IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 19, NO. 1, FEBRUARY 2004 549

Online Reconfiguration Considering VariabilityDemand: Applications to Real Networks

Enrique Lpez, Hugo Opazo, Luis Garca, and Patrick Bastard

AbstractThis paper presents how the application of the min-imal loss reconfiguration in distribution networks can help to eval-uate online reconfiguration benefits, considering the time varyingnature of loads, using daily load profiles in each node of the system.The emphasis in this evaluation is hourly reconfiguration in dis-tribution systems, compared to fixed topologies, considering max-imum and average demand of the system. The results of applica-tions to two real systems show unexpectedly that hourly reconfig-uration is not so effective, if compared to a simple maximum oraverage demand reconfiguration. These conclusions are based onthe decrease of obtained losses and on the amount of commutationsinvolved in the hourly reconfiguration process.

Index TermsDistribution networks, losses, reconfiguration,variability demand.

NOMENCLATURE

Resistance of branch b.Complex current in branch b.

i Current vector of branches.Maximum current of branches.

I Vector of node currents.[A] Incidence matrix.V Node voltage.

Minimum node voltage.Maximum node voltage.Number of total branches.

M Branch number of radial network.N Number of nodes.

Number of sources.

I. INTRODUCTION

D ISTRIBUTION networks reconfiguration based on min-imal loss has a natural tendency to improve the operationalconditions. One of the first papers about this topic was presentedby Merlin and Back [1]. The discrete branch and bound opti-mization technique was used from a meshed distribution net-work. However, its application to real systems is not very easydue to the required significant computer effort. Another investi-gation line has been proposed in [2], who derived a formula inorder to estimate the loss reduction produced by open and close

Manuscript received April 23, 2003.E. Lpez and H. Opazo are with the Department of Electrical Engineering,

Universidad de Concepcin, Casilla 160-C Correo 3 Concepcin, Chile (e-mail:elopez@die.udec.cl; hopazo@die.udec.cl).

L. Garca and P. Bastard are with the cole Suprieure dlectricit, Paris91192, France (e-mail: luis.garcia@supelec.fr; patrick.bastard@supelec.fr).

Digital Object Identifier 10.1109/TPWRS.2003.821447

actions on the switching elements without altering the systemradiality. Many papers proposed then modifications which im-proved accuracy and computational efficiency of this method[3].

There are other works proposing optimization techniquessuch as expert system, modified simulated annealing, geneticalgorithms, and artificial neural networks [4][7]. Algorithmsof transport with quadratic costs [8] and heuristic methods havealso been proposed [9][14]. A survey on distribution systemsreconfiguration is presented in [15], ranging from Merlin andBack fundamental work, to the state of art in 1993. Reference[16] obtained loss minimization by installing capacitors andby network reconfiguration considering load modeling impact.An algorithm that is based on distribution network partitioninginto groups of load busses is proposed in [17]. A heuristicconstructive method for minimal losses reconfiguration isproposed in [18]. By means of a loss incremental evaluation,a new node is added in each stage that introduces minimallosses. Finally, [19] presented an algorithm for minimal lossreconfiguration, based on dynamic programming approach.This algorithm, based on formal technique, is quite simple, andresults in a very short computing time. Therefore, it opens away to real time reconfiguration of networks.

The main objective of this paper is to take into account howload varies in distribution systems, depending on the season, onthe day, and on the hour. The switching actions to reduce lossestake into account the time varying nature of loads. The loadprofiles are a function of customer types and they vary fromone connection point to another one all over the network. Thus,the load diversity is taken into account by the daily demands ofvarious customer types.

About reducing distribution losses through reconfigurationon real-time operation considering load variability, [20] indi-cated that an important loss reduction was obtained through sim-ulations in Canadian networks on a one-year period. Reference[21] presented algorithms to reduce losses through load estima-tion considering load variability. Reference [22] showed hourlyreconfiguration benefits based on short- and long-term loss re-duction. An optimal power flow model for minimal losses isapplied in [23]. This paper emphasizes results and conclusionsabout hourly reconfiguration for online power operation in anenergy control center.

Most of the papers emphasize online reconfiguration advan-tages, even if sensibility studies for online applications are notalways performed. In this paper, we apply the minimal loss re-configuration method in real distribution systems to evaluate on-line reconfiguration advantages. The studies are made with themodel developed in [19], including demand aspects such as the

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550 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 19, NO. 1, FEBRUARY 2004

model itself (P, Z, or I constant), the actual type of load (indus-trial, commercial, residential, and mixed), and hourly variation[24], [25]. The aim is to evaluate online reconfiguration benefitin terms of loss reduction.

II. MINIMAL LOSS PROBLEM

The minimal loss reconfiguration problem in distribution sys-tems, through topological changes, can be written as follows [1],[19]:

Minimize (1)

Subject to(2)(3)(4)(5)

Equation (2) corresponds to the balance of load currents ineach node. Equation (3) corresponds to the respect of feedersthermal limits and the maximum capacity of substations. Equa-tion (4) considers the voltage restriction in each node. Eventu-ally the fourth constraint (5) is the radiality restriction in a pri-mary distribution system.

A. General Algorithm of ReconfigurationThe minimal loss reconfiguration is solved by dynamic pro-

gramming approach with graph compression and radial loadflow. The following algorithm describes the method [19].

i) Data system: Number and rating of power substationsand feeders, topology, and switching possibilities of thepower apparatus connected to the network.

ii) Actual operation: Using fast radial load flow, evaluatethe actual system conditions as node voltage, real, andreactive losses.

iii) Graph compression: When there is a set of nodes withnonreconfigurable radial topology, consider an equiv-alent node representing the load of the subsystem. Sononvalid options for reconfiguration are eliminated.

iv) Possible node connections: The process goes from eachsource node of the network (substation) forward thefinal load nodes, for connecting new possible node.

v) Radial load flow: To determine voltage profile, currents,and losses. In this case, nodes are considered accordingto the load type (P, Z, or I, constant).

vi) Losses functional evaluation: In each stage, the connec-tion of a new node that produces the lowest increment inlosses functional is added to the tree (forward process).

vii) Backtracking process: Applying a backward process theeffect of the last load connection in the structure is eval-uated.

viii) Constraints: Verification of thermal limits in substationsand feeders, voltage profiles, and other constraints. If aconstraint is not fulfilled, a transfer of loads betweensubstations should be made and step iv) should be per-formed again.

ix) Radial systems: The process goes on until all loads areconnected to the network; if not, they all go to iv).

x) Final loss evaluation: A fast radial load flow is appliedto determine networks final losses.

III. DEMAND CHARACTERIZATIONIt is important to consider the demand characterization, for

each node of the real distribution systems.The demand on distribution systems depends on the time and

also on the load type as industrial; commercial; urban residential; street lighting; mixed load.

Modeling load patterns of various customer types requires totake into account daily load profiles. The aim is to incorporatethese profiles in loss minimizing reconfiguration procedures.This has been done considering a 24-h period (in a winter day).

A. MethodologyThe methodology for demand characterization in real distri-

bution networks is as follows.i) Load recognition is done for the distribution system. The

aim is to identify a typical load model for each node.ii) Load profiles of different demand types are determined.

This information was mainly obtained by means of mon-itoring the network presently and historically.

iii) In some nodes, when the load profile is unknown, itis approached starting from nominal values and consid-ering: diversity factors, demand factors, utilization fac-tors, and recommendations of the utility personnel. Theload curves are built and typical load curves are associ-ated, as described in [24] and [25].

iv) Once the load patterns are obtained, one builds the data-base of an hourly system.

B. Load CurvesThe types of loads considered sum up an infinity of individual

characteristics. The global behavior of the load is the result oftypical consumption cycles. The loads can be characterized bycurves, as shown in Figs. 14, obtained firsthand for typicalloads on a winter day, as described in [25].

IV. APPLICATIONS

Table I gives the main characteristics of the two real systems(i.e., number of sources, nodes, lines, and power). The data in-dicate that it is necessary to open 14 lines in the system 1 and43 lines in the system 2, to obtain radial topology. Table II givesthe main characteristics of the load type in real systems.

From the demand behavior at each node, it is possible to per-form a minimal loss reconfiguration hourly for both systems.

A. Operational ConditionsOperational conditions in which the losses are evaluated in

both systems are the following:

LPEZ et al.: ONLINE RECONFIGURATION CONSIDERING VARIABILITY DEMAND: APPLICATIONS TO REAL NETWORKS 551

Fig. 1. Residential load.

Fig. 2. Commercial load.

Fig. 3. Industrial load.

Fig. 4. Public light.

i) actual configurationit corresponds to the hourly lossevaluation, in a period of 24 h, using the actual networkconfigurations;

ii) hourly configurationit finds optimal topology and as-sociated losses, for each one of the 24-h intervals, infunction of the different hourly demand profiles of thesystems.

iii) configuration for maximum demandin this case, theoptimal topology is determined for the maximum de-mand condition of each node. Later on, maintaining thistopology, the loss behavior for the 24 h, in function ofhourly load profiles in each node is analyzed.

TABLE IREAL SYSTEMS CHARACTERISTICS

TABLE IILOAD TYPE IN REAL SYSTEMS

iv) configuration for average demandit is similar to theprevious case, but average demand in each node is con-sidered and for the topology obtained, the systems hourlyloss is evaluated, in a period of 24 h considering time loadvariation.

For system 1, we first consider the actual operation condition(i.e., the actual topology, then the hourly reconfiguration to min-imal loss condition is done in the system and, finally, the hourlyoperation is evaluated, but maintaining the topology obtainedconsidering maximum demand. For the system 2, the same oper-ative conditions were analyzed as in the previous case; also, thecase of hourly operation system was analyzed, but maintainingthe topology obtained for average demand reconfiguration andmaximum demand reconfiguration in the system.

V. RESULTS

Results obtained for system 1 are presented in Table III. Theyshow hourly loss for actual operational condition, online loss,for hourly minimal loss reconfiguration. Then, hourly losses areevaluated for the topology obtained for maximum demand oper-ational condition. Table III also shows loss percentage reductionin respect to the actual configuration. Fig. 5 shows loss evolu-tion for the system 1.

Table IV, like in the previous case, shows results for system2. It presents hourly losses for the actual operational conditions,losses obtained when hourly reconfiguration is applied (consid-ering the online minimization), and hourly losses maintainingtopology obtained at maximum demand and at average demand.Fig. 6 shows system 2, hourly loss behavior.

Table V presents the number of necessary commutations forhourly reconfigurations in system 2. Finally, Table VI shows thenumberofnecessarycommutationsforaveragedemandreconfig-uration and maximum demand reconfiguration in the system 2.

VI. RESULT ANALYSIS

Table III shows an important loss reduction when hourly re-configuration is made. The maximum loss reduction is 0,0452p.u., corresponding to 52% (0.0875 p.u. at 0.0423 p.u, hour 12)and the minimal loss reduction is 0,0097 p.u. and corresponds to48% (0.0204 p.u. at 0.0107 p.u, hour 3). In case of loss evalua-tion for the topology of maximum demand, there is a loss reduc-tion similar to the previous case (52% in hour 9), but in the case

552 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 19, NO. 1, FEBRUARY 2004

TABLE IIIHOURLY LOSSES IN SYSTEM 1

Fig. 5. Hourly loss evaluations in system 1.

of lowest reductions, these are of 0.0114 p.u. and correspond to46% (0.0249 p.u. at 0.0135 p.u., hour 24). Practically all hoursevaluated have minor losses in the hourly reconfiguration, as ex-pected. There is also correlation in the levels of obtained lossesin some evaluated hours. In all of the cases, loss reduction levelsare not very different for both methods of reconfiguration thatwere analyzed, as shown in Table III as well as in Fig. 5. System1 presents an average loss of 0.0543 p.u., which is reduced withhourly reconfiguration to 0.0272 p.u., with 49% of reductionand it is reduced to maintain the maximum topology demand ina fixed way to 0.0280 p.u. with 47% loss reduction.

Table VI shows active losses for system 2. As in the previouscase, hourly reconfiguration makes losses decrease. Loss reduc-tion reaches a maximum of 21.42% (hour 19), and the minimalloss reduction is 0.91% (hour 7). For optimal topology of max-imum demand, the maximum loss is reduction of 20.70% (hour19), and in less favorable condition 0.50% (hour 7). In the op-timal topology case for average demand, there are loss reduc-tions of up to 20.60% (hour 19) and minimal loss reductions of0.40% (hour 7). The loss medium percentages are reduced withhourly reconfiguration of 9.75%, unlike the maximum demand

TABLE IVLOSSES TO HOURLY RECONFIGURATION AND CONSIDERING TOPOLOGIES

FOR MAXIMUM AND AVERAGE LOAD

Fig. 6. Hourly loss evaluations in system 2.

case and average which are reduced 7.98% and 7.37%. In Fig. 6,note the loss evolution in system 2.

Tables V and VI show the total number of commutations re-quired in each level of reconfiguration of system 2. Note that ineach interval it requires a change of 43 switches (i.e., 86 commu-tations) of which in average only 6.6 switches must be changed ineach level of loss minimization (each hour), with a total of 13.25hourly commutations. When considering hourly reconfiguration(24h), thereexistsadailytotalof318commutations,withtherisksinvolved, unlike with the average topology demand or maximum,which only requires the changing of nine switches.

VII. CONCLUSIONThis paper presents online reconfiguration evaluation in

power networks, dealing with actual applications, consideringdifferent load patterns, and analyzing hourly reconfiguration.

LPEZ et al.: ONLINE RECONFIGURATION CONSIDERING VARIABILITY DEMAND: APPLICATIONS TO REAL NETWORKS 553

TABLE VTOTAL COMMUTATIONS ASSOCIATED TO HOURLY RECONFIGURATION

TABLE VITOTAL COMMUTATIONS ASSOCIATED TO MAXIMUM AND AVERAGE

RECONFIGURATION

Online reconfiguration has been compared with optimal topolo-gies obtained for maximum demand and average demand.

The results show that a loss reduction appears when the net-work is reconfigured hourly. Nevertheless, this reduction is as-sociated with an important number of commutations necessaryto modify the system topology. Even if losses are lower, thisreduction may be not relevant when compared with losses ob-tained with a fixed topology optimized for maximum or averagedemand. It depends mainly on the cost of the commutation de-vices compared to the actual benefit of online reconfiguration. Itis not sure that a general response can be given: a specific studyshould certainly be carried out for any given network. More-over, online reconfiguration can lead to overvoltage transients,which can decrease the system reliability. In conclusion, onlinereconfiguration must be carefully evaluated, considering loadvariability, and fixed topologies determined from maximum oraverage demand could be more efficient.

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Enrique Lpez was born in Lota, Chile. He received the electrical engineerdegree from the Universidad Tcnica del Estado, Chile, and the Ph.D. degreein electrical engineering from Institut National Polytechnique de Grenoble(INPG), Grenoble, France.

Currently, he is an Associate Professor in the Electrical Engineering Depart-ment at the Universidad de Concepcin, Concepcin, Chile. His interest areasare planning, optimization, control, reliability, and quality of the electrical sys-tems.

Hugo Opazo was born in Lota, Concepcin, 1963. He received the electricalengineering and M.S.E.E. degrees from the Universidad de Concepcin, Con-cepcin, Chile, in 1989 and 2001, respectively. He is currently pursuing thePh.D. degree at the Universidad de Concepcin.

Currently, he is Assistant Professor in the Electrical Engineering Departmentof the Universidad de Concepcin, where he has been since 1992. He was withCompaia Manufacturera de Papeles y Cartones in Spanish (CMPC), Celulosa,Chile, from 1989 to 1992. His research interests include distribution and powersystems, optimization, and power systems protection.

Luis Garca was born in Concepcin, Chile. He received the electrical engineerdegree from the Universidad de Concepcin, Concepcin. He is currently pur-suing the Ph.D. degree in electrical engineering at the Universit Pierre et MarieCurie, Paris, France, and cole Suprieure dlectricit Supelec, Paris, France.

Currently, he is an Instructor Professor in the Electrical Engineering Depart-ment at the Universidad de Concepcin. His areas of interest are the optimizationof operation and exploitation of the electrical systems.

Patrick Bastard was born in Pont-Auderner, France. He graduated fromSuplec, Gif-sur-Yvette, France, in 1988. He received the Ph.D. degree inelectrical engineering from the University of Paris XI, Paris, France, in 1992.

Currently, he is a Professor in Suplec, France, where he manages a researchgroup about power systems. From 1989 to 1992, he was a Research Engineerwith Group Schneider, Grenoble, France, in the field of power system relays.