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IEEE TRANSACTIONS ON POWER SYSTEMS 1

Multi-Agent System for DistributedManagement of Microgrids

Y. S. Foo. Eddy, Student Member, IEEE, H. B. Gooi, Senior Member, IEEE, and S. X. Chen, Member, IEEE

Abstract—In market operations, distributed generators (DGs)and price-sensitive loads participate in a microgrid energy marketimplemented in JADE. Each DG and each price-sensitive loadis represented by the respective agents which perform variousfunctions such as scheduling, coordination and market clearingsubject to system, DG and load constraints. Each agent is as-signed to one of the several agent objectives which maximizeseither DG or load surpluses or both. In simulated operation ofa microgrid, hourly power reference signals and load controlsignals from JADE are passed to DG and load models developedin MATLAB/Simulink using MACSimJX. Simulated operationof DGs and loads are studied by performing simulations underdifferent agent objectives. Results from simulation studies demon-strate the effectiveness of implementing multi-agent system (MAS)in the distributed management of microgrids.

Index Terms—Deregulated energy market, distributed genera-tion, JADE, MACSimJX, microgrid, multi-agent system.

I. INTRODUCTION

T HE general trend of distributed generator (DG) installa-tions at distribution voltage level coupled with advances

in communication and control, increased environmental aware-ness and escalating fuel prices have generated a significant in-terest in the research of microgrids [1]. High penetration of DGtechnologies such as diesel engines, combined cooling heat andpower generating units (CCHPs), microturbines (MTs), photo-voltaics (PVs), wind turbines (WTs) and fuel cells have trans-formed regulated power generation into restructured entities [2].As a result, integration of DGs has become an important aspectfor successful operation of microgrids among other operationaland technical challenges faced [3], [4].The concept of microgrids [5], [6] basically involves DGs,

renewable energy sources, clusters of controllable loads and en-ergy storage systems (ESSs) operating in a coordinated manner

Manuscript received March 14, 2013; revised July 10, 2013, November 07,2013, and March 26, 2014; accepted April 29, 2014. This work was supportedby the Singapore National Research Foundation under its Campus for ResearchExcellence and Technological Enterprise (CREATE) programme, CambridgeCentre for Carbon Reduction in Chemical Technology (C4T) and CambridgeCentre for Advanced Research in Energy Efficiency in Singapore (CARES) Ltd.Paper no. TPWRS-00309-2013.Y. S. Foo. Eddy and H. B. Gooi are with the School of Electrical and

Electronic Engineering, Nanyang Technological University, Singapore 639798(e-mail: [email protected]; [email protected]).S. X. Chen is with DNV GL Energy (formerly KEMA), Singapore 118227

(e-mail: [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/TPWRS.2014.2322622

to supply reliable electricity and reduce energy prices. In addi-tion, microgrids are expected to improve power quality, reducetransmission losses, provide system reliability and enable inte-gration of DGs and renewable sources [7]. An IEEE standard [8]was also established which provides a set of guidelines for in-terconnection of distributed energy resources (DERs). Further-more, microgrids have the capability to operate in either gridconnected or islanded mode. In grid connected mode, micro-grids aim at satisfying demand through local generation. Excessor deficit power in a microgrid can be absorbed or supplied bythe grid respectively. In islanded mode, power balance withinthemicrogridmust be observed between generation and demandin order to maintain system frequency and stability.Market operations and distribution networks become increas-

ingly complex as the power industry moves towards decen-tralization [9]. The presence of DERs at distribution voltagelevel will inevitably change the way power flows within thenetwork causing it to change from a passive to an active one.Consequently, centralized supervisory control and data acqui-sition (SCADA) which was originally designed for traditionalpassive networks may be inadequate to cope with the high pen-etration of DERs and complex control decisions due to the lackof flexibility and extensibility [10]. Moreover, assumptions ap-plied to conventional power systems may not be valid for activedistributed systems which raise challenges in the operation ofmicrogrids [11].Main issues regarding integration of DERs are also high-

lighted in [12]–[14] which primarily include 1) the need forscheduling and dispatch of DGs under supply and demanduncertainties, 2) design of new market models that enablescompetitive participation within a microgrid, 3) development ofmarket and control mechanisms which exhibits plug-and-playfor seamless integration of DERs, 4) cooperation and controlwhich are distributed and realized with minimal informationexchange with the central controller and finally, 5) communi-cation networks which are based on standard components suchas TCP/IP protocol. Most of these issues can be addressed byproviding an agent platform with a common communicationinterface in the distributed system [15].MAS has been widely proposed as a feasible approach for

managing complex distributed systems [16]. The extension ofMAS into microgrid applications is also evident in [17]–[19]where various research activities ranging from agent basedmarket operation, fault protection schemes and distributedenergy management systems to real time implementations.In addition, proposed guidelines and requirements on the useand applications of MAS in power systems are discussed indetail [20], [21]. Key motivation for proposing MAS in power

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2 IEEE TRANSACTIONS ON POWER SYSTEMS

systems basically lies in its inherent benefits such as flexibility,scalability, autonomy and reduction in problem complexityamong other factors.In [22], MAS was used to simulate multiple microgrid

market scenario involving load and generation agents with andwithout storage systems. MAS was also implemented in energymarket simulation using risk-based continuous double auctionalgorithm [23]. In [24], MAS was applied to microgrids toparticipate in ancillary service markets. The proposed auctionalgorithm which solves asymmetric assignment problems isdiscussed in [25]. In [17], MAS implementation for operation ofa microgrid is presented. The MAS design and implementationof microgrids for seamless transition from grid-connected toislanded mode in MATLAB/Simulink environment is discussedin [19].Although many microgrid research activities involving MAS

have been reported, no proper MAS platform was implementedvia integration of microgrid market operations and DERs. Thework reported in the literature specifically focuses on eitherintelligent market operations or DER implementations whilecoordinated actions between market operations and DER im-plementations in microgrids are seldom addressed. This paperaddresses this issue by proposing a multi-agent based systemwhich integrates microgrid market operations and implementa-tion of DERs using Simulink, Java agent development frame-work (JADE) [26] and multi-agent control simulation Jade ex-tension (MACSimJX) [27]. The key intention of this paper is tocoordinate agent-based market activities with DER implemen-tations which are separately addressed in the literature. In addi-tion, the developed multi-agent system acts in accordance to thefoundation for intelligent physical agents (FIPA) [28] specifica-tions which provide standards for agent development and im-plementation. The proposed multi-agent based system models amarket scenario where each energy seller or each energy buyeris represented by an agent that aims to maximize the bene-fits according to the defined agent objectives while ensuringthe smooth operation and proper execution of microgrid opera-tions under the simulated real-time environment. Two functionsof microgrid operations were also demonstrated which includeprice-driven generation and demand scheduling and locationalmarginal pricing (LMP) [29]–[31] for various participants in adistributed microgrid energy market.The remaining paper is organized as follows. Section II dis-

cusses the types of microgrid control architecture. Section IIIformulates the problem mathematically. Section IV describesthe proposedmulti-agent based system. Section V provides sim-ulation studies and result analysis. Section VI provides conclu-sions for the paper.

II. MICROGRID CONTROL ARCHITECTURE

Control architecture of a power system can be broadly classi-fied into two distinctive categories which are centralized anddecentralized controls. The main difference between the twotypes of control lies in the management of tasks and responsibil-ities given to the respective controllers. In a dynamic microgridwhere DGs and loads typically have different ownerships, varia-tions in generation and demand raise challenges in forecasting.

Fig. 1. Schematic diagram of hierarchically controlled microgrid.

They result in high levels of uncertainties. In addition, infor-mation on power quantity and cost function of DGs and loadsis also not readily known [11]. Therefore, appropriate controlschemes and coordination strategies are necessary for efficientmicrogrid operations.

A. Centralized Control

A fully centralized control usually consists of a dedicatedcentral controller which executes a range of functions such asgathering data, performing calculation and optimization and de-termining control actions for all units. In addition, all of thesefunctions are done at a single point which requires an extensivecommunication infrastructure for the central controller and con-trolled units to interact.However, due to the need for processing large amounts of

information at a single point simultaneously, centralized con-trol is unable to exhibit plug-and-play feature which is requiredin a microgrid setup. Consequently, this restricts power systemexpansion and poses limitations on planning of power systemsamong other factors [32]. Generally, centralized control is moresuited for standalone power systems which need to maintaincritical supply and demand balances in a slow changing infra-structure.

B. Hierarchical Control

A fully decentralized control typically consists of many localcontrollers where each controller controls a single unit. Thesecontrollers only gather local information about the unit undercontrol and is neither fully aware of system level parameters norcontrol actions from neighboring controllers [33]. However, ina system where the presence of strong coupling between var-ious operating units requires a minimum level of coordination,a fully decentralized control is unable to achieve stable opera-tion based on local information alone. As a result, a hybrid formof control known as hierarchical control is proposed in Fig. 1for the microgrid due to the presence of numerous controllabledevices and stringent performance requirements [34], [35]. Ingeneral, decentralized control is applicable for grid-connectedmicrogrids comprising many fast changing DGs with differentownerships.The proposed MAS framework in this paper basically con-

sists of a group of intelligent agents interacting with each otherto achieve local and global objectives. Each agent has limitedknowledge of the environment and is provided a set of allocated


FOO. EDDY et al.: MULTI-AGENT SYSTEM FOR DISTRIBUTED MANAGEMENT OF MICROGRIDS 3

tasks and responsibilities in order to achieve its goals through in-formation exchanges with other agents [36]. Agents also exhibitother key attributes which include sociability, reactivity, pro-ac-tiveness, reliability and mobility [20]. These attributes coupledwith the autonomy given to agents make MAS a suitable al-ternative for coordinated microgrid operation in a competitivemarket environment with numerous DG owners [17].

III. PROBLEM FORMULATION

As mentioned previously, MAS is proposed for generationand demand scheduling and the LMP for various participantsin the distributed microgrid energy market. Generator and loadagents retrieve power scheduling information based on their in-cremental cost slope and price signals obtained from the pro-posed microgrid energy market. The power reference and loadcontrol signals are then sent to the generator and load corre-spondingly. The price signal refers to the market clearing price(MCP) which is derived from the submitted bids of generatorand load. Subsequently, the derived MCP establishes the pricereference in LMP among other variables where the participantswill pay or be paid at that price. The amount that each generatoror load agent receives or pays respectively depends on some ob-jectives imposed on the agents.

A. Market Clearing Price and Scheduling Problem

The idea of proposing a microgrid market structure is to en-courage a competitive electricity market since DERs are consid-ered more economical to generate energy locally at least for acertain peak period compared to buying directly from the maingrid [37]. In addition, a real-time market clearing technique isused to determine the MCP. The objective in determining MCPis to dispatch an aggregate of different types of DGs to an aggre-gate of different consumers. A double-sided biddingmechanismis considered. All bids to sell energy will be priced at the mar-ginal cost of the energy.Consider the power generated by the th generation bidder

where the supply curve is approximated based on the fuel con-sumption data. It is expressed as

(1)

where is the active power of the th generator; is theprice for generating ; and is the gradient of the thsupply curve and is commonly referred to as the bidding rate ofthe th generator in the subsequent sections.Likewise, consider the load required by the th demand

bidder where the demand curve is expressed as

(2)

where is the active demand required by the th demandbidder; is the price intercept of the th demand curve;is the price offered by the th load to consume ; andis the bidding rate of the th demand bidder.In a balanced system where the total generation equals the

total load demand, MCP can be determined [38] as expressed

(3)

where is the number of generators; and is the number ofloads participating in the competitive market.The dispatch for generators and loads can be determined by

substituting the value of MCP obtained from (3) into (1) and (2)respectively subject to the following constraints.The total microgrid generation and load must be balanced at

all times with the utility grid either injecting or absorbing energyduring unbalanced periods:

(4)

where is the active power delivered from/to the utility gridto maintain power balance within the microgrid.Each generation unit has to abide by generation limits:

(5)

where is the minimum power generated by the th gen-eration; and is the maximum power generated by the thgeneration.Similarly, each load has a consumption limit to follow:

(6)

where is the minimum power required by the th loadwhich is also regarded as critical load; and is the max-imum power required by the th load based on the maximumcapacity rating.In addition, whenever the utility grid is required to main-

tain power balance in the microgrid due to either insufficientor excess generation, market participants will follow utility gridprices accordingly during market clearing.

B. Locational Marginal Pricing

The nodal pricing or LMP refers to the Lagrangianmultiplierswhich are derived from active power flow equations at each buswithin a system [29]. LMP was selected as the pricing mech-anism because it takes into account power losses among otherfactors which are typical in a medium-low voltage network. Ba-sically, LMP at any node in the system consists of three com-ponents which include reference marginal cost, a marginal losscomponent and a congestion component expressed as

(7)

where is the reference marginal cost which is ob-tained from (3) and is the same for all nodes in the system;

is the marginal loss component which isfurther explained in the subsequent section; and



is the congestion component which generally consists of ashadow price and a generation shift factor.Therefore, the LMP for each market participant will be loca-

tion specific factoring into account marginal losses component.In subsequent sections, the congestion component will not beconsidered as no congestion is assumed in the illustrated system,i.e., equals zero.

C. Marginal Loss Factor

The loss sensitivity factor, , is part of the mar-ginal loss component in (7) and is referred as incremental loss[39]. Loss sensitivity factors in a power system are derived di-rectly from AC power flow. The first step determines the ratioof change in power at the reference bus, when a changein power at bus , or is made. It can be written forbuses in the network and is expressed as

...... (8)

where is the transpose of the inverse Jacobian matrixfor the network. The phase angles and voltage magnitudes inthe network can then be obtained by employing any nonlinearprogramming techniques available. An Interior Point Method(IPM) [40] is used in this paper for determining changes in phaseangle and voltage magnitude parameters in the network.Next, evaluate the loss sensitivity factor which is the ratio of

change in real system losses when a change in power at bus ,is made. Likewise, it can be extended to buses and is

expressed as

......

. . ....

... (9)

where are the values obtained from (8) for everyand and are obtained by taking the derivatives of

with respect to . Therefore, the incremental loss at eachbus in the network is obtained and will be subsequently used byLMP to compute the nodal price at each node in the network.

D. Agent Optimization Objectives

The generation and load agents developed in this paper aregiven certain objectives to accomplish. There are basically threedifferent objectives available where each objective specifies adifferent policy of market operation.The first objective that agents may be tasked to perform is to

maximize DG surplus. Agents tasked with this objective aim atmaximizing the profit of generation agents which participate inthe trading process. Generation agents can either sell their en-ergy to load agents or the utility grid depending onMCP derivedin (3) or the grid buyback price. The profit for each generationagent is expressed as

Fig. 2. Schematic diagram of proposed multi-agent system.

(10)

where is the grid buyback price for the power injectedback into the utility grid; is the power flow ex-change between the utility grid and the th DG; is thepower sold to the microgrid load; and is the total sched-uled power sold to the microgrid load and the utility grid.Similarly, the second objective is to maximize load surplus.

Agents under this objective aim at maximizing savings for theload which also implies minimizing the load cost for each loadagent. Load agents can either buy energy from the generationagent or directly from the utility grid depending on energy pricesduring trading. The load saving for each load agent can be ex-pressed as

(11)

where is the total scheduled power bought from gener-ation agents and the utility grid by the th load agent;is the retail price offered by the utility grid; is theamount of power bought from the utility grid; andis the amount of the power bought from generation agents.Consequently, the third objective is to maximize both DG

and load surplus simultaneously which is the combination of(10) and (11). In (10), the maximization of DG surplus focusesmainly on the generation agents such that it also maximizes loadcosts at the same time. Similarly, (11) focuses mainly on loadagents such that it minimizes DG profits at the same time. There-fore, the third objective aims at maximizing generation and loadagent surplus simultaneously.

IV. PROPOSED MULTI-AGENT PLATFORM

The proposed MAS approach for simulating market environ-ment as well as simulated response of DERs is shown in Fig. 2.The developed multi-agent and coordination system operate incompliance with IEEE FIPA specifications [28]. Market andcontrol operations were implemented in JADE and the coordi-nation layer between Simulink models and agents were imple-mented in MACSimJX which uses TCP/IP protocol and Win-dows pipe for its communication channels.JADE was used for multi-agent implementation because it

has an agent platform that complies with FIPA and mainly con-sists of an agent management system (AMS), a directory facil-



Fig. 3. FIPA compliant agent platform reference model.

Fig. 4. Agent process flowchart.

itator (DF) and a message transport system as shown in Fig. 3.In addition, JADE exhibits numerous inherent features foundin distributed systems. Most of JADE’s complexities are hiddenfrom users enabling more focus on logical aspects of the system.Furthermore, JADE provides graphical interface for monitoringagents as well as an extensive library of classes with methodsbased on FIPA standards.Similarly, MACSimJXwas used as the coordination layer be-

cause it specifically integrates Simulink with JADE and facili-tates the development of software control structures with a rangeof features [27]. In addition, MACSimJX has a client-server ar-chitecture and has the capability of parallel processing informa-tion as well as handling multi threaded programs, a requirementfor distributed systems.

A. Agents Developed in Proposed MAS

The proposed multi-agent system comprises many intelligentagents representing various components in a microgrid. Eachagent has a localized knowledge base, containing rules and be-haviors, which governs its decision making process. The agentinternal process is shown in Fig. 4 which illustrates the statesinvolved when decisions are made.Microgrid agents developed in the proposed MAS include

generation (DG) agents, load (Demand) agents, market clearingengine (MCE) agent, coordination (CO) agent, utility grid (UG)agent, and other ancillary agents. A brief description of the func-tionalities for the main agents are given below.DG Agent: This agent models combined heat and power

(CHP) units, dispatchable units which may include MTs, fuelcells and energy storage systems and non-dispatchable unitswhich may include PVs and WTs as an aggregated equivalentDG unit under the same owner to participate in microgridmarket operations through negotiations with Demand Agents.It also regulates and controls output power and status of DGunits. Information contained in this agent includes an agent

identifier, minimum and maximum generation limits and dy-namic data such as bidding rate, generation settings, revenueand DG surplus.Demand Agent: This agent models an aggregated equivalent

load unit under the same consumer to participate in market oper-ations through negotiations with DG Agents. It is also capableof regulating and controlling the power demand and status ofthe respective load units based on energy prices. Informationresiding in this agent includes an agent identifier, minimum andmaximum demand limits and dynamic data such as bidding rate,power demand settings, load costs and savings.MCE Agent: This agent becomes active if the MCP compu-

tation of the microgrid is required for a specified period. It takesin bids from DG and Demand Agents and liaises with CO Agentduring market operations. Furthermore, it contains aggregatedinformation on microgrid generation and demand levels.CO Agent: This agent is responsible for coordinating the en-

tire microgrid market operation as well as monitoring the net-work for any technical violations. It coordinates with MAC-SimJX Agent to signal the beginning and end of market opera-tions for that period.UG Agent: The utility grid agent basically behaves similar

to an independent system operator (ISO) and is responsible forpower balance in the microgrid. It also broadcasts retail pricesignals to DG and Demand Agents at regular intervals to facili-tate decision making in the corresponding agents.MACSimJX Agent: This agent basically performs coordi-

nation between Simulink and JADE as well as manages anAgent Task Force (ATF) which consists of several other ancil-lary agents. In addition, an Agent Environment (AE) residingwithin this agent also acts as an interface for coordinatingcontrol signals between Simulink and JADE.

B. Agents Interaction and Coordination

A hierarchical control architecture [34], [35] is used for thesimulation of microgrid operation. The entire microgrid marketoperation and simulation are achieved by distributing responsi-bilities to the various agents. Agents accomplish assigned tasksby proactively interacting among themselves as shown in Fig. 5.From Fig. 5, steps 1 to 7 execute a few hours before gate closurewhile steps 8 to 10 execute after gate closure, say about an hourbefore the actual energy delivery. They define the pre-gate andgate closure periods typical in electricity markets.During initialization, AE will instruct ATF to inform CO

Agent to begin market operation. After which, MCE Agentrequests for DG and Demand Agents bidding data as well asgrid pricing data. Once all bids have been received, MCE Agentthen computes MCP and informs relevant agents about theresults. Every DG and Demand Agent then perform an internalscheduling and concurrently check for any technical violationswith its ATF counterpart.The energy trading which solves a symmetrical assignment

problem [17] between DG and Demand Agents will commenceafter all technical violations have been resolved. DG and De-mand Agents will aim at achieving a certain objective basedon one of the three agent objectives assigned as describedin Section III. Under maximizing DG surplus objective, DG



Fig. 5. Interaction between agents for market operation and implementation.

Agents will search through DF and negotiate with every De-mand Agent and UG Agent to sell their energy at the highestoffered price. Similarly for maximizing load surplus objective,Demand Agents will search through DF and negotiate withevery DG Agent and UG Agent to buy energy at the lowestoffered price. For maximizing both the DG and load surplusobjective, DG Agents, Demand Agents and UG Agent willnegotiate among themselves to arrive at a common price thatbenefits both DG and Demand Agents. UGAgent then performspower balance for the microgrid whose generation or load isstill not satisfied after negotiations by either buying back theexcess power from DG Agents or selling power to DemandAgents. Market operation ends when DG and Demand Agentsinform CO Agent about the trading results and the updateddispatch will be passed on to ATF and AE for implementationin Simulink.In the event that the power available from/to the grid to/from

the microgrid is limited, the UG Agent is first to know the statusthrough the real-timemeasurement from the smart meter locatedat the point of common coupling (PCC). Subsequently, the UGAgent will inform every DG and Demand Agent concerningthe shortfall/excess in active power at the PCC through JADEandMACSimJXwhich can provide the basic necessary servicesfor agent communication. At the same time, DG and DemandAgents will also sense a change in the system frequency throughthe real-time measurement on the respective local controllers.The local DG controllers then perform primary control whichactively adjusts by raising/lowering the active power output ofeach DG until the system frequency is stabilized. In addition,Demand Agents are expected to perform local voltage control atthe load terminals by controlling the amount of injected reactivepower from the local capacitor bank. It should also be noted thatagents are required to interact and coordinate among themselvesvia AE, ATF, and COAgent as described in Section IV-A so thatthe DG and Demand Agents are aware of the system dynamicsand are able to make informed control decisions.

Fig. 6. Single-line diagram of microgrid in Simulink.

TABLE IMICROGRID NETWORK PARAMETERS

TABLE IIDETAILS OF DISTRIBUTED GENERATORS AND LOADS

V. SIMULATION STUDIES AND RESULTS

A. Seven-Bus Microgrid System

Simulation studies were carried out on a three-phase, seven-bus, 400-V grid-connected microgrid. The microgrid consistingof three equivalent DGs, three dynamic equivalent loads and autility grid power source was modeled in Simulink. An equiva-lent single-line diagram is shown in Fig. 6. Details on networkparameters and the number of DGs and loads in each equivalentDG and load are given in Tables I and II. This setup considers ascenario with three DG owners and three load users that servesas a building block for a larger dynamic power system.In this setup, equivalent DGs and loads ranging from 0.75

MW to 3.5 MW were connected to the microgrid. Each equiv-alent DG consists of dispatchable and non-dispatchable unitshaving various sizes. The minimum DG power represents thesituation when cogeneration units are programmed to provideminimum heating requirements to the local community. It isthe sum of all minimum power requirements of the cogenera-tion units and minimum power of any online units with similarphysical constraints under the corresponding DG owner. Con-versely, the maximum DG power is the sum of all units’ ratedpower when all units under the respective DG owner partici-pate in market operations. Local voltage regulating devices are



TABLE IIIBIDDING RATES ( , IN $/MWh/MW) OVER 24-H PERIOD

Fig. 7. Grid and market clearing prices.

placed at load buses to improve load voltage profile when neces-sary. Network parameters were taken from a typical low voltagedistribution network. The no-load price in Table II refers to theprice intercept for the corresponding loads as described in (2).The utility grid acts as a power balancing source to maintainpower balance in the microgrid during normal operation.Simulation studies for a typical day were carried out. The

24-h bidding rates for DGs and loads are given in Table III. Re-sults of market clearing prices and grid prices [41] for a typ-ical day are shown in Fig. 7. High grid prices are observed be-tween 0800 h to 1600 h which also coincide with the peak hoursof a study day. Bidding patterns of DGs and loads also followgrid pricing trend resulting in MCP having a similar price trendduring the same period.Fig. 8 shows the simulation results of DG scheduling and

power supplied by the utility grid for the same study day. Be-tween 0000 h to 0800 h and 2100 h to 2400 h, DG 2 was sched-

Fig. 8. Simulated DG scheduling.

uled to generate 2 MW. At 0800 h and 1200 h, DG 2 was sched-uled at 3.25 MW. Likewise, DG 1 and DG 3 were also sched-uled accordingly. All DGs operate within the MW limits shownin Table II. By observing the power generated from the utilitygrid at the various hourly intervals, the microgrid experiencesdifferent conditions during the study day. A slight variation inutility grid power is observed at each hour to maintain powerbalance in the microgrid except at 0800 h when the microgridhas excess generation and exports 1 MW back to the utility grid.At 0900 h, the microgrid’s generation and load demand are bal-anced resulting in no power exchange from the utility grid. At2100 h, the microgrid experiences a slight shortage in genera-tion and imports about 0.07MW from the utility grid tomaintainpower balance. This shows that the power management by theagents is able to handle various microgrid conditions.Fig. 9 shows the outcome of load scheduling and system

losses for the day. Between 0000 h to 0800 h and 2100 h to2400 h, it is observed that Load 1 requires a constant 2 MWwhich shows that the simulated load scheduling works as theminimum power for Load 1 has been reached. Similarly, Load2 reaches maximum demand of 3 MW at 0800 h and 1200 hwhile Load 3 reaches peak demand of 2.5 MW at 1200 h. Thefirst system load peak occurs from 0800 h to 1500 h. Its peak be-haviors coincide with peak DG generation and high grid retailprice in Figs. 8 and 7, respectively. The second load peak oc-curs from 1800 h to 2100 h whose behaviors coincide with thesecond DG generation peak but at a lower grid retail price. Thetotal system power losses remain around 0.1 MW throughoutthe day and account for approximately 3% of the total load de-mand.From Fig. 9, it is observed there is a sudden drop in load de-

mand at the beginning of 1500 h since the market clearing en-gine is assumed to clear once for each hourly interval. In reality,the real-time load will change gradually over the entire hour andthe load increment/decrement will not be so sudden and huge.The DGs respond to the sudden load drop by adjusting their ac-tive power reference settings accordingly. From Fig. 8, DG 1changes its production from 2.35 MW to 1.62 MW while DG 2changes from 2.73 MW to 2 MW and DG 3 changes from 2.59MW to 2.05 MW to accommodate the change in load at 1500 h.DG 1 is modeled as an equivalent power electronics generatorin the simulation of this paper. From Fig. 10, DG 1 generates2.35 MW in the previous hour until at 1500 h, its generation



Fig. 9. Simulated load scheduling and system losses.

Fig. 10. DG 1 active power response during 1500 h.

Fig. 11. Load 1 per unit voltage profile during 1500 h.

decreases to 1.65 MW within 0.5 s to meet the new operatingcondition. This shows the fast response of the DG for dynamicmicrogrid operation.In Fig. 11, it is observed that the per unit voltage at Load 1

terminal at 1500 h rises momentarily by about 0.6% to 1.006p.u. during the first 0.04 s before settling down at about 1 p.u.after 0.2 s. The voltage rise is attributed to the sudden drop inload demand while the system takes some time for the dynamicsto settle down before it reaches a new steady-state operatingcondition. During transition, a brief period of excess generationcauses the load terminal voltage to rise momentarily. The after-wards effect on voltage is regulated at 1 p.u. which is maintainedby a local capacitor bank.Comparisons of DG profit and load savings under different

agent objectives are given in Figs. 12 and 13. Fig. 12 shows asnapshot of DG 1 profits for different agent objectives in thesame study day. A base objective where agents trade directlywith the utility grid and do not participate in the microgridmarket operations is used for comparison. It is observed fromFig. 7 that during periods where MCP is below the grid retailprice, DG 1 agent reports similar profits when assigned underthe agent maximization objectives. However, during periods

Fig. 12. DG 1 profits with different agent objectives over a study day.

Fig. 13. Load 1 savings with different agent objectives over a study day.

where MCP is above the grid retail price, DG 1 agent reportsvarying profit levels when assigned different agent objectives.In addition, DG 1 agent reports negative profits during certainperiods when assigned the load surplus maximization or baseobjective indicating that the generation cost is higher than therevenue. This shows that the grid price can affect DG profits.Similarly, Fig. 13 shows a snapshot of Load 1 savings when

subjected to different agent objectives for the same study day.Load 1 agent reports similar savings whenever MCP is belowgrid retail price and varying level of savings when MCP ishigher than the grid retail price. Load 1 agent also reports neg-ative savings between 0800 h and 1400 h when the agent wasassigned the base objective. Referring to Fig. 7, the negativesavings are attributed to high grid retail prices during the sameperiod which results in a higher amount payable by Load 1 ascompared to the load cost.Numerical results for DG revenues and profits as well as load

costs and savings over a 24-h period are given in Tables IV andV. Table IV shows the revenue and profit for each DG as wellas the aggregated amount. In general, DG agents have a betterprofit when agents are assigned optimization objectives com-pared to the base objective. Although maximizing DG surplusyields the highest profit, load savings are minimized simultane-ously. This shows that maximizing DG surplus favors the gen-eration side and likewise maximizing load surplus favors theload side. However, DG agents assigned to maximizing the DGand load surplus objective yield optimal profits which maximizeboth the DG profits and load savings simultaneously. It is alsoobserved that DG 2’s profit is negative under the base objective.This indicates that DG 2 has accumulated a net negative profit



TABLE IVDISTRIBUTED GENERATORS REVENUE AND PROFIT OVER 24-H PERIOD

TABLE VLOAD ENERGY COSTS AND SAVINGS OVER 24-H PERIOD

of $0.82 over the 24-h period which is due to a higher gener-ation cost compared to the revenue collected.Table V shows the load costs and savings for each load agent

under different agent objectives. It is observed that load agentsassigned to maximizing the DG and load surpluses yield op-timal savings. In addition, load savings are significantly higherwhen load agents are assigned the optimization objectives com-pared to base objective. Load 3 has negative savings under thebase objective indicating that the amount payable by Load 3 ishigher than the load cost. Since Load 1 reports the highest sav-ings under the base objective, the total load savings for the mi-crogrid remains positive despite negative savings from Load 3.From the results in Tables IV and V, it is evident that maxi-

mizing both DG and load surplus objective provide optimal ben-efits to both the DGs and the loads and is not biased to eitherside. Similar trends are also observed for each hour during thestudy day. During each hourly interval, DG and Demand Agentscoordinate and trade according to assigned agent objectives. Atthe end of each trading session, the UG Agent maintains powerbalance by accepting all sale requests from DG agents havingexcess generation and accepting all purchase requests from De-mand Agents having unsatisfied load. This shows the effective-ness of agents in handling various conditions.

B. Extended Analysis on IEEE 14-Bus Test System

The proposed approach is further tested on a modified IEEE14-bus test system [40]. In this system, there is one utility gridgenerator, four DGs and eleven loads. The utility grid generatoris located at bus 1 which is also the reference bus. The four DGsare located at buses 2, 3, 4, and 12. The eleven loads are locatedat buses 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, and 14. The utility gridgenerator is represented by a UGAgent. Each DG is representedby a DG agent and each load by a Demand Agent. Generator andload data is obtained from [40]. The line parameters are obtainedfrom the IEEE 14-bus test system [42].The dispatched power, marginal loss factor values, LMP

and MCP over a 24-h period are shown in Table VI. There arefour DG Agents and eleven Demand Agents in the 14-bus testsystem. Results for the DG connected at bus 3 and the loadat bus 9 are shown in Table VI. It can be observed that theLMP values differ slightly from MCP due to the contribution

TABLE VIRESULTS OF DG AND LOAD FOR 14-BUS SYSTEM OVER 24-H PERIOD

of marginal loss factors. Positive marginal loss factor valuesindicate that the change in power injection at the correspondingbus results in an increase of system losses while negative valuesindicate a reduction of system losses due to a change in powerinjection at the bus. Based on the values obtained, DGs willfetch a higher price for positive marginal loss factor and willfetch a lower price for negative values. Conversely, loads willbe penalized for positive marginal loss factors and rewardedfor negative values according to (7) when the energy tradingalgorithm is executed.Market results for DG and Demand Agents for the 14-bus

test system over a 24-hour period is shown in Table VII. Themaximization of DG and load surplus objective was comparedwith the base objective. There are no market results for the DGconnected at bus 4 because it is the most expensive unit and



TABLE VIIMARKET RESULTS FOR 14-BUS SYSTEM OVER 24-H PERIOD

did not participate in the market operation during the 24-h pe-riod. From Table VII, when compared to the base objective, itis observed that DGs yield higher profits and loads have highersavings. They follow the same trend as those of the 7-bus mi-crogrid system.

VI. CONCLUSION

This paper presents a MAS approach for distributed manage-ment of microgrids. The proposed MAS was developed usingIEEE FIPA standards, and market operations were coordinatedwith implementation of the microgrid. Simulation studies andresults demonstrate the effectiveness of the proposed distributedmarket operation and control technique displaying much po-tential for the autonomous operation of microgrids. It is foundthat maximizing the benefit for both energy buyers and sellerspromotes unbiased transactions between them. The proposedmarket structure can be extended to manage a larger networkcomprising numerous participants. Furthermore, the proposedapproach can be implemented in actual microgrids withminimaladditional software cost by replacing the Simulink models withthe actual microgrid and the communication network interfacebetween MACSimJX and actual microgrids can be achievedthrough TCP/IP and the necessary SCADA I/O devices. Simula-tion studies have shown that the proposed distributed system iscapable of handling the economical and technical requirementsof microgrids. Coordination of multiple microgrids will be con-sidered in the future which will reinforce the research and de-velopment of smart grids.

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Y. S. Foo. Eddy (S’09) received the B.Eng. degree inelectrical and electronic engineering from NanyangTechnological University, Singapore, in 2009, wherehe is currently pursuing the Ph.D. degree in the Lab-oratory for Clean Energy Research, School of Elec-trical and Electronic Engineering.His research interests are multi-agent systems, mi-

crogrid energy management systems, electricity mar-kets, and renewable energy resources.

H. B. Gooi (SM’95) received the B.S. degree fromNational Taiwan University, Taipei, Taiwan, in1978, the M.S. degree from the University of NewBrunswick, Fredericton, NB, Canada, in 1980,and the Ph.D. degree from Ohio State University,Columbus, OH, USA, in 1983.From 1983 to 1985, he was an Assistant Pro-

fessor with the Electrical Engineering Department,Lafayette College, Easton, PA, USA. From 1985 to1991, he was a Senior Engineer with Empros (nowSiemens), Minneapolis, MN, USA, where he was re-

sponsible for the design and testing coordination of domestic and internationalenergy management system (EMS) projects. In 1991, he joined the Schoolof Electrical and Electronic Engineering, Nanyang Technological University,Singapore, as a Senior Lecturer, where he has been an Associate Professor since1999. His current research focuses on microgrid EMSs, electricity markets,spinning reserve, energy efficiency, and renewable energy sources.

S. X. Chen (M’13) received the B.S. dual degreein power engineering and business administrationfrom Wuhan University, China, the M.S. and Ph.D.degrees in Power Engineering from Nanyang Tech-nological University (NTU), Singapore, in 2007,2008, and 2012, respectively.From 2012 to 2013, he was a research fellow

of Energy Research Institute at NTU, Singapore.Currently, he is a consultant working at the CleanTechnology Center in DNV GL Energy (formerlyKEMA). His research interests are smart energy

management systems, energy efficiency, power system operation and planning,renewable energy sources, and energy storage systems.

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