus power system [14], and each physical bus is allocated a BA. BA1 is associated with GLEN LYN Generator, and
can control the generator’s output power.
In order to effectively implement BAs, we use the Belief-Desire-Intention (BDI) agent model. The BDI agent model is a
software model for intelligent agents, and provides a mechanism for agents to select plans through deliberation. Beliefs represent the informational state of the agent and the world. Desires
represent the motivational state of the agent. They represent objectives or situations that the agent would like to accomplish
or bring about. Intentions represent the deliberative state of an
agent—what the agent has chosen to do. Intentions are desires
to which the agent has to do some extent committed.
is illustrated. Firstly, the
In Fig. 4, the procedure of a
Sensors component transfers the signal inputs from the power
system to the conceptual input of the Belief Revision Function
will construct the Belief component to
component. The
represent the existing status of the physical bus at time (i.e.,
the reactive power
and the voltage ) and the Desire component to indicate the theoretical expectation on Bus . Secemploys the Filtering function to choose a practical
ondly,
status that Bus should be by comparing its beliefs and desires,
and the Intentions component is generated (containing intenand voltage ). Then we employ the
tion reactive power
case-based reasoning (CBR) as the internal logic to perform the
action selection function, and the selected actions will be exe’s intentions. If faults are detected, the Reaccuted to fulfill
tive Layer will be triggered and countermeasure actions will be
selected from Plans to restore the bus. Let
indicate all cases in the Plan component, and each case in the li.A
brary is defined as
records a fact that
ever executed an action
in
case
milliseconds after a fault (i.e., fault reactive power is
and voltage is
) was detected, and the action successfully
and voltage to
. Suprestore Bus ’s reactive power to
pose a fault
is detected by
at time ,
in order to select a reasonable countermeasure from its Plans,
firstly retrieve its Plans and evaluate each case in the Plans
according to (1). The purpose of Equation 1 is to calculate the
by considering
difference between the fault and the case
the time, the magnitude of the fault, and the effect of the action:
(1)
where is the time when the evaluation is performed and
is the normalization function.
Suppose case
is most similar one as the fault
, then its action
is selected and executed by
, and the consequent results [i.e., reactive power
) and voltage (
)] are recorded. A new case
(
is added
’s Plans. If the results satisfy
’s Intentions,
into
will turn itself back to the Proactive Layer. Otherwise, if the
’s Intentions,
will ask help from
results do not satisfy
a CA and the Social Layer will be activated.
B. Coordination Agents
A CA works on the Social Layer, and is not fixed to any
physical bus. A CA does not permanently exist, but is created
under a request and is eliminated when its tasks are completed.
Our proposed CA has three features which make its differences
from related work [10], [12]. 1) It can reduce the system cost.
A CA will be created only when a BA cannot solve the problem
caused by a system fault alone. After the problem is solved, the
CA will be eliminated. In Fig. 3, when BA23 detects a fault but
cannot solve the problem, CA1 is created under the request of
BA23. CA1 controls BA23 and BA23’s neighbors (i.e., BA15 and
BA24), and tries to solve the problem within this group. After
the problem is solved, CA1 will release all controlled agents
and CA1 will be eliminated as well. 2) More than one CA can
work concurrently on multiple heterogeneous buses for solving
different problems. In Fig. 3, when CA1 is solving a problem
through coordinating BA15, BA23, and BA24, CA2 is working
on another issue by managing BA1, BA2, and BA3. These BAs
and CAs work on different facilities and have different functionalities. 3) The proposed CAs are robust and scalable for solving
problems in different complexities. In Fig. 3, when CA2 cannot
solve a problem within the existing group (contains BA1, BA2,
and BA3), CA3 is created under the request of CA2. Then CA3
will coordinate CA2, (but CA2 still controls its original group
members), and also coordinate all neighbors of CA2’s group
members. CA3 will lead the new group to solve the problem.
4
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Fig. 5. Coordination agent.
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In the extreme case, a top CA will be created to control all other
agents, and the global solution will be provided.
In Fig. 5, the coordination procedure of CAs is illustrated.
Firstly, under the request of a Servant Agent (SA) (a BA or a
CA), a CA will be created and performs a team forming process
to generate a group (the detail about team forming will be introduced in Section III-C), and the CA becomes a Master Agent
(MA) to control all group members. Then the CA will collect
information from its SAs. By combining all SAs’ plans and possible behaviors, the Plan Set and the Action Set can be created.
Thirdly, the CA will perform a Practical Reasoning process to
analyze the fault reported by its SA and to find a countermeasure based on the collected information. We still employ the
case-based reasoning for this purpose. Let
be the Plan Set, where
includes plans from
. Let
be the Action Set, where
indicates accan fulfill. Suppose
fails to solve the problem
tions
cased by a fault alone, and request a help from
.
will
, and its counterreport the original fault
measures and consequent results [i.e.,
] to
. Then
will forward time
to its SAs, and request each SA to provide a norperiod
to indicate how it is affected by the fault
malized value
reported and
’s actions.
is calculated by considering
is directly impacted by
, and defined in (2):
how
(2)
where
at time
[
,
, refer to (1) and Section III-A
for details], respectively. Because different SAs may have different knowledge and action plans, conflicts may happen during
will perform as a
their cooperations. In such a case, the
mediator to exchange intentions between the selected SAs, and
adapt their intentions gradually until the conflicts are resolved.
Such process is named as Negotiation. Generally, in order to
will order
reach an agreement during the negotiation, the
SAs to concede their intentions between the most expected bus
status and the existing bus status according to the time past and
the bus’ priority. Because of page limitation, the principle of
agent negotiation can be gotten in the literatures [13], [15] or
our previous work [16], [17]. Lastly, the Task Allocation process
allocates tasks to SAs according to the action selection results
and the negotiation outcomes (if applicable). After SAs fulfill
their tasks, the feedback from the physical bus will indicate the
will be elimiperformance. If the problem is solved, the
will request
nated and all SAs will be released. Otherwise,
helps from another Coordination Agent and a bigger group will
be created (see Section III-C for details). Such a process is repeated until the problem is solved or the group size reaches the
maximum number, i.e., including all BAs.
and
and
, respectively.
are defined as follows:
indicate
,
’s status
, and
(3)
otherwise.
(4)
ranks the SAs according to the
values, and
Then
values are smaller than a predefined
picks up the SAs whose
threshold. After that, each selected
will perform an action selection process based on the result of
C. Dynamic Team Forming Mechanism
Let Agent be an agent in the MAS, if Agent is a BA, set
NP will include all agents which have a direct connection between Agent ; and if Agent is a CA, set NP will include all
agents which have a direct connection with any agent controlled
by Agent . Suppose that a fault happens and is detected by
Agent , Agent will try to solve the problem without helps
from any other agents. However, if Agent fails to solve the
problem alone, a Team Forming Algorithm (TFA) will be per. Then
formed. Firstly, Agent asks help from a CA, i.e.,
will create a group (i.e., Group G), and add Agent as a
. Secondly,
will contact Agent
group member, i.e.,
’s neighbors, i.e., all agents in set NP, for team forming purpose, and wait for their responses. If a neighbor of Agent is not
’s
controlled by any other agents, this neighbor will reply to
request, and becomes a member of Group G. In the case that a
neighbor of Agent is already controlled by another agent, this
’s request to its controller, and let the
neighbor will forward
controller decide whether they will join Group G. Lastly, when
gets all responses from all Agent ’s neighbors, Group G
is finalized. Then
will behave as a MA and controls all
agents in group G. The result of TFA is a hierarchical MAS
is the controller. By comparison with simply
structure, and
adding more BAs to the same group and control the group using
just one CA, the proposed TFA has the following merits. 1) The
proposed TFA generates a hierarchical structured MAS, which
can effectively share work loads among CAs according to fault
complexities, so as to improve the whole system’s efficiency. 2)
The proposed TFA provides the system with the ability to solve
parallel problems, which is difficult to be fulfilled in single CA
structure. 3) The proposed TFA is more robust in the case when
a CA fails, and can prevent the failure of the whole system (see
Team Reforming Algorithm for details 2). In Algorithm 1, we
formally define the TFA.
REN et al.: CONCEPTUAL DESIGN OF A MULTI-AGENT SYSTEM
Algorithm 1: Team forming algorithm
1: Input: Agent
(
),
2: Output: A new CA
and its controlled Group G
3: create a new CA
4: set
to
, G to , and
to
5:
6: for each neighbor agent (Agent
8:
if
then
set
to
9:
10:
else
11:
12:
while
13:
set
do
to
14:
end while
15:
set
16:
17:
do
end if
18: end for
19: return CA
interconnections in a large scale power system will impact the
performance of the proposed TFA. If a power system contains
too many interconnections, each BA will have a large number
of directly connected agents, so as the agent number in each
group will be large and the group controller will spend a long
time to plan the countermeasures and organize each group
members (i.e., similar as the disadvantages in CCS). On the
other hand, if the interconnections in a power system are few
and BAs have very limited directly linked neighbors, many
CAs will be created before an effective solution is generated.
This is because few new agents will be added to an existing
group. Each time a new CA is created, the new CA may not
have significant improvement on its knowledge for the problem
solving, and hence will keep on creating new CAs until an
effective solution is proposed. According to our experience,
if a bus has only one connection with its neighbor, the team
forming process may become time-consuming; and if a bus
has more than 6 connections, the decision-making process may
become time-consuming. Solving such a problem will be one
of our focuses in the further research.
However, during the TFA process, an agent may fail and become unreachable. In order to solve such an issue, a Team Reforming Algorithm (TRA) is proposed. Generally, if a controller
agent notices that a controlled agent is unreachable, the controller agent will eliminate the controlled agent from its group
straightway. If the controlled agent is the one who originally
detects the fault (a BA) or forwards the fault (a CA) to the controller agent, then the controller agent will destroy the group.
The controlled agent will perform the TFA process to request
a help from another CA to solve the problem. If a controlled
agent finds that its controller agent becomes unreachable, the
controlled agent will leave the controller agent’s group, and turn
itself back to an MA. Then the controlled agent will perform the
TFA process again if necessary. The TRA is shown in Algorithm 2.
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7:
) of
5
to
and Group G
In order to check the dominance relationship of an agent ,
a query with response requirement is sent to Agent . Agent
will reply ’MA’ if it is a controller, or ’ ’ if it is controlled
by Agent , then another query will be further sent to Agent
. Such a process is repeated until an agent replies ’MA’, and
the dominance relationship is got by recording the sequence
of replies. For example, suppose we are going to check the
dominance of BA3 in Fig. 3, the following steps are performed:
,
;
1)
2)
,
;
,
; 4)
3)
(where
indicates Agent
return
is controlled by Agent ). Theoretically, a CA can control
any number of agents. A CA’s problem solving ability will be
improved as its group member increases. That is because with
more group members, a CA can collect more information about
a power system, and organize more complex operations within
the group. For instance, CCS can be considered as an extreme
case that a CA dominates a group containing all agents in a
power system. With a large group members, CCS can produce
the global optimal solution, but will spend much longer time.
So getting the balance between effectiveness and efficiency is
very important. Therefore, usually the agent number in a group
is small at the beginning, and gradually increased. Generally,
the proposed method is scalable. However the complexity of
Algorithm 2: Team reforming algorithm (TRA)
1: Input: CA , ’s controlled group G, and ’s controller CA
2: Output: The CA
3: for each agent
4:
and its Group G will be reformed
in Group G do
if
then
5:
6:
set
7:
if
to
then
8:
9:
if
then
10:
11:
else
12:
if
13:
is CA then
6
IEEE TRANSACTIONS ON POWER SYSTEMS
14:
end if
15:
end if
16:
17:
simulated by using the MATPOWER package [19] on Matlab,
and all agents are implemented by using the Jack Agent Software which can allow many agents to be simulated and includes
interagent communication. During the simulation, the data generated from the Matlab on each physical bus are firstly recorded
by Microsoft Excel files, and read by our agents automatically.
After agents make decisions on the following actions, the adjustments on each physical bus will be delivered to the corresponding Excel files. Then the Matlab updates the settings on
each physical bus according to the Excel files, and re-generates
new simulation results. Finally, by recording these simulation
results continuously, the simulation curve for each physical bus
can be generated. In this section, three cases are investigated.
The first case mimics a situation where only one bus has a fault
and the load shedding operation is not necessary to restore the
fault. The second case mimics a situation where a generator bus
has a fatal fault and the load shedding operation is necessary.
The third case mimics a situation where two buses have faults
concurrently. Through the case studies, the dynamic managing
ability of the proposed MAS is demonstrated in the aspect of
restoring a power system.
end if
end if
18: end for
19: if
then
20:
set
21:
if
to
22:
23:
else
24:
25:
end if
26: end if
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then
After a group of agents (Group G) successfully solves a
system problem, the group will be eliminated to reduce the
be the group leader, the TDA is
system operation cost. Let
defined in Algorithm 3. Generally, TDA is a recursive algorithm
solves all allocated problems, and is
and is activated when
will release all agents
not controlled by any agent. Firstly,
in Group G, and then be eliminated. Secondly, if a member
agent of Group G is a CA, the above process will be applied
again on this member agent. Such a recursive procedure will be
continued until all agents in the hierarchical structure controlled
are released or a CA’s tasks are not complete.
by
Algorithm 3: Team dismissing algorithm (TDA)
1: Input: A CA
and its controlled group G
2: Output: The CA
3: if
4:
5:
and
for each agent
set
if
10:
end if
end for
11:
12: end if
in Group G do
is CA then
8:
9:
then
to
6:
7:
and Group G will be eliminated
IV. CASE STUDIES
The IEEE 30-bus power system [14] (Fig. 3) is used to test
the proposed MAS, and the restoration results with the countermeasures introduced in [18] are compared. The 30-bus system is
A. Case I
Case I mimics the situation that only one bus has a fault, and
the load shedding operation is not necessary for restoring the
system. We assume that Bus 3 is broken and the countermeasures introduced in [18] is employed to restore the system. The
countermeasures are chosen to avoid voltage collapse due to the
restoration in generation reactive power output which is around
50 s. Typical countermeasures against voltage drop such as sc
voltage increase, generation and load tap change operation are
carried out in the first 30 s and if the countermeasure has not
caused the voltage to recover, load shedding is actuated at 30 s
giving a manager of 20 s before the reactive power of the first
generator is rescheduled (which can lead to fast voltage collapse region). Such countermeasures are centralized controls,
and must have global view of the power system. The countermeasures are displayed in Table I. In Step 1, the generator
voltage in Bus11 is increased. In Steps 2–3, the transformer ratios in Bus4 and Bus6 are modified. In Steps 4–5, Bus30 and
Bus29 perform load shedding operation. We monitor the voltage
of Bus30 and the power of Bus11 in Fig. 6. It can be seen that
without countermeasures, the power system will collapse, i.e.,
Bus30’s voltage will drop to zero, and Bus11’s generator will
be damaged, and with the traditional protection introduced in
[18], Bus30’s voltage is kept around 1.02 pu, and Bus11’s reactive power is kept around 20 Mvar. Even though the traditional countermeasures can solve the system faults, and restore
the system from vulnerable state, several components (i.e., generators and transformers) are impacted, and the load shedding
operation needs to be performed.
In Table II, the proposed MAS countermeasures are displayed. Firstly, BA3 detects the fault, and activates the Reactive
Layer and Social Layer. Then CA1 is created to solve the
, but it fails. Immediately, a higher
problem within Group
.
level CA2 is created to solve the problem within Group
Finally, CA2 orders BA5 to modify its generator’s voltage,
. After the power
and the problem is solved within Group
REN et al.: CONCEPTUAL DESIGN OF A MULTI-AGENT SYSTEM
Fig. 6. Restoration results comparison (Case I).
are displayed in Fig. 7. It can be seen that with such a protection, the power system is saved. Bus30’s voltage is increased to
0.9 pu, and Bus11’s reactive power is kept round 18 Mvar.
The proposed MAS countermeasures are displayed in
Table III. BA2 firstly detected the fault and asked help from
CA1. CA1 ordered BA1 and BA5 to increase their generators’
voltage and BA4 and BA6 to decrease their transformers’ ratio
in Step 4. However, such a rescue failed and hence CA2 was
generated. In Step 6, CA2 ordered BA13 to increase its generator’s voltage and BA28 to decrease its transformer’s ratio, but
it cannot stop the system collapse as well. Under the request of
CA2, CA3 controlled more BAs and ordered BA11 to increase
its voltage. Such an operation failed as well, and CA4 cannot
perform any new operation to stop the system collapse. Finally,
CA5 was created to control all BAs and CAs in the system.
Because CA5 had the global view of the system, so it ordered
BA29 and BA30 to perform the load shedding operation. The
system was saved and prevented from collapse. It can be seen in
Fig. 7 that before the load shedding operation was performed,
even though several operations was performed to rescue the
power system, BA11’s power dropped dramatically. After the
load shedding operation was performed by CA5, BA11’s power
was recovered and BA30’s voltage became stable. Our proposed
MAS spent similar time as the traditional countermeasures to
solve the fault in Case II. The simulation results indicates
that our proposed MAS approach can also perform the load
shedding operation when it is needed.
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TABLE I
TRADITIONAL COUNTERMEASURES
7
TABLE II
PROPOSED MAS COUNTERMEASURES (CASE I)
C. Case III
Fig. 7. Restoration results comparison (Case II).
system become stable, CA1 and CA2 are eliminated, and all
BAs are back to the Proactive Layer. The restoration results are
compared with the traditional approach’s results in Fig. 6. It
can be seen that by using the proposed MAS, Bus30’s voltage
is kept around 0.99 pu, and Bus11’s reactive power is kept
around 17 Mvar. Although Bus30’s voltage is not restored to
the same level, the proposed MAS can also prevent the power
system from collapse. Furthermore, the proposed MAS only
modifies the generator voltage of Bus5, and does not perform
a load shedding operation, and hence minimizes the impact to
the system users. Also, the traditional countermeasures spent
30 s to restore the system, while our proposed MAS only spent
7 s to solve the fault in a local group.
B. Case II
Case II simulates a situation that a generator bus (i.e., Bus2)
has a problem and the generator is disconnected. In such a case,
the load shedding operation is necessary in order to prevent
system collapse. Firstly, we employ the traditional countermeasures (see Table I) to restore the power system, and the results
Case III mimics a more complex situation, namely that two
faults occur at the same time in a power system. The connection between Bus2 and Bus6 and the connection between Bus24
and Bus25 are broken. Initially, we still employ the traditional
countermeasures (see Table I) to restore the power system, and
the results are displayed in Fig. 8. It can be seen that with such
a protection, the power system is saved. Bus30’s voltage is increased to 1 pu, and Bus11’s reactive power is kept round 20
Mvar. However, as the problem in Case I, the traditional countermeasures require a centralized controller and a global system
view, so its efficiency is decreased. Also, the traditional countermeasures will significantly impact system users because of the
load shedding operation.
In Table IV, the proposed MAS countermeasures are listed.
When faults happen in Bus6 and Bus24, BA6 and BA24 detect
faults, respectively. Firstly, both of them try to solve the problem
alone, but both fail. Then they ask for help from CAs. CA1 con, and solves its problem by ordering BA2 to introls Group
crease generator voltage in Step 4. At the same time, because
CA2 cannot solve its problem, and it requests help from CA3.
is released and CA3 orders both BA10 and
In Step 5, Group
BA27 to increase their transformer ratios, and the problem is
solved. Finally, CA3 and CA2 are eliminated in sequence. The
restoration results are illustrated in Fig. 8. It can be seen that our
MAS also solves the two concurrent system faults using only
local system views, and keeps Bus30’s voltage around 1 pu and
Bus11’s reactive power around 17 Mvar. Obviously, the restoration efficiency is increased, and the impact on system users is decreased. Also, our proposed MAS only spent 13 s to restore the
8
IEEE TRANSACTIONS ON POWER SYSTEMS
fulfill self-healing strategies, i.e., the reactive layer, the coordination layer, and the deliberative layer. Even though SPID
employs distributed agents, the architecture of SPID is static.
Nagata et al. proposed a multi-agent approach for decentralized
power system restoration in a distribution system network [10].
The load agent collects information about the power system,
and the feeder agent controls the entire restoration process in accordance with the priority in the restoration strategy. However,
their approach has a difficulty to provide a global solution for a
catastrophic failure. Solanki et al. proposed a distributed multiagent system to analyze faults in a power system, and to restore
the system after faults [1]. When a fault is detected by a load
agent, an alert message is sent to generator agents. Then generator agents try to restore the fault by sending their remaining
available transfer capacity to the problem load agent by passing
through switch agents. However, their system only considers to
control generators’ actions, but does not involve other facilities.
Lin et al. proposed a centralized multi-agent system to model
the power distribution between substations and end-users[21].
The distribution feeders and substation transformers are modeled by FCB agent and MTR agent, respectively. When a fault
happens in an end-user, the FCB agent firstly allocates the fault
by checking both the upstream and downstream switches’ current readings. Then restoration requirements are forwarded to
the MTR agent. The MTR agent will perform a heuristic reasoning process to decide which loads should be restored or shed
based on capacities of transformers and feeders, and the priorities of each load. Finally, the power will be transferred to the
problem loads through valid feeders, and the distribution system
can be restored.
By comparison with the above work, our proposed
multi-agent system has three merits. 1) Our proposed
multi-agent system consists of three layers and manages
power systems from day-to-day operations to dynamical faults
restoration. 2) Our proposed multi-agent system uses DTFM to
dynamically modify the system architecture. Distributed architectures are generated to perform normal operations and/or to
restore the system by using individual agents, and hierarchical
architectures are created to provide global solutions for faults.
3) Through dynamically adjusting the system architecture and
the number of agents, our proposed multi-agent system can
trade-off the system cost and the system efficiency.
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TABLE III
PROPOSED MAS COUNTERMEASURES (CASE II)
Fig. 8. Restoration results comparison (Case III).
TABLE IV
PROPOSED MAS COUNTERMEASURES (CASE III)
VI. CONCLUSION AND FUTURE WORK
system by using two local groups, while the traditional countermeasures spent 30 s and the system global view was also needed.
V. RELATED WORK
In this section, some related work is discussed and compared
with our approach. Liu et al. [20] proposed a Strategic Power
Infrastructure Defense (SPID) system to prevent catastrophic
failures in power systems. The principle of SPID is to provide
self-healing and adaptive reconfiguration capabilities for power
grids based on wide-area system vulnerability assessment. A
three-layer hybrid multi-agent system model was proposed to
In this paper, a conceptual MAS design is proposed for autonomous power systems management and restoration. The Bus
Agent was introduced to control each individual, physical bus
in a power system, and the Coordination Agent was introduced
to manage behaviors of Bus Agents. A dynamic team forming
mechanism was proposed for agent coordination purposes. The
architecture of the proposed MAS is changeable, and is dynamically and automatically modified according to power system
status. The simulation results demonstrated the feasibility of the
proposed MAS.
Our future work on this research will focus on implementation of the MAS in a real-world application and testing the
system in more complex scenarios.
REN et al.: CONCEPTUAL DESIGN OF A MULTI-AGENT SYSTEM
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9
Fenghui Ren, photo and biography unavailable at time of publication.
Minjie Zhang, photo and biography unavailable at time of publication.
Danny Soetanto, photo and biography unavailable at time of publication.
XiaoDong Su, photo and biography unavailable at time of publication.