A Fuzzy-Multiagent Service Restoration Scheme for Distribution System With Distributed Generation

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A Fuzzy-Multiagent Service Restoration Scheme forDistribution System With Distributed Generation

Akram Elmitwally, Member, IEEE, Mohammed Elsaid, Mohammed Elgamal,and Zhe Chen, Senior Member, IEEE

Abstract—This paper proposes a new multiagent control sys-tem (MACS) for service restoration in distribution systems withintegrated distributed generation (DG) units. First, the MACSdetects and locates faults, then decides the optimal reconfigura-tion of the network for restoring de-energized loads, and finallyregulates the nodes voltages. Unintentional islanding operationof DG units is avoided and different postfault response modesof DG unit are addressed in the MACS design. The MACS hasa hybrid centralized–decentralized structure where agents arearranged in two layers with different responsibilities and commu-nication capabilities. Agents at load buses in the first layer canonly communicate directly with its next neighbor load agents onthe same feeder and to its feeder agent, whereas the higher levelagents in the second layer can communicate directly with eachother. This MACS structure reduces the possibilities of controlsystem failures for a moderate communication network infrastruc-ture. Full dynamic simulation model for evaluating the MACS isimplemented.

Index Terms—Distributed generation (DG), multiagent,restoration.

I. INTRODUCTION

F AULTS and outages are inevitable in distribution net-works. Consequently, the faulted area and some unfaulted

areas may lose power supply. The system reconfigurationsare aimed to restore as many loads as possible by transfer-ring de-energized loads in the out-of-service areas to otherbackup supporting feeders/laterals without violating operatingconstraints. Power system distribution automation improves thereconfiguration process. It results in a fast response to opera-tion problems, reduces operator intervention and human error,and decreases the duration of outages [1]–[3]. The approachesproposed for performing system reconfiguration mostly oper-ate in a centralized approach [4]–[8]. A central optimizationsolver collects all the system data and then processes them toproduce corrective actions [9]–[16]. All these methods needa control center to make reconfiguration decisions. For large-scale power systems, the topology of the power system becomescomplicated. The computational burden of the control centergreatly increases due to the voluminous collected data. This

Manuscript received June 17, 2014; revised November 30, 2014 andFebruary 01, 2015; accepted March 12, 2015. Paper no. TSTE-00296-2014.

A. Elmitwally, M. Elsaid, and M. Elgamal are with the Department ofElectrical Engineering, Mansoura University, Mansoura 35516, Egypt (e-mail:[email protected]).

Z. Chen is with the Department of Energy Technology, Alborg University,Alborg 9220, Denmark.

Digital Object Identifier 10.1109/TSTE.2015.2413872

slows down the reconfiguration decision-making and limits theperformance of the service restoration system [12].

It has become a challenge to manage the network from acentral control system. The current centralized SCADA sys-tem is no longer sufficient for some control operations [1]. Thesmart grid integrates advanced sensing technologies, controlmethods and two-way communications into current electric-ity grid at generation, transmission, and distribution levels[15]. Smart grids develop a solution for this challenge byadopting decentralized control schemes [15], [16]. The decen-tralized approach for power system reconfiguration is robust.Occurrence of single-point failure is improbable because thereis no central controller in the decentralized approach. Also, ithas more flexibility compared to the centralized approach [13].Multiagent control system (MACS) based on two-way commu-nication technologies have emerged as a possible decentralizedcontrol approach. The MACS has been applied to several areasin power systems including reconfiguration, service restoration,fault detection, protection coordination, and voltage control[16]–[22].

The authors in [20]–[22] proposed a MACS for protection ofdistribution generation (DG) systems. The MACS can do faultdetection, fault location, and load shedding. Reference [16]developed a multiagent framework for power system restora-tion. This framework has a centralized design, consisting ofseveral bus agents and a single facilitator agent. Bus agentswere used to decide suboptimal target configuration after afault occurrence, while the facilitator agent acted as a managerin the decision process. Reference [14] refined the frameworkin [16] and utilized power generation/feeder agents (FAs), busagents, and circuit breaker agents to distribute the reconfigu-ration functionalities. The methods proposed in [14] and [16]are centralized in nature, which suffers from lower reliability,limited security, and uneasy extensions.

To overcome the defect of centralized methods, some decen-tralized MACS structures have been presented in [17]–[19].Reference [17] provided a multiagent framework to restore apower grid after a fault occurrence. The framework does therestoration process by cascading messages from the affectedFA through load agents (LAs) up to the backup FA. In turn,the backup FA replies by flow of cascading messages throughLAs up to the affected FA. Hence, this may jeopardize arestoration process if one of LAs fails to receive or forwardthe cascading message. The multiagent framework in [18]integrates the advantages of both centralized and decentral-ized structures to improve the multiagent framework in [17].It is noticed that [14], [16]–[18] have applied their MACS

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restoration structures to special simple test systems (not stan-dard). Moreover, reported techniques in [14] and [16]–[19] havenot considered the following:

1) integrating a fault identification module into the MACS;2) DG participation in service restoration;3) postrestoration operational constraints;4) developing a mechanism to adjust the voltage after the

service restoration; and5) full dynamic simulation model for verifying the effective-

ness of the MACS.The MACS in [19] can locate and isolate faults, then decide

and implement the switching operations to restore the out-of-service loads. The MACS structure has two layers: 1) bus agentlayer and 2) FA layer. The function of bus agents’ layer is mon-itoring, making simple calculations, and implementing controlactions. FAs’ layer is assigned to negotiation. All bus agentsnotify the affected FA to locate and isolate the fault. If this FA,acting as a facilitator, fails, the second stage (restoration of out-of-service loads) will fail. Also, the scheme given in [19] doesnot consider DG security against unintended islanding opera-tion (when the DG unit is within the out-of-service zone), loadpriorities, and the distance of the backup feeders.

The general understanding is that the smart grid is the con-cept of modernizing the electric grid. Through the additionof advanced technologies, the smart grid becomes more flex-ible, interactive, and has a self-healing capability. So, smartgrid can intelligently integrate the actions of all connectedparticipants to deliver sustainable, economic, and secure elec-tricity supply. A smart grid employs innovative products andservices in areas of intelligent monitoring, control, commu-nication, and computing [23]. Roche et al. [24], and Li andZhou [25] described the operation of MACS as an efficienttool for energy management in smart grid. A flexible opera-tion system of smart grid is presented in [26]. It can minimizeoperating cost by energy management of power plants. Also, itcan manage the demand response to make the grid interactive.Reference [27] reported the application of MACS for control ofsmart grid, demand response management, and service restora-tion. Some papers like [28] and [29] provide a comprehensivereview of smart grid technologies in terms of infrastructure,management, and protection. Reference [30]–[32] discussed theapplications, requirements, and challenges of wireless com-munication technologies in the smart grid. The design of apeer-to-peer communication framework for smart grid MACSis thoroughly described in [23].

The new aspects presented in this paper are as follows.1) This paper proposes a new design of a MACS for ser-

vice restoration in smart distribution systems integratingDGs. The MACS is new in terms of functions, structure,arrangement of agents, communication capabilities ofagents, and rules of operation. The new MACS is a hybridcentralized–decentralized framework where agents arearranged in two layers. Agents at load buses includedin the first layer can only communicate directly withits next neighbor LAs on the same feeder and to itsFA, whereas higher level agents, such as feeders’ andregulators’ agents, included in the second layer can com-municate directly with each other. In this way, all agents

installed at feeders, regulators, and loads can commu-nicate and coordinate its actions. Thus, the proposedMACS design improves the MACS reliability and min-imizes the possibility of single-point failures which arethe main drawbacks of the centralized MACS. Besides, itoptimizes the number of required communication chan-nels and structure of the communication subsystem thatrestrict the fully decentralized MACS.

2) The proposed scheme considers:a) integrating a proposed fault identification module into

the MACS;b) DG security against unintended islanding operation;c) the distances between the backup feeders and the

affected feeder; andd) a mechanism to regulate the voltage after the service

restoration.3) A parallel full dynamic MATLAB-JADE simulation

model is implemented for evaluating the MACS.Furthermore, the proposed fault identification algorithm can

distinguish and detect both line and bus faults. A fuzzy rule-based system is used for decision-making support in the MACS.

Next to this introduction, Section II presents the formu-lation of the supply restoration problem in the presence ofDGs. Section III describes the structure and behavior ofthe proposed MACS. The fuzzy logic-based decision supportsystem is analyzed in Section IV. Description and imple-mentation of the dynamic simulation setup is provided inSection V. Performance results of the proposed service restora-tion scheme are presented in Section VI followed by conclusionin Section VII.

II. DG-SUPPORTED SERVICE RESTORATION

The proposed MACS operates the sectionalizing switches toisolate the fault zone(s). Then, service restoration mechanism isapplied. For safety reasons, if the DG lies in the affected zone(faulted zone), it will be tripped to prevent unintended islandingoperation. DG could participate in service restoration in twoways as follows [33], [34].

1) If the DG is within the out-of-service (de-energized) zone,activation of DG depends on the capability of backupfeeders to supply incoming de-energized loads. If thesupporting backup feeders could absorb all incomingde-energized loads, reconnecting the DG is not done.However, if only partial load transfer to the backup feed-ers is possible, DG could offer additional help. Prior toactivating DG, the additional loads that can be restored byDG should be carefully calculated to avoid overloadingon the backup feeders and maximize service restorationof out-of-service loads.

2) If the DG is outside the faulted zone, DG remains con-nected to network after the emergency state. The opera-tion status of DG after the fault clearance is very impor-tant for the service restoration process. it is classified intotwo categories as follows:a) Survived DG (SDG): DG unit that maintains its inter-

connection to the distribution network after faultclearance.


ELMITWALLY et al.: FUZZY-MULTIAGENT SERVICE RESTORATION SCHEME FOR DISTRIBUTION SYSTEM 3

Fig. 1. Communication flow of agents.

b) Nonsurvived DG (NSDG): DG unit that loses inter-connection to the distribution network during a faultand cannot automatically recover after fault clearance.This can occur because of the loss of synchronismbetween a DG unit and the network, or improperprotection coordination of a DG unit [33].

Restoration scheme caters to maximize the capacity of theserved loads considering load priority [12]. Several associatedconstraints are taken into account in this study to prevent anyviolations after the restoration process.

1) Feeder flow is below the limit.2) Bus voltage is kept within 0.95 and 1.05 p.u.3) Distribution network radial configuration must be main-

tained. This is assured by limiting the number of branchesentering a bus to one branch at most [16]–[19].

III. PROPOSED MULTIAGENT-BASED STRATEGY

The proposed MACS architecture is shown in Fig. 1. An FAis allocated to each feeder. An LA is allocated to each load bus.A regulator agent (RA) is allocated to each substation trans-former. The LA monitors the loads and DG is connected toits bus. It can control two local switches on both sides of itsbus to isolate it if necessary (see Fig. 2). It can also control thetie switches if available at this bus to connect the feeder to anadjacent feeder. The LA reports to the FA about any abnormallocal changes and can receive commands from the FA to con-nect or disconnect any local switch or load (see Fig. 2). EachLA is assumed to have direct communications with its FA andthe next neighbor LAs on the same feeder only. This limits theamount of the required communication facilities in the system.

The FA negotiates with other feeders/laterals to restore theservice in case of load power outage. If the FA could sub-stitute outage power to its LAs from another feeder, it sendscommands to the appropriate LAs with tie switches to connectthe backup feeders/laterals. The information flow between theseagents is illustrated in Fig. 1. The RA can control its substationtransformer tap to adjust buses voltages on receiving a requestfor changing the reactive power supply (RPS) from FAs. Eachagent type has its own set of operation rules as described below.

Fig. 2. Autonomous control manner of LA.

A. Load Agent

The LA monitors the power flowing into its load, lines con-nected to its bus, and DG unit (if available). Fig. 2 shows theautonomous control manner of an LA at bus k.

1) Fault Isolation Algorithm:a) Each LA monitors the load power and voltage at its

own node and upstream and downstream current flows.Current direction is identified based on its phase anglethat is measured by local phase angle meters at the LA.

b) If LA at node k detects that:i) the voltage is blow the lower limit;

ii) upstream current (Ik,up) is more than three times of itsmaximum value (Ik,up,max), and flows in downstreamdirection; and

iii) the downstream line current (Ik,down) is less than itsmaximum value (Ik,down,max) and flows in down-stream direction, or Ik,down flows in upstream direc-tion.

Then, the fault exists at node k (local fault) [35]–[37].Therefore, LA sends a command to the switches on both sidesof bus k to isolate the bus.

c) If LA at node k detects that:i) its voltage is blow the lower limit;

ii) upstream and downstream currents exceed three timesof their maximum values; and

iii) their flow directions are both downstream.Then, LA at node k sends a query message to downstream

agent at node (k + 1) to inquire about the status of fault exis-tence or nonexistence and direction of current flow at its node.

d) If the downstream agent at node (k + 1) replies that thefault exists and Ik+1,up flows in upstream direction (whenthere is downstream DGs), or that the fault does not existand Ik+1,up flows in downstream direction, then a faultexists in-between node (k) and node (k + 1). Hence, theLA at node k sends a request message (RM) to down-stream agent at node (k + 1) to disconnect the near switch



Fig. 3. Schematic diagram for fault detection and isolation algorithm (a) linefault and (b) bus fault.

on the feeder section joining (k + 1 and k). Also, it sendsa command to open its local switch on the feeder sectionjoining (k + 1 and k) to isolate the faulted section.

2) Operation Rules:a) FA sends an RM to LA to disconnect a DG unit existing

in faulted zone to prevent unintended islanding operation.b) If LA at any node detects that its voltage is zero (VK = 0)

after a fault clearance, then it sends an RM to its FA torestore its prefault voltage and load power.

c) LA with local DG in a de-energized zone sends an informmessage (IM) to its FA telling the available power of itsDG unit.

d) If an LA detects that voltage drops below 0.95 p.u. orarises above 1.05 p.u., its FA sends an RM to RA tochange RPS by resetting the tap position of substationtransformer. If the voltage is corrected, it will send an IMto RA to stop changing RPS.

Fig. 3 reveals a schematic diagram for fault detection andisolation algorithm. Referring to Fig. 3, LAk is the LA at busk. Ik,up is the current of upstream line for bus k. Ik,down isthe current of downstream line for bus k. Ik,up,dir is the flowdirection of upstream line current for bus k. Ik,down,dir is theflow direction of downstream line current for bus k. SWk,up

is upstream line switch at bus k. SWk,down is downstream lineswitch at bus k.

B. Feeder Agent

FA makes proper reconfiguration decisions for servicerestoration. It applies the following rules.

1) If an FA detects that the feeder current (If ) is more thanthree times of its maximum value (If,max), it sends a mes-sage to downstream LAs to query about the status of faultexistence and direction of current flow.

2) If a downstream LA replies that the fault exists and cur-rent flow direction is upstream, the FA sends an RMto this downstream LA to disconnect its upstream lineswitch and sends a command to open its feeder switchto isolate the fault. If all downstream LAs reply that nofault exists, the FA sends a command to open its feederswitch to isolate a probable undetected fault.

3) After the fault is cleared, the FA sends RMs to itsLAs located downstream the fault section to discon-nect sectionalizing switches and DG units (if avail-able) to prevent unintended islanding operation. Then,the FA waits for notification messages from LAs aboutabnormal operations such as load outage and voltageviolation.

4) If an FA receives RMs from its LAs to restore their loadpower, it sends RMs to all available neighboring FAs.

5) If a backup FA receives an RM and it has extra capac-ity, then it sends a message with its extra capacity to therequesting FA. Otherwise, it will send a refuse messageto the concerned FA.

6) After the concerned FA receives all proposals from neigh-boring FAs, it chooses the best proposals based on a fuzzylogic system described in Section IV.

7) If the total proposals are greater than the required poweramount, then the concerned FA sends accept messagesto the selected proposals and reject-messages to the restof proposals. Also, it sends commands to the LAs withappropriate tie switches to connect the FAs with theselected proposals.

8) If the total proposals are less than the required poweramount, then the concerned FA sends accept messagesto all proposing FAs. The concerned FA will decide theamount of loads which can be restored based on theirpriorities. It will command its LAs to either connect ordisconnect its load.

9) If a feeder is supplying some loads of other feeder and itsFA receives RMs from any of its LAs about any abnor-mal conditions, it will send cancel messages to the FAsof the supported feeders. Also, it sends commands to theappropriate LAs to disconnect the tie switches to thesefeeders.

10) When a fault is repaired, the sectionalizing switches onboth sides of the faulty section are reclosed manually.Then, the local LAs report to their FA that sends messagesto neighboring FAs to disconnect the tie switches.

Fig. 4 shows the negotiations algorithm of FA for powerrestoration. Fig. 5 shows the negotiations algorithm of FA forpower subscription.

C. Regulator Agent

The behavior of the RA is described below.1) RA monitors the voltage on both sides and the power flow

(SR) of the transformer.



Fig. 4. Negotiations algorithm of an affected FA for power restoration.

Fig. 5. Negotiations algorithm of FA for power subscription.

2) When an RA receives an RM for RPS changing, it checksits operating conditions. Then, it sends back an agree orrefuse message to the sender FA to tell its offer of support.

3) If an RA receives a message for RPS changing, it sends amessage to each FA to provide the maximum voltage andthe minimum voltage of its nodes. Once the RA receives

this information, it determines the overall maximum andminimum node voltages in the network. Then, the RAchecks if [38]

Vmax,network − Vmin,network < Vmax,allow − Vmin,allow

Vmax,network+ΔVtap≤Vmax,allow (forRPS increase request)

Vmin,network−ΔVtap≥Vmin,allow (forRPSdecrease request)

where Vmax,nework is the maximum bus voltage in thedistribution network, Vmin,network is the minimum busvoltage in the distribution network, Vmin,allow is the maxi-mum allowable voltage, Vmin,allow is the minimum allow-able voltage, and ΔVtap is the voltage change per eachtap move of ULTC. For the proposed system, Vmin,allow

is 0.95 p.u., Vmin,allow is 1.05 p.u., and ΔVtap is 0.01 p.u.If the above two condition are satisfied, then the RA

performs the requested tap move of the ULTC. Otherwise,the RA will send a refuse message to the requesting FA.Concerned FAs then behave locally to fix the problem.They may apply load shedding strategy to adjust nodesvoltages as given in [4].

4) If the RA receives contradictory RMs for RPS increas-ing and decreasing at the same time or normal operatingconditions are violated, it will send refuse messages toall requesting FAs. Concerned FAs may then apply loadshedding strategy to adjust nodes voltages.

D. Effect of Communication Failures

If communication between two agents fails for some rea-son, the system should be able to continue the execution of thealgorithm. A specific waiting interval is set at a sending agentthat defines the maximum time period during which a receiv-ing agent should have completed execution of tasks and sendsback a response message. Waiting interval of LA is 1 s. If noresponse is received, the message sending is repeated for threetimes. Then, if no response is received, the sending agent adaptsits algorithm to overcome the problem. LAs, arranged in thefirst layer, communicate with its next neighbor LAs on the samefeeder and with its FA. As a backup solution, if one LA losescommunication with its next neighbor LAs, it sends its messageto its FA (i.e., rerouting the communication path). Accordingly,the FA will forward this message to the intended LAs, receivethe answer messages, and then forward them to the sending LA.Furthermore, if an LA loses communication with its FA, it cansend its message to a neighboring LA. In consequence, the latteragent will forward this message to their FA, receive the answermessage and then forward it to the sending LA.

The communicated agent (RA or FA) may not answer themessage of the sender agent (FA or LA) due to agent or com-munication failure. In this case, the sender agent will resend themessage to the target agent after 2 s as a waiting interval. If noresponse is received within 2 s, the message sending is repeatedfor three times. Then, if the nonresponding agent is an FA, thesender LA will act locally. If the nonresponding agent is an RA,the sender FA will command LAs to act locally. For example,LA can do load shedding strategy for solving an under-voltageor feeder congestion problem.



Fig. 6. Fuzzy membership functions. (a) Proposer distance (p.u.); (b) proposedpower (p.u.); and (c) selection priority index.

TABLE IFUZZY RULES FOR PROPOSALS RANKING

IV. EVALUATION AND SELECTION OF THE PROPOSALS

During service restoration process, the FA having de-energized loads on its feeder determines the load restorationpossibility index (RPI). It is computed as the ratio between thesum of apparent power supply proposals of supporting feed-ers to the total de-energized load power on the affected feeder.The restoration will be complete if RPI ≥ 1. It will be par-tial if RPI < 1. Then, the concerned FA ranks supportingfeeders/laterals proposals based on the higher offered powermargin and the shorter distance between tie switches by a pro-posed fuzzy system. The fuzzy system has two inputs: 1) theproposed power (PP) and 2) the proposer distance (PD). PP iscalculated in p.u. based on the total de-energized load power(KVA). PD is calculated in p.u. based on the distance to theremotest backup feeder.

For the first input (PP), three fuzzy sets are defined. They arelow (L), medium (M), and high (H). For the second input (PD),two fuzzy sets are defined. They are low (L) and high (H). Thefuzzy system output is the selection priority index (SP). Sixfuzzy sets are assigned to cover the SP possible range. Theyare very high (VH), high (H), medium (M), low (L), very low(VL), and very–very low (VVL). The membership functionsof output and input variables are trapezoidal shaped as shownin Fig. 6. The fuzzy rule base consists of six rules as given inTable I. Min–Max inference method and centroid defuzzifica-tion method are used to get the SP value for each proposal [39].

V. SIMULATION MODEL IMPLEMENTATION

A parallel MATLAB-JADE arrangement is constructedfor dynamic simulation of the distribution network and theMACS. JADE is the platform for agent and multiagentscheme development [40]. Power system is simulated on

Fig. 7. Schematic diagram of parallel operation of MATLAB-JADEarrangement.

MATLAB/PSAT [41]. An interface for linking MATLAB toJADE is designed using data agents. Data agents are Javaobjects that contain different data structures. Two main subrou-tines GetStatus and SetStatus were constructed in java languagefor reading status from power system and also setting con-trol actions to it. During time-domain simulation, informationabout power system operating conditions at each integrationstep passes from MATLAB environment to JADE by meansof data agents. Then, RA, FAs, and LAs constructed in JADEenvironment process the information, produce control actionsif needed, and put information about control actions inside dataagents to carry to MATLAB environment. Fig. 7 illustrates theintegration of MATLAB/ PSAT-JADE in the simulation model.Case study power system used to evaluate the MACS is amodified version of the 33-bus test distribution system usedin [42]–[44] and shown in Fig. 8. An RA is located betweensubstation and bus 1 as depicted in Fig. 8. DGs are located atbuses 16, 22, and 30. Each DG unit has a capacity of 500 kWand operates at unity power factor. An FA governs each feedermarked as FA1, FA2, etc., in Fig. 8.

The 33-bus test distribution system has 5 feeders, 32 feedersections, and 5 tie switches. Nominal voltage is 12.66 kV. Totalsystem load is 3.72 MW and 2.3 MVAR. All the sectionalizingswitches are initially closed, while all the tie switches are ini-tially open. The current-carrying capacity of sections of feeder1 is 400 A (5.064 MVA). For other feeders sections and all thetie lines, the current-carrying capacity is 200 A (2.532 MVA).The distances between the five feeders are shown in Table II.Main substation transformer is assumed to have an ULTC withtap range of ±12 steps. Time delay of the ULTC tap movementsis 5 s. Initial state of ULTC tap is at zero tap.

VI. TEST RESULTS

Simulations are executed using an Intel(R) core (TM) i3-2350M CPU at 2.3 GHz, 4 GB RAM PC. MATLAB version7.8.0.347, PSAT version 2.1.6, and JADE version 4.1.1 areused. In the following, two example tests are presented to showdifferent operation modes of the proposed MACS.

A. Test 1

At the 1st second of simulation, a large sudden increaseoccurs simultaneously to the loads on buses 11–13 and 30–33



Fig. 8. Modified 33-bus test distribution system.

TABLE IIDISTANCE BETWEEN THE TIED FEEDERS

by 1.5 times of their original values. Also, loads on buses 26–29 increase by 2.5 times of their original values. Besides, atthe 2nd second of simulation, a three-phase fault occurred atbus 26 through a fault impedance of 0.1 + j0.3 p.u.. The faultdetection and isolation is achieved as follows. The LA at bus 26detects that its voltage is decreasing. The upstream and down-stream currents largely increase and all flows’ directions areheading toward bus 26. Hence, LA at bus 26 ensures that thisis a local fault on bus 26 (at about 2.02 s). Therefore, it sendsa command to open the local sectionalizing switches on bothsides. LA at bus 26 notifies FA4 with the detected and iso-lated fault. Hence, FA4 sends an RM to LAs at buses 27–33to disconnect the DG units (if available) and loads to preparefor service restoration process.

Two situations are assumed for DG units after faultclearance:

1) All DG Units Are Reactivated: Then, FA4 sends mes-sages to the neighboring FAs to restore its de-energized loads(1.2086 MVA). The backup FAs reply by messages offering

their extra capacity. FA5 proposes an extra capacity of (2 MVA)and FA3 proposes an extra capacity of (1.339 MVA). After FA4receives these proposals, it evaluates them and selects the opti-mal proposals based on the fuzzy system. SP value is 0.92 forFA3 offer and 0.5 for FA5 offer. So, FA4 sends an accept mes-sage to FA3 to restore all de-energized loads and it sends a rejectmessage to FA5. FA3 sends an RM to LA at bus 25 to close thetie switch at bus 25 tied to feeder 4. FA4 sends an RM to theLA at bus 29 to close its tie switch connecting feeder 3. In addi-tion, FA4 sends RMs to LAs at buses 27–33 to connect the DGunits and local loads. The de-energized loads are restored atabout 2.43 s.

Just after the service restoration, the LAs at buses 27–33 detect that their voltages go below the 0.95-p.u. limit.Therefore, they send RMs to RA to increase RPS. The RA sendsagree messages to violated voltage LAs to increase RPS. Fiveseconds later, the ULTC on substation transformer starts work-ing to raise the voltage on violated voltage LAs to above the0.95-p.u. limit. At about 9.5 s, the voltages of all violated volt-age buses return to above the 0.95-p.u. limit. Therefore, theirLAs send IMs to RA to stop RPS increasing. Computer runtime for dynamic simulation of this test is 131 s.

2) Only the DG Unit at Bus 22 Is Reactivated: In thiscase, the required power to re-energize all loads of feeder 4is 1.7086 MVA. After negotiations with neighboring FAs, FA5offers an extra capacity 1.5 MVA and FA3 offers an extracapacity 1.339 MVA. After evaluating these offers by the fuzzysystem, FA4 finds that the offer of feeder 3 is better (SP = 0.72for feeder 3 and SP = 0.67 for feeder 5). So, it sends anaccept message to FA3 to transfer loads of buses 27–31 (nearlyabout 1.251 MVA) and then it also sends an accept message toFA5 to transfer remaining loads of buses 32–33 (nearly about457 KVA). LAs at buses 10–18 and buses 26–33 detect thattheir voltages go below the 0.95-p.u. limit after service restora-tion. Therefore, they send RMs to the RA to increase the RPS.Five seconds later, the ULTC on substation transformer startsto raise the voltage on the violated voltage buses to above the0.95-p.u. limit. At about 19.5 s, the voltages at buses 10–15and buses 28–31 return to above the 0.95-p.u. limit. However,at about 24 s, the RA sends IMs to the LAs of the rest of theviolated voltage buses showing that it can’t increase RPS asits power flow reached the maximum limit. Hence, these LAswould shed some load if their voltages were under 0.9 p.u.Computer run time for dynamic simulation of this test is 134 s.

Fig. 9 shows the power flow changes in line between buses 31and 32, tie line between buses 18 and 33, tie line between buses25 and 29, and the load at bus 33 for the two DG situations.The power flows of lines 31–32 and 18–33 after reconfigurationdepend on the status of DGs after fault isolation. After reconfig-uration, the initially open tie line 25–29 carries nearly the samepower to supply de-energized loads in both DG situations. Loadat bus 33 is de-energized after fault isolation, but it is restoredshortly after reconfiguration. Fig. 10(a) reveals the voltagechanges at bus 32. The voltage of this bus drops to zero afterfault isolation, but the voltage is recovered in less than 0.5 safter reconfiguration. Fig. 10(b) depicts the steady-state busvoltage level after supply restoration for the two DG situations.All buses’ voltages are kept within 0.95 and 1.05 p.u. when



Fig. 9. Dynamics of power flow with DGs activated and deactivated: (a) line31–32; (b) tie line 18–33; (c) tie line 25–29; and (d) load at bus 33.

Fig. 10. Load bus voltage with DG units activated and deactivated: (a) dynam-ics of voltage at bus 32 and (b) final steady-state load buses voltages.

all DGs are reactivated. Whereas, few buses’ voltages dropslightly below 0.95 p.u. when DGs are deactivated. Switchingactions and postdisturbance steady-state active power losses,minimum bus voltage, maximum bus voltage, and nonrestoredload percentage are provided in Table III.

TABLE IIIPERFORMANCE FOR TEST 1

*U refers to upstream direction and D refers to downstream direction.

B. Test 2

At the 1st second of simulation, a sudden increase of 50% ofthe loads on buses 4–22 occurs, and at the same time, a suddenincrease of 33.3% to the loads on buses 23–33 occurs. At the2nd second, a three-phase fault occurred between buses 4 and 5through a fault impedance of 0.1 + j0.3 p.u.. Accordingly, theLA at bus 4 detects a voltage decrease and a large increase in theupstream and downstream currents flowing in the downstreamdirections. Therefore, it sends a message to the LA at bus 5 toquery about the fault status. It replies that the fault exists and itscurrents’ flow direction is upstream. So, the LA at bus 4 sendsa command to open its sectionalizing circuit breaker betweenbuses 4 and 5 to isolate the fault. The LA at bus 5 does thesame (at about 2.03 s). Feeders 4 and 5 are thus de-energized.FA1, FA4, and FA5 send RMs to all LAs at buses 5–10, LAsat buses 11–18, and LAs at buses 26–33, respectively, to openthe sectionalizing switches, DG units (if available), and loadsswitches to prepare for service restoration process.

Two situations are assumed for DG units after fault clearance.1) All DG Units Are Reactivated: LAs at buses 5–10 send

RMs to their FA (FA1) to restore their de-energized loads(1050 kVA). LAs at buses 11–18 send RMs to their FA (FA5) torestore their de-energized loads (434 kVA). LAs at buses 26–33send RMs to their FA (FA4) to restore their de-energized loads(834 kVA). Accordingly, FA1, FA4, and FA5 start the negotia-tions with the neighboring FAs. FA2 offers an extra capacity of(1.1 MVA) to FA1 and an extra capacity of (600 kVA) to FA5.FA3 offers an extra capacity (1.02 MVA) to FA4.

Thus, FA1 and FA5 send accept–proposal messages to FA2,and FA4 sends an accept–proposal message to FA3. Therefore,FA2 sends RMs to LAs at buses 21 and 22 to close the tieswitches tied to feeders 1 and 5, respectively. FA3 sends anRM to LA at bus 25 to close the tie switch tied to feeder 4.In addition, FA1, FA4, and FA5 send RMs to proper LAs toclose the needed switches for re-energizing the loads and recon-necting DGs. The sectionalizing switches between buses 10–11and 6–26 are kept open. The de-energized loads are restoredcompletely at about 2.55 s. Then, the LAs at buses 5–10, buses17–18, and buses 26–33 detect that their voltages are below0.95 p.u. Therefore, they send RMs to RA to increase RPS.Five seconds later, ULTC on substation transformer starts work-ing to raise the voltage on the violated-voltage LAs to abovethe 0.95-p.u. limit. At about the 10th second, the voltages ofviolated-voltage LAs return to the 0.95-p.u. limit. So, they sendmessages to RA to stop increasing RPS. Computer run time fordynamic simulation of this test is 132 s.

2) All DG Units Are Deactivated: In this case, de-energizedload of feeder 1 is 1.05 MVA (not changed). De-energizedload of feeder 5 is 934 kVA. De-energized load of feeder 4



Fig. 11. Dynamics of power flow with DGs activated and deactivated: (a) tieline 22–12; (b) tie line 25–29; and (c) load at bus 33.

is 1.33 MVA. FA1, FA4, and FA5 requests offer from neigh-boring FAs. FA2 offers an extra capacity of (1.1 MVA) toFA1 and an extra capacity of (600 kVA) to FA5. FA3 offersan extra capacity of (1.02 MVA) to FA4. The offer of FA2 isless than the needed power for de-energized loads of FA1 andFA5 together. Also, offer of FA3 is less than the needed powerfor de-energized loads of FA4. Therefore, FA1, FA4, and FA5move to partial-restoration state to restore loads with highestpriorities. FA1, FA4, and FA5 send RMs to LAs at buses 5–10,LAs at buses 26–33, and LAs at buses 11–18, respectively, toclose their sectionalizing switches. Load switches are closed,except those at buses 17, 18, 32, and 33 where lower priorityis assumed. Just after load restoration, LAs at buses 5–18 and26–33 detect that their voltages are below the 0.95-p.u. limit.Therefore, they send RMs to RA to increase RPS. The RAreplies by refuse messages to all as the local power flow reachedits limit. Hence, these LAs shed loads if their voltages are under0.9 p.u. However, since their voltages are above 0.9 p.u., loadshedding is avoided. Computer run time for simulation of thistest is 135 s.

Fig. 11 shows the power flow changes in tie line betweenbuses 22 and 12, tie line between buses 25 and 29, and theload at bus 33 for the two DG situations. The power flow intie line 22–12 after reconfiguration is slightly larger with minorovershoot when all DGs are activated. The steady-state powerflow in tie line 25–29 is the same for both DG situations. Loadat bus 33 loses supply for about 0.5 s after fault isolation beforeit restores supply by reconfiguration. Fig. 12(a) reveals the volt-age changes at bus 18. The voltage of this bus drops to zero afterfault isolation, but the voltage is recovered in less than 0.5 safter reconfiguration for both DG situations. Fig. 12(b) depictsthe steady-state bus voltage level after supply restoration forthe two DG situations. All buses voltages are kept within 0.95and 1.02 p.u. when all DGs are reactivated. However, due tosupply shortage, many buses’ voltages drop between 0.93 and

Fig. 12. Load bus voltage with DG units activated and deactivated: (a) dynam-ics of voltage at bus 18 and (b) final steady-state load buses voltages.

TABLE IVPERFORMANCE FOR TEST 2


0.95 p.u. when DGs are deactivated. Steady-state power flowsin all feeders sections are provided in the Appendix for differentconditions. Switching actions and postdisturbance steady-stateactive power losses, minimum voltage, maximum voltage, andload not restored percentage are given in Table IV.

C. Cost-Benefit Analysis

A set of required devices must be installed on each agent ofthe proposed MACS. This set includes:

1) current and voltage sensors for the real time measurementof the voltage and the current of every phase of the feeder;

2) data acquisition system for measured data collecting, pre-processing, organizing and transfer to the agent processor;

3) computer powered by uninterruptible power supply forhosting the agent software and implementing the agent’sintelligence and functions;

4) two-way communication facilities, for implementing theinformation transfer among the agents.

Also, two proper switches are assumed already installed onboth sides of the agent bus. The cost of the proposed MACS



components can be expressed as follows:

MACS cost = (2Nb +Nf +Nr)× (cost of current sensor)

+ (Nb +Nr)× (cost of voltage sensor) + (Nb+Nf+Nr)

×(cost of data acquisition unit+cost of agent computer)

+ cost of communication facilities

where Nb is the number of load buses, Nf is the number offeeders, and Nr is the number of regulators. For the 33-bus casestudy system, Nb = 33, Nf = 5, and Nr = 1.

The cost of smart grid control system is estimated to be20 000 $ per bus in [45]. The MACS cost in a similar distribu-tion network is estimated to be 30 000 Euros per bus in [46].This is about 70% higher than the conventional distributionautomation cost estimates [47].

Application of MACS benefits the distribution network inmany aspects as

1) Reduced restoration time.The proposed MACS isolates the fault and restores theservice of the de-energized loads in few seconds. But forthe conventional control systems like SCADA, it takesseveral minutes to restore supply to de-energized loads[47]. Thus, MACS reduces the cost of energy not servedand improves system reliability.

2) Reduced restoration cost.This cost can include line crew, support services such ascall centers, media relations, and other professional stafftime associated with service restoration [46], [47].

3) Reduced sustained outages.MACS reduces the likelihood that there will be an unre-stored outage, and allows the system to be rapidly recon-figured to restore service to as many customers as possible[46], [47].

4) Reduced distribution system losses.Customer voltages remain within service tolerances,while minimizing the amount of transferred reactivepower. This improves the power factor and reduces linepower losses.

These technical advantages will result in significant eco-nomic benefits to the distribution network. The MACS benefitto cost ratio is estimated as around 2 in [46] considering hard-ware replacement costs in 30-year life cycle. Pay-back periodof MACS is found as 10–15 years [46], [47].

D. Performance Comparison

At the 1st second of simulation, a large sudden increaseoccurs to the loads on buses 11–13 and 30–33 by 1.5 timesof their original values. Also, loads on buses 26–29 increaseby 2.5 times of their original values. Besides, at the 2nd sec-ond of simulation, a three-phase fault occurs between buses 26and 27 through a fault impedance of 0.1 + j0.3 p.u.. If the pro-posed MACS is applied, the control actions will be similar tothat explained in Section VI-A for (Test 1).

When the MACS in [19] is applied under the same condi-tions, it identifies and isolates the fault. Then, it starts the loadrestoration process. Two situations are assumed for DG unitsafter fault isolation:

TABLE VPERFORMANCE COMPARISON OF MACS


1) All DG Units Are Reactivated: FA4 sends messagesto the neighboring FAs to restore its de-energized loads(1.2086 MVA). FA5 proposes an extra capacity of (2 MVA)and FA3 proposes an extra capacity of (1.339 MVA). After FA4receives these proposals, it selects the higher power offer. So,FA4 sends an accept message to FA5 to restore all de-energizedloads and sends a reject message to FA3. But, after the servicerestoration, the voltages at buses 16–18 and 27–33 are below0.9 p.u.

2) Only the DG Unit at Bus 22 Is Reactivated: FA4 sendsmessages to the neighboring FAs to restore its de-energizedloads (1.7086 MVA). FA5 proposes an extra capacity of(1.5 MVA) and FA3 proposes an extra capacity of (1.339 MVA).So, FA4 sends an accept-message to FA5 to transfer loads ofbuses 30–33. Then, it also sends an accept message to FA3 totransfer remaining loads of buses 27–29. But, after the servicerestoration, the voltages at buses 13–18 and 30–33 are below0.9 p.u.

Table V lists the performance indices of the proposed MACScompared to that of the MACS in [19]. Although both MACScan restore the load, the MACS in [19] produces much higherpower loss and cannot prevent significant voltage reduction atsome buses. Moreover, if the MACS in [19] is applied whenthe fault occurs exactly on the bus as in Section VI-A (Test 1),it fails to locate this fault. This is because the embedded faultidentification algorithm deals only with faults on feeder sec-tions. Therefore, that MACS cannot make correct restorationdecisions in this case.

If the MACS in [17] is applied under the conditions of Test 1(Section VI-A), it will make a larger number of messagescompared to the proposed MACS. Proposed MACS needs20 messages, while MACS in [17] needs 65 messages toaccomplish service restoration. Also, the MACS in [17] canfail, if one of the LAs fails to forward the message sent bythe affected feeder. Therefore, the proposed MACS is faster,more flexible, more robust, and more reliable compared to theMACS in [17] and [19].

VII. CONCLUSION

This paper proposes a new MACS for service restorationin distribution systems integrated with DGs. Many impor-tant factors are considered in constructing the MACS. A faultidentification algorithm is integrated. Load priority, DG secu-rity, and voltage quality are addressed. The new MACS hasa hybrid centralized–decentralized framework where agentshave different communication capabilities. Agents at load buses



can only communicate directly with its next neighbor LAs onthe same feeder and to its FA. Other higher level agents cancommunicate directly with each other. Thus, all agents cancommunicate and coordinate its actions. This avoids single-point failure in coordinator-based MACS and improves thereliability of decision-making system without the need of a toocomplicated communication network. Full dynamic simulationmodel for evaluating the MACS is implemented. It employsa parallel MATLAB/PSAT-JADE arrangement. Test results areprovided under different contingency states and DG operationmodes. If most of DG units are deactivated after fault clear-ance, the MACS can restore rapidly most of the de-energizedload. If most of DG units are activated after fault clearance,the MACS can faster restore the entire de-energized load. Forthis later case, the required switching operations are less, powerlosses are less, and better buses voltages are globally reached.

APPENDIX

TABLE A1LINE FLOWS AFTER FAULT ISOLATION

TABLE A2LINE FLOWS AFTER RECONFIGURATION

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Akram Elmitwally (S’02–A’02–M’03) received the B.Sc., M.Sc., and Ph.D.degrees in electrical power engineering from Mansoura University, Mansoura,Egypt, in 1989, 1995, and 2002, respectively.

Between 1998 and 2000, he has been a Visiting Researcher at the Departmentof Electronic and Electrical Engineering, University of Bath, Bath, U.K..Currently, he is an Associate Professor with the Department of ElectricalEngineering, Mansoura University. He has authored more than 60 papersin journals and conferences. His research interests include power quality,power system protection, distributed generation, and AI applications in energysystems.

Mohammed Elsaid is a Professor with the Department of ElectricalEngineering, Mansoura University, Mansoura, Egypt. He has authored morethan 30 papers in journals and conferences. His research interests include powerquality, power system control, and renewable energy systems.

Mohammed Elgamal is a Postgraduate Research Student with the Departmentof Electrical Engineering, Mansoura University, Mansoura, Egypt. His researchinterests include smart grids, power system operation, and control of renewableenergy systems.

Zhe Chen (M’95–SM’98) is a Professor with the Department of EnergyTechnology, Aalborg University, Aalborg, Denmark. He is the author of morethan 360 research papers in journals and conferences. His research interestsinclude power system operation, power electronics, and wind energy.

Prof. Chen is a Fellow of IEE. He is the Editor of IEEE TRANSACTIONS ON

POWER SYSTEMS and Associate Editor of IEEE TRANSACTIONS ON POWER

ELECTRONICS.

A Fuzzy-Multiagent Service Restoration Scheme for Distribution System With Distributed Generation

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