Multi-Agent Systems for Power Engineering Applications—Part I:Concepts,Approaches, and Technical Challenges
Stephen D.J.McArthur,Senior Member,IEEE,Euan M.Davidson,Member,IEEE,
Victoria M.Catterson,Member,IEEE,Aris L.Dimeas,Student Member,IEEE, Nikos D.Hatziargyriou,Senior Member,IEEE,Ferdinanda Ponci,Member,IEEE,and
Toshihisa Funabashi,Senior Member,IEEE
Abstract—This is thefirst part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society’s Multi-Agent Systems(MAS)Working Group.
Part I of this paper examines the potential value of MAS tech-nology to the power industry.In terms of contribution,it describes fundamental concepts and approaches within thefield of multi-agent systems that are appropriate to power engineering applica-tions.As well as presenting a comprehensive review of the mean-ingful power engineering applications for which MAS are being investigated,it also defines the technical issues which must be ad-dressed in order to accelerate and facilitate the uptake of the tech-nology within the power and energy sector.
Part II of this paper explores the decisions inherent in engi-neering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented.
Index Terms—Multi-agent systems.
I.I NTRODUCTION
F OR over a decade,the proposed use of multi-agent sys-
tems(MAS)to address challenges in power engineering has been reported in IEEE transactions and conference papers. MAS technology is now being developed for a range of applica-tions including diagnostics[1],condition monitoring[2],power system restoration[3],market simulation[4],[5],network con-trol[5],[6],and automation[8].Moreover,the technology is maturing to the point where thefirst multi-agent systems are now being migrated from the laboratory to the utility,allowing industry to gain experience in the use of MAS and also to eval-uate their effectiveness[1].
Nevertheless,despite a growing awareness of the technology, some fundamental questions often arise from other researchers and,in particular,industrial partners when discussing multi-agent systems and their role in power engineering.These are:
Manuscript received September26,2006;revised May31,2007.Paper no. TPWRS-00656-2006.
S.D.J.McArthur,E.M.Davidson,and V.M.Catterson are with the Institute for Energy and Environment,University of Strathlcyde,Glasgow,U.K.(e-mail: s.mcarthur@eee.strath.ac.uk).
A.L.Dimeas and N.D.Hatziargyriou are with the Power Division,Elec-trical and Computer Engineering Department,National Technical University of Athens,Athens,Greece.
F.Ponci is with the Electrical Systems Department,University of South Car-olina,Columbia,SC29208USA.
T.Funabashi is with the Meidensha Corporation,103-8513Tokyo,Japan. Digital Object Identifier10.1109/TPWRS.2007.908471what benefits are offered by multi-agent systems?What differ-entiates them from existing systems and approaches?To what sort of problem can they be applied?
If and when MAS technology is deemed appropriate for a particular power engineering application,then other questions naturally follow:how should multi-agent systems be designed? How should multi-agent systems be implemented?Are there any special considerations for the application of MAS in power engineering?
The IEEE Power Engineering Society’s(PES)Intelligent System Subcommittee(within the PSACE Committee)has formed a Working Group to investigate these questions about the use of multi-agent systems.Itsfirst remit is to define the drivers for and benefits gained by the use of multi-agent sys-tems in thefield of power engineering.As MAS are a relatively new technology,a number of technical challenges need to be overcome if they are to be used effectively.The Working Group’s second remit is to identify and disseminate details of those challenges.Its third andfinal remit is to provide technical leadership in terms of recommendation and guidance on the appropriate use of the standards,design methodologies,and implementation approaches which are currently available. This paper reports on the research of the Working Group.It begins by describing key concepts and approaches associated with multi-agent systems.As a result of research and discus-sions by the Multi-Agent Systems Working Group,definitions of MAS terminology and concepts have been tailored for use by the power engineering community.
陈德容图片The engineering drivers behind the use of MAS and the bene-fits they may offer are presented.The recent increase in activities in this area has led to some inappropriate uses of the technology; hence,this paper considers the principal problems which can be tackled by MAS.Comparisons with existing technologies,such as web services,grid computing,and intelligent systems tech-niques,are d
rawn to illustrate how MAS differ. Additionally,this part of the paper(part I)presents a com-prehensive review of the power engineering applications for which MAS technology is being investigated,and outlines the key technical issues and research challenges which the authors believe need to be addressed if MAS technology is to be de-ployed within the power industry.
The uptake of multi-agent systems has increased over the last few years,in terms of number of research projects.However, it is essential at this stage of maturity of research into the ap-plication of MAS that appropriate standards and guidance are
0885-8950/$25.00©2007IEEE
available for those developing multi-agent systems in the power engineering community;these are discussed in the companion Part II paper.
II.C ONCEPTS:T ERMINOLOGY AND D EFINITIONS
In order to explore the potential benefits of MAS to power en-gineering and the areas where their application may be justified, the basic concepts and approaches associated with multi-agent systems need to be understood.This leads us to a basic but es-sential,and unfortunately difficult,question:what is an agent?
The computer science community has produced myriad defi-nitions for what an agent is[9]–[13].The fact that so many dif-ferent definitions exist testifies to the difficulty in defining the notion of agency.A comparison of these definitions and discus-sion of their relative merits and weaknesses,from a computer science perspective,can be found in[14].
While all the definitions referenced above differ,they all share a basic set of concepts:the notion of an agent,its environment, and the property of autonomy.Wooldridge’s basic definition of an agent[13]echoes that of Russell and Norvig[9]and Maes [10].According to Wooldridge,an agent is merely“a software (or hardware)entity that is situated in some environment and is able to autonomously react to changes in that environment.”The environment is simply everything external to the agent.In order to be situated in an environment,at least part of the environ-ment must be observable to,or alterable by,the agent.The en-vironment may be ,the power system),and there-fore observable through sensors,or it may be the computing ,data sources,computing resources,and other agents),observable through system calls,program invocation, and messaging.An agent may alter the environment by taking some action:either physically(such as closing a normally-open point to reconfigure a network),or otherwise(
<,storing diag-nostic information in a database for others to access).
The separation of agent from environment means that agents are inherently distributable.Placing copies of the same agent in different environments will not affect the reasoning abilities of each agent nor the goals it was designed to achieve;rather, the specific actions taken by each may differ due to different observations from the two environments.This means that an agent can operate usefully in any environment which supports the tasks the agent intends to perform.
Under Wooldridge’s definition,an entity situated in an en-vironment is only an agent if it can act autonomously in re-sponse to environmental changes.Autonomy is a somewhat elu-sive term,used in all definitions of agency,but rarely defined. The loosest definition of autonomy says that an agent“exercises control over its own actions”[14],meaning that it can initiate or schedule certain actions for execution.Russell and Norvig go further,by requiring the scheduling of actions to be in re-sponse to some change in the environment,and not simply the result of the agent’s in-built knowledge[9].This requirement for environmental change is in agreement with Wooldridge,and makes intuitive sense;can an agent really be considered au-tonomous if it takes action at times predefined by the agent designer,regardless of external changes in circumstance?Au-tonomy is therefore the ability to schedule action based on en-vironmental observations.
From an engineering perspective,this definition is problem-atic:it does not clearly distinguish agents from a number of ex-isting software and hardware systems.Arguably,under the defi-nition above,some existing systems could be classed as agents. For example,a protection relay could be considered as an agent. It is situated in its ,the power system.It reacts to changes in it ,changes to voltage or/and cur-rent.It also exhibits a degree of autonomy.Similar arguments can be made for software systems such as Unix daemons and virus checkers.
Renaming existing systems or new systems built using ex-isting technologies as“agents”offers nothing new and no con-crete engineering benefit.While Russell and Norvig[9]argue that“The notion of an agent is meant to be a tool for analyzing systems,not an absolute characterization that divides the world into agents and non-agents,”being able to distinguish agent sys-tems from existing systems is important.There is a need to know how agents and multi-agent systems differ from existing sys-tems and system engineering approaches.Moreover,it is the po-tential advantages gained through these differences that interest us as power engineers and that have motivated the exploration of the application of MAS to power engineering problems. B.Definition of an Intelligent Agent
In order to help differentiate MAS from existing systems,the authors have adopted the definition of a
gency as proposed by Wooldridge[13].Wooldridge extends the concept of an agent, given above,to that of an intelligent agent by extending the def-inition of autonomy toflexible autonomy.An agent which dis-playsflexible ,an intelligent agent,has the fol-lowing three characteristics.
•Reactivity:an intelligent agent is able to react to changes in its environment in a timely fashion,and takes some ac-tion based on those changes and the function it is designed to achieve.
•Pro-activeness:intelligent agents exhibit goal-directed be-havior.Goal-directed behavior connotes that an agent will dynamically change its behavior in order to achieve its goals.For example,if an agent loses communication with another agent whose services it requires to fulfill its goals, it will search for another agent that provides the same services.Wooldridge describes this pro-activeness as an agent’s ability to“take the initiative.”
•Social ability:intelligent agents are able to interact with other intelligent agents.Social ability connotes more than the simple passing of data between different software and hardware entities,something many traditional systems do.
It connotes the ability to negotiate and interact in a coop-erative manner.That ability is normally underpinned by an agent communication language(ACL),which allows agents to converse rather tha
n simply pass data.
While an agent,in terms of our earlier definition,and many existing systems display the characteristic of reactivity,in order to be classed as an intelligent agent under Wooldridge’s defini-tion,an agent must also have some form of pro-activeness and
MCARTHUR et al.:MULTI-AGENT SYSTEMS FOR POWER ENGINEERING APPLICATIONS—PART I1745
some form of social ability.It is the goal-directed behavior of individual agents and the ability toflexibly communicate and interact that set intelligent agents apart.
Not only do the characteristics of reactivity,pro-activeness, and social ability help us distinguish agents from traditional hardware and software systems,it is from these characteristics, as shall be discussed in the following sections,that many of their benefits are derived.
C.Definition of a Multi-Agent System
A multi-agent system is simply a system comprising two or more agents or intelligent agents.It is important to recognize that there is no overall system goal,simply the local goals of each separate ag
ent.The system designer’s intentions for the system can only be realized by including multiple intelligent agents,with local goals corresponding to subparts of that intention.
Depending on the definition of agency adhered to,agents in a multi-agent system may or may not have the ability to commu-nicate directly with each other.However,under Wooldridge’s definitions,intelligent agents must have social ability and there-fore must be capable of communication with each other.
For the sake of this paper,the authors have focused on MAS where this communication is supported.This clearly differenti-ates the type of MAS discussed in this paper from other types of systems.
III.P OTENTIAL B ENEFITS OF MAS T ECHNOLOGY AND
D RIVERS FOR I TS U S
E IN P OWER E NGINEERING A PPLICATIONS To answer the question of how(and why)MAS may be ap-plied in power engineering requires an understanding of the basic ways MAS can be exploited.In this paper,the authors have called these“approaches.”
To date,MAS have a tendency to be exploited in two ways: as an approach to buildingflexible and extensible hardware/soft-ware systems;and as a modeling approach.
A.MAS as an Approach to the Construction of Robust, Flexible,and Extensible Systems
There are many power engineering application areas for whichflexible and extensible solutions are beneficial. Flexibility connotes the ability to respond correctly to dy-namic situations,and support for replication in varied situa-tions(environments).This sounds very similar to autonomy and therefore intelligent agents should automatically beflexible;but if autonomy is the ability of an agent to schedule its own ac-tions,flexibility relates to having a number of possible actions from which to select the most appropriate.Some specific exam-ples offlexible behavior would be correct handling of different formats of one type of data(such as temperatures in Centigrade or Fahrenheit);or the ability to construct a new plan if a par-ticular control action fails;or a system that can be deployed on any feeder,which senses the connection of distributed genera-tion and changes protection settings accordingly. Extensibility connotes the ability to easily add new func-tionality to a system,augmenting or upgrading any existing functionality.For example,a condition monitoring system may gain a new type of sensor,and require a new data anal-ysis algorithm.A state-estimator system may be upgraded to use a faster load-flow calculation algorithm.For distribution networks,a distributed netwo
rk control and management system responsible for voltage control may be extended to also automate restoration and the management of distributed generation.Importantly,a truly extensible system will allow new functionality to be added without the need to re-implement the existing functionality.
Across many applications in power engineering,there is also a requirement for fault tolerance and graceful degradation: should part of the system fail for whatever reason,the system should still be able to meet its design objective or,if that is not possible,it should accomplish what it can without interfering with other systems.
MAS can provide a way of building such systems.Indeed, the ability of MAS to beflexible,extensible,and fault tolerant is often part of the justification for their use.However,in order for that justification to be valid,the way in which MAS pro-videflexibility,extensibility,and fault tolerance needs to be un-derstood.The properties of agents and MAS that produce these qualities are examined below.
1)Benefits of Autonomy and Agent Encapsulation:An agent encapsulates a particular task or set of functionality,in a similar way to modular or object-oriented programming.This means that the benefits
of standard interfaces and information-hiding are also available with agent programming through the use of messaging with a standard agent communication language,but there is also the additional capability of autonomous action. Recall that autonomous action means each agent is able to schedule its own activity in order to achieve its goals.In a mod-ular programming situation,external modules can call functions which the module has no choice but to execute.With agent pro-gramming,external agents can only send messages requesting the agent take some action:the autonomous agent can decide whether to fulfill the request,the priority of the task,and if other actions should also be scheduled.This can be useful in situations when an agent is receiving many requests and cannot fulfill them all within a reasonable timescale,such as with multiple requests for a processing-intensive task like a load-flow calculation. The autonomy of each agent and the messaging interface are what contribute most toflexible and extensible systems. Because agents are not directly linked to others,it is easy to take one out of operation or add a new one while the others are running.Any agents interacting with the stopped one can use the standard service location facilities to locate another agent that performs the same task,and by this mechanism,new agents can be included within the system.The agent framework provides the functionality for messaging and service location, meaning that new agent integration and communications are handled without effort from the system designer.
This allows systems to be extensible:extra functionality can be added simply by deploying new agents,which use service location tofind others to communicate with;and parts of sys-tems can be upgraded by deploying a replacement agent and removing the obsolete one.Flexibility also follows:the appro-priate mix of agents can be deployed tofit the details of indi-vidual situations,andflexible handling of messages between
1746IEEE TRANSACTIONS ON POWER SYSTEMS,VOL.22,NO.4,NOVEMBER2007
agents allows the system to self-configure.Finally,legacy sys-tems can be incorporated within the system simply by wrapping legacy functionality in a layer of agent messaging.
2)Benefits of Open MAS Architectures:An open agent archi-tecture places no restrictions on the programming language or origin of agents joining the system,and allowsflexible commu-nication between any agents.This is achievable through adher-ence to messaging standards:the separation of an agent from its environment means that the messaging language an agent under-stands is important for inter-agent communication,rather than the programming language in which it was implemented.
An example of a set of standards for an open architecture is that defined by the Foundation for Intelliずっと好きだった迅雷资源
gent Physical Agents(FIPA)[15].The FIPA Agent Management Reference Model covers the“framework within which FIPA agents exist,”defining standards for creating,locating,removing,and com-municating with agents.This is more generally called the agent platform,and is simply one part of an agent’s environment.One requirement of an open agent architecture is that the platform places no restrictions on the creation and messaging of agents, while a second is that some mechanism must be available for locating particular agents or agents offering particular services within the platform.Under the FIPA model,this is achieved through a separate agent called the Directory Facilitator:an agent which manages a searchable list of services offered by other agents within the platform.
王洛宾歌曲Early agent systems tended to be closed architectures,as one set of agents would be deployed every time the system was run, with all communication explicitly defined by the system cre-ator.An example is the ARCHON system for distribution net-work management,originally built to integrate four legacy sys-tems[16].Such an architecture is said to be closed because new agents cannot be added to the community:even if a new agent is created and run,other agents have no way of locating it and communicating with it.A closed architecture removes the pos-sibility of an extensible orflexible system,severely limiting the benefits of using agents.
How to specifically design an open agent architecture is dis-cussed in detail in Part II of this paper.
3)Platform for Distributed Systems:An agent is distinct from its environment,meaning that it can be placed in different environments and still have the same goals and abilities.How-ever,the environment impacts upon which actions an agent takes and in what order,as the agent autonomously schedules action in response to sensor inputs and messages.
红石头歌词For this reason,an agent is inherently distributable,having no fixed ties to its environment.In practice,distribution of agents across a network is supported by the agent platform:the plat-form is run on every computer that will host an agent,and the agents are deployed within the platform as usual.To agents within one platform,there is no difference between agents on the same computer and agents on a different computer,as the instances of the platform running on separate machines seam-lessly connect and appear as a single instance.
This means that the same set of agents can be deployed on one computer,and alternatively on multiple networked computers, without modifying or changing the agent code.
4)Fault Tolerance:Building redundancy into systems is one of the standard engineering approaches to gaining fault toler-ance.Building redundancy into MAS simply involves providing more than one ag
ent with a given set of abilities.If an agent needs the services of a second agent in order to fulfill its goals, and the second agent fails,the agent can pro-actively seek an al-ternative agent(perhaps using the Directory Facilitator)to pro-vide the services it requires.
This redundancy may be provided by simple duplication of each agent,possibly with distribution of duplicates across dif-ferent computers.This would provide a tolerance to physical faults,such as the loss of a network connection,or damage to a computer.Tolerance to programming-related faults would re-quire a more design-intensive solution:rather than simply run-ning two copies of a single agent,the same functionality would be coded differently in two agents.Various applications and op-erating environments will have differing requirements for levels of robustness and fault tolerance,and so the approach taken must be application-specific.
However,theflexibility offered by an open architecture of agents with good social ability easily leads to the design of a fault-tolerant system.
B.Multi-Agent Systems as a Modeling Approach
Multi-agent systems are more than a systems integration method,they also provide a modeling approach.By offering a way of viewing the world,an agent system can intuitively represent a real-worl
d situation of interacting entities,and give a way of testing how complex behaviors may emerge. Natural representation of the world has previously been given as an advantage of object-oriented(OO)systems design,where entities in a system are modeled as objects.This has recently found favor with the power engineering community in stan-dards such as the Common Information Model(CIM)[17]and IEC61850[18].The main benefit of the object approach is data-encapsulation:the particular data structures used to hold at-tributes of an object are hidden from external objects,but are in-directly accessible through method calls and standard interfaces. Agent-based design adds another level of abstraction to this:not only are internal data structures hidden,but the“methods”(ac-tions)an agent can perform are also hidden,yet indirectly ac-cessible through standard messaging interfaces.
This is a very natural way of modeling actors in some sys-tems such as markets:in a real market,actors have attributes (such as desired price and lowest price for a seller)and pos-sible ,start auction,accept bid)which other actors cannot manipulate directly.Indirect access is available by,for example,presenting the seller with a high bid,in the hope that it will be accepted.By modeling each market participant as a separate agent in a multi-agent system,it is easy to run simu-lations of different market scenarios;the attributes of single or multiple market participants can be altered by changing the ini-tial conditions of one or more agents.
董子健身高Marketplace simulation is an application in which the bene-fits of using intelligent agents to represent autonomous actors are fairly clear.By modeling the behavior and communication of individual agents,operation of the market can be studied for
MCARTHUR et al.:MULTI-AGENT SYSTEMS FOR POWER ENGINEERING APPLICATIONS—PART I1747
emergent behavior patterns.However,many other power engi-neering applications can usefully apply this way of viewing the world,such as power systems operation and control.Generators have a degree of autonomy and cannot be directly affected by external system actors,lending themselves to being represented by agents.Such an application would be using agents for both their modeling properties and also as a way of building aflex-ible,extensible system.
Through their use for systems integration or modeling,MAS offer significantly different approaches to designing systems for typical power and energy applications.
Pˇe chouˇc ek and Thompson provide interesting perspectives on industry applications of multi-agent systems in a report from the Industry Track of the Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems(AAMAS 2005)[19].They indicate that most industrialis
ts are interested in agents for the following applications:planning;scheduling; resource and strategic decision making;diagnostics;control and real-time replanning;software systems integration;interoper-ability;knowledge integration;ontologies;and simulation and modeling.Many of these underpin the applications of multi-agent systems within the power industry which are discussed in this paper.
IV.MAS,G RID C OMPUTING,W EB S ERVICES,AND
A RTIFICIAL I NTELLIGENCE T ECHNIQUES
Before exploring the applications of MAS technology in power engineering,it is worthwhile considering the relation-ship between multi-agent systems,grid computing[20],web services[21],and artificial intelligence techniques:what the technologies have in common and what makes them different. The commonality between thefirst three is easiest to deal with:all three technologies offer a perspective on the prob-lems associated with distributed ,harnessing dis-tributed hardware and software resources to complete a specific objective or task.They all tend to support some form of mes-saging between their component parts.
How do they differ?Firstly,they differ in scope of applica-tion.Grid computing is normally focused on harnessing hard-ware resources(computational power)to solve computationally complex problems.W
eb services,on the other hand,are de-signed to offer interoperability between software systems,pro-viding the mechanisms for the discovery of those systems and their communication across a network.
Atfirst glance,web services and multi-agent systems look deceptively similar.Similar styles of interaction diagrams are often used to describe web services and to describe agent inter-actions.The ideas of the“services”and the“brokerage of ser-vices”are common to the technologies.However,standards for multi-agent ,[15])support a richer set of ,support for negotiation,than those required for the brokerage of services as supported by web services.So while web services support the interoperability between software sys-tems,the nature of that interoperability is more limited than that for multi-agent systems.
The key differentiator between multi-agent systems,grid computing,and web services is the notion of autonomy.Under the current standards,there is no provision for autonomy in web services[22].Similarly there is no requirement for nodes in computational grids to exhibit autonomy.
It is also the social ability and proactive nature of agents that set them apart from grid computing and web services,so much so that MAS technology has been mooted as a mechanism for delivering improved web services[22]and grid computing systems.
Hence,applications where the use of agents is justified are normally cases where the characteristic of autonomy offers tan-gible benefits.
Another common question regards the difference between MAS and AI techniques per ,expert systems,model-based reasoning(MBR)systems,case-based reasoning systems, artificial neural networks(ANNs).
This question is understandable from the perspective that the techniques above have been applied to similar problems(fault diagnosis,condition monitoring,decision support)and that MAS are often seen as another AI technique.However,this question also represents a misunderstanding,as MAS are not an alternative or competitor to classical AI techniques.Indeed, there are many cases in the literature where expert systems, ANNs,and MBR systems are used to provide agents with their abilities to reason and achieve the goals for which they were designed.
What MAS do provide is a framework for building hybrid systems which integrate different AI techniques.Examples of where such an approach can be beneficial are fault diagnosis[1] and condition monitoring[2].
V.B IBLIOGRAPHICAL A NALYSIS OF A GENT R ESEARCH
A bibliographical analysis of agent research was undertaken in the preparation of this paper.Its aim was to provide an indication of the active areas of agent research,with respect to power systems and related applications.For conferences,the sources were restricted to the Proceedings of the Intelligent Systems Application to Power Systems conferences for2001, 2003,and2005[23]–[25].This is a representative forum for agent-based research in the power industry.In addition, papers from relevant IEEE and IEE journals were sought and categorized.These included the IEEE T RANSACTIONS ON P OWER S YSTEMS,IEEE T RANSACTIONS ON P OWER D ELIVERY, IEEE T RANSACTIONS ON E NERGY C ONVERSION,and IEEE T RANSACTIONS ON E VOLUTIONARY C OMPUTING.Further searches included the IEEE Power and Energy Magazine and relevant IEE journals.All searches dated from1998onwards. These sources and timescales are representative of the body of research undertaken in thisfield.
Four categories of applications were discovered:monitoring and diagnostics,distributed control,modeling and simulation, and protection.From the survey results in Table I,it is clear that most papers have concerned the use of agents for modeling and simulation or distributed control.This is not surprising,as these are two complexfields where the power industry faces real challenges.
Protection applications represent the least active area in terms of journal publications,with onlyfive journal papers[26]–[30]. All the journals focused on monitoring and diagnostics have
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TABLE I
B IBLIOGRAPHI
C S URVEY OF A GENT P
APERS
arisen from the research activities at the University of Strath-clyde [1],[2],[31]–[34].In terms of journal papers,there is a wide diversity of authors publishing work in the area of dis-tributed control [3],[6],[8],[35]–[46]and modeling and simu-lation [47]–[62].
VI.A PPLICATION OF MAS IN P OWER E NGINEERING As described in Section III,agent technology offers two main approaches to developing innovative applications.The four broad fields of agent applications in power,identi fied through the bibliographical analysis,each use the property of flexible autonomy to bring a new suite of techniques and abilities to bear on traditional issues and problems in the industry.
Based on this,multi-agent systems should be considered for applications which display one or more of the following characteristics.
•There is a requirement for interaction between distinct con-ceptual entities,such as different control subsystems and plant ,controlling a microgrid while taking ac-count of thermal constraints,voltage control,and renew-able energy sources.
•A very large number of entities must interact,where it would be impossible to explicitly model overall system ,simulation of an energy marketplace where each individual generator,indepen
dent system operator,and customer is modeled.
•There is enough data/information available locally to un-dertake an analysis/decision without the need for commu-nication with a central ,substation-based diag-nostics from transformer,switchgear,and protection anal-ysis systems.
•New functions need to be implemented within existing plant items and control ,extending substa-tion-based condition monitoring systems by adding data interpretation functions.
•Over time,there is a requirement for functionality to be continually added or ,asset management through the use of real-time condition monitoring on multiple plant items.
The speci fic bene fits of MAS technology for the four fields of application are considered below.A.Monitoring and Diagnostics
A key application area for multi-agent systems is the manage-ment and interpretation of data for a wide variety of power engi-neering monitoring and diagnostic functions.MAS technology
is an excellent tool for collecting and manipulating distributed information and knowledge.
1)Condition Monitoring:Condition monitoring of equip-ment and plant items offers a number of challenges:•gathering data from a variety of sensors;
•interpreting the data to extract meaningful information.This often requires the use of multiple algorithmic and in-telligent system-based approaches;
•combining the evidence and information from different in-terpretation algorithms to generate an overall diagnostic conclusion;
•delivering the diagnostic information in the correct format to relevant engineers;and
•automatically altering power system and plant settings based on the condition of the plant.
If we consider plant items such as transformers,there are var-ious sensors which can be used to monitor them,such as UHF monitoring of partial discharge,acoustic monitoring of partial discharge,and online dissolved gas in oil measurement.Further-more,operational information about the circuit loading and fault conditions from digital fault recorders can also be used to inform the diagnostic process.Agent technology allows the combina-tion of data from all these sources in a flexible manner:infor-mation is used when it is available and relevant by delegating the task of monit
oring each source to an autonomous agent.As an example,an agent responsible for monitoring the output from UHF sensors can inform the engineer or diag-nostic algorithms when signi ficant partial discharge activity has been detected.The autonomy of the agent allows it to determine when such information should be communicated,and to whom.The property of flexibility allows integration of as much diagnostic data,information,and knowledge as is currently available.New sensors and interpretation algorithms can also be introduced seamlessly into the overall system,since the open architecture allows extensibility.
Using these principles,some of the authors have developed a transformer condition monitoring multi-agent system [2].
As a further idea,condition monitoring agents could also be capable of modifying the measurement set-up by,for example,altering the data acquisition rate.While the physical instrument connection can rarely be changed,in a framework of virtual in-strumentation (e.g.,LabVIEW),the monitoring agent can con-trol execution of speci fic virtual instruments.This would bring advantages such as the optimization of resources like battery and computation power.
2)Post-Fault Diagnosis of Power System Faults:When op-erational engineers investigate the causes
and impact of power system faults,they employ a number of data sources.These include supervisory,control and data acquisition (SCADA)system data,digital fault recorder data,and traveling-wave fault locator data.In a similar fashion to the condition monitoring problem discussed previously,automation of the analysis of such data provides essential decision support to operational engineers.For example,[1]reports on work with a U.K.utility which experienced an in flux of 15000SCADA alarms and 1695digital fault records during a single storm.The engineers require effective supporting analysis tools to combat such situations.
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