agent based restoration with distributed energy storage support in smart grids

8/10/2019 Agent Based Restoration With Distributed Energy Storage Support in Smart Grids

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IEEE TRANSACTIONS ON SMART GRID, VOL. 3, NO. 2, JUNE 2012 1029

Agent Based Restoration With Distributed EnergyStorage Support in Smart Grids

Cuong P. Nguyen, Member, IEEE, and Alexander J. Flueck, Senior Member, IEEE

AbstractThe goal of this paper is to present a new andcompletely distributed algorithm for service restoration with dis-tributed energy storage support following fault detection, location,

and isolation. The distributed algorithm makes use of intelligent

agents, which possess three key characteristics, namely autonomy,local view, and decentralization. The switch agents will detect,locate and isolate the fault, then restore the load. The distributedenergy storage agent will support the system in grid-connected as

well as islanded operation. Important restoration issues such asload priority restoration and islanding coordination of multiple

distributed energy storage systems will be discussed. Two casestudies on the modified IEEE 34 node test feeder will be presented.

Index TermsActive network management, advanced distribu-

tion automation, distributed energy storage, islanded operation,

islanding coordination, multiagent systems, priority load restora-tion, smart grid.

I. INTRODUCTION

RESTORATION is an important part of Advanced Dis-tribution Automation (ADA), which seeks to restore theunfaulted and outaged parts of a system due to the isolation

of a fault. Mathematically, the restoration problem is a combi-

natorial problem with the objective of maximizing the supply

of power for as many customers as possible while satisfyingsource, line/cable loading, and often radial network constraints.

Different reconfiguration techniques have been proposed to

solve this problem, such as deterministic mathematical pro-

gramming, heuristic techniques and knowledge-based systems.

In deterministic mathematical programming, the restoration

problem is often formulated as a mixed integer programming

(MIP) problem. Then, any available MIP technique can be

applied to solve the restoration problem. Nagataet al.[1] pro-

posed a two-stage algorithm which decomposes the restoration

problem into two subproblems (the maximization of available

power to the de-energized area, and the minimization of the

amount of unserved energy). The algorithm is limited to dcmodels which do not take into account the reactive power as

well as voltage variations. In [2], the authors presented the

Manuscript received December 20, 2010; revised June 20, 2011; acceptedJanuary 18, 2012. Date of current version May 21, 2012. This work was sup-ported by the Perfect Power project at Illinois Institute of Technology, fullyfunded by the Department of Energy, Illinois Institute of Technology, and S&CElectric Company under Award DE-FC26-08NT02875. Paper no. TSG-00381-2010.

C. P. Nguyen is with the GE Energy, Schenectady, NY 12345 USA (e-mail:[email protected]).

A. J. Flueck is with the Department of Electrical and Computer Engineering,Illinois Institute of Technology, Chicago, IL 60616 USA (e-mail: [email protected]).

Digital Object Identifier 10.1109/TSG.2012.2186833

network reconfiguration for service restoration in shipboard

power distribution systems. The problem is formulated as a

variation of the fixed charge networkflow problem, and solved

by the CPLEX commercial package. Due to the linearization

of some of the problem constraints, the solution does not

guarantee a global optimum. Perez-Guerrero et al. [3] solved

optimal restoration of distribution systems using a dynamic

programming approach. During the solution process, many

states close to each other are grouped. Then, only the best states

are selected to reduce the problem size, so that solution speedup

is achieved. While mathematical programming might guaranteea global optimal solution, its drawback is the computational

intensity, so it might not be practical for large distribution

systems.

On the other hand, heuristic techniques and knowledge based

systems utilize the operators knowledge and experience to

narrow down the search space for the combinatorial restoration

problem. Thus, the solution is achieved in a shorter time. In [4],

Liu et al. reported an expert system algorithm for restoration

and loss reduction of distribution systems. The authors con-

structed a knowledge base which contains rules that implement

a solution approach that system operators can use in order to

restore as many loadsas possible. A recent work by Tsai [5] ex-tended [4] by considering load variation so multiple restoration

plans are obtained. An early work on heuristic search approach

to distribution system restoration was introduced by Morelato

and Monticelli [6]. The proposed approach used a knowledge

guided search strategy to reach the solution. Artificial neural

network (ANN) [7], ant immune system-ant colony optimiza-

tion (AIS-ACO) [8], Genetic Algorithm (GA) [9], among

others are also used extensively for restoration problems.

However, most of the restoration techniques surveyed above

solve the reconfiguration problem from a centralized point of

view, which in reality often requires a low-latency communica-

tion system transferring a potentially large amount of data be-

tween fielddevices and the control center. In addition, the con-

trol center requires expensive computing capability. As a result,

centralized techniques are subject to a single point of failure risk

and large capital costs.

Meanwhile, multiagent systems (MAS) have recently

emerged as a competitive technology for the advanced distribu-

tion automation requirements of smart grid. Essentially, a smart

grid is an advanced grid that makes use of distributed intelli-

gence to fulfill its duties of self-healing, high reliability, high

quality, and demand response [10]. The MAS can overcome the

disadvantages of centralized control by distributing the control

at the component level, thus avoiding a single point of failure,

while utilizing peer-to-peer communication for collaboration to

1949-3053/$31.00 2012 IEEE


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NGUYEN AND FLUECK: AGENT BASED RESTORATION WITH DISTRIBUTED ENERGY STORAGE SUPPORT IN SMART GRIDS 1031

Fig. 2. High level switching agent state diagram.

Fig. 3. Single line diagram of medium voltage application[24].

fault. In the latter case, the agent will detect the fault, then

locate and isolate the fault via MAS communication.

3) TRANSIENT: This contains the intermediate state where

the agent stays temporarily before transitioning to another

state.

4) RESTORATION: After moving to this block, the agentwill start the restoration process, including checking if the

team is ready for restoration, finding the restoration path,

and closing the switch to restore the team load.

During operation, the measurements including present status,

voltages and , and current , are sampled regularly

and processed by the agent. Based on the measurements and

MAS communication, the agent will determine the new desired

status of the switching device. In addition, the switching agent

is also given the total load in each of the X and Y teams, the

capacity rating of the X and Y team line segments as well as

the capacity of the switching device, and the capacity rating

of a connected source, if any. These pieces of information are

used in the restoration process to describe source and network

constraints local to each agent.

IV. DISTRIBUTEDENERGYSTORAGESYSTEM

This section presents the power system model and agent

model for the electronic converter storage system (ECS)a

popular type of distributed energy storage system.

ECS consists of a power electronics system and storage

system. The storage system, typically batteries, is of dc type. It

interfaces with an ac electric grid through an advanced power

electronics system. Fig. 3 shows an example of an ECS system.

Sodium sulfur battery (NaS) is a molten metal battery system

that is constructed from a molten sulfur (Na) positive electrode

and a molten sodium (S) negative electrode separated by a solid

Fig. 4. Model of DES in different modes.

beta alumina ceramic electrolyte. A NaS battery is characterized

by:

High operating temperatures of to .

High energy density: discharge duration can reach 6 hours

at rated output.

High efficiency of charge/discharge (82%92%).

A life cycle of 15 years.

Inexpensive material.

With these characteristics, a NaS battery is very attractive for

large-scale nonmobile applications such as distributed energy

storage for a distribution power system. Based on [26], the NaS

battery has been applied widely in the Japanese power grid. As

of 2009 the total installed capacity is about 270 MW. In the

United States, 9 MW of NaS battery system has been deployed

so far for peak shaving, backup power, firming wind capacity,

and other applications.

A. Power System Model

Depending on the condition of the grid, DES can work in

one of three modes, namely POWER CHARGE mode, POWER

DISCHARGE mode, or VOLTAGE CONTROL mode (Fig. 4).

First, in POWER DISCHARGE mode, the objective is to

discharge power into the grid at a user-defined complex power

setpoint. The DES can be modeled as a voltage dependent

current source. That is, given a total complex three-phase

power discharge setpoint , if the terminal voltages

are , then a set of three-phase balanced currents

can be injected into the system to meet the setpoint:

(1)

(2)

Therefore,

(3)

(4)

(5)

It is seen from (3)(5) that both current magnitude and current

angle vary according to the three-phase terminal voltage.

Second, in POWER CHARGE mode, the objective is to

charge or draw the power from the grid at a user-defined

complex power setpoint. In this case, the DES can be modeled

as a voltage dependent current sink. That is, given the total

three-phase power charge setpoint at the rated

voltage level , if the terminal voltages are ,


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1032 IEEE TRANSACTIONS ON SMART GRID, VOL. 3, NO. 2, JUNE 2012

then a set of three-phase balanced currents can be

drawn from the system:

(6)(7)

(8)

It is seen from (6)(8) that only the current angle varies ac-

cording to the three-phase terminal voltage. The current magni-

tude is always constant in POWER CHARGE mode. Typically,

the charging power factor is maintained at 1.0, so in (6) is

zeroed out.

Third, in VOLTAGE CONTROL mode, the objective is to

control the DES terminal voltage. The DES works like a gener-

ator that regulates its terminal voltage. That is, given the desired

voltage setpoint , a set of balanced three-phase terminal volt-ages is maintained as follows:

(9)

(10)

(11)

An example of DES powerflow data is given in Table I with

the parameters explained as follows.

the rated power (kVA).

the rated line to line voltage (kV).

the maximum energy storage capacity(kWh).

the real power setpoint (kW) in POWER DIS-

CHARGE mode.

the power factor setpoint (%) in POWER DIS-

CHARGE mode.

the available stored energy (kWh).

the real power setpoint (kW) in POWER

CHARGE mode.

the power factor setpoint (%) in POWER

CHARGE mode.

the minimum energy threshold

(%) before charging is required. the high voltage limit (per unit).

the low voltage limit (per unit).

the maximum injected current

threshold (% of rated current) when a fault occurs while

working in POWER DISCHARGE mode.

the voltage setpoint (kV) in VOLTAGE

CONTROL mode.

the maximum injected current threshold

(%) in VOLTAGE CONTROL mode.

the time (seconds) before shutdown in

POWER DISCHARGE mode when the terminal voltage

is less than .

the overload trip time (seconds) in

VOLTAGE CONTROL mode.

TABLE IPOWERFLOW DATA OF THEMODIFIED IEEE 34 NODETEST FEEDER

Fig. 5. High level DES agent state diagram.

the linear current increase time (sec-

onds) in POWER DISCHARGE mode.

B. Agent Model

The DES agent is represented by the SYSTEM CONTROLblock seen in Fig. 3. During operation, the agent receives the

measurements including interface switch status (not shown),

mode, terminal voltages, and injection currents. Then, the agent

decides the proper interface switch status, mode, and settings

for the DES.

Fig. 5 shows a high level DES agent state diagram. A de-

scription of the detailed diagram with 12 states is given in [25].

Basically, there are three blocks and two states.

1) POWER DISCHARGE: The DES injects power into the

grid at the user setpoint of .

2) POWER CHARGE: The DES draws power from the

grid to charge its battery system at the user setpoint of. This mode is not allowed

while the DES is operating in an island. Before the DES

enters this mode, MAS communication is required to de-

termine if the distribution network and associated source

can supply the charging current.

3) VOLTAGE CONTROL: The DES works as a voltage

source in an island. A voltage setpoint of

is maintained at the DES terminals. Within an island,

only one DES is allowed to work in VOLTAGE CON-

TROL mode. That DES is also called the master DES.

The decision on which DES is the master depends on

the availability of the energy and size of the DES units

in the island, which is determined in real-time via MAS

communication.


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Fig. 6. Configuration of modified IEEE 34 node test feeder equipped with agent-controlled switches, and distributed energy storage.

4) SHUTDOWN: The DES has to be disconnected in this

state due to abnormalities in the network such as short-cir-

cuit or battery drain in the DES.

5) CLOSING: The DES is connected back to the network in

this state.

V. RESTORATION SIMULATION

The simulation on the IEEE 34-node distribution feeder [27]is presented in this section, see Fig. 6. The 24.9 KV substation

resides at node 800. For the simulation, eight agent-controlled

switching devices [e.g., switch (SWI), breaker (BRK), and sec-

tionalizer (SCT)] are added. In addition, three agent-controlled

distributed energy storage units DES_16, DES_58, and DES_60

are added for restoration support. The power system data of the

DES units are given in Table I. The 11 agents form a multiagent

system of nine teams. The naming convention for each agent is

AGT_ followed by the name of the device. This simulation

uses a timestepof 0.1 ms.

The agents communicate using Foundation for Intelligent

Physical Agents (FIPA) standard [28]. Some message perfor-mative examples include:

REQUEST: the sending agent requests the receiving agent

to perform some action.

QUERYIF: the sending agent asks the receiving agent

whether or not a given proposition is true.

INFORM: the sending agent informs the receiving agent

that a given proposition is true.

A. Case 1: Partial Restoration

The simulation starts at time 0. Then, a permanent unbalanced

bolted phase-A-to-phase-B fault (or A-B fault) occurs at node

828 at time 5.0 s. The simulation ends at 20.0 s. The Team 9 load

is critical while all other team loads are noncritical. The initial

energy capacity of DES_58 is decreased down to

, so it will need to charge during the simulation.

The switching sequence of switching devices and DES units

is given in Table II, while a short list of 184 messages being

exchanged in the MAS is shown in Fig. 7. The time line of the

simulation is explained as follows.

At 0.0000 s, the system operates in the normal state, i.e., all

switching devices are closed and work in NORMAL mode,

and all distributed energy storage system units operate inPOWER DISCHARGE mode at the desired setpoint.

At 0.7503 s, DES_58 available energy decreases

down to the minimum level

. AGT_DES_58 opens its

interface switch to isolate the unit, then sends out a

REQUEST cap=17.39 message to all Team 5 team-

mates for allocating a charge capacity of 17.39 amps.

The message gets propagated to all parts of the MAS to

search for the capacity (messages #2, #8, and #13). The

corresponding successful INFORM done messages are

#17, #19, and #20. Therefore, the charge path will be

. At 1.7180 s, AGT_DES_58 closes its interface switch to

start charging the unit in POWER CHARGE mode. Fig. 9

shows that while charging, the real power output is nega-

tive. In other words, DES_58 draws power from the grid.

At 5.0000 s, a short-circuit fault occurs at node 828.

All the DES units sense the voltage dip at their ter-

minals. AGT_DES_16 reacts to that by increasing the

output current linearly toward the

within

window to inject more power into the grid to meet the

power setpoint. Accordingly, the DES_16 current in Fig. 8

is seen to increase linearly from 5.0001 s to 5.0169 s.

Also due to the fault, AGT_BRK_02 and ACT_SCT_28

move to ABNORMAL mode (excessive current) while all


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other switching agents move to TRANSIENT mode (low

voltage).

At 5.0168 s, AGT_SCT_28 opens its sectionalizer to iso-

late the fault after counting a given number of fault current

samples. Consequently, the voltage profile in the feeder up-

stream of the fault is recovered, causing AGT_DES_16 to

move back to normal discharge state at 5.0169 s.Meanwhile, having isolated the fault, AGT_SCT_28 cur-

rently working in ABNORMAL mode, starts locating the

fault by sending out QUERYIF isFaulted messages to

Team 4 (message #23) and Team 2 (messages #22 and

#25) switching teammates. The respective replying mes-

sages #28 (INFORM TRUE), #27 (INFORM FALSE),

and #30 (INFORM FALSE) show that the fault current

is passing through Team 2, but not Team 4. Therefore, the

fault must be in Team 4. After fault isolation, the distribu-

tion system is split up into two parts. The part upstream of

Team 4 is still energized by the substation, while the part

downstream of Team 4 is islanded.

At 5.0169 s, AGT_DES_58 transitions from POWER

CHARGE mode to SHUTDOWN mode dueto voltageloss,

turning off DES_58. Since there aretwo DES units in Team

5, the islanded operation starts by determining who will

operate in VOLTAGE CONTROL mode because only one

DES, also called themasterDES, is allowed to control the

voltage. AGT_DES_58 sends a QUERYIF KVA=750

message #21 to AGT_DES_60 in Team 5 to query for

VOLTAGE CONTROL mode. The INFORM FALSE

reply #26 is received at 5.2838 s due to the smaller rated

kW capacity of DES_58. Therefore, AGT_DES_58 can not

work in VOLTAGE CONTROL mode.

At 5.0319 s, SCT_52 is opened due to extended loss

of voltage. The corresponding agent transitions to

RESTORATION mode.

At 5.0349 s, SWI_42, SCT_46, SWI_32, and SWI_62 are

also opened due to extended loss of voltage. The corre-

sponding agents transition to RESTORATION mode.

At 5.1003 s, AGT_DES_60 transitions from POWER

DISCHARGE mode to SHUTDOWN mode due to the

extended loss of voltage, turning off DES_60. Then,

AGT_DES_60 sends a QUERYIF KVA=1500 message

#24 to AGT_DES_58 in team 5 to query for VOLTAGE

CONTROL mode. The replying INFORM TRUE mes-

sage #29 is received at 5.4005 s due to the larger rated

kW capacity. Therefore, AGT_DES_60the master

DEScan work in VOLTAGE CONTROL mode.

At 5.4007 s, after transitioning from SHUTDOWN

mode to VOLTAGE CONTROL mode, AGT_DES_60

broadcasts QUERYIF isClear messages #31, #32, #33,

and #34 to all Team 5 switching members to query if

the team is clear for restoration. All replies (INFORM

TRUE messages #35, #36, #37, and #38) are received

by AGT_DES_60 by 5.7675 s, confirming Team 5 is ready

for restoration.

At 5.7677 s, AGT_DES_60 closes its interface switch to

regulate the voltage in Team 5, see Fig. 10. Then, it starts

searching for priority restoration path(s) by sending out

REQUEST priPath messages #40, #41, #42, and #52 to

Team 5 switching teammates. The request is forwarded to

other parts of the MAS, including messages #56 and #67.

The corresponding reply messages are #57, #58, #71, #72,

#78, and #80. Since only Team 9 has priority load, the pri-

ority restoration path is asseen in messages #72 and #80. Note that Team 6 does not

have priority load, but it still lies on the priority restoration

path. During the restoration process, team loads in a pri-

ority restoration path are restored first.

At 5.7679 s, after sensing good voltage at its terminal,

DES_58 decides to come back online to inject power into

the grid in POWER DISCHARGE mode.

At 10.5168 s, SWI_42 is closed after Team 6 is con-

firmed clear (messages #54 and #68) and the REQUEST

cap=10.3 message #109 is accepted (message #114) to

restore Team 6 load. The agent first transitions to TRAN-

SIENT mode, then to NORMAL mode. At 11.0942 s, DES_58 is shut down due to energy insuf-

ficiency. Note that in islanded operation, DES_58 is not

allowed to charge. Therefore, any charge request will be

rejected as seen in messages #129 and #138.

At 11.0992 s, SCT_46 is closed after the REQUEST

cap=9.82 amps (messages #115 and #116) is accepted

(messages #121 and #126) to restore Team 9 load. The

agent first transitions to TRANSIENT mode, then to

NORMAL mode.

After restoring the priority load, AGT_SCT_46 sends out

REQUEST updPath messages #130 and #133 to the

MAS to update the priority restoration path, the replyingINFORM done messages are #141, #144. As a result,

the priority restoration path is removed, allowing all other

nonpriority teams to start their restoration.

At 18.1165 s, SWI_62 is closed after the REQUEST

cap=2.09 amps (message #169) is accepted (message

#178) to restore Team 8 load. A similar attempt (message

#168) is also made by SWI_32 to restore its Team 7 load

of 12.7 amps in message #168, but due to the limited ca-

pacity of source DES_60, the attempt was not successful

(message #175).

This case study demonstrates partial service restoration fol-

lowing a serious fault. Due to the serious fault, the distribution

system is divided into two parts. The part upstream of the fault

is still energized by the substation while the part downstream

of the fault is supplied by the DES units. Load priority is taken

into account during the restoration process. The distributed en-

ergy storage system agents work in different modes based on the

different conditions of the distribution system. In particular, the

coordinationof the two DES unitsin thesameisland is achieved.

If there are three or more DES units in the same team, then

a similar QUERYIF isMaster communication process can be

used for each DES. The DES that has available energy and the

largest capacity will be the master DES, which can work in

voltage control mode, while all other DES units will work in

power discharge mode.


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Fig. 7. Case 1: Abbreviated list of agent communication messages.

B. Case 2: Full Restoration

The rated capacity of DES_60 is increased to 2250 KVA

for the full restoration simulation. The simulation starts at time

0. Then, a temporary breaker failure occurs at 5.0 s, causing

BRK_02 to open. The simulation ends at 20 s. Also, at 5.0 s,

the communication link from AGT_BRK_02 to AGT_SWI_18

fails permanently. Team 9 load is critical while all other team

loads are noncritical.

Fig. 8. Case 1: DES_16 output.



This restoration case will employ the break-before-make

transfer scheme to guarantee the radial configuration of the dis-

tribution network at all time. The scheme requires that the en-

ergized line segment has to be de-energized from the existing

source or break before it can be re-energized to an alternate


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TABLE IICASE2: FULL RESTORATIONSWITCHING SEQUENCE

TABLE III

CASE2: FULL RESTORATIONSWITCHING SEQUENCE

source or make. The advantage of this common practice is to

avoid switching surges and loop currents. But the disadvantage

is that the line segment is subject to a momentary outage. On

the other hand, if make-before-break were to be used, then

the synchronization of the sources would be carried out before

the make action occurs. This may lead to longer restoration

times, but there would be no outage during the transfer.

The switching sequence of switching devices and DES units

is given in Table III. There are totally 230 messages being ex-

changed in the multiagent system during the simulation. The

time line of the simulation is explained as follows.

At 0.0000 s, all switching devices are closed and all

DES units work in POWER DISCHARGE mode in the

PQ_NORMAL state.

At 5.0000 s, BRK_02 opens due to temporary breaker

failure. The communication link from AGT_BRK_02 to

AGT_SWI_18 is also unavailable from this time onward.Therefore, the distribution system can not be restored

back to the substation because BRK_02 fails to get the

QUERYIF isClear replies from all Team 2 teammates.

The distribution system can only be restored by DES units.

At 5.0151 s and 5.0181 s, after sensing the extended

voltage loss, all closed switching devices are opened by

the corresponding switching agents.

At 5.0835 s, all DES units are also opened due to the

extended voltage loss. After this time, similar to case 1,

AGT_DES_60 in Team 5 will work in VOLTAGE CON-

TROL mode while AGT_DES_58 in the same team will

operate in POWER DISCHARGE mode. AGT_DES_16 is

the only DESagent in Team 2, so it will work in VOLTAGE

CONTROL mode by default.

At 5.4570 s, AGT_DES_16 closes its interface switch

to work in VOLTAGE CONTROL mode after con-

firming Team 2 to be clear by MAS communication.

DES_16 acts as a new source to regulate the voltage in

Team 2. The part of the distribution system energized

by this source will be referred to as the upstream is-

land. After closing its interface switch, AGT_DES_16starts searching for priority load using MAS commu-

nication. The priority restoration path is found to be

.

At 5.8176 s, AGT_DES_60 (master DES) also closes

its device to work in VOLTAGE CONTROL mode after

Team 5 is clear. DES_60 acts as a new source to regu-

late the voltage in Team 5. The part of the distribution

system energized by this source will be referred to as

the downstream island. After closing, AGT_DES_60

starts searching for priority load using MAS commu-

nication. The priority restoration path is found to be

. At 5.8178 s, after sensing good voltage at its terminal,

AGT_DES_58 also closes its interface switch to discharge

power into Team 5.

At 6.1728 s, SCT_28 in the upstream priority restoration

path is closed to restore Team 4 load of 1.71 amps.

At 6.6001 s, SWI_42 in the downstream priority restora-

tion path is closed to restore Team 6 load of 10.3 amps.

At 7.2158 s, SCT_46 in the downstream priority restora-

tion path is closed to restore Team 9 load of 9.82 amps.

After restoring the priority load, AGT_SCT_46 starts a re-

quest for updating the priority restoration path. As a re-

sult, the upstream and the downstream priority restoration

paths are removed, allowing other nonpriority loads to be

restored.

At 10.1331 s, SWI_62 is closed to restore Team 8 load of

2.09 amps.

At 10.1997 s, SWI_32 is closed to restore Team 7 load of

12.7 amps.

At 18.3741 s, the request for allocating Team 3 load of

13.06 amps from AGT_SWI_18 is successfully autho-

rized by the downstream island. The upstream island can

not satisfy Team 3 capacity request due to the limited

capacity of the source DES_16. The successful restoration

path is . This

is a special restoration path because SCT_52 is open.Therefore, the break-before-make transfer scheme must


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be activated to reconfigure the network such that the up-

stream island can be merged with the downstream island.

Initially, SCT_28 is opened to break Team 4 from the

upstream island.

At 18.3743 s, SCT_52 is closed to make Team 4.

At 18.6077 s, DES_16 is shut down to break Team 2.

At 18.6079 s, SCT_28 is closed to make Team 2.

At 18.6081 s, after sensing good voltage at its terminal,

DES_16 is reconnected to operate in POWER DIS-

CHARGE mode.

At 18.7745 s, SWI_18 is closed to restore Team 3.

This case study shows the full service restoration following

breaker failure and communication failure events. The failures

cause the system to operate in two islands initially. Due to the

limited capacity of one of the DES units, merging is necessary

to achieve full service restoration. Priority load is also consid-

ered during the restoration process. The break-before-make

transfer scheme is employed for reconfiguring the network

during the restoration.

C. Loss Reduction and Voltage Improvement in Normal

Operation

Loss reduction is observed in the distribution system under

normal operation. With the presence of the DES units, the total

system loss is 120.056 kW as compared to 274.673 kW without

the DES units. The loss is reduced mainly because part of the

load is locally supplied by the DES units rather than the substa-

tion, which is electrically farther away.

In terms of voltage profile, most of the node voltages are in-

creased slightly with the presence of the DES units. In particular,

the maximum voltage increase of 7.4% occurs at node 814.

VI. CONCLUSION ANDFUTUREWORK

As the communication network penetrates deeper into the

distribution system and as distributed computing power im-

proves in both speed and cost, the implementation of agent

based advanced distribution automation is becoming more

practical. The dNetSim-JADE simulation package provides a

realistic testing environment for advanced distribution automa-

tion via autonomous agents.

A new model and distributed control strategy for DES were

presented. Multiple benefits of DES were demonstrated through

two test cases: 1) In normal operation, the total system losswas cut by more than a half and the system voltage profile was

slightly improved; 2) in abnormal operation, dynamic islanding

was achieved to restore the islanded portion of the grid; 3) Co-

ordination of multiple DES units in the same island as well as in

different islands was achieved; 4) Load priority was considered

during restoration.

Future work will explore the optimal dispatch problem of

multiple distributed energy storage system units from the dis-

tributed perspective.

ACKNOWLEDGMENT

The authors would like to thank the sponsors for theirfinan-

cial support.

Department of Energy Disclaimer: Neither makes any war-

ranty, express or implied, or assumes any legal liability of re-

sponsibility for the accuracy, completeness, or usefulness of any

information, apparatus, product, or process disclosed, or rep-

resents that its use would not infringe privately owned rights.

Reference herein to any specific commercial product, process,

or service by trade name, trademark, manufacturer, or otherwise

does not necessarily constitute or imply its endorsement, recom-

mendation, or favoring by the U.S. Government or any agency

thereof. The views and opinions of authors expressed herein do

not necessarily state or reflect those of the U.S. Government or

any agency thereof.

REFERENCES

[1] T. Nagata, S. Hatakeyama, M. Yasouka, and H. Sasaki, An efficientmethod for power distribution system restoration based on mathemat-ical programming and operation strategy, in Proc. Int. Conf. PowerSyst. Technol., 2000.

[2] K. Butler, N. Sarma, and V. R. Prasad, Network reconfiguration forservice restoration in shipboard power distribution systems, IEEETrans. Power. Syst., vol. 16, no. 4, pp. 653661, Nov. 2001.

[3] R.Perez-Guerrero, G. Heydt,N. Jack, B. Keel, andA. Castelhano, Op-timal restorationof distribution systems usingdynamic programming,

IEEE Trans. Power. Del., vol. 23, no. 3, pp. 15891596, 2008.[4] C.-C. Liu, S. Lee, and S. Venkata, An expert system operational aid

for restoration and loss reduction of distribution systems,IEEE Trans.Power. Syst., vol. 3, no. 2, pp. 619626, May 1988.

[5] M. Tsai, Development of an object-oriented service restoration expertsystem with load variations,IEEE Trans. Power. Syst., vol. 23, no. 1,pp. 219225, 2008.

[6] A. Morelato and A. Monticelli, Heuristic search approach to distribu-tion system restoration, IEEE Trans. Power. Del., vol. 4, no. 4, pp.22352241, Oct. 1989.

[7] Y. Hsu and H. Huang, Distribution system service restoration using

theartifi

cial neural network approach and pattern recognition method,IEE Proc. Gener., Transm., Distrib., vol. 142, no. 3, pp. 251256, May1995.

[8] A. Ahuja, S. Das, and A. Pahwa, An ais-aco hybrid approach formulti-objective distribution system reconfiguration, IEEE Trans.

Power. Syst., vol. 22, no. 3, pp. 11011111, 2007.[9] Y. Kumar, B. Das, and J. Sharma, Multiobjective, multiconstraint ser-

vice restoration of electric power distribution system with priority cus-tomers,IEEE Trans. Power. Del., vol. 23, no. 1, pp. 261270, 2008.

[10] A. Flueck and Z. Li, The journey to perfect power at Illinois Instituteof Technology, IEEE Power Energy Mag., vol. 6, no. 6, pp. 3647,Nov.Dec. 2008.

[11] S. McArthur, E. Davidson, V. Catterson, A. Dimeas, N. Hatziar-gyriou, F. Ponci, and T. Funabashi, Multi-agent systems for powerengineering applicationsPart I: concepts, approaches, and technicalchallenges,IEEE Trans. Power. Syst., vol. 22, no. 4, pp. 17431752,2007.

[12] U. Doe, Grid 2030: A national vision for electricitys second 100years, 2003 [Online]. Available: http://www.climatevision.gov/sec-tors/electricpower/pdfs/electric_vision.pdf

[13] A. Nourai and C. Schafer, Changing the electricity game, IEEEPower Energy M ag., vol. 7, no. 4, pp. 4247, 2009.

[14] P. Mercier, R. Cherkaoui, and A. Oudalov, Optimizing a batteryenergy storage system for frequency control application in an iso-lated power system, IEEE Trans. Power. Syst., vol. 24, no. 3, pp.14691477, 2009.

[15] D. Lee and L. Wang, Small-signal stability analysis of an autonomoushybrid renewable energy power generation/energy storage system parti: time-domain simulations,IEEE Trans. Energy Convers., vol. 23, no.1, pp. 311320, 2008.

[16] K. Qian, Z. Li, C. Zhou, and Y. Yuan, Benefits of energy storage inpower systems with high level of intermittent generation, inProc. 20thInt. C onf. Exhib. Electr. Distrib.Part 1, 2009, pp. 14.

[17] Z. Yang, C. Shen, L. Zhang, M. Crow, and S. Atcitty, Integration ofa statcom and battery energy storage, IEEE Trans. Power. Syst., vol.16, no. 2, pp. 254260, May 2001.


10/10


[18] L. Zhang, F. Wang, Y. Liu, M. Ingram, S. Eckroad, and M. Crow,Facts/ess allocation research for damping bulk power system low fre-quency oscillation, in Proc. IEEE 36th Power Electron. SpecialistsConf., 2005, pp. 24942500.

[19] A. Nourai, V. Kogan, and C. Schafer, Load leveling reduces t&d linelosses,IEEE Trans. Power. Del., vol. 23, no. 4, pp. 21682173, 2008.

[20] G. Koeppel, M. Geidl, and G. Andersson, Value of storage devicesin congestion constrained distribution networks, in Proc. Int. Conf.

Power Syst. Technol., 2004, vol. 1, pp. 624629.[21] D. Staszesky, D. Craig, and C. Befus, Advanced feeder automation is

here,IEEE Power Energy Mag., vol. 3, no. 5, pp. 5663, 2005.[22] A. Flueck and C. Nguyen, Integrating renewable and distributed

resourcesIIT perfect power smart grid prototype, in Proc. IEEEPower Energy Soc. Gen. Meet., 2010, pp. 14.

[23] C. Nguyen and A. Flueck, Modeling of communication latency insmart grid, in Proc. IEEE Power Energy Soc. Gen. Meet., 2011, pp.16.

[24] A. Jones, Whitepaper: Thepotential forenergy storage in transmissonand distribution systems,S&C Electr., pp. 16, May 2009.

[25] C. Nguyen, Power system voltage stability and agent based distribu-tion automation in smart grid, Ph.D. dissertation, Illinois Institute ofTechnology, Chicago, IL, 2011.

[26] B. Roberts, Capturing grid power,IEEE Power Energy Mag., vol. 7,no. 4, pp. 3241, 2009.

[27] W. Kersting, Radial distribution test feeders, in Proc. IEEE Power

Eng. Soc. Winter M eet., 2001.[28] FIPA communicative act library specification, 2002.

Cuong P. Nguyen (S06M11) received the Ph.D.degree in electrical engineering from the Illinois In-stitute of Technology, Chicago, in 2011.

He is currently an Application Engineer withinthe Power Systems Software Group at GE Energy,Schenectady, NY. His research interests includeagent-based distribution automation in smart grids,modeling of smart grid components, power systemvoltage stability and contingency ranking usingcontinuation methods, and large scale productionsimulation software.

Alexander J. Flueck(S90M96SM09) receivedthe B.S., M.Eng., and the Ph.D. degrees in electricalengineering from Cornell University, Ithaca, NY, in1991, 1992, and 1996, respectively.

He is currently an Associate Professor at IllinoisInstitute of Technology, Chicago. His research inter-ests include autonomous agent applications to powersystems, transfer capability of large-scale electricpower systems, contingency screening of multiplebranch or generator outages with respect to voltagecollapse, transient stability, and parallel simulation

of power systems via message-passing techniques on distributed-memorycomputer clusters.

agent based restoration with distributed energy storage support in smart grids

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