A Cooperative Multiagent Framework for Self-Healing ... - IEEE Xplore

IEEE TRANSACTIONS ON SMART GRID, VOL. 3, NO. 3, SEPTEMBER 2012

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A Cooperative Multiagent Framework for Self-Healing Mechanisms in Distribution Systems Aboelsood Zidan, Student Member, IEEE, and Ehab F. El-Saadany, Senior Member, IEEE

Abstract—Because of society’s full dependence on electricity and high cost of system outages, one important goal is to increase the reliability of the power system, which means that a salient attractive feature of smart grid is its self-healing ability. Smart grids will develop and enhance the automation of distribution by operating in a distributed manner through new digital technologies such as monitoring, automatic control, two-way communication, and data management. In this work, the smart grid concept and technologies have been applied to construct a self-healing framework for use in smart distribution systems. The proposed multiagent system is designed to locate and isolate faults, then decide and implement the switching operations to restore the out-of-service loads. The proposed control structure has two layers: zone and feeder. The function of zone agents in the first layer is monitoring, making simple calculations, and implementing control actions. Feeder agents in the second layer are assigned to negotiation. The constraints include voltage limits, line current limits, and radial topology. Load variation has been taken into consideration to avoid the need for further reconfigurations during the restoration period. The results of the simulation conducted using the new framework demonstrate the effectiveness of the proposed control structure. Index Terms—Distributed control, distribution system, fault location, multiagent, service restoration, smart grid.

I. INTRODUCTION

T

HE AUTOMATION of distribution provides increased efficiency in day-to-day operations and improves the restoration process, which includes responding rapidly to outages, reducing operator error, and shortening the duration of outages [1], [2]. Detecting and isolating fault locations and restoring service are among the important functions of distribution automation (DA) and form the cornerstone of strategies for developing smart grids [3]. Service restoration is defined as finding suitable backup feeders and laterals to transfer the loads in out-of-service areas using operational criteria through a series of switching operations [4]. Restoration is achieved through the switching operation of sectionalizes (normally closed) and tie switches (normally opened). Different restoration methods thus entail different configurations, which may affect service quality. In addition, because the task of restoration is usually

Manuscript received November 24, 2011; revised March 12, 2012; accepted April 29, 2012. Date of publication May 25, 2012; date of current version August 20, 2012. Paper no. TSG-00653-2011. The authors are with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1 Canada (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSG.2012.2198247

performed under emergency conditions, time constraints can add to the complexity of the problem [4], [5]. Currently, many distribution systems are not fully automated. Therefore, in most cases, the corrective actions that are required for the fault location, isolation and service restoration (FLISR) function are performed manually by human operators. This process may require more time than the self-healing feature of a smart grid, which can detect faults and reconfigure the network automatically. Therefore, one vital pillar of smart grids is self-healing, which can be described as the quality of a system that enables it, when subjected to a fault, to automatically and intelligently perform corrective actions to restore the system to the best possible state, thus enabling it to perform basic functions without violating any system constraints. The ability to quickly and flexibly reconfigure the network to restore the loads previously deenergized by the fault, therefore, represents the key component of the self-healing function. This automated FLISR or self-healing function will: minimize the workload for the field human operators, provide almost immediate restoration for the customers, and improve reliability and robustness of distribution networks. The approaches currently used to solve this problem (heuristics, expert system, meta-heuristic, and mathematical programming) operate in a centralized way so that a central optimization solver must read all of the system data and then process them in order to obtain the solution [6]. The main advantage of centralized control is that it may provide the best solution, especially for small-scale systems. However, several challenges might impede the use of centralized control in future distribution networks: the large number of small DG units and the increase in the level of uncertainty due to renewable resources, electric vehicles, and variable demand. Smart grids therefore represent a promising solution for tackling these challenges through the development and enhancement of distribution automation, the distributed operation of functions, and the reduction or elimination of human intervention through the enhanced deployment of information, two-way communication technologies, and data management. Multiagent systems (MAS) are composed of multiple interacting computing elements, known as agents. These agents react to changes in the environment and are capable of acting (making decisions) in order to achieve specified goals [7]. A multiagent system can be considered the plate form of distributed processing, parallel operation, and autonomous solving. It can also be much faster for solving discrete and nonlinear problems [8]. In the last few years, some research studies have been directed at implementing the self-healing feature of a smart grid in distribution systems [9]–[15]. In [9], the application of a MAS for planning the service restoration of a distribution system was pro-

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posed; however, the authors did not consider load shedding, load priorities, or the obtaining of extra available capacity through load transfers from the main backup feeders to their neighbors. Their work was limited to service restoration and did not include the use of agents for the detection and isolation of fault locations. In [10]–[15], a multiagent framework was proposed for the self-healing of a distribution system, but, no studies have presented a well-defined control structure, which should include the following: 1) an appropriate control structure and operation mechanism to be implemented in each agent; 2) appropriate coordination and communication among agents in a distributed fashion; 3) a general procedure for a restoration algorithm that is effective for any distribution network (i.e., without systembased rules); 4) consideration of load variation, load priorities and operational constraints; 5) a proper simulation model for verifying the effectiveness of the proposed control structure. The work presented in this paper has the goal of including consideration of the above points, which have not yet been well defined in previous studies, in the design of an appropriate distributed control system for self-healing distribution feeders. The remainder of this paper is organized as follows. Section II presents the formulation of the restoration problem and a discussion of related practical issues. In Sections III and IV, the structure of the proposed multiagent control is described. The operating mechanism of each control agent has been defined according to the concept of intelligent agents, and the decision makers of the control agents are coordinated using expert-based Foundation for Intelligent Physical Agents (FIPA) communication protocols. Section V discusses important issues related to the practical implementation of the proposed algorithm. Section VI presents the results of a simulation conducted in order to validate the proposed algorithm, and Section VII provides conclusions and summarizes the main contributions of this research. II. RESTORATION PROBLEM IN DISTRIBUTION SYSTEMS A. Problem Formulation The service restoration problem can be formulated as a multiobjective multiconstraint optimization problem [16]. The proposed objectives and constraints considered are as follows: a) Objective Functions: 1) maximizing the load restored with consideration of load priority (1) : the number of energized buses after service where restoration; : load at the th bus; : status of the load at the th bus (i.e., equals 1 for a restored load and 0 for an unrestored one); and : priority or importance level of the load at the th bus;

2) minimizing the number of switching operations and thus reducing the time required for and the operational cost of the restoration [4]

(2) where : the total number of switches; : status of the th switch in the restored network (i.e., equals 1 for a closed one and 0 for an opened one); and : status of the th switch immediately after the fault has been isolated; 3) minimizing losses during the restoration period (3) : total number of branches; : current in the th where branch; and : resistance of the th branch. b) Constraints: 1) radial network structure should be maintained; 2) bus voltages at all buses should be kept within limits (4) where : voltage at the th bus, and : maximum and minimum acceptable bus voltages (in the case study for this research, they are 1.05 p.u. and 0.9 p.u., respectively); 3) all branch currents should be kept within their limits (5) where : current in branch j, and current.

: maximum line

B. Practical Issues Related to the Service Restoration Problem This section highlights some of the important aspects related to the operational practices of the restoration problem, which were used in extracting the rules for designing the expert-based decision makers for the control agents described in Section IV. The related issues are as follows: • For locating and isolating the fault and coordinating the protection device, it is important to maintain the radial network topology during the service restoration process [16]. • In the case of limited available capacity for restoration or partial restoration, the restoration should begin with the highest priority customers first (i.e., hospitals, industrial loads, etc.) as formulated in (1) [16]. • Load variation during the restoration period should be considered during the building of the restoration plan. Using the rated or the daily peak load may lead to the limited restoration of some loads. Using the prefault load may cause overloading in some backup feeders [5]. Therefore, in order not to violate the operational constraints (i.e., voltage and current limits) and to benefit from the available capacity for restoration (to restore as much load as possible), the peak load over the restoration period (i.e., the average duration for fault repair) should be used to build the restoration plan [5], [17]. This point is aligned with the second objective in (2) because considering load

ZIDAN AND EL-SAADANY: A COOPERATIVE MULTIAGENT FRAMEWORK FOR SELF-HEALING MECHANISMS IN DISTRIBUTION SYSTEMS

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Fig. 1. Levels of backup feeders for the restoration process.

variation will prevent the need for further reconfigurations during the future restoration hours, thus reducing the number of switching operations. • Restoration is accomplished by transferring loads in the out-of-service area to their neighboring backup feeders through the on-off control of tie-sectionalizing switches. Because the time required for the restoration process depends on the number of switching operations [4], [16], that number should be kept as low as possible, as formulated in (2). For example, the typical operating time of remote-controlled and manually controlled switches are 50 s and 1200–1500 s, respectively [6]. Reducing the number of switching operations also reduces the possibility of switching surges, the risk of outages, and the number of transient disturbances in the system due to multiple switching operations. The operational cost of the switching operations is also lowered (i.e., the cost of maintenance and dispatching technicians for nonautomated systems as well as the costs associated with the reduced lifetime of the switches). • If the restoration plan provides a network configuration with a topology closer to that of the prefault configuration, it will be easier to return to the normal configuration after the fault is cleared. • Based on the last two points, if the capacity of the backup feeder is sufficient, the out-of-service area can be restored as a single group through one switching operation using one backup feeder (i.e., group restoration) [18]–[21]. This option minimizes the response time by reducing the number of switching operations to one. If no backup feeder has the capacity required for group restoration, the out-of-service loads can be restored as multigroups (i.e., zone restoration). A zone indicates a segment of a distribution feeder that is bounded by two or more switches. Zone restoration is accomplished by finding a plan to restore as many zones as possible through suitable paths [20], [21].

• If any zones are still unrestored after zone restoration, loads can be transferred from the main backup feeders to their neighbors in order to secure what might be a sufficient margin for the main backup feeders to restore the remainder of the unrestored loads, as shown in Fig. 1. • If any zones are still unrestored after the load transfer, shedding of the least priority loads can enable the remainder of the unrestored loads to be restored with the remaining limited capacity. • For practical purposes, the loss reduction calculated according to (3) is definitely to be used in normal operating conditions and is not appropriate for service restoration during an emergency situation [16], [20], [21]. III. THE PROPOSED MAS CONTROL STRUCTURE A. Structure of the Proposed MAS A cooperative MAS control structure is proposed for the implementation of the self-healing operation. The control agents possess the intelligent ability to use communication and negotiation in order to determine the current and predicted states of the system and then set their actuators and switches in a way that will achieve their own objectives as closely as possible while satisfying any constraints. Each distribution feeder is divided into segments (zones) based on the location of protective devices. Thus, each section bounded by two or more switches is a zone. To help in the restoration process when a fault occurs, feeders of the distribution networks are connected by tie switches that are normally open. The proposed control structure therefore has two layers: zone and feeder. The function of the zone agents in the first layer is monitoring, making simple calculations, and implementing control actions. The feeder agents in the second layer are assigned to negotiation. Each feeder agent communicates only with its neighbored feeder agents which are connected with them by tie switches. Fig. 2 shows a depiction of

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Fig. 2. Concept of the distributed control structure.

the concept of the intelligent control agents and their two-way communication for the proposed control structure. B. Coordination Among Control Agents via Communication The Foundation for Intelligent Physical Agents (FIPA) was originally formed in 1996 to specify software standards for agent-based systems [22]. FIPA developed an agent communication language (ACL) for agent communications [22], [23], which consists of 20 ACL message types (e.g., informing, requesting, and composite speech acts). These messages have been used in this work as a means of coordinating the proposed intelligent control agents in order to achieve self-healing. IV. THE PROPOSED OPERATION MECHANISM AND COORDINATION BETWEEN CONTROL AGENTS Fig. 3 shows the objectives and the two-way communication among the control agents. As shown in the figure, the objectives common to all agents are the maximization of the loads restored and the minimization of the number of switching operations. Each agent also has an additional objective. For example, the initiator feeder agent (i.e., the feeder that has been subjected to a fault at one of its components and hence has at least one out-ofservice zone) will locate and isolate the faulty zone before beginning the restoration process. The responder feeder agents (i.e., level-1 or immediate-neighbor backup feeders of the initiator) and the subcontractor feeder agents (i.e., level-2 or immediate- neighbor backup feeders of the responder feeders) will provide whatever capacity they have to assist with the restoration without violating their operational constraints. This section describes the operating mechanism of each agent and the mechanism for their coordination using two-way communication during both stages of the self-healing operation (i.e., the first stage, which is the detection and isolation of the fault location, and the second stage, which is the restoration of the out-of-service load).

A. Fault Location Detection and Isolation Algorithm (Stage 1) Once a permanent fault occurs in a distribution feeder, the feeder circuit breaker is tripped in real-time operation. The fault location detection and isolation algorithm is then applied in order to locate and isolate the faulty section from both directions. As soon as the faulty section is isolated, the upstream out-of-service loads are restored through the closing of the feeder circuit breaker. A restoration algorithm is applied to restore the downstream out-of-service loads. When the faulty section is repaired, the reverse switching sequence is applied so that the distribution system is returned to its normal configuration. Due to the radial topology of the distribution feeders, the occurrence of a fault in a distribution feeder affects only its sections [i.e., sections between the substation and the faulty section as well as the downstream sections, when distributed generation units (DGs) are present]. Therefore, only the control agents of the feeder that has the faulty section will participate at this stage. Due to the voltage potential difference, the normal power flows being from the source to the grid. However, the introduction of DG units may change the direction of power flows from unidirectional to bidirectional. When a fault occurs somewhere in the distribution system, the power flow magnitude and direction change. Fault current flows from the substation and DG units to the lowest potential point at the fault location. Therefore, when a fault occurs in one of the zones between the substation and other zones that involve DG units, the following two conditions apply [13]: 1) the fault is fed by both the substation and the DG units in the downstream zones. The current in both boundary breakers of this zone will thus flow into the zone; 2) the current in at least one of its breakers will exceed its limit. The former condition means that there is no fault outside this zone. The latter condition always applies because the former one can be implemented under normal conditions (i.e., a reverse power flow due to a high generation level produced


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Fig. 3. Coordination via two-way communication among the control agents.

TABLE I LOGIC CIRCUIT FOR ZONE BINARY SIGNAL AND FAULT CURRENT FLOW

from DG units in the downstream zones). On the other hand, when a fault occurs in a zone that has no downstream zones containing DGs, its entrance breaker current will exceed its limit. Based on these conditions for fault occurrence and on the proposed control structure in Fig. 3, the fault location detection and isolation algorithm for a single fault at a time can be described as follows: 1) Monitoring devices using direction and over-current relays provide two signals to indicate a change in the status of

the current flow. One signal indicates that the magnitude of the current exceeds its limit, and the other indicates the direction of the current. 2) Zone agents utilize these signals in a logic circuit to generate simplified binary status signals (0 or 1), as shown in Table I. 3) They then send these binary signals to their feeder agent through inform messages. 4) The feeder agent determines which zone is the faulty zone, as shown in Table I:

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• if it receives a binary signal with a value of one from a type 1 zone, it sends a request message to this zone agent asking it to open its boundary breakers; • if it receives a binary signal with a value of one from a type 2 zone, it sends a request message to this zone agent asking it to open its boundary breakers; • if it receives a binary signal with a value of one from more than one type 2 zones, it sends a request message to the last zone agent (i.e., the zone at the feeder end side) asking it to open its boundary breakers; • if it receives a binary signal with a value of zero, no action is taken.

the number of switching operations as much as possible. Fig. 4 shows the overall procedure for the proposed agents. The remainder of this section describes the operating mechanism of each agent during the restoration process. 1) Initiator Control Agent Operating Mechanism: After the fault is isolated, the downstream zones are isolated. The affected zones communicate with their feeder agent (initiator), as shown in Figs. 2 and 3, in order to build a restoration plan. The details of the proposed architecture are as follows: 1. Each zone agent in the out-of-service area sends a request message to the initiator, including its load demand and priority

B. Service Restoration Algorithm (Stage 2) After the faulty section is isolated, the upstream out-of-service loads are restored through the closing of the feeder circuit breaker. The restoration algorithm is applied in order to restore the downstream out-of-service loads. The restoration problem is a combinatorial problem of large scale due to many combinations of possible switching operations that increase with the increase of networks’ dimensions. For this reason, most of the published work didn’t apply global optimization algorithms (i.e., metaheuristic and mathematical programming) to solve the restoration problem. Expert-based algorithms are preferred over global optimization algorithms to determine restoration plans [20] for two main reasons: i) while global optimization algorithms may arrive at high quality solutions, they take an inappropriately long time to reach a solution, hence, slow specially for realistic distribution networks [16], [20]. Furthermore, a well-designed expert-based algorithm may arrive at equally good solution and the benefit from the computational burden of global techniques may not always be realized [20]. ii) Global optimization algorithms propose the final configurations only. In contrast, expert-based algorithms provide a feasible sequence of switch operations to guarantee that implementing the restoration plan will not cause unwanted effects on the network [20]. Therefore, in this work, an expert-based decision-making algorithm has been used to govern the control agents. The rules have been extracted from the practical issues related to the service restoration problem, as presented in Section II of this paper. The restoration plan has two primary objectives: 1) to provide as much service as possible to the customers with consideration of their priorities (1); and 2) to be implemented as fast as possible that can be translated into minimization of the number of switching operations to be performed (2). Hence, based on the problem objectives, the proposed algorithm implements a multistep procedure while avoiding visits to infeasible solutions such that: 1) the outage loads are restored as a single group if there is supporting feeder that has enough capacity; 2) if group restoration is not valid, zone restoration is implemented by restoring as many zones as possible through suitable paths; 3) load transfer from the main backup feeders to their neighbors is applied to restore the remainder of the unrestored loads; and 4) load shedding of the least priority loads is applied to restore the remainder of the unrestored loads with the remaining limited capacity. In this way, the proposed algorithm builds its restoration plan with: reenergizing as much as possible of the out-of-service loads with the consideration of their priorities and minimizing

(6)

2.

3.

4.

5.

: weighting factor and load demand of cuswhere tomer in zone , respectively, and : total number of customers in zone . The initiator control agent starts negotiations using a contract net protocol (CNP) [23] by sending call for proposal (CFP) messages to the responder feeder agents. After the responder feeder agents reply with their proposal messages, which contain their available remaining capacity (ARC), the initiator agent sends these two input items to its decision maker. The input consists of the load demands and priorities from the out-of-service zone agents, and the ARCs from the responder agents. The decision maker component in the initiator agent uses expert-based rules that have been extracted from the practical issues explained in Section II along with the input it has received in order to determine its output. The initiator agent compares the maximum ARC with the total demand from the out-of-service zones (7)

where : load demand of zone : the total number of out-of-service zones; and : the total number of responder agents. 6. If (7) is satisfied, the initiator decides to initiate group restoration by restoring all out-of-service zones through one switching operation. 7. The actions of the initiator agent are therefore to send an accept-proposal message to the responder agent that has the highest ARC and to send a request message to its zone agent that is the neighbor of the selected backup feeder asking it to close its tie switch for the completion of the restoration process. 8. If (7) is not satisfied, the initiator decides to initiate zone restoration by building a zone/switch relationship table [9]. The initiator identifies all possible out-of-service zone combinations as follows: (8) where

: a collection of

adjacent zones bounded by


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Fig. 4. Overall self-healing procedure using the proposed agents.

two or more switches, at least one of which is a tie switch. After the elements in are identified, the initiator builds its zone/switch relationship table. The information in this table is: 1) the zones in each combination ; 2) the bounded switches for each combination ; 3) the load demand in each combination according to (9); and 4) the equivalent priority index for each combination according to (10). (9)

(10) 9. Based on the zone/switch relationship table and the ARCs communicated from the responder agents, the initiator agent searches for possible combinations of zone restoration. It compares the ARCs with the elements of , which are listed in descending order based on their priority indices. 10. After checking for the available restoration possibilities, the initiator agent actions are: 1) to send accept-proposal

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messages to those responders that will be used in the restorations; 2) to place tie switches between the feeder agents that have accepted proposals and the selected combinations for the restoration in a switch-to-be-closed list (SCL); 3) to place the bounded sectionalizing switches of the selected combinations for restoration in a switch-to-be-opened list (SOL) in order to satisfy the radial constraint; and 4) to update the zone/switch relation table. 11. It then checks to determine whether the table is empty (i.e., whether all zones have been restored). 12. If the table is empty, the initiator sends request messages to the appropriate zone agents asking them to open their sectionalizing switches that are included in the SOL list in order to partition the outage area and then to close their tie switches that are included in the SCL list. 13. If the table is not empty, the initiator sends request messages to the responder feeder agents that are neighbors of the remaining unrestored zone combinations. This request prompts these responders to start negotiations with their neighbors (subcontractors) to find load transfers that can provide additional ARC. The request message includes the load demand required for the remaining unrestored zone combinations. 14. After these responders reply with their ARC, the initiator agent repeats steps 9–11. 15. If the table is empty, the initiator sends request messages to the appropriate zone agents to open their sectionalizing switches included in the final SOL list and then to close their tie switches included in the final SCL list. 16. If the table is not empty, the initiator determines the necessity for load shedding of the lowest priority load (i.e., the lowest priority zone index) in the remaining unrestored zone combinations. It then checks to determine whether all zones have been restored, as in step 9. 17. The initiator agent repeats step 16 until the zone/switch relationship table becomes empty, when it then executes step 15 in order to implement the switching actions required for the completion of the restoration process. Fig. 5 shows the overall procedure for the operation of the proposed initiator control agent. 2) Level-1 Backup Feeder (Responder) Operating Mechanism: The available remaining capacity (ARC) of each supporting feeder represents the maximum load that can be supplied by this feeder without violation of its current and voltage constraints. During the restoration period, higher currents will appear only in branches between the root node (i.e., substation) and the correspondent node at the tie switch with the affected feeder (i.e., restoration path - - as shown in Fig. 6). Hence, the maximum additional load, which will not lead to overloading in the supporting feeder can be obtained by considering the current constraint at zones of this restoration path only [24]. In addition, due to the radial topology of distribution networks, as the load point comes far away from the root node, its voltage drop increases, and it becomes maximum at points located at the end of the feeder (i.e., point as shown in Fig. 6). Hence, the maximum additional load, which will not lead to unpermitted low voltages in the supporting feeder can be obtained by consid-


ering the voltage constraint at zones including these points only [25]. The operating mechanism of each level-1 backup feeder (responder) agent will be as follows: 1. after the responder agent receives a CFP message from the initiator, it starts to build its proposal (ARC); 2. it sends query messages to its appropriate zone agents about their spare capacities and bus voltage values; 3. each zone agent replies by sending an inform message that includes the spare capacity of its branch and/or the bus voltage magnitude of its bus: (11) where : represents the available capacity of each zone before it becomes overloaded and before its protection device operates; : upper bound current in branch ; and : magnitude of the current flow in branch ; 4. if any zone has more than one branch, it sends the minimum spare capacity of its branches, and a zone with more than one bus sends the lower voltage magnitude of its buses; 5. after the responder agent receives these replies, it calculates its ARC as follows [24]: (12) (13) (14) where : zones along the restoration path (i.e., path - in Fig. 6); : the lowest bus voltage magnitude of the values received from zone agents; : minimum allowable voltage magnitude in the network (i.e., 0.9 p.u.); : series impedance of the path between the substation and the node closest to node on the restoration path (i.e., part of the restoration path between root node and node in Fig. 6). This impedance could be determined by carrying out offline simulation if the forecasted load is available (i.e., to determine which point among the points located at the end of the feeder will have minimum voltage, hence, its will be used). Another option is to determine for those possible points in advance and based on the received minimum voltage value, the appropriate impedance is used. In this paper the first option was used to determine this : maximum spare capacity of the restoration path without overloading (current limit constraint); : maximum spare capacity of the restoration path to avoid under-voltage at any node (voltage limit constraint); and : maximum spare capacity of the restoration path without violating operating constraints. 6. If this spare capacity from this supporting feeder will be used to restore an out-of-service load at voltage (15)


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Fig. 5. Procedure for the proposed initiator control agents.

to include the voltage limit feeder will thus be

pu, the ARC for this

(16)

7. 8.

9.

10. Fig. 6. Restoration path and

for supporting feeder.

This ARC guarantees that voltage limits will not be violated for the restored zones. The responder sends to the initiator agent a propose message that includes this ARC. If the responder receives an accept-proposal message from the initiator, it replies to the initiator by sending an inform message to indicate that it is committed to the completion of the task. If the responder receives from the initiator a request message for additional ARC through load transfer, it begins negotiations by sending CFP messages to its neighboring feeders, if available (i.e., level-2 backup feeders or subcontractor agents). This load transfer from a level-1 backup feeder to a level-2 backup feeder would involve the responder securing

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Fig. 7. Procedure for the proposed responder control agent.

a margin that could enable it to restore the remaining out-of-service zone combinations. The best amount of the transferred load (TL) is then

load of remaining unrestored zone combinations remaining ARC of this level-1 backup feeder

(17)

11. Due to the discrete nature and possibly the limited ARC of the level-2 backup feeders, the TL cannot be exactly the same as what is required. The responder thus selects its zones to be transferred to the level-2 backup feeder as follows:

Transferred zone

ARC of level (18)

12. After the responder determines the zones to be transferred, it sends a propose message to the initiator with its new ARC. 13. If the responder receives an accept-proposal message from the initiator, it sends a confirm message to the subcontractor agent and request messages to the appropriate zone agents asking them to open the bounded sectionalizing switches for the selected zones to be transferred and to close the tie switch to complete the load transfer to the subcontractor. Fig. 7 shows the overall procedure for the proposed responder control agent. 3) Level-2 Backup Feeders (Subcontractors) Operating Mechanism: The operating mechanism of the subcontractor agents is similar to steps 1–7 in the mechanism of the responder agents, and subcontractor agents also negotiate with responder agents.

V. PRACTICAL ISSUES RELATED TO THE IMPLEMENTATION OF THE PROPOSED SELF-HEALING ALGORITHM In this section, issues that are related to the practical implementation of the two-way communication control algorithm are presented. A. Implementation of Two-Way Communication Two-way communication is the key technology in the implementation of the smart grid concept. The appropriate design for the physical, data, and network communication layers is still a topic of intense debate. Several types of communication technologies can be used (e.g., fiber optics, wireless, and wire line). The harsh and complex power system environment entails significant challenges with respect to the reliability of wireless communications for smart-grid applications [26]. Power line communication (PLC) may be considered a good candidate for smart-grid applications because it is the only technology that has a deployment cost that can be considered comparable to that of wireless systems since the lines already exist. A power grid will therefore be the information source and the information delivery system [27]. B. Communication Link Failure Issue For MAS-based self-healing, communication is essential for the collection of enough information to enable good decisions [10]. Thus, if communication between two agents fails for any reason, the system should be able to continue the execution of the algorithm. A time-out procedure is simply the expectation of executing specific tasks within a specific maximum predetermined time interval and can be introduced for communication between agents. When the time expected for the receipt of a message from another agent is exceeded, the receiving agent will modify the execution of its algorithm in order to tackle the problem. For example, in this proposed algorithm, communication among agents is limited to two levels: the first level between each zone agent and its feeder agent, and the second level


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Fig. 8. Current implementation of the procedure with data flow between the MAS and the simulated distribution network.

between neighboring feeder agents. Because of their high numbers in practical systems, to reduce the amount of communication and hence the delay time during the negotiation process, the algorithm does not allow zone agents to negotiate with one another. As a backup algorithm, if one zone agent were to lose its ability to communicate with its feeder agent, it can send its message to its neighboring zone agent (i.e., rerouting the communication path). In consequence, the latter agent will forward this message to their feeder agent and then forward its reply to the former zone agent. VI. CASE STUDIES The proposed MAS-based self-healing algorithm was tested on a distribution system having two substations, four feeders, and 70 nodes [28]. The following factors were taken into consideration: 1. Renewable DG units, such as those generating power from wind and solar energy, are characterized as fluctuating power sources due to changes in wind speed and solar irradiance. Therefore, at each time interval (depending on the accuracy of the forecasting), a zone agent that includes this type of DG sends its feeder agent an inform message containing the power predicted to be generated during this time interval. 2. The typical hourly load patterns of residential, commercial, and industrial customers are used [29] to include the priorities of the loads and to estimate accurately the loading of zones along each feeder as well as the ARC of the supporting feeders. 3. Based on the policies of each utility regarding the number of switching operations allowed and the forecasting accuracy with respect to renewable DG units and load demand, the service restoration algorithm can provide an overall plan for the whole restoration period or multiple plans (e.g., one plan for each time interval).

4. As mentioned in Section II of this paper, the peak load and the lowest generated power from renewable-energy-based DGs over each restoration plan period are used to build this restoration plan. 5. The islanded operation of DGs is not included in this work. Utilities have installed intelligent electronic devices (IEDs) such as modern protective relays and recloser controls in order to provide local measurement and control capabilities. IEDs provide the required input and output signals for the control system, such as switch open/close indication, live/dead voltage indication, fault current indication, load current, and open/close commands. The data required for the simulation were obtained from load flow calculations in Matlab software. In this work, the algorithm for each control agent was implemented in a Java Agent Developing Framework (JADE), which provides the required communication and platform services, and the distribution system was modeled with Matlab. Fig. 8 shows the simulation sequence whereby the forecasted load demand and DG generation are fed into the distribution network. This step is followed by the sending of the generated data to the MAS in JADE. The MAS executes the algorithm, and the switching actions are sent back to the distribution network and held until the end of the plan time interval. For the sake of verification and comparison, a comparison among three scenarios (1st proposed scenario with consideration of variant load, 2nd scenario based on prefault load, and 3rd scenario based on peak load) to show the effect of load variation on restoration plans is presented. The first scenario implements the proposed algorithm so that [17], [30]–[32]: 1) the peak load at each out-of-service zone over the restoration time interval is determined; 2) for the candidate supporting feeders, the network simulator runs hourly power flow over the restoration time interval; 3) in this way, the branch current flow and voltage for each zone per any candidate supporting feeder are estimated (i.e., the highest branch current and lowest voltage); 4) if all zones in a candidate supporting feeder have similar

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Fig. 9. Four-feeder test system with zone numbers.

load patterns, one power flow for the peaking hour will be required; and 5) these estimated currents and voltages are sent to responder agents so that they can calculate their ARC, as explained in Section IV. The second scenario builds its restoration plan based on the prefault values, and then responds to any overloading through post-restoration load management using load transfers and shedding. The third scenario builds its restoration plan based on the maximum known daily values of loads. A. Simulation Study Without DG Fig. 9 shows the system with zone numbers. Feeders F1 and F2 are residential feeders, feeder F3 is an industrial feeder, and feeder F4 is a commercial feeder. A fault is assumed to have

occurred at Z1 in feeder F4. Two outage periods were selected: 1) during the lightest loading period of the system (i.e., from 1 AM to 6 AM); and 2) during the highest loading period of the system (i.e., from 5 PM to 10 PM). 1) Case 1: Fault Occurred During the Lightest Loading Period: a) First Scenario: Based on the proposed fault location and isolation algorithm, the Z1 agent (ZAG1) sends an inform message to its feeder agent (FAG4) that includes a binary signal with a value of one. FAG4 then sends a request message to ZAG1 to open its sectionalizing switch s1 for fault isolation, and FCB4 will also open in response to the fault. After the fault has been isolated, the out-of-service zones (zones 2–10) start the restoration algorithm by sending request messages to


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TABLE II OPEN SWITCHES, LOSS, MINIMUM VOLTAGE, SWITCHING OPERATIONS, AND LOAD SHED

TABLE III OPEN SWITCHES, LOSS, MINIMUM VOLTAGE, SWITCHING OPERATIONS, AND LOAD SHED

their FAG4. The contents of the request messages are load demand and priority index. FAG4 (initiator) starts the negotiation process by sending CFP messages to its neighbors FAG1, FAG2, and FAG3 (responders). FAG1, FAG2, and FAG3 send query messages to their ZAGs to obtain their ARCs. These ZAGs then send inform messages to their FAGs. The contents of the inform messages are spare capacity and voltage magnitudes. Each responder FAG calculates its ARC using these data and sends a propose message to the initiator. FAG4 evaluates these proposals by comparing the proposed ARCs with its required demand. In this case, the available ARC from FAG3 is sufficient compared to that required; FAG4 thus determines to use group restoration by closing only s4. FAG4 sends accept-proposal message to FAG3. FAG3 then reply with inform message to indicate that it is committed to completing the task. FAG4 places switch s4 on the switch-to-be-closed list (SCL). In consequence, FAG4 sends a request to ZAG4 to close its tie switch s4 for restoration. b) Second Scenario: The sequence is the same as in the first scenario. Based on the prefault data, group restoration is implemented by closing s4. There is no constraint violation will occur till the end of the restoration period. c) Third Scenario: Based on the peak load data, zone restoration and load transfer are implemented. FAG3 transfers its load (L50) to FAG1 by opening switch s13 and then closing switch s14. Hence, it will enable FAG4 to restore all outage zones. FAG4 will implement zone restoration by restoring zones (2–7) from FAG3, zones (8–9) from FAG2, and zone 10 from FAG1. The switching operations to be implemented are: opening switches s8 and s11 to divide the outage area into three groups; then closing switches s4, s10, and s12 to complete the

restoration process. There is no constraint violation will occur till the end of the restoration period. Table II shows the open switches, losses, minimum voltage, switching operations, and the load shed during the restoration plan [Base: 11 KV, 1 MVA]. The second scenario (prefault) has the same restoration plan as the first scenario. Based on these results, the third scenario provides higher voltage and lower losses because it has been built based on the peak load. However, the third scenario required a higher number of switching operations (seven switching operations compared to the first scenario that required one switching operation only), leading to the conclusion that the first scenario is a good alternative to the third one, thereby aligning more closely with operational practices. 2) Case 2: Fault Occurred During the Highest Loading Period: Table III shows the open switches, losses, minimum voltage, switching operations, and the load shed during the restoration plan for the three scenarios. Both of the first and the second scenarios required the same switching actions (i.e., opening s8, s11, and closing s4, s10, s12). In the first scenario, these switching actions were implemented at the beginning of the restoration period. In the second scenario, the switching actions had to be implemented in two stages: opening s8 and closing s4, s12 at the beginning of the restoration period and then opening s11 and closing s10 at 6 PM in order to relieve the overloading of feeder 1. This process causes customers at zones Z8 and Z9 to be interrupted again during the switching actions at 6 PM. In addition, as shown in Table III, the first scenario provides a higher voltage and lower losses during the restoration period. Also, the third scenario required a higher number of switching operations (7 switching op-

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Fig. 10. Load and DG power profiles over 24 hours.

TABLE IV OPEN SWITCHES, LOSS, MINIMUM VOLTAGE, SWITCHING OPERATIONS, AND LOAD SHED

erations compared to the first scenario that required 5 switching operations). B. Simulation Study With DG For the sake of comparison with cases 1 and 2, the outage periods and fault location were selected to be the same as in the previous cases. Three wind-based DG units, each with a rating of 0.5 MW were inserted at load points L13, L27, and L40 as shown in Fig. 9. Fig. 10 shows the load and the total generation from these DG units over 24 hours. The sequence among agents is the same as in the previous cases. Table IV shows the open switches, losses, minimum voltage, switching operations, and the load shed during the restoration plan. The second case (i.e., fault occurred during the highest loading period from 5 PM to 10 PM) shows the effectiveness of DG units in supporting the ARC for responders and then in reducing the number of switching actions. For example, in case without DG, 5 switching operations were required, and in case with DG, only 3 were needed.

From these case studies and based on the results shown in Tables II, III, and IV it appears that the proposed algorithm has the following advantages: • it can locate and isolate the faulty component; • during the mean time to repair the fault, it provides a restoration plan that includes the following: 1) consideration of variable demand and generation; 2) consideration of load priorities; 3) satisfaction of operational constraints (voltages and currents do not violate their limits); 4) provision of the minimum number of switching operations to reduce operational costs and to avoid further switching that causes interruption to some customers, as occurred in the second scenario, as shown in Table III; 5) benefits of supporting power derived from DG units It can be concluded that: • Scenario 1 is more effective than scenario 2 because the former results in a restoration plan that may involve a lower number of switching operations and that does not require further reconfigurations and customer interruptions. • Scenario 1 is more effective than scenario 3 because the former results in a restoration plan that involves a lower number of switching operations and it avoids unnecessary load shedding that may be required by scenario 3. Therefore, scenario 1 is aligning with the operational practices. VII. CONCLUSION In this paper, a proposed distributed control structure for the self-healing of smart distribution systems has been presented.


The proposed control structure consists of two main types of controllers: zone and feeder. The operating mechanism of each controller has been designed based on the concept of a multiagent system. An expert-based decision maker has been proposed for each agent in order to achieve its objectives and satisfy its constraints. Coordination via two-way communication protocols has been proposed as a means of achieving effective cooperation among agents and of implementing both the detection and isolation of a fault location and the restoration objectives. For the purposes of verification of the proposed algorithm, a simulation model was developed. The results show that cooperation among agents through two-way communication provides a good solution for fault location and isolation and for building an effective restoration plan. This proposed algorithm and the implementation of the procedures for the agents could enable the concept of a self-healing smart grid to be employed in future distribution systems. REFERENCES [1] R. Pérez-Guerrero, G. Heydt, N. Jack, B. Keel, and A. Castelhano, “Optimal restoration of distribution systems using dynamic programming,” IEEE Trans. Power Del., vol. 23, no. 3, pp. 1589–1596, Jul. 2008. [2] S. Kazemi, M. Fotuhi-Firuzabad, M. Sanaye-Pasand, and M. Lehtonen, “Impacts of automatic control systems of loop restoration scheme on the distribution system reliability,” IET Gener. Transm. Distrib., vol. 3, no. 10, pp. 891–902, 2009. [3] X. Mamo, S. Mallet, T. Coste, and S. Grenard, “Distribution automation: The cornerstone for smart grid development strategy,” in Proc. IEEE Power Energy Soc. General Meeting, Calgary, AB, Canada, 2009, pp. 1–6. [4] W. Chen, “Quantitative decision-making model for distribution system restoration,” IEEE Trans. Power Syst., vol. 25, no. 1, pp. 313–321, Feb. 2010. [5] M. Tsai, “Development of an object-oriented service restoration expert system with load variations,” IEEE Trans. Power Syst., vol. 23, no. 1, pp. 219–225, Feb. 2008. [6] T. Nagata and H. Sasaki, “An efficient algorithm for distribution network restoration,” in Proc. IEEE Power Eng. Soc. Summer Meeting, Vancouver, BC, Canada, 2001, vol. 1, pp. 54–59. [7] P. Li, B. Song, W. Wang, and T. Wang, “Multi-agent approach for service restoration of microgrid,” in Proc. 5th IEEE Conf. Indust. Electron. Appl., Taichung, Taiwan, 2010, pp. 962–966. [8] J. Wu, T. Lee, C. Lu, and S. Su, “An autonomous decision approach for fault allocation and service restoration in electrical distribution systems by multi agent system,” in Proc. 9th Int. Conf. Hybrid Intell. Syst., Shenyang, China, Aug. 2009, pp. 89–94. [9] M. Tsai and Y. Pan, “Application of BDI-based intelligent multi-agent systems for distribution system service restoration planning,” Euro. Trans. Electr. Power, pp. 1783–1801, 2011. [10] X. Yinliang and L. Wenxin, “Novel multiagent based load restoration algorithm for microgrids,” IEEE Trans. Smart Grid, vol. 2, no. 1, pp. 152–161, Mar. 2011. [11] D. Ye, M. Zhang, and D. Sutanto, “A hybrid multiagent framework with Q-learning for power grid systems restoration,” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 1–8, Nov. 2011. [12] L. Liu, K. P. Logan, D. A. Cartes, and S. K. Srivastava, “Fault detection, diagnostics, and prognostics: Software agent solutions,” IEEE Trans. Vehic. Technol., vol. 56, no. 4, pp. 1613–1622, Jul. 2007. [13] T. Kato, H. Kanamori, Y. Suzuoki, and T. Funabashi, “Multi-agent based control and protection of power distributed system—Protection scheme with simplified information utilization—,” in Proc. 13th Int. Conf. Intell. Syst. Appl. Power Syst., Arlington, VA, Nov. 2005, pp. 49–54. [14] T. Nagata and H. Sasaki, “A multi-agent approach to power system restoration,” IEEE Trans. Power Syst., vol. 17, no. 2, pp. 457–462, May 2002. [15] J. M. Solanki, S. Khushalani, and N. Schulz, “A multi-agent solution to distribution systems restoration,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1026–1034, Aug. 2007. [16] Y. Kumar, B. Das, and J. Sharma, “Multiobjective, multiconstraint service restoration of electric power distribution system with priority customers,” IEEE Trans. Power Del., vol. 23, no. 1, pp. 261–270, Jan. 2008.

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[17] V. Donde, Z. Wang, F. Yang, and J. Stoupis, “Short-term load forecasting based capacity check for automated power restoration of electric distribution networks,” in Proc. 2010 IEEE PES Trans. Distr. Conf. Exposition, New Orleans, LA, Apr. 2010, pp. 1–8. [18] A. Zidan and E. F. El-Saadany, “Service restoration in balanced and unbalanced distribution systems with high DG penetration,” in Proc. IEEE Power Energy Soc. General Meeting, Detroit, MI, July 2011, pp. 1–8. [19] W. Lin and H. Chin, “A new approach for distribution feeder reconfiguration for loss reduction and service restoration,” IEEE Trans. Power Del., vol. 13, no. 3, pp. 870–875, Jul. 1998. [20] K. Miu, H. Chiang, and R. McNulty, “Multi-tier service restoration through network reconfiguration and capacitor control for large-scale radial distribution networks,” IEEE Trans. Power Syst., vol. 15, no. 3, pp. 1001–1007, Aug. 2000. [21] 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. 619–626, May 1988. [22] FIPA, [Online] [Online]. Available: http://www.fipa.org [23] G. Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Cambridge, MA: The MIT Press, 2000. [24] R. Ciric and D. Popovic, “Multi-objective distribution network restoration using heuristic approach and mix integer programming method,” Electr. Power Energy Syst., pp. 497–505, 2000. [25] K. Aoki, T. Satoh, M. Itoh, H. Kuwabara, and M. Kanezashi, “Voltage drop constrained restoration of supply by switch operation in distribution systems,” IEEE Trans. Power Del., vol. 3, no. 3, pp. 1267–1274, July 1988. [26] V. Gungor, B. Lu, and G. Hancke, “Opportunities and challenges of wireless sensor networks in smart grid,” IEEE Trans. Indust. Electron., vol. 57, no. 10, pp. 3557–3564, Oct. 2010. [27] S. Galli, A. Scaglione, and Z. Wang, “For the grid and through the grid: The role of power line communications in the smart grid,” Proc. IEEE, vol. 99, no. 6, pp. 998–1027, Jun. 2011. [28] D. Das, “Reconfiguration of distribution system using fuzzy multi-objective approach,” Electr. Power, Energy Syst., vol. 28, pp. 331–338, 2006. [29] E. Lopez, H. Opazo, L. Garcia, and P. Bastard, “Online reconfiguration considering variability demand: Applications to real networks,” IEEE Trans. Power Syst., vol. 19, no. 1, pp. 549–553, Feb. 2004. [30] M.-Y. Huang, C.-S. Chen, and C.-H. Lin, “Innovative service restoration of distribution systems by considering short-term load forecasting of service zones,” Electr. Power Energy Syst., vol. 27, no. 5–6, pp. 417–427, Jun.–Jul. 2005. [31] K. Aoki, K. Nara, M. Itoh, T. Satoh, and H. Kuwabara, “A new algorithm for service restoration in distribution systems,” IEEE Trans. Power Del., vol. 4, no. 3, pp. 1832–1839, Jul. 1989. [32] S. Devi, D. P. S. Gupta, and S. Sargunaraj, “Optimal restoration of supply following a fault on large distribution systems,” in Proc. Int. Conf. Adv. Power Syst. Contr., Operation Management, Hong Kong, Nov. 1991, vol. 2, pp. 508–513. Aboelsood Zidan (S’11) was born in Sohag, Egypt, in 1982. He received the B.Sc. and M.Sc. degrees from Assiut University, Assiut, Egypt, in 2004 and 2007, respectively, both in electrical engineering. He is currently working toward the Ph.D. degree in electrical and computer engineering at the University of Waterloo, Waterloo, ON, Canada. His research interests include distribution automation, renewable DG, distribution system planning, and smart grids.

Ehab F. El-Saadany (M’01–SM’05) was born in Cairo, Egypt, in 1964. He received the B.Sc. and M.Sc. degrees in electrical engineering from Ain Shams University, Cairo, Egypt, in 1986 and 1990, respectively, and the Ph.D. degree in electrical engineering from the University of Waterloo, Waterloo, ON, Canada, in 1998. He is currently a Professor in the Department of Electrical and Computer Engineering, University of Waterloo. His research interests are distribution system control and operation, power quality, distributed generation, power electronics, digital signal processing applications for power systems, and mechatronics.