1

Abstract—The paper presents results of a smart grid project,

currently on-going, in collaboration with an Italian electricity distribution company (DisCo). The project is aimed at defining new optimization tools to be integrated in the SCADA/DMS that is already implemented within the DisCo control center. In this paper, a DMS architecture that includes Optimal Network Reconfiguration (ONR) functions is presented. ONR, being based on data received through a metering infrastructure and a topology processor, can be performed not only for planning, but also in extended real-time during system operation. Test results are presented for two different operative problems and are carried out on a realistic sized model of a distribution network.

Index Terms—Optimal Network Reconfiguration, Advanced Distribution Management, Smart Grids, Distribution Network, Simulated Annealing, Meta-heuristics.

I. INTRODUCTION

ISTRIBUTION grids are mostly passive networks operated through manual procedures, whose efficacy

relies on the experience of an aging work force. In the next future, distribution systems will evolve progressively from passive to active grids, integrating customers and utility resources. This is a great challenge for DisCOs that will have to manage suitably huge amounts of signals and control multiple devices, including a fast growing number of Distributed Generation (DG) units.

The increasing penetration of Distributed Energy Resources (DERs) and the deployment of Automated Meter Infrastructures (AMI), will require the update of current Distribution Management System (DMS) schemes, pushing towards the development of Advanced DMS (ADMS) designs and implementations [1]. ADMS will allow to achieve complete automation of dispatching, operation, distribution and services, ensuring at the same time the fulfillment of reliability and economical targets.

In this paper, a scheme of the proposed advanced DMS (ADMS) architecture is presented. ADMS must be able to provide most of the requested innovative functionalities of a

The present study was funded under the grant PST #44 “Smart-Grids:

Advanced Technologies for utilities and energy”, received by the Regione Puglia as Strategic Project in the Framework Program Agreement on the scientific research sector in the Apulian region.

S. Bruno, S. Lamonaca, M. La Scala, U. Stecchi are with the Electrical and Electronic Department (DEE) of the Polytechnic School of Bari (Politecnico di Bari), Bari, 70125, Italy. (e-mail: bruno@deemail.poliba.it, lascala@poliba.it).

smart distribution system and is responsible for managing, storing and elaborating available data and perform several management applications that can be carried out in the medium-long term, for planning, or in extended real-time for system operation [1]. Most of such functions and methodologies were already illustrated and formulated in [2]-[4], whereas the integration of Optimal Network Reconfiguration (ONR) tools is described in this paper.

From a mathematical point of view, distribution network reconfiguration is based on finding an optimal solution, with regards to an objective function, through the combination of network switches under specific constraints [5]. ONR is a combinatorial optimization problem that, applied to large electric systems, might require remarkable computational efforts. Formulation and solution of this problem have been largely discussed at transmission level whereas, nowadays, it finds growing applications at distribution level. Several application of distribution network reconfiguration can be found in the literature, solving multi-objective planning and off-line reconfiguration problems through heuristics and meta-heuristics [5]-[10].

The abovementioned studies refer to off-line approaches but, since smart distribution grids are characterized by extensive networks, continuous demand and generation fluctuations, real-time ONR applications should be developed [11]. In [11], a robust and efficient real-time ONR algorithm is proposed for real-time management of smart distribution grids and losses reduction.

In this paper an algorithm based on simulated annealing (SA) is proposed for developing ONR applications in ADMS extended real-time framework. The ONR optimization block developed in this paper is characterized by a perfect modularity and is compatible with all other tools developed for system analysis and optimization. This means that ONR can easily give support to any other optimization or decision tool, helping in solving several operative problem such as losses minimization, congestion management, security violations, volt-var optimization and so on.

The feasibility of this approach for on-line applications is tested for solving the common operative problem of losses reduction and for congestion management. Test results are obtained carrying out simulations on a detailed representation of a real sized urban distribution network (about 1000 nodes).

Integration of Optimal Reconfiguration Tools in Advanced Distribution Management System

Sergio Bruno, Member, IEEE, Silvia Lamonaca, Member, IEEE, Massimo La Scala, Fellow, IEEE, and Ugo Stecchi, Member, IEEE

D

2

II. OPTIMAL RECONFIGURATION FUNCTIONS IN ADVANCED

DMS SCHEME

This paper presents results of the research activity on smart grids involving Politecnico di Bari (Italy) and two local energy distribution companies (AMGAS Bari and AMET Trani). AMET supplies electricity to the densely populated urban area of Trani, a typical middle-sized city in the South of Italy (about 60,000 inhabitants). The network serves also a large rural area and reaches altogether about 35,000 customers in about 100 km2.

Such as many other municipal areas in the Apulian Region, the Trani’s area has been experiencing in the last few years a rapid, and almost uncontrolled, growth of middle sized photovoltaic power plants. Such plants, having usually a capacity close to 1 MW, are directly connected at MV level to the distribution grid causing growing concerns for allocating further distributed energy resources and system operation.

The aforementioned project with Politecnico di Bari was started in year 2009 as follow up to a smart meter deployment campaign that brought to the substitution of about 95% of all installed metering devices and to the implementation of a SCADA system that allows to monitor and control the status of a large portion of power disconnector switches. One of the main goals of such project is to provide smartness to the DMS functions that are (or are to be) developed within AMET control center.

In previous studies it was showed how main control functions can be developed through the implementation of a Three-phase Distribution Optimal Power Flow (TDOPF), able to deal with all distributed resources, upcoming operative requirements and security limits [2]-[4].

The main idea presented in this paper is to take advantage of the existing switch control infrastructure in order to provide control tools to be applied in both planning or extended real-time operation. For real time applications a snapshot of the

system can be obtained considering real time measurements provided by automated metering infrastructure (AMI) along with measurements (or generation forecasts) from all producing units and data from a topology processor.

At the current stage of development the control center is able to interrogate controlled switchboards via GSM and obtain information on the status of each power disconnector switch. This procedure takes several minutes and is usually performed once in a day. Since any closure/opening of controlled switches is promptly communicated to the control center, and since disconnectors are switched on/off very seldom (usually just after faults or during maintenance operations), at any instant it is possible to have an exact description of disconnectors’ status and evaluate the topology of the network.

GSM does not provide the top notch technology for communication but proved very reliable in terms of system security (for example a robust firewall to external intrusions is obtained by programming modem in switchboards to answer only to incoming calls from known numbers).

An enhancement of system automation level will require the implementation of faster communication channels (GPRS or optical fibers) allowing to keep such components on-line and have a continuous flows of info from/to the control center. Given the actual time framework in which the system can be operated (an operative time window of about 15-30 minutes), the current monitoring and control system is still appropriate.

In fig. 1, the ideal scheme of the proposed monitoring and control architecture for AMET grid is showed. In such scheme the ADMS block represents a modular system combination of several functions (i.e. conservative voltage regulation, distribution power flow, adaptive relaying, store management system, etc.) supported by bidirectional communications with field equipments and RTUs. In this same figure, those elements framed with a bold and continuous line were fully

Fig. 1: Basic ideal scheme of the proposed monitoring and control architecture

ADMS

°°

ODPF

TP

SE

ONR

VVO

CVP

SC

AR SM

• AMR: Automatic Meter Reading

• AR: Adaptive Relaying

• CA: Contingencies Analysis

• CVP: Capacitor/Voltage regulator Placement

• CVR: Conservative Voltage Regulation

• DG: Distributed Generation

• EDA: Environmental Data Acquisition

• MDI: Meter Data Integration

• ODPF: Optimal Distribution Power Flow

• ONR: Network Reconfiguration

• OTS: Operator Training Simulator

• RCS: Remote Controlled Switches

• SC: Short Circuit analysis

• SCs: Switching Capacitors

• SDF: Supply and Demand Forecast

• SE: State Estimator

• SF: Storage Facilities

• SM: Switch Management

• SMS: Storage Management System

• TP: Topology Processor

• ULTC: UnderLoad Tap Changer

• VVO: Voltage – Var Optimization

SDF

CA

SMS

CVR

EDA

OTS

Off‐line

Real time

concentrator

AMR

AMR

AMR

concentratorSERVER

MDI

GIS INTERFACE

EnvironmentalMonitoringStations

CONTROL CENTER

Signals from RTUs

AMI

DISTRIBUTION NETWORK

RCS

SCs

ULTC

DG

DGSF

3

described in [4], whereas bold and dotted contours identify the elements necessary for developing ONR tools.

The main idea is to reconfigure the network during the day-ahead or the extended real time (about 15-30 minutes [12]) framework of system operation. The starting configuration must be identified through a topology processor (TP) and a state estimator (SE). TP is an on-line module that collects the status of each switch and gives back an actual snapshot of the grid. The state estimator module calculates voltages, currents and angles basing on pseudo-measurements from the field. ONR evaluates the necessary switching maneuvers for implementing the best grid configuration in terms of loss reduction or that minimizes control effort during active power rescheduling.

A. Formulation of the ONR problem

The ONR problem can be formulated as a ),,(Cmin obj

,uxV

ux (1)

subject to equality and inequality constraints 0),,(f uxV (2)

0),,(g uxV (3)

with xx and uu .

In (1) function Cobj is the objective function to be minimized; V, x and u represent respectively the set of node voltages, the set of continuous variables (for example generated active and reactive power) and the set of discrete control variables (i.e the status open/closed of each switch). Eqn. (2) takes into account the non-linear load flow equations, whereas in (3) all technical and operational requirements are taken into account (line and transformer thermal limits, minimum and maximum voltages, etc.) adding non linearity to the problem. Since the ONR might contain both integer and real variables, the resulting overall problem is usually a mixed integer non linear optimization problem (MINLP).

The overall MINLP can be solved by means of several techniques that might involve relaxation of integer variables [13] or problem decomposition. Given the good performance of SA in solving non linear integer problems, a common approach consists in decomposing the problem by means of Bender decomposition [14] or formulating it as a two stage optimization [15]-[16].

In this paper, for the sake of preserving modularity of DMS tools, the optimization algorithm adopts SA for the sole solution of a integer non linear problem whereas all operational constraints and continuous variables are taken into account by means of an optimization code based on non linear programming techniques (basically a TDOPF). This means that to each configuration selected by the SA code, a single feasible solution coming from the optimization tool is associated. This methodology allows to embed network constraints in the OPF code and not in the SA. This might result in a higher computational burden but it avoids the definition of time varying weights or constraint relaxation rules in the first steps of SA as in [17].

B. Objective functions

Objective functions vary according to the specific operative problem that must be solved. In the case of minimization of system losses the objective function can be formulated as:

2

1

0

nbus

iLi

obj

P

LossesTotalC (4)

Other formulations can be aimed at the minimization of control effort during power system rescheduling. Hypothesizing that, at a certain moment of the day, too much power is produced causing the violation of one or more security limits and that the generated output must be rescheduled, the objective function will be formulated as in the following expression :

ngen

i schedGi

GischedGii,obj P

PPC

1

2

0 (5)

where, for the i-th generator, PGi is the generated power after rescheduling and PGi sched is the amount of power that was scheduled for production. Clearly the solution of this problem assumes the availability of dispatchable generating power resources. Further operative constraints in redispatching can be taken into account as hard limits.

Other possible operative problems might be solved adopting different formulations of the objective function and assuming different set of control variables. Other possible formulations can be found in [2]-[4].

C. Penalty functions

Penalty functions are introduced in order to take into account inequality constraints in (3). Typically inequality constraints are referred to thermal limits of branches and to acceptable voltage profiles, but can clearly take into account several other operative and technical constraints.

In this paper three penalty functions (Cp1, Cp2 and Cp3) have been formulated. These functions are referred respectively to line, transformer and voltages constraints, and were formulated according to the following formulations:

nlines

i imax,

imax,ii,p I

IIC

1

2

11 (6)

with imax,ii, IIif 01 ;

ntrasf

j jmax,

jmax,jj,p S

SSC

1

2

22 (7)

with jmax,jj, SSif 02 ;

nbus

k klim,

klim,kk,p V

VVC

1

2

33 (8)

with

kmax,kkmin,k,

kmin,kkmin,klim,

kmax,kkmax,klim,

VVV

VVVV

VVVV

if0

if

if

3

.

4

In (6)-(8), I, S and V represent respectively line current, transformer apparent power and node voltage, whereas α coefficients are relative weights for penalty functions. Suffixes max e min refer to maximum and minimum technical constraints.

Under these assumptions the ONR problem can be formulated as ),,(Cmin

,uxV

ux (9)

subject to equality constraints 0),,(f uxV (10)

with 321 pppobj CCCCC (11)

and xx and uu .

D. Solving algorithm

The ONR algorithm proposed in this paper was formulated in such way that the modularity of ADMS tools is ensured. Basically the algorithm is structured with a SA algorithm that searches and selects radial network configurations and a slave code that performs a mere Distribution Three-Phase Load Flow (DTLF) or solves a more complex optimization problem (for example a TDOPF).

The ONR algorithm was implemented in a Matlab/OpenDSS environment, exploiting the data exchange COM (Component Object Model) interface that is available in the OpenDSS package [18]. The ONR algorithm, based on simulated annealing meta-heuristic method, is implemented in a Matlab code. The algorithm looks for new configurations and communicates necessary topological changes to the OpenDSS grid model. Each state contains the status of about 100 controllable disconnector switches that interconnect laterals and backbones of the eleven urban feeders.

If ONR does not involve the optimization of other variables than the status of disconnectors, the slave code is constituted by a simple DTLF. In this case, the OpenDSS simulation engine performs a distribution load flow for each new state, evaluate losses and calculates voltages and power flows.

When other control variables have to be optimized the slave code will perform an optimization of all control variables (i.e. active and reactive resources, loads, switching capacities, under-load tap changers, etc.) minimizing the chosen objective function. All inequality constraints are taken into account by means of suitable penalty functions that are minimized concurrently to the objective function.

The optimization block is developed in a Matlab/OpenDSS environment and is based on the work presented in [2]-[4]. An important feature of such optimization code is that it can treat concurrently both single- and three-phase system representation. Each system element, represented as a single object with its single- or three-phase model, can be controlled by the optimizing code, overcoming the limitations of the nodal approach.

In Fig. 2 a schematic representation of the proposed algorithm is given. First of all, it must be remembered that in this approach only radial configuration can be accepted. In fact, AMET system is always operated with a multiradial

configuration and any new network state must be characterized by this same property. Since radial solutions are very few within the entire search space the state boundary was restricted to those configurations that can be reached by closing and opening two elements of the grid per time. This approach allows not only to take into account just radial solutions, but also to obtain states that can be reached by the first state through a limited number of switching maneuvers, keeping a radial configuration after each maneuver.

In order to find only radial solutions a simple strategy, very close to the one proposed in [15]-[17], is followed. Starting from a radial known configuration (for example the actual configuration produced by the TP) an open disconnector is randomly selected for being closed. Since we have added a closed branch to a connected radial graph the resulting configuration is meshed. At this point, if one disconnector is opened and the system remains connected the resulting configuration is radial. The algorithm searches among all open switches for those ones that can be opened without losing the connectivity of the network, and randomly selects one of them, producing a new radial system configuration.

select or detect initial

configurationk=0 u=u0

evaluate objective function Cthrough DTLF or TDOPF

k=k+1close a random switch

search all switches that can be opened; open one randomly and set new configuration u=uk

is uk a new configuration

?

evaluate objective function Ck

through DTLF or TDOPF

Ck< Ck-1?

yes

no

yes

accept new configuration uk

and reduce temperature T

no select a random number R and set probability P(T)

R< P(T)?yes

T< Tmin?

yes

STOP

no discard new configuration and restore old one

u=uk-1

no

Fig.2: Flow chart of the proposed ONR algorithm

Once a new configuration is selected for study, the algorithm optimizes/evaluates objective and penalty functions for that configuration by a simple DTLF or through an optimization of all available control variables. In order to save computation time, such optimization is performed only if this configuration has not been analyzed before. The objective function evaluated for this configuration is compared to the one evaluated at the previous step, and the new configuration is chosen or discarded according to SA working principles. The algorithm stops after the temperature is below a certain

5

threshold or having reached a maximum number of iterations or whenever a certain objective is reached (for example C is below a certain tolerance value).

III. TEST RESULTS

Test results have been obtained carrying out simulations on a realistic sized representation of the AMET distribution system. The AMET system is interconnected with the subtrasmission system through a single HV/MV substation and supplies energy by means of two 150kV/20kV transformers equipped with controllable tap changers and eleven 20kV feeders. The two transformers have respectively a capacity of 30 and 25 MVA, with a base load of 20 MW and peaks of about 35 MW. A simplified scheme of the distribution substation is given in Fig. 3.

Fig.3: Simplified scheme of the AMET distribution substation

The system model, comprising all HV and MV elements,

consists of 930 buses, 1000 distribution lines (cabled and overhead), 100 controllable switches, 500 load buses.

Test results were obtained considering the whole representation of the system comprising an urban area, served by the 30 MVA transformer and the main four feeders, and the rural network supplied by a 25 MVA transformer.

An important feature of this network, makings ONR applications particularly appealing, is that the network is characterized by a tangled architecture that allows several reclosures on elements belonging to different feeders. Such tangled structure is a legacy of an inhomogeneous urbanization process that led to an incremental development of the distribution network. The distributor in fact had to connect, one after the other, blocks or buildings as soon as the they were completed and new connections to the grid were requested. The result is that very often laterals of a feeder can be directly connected to laterals of different feeders. Moreover each backbone has at least one reclosure switch at the end of the line.

A. Case A

The first case was aimed at finding the optimal network configuration for losses reduction. The adopted objective function is the one formulated in (4). The initial configuration

is characterized by a value of losses of about 1100 kW. In figs. 4 and 5, the overall convergence behavior is shown.

Figure 4 shows how the overall objective function is minimized along the iterative method. It also suggests how the method is able to move out of suboptimal areas and search for a global minimum. At the last iterations the overall amount of losses is reduced to about 690 kW.

In Table I the value of each minimized function, at the beginning and at the end of the optimization, is shown. At the last iteration all penalty functions are null, indicating how to method is able to minimize losses and remove security violations at the same time.

0 20 40 60 80 1000.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

iteration #

C [p

.u.]

Fig.4: Case A, function C along the SA algorithm

0 20 40 60 80 100650

700

750

800

850

900

950

1000

1050

1100

iteration #

activ

e po

wer

loss

es [k

W]

Fig.5: Case A, overall losses along the SA algorithm

TABLE I

CASE A. OBJECTIVE AND PENALTY FUNCTIONS AT FIRST AND LAST ITERATION Iter. # C Cobj Cp1 Cp2 Cp3

1 1.749 0.992 0.756 0.000 0.001 100 0.413 0.413 0.000 0.000 0.000

B. Case B

The second test case was carried out introducing in the network a significant amount of power generators (about 13 generators, producing about 32 MW). In this case the huge amount of generated power gives rise to congestions on several distribution lines (see Fig. 6).

By applying the proposed methodology and employing the objective function formulated in (5), an optimal configuration is found. As shown in Fig. 7, the methodology converges very quickly to a solution where all constraints are respected and rescheduling can be avoided. After reconfiguration the current flowing in each distribution line is below its ampacity (Fig. 8).

6

0 100 200 300 400 500 600 700 800 9000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

# line

I / Im

ax [p

.u.]

Fig.6: Case B, line current vs. ampacity (before ONR)

0 5 10 15 20 25 300

0.5

1

1.5

2

2.5

iteration #

C [p

.u.]

Fig.4: Case B, function C along the SA algorithm

0 100 200 300 400 500 600 700 800 9000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

# line

I / Im

ax [p

.u.]

Fig.8: Case B, line current vs. ampacity (after ONR)

C. Case C

A third test case was performed considering a significant increase of power that has to be generated. All other hypotheses are analogous to case B. The amount of power to be generated (about 60 MW) is so massive that, even with the best system configuration, it will be affected by a generation rescheduling. The ONR scheme is aimed at the minimization of the controlling effort and, therefore, at ensuring that the maximum amount of power is generated.

Figure 9 shows how the method converges towards an optimal solution in about 150 iterations. Figure 10 shows how the total generated power is maximized along the process. At the final iteration the system configuration allows the production of about 47.9 MW, whereas at the first iteration the total power would have to be cut at 30 MW.

0 50 100 1500

0.5

1

1.5

2

2.5

iteration #

C [p

.u.]

Fig.9: Test case C, function C along the SA algorithm

0 50 100 15020

25

30

35

40

45

50

iteration #

tota

l gen

erat

ed p

ower

[MW

]

Fig.10: Test case C, function C along the SA algorithm

In both cases the system is not able to produce the entire quantity (60 MW) and generation has to be curtailed through generated active power control. In this case, thanks to system reconfiguration, DSO would be able to reduce the amount of power to be curtailed from 30 MW to 12.1 MW.

In Tables II and III, it is shown how the DTOPF code finds the minimum acceptable curtailment in the presence of all above quoted inequality constraints. In both cases (first and last iteration of SA) the DTOPF converges rapidly in about 10 iterations.

TABLE II

CASE C. MINIMIZATION OF CONTROL EFFORT (INITIAL CONFIGURATION) Iter. # C Cobj Cp1 Cp2 Cp3

0 54.908 1.243 53.654 0.000 0.011 1 19.947 0.532 19.415 0.000 0.000 2 19.174 0.530 18.643 0.000 0.000 3 6.649 0.501 6.148 0.000 0.000 4 2.951 0.465 2.486 0.000 0.000 5 2.296 0.446 1.850 0.000 0.000 6 2.216 0.434 1.782 0.000 0.000 7 2.196 0.423 1.773 0.000 0.000 8 2.190 0.404 1.786 0.000 0.000 9 2.180 0.410 1.770 0.000 0.000

10 2.177 0.408 1.769 0.000 0.000

7

TABLE III CASE C. MINIMIZATION OF CONTROL EFFORT (OPTIMAL CONFIGURATION) Iter. # C Cobj Cp1 Cp2 Cp3

0 51.790 1.243 50.530 0.000 0.017 1 81.051 0.000 81.051 0.000 0.000 2 77.026 0.000 77.026 0.000 0.000 3 8.827 0.069 8.758 0.000 0.000 4 0.912 0.134 0.778 0.000 0.000 5 0.398 0.150 0.248 0.000 0.000 6 0.274 0.153 0.121 0.000 0.000 7 0.182 0.155 0.027 0.000 0.000 8 0.161 0.155 0.006 0.000 0.000 9 0.162 0.157 0.005 0.000 0.000

D. CPU timings

All simulations were carried out on an ordinary desktop PC, HP Compaq 8000 Elite CMT PC, with Intel Core 2 Quad CPU Q 9650 3.00 GHz and 4.00 GB RAM. The total elapsed time is show for each case in Table IV.

All timings appears to be compatible with extended real-time system operation and a 15-30 minutes control framework. The computation effort for Case C is about 27 minutes, but it can be drastically reduced through some code optimization and executing the simulation on a more powerful machine. Furthermore, in the presence of severe time requirements, it is always possible to accept suboptimal solutions that are available to the SA algorithm after few tens of iterations.

TABLE IV

CPU EXECUTION TIME Case Time [s]

A 295 B 386 C 1650

IV. CONCLUSIONS

The paper presented an Advanced Distribution Management System scheme that integrates Optimal Network Reconfiguration (ONR) within its monitoring and control functions. ONR problem was solved through a simulated annealing based algorithm that allows an easy integration with other system analysis and optimization tools.

Test results obtained by implementing such algorithm on a detailed representation of a medium-sized urban distribution network showed how the methodology can be applied in the extended real-time operative framework of such system. The methodology is able to ensure the selection of an optimal configuration minimizing system losses and respecting all technical and operative constraints. Moreover, when associated to other DMS tools such as a TDOPF, the methodology permits to minimize further the control effort requested for removing security violations.

The main foreseeable bottleneck relies in the on-line implementation of system state estimators that will have to exploit data received from the field from smart meters and distributed generators, and build the system model to be

adopted in the optimization code.

V. ACKNOWLEDGMENT

The present study was funded under the grant PST #44 “Smart-Grids: Advanced Technologies for utilities and energy”, granted by the Regione Puglia as Strategic Project in the Framework Program Agreement on the scientific research sector in the Apulian region.

The authors would like to thank P.Eng Walter Leggieri and all personnel at AMET for the help provided during the modelling of the system representation. The authors would also thank Antonio Ripa for his help in the preparation of numerical results.

VI. REFERENCES [1] J. Fan and S. Borlase, “The Evolution of Distribution”, IEEE Power &

Energy, Vol. 7, pages 63-68, No. 2, Mar/Apr 2009. [2] S. Bruno, S. Lamonaca, G. Rotondo, U. Stecchi, M. La Scala,

“Unbalanced Three-phase Optimal Power Flow for Smart Grids”, IEEE Transactions on Industrial Electronics, Vol. 58, No. 10, pp. 4504-4513, October 2011.

[3] M. Bronzini, S. Bruno, M. La Scala, R. Sbrizzai, “Coordination of Active and Reactive Distributed Resources in a Smart Grid”, PowerTech 2011, Trondheim (Norway), 19-23 June, 2011.

[4] S. Bruno, M. La Scala, U. Stecchi, “Monitoring and Control of a Smart Distribution Network in Extended Real-Time DMS Framework”, Cigré International Symposium - The Electric Power System of the Future. Integrating supergrids and microgrids, Bologna, Italy, September 13-15, 2011.

[5] G. Celli, F. Pilo, “Optimal distributed generation allocation in MV distribution networks”, in proc. Power Industry Computer Applications, 2001 (PICA 2001) - Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society International Conference on, pp. 81-86.

[6] G. Celli, E. Ghiani, S. Mocci, F. Pilo, “A Multiobjective Evolutionary Algorithm for the Sizing and Siting of Distributed Generation”, IEEE Transaction on Power Systems, Vol. 20, No. 2, pp. 750-757, May 2005.

[7] A. Silvestri, A. Berizzi, S. Buonanno, “Distributed generation planning using genetic algorithms”, Conference on Electric Power Engineering, PowerTech Budapest 99 International, Budapest (Hungary) 29 Aug -02 Sep 1999.

[8] J.M. Nahamn, D.M. Perić, “Optimal Planning of Radial Distribution Networks by Simulated Annealing Technique”, IEEE Transactions On Power Systems, vol. 23, no. 2, May 2008.

[9] M. Gandomkar, M. Vakilian, M. Ehsan, “A combination of genetic algorithm and simulated annealing for optimal dg allocation in distribution networks”, presented at Electrical and Computer Engineering, 2005. Canadian Conference on, pp. 645 – 648, Saskatoon, Saskatchewan Canada, 2005.

[10] Ma Zhigang, “Study on Distribution Network Reconfiguration Based on Genetic Simulated Annealing Algorithm”, in Proc., Electricity Distribution, 2008. CICED 2008. China International Conference on, pp. 1-7.

[11] A. Vargas and M.E. Samper, “Real-Time Monitoring and Economic Dispatch of Smart Distribution Grids: High Performance Algorithms for DMS Applications”, under publication on IEEE Transaction on Smart Grids, Digital Object Identifier 10.1109/TSG.2012.2187078.

[12] S. Paudyaly, C.A. Canizares, K. Bhattacharya, “Three-phase distribution OPF in smart grids: Optimality versus computational burden”, Innovative Smart Grid Technologies (ISGT Europe) 2011, Manchester, December 5-7, 2011.

[13] F.V. Gomes, S. Carneiro, J.L.R. Pereira, M.P. Vinagre, P.A.N. Garcia, Leandro Ramos de Araujo, “A New Distribution System Reconfiguration Approach Using Optimum Power Flow and Sensitivity Analysis for Loss Reduction”, IEEE Transactions on Power Systems, Vol. 21, No. 4, pp. 1616-1623, 2006.

[14] H.M. Khodr, J. Martinez-Crespo, M.A. Matos, J. Pereira, “Distribution Systems Reconfiguration Based on OPF Using Benders

8

Decomposition”, IEEE Transactions on Power Delivery, Vol. 24, No. 4, pp. 2166-2176, 2009.

[15] H.-D. Chiang, R. Jean-Jumeau, “Optimal network reconfigurations in distribution systems. I. A new formulation and a solution methodology”, IEEE Transactions on Power Delivery, Vol. 5, No. 4, pp. 1902-1909, 1990.

[16] H.-D. Chiang, R. Jean-Jumeau, “Optimal network reconfigurations in distribution systems. II. Solution algorithms and numerical results”, IEEE Transactions on Power Delivery, Vol. 5, No. 3, pp. 1568-1574, 1990.

[17] Young-Jae Jeon, Jae-Chul Kim, Jin-O Kim, Joong-Rin Shin, K.Y. Lee, “An efficient simulated annealing algorithm for network reconfiguration in large-scale distribution systems”, IEEE Transactions Power Delivery, Vol. 17. No. 4, pp. 1070-1078, 2002.

[18] OpenDSS, available at http://sourceforge.net/projects/electricdss/.