A Complex Systems Modelling Approach forDecentralized Simulation of Electrical Microgrids

Enrique Kremers and Pablo ViejoEuropean Institute for Energy Research - EIFER

EDF and the University of KarlsruheKarlsruhe, Germany.

Email: enrique.kremers@eifer.uni-karlsruhe.de

Oscar Barambones and Jose Gonzalez de DuranaUniversity College of EngineeringUniversity of the Basque Country

Vitoria-Gastez, SpainEmail: josemaria.gonzalezdedurana@ehu.es

Abstract—The structure and behavior of Electrical Grids sharemany of the properties of Complex Computer Systems, withmicrogrids and other decentralized electrical systems attachedto them, so they can be interpreted as Systems of Systems.Furthermore, the evolution of the future electrical system willbring a higher degree of decentralization concerning speciallyproduction and control. To deal with this paradigm change,new models and tools are necessary. In this paper a modelof an electrical microgrid is presented. The approach used inthe development of the model is Agent-Based in combinationwith System Dynamics. By mixing these approaches the differententities of the electrical system (production, demand, storage,etc.) have been represented. Through the individual behavior ofthe agents it is possible to reproduce the complex behavior ofthe system as a whole. This behavior can produce expected andunexpected emergent properties on the interconnected systemthat can be analyzed through simulation. A case study ispresented to analyze the capabilities of such kind of models. Theexample shows the simulation of an integrated microgrid system,where different components such as renewable energy sourcesand storage have been implemented. The simulation results ofthis case study are discussed.

Index Terms—electrical grid, microgrid, agent-based models,complex computer systems, system of systems.

I. INTRODUCTION

The introduction of renewable energy carriers in developedcountries implies one of the greatest changes in the structureof their energy systems. These systems are moving awayfrom a centralized and hierarchical energy system, where theproduction follows a top-down principle under strict control ofthe electricity supply companies, towards a new system wheredistributed actors influence the energy supply. Production isnot limited any more to large energy providers, as smalldecentralized producers enter the network and inject energyat much lower tension levels than before.

II. MICROGRIDS

A microgrid is an integrated energy system consisting ofinterconnected loads and distributed energy resources which asan integrated system can operate in parallel with the grid or inan intentional island mode [2]. A microgrid is as well a set ofsmall energy generators arranged in order to supply energy fora community of users in close proximity. It is a combinationof generation sources, loads and energy storage, interfacedthrough fast acting power electronics. In the course of this

paper we argue how an the electric grid and also a microgridcan be understood as a “Complex Computer System”.

Microgrids represent a form of decentralization of electricalnetworks. They comprise low or medium voltage distributionsystems with distributed energy sources, storage devices andcontrollable loads.

The European Commission [4] defines microgrids as smallelectrical distribution systems that connect multiple customersto multiple distributed sources of generation and storage. Italso described that microgrids typically can provide powerto communities up to 500 households at low voltage level.This shows that this technology is not only suitable forelectrification of small remote islands or remote zones indeveloping countries but for the integration into existing highvoltage transmission grids, too. Therefore microgrids can havea wide application spectrum in near future.

During disturbances, the generation and corresponding loadscan autonomously disconnect from the distribution system toisolate the load of the microgrid from the disturbance withoutdamaging the integrity of the transmission grid.

From the point of view of the customer, it can be seen asa low voltage distribution service with additional features likeincrease in local reliability, improvement of voltage and powerquality, reduction of emissions, decrease in cost of energysupply, etc.

A microgrid is connected to the distribution network througha single point of common coupling (PCC) and appears to thepower network as a single unit.

A number of authors argue that power electronics wouldbe a crucial feature regarding microgrids since most of thepower microsources must be electronically controlled to gainthe required characteristics for the system.

1) Microgrid architecture: A report by Navigant Consult-ing [2] prepared for DOE’s Office of Electricity Delivery andEnergy Reliability identifies four classes of Microgrids:• Single Facility Microgrids

These Microgrids include installations such as industrialand commercial buildings, residential buildings, and hos-pitals, with loads typically under 2 MW.

• Multiple Facility MicrogridsThis category includes Microgrids spanning multiplebuildings or structures, with loads typically ranging be-

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Bus

Collecter

Bus

Infinite

Feeder1

Feeder2

123

4

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Load2

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Diesel

200kW

CHP

200kW

Motor

30kW

100kW

PV2

Load3

30kW

100kW

PV4

Load4

30kW

Load5

30kW

PCC

Fig. 1. Microgrid schemme

tween 2 and 5 MW. Examples include campuses (medical,academic, municipal, etc), military bases, industrial andcommercial complexes, and building residential develop-ments.

• Feeder MicrogridsThe feeder Microgrid manages the generation and/or loadof all entities within a distribution feeder, which canencompass 5-10MW. These Microgrids may incorporatesmaller Microgrids – single or multiple facility– withinthem.

• Substation MicrogridsThe substation Microgrid manages the generation and/orload of all entities connected to a distribution substation,which can encompass 5-10+MW.

Figure 1 shows a Multiple Facility Microgrid, with twointegrated Feeder Microgrids.

2) Microgrid control: Concerning the control of micro-grids, direct control over local distribution and transmissionof electricity from the interconnect point across a facility is avery common practice.

Electricity grids are becoming intelligent, true complexcomputer systems, with flexible, controlled power flows sup-ported by advanced information technology [6]. Under the newsmart grid paradigm emerged in the last decade, new capa-bilities for measurement, control and communication, with atwo-way flow of energy and information between customerand supplier, are demanded, in order to rise efficiency and geta cleaner electricity generation.

Some of such new operational measurement and controlcapabilities that are needed to manage smart grids are:• Network based communication and control• Monitor energy generation and consumption• Optimization of production and consumption• Generation and load dispatching• Energy production and consumption control• Tieline control

• Microgrid active and reactive power control• Microgrid connection and islanding control• Security standards• User-based billingSome of the key technical issues not entirely solved at

present are• Real time power flow balancing• Voltage control and security during disconnection (island-

ing) from the point of common coupling (PCC)• Failures protocol response• Protection and stability aspects• Dynamic short term and long term response• Active and reactive power control• Communication technologies adaptation• Tieline controlOn these points, research is still required and so modelling

and simulation of microgrids is an unavoidable instrument onthe way to an efficient and secure physical implementation.

III. MODELS

For modeling electrical grids, it is necessary to use newdesign tools (different to the traditionally used in ElectricalEngineering) and agent based software able to capture thecomplex system behavior, the wanted and unwanted emer-gent behavior, the internal and external events, the systemcommunications, etc. Simulation can be very helpful fordesigning electrical grids and microgrids, because while alot of intelligent network components do not exist yet, theycan be modeled and used in the simulation. Therefore, it ispossible to use components before their market deployment inthe simulation and future designs of intelligent networks canbe tested.

The analysis of system architectures can be a relevant firststep for future grid development. Despite the results of the sim-ulation may fit for some ideal architectures, the developmentof “enabling” technologies is necessary. Some of them canbe already available in the market or being employed in othersectors, but probably other are completely new or still far awayfrom commercialization and widespread deployment. Whilewaiting for research, development and commercialization ofsuch elements, simulation can be very useful to analyze thecapabilities of future systems.

A. The electrical grid as a Complex Computer System andSystem of Systems

The electrical grid can be considered as a true ComplexComputer System whose aim is to manage power flows.Indeed it consists of a set of vertices (nodes or buses with acomputer) with two types of edges arranged between them: thefirst one (power lines) is dedicated to carry out the power flowand the second one (information channels) to broadcast theinformation flow. In the authors opinion, the assumption thata computer is placed on each node does not appear to involve agreat loss of generality to the model. This artificial connectionprecisely defines the ”outside view” or environment of the

Fig. 2. Different layers in an electrical microgrid

system. Therefore, the electrical system can be split in twolayers:• Physical layer: The first layer comprehends the physical

structure of the electrical grid itself, including all thepower transmission lines. It includes the power flowsas wells as all the electrical parameters concerning thecorrect operation of the grid.

• Logical layer: This second layer, which represents themain part the upcoming generation of electrical grids isnot yet present in contrast to the physical layer. Thislayer includes all the information exchange that has to bearranged to control DG, dispatchable loads and all othersmart equipment in future grids. It has to be underlinedthat the communication paths have not to be the samethan the links in the first layer, although they could beexploited for that aim (Power line communication, PLC).

Concerning the second layer, in this work a specific infor-mation and communication technology is intentionally do notfixed, it is rather treated from the theoretic point of view. Thespecifications of this layer include a real time communicationbetween the elements of the grid. In [3] an Internet of Energy issuggested as a parallelism to computer networks. Particularlythis medium could itself serve as a communication platform.More examples for the implementation of the logical layercould be:• Wired-based technologies

– Using the grid infrastructure: Power line communi-cation

– Other existing or new wired networks such as theInternet or other cable networks

• Wireless technologies, such as WLAN or WMANAlso geographic network localisation, distribution of pro-

cessing and databases, interaction with humans, and unpre-

High Voltge 380 kV

Low Voltge 50 kV Distribution Grid

Transmision Grid

PCC

+

Fig. 3. Integrated microgrid

dictability of system reactions to unexpected external eventsare present in this kind of networks.

Figure 3 shows a schematic drawing of an integrated mi-crogrid, showing the point of common coupling (PCC), butwithout the information channels.

One can easily argue that the electrical grid, and also amicrogrid, (nearly) satisfy the five principal characteristics thatdistinguish them as true systems-of-systems [5].

1) Operational Independence of the Elements: Electricalgrid (and microgrid) systems are able to usefully operateindependently. So generators, loads and other elementswork properly when they are not connected to the grid.

2) Managerial Independence of the Elements: The electricalgrid (and microgrid) systems not only can operate inde-pendently, they do operate independently. They maintaina continuing operational existence independent of thesystem-of-systems.

3) Evolutionary Development: The electrical grid (and mi-crogrid) does not appear fully formed. Its future devel-opment and existence is evolutionary and depends ofother environmental agents.

4) Emergent Behavior: The electrical grid (and microgrid)performs functions and carries out purposes that do notreside in any component system. These behaviors areemergent properties of the entire system-of-systems andcannot be localized to any component system. The maingoals of electrical grid and microgrid are given by thesebehaviors.

5) Geographic Distribution: The geographic extent of theelectrical grid is large and also microgrids can be ofsome extension. The electrical grid components canreadily exchange information (but in this case they canalso exchange energy).

IV. APPROACH

A model of a ”Microgrid” implemented in AnyLogic ispresented below. The model is completely open so it can beused to address a number of issues of design and computationthat arise in such networks, some of which are:• Grid and microgrid simulation, control, being basis for

other models• Dynamic power flow computing• Loads power consumption, load dispatching, optimize

energy consumption, generation and storage, collect andreport

• Grid and microgrid architecture design• Centralized and decentralized control design• Load connection and disconnection• Microgrid connection and islanding modes• Branch or node (substation) deleting• Microgrid optimal design• Economic benefits demonstrationThe approach presented in the following is intended to

obtain a suitable electrical microgrid model. It is a contin-uation of the authors previous work in this area [11], [12]. Inthese works, the mesh method was used to obtain voltages atthe mesh nodes and currents through the branches, assumingknown voltages given by generators. In the present workthough a new power flow method has been built. An importantpart of the work has been to adapt or rather redesign the oldpower flow method to the dynamic case, using an agent basedmethod, in which charges, voltages in generators, and even thestructure of the grid, may change.

This new method has been implemented with the Anylogic’sAgent Based paradigm, using agents for representing the busesof the grid.

A. AnyLogic modeling

AnyLogic is a simulation program developed by XjTek[9] using a subset of UML for Real Time (UML-RT) as amodeling language, and extended it to incorporate continuousbehavior.

Three simulation paradigms are included in AnyLogic:• System Dynamics (SD)• Discrete Event (DE)• Agent Based (AB)SD and DE are more traditional approaches whereas AB

is relatively new. This methods can be combined in a model,for example it is possible to include SD and DE methods intoan agent model, so each agent can be an hybrid system. Withthis kind of models a very high refinement level can be areobtained.

The Dynamic System (DS) paradigm, the main method usedin block based programs like Matlab-Simulink, is consideredto be as a part of SD paradigm in AnyLogic since version 6appeared.

Although AnyLogic is specially well featured to modelHybrid Systems, like electric and electronic circuits, there arenot many papers about modeling electrical networks nor power

system generation and distribution made using this software[10].

B. Power Flow analysis

To design an electrical network engineers need to be ableto estimate the voltages and currents at any places within thecircuit. Usually an undirected graph shows the grid topologyand mesh analysis or nodal analysis is often used to posean equation system and solve it for the voltage and currentat any place in the circuit. Standard lineal algebra methodsare suitable for linear systems, but other non linear circuitsonly can be solved using estimation techniques or specializedsoftware programs. The most widely used of all these isthe Power Flow analysis. Summarized, the method can bedescribed as follows.

In an electrical network with n nodes, each one associatedto a pair of complex numbers V (voltage) and S = P + Qi(apparent power).

The data is the following:• |V | and ∠V = 0 are given at the reference node• |V | and P are given at control nodes• P and Q are given at load nodes (the other nodes)Applying the electrical laws gives us a nonlinear system of

2n equations with 2n real unknowns

g(x) = 0

where x, g(x) ∈ R2n, with the unknowns:• P,Q for the reference or slack bus,• ∠V and Q for control buses,• |V |,∠V for load buses.To solve the system, the most common approach in literature

is to apply the Gauss-Jordan (iterative, of slow convergence)or the Newton-Raphson (faster, but requires to calculate theJacobian matrix and invert it) method.

When talking about power flows, for example [1], it isoften assumed that the electrical network is static. In fact it isassumed that all data values are known constants and the effectof actual load variations with time is studied by consideringa number of different cases for which steady state conditionsare assumed.

Even though, there are situations where after a value changein some nodes (e.g. when connecting or disconnecting loadsand generators), the values of the unknowns have to be updatedin a short time, because these values are used for the controlof the network or for its simulation.

C. Agent based modeling the electrical grid

As said before, the traditional method used for the flowcalculation of electrical network is based on static power flowcalculations. These require the computation of a steady state ofthe network, and have to be rerun each time a change occurs ifa continuous simulation wants to be executed. This can lay toa large number of re-calculations, leading to redundant steps,when only small changes affect small parts of the network.Implementing an agent based approach could try to solve these

issues by providing more flexible and dynamic algorithms,and through the combination of traditional and agent basedmodeling techniques.

In the authors opinion, agent based modeling is speciallywell suited to cope with all difficulties one finds trying toobtain a dynamic model of the electrical grid. The proposedmodel use the three central ideas of this paradigm: agents torepresent electrical buses, the environment object to representin space and time the geographic area where the power linesare wired, and then single rules are bring up to each agent inorder to induce the necessary complexity to the model. Then,emergent properties of the model will represent the electricalgrid behavior [7].

Agent interactions are created at two levels (see 2). At thephysical level they are given by electrical circuits laws (Ohmand Kirchhoff laws, etc.), translated to computational powerflow algorithm rules. At the logical level, interactions representinternal and external events and they are implemented as agentmessages in the model. In this way it is possible to createintelligent agents (buses) which are able to easily carry outsome typical grid tasks as power flow control, generationsources dispatching, load dispatching, grid connection anddisconnection, etc.

V. CASE STUDY: AN INTEGRATED MICROGRID

Hybrid Renewable Energy Systems include behaviors ondifferent time scales, so the establishment of a single modelcovering all details would have as advantage the universality,but it also have some drawbacks, as for example the difficultyto understand the dominant behavior of the system and also,due the high complexity of the model, it would get anexcessively long simulation time. The authors have developed

Fig. 4. Project view

a computational hybrid, agent based, system modeling andsimulation method, as a valuable tool for analysis, design andvalidation of micro-grids and, trying to simplify the expositionand the result analysis, and also for didactic reasons, themethod is applied to a rather simple one.

The model has been built in AnyLogic using formulae givenin [14] for the development of some of the components. It iscomposed of twelve Active Object Agent classes. Seven ofthem, Battery, Consumer, Diesel, Node, Substation, PV andWind are intended to model the elements at the logical layer(see figure 2) and the other five, Bus, Slack, Control, Load andLine besides the Main class are for the physical layer elements.Figure 4 shows the AnyLogic Project window where the objecttree can be seen.

The intention of this paper is no to describe all the elementsin detail, but rather give an systemic view describing the keyfeatures of each agent.

The modeling of the microgrid consists in the integration ofthe power flow implementation in Anylogic into the ”behavior”model of the agents. This is comparable to the two layers,the power flow symbolizing the physical flows, and the agentbehavior the logical layer, i.e. the behavior of the agents. Thepower flow is performed at each time step, and computesthe voltage angles and bus unknowns for all nodes. The timeresolution chosen for calculation was in minutes as a first stepbut can be adapted to finer values, so that transient effects canbe analyzed closer.

A. The model of the microgrid

The microgrid consists in a grid attached to a transmissionnetwork through a substation, as it can be seen in Figure 3.The grid includes the following elements• Diesel generator• Wind power generator• Photovoltaic element and Battery• Two loads• Substation

The photovoltaic panel is connected to the grid through anelectronic inverter. The topology of the grid can be see in Fig-ure 3. Each agent has an autonomous behavior, correspondingto its technical characteristics, intended or unintended randomchanges, etc. The model of each type of agent comprisingthe microgrid and their behavior that results therefrom aredescribed in the following section.

1) Diesel generator: In the case the microgrid is turnedon islanding mode and the load requirements are not met byeither renewable energy system or by batteries, the power thatis still required must be provided by the generator. The overallefficiency of a diesel generator is defined as follows:

ηoverall = ηbrakethermal · ηgenerator,

where ηbrakethermal is the brake thermal efficiency of dieselengine. Normally, diesel generators are modeled to controlhybrid power systems in order to achieve required autonomy.It can be observed that only high power operation mode (70-90% of full load) is efficient and economically reasonable. In

Fig. 5. Topology of the microgrid used in the case study

the absence of peak demand, the diesel generator can be usedfor battery charging, too. It has to be noted that the dieselgenerator here symbolizes a dispatchable unit that can supplyenergy in critical situations, but it could be also replacedby any other controllable source such as a (micro-)CHP, forexample. The generator is modeled as a PV-bus.

2) Wind system simulation: Wind power is the renewableenergy source (RES) that has achieved the greatest penetrationrates in the last decades. Despite of being relatively economicand profitable and gaining a high popularity, wind powerinstallations implicate a highly fluctuating generation and aredifficult to predict. This is often a cause of grid stabilityproblems. The model described here aims to consider the mainpoints that characterize this generation source. The model canbe split into two parts, the wind turbine generator itself andthe simulation model for the wind speed.

The power output of wind turbine generator at a specific sitedepends on wind speed at hub height and speed characteristicsof the turbine. Wind speed at hub height can be calculated byusing power-law equation [14]:

Vz = Vi

(Z

Zi

)xwhere Vz and Vi are the wind speed at hub and referenceheight Z and Zi, and x is power-law exponent.

The power output Pw (kW/m2) from wind turbine gener-ator can be calculated as follows [14]:

Pw = 0, V < Vci

Pw = a V 3 − bPr, Vci < V < Vr

Pw = Pr, Vr < V < Vco

Pw = 0, V > Vco

where a = Pr/(V 3r − V 3

ci), b = V 3ci/(V

3r − V 3

ci), Pr is therated power, Vci, Vco and Vr are the cut-in, cut-out and ratedspeed of the wind turbine.

Pr

Pw

Vci

Vr co

V V

Fig. 6. Power output

In [15] a combined wind simulator and wind power gen-erator model was developed to show agent based capabilitiesin multiscale simulation. This model is able to simulate windspeed from real time requirements up to simulations on midtime scales using hourly mean values. For this case, thedetailed model is used as real time wind speed has to beconsidered in the microgrid model.

Using historical wind speeds from a specific site, futurehourly data can be simulated using the time series model. Thesimulated hourly mean wind speed can be obtained by

vh(t) = µh + σh · yt (1)

where µh is the observed hourly mean wind speed and σh thehourly standard deviation. The method is explained in detailin [15] and [16].

The time series model of yt is based on an ARMA (Auto-Regressive Moving-Average) model which is given by

yt = φ1 yt−1 + φ2 yt−2 + . . .+ φn yt−n

+ αt + θ1 αt−1 + θ2 αt−2 + . . .+ θm αt−m(2)

A data series is used for yt to build the model, i.e. to calculatethe auto-regressive φi i = 1, 2, . . . , n and the moving averageparameters θj j = 1, 2, . . . ,m. {αt} is a Gaussian white noiseprocess with zero mean and standard deviation of σα.

After having simulated the hourly means, a real timemodel is used to recreate the turbulent phenomena on thisslow varying part. The so called fast term is modeled bythe following differential equation that simulates the highlyfluctuating signal:

dw

dt(t) = −w(t)

T+ κvh(t)

√2Tξ(t) (3)

where T = L/v, being L the turbulence length scale, κ a factorthat depends on the geographical location of the wind turbinesite, ξ(t) a Gaussian white noise and vh(t) the hourly meanwind speed. The equation describes a stationary Gaussianprocess.

Having gained the wind speed, the wind generator modeldescribed above determines at each time step the power outputof the wind generator, that is used to feed into the microgrid.This power is delivered to the corresponding PV-bus.

3) Photovoltaic (PV) panel: The solar radiation on aninclined plane is given by [14]:

IT = IbRb + IdRd + (Ib + Id)Rr,

where Ib and Id are direct normal and diffuse solar radiations,Rd and Rr are the tilt factors for the diffuse and reflected partof the solar radiations. TT depends on position of sun in thesky, which varies from month to month.

Hourly power output from PV system with an area Apv (m2)on an average day of jth month, when total solar radiation ofIT (kW h/m2) is incident on PV surface, is given by [6]

Psj = ITjηAPV ,

where system efficiency η is given by

η = ηmηpcPf

and the module efficiency ηm is given by

ηm = ηr[1− β(Tc − Tr)],

where Zr is the module reference efficiency, Zpc is the powerconditioning efficiency, Pf is the packing factor, β is thearray efficiency temperature coefficient, Tr is the referencetemperature for the cell efficiency and Tc is the monthlyaverage cell temperature, calculated as

Tc = Ta +ατ

ULIT ,

where Ta is the instantaneous ambient temperature, UL/ατ =IT,NOCT/(NOCT − Ta,NOCT), and NOCT is normal oper-ating cell temperature, Ta,NOCT = 20◦ C and IT,NOCT =800 W/m2, for a wind speed of 1 m/s. Having a known outputpower and tension, the photovoltaic cell is modeled as a PV-bus.

4) Battery storage system: Storage systems comprehenda main part in microgrid networks. Storage elements arefundamental when fluctuating energy sources are used inthe grid, as a temporary displacement between the actualgeneration and consumption exists. As we have to assure thatthe power injected to the grid matches exactly the demand atany time to assure stability, the introduction of not dispatchablesources like wind or sun leads to a balancing problem. Thisis specially the case when the fluctuating sources hold a highpenetration rate and thus cannot be seen further as negativeconsumers (by being neglected including them virtually to thedemand as a reduction of it). In this case, as a control ofthe injected renewable energy is hardly possible, a ”passive-external” control in the sense of storing superfluous energywhen demand is satisfied and release it when this is not thecase is needed. In the model, the implemented battery storagewas modeled as an energy storage device. The storage is sizedto meet the load demand during non-availability period ofrenewable energy source. The battery was connected to thesame bus as the PV panel, as it is has become common toinstall these type of device together as they complement eachother. The required battery capacity in ampere hour is givenby

Brc =Ec(Ah)Ds

(DOD)maxηt,

where Ec(Ah) is the load in ampere hour, Ds is the batteryautonomy or storage days, DODmax is the maximum batterydepth of discharge, ηt is the temperature correction factor.

The charge quantity of the battery bank at the time t canbe calculated by

EB(t) = EB(t− 1)(1− σ) + (EGA(t)− EL(t)/ηinv)ηbattery

where EB(t) and EB(t−1) are the charge quantities of batterybank at the time t and t − 1, σ is the hourly self-dischargerate, EGA(t) is the total energy generated by renewable energysource after energy loss in controller, EL(t) is load demandat the time t, ηinv and ηbattery are the efficiency of inverterand charge efficiency of battery bank.

Charge quantity of battery bank is subject to the followingconstraints:

EBmin ≤ EB(t) ≤ EBmax ,

where EBmax and EBmin are the maximum and minimumcharge quantity of battery bank.

Like the other generation sources, the battery is modeled asa PV-bus.

Fig. 7. Supervisor control

5) Load modeling: The modeling of loads can be a quitecomplex issue. The behavior of a load mainly relies on theelectrical energy consumption created by a device. Consideringa load in the traditional, electrical engineering sense , asingle impedance with some fixed characteristics is meant. Butregarding microgrids, the connected loads normally will behouseholds or some other complex consumers that consist ofa large number of devices. These devices create an aggregatedenergy demand that is influenced by each one of the devices.The modeling and simulation of such a demand is a wide topicwhere still a lot of questions have to be solved. Analyzing

Fig. 8. Simulation view: Power Flow

the load curves of typical households, patterns can be rec-ognized. There are normally some increasing demand peaksin the morning, noon and evening. This peaks are speciallypronounced during weekdays, as they coincide with the usagetimes of most of the high power devices. For representing sucha type load in the model, the consumer agents were simplifiedand a typical curve was assumed. This curve represents anactive power demand (almost no reactive power is consumedby households). Thus, the consumer is modeled as a PQ-bus,where the active P and reactive Q power values are known.

6) Substation: Electrical substations are used for powertransfer between different levels of the grid. The substationserves mainly as power compensation in order to exchangepower with the grid and can be considered as an infinite bus.The simplest substation of this type consists in a transformer.The model can be realized according to the ideal transformerequation

Pin = Vpri · Ipri = Pout = Vsec · Isec

where Pin and Pout are the powers, Vpri and Vsec the voltagesand Ipri and Isec the currents on the primary and secondarywinding, respectively. Using an equivalent circuit for a realtransformer, the losses can be included, such as windingresistances, hysteresis losses, etc. The substation is modeledas the slack bus, that means that the complex voltage valueis fixed, normally the magnitude to the base voltage value ofthe per unit system, and the voltage angle is set to zero. Itcorresponds to the Point of Common Connection (PCC).

VI. SIMULATION RESULTS

Once all the objects had been created it is possible toconnect them and run simulation. Figure 8 represents the

Fig. 9. Simulation view: Graphics results for power and voltage

Fig. 10. Simulation view: Graphics results for substation

AnyLogic simulation window of a simple example with a sub-station (substation), a battery accumulator (battery), a dieselgenerator (diesel) a wind turbines (wind), and two consumer(consumer1 and consumer2). The data appearing beside thenodes represent the (simulation time) power flow data: powerand voltage levels at each bus.

This example shows the microgrid running in connectedmode. In this case, the diesel generator is assumed to runat a constant (economically feasible) level. Exceeding andneeded power is exchanged with the high tension grid throughthe substation. The active and reactive power flows can beobserved at Figure 10, where positive values represent a flowinto the microgrid (shortage operating mode) and negativevalues an excess of power produced that is absorbed by thetransmission grid (surplus operating mode). This is the typicalfunction of the slack bus, regarding the physical layer. Thebattery is scheduled to charge during the daytime, where thePV panel is supposed to feed in its power. The stored energyis the released at evening and night. The battery storage cyclesare assumed on daily cycles.

Figure 9 shows the active and reactive power injections

of the different buses into the grid, as well as the voltageangles and magnitudes. The randomness of the wind powerinjection can be observed, as well as the photovoltaic elementinjection to the grid. The magnitudes of the buses remainrelatively stable, as well as the angles for the generationbuses. The reactive flows remain small, as there is no reactiveconsumption on the microgrid.

It can be seen that supply is guaranteed even if load demandof power or meteorological conditions change. This can bemade specially illustrative and didactic by letting the userclicking at houses as order to change load demand of eachone while historical meteorological conditions also change. Inthis way it is possible to study the microgrid behavior in allpossible conditions which could be very interesting in orderto size microgrid elements and design its structure.

Figure 8 shows some graphical results.

VII. CONCLUSION

An agent based model for simulation of microgrids has beenimplemented using AnyLogic. The model offers a clear twolayer structure, which allows representing both physical andlogical interactions between the elements. The logical layeroffers a robust base to implement agent communication in realtime. The physical layer provides the technical results of thepower flow calculation integrated in the model. This allows tocompute real time simulations of the grid, which can providevaluable information before having implemented the real grid.

The model is mainly intended to design and test micro-grids and it can be used as a tool for design, developmentand demonstration of control strategies specially CentralizedSupervisor Control and Decentralized Load-Dispatch Control,design and demonstration of microgrid operation strategies,design and trying of microgrid communication buses, micro-grid optimal design, and economic benefits demonstration.

REFERENCES

[1] A.R. Bergen and V. Vittal, Power System Analysis, 2nd ed. PrenticeHall, New Jersey, 2000.

[2] Navigant Consulting. Microgrids Research Assessment: Phase 2 FinalReport. Prepared for DOE’s Office of Electricity Delivery and EnergyReliability. California, May 2006.

[3] (2009, Nov.) Bundesministerium fuer Wirtschaft und Technologie. Pro-jekt E-Energy. [Online]. Available: http://www.e-energy.de/

[4] (2009, Oct.) European Commission website on Microgrids. [Online].Available: http://www.microgrids.eu

[5] M.W. Maier. (2009, Oct.) TiAC White Paper: Architecting Principles forSystems of Systems. TiAC Enterprise Architecture Survey. InformationalTechnology Educational Services Inc.Needham, MA [Online]. Available:http://www.infoed.com/Open/PAPERS/systems.htm

[6] (2006, Jan.) European SmartGrids Technology Platform: Visions andStrategy for Europe’s Electricity Networks of the Future. European Com-mission: Directorate-General for Research. EUR 22040, 2006 [Online].Available: http://ec.europa.eu/research/energy/pdf/smartgrids en.pdf

[7] Agent-based model. From Wikipedia, the free encyclopediahttp://en.wikipedia.org/wiki/Agent-based model

[8] Northeast Blackout of 2003. From Wikipedia, the free encyclopediahttp://en.wikipedia.org/wiki/Northeast Blackout of 2003.

[9] (2009,Nov.) XJ Technologies website. [Online].Available:http://www.xjtek.com/

[10] Y.G. Karpov, R.I. Ivanovski, N.I. Voropai, D.B. Popov, “HierarchicalModeling of Electric Power System Expansion by AnyLogic SimulationSoftware”, Power Tech, 2005 IEEE Russia, 27-30 June 2005 pp. 1 – 5.

[11] J.M. Gonzalez de Durana, O. Barambones, E. Kremers, and P. Viejo,“Complete agent based simulation of Mini-Grids. The Ninth IASTEDEuropean Conference on Power and Energy Systems”, The NinthIASTED European Conference on Power and Energy Systems, EuroPES2009, Palma de Mallorca, Spain, September 7-9, 2009.

[12] J.M. Gonzalez de Durana, O. Barambones, Object oriented simulation ofHybrid Renewable Energy systems focused on Supervisor Control. 14th

IEEE International Conference on Emerging Technologies and FactoryAutomation, ETFA2009, September 22-26, 2009, Palma de Mallorca,Spain.

[13] T. Minagawa, Y. Ichikawa, M. Sato, Y. Ishihara, “Recognition ofcoherent swing in power systems and detection of step-out using powerand current measured on tie lines”, Electrical Engineering in Japan,Volume 119 Issue 4, Pages 22 - 31.

[14] M.K. Deshmukh, S.S. Deshmukh. Modeling of hybrid renewable energysystems. Renewable and Sustainable Energy Reviews 12 (2008) pp. 235–249.

[15] E. Kremers, N. Lewald, J. M. Gonzalez de Durana, O. Barambones,“An agent-based multi-scale wind generation model”, The Ninth IASTEDEuropean Conference on Power and Energy Systems, EuroPES 2009,Palma de Mallorca, Spain, September 7-9, 2009.

[16] R. Billinton, H. Chen and R. Ghajar, “Time-series Models for ReliabilityEvaluation of Power Systems Including Wind Energy”, Microelectron.Reliability, Vol.36. No.9. 1996, pp 1253-1261.

[17] E. Ortjohann, O. Omari, R. Saiju, N. Hamsic, D. Morton, “A SimulationModel For Expandable Hybrid Power Systems”, University of AppliedSciences S udwestfalen, Division Soest, Laboratory of Power Systemsand Power Economics. Internal Report.

[18] F. Valenciaga, P.F. Puleston “Supervisor Control for a Stand-AloneHybrid Generation System Using Wind and Photovoltaic Energy”, IEEETransactions on Energy Conversion, vol. 20, no. 2, June 2005.