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IEEE TRANSACTIONS ON POWER SYSTEMS 1

Comprehensive Operational Planning Frameworkfor Self-Healing Control Actionsin Smart Distribution Grids

Seyed Ali Arefifar, Member, IEEE, Yasser Abdel-Rady I. Mohamed, Senior Member, IEEE, andTarek H. M. EL-Fouly, Member, IEEE

Abstract—Self-healing is a major driving force in the smart gridvision. This paper proposes a comprehensive design and opera-tional planning framework to generate optimum self-healing con-trol actions in a distribution system. For this purpose, a distribu-tion system with optimally allocated distributed generators (DGs)is divided into a set of microgrids with high self-adequacy throughallocation of distributed energy storage resources (DESRs) and dis-tributed reactive sources (DRSs). Afterwards, by using the pre-dicted load and generation of renewable-based distributed genera-tors for the next hour of the day and other important factors (self-adequacy in the unfaulted microgrids, total distribution system’senergy losses and the total supplied loads according to their re-quested reliability), the optimum self-healing strategy is plannedfor the system for all possible future faults. The IEEE 123-bus dis-tribution system is selected as the test system; optimummicrogridsare designed and several case studies are presented to demonstratethe effects of optimization coefficients on the optimum self-healingcontrol actions.

Index Terms—Comprehensive planning, control, optimumstrategy, self-healing, smart grid.

I. INTRODUCTION

P OWER engineers nowadays are facing new challenges toimprove the operation of power distribution systems [1].

Introducing distributed energy resources, advanced metering,communication and control technologies at the distributionlevel have changed the structure of conventional distributionsystems to a faster-acting, potentially more controllable andreliable grid, so-called “smart grid” [2]. Smart power distri-bution grids are distinguished from conventional distributionsystems from their reliability, self-healing, self-sufficiency andinteractive characteristics. Self-healing is an important featurein smart distribution systems, which deals with the algorithmsfor taking preventative actions or to handle problems afterthey occur [3]. In case of major faults in a distribution system,the system’s ability to restore itself and supply power to as

Manuscript received October 15, 2012; revised January 30, 2013; acceptedApril 09, 2013. Paper no. TPWRS-01165-2012.S. A. Arefifar and T. H. M. EL-Fouly are with CanmetENERGY, Natural

Resources Canada (NRCan), Varennes QC J3X 1S6, Canada (e-mail: [email protected]; [email protected]).Y. A.-R. I. Mohamed is with the Department of Electrical and Computer

Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada (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.2013.2259852

many loads as possible, makes the system more stable andtrustworthy.Self-healing has been studied in transmission networks [4],

[5] and gained interest from distribution system operatorsduring the last decade. Several papers have been published inthis area. For instance, Amin in [6]–[8] presented a descriptionof a comprehensive, multi-layered self-healing power systeminfrastructure. In [9] and [10] the authors presented a generalmethodology for business case analyses to assess the costs andbenefits of implementing a high performance IT infrastructurefor a self-healing grid. Later in [11], the authors presentedsteps to implement self-healing distribution automation. Theyexplained how their self-healing solution improved the networkreliability. In 2011, the issues associated with improving accu-racy of fault location methods was presented as an importanttask for realization of self-healing networks [12]. Recently in2012, security needs and challenges of smart grids and requiredresearch and developments including self-healing were de-scribed [13]. These papers and similar researches on the subjectin literature have not properly addressed the need for a general,systematic and optimized approach for performing self-healingin smart grids.This paper, considering the probabilistic and intermittent

characteristics of DGs and hourly variation of loads in thesystem, proposes a comprehensive operational planning frame-work for performing self-healing control actions in distributionsystems. The procedure is divided into two stages, planningand operation. At the system planning stage, the system isdivided into a set of virtual microgrids considering the inter-mittent nature of distributed energy resources and variation ofloads. The design at this stage makes the self-healing controlactions more effective and is optional for the distributionsystems that are already clustered into one or more microgrids.At the operational stage, the self-healing control actions areplanned for the system considering the intermittent nature ofdistributed energy resources and variation of loads, as well asthree key issues in the distribution systems: self-adequacy inthe unfaulted microgrids, total distribution system’s energylosses and the total supplied loads according to their requestedreliability. Depending on the importance of each factor for thedistribution system operator, an optimization objective functioncan be defined. The optimum self-healing action(s) then will bea set of one to three actions including system reconfiguration,DG output adjustment and load shedding. The IEEE 123-busdistribution system is selected as the test system and severalcases are studied to investigate the effects of the optimization

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2 IEEE TRANSACTIONS ON POWER SYSTEMS

objective function on the predicted self-healing control actions.The contributions of this paper to the research field are asfollows.• Development of a systematic strategy for constructing self-sufficient microgrids, using DG and energy storage units.

• Account of the probabilistic and intermittent nature of DGsand hourly variation of loads for constructing the micro-grids and for performing improved self-healing actions.

• Proposal of a comprehensive operational plan for per-forming optimum self-healing control actions in a givensystem, including system reconfigurations, controlling theDGs’ outputs and load shedding.

• Simultaneous consideration of self-adequacy in unfaultedmicrogrids, total distribution system energy losses, andtotal supplied loads according to their requested reliabilityin performing self-healing control actions.

The paper is organized as follows. Section II explains thedesign and strategy concepts of the self-healing process. Themodels used for system components are presented in Section III.Problem formulation is presented in Section IV. Section V ex-plains the solution algorithms and Section VI discusses the clus-tering of the IEEE 123-bus distribution system. In Section VII,some case studies are presented and the paper is concluded inSection VIII.

II. DESIGN AND STRATEGY CONCEPTS

The concepts behind the two research stages, planning andoperating, are further explained in this section and are shown inFig. 1.

A. Planning Stage (Optional)

Following a disturbance, a self-healing system reconfigura-tion could split a power network into self-sufficient islands.One of the important factors to determine the boundaries of theislands is to minimize the real and reactive power imbalancewithin them [14]. Although the controllable generation and loadmust be balanced once a system partitions, if minimization ofpower imbalance is done at the planning stage, balancing thegeneration and load will be much easier and more effective inthe operating stage. Therefore, the goal at the planning stage isto design supply-adequate microgrids in a system including sev-eral dispatchable and intermittent distributed energy resources.For this purpose, first, the active distribution system is devel-oped by introducing different types of DG units such as windturbines, photovoltaic (PV) modules and biomass generators.Considering the probabilistic nature of wind turbines and PVmodules and the hourly load profile, the three types of genera-tors are optimally sized and located in the system to minimizethe total annual energy losses. The physical and environmentalissues at this stage can also be considered in selection of can-didate buses for installing DGs. Next, by taking advantage ofDESRs and DRSs, the system is partitioned into microgrids byoptimally sizing and allocating these devices and at the sametime, minimizing the energy imbalance in the microgrids. TheDESRs can store the otherwise spilled energy generated by re-newable-based resources or even store during off-peak hourswhile the energy prices are usually lower. Storing the energyand releasing it during on-peak period will increase the self-ad-equacy of the microgrids during discharge periods. On the other

Fig. 1. Optional design and self-healing strategy concepts.

hand, DRSs which could be an independent reactive source orDG connected units, inject reactive power to the distributionsystem and improve the load-generation imbalance in micro-grids [15]. Considering the benefits of both DESRs and DRSs,if these units are installed in the distribution system simultane-ously, the constructed microgrids will have less load-generationimbalance. This design in the planning stage facilitates robustinfrastructure for smart distribution systems operation and con-trol, especially self-healing control, by using virtual microgridsas building blocks in future distribution systems [15]. It is alsoin line with the IEEE Std. 1547.4-2011, which presented a mi-crogrid structure as the building blocks of active distributionsystems [16]. The main design concepts at the planning stageare shown in Fig. 2.

B. Operating Stage

In response to disturbances, an appropriate self-healing actioncan stop the propagation of disturbances and avoid cascadingevents. Self-healing usually includes two phases [17]. At thefirst phase, as an emergency reaction, a faulty condition is de-tected and its effects on other parts of the system are minimized.This is usually done through isolation of the faulted part of thesystem. The second phase, which is restoration of the system,includes some actions to improve the overall system conditionssuch as system reconfiguration, load shedding or controllingdispatchable generators’ output powers. Each of the actions re-quires detailed studies and makes this stage more complicatedrequiring more complex arrangements.


AREFIFAR et al.: COMPREHENSIVE OPERATIONAL PLANNING FRAMEWORK FOR SELF-HEALING CONTROL ACTIONS 3

Fig. 2. Main design concepts and the virtual microgrids.

In order to have an optimum operational plan, the two men-tioned phases, emergency reaction and restoration of the system,will be done simultaneously. Since the problem is to plan anoperating scheme for steady state conditions, the dynamics andtransients of the system which do not last for a long period arenot considered here. The emergency reaction is usually per-formed momentarily after the fault. Therefore, to have bothphases’ actions at the same time, the system conditions in termsof loads and generation should be predicted and different faultsin different zones should be examined beforehand. By doing so,the system operator knows about the system conditions, for in-stance one hour ahead, and selects the optimum restorative ac-tion in case of faults in any of the microgrids. The optimumrestorative action can be done by performing each or all of thefollowing: 1) system reconfiguration, 2) load shedding based ontheir requested reliability, and 3) changing the output power ofDG units.The question here is which action should be taken and how

it should be performed to get the maximum benefits. To an-swer this question, one should know the benefits that can beachieved by performing self-healing actions in a distributionsystem. Self-adequacy of microgrids, maximum total suppliedloads in case of emergencies and minimum energy losses aresome important features of the distribution systems that are ben-eficial for the distribution companies and electricity customersand can be achieved by properly performing self-healing con-trol actions. Planning self-adequate microgrids and maximizingthe total supplied loads in case of emergencies will improve thereliability and supply security of the distribution system. Thesystem losses might not be as important as the other factors,during such situations; however, it still can be considered for op-timizing the operating plans, especially if the restoration time islong and system configuration is changed or loss minimizationis not performed properly in the planning stage. Other importantcharacteristics of a distribution system could also be used in asimilar approach to optimize the efficiency of self-healing con-trol actions. Depending on selection of one of the mentioned is-sues as the main objective, the optimum self-healing control ac-tions will be different. For instance, to reduce the system energylosses, system reconfiguration and DG power adjustment can bedone; to maximize the self-sufficiency of microgrids accordingto the requested load reliability and to maintain voltage limit re-quirements, load shedding can be performed; and to maximize

the self-adequacy of microgrids and improve the voltage pro-file, the DGs’ output powers can be adjusted. Selecting systemenergy losses, loads’ reliability or self-sufficiency as an indexto maximize the benefits, should be carried out based on eco-nomic and system related studies, which is out of the scope ofthis paper. This will result in the choice of the coefficients ,and , shown in Fig. 1 and presented in Section IV. In this

paper, these coefficients are selected arbitrarily to demonstratethe proposed algorithm.After selection of the coefficients and solving the opti-

mization problem by using the predicted probability densityfunctions of load and generation data, the optimum self-healingstrategy for all possible fault scenarios, including simultaneousdifferent types of faults, should be developed, by using thepredicted load and generation data, for the next hour, as a table.This process should be done continuously and the table shouldbe updated accordingly. In case of any kind of faults in thesystem, the control center of the smart grid can use these tablesand perform the self-healing control actions instantaneously.

III. MODELING OF SYSTEM COMPONENTS

In order to have a more feasible and robust design in the pres-ence of the uncertain nature of DG units and loads, the charac-teristics of loads and DG units should be considered in all thestages. Therefore, the generation units and loads are modeledprobabilistically with several probability states for both plan-ning and operating stages.With such design, the system analysiswill be more feasible and the proposed algorithms will be morerobust against generation/load modeling errors which usuallyexist in deterministic modeling of distribution system compo-nents. For this purpose, DG units are assumed to be from typicalDG types, PV modules, wind turbines and biomass generators.The nature of renewable resources are probabilistic, therefore,for the planning stage, the solar irradiance and wind speed foreach hour of the day are modeled by Beta and Weibull prob-ability density functions (PDF), using historical data [15]. Al-though the PDFs may not accurately represent the uncertaintiesin the generation level of DG units, they are still among the bestchoices to characterize them and can provide significantly moreaccurate results comparing to deterministic approaches. To getthe PDFs, four days, one for each season, can be used to repre-sent the entire year. The day representing each season is furtherdivided into 24-hour time segments, each having a probabilitydensity function for solar irradiance and wind speed. The load isalso modeled as hourly shape using IEEE-RTS [18]. In order tointegrate the output power of PV modules and wind turbines asmulti-state variables in the formulations, their continuous PDFsare divided into different states. The selected number of statesaffects the accuracy and complexity of the formulation. In thisresearch, the output power of the wind turbines and the PVmod-ules for each hour of the day is divided into twelve segments.Assuming that solar irradiance and wind speed states are inde-pendent, the probability of any combination of the load and gen-eration is obtained by convolving the two probabilities. There-fore for each hour there are 144 (12 12) number of states withdifferent probabilities and for each day there are 3456 (24144) states. In order to get the power and energy losses for thesystem, deterministic power flow is run for each state and the



results such as power losses or lines’ real and reactive powersare accumulated considering the probability of each state.In the operating stage as well, the output power of DGs and

loads, for the next hour, are modeled using probability densityfunctions. For this purpose, similar to the planning stage, thegeneration level of wind turbines and PV modules are mod-eled using Beta and Weibull PDFs, respectively, and the loadsare modeled using Gaussian PDF. For predicting the probabilitydensity functions of solar irradiance and wind speed for a spe-cific hour of the day, weather stations data or prediction methodscan be used. As an example, [19] presents a conditional proba-bilistic method within a time series model to predicted the PDFof wind speed for a short term period and [20] presents a methodto predict the PDF parameters of a PV module for each hour ofa typical day.By using probabilistic models, the self-healing control ac-

tions will be obtained in a probabilistic manner to account forestimation uncertainty and this will enhance the robustness ofthe proposed algorithm.

IV. PROBLEM FORMULATION

This section explains formulation of the problems at the twostages, planning and operation, as well as optimization con-straints and control variables.

A. Planning Stage Optimization (Optional)

The planning stage includes two different optimization prob-lems. At the first phase, the goal is to minimize annual en-ergy losses by optimally allocating different types of distributedgenerators. The first goal is applicable in distribution systemswithout DG units and faced with a large number of DG con-nection applications. In this scenario, it is necessary to find thecandidate buses for DG locations considering physical and envi-ronmental issues, and then find the optimum locations and sizesof DG units from the candidates under typical DG/load uncer-tainties. The objective function for the first part is defined as theminimization of (1):

(1)

where is number of states for a year. is the systemlosses for state “ ”, is the probability of the related state andis the time segment of the related state which is one hour in

this research. Minimizing the total energy losses of the systemis achieved by optimally allocating the DGs. Once this phase isdone, the system is ready to be partitioned into microgrids.The goal of the second phase is to divide the distribution

system with different types of DGs into several microgrids. Thesecond phase can be applied directly to distribution systemswith an existing mix of renewable and dispatchable DGs. Theobjective here is to minimize the real and reactive power im-balance between generation and loads within the microgrids byintegrating DESRs and DRSs. In order to minimize the powerimbalance in the microgrids and consider the probabilistic na-ture of the renewable resources, two probabilistic indices aredefined to be minimized and they are presented in the followingobjective function:

(2)

(3)

where and are the defined indices to representthe probabilistic real and reactive power flow of the virtual cutset lines connecting the microgrids together, and are calculatedas shown in (3). The is the number of constructed mi-crogrids and the denominator is set to normalize the cost func-tion. The coefficients and can be set based on the impor-tance of real or reactive power imbalance in the zones. For thecase studies presented in this paper they are set as 0.5. In (3),and are the nodes connecting two microgrids together, isthe number of possible states for each year [365 3456(states/day)], and is the probability of the state in which the realand reactive powers are calculated. The power balance objectivefunction can be calculated by performing probabilistic powerflow in the system. The control variables for this part are dividedinto two sets, 1) the locations and sizes of the DESRs and DRSsin the distribution system and 2) the cut set lines that connect themicrogrids together. The optimum design at the planning stagewill minimize the required efforts at the operating stage for bal-ancing the load-generation in the microgrids.

B. Operating Stage Optimization

This stage includes planning of the optimum self-healing con-trol action(s) for the next hour of the day considering three dif-ferent objective functions. The first objective function is to max-imize self-adequacy in unfaulted microgrids and minimize theload-generation imbalance in them as calculated in (4):

(4)

where and are the defined indices to representthe probabilistic real and reactive power of the sectionalizingswitches connecting the microgrids together and are calculatedusing probabilistic power flow. Note that (2) and (4) are sim-ilar; however, (2) is in planning stage and calculated for a oneyear period, but (4) is in operating stage and calculated for therestoration time only. When the system configuration changesand restoration time is long, the self-adequacy of microgrids isimportant in terms of their operation and control and will bebeneficial for the utilities and customers. Note that due to thepower flow and voltage limit constraints in all unfaulted micro-grids, minimizing the transferred power between them will notaffect the interconnection of them during the disturbances.The second objective is to minimize total distribution system

energy losses. Asmentioned in Section II, although losses mightnot be important during the disturbance, it can be considered foroptimizing the operating plans with less priority, especially ifthe restoration time is long and system configuration is changedduring the fault or loss minimization is not performed properlyin the planning stage. The losses for the disturbance period, iscalculated as shown in (5):

(5)



Finally, the third objective is to maximize the total suppliedloads orminimize the total disconnected loads in the distributionsystem during a fault according to their importance and theirrequested reliability:

(6)

where is the reliability index defined for the load .is between “0” and “1”, with “1” representing the most and“0” representing the less important loads. By increasing thisindex one can avoid load shedding as a restorative action for theself-healing process. In this research, the requested reliability ofloads is assumed to be proportional to the size of each load. Thefinal objective function could be each or the weighted summa-tion of the three defined objective functions as shown in (7):

(7)

and the coefficients are selected depending on the system cir-cumstances, to maximize the benefits and minimize the costs.

C. Optimization Constraints

There are several practical constraints for solving the prob-lems defined in this paper. These constraints can be summarizedas follows:• Penetration level of different kinds of DGs, as W% offeeder capacity, wind based power; S% of feeder capacity,solar power; and B% of feeder capacity biomass genera-tion at the planning stage:

(8)• Power flow equations:

(9)

(10)

The power of DESR units is positive in the discharging periodand negative in the charging period.• Voltage limits at all the system buses:

(11)

• Reverse power flow limitation which guarantees that thereverse power flow will not exceed 0.6 of the substationrating. This is a typical constraint in substations, wheresystem protection and transformer tap-changer operationcan limit the allowed percentage of reverse power flow:

(12)

• The total energy capacity of DESRs and their power rat-ings, as well as total reactive power of DRSs should beconsidered as follows:

(13)

(14)

D. Optimization Control Variables

At the planning stage, the control variables are locations andsizes of different DGs in the system, the locations and sizes ofthe DESRs and DRSs, as well as the virtual cut set lines thatconnect the microgrids together. At the operating stage there arethree control variables: 1) the status of reconfiguration switches,2) the loads to be shed, and 3) the output power of controllableDGs in microgrids. These parameters will be optimized at eachstage using optimization techniques explained in the next sec-tion.

V. SOLUTION ALGORITHMS

The problems defined in the planning stage include 1) op-timal DG allocation for loss minimization, considering proba-bilistic nature of DGs and loads, and 2) optimum supply-ade-quate microgrid design. At the operating stage, there are threedifferent optimizations to have: 1) optimum supply-adequatemicrogrids, 2) minimum energy losses, and 3) minimum dis-connected loads. The defined optimization problems are com-prehensive constrained problems with a combinatorial naturedemanding efficient solution algorithms. Three different typesof algorithms are used in this paper including Tabu Search (TS,as the main optimization method), graph theory-related algo-rithms, and forward-backward-based probabilistic power flowmethod, which are explained in the following sub-sections.

A. Tabu Search (TS)

TS is a heuristic search algorithm that uses different memorystructures to guide the search to a good solution both economi-cally and effectively [21], [22]. It is an iterative-based algorithmin which the optimization process starts with a feasible solutionand continues searching in the neighborhood until certain cri-terion, which is usually the maximum number of iterations, isreached. The first step in TS is to select the starting point. Thestarting point can be selected as control variables with arbitraryvalues. The control variables for the planning stage can be rep-resented as the following vectors:

(15)

(16)

(17)

(18)

Each component of , and represents therated capacity of DGs, DESRs or DRSs in a specific bus. Thecomponents of the vector represent one physical line in thesystem, where number “1” and “0” for each component repre-sent an inside or between microgrids line, respectively.For the operating stage, the control variables can be repre-

sented as the following vectors:



(19)

(20)

(21)

Each component of vector represents one reconfigurationswitch with “1” and “0” for closed or open switch, respectively.

represents the amounts of loads to be shed on eachbus, and represents the output power of DG units inunfaulted microgrids.The next step is to make sets of neighbors for all the starting

points. Each neighbor is selected by changing some components(e.g., 5) of each vector and checking the feasibility of the resul-tant design in terms of the constraints. Then the best neighbor isset as the new starting point and the process continues until themaximum number of iterations, is reached. To avoid stopping ina local optima, and to prevent cycling around it, some Tabu re-strictions are imposed by using a list called Tabu List (TL). TheTL keeps the best solutions that have been visited in previousmoves or the moves that have resulted in the optimum point inprevious regions and avoids revisiting them. In this paper, theTL was constructed from the best recently visited solutions byusing a quantity which is unique for each vector, as shown in(22):

(22)

As an aspiration criterion to make the search process moreintelligent, when the newly met solution point has better prop-erties than the optimum point reached so far, it will come out ofTL. Further, to avoid random searching, two memory structuresare used in TS, Short-Term Memory (STM) and Long-TermMemory (LTM). The STM, by memorizing the common fea-tures of sub-optimal solutions, guides the search in each regionto high quality solutions rather than random undirected move-ments. The LTM, by keeping track of common features of allinitial starting points, diversifies the search by jumping to a newregion and allow the algorithm to go through all the possible so-lutions to find the global optima. In this paper, the two types ofmemories have been implemented by using two sets of vectorswhich are updated every iteration.

B. Graph-Theory-Related Algorithms

The graph-theory-related algorithms are used for two opti-mization problems which are the optimal microgrid design andthe optimal system reconfiguration. For the optimal microgriddesign, for each set of neighbors in TS, these algorithms are usedto check whether all the system buses are considered in differentmicrogrids as a tree. For the optimum system reconfigurationproblem, again for each set of neighbors in TS, the algorithmsare used to check whether all the unfaulted microgrids are con-nected together as a tree for the selected configuration and thereis no loop in the system.For prevention of loops in the design, since the graph is a con-

nected one, we can check the number of buses/edges (NE) andnumber of branches and switches/vertices (NB) in the systemconfiguration. For a connected graph to be a tree we should have

(23)

These conditions can be checked by using the shortest pathalgorithm [23]. The shortest path algorithm adopted in this re-search finds a path between two vertices (buses) in the systemsuch that the sum of weights of its constituent edges is mini-mized. The branch impedances are used as the weight of edges.Since the main focus is to check the connectivity of the busesand microgrids, we can assume undirected graph, which is gen-erated from distribution system topology. If there is a path fromany bus to all system buses and (23) is satisfied, we can con-clude that the system buses are connected as a tree. Several algo-rithms are proposed to solve shortest path problem. In this paper,the Dijkstra’s algorithm [24], which solves the single-sourceshortest path problems, has been used.

C. Forward-Backward-Based Probabilistic Power Flow

Power flow algorithms are needed at both planning and oper-ation stages and at each iteration of TS for this research. The for-ward-backward power flow algorithm is a well-known methodused for power flow calculations in radial distribution systems.It is designed to overcome numerical issues associated withother load flow solution algorithms such as Newton-Raphsonwhen applied to sparse or meshed distribution systems.The algorithm starts with assuming a flat profile for voltages

of all the buses and follows an iterative-based procedure of threesteps: 1) calculation of bus currents, 2) backward sweep, to cal-culate the branch currents, and 3) forward sweep, to calculatethe bus voltages. These three steps are repeated until conver-gence is achieved [25]. During the optimization process, theforward-backward power flow is run for each load-generationstate and the results, such as energy losses or power indices, areadded considering the probability of the states.In the next sections the planning and operational planning

algorithms are applied to the IEEE 123-bus distribution systemand the results are presented.

VI. CLUSTERING THE ACTIVEDISTRIBUTION SYSTEM (OPTIONAL)

The IEEE 123-bus distribution system is selected as the testsystem for implementation of the algorithm and sensitivitystudies. It is assumed that the whole system is energizedthrough one main feeder. In the cases that back up feedersexist, the algorithms can be applied similarly. In this section,the DGs are optimally allocated in the system to have minimumannual energy losses, and maximum self-adequate microgridsare designed by optimally allocating DESRs and DRSs in thesystem.

A. Optimal Allocation of DGs in the Distribution System

For the case studies in this research, the biomass generatorsare set as controllable DG units. It is assumed that total ratedpower of the wind turbines, PV modules and biomass genera-tors are 400 kW , 200 kW , and600 kW , respectively. The optimum locationsand ratings of different types of DGs are presented in Table I.After installing DG units, the annual energy losses of the systemreduced from 1238.6 MWh to 684.77 MWh. The optimally de-signed system at this stage, with optimum location and ratedcapacities of DGs, is used in the next section for construction ofoptimum microgrids.



TABLE IOPTIMUM DG LOCATIONS AND CAPACITIES

TABLE IIOPTIMUM CONSTRUCTED MICROGRIDS

Fig. 3. The 123-bus distribution system clustered into zones.

B. Optimal Microgrid Design

In order to design optimum microgrids, both DESRs andDRSs are added to the system simultaneously and the totalload-generation imbalance in the zones is minimized. It isassumed that the total capacities of energy storage devices andreactive sources are 450 kW and 350 kVAr, respectively. Thelocations and capacities of distributed energy storage devicesand distributed reactive sources, as well as the optimum lo-cations for sectionalizing switches are presented in Table II.Fig. 3 shows the system partitioning results. In this figure,

represents the th zone or microgrid and representsthe sectionalizing switch between zones and . Throughthis design, the system is divided into 15 microgrids based onmaximum self-adequacy and minimum power imbalance in themicrogrids. Since each zone has minimum power imbalance,the self-healing control actions can be done with minimumrequired actions and the disconnected microgrids can operatein autonomous mode with less amount of load shedding.

VII. OPTIMUM SELF-HEALING STRATEGY PLANNER

In this section, firstly, all three factors, self-adequacy in theunfaulted microgrids; total distribution system’s energy losses;and the total supplied loads according to their requested relia-bility, are considered individually and then the optimum self-healing strategy is planned by combining them. It is assumed

TABLE IIIOPTIMUM SELF-HEALING STRATEGY FOR CASE A

that the restoration time or duration of fault clearance is onehour and the optimum strategy is planned for the next hour ofthe day. In the case studies presented here, two different faultlocations are considered in zones and . Self-healing con-trol actions can also be planned for several simultaneous faultsin different zones. In such cases, the reconfiguration switcheswill detach the faulted zones and the rest of the system willbe treated similarly in the proposed algorithm to determine theoptimum self-healing strategy. The reconfiguration is achievedconsidering all switches in the system, load shedding and con-trolling the DG units’ powers are done in the zones that theirsupply bus has been changed through system reconfigurations.The load shedding and controlling the DGs’ outputs can also bedone for all the system buses and the results will be different.For this system, it is assumed that the biomass DGs’ outputs cangenerate up to 110% of their rated capacity (might be differentfor different DGs) for a short period of time until the system re-turns to its normal operating conditions.

A. Self-Adequacy-Based Strategy

Self-adequacy of unfaulted microgrids for operation in au-tonomous mode is considered in this section for planning theoptimum self-healing strategy. The results related to this caseare shown in Table III. This table shows that for faults in ,some switches should be opened and some should be closed tokeep the system radial. Also in the affected zones that are ,, and , the loads on buses 29, 33, 38, and 40 should be

shed and the output of the biomass DGs on buses 33, 48, and 49should be changed to 90%, 110%, and 110%, respectively.

B. Energy Loss Minimization-Based Strategy

Minimizing distribution system losses has always been anissue for power system engineers. Although the losses in emer-gency conditions might not be as important as losses duringnormal operating conditions, it can still be considered for op-timizations. In the cases that the restoration time is long, thesystem configuration is changed due to the fault and the lossminimization is not performed properly in the planning stage,the loss minimization in the operating stage will greatly affectthe objective function. The self-healing actions could be donesuch that the total distribution system losses is minimized andsubsequently reduce the total cost of electricity for the utilitiesand customers. In this section, the self-healing control actions isplanned in order to have minimum power losses in the system.The results related to this case are shown in Table IV. It is seen



TABLE IVOPTIMUM SELF-HEALING STRATEGY FOR CASE B

TABLE VOPTIMUM SELF-HEALING STRATEGY FOR CASE C

that, similar to case A, all three actions including system re-configurations, load shedding and controlling the DGs’ outputpowers is required for performing optimal self-healing actions.

C. Load Reliability-Based Strategy

In a distribution system there are different loads with dif-ferent requested reliability for electric energy. In this section,the self-healing control actions are planned considering only thereliability of the loads in the distribution system. In this case, tohave the minimum objective function, the amount of load shed-ding should be zero; however, in some cases due to the voltageconstraints in the microgrids, we have to curtail some of theloads. This has to be done in a way that the loads that have re-quested higher reliability, such as hospitals, remain connectedto the network. The results for this case are shown in Table V.It is seen that for faults in there is no need for load sheddingbut for faults in we have to perform load shedding on somebuses to meet the voltage level requirements.

D. Combination of Objectives

In some applications, the service providers may need to con-sider some or all of the above mentioned factors for designingself-healing strategies. In this section, as an example, the objec-tive functions are combined together to make a weighted sum-mation single objective function. In order to make the resultsmore feasible, the objective functions, and are nor-malized using the optimum values of previous cases (A and B),the coefficients and are set to one and is set to zero. Theresults related to this case are shown in Table VI. The optimumself-healing strategy for case D and fault in is shown inFig. 4. It is seen that all three self-healing actions should be per-formed to optimize the objective function. The coefficients inthe objective function have significant effects on the optimally

TABLE VIOPTIMUM SELF-HEALING STRATEGY FOR CASE D

Fig. 4. Optimum planned self-healing actions for faults in Zone 10.

planned self-healing control actions. They can be determinedfor the objective function to represent the costs when detailedcost information is available for a typical distribution system.When the coefficients are determined, the tables consisting allfault scenarios and associated optimum self-healing actions canbe developed using the predicted load and generation data. Incase of any kind of faults in the system, the control center ofthe smart grid can use these tables and perform the optimumself-healing control actions immediately.

VIII. CONCLUSIONS

This paper introduces a comprehensive design and operationstrategy for performing optimum self-healing control actions ina smart distribution grid. For this purpose, as an optional plan-ning stage, the active distribution system is divided into sev-eral microgrids considering the probabilistic nature of DG unitsand loads in the system. Afterwards, at the operating stage, anovel formulation for performing self-healing control actions ina probabilistic manner is presented. The idea is to combine thetwo phases, emergency reaction and restoration of the system,and perform them simultaneously. The control actions includesystem reconfiguration, load shedding and controlling the DGs’outputs. The optimum control strategy is planned based on se-lection of any of the three important factors, including self-ad-equacy in the unfaulted microgrids, total distribution system’senergy losses and the total supplied loads, or all together as aweighted summation objective function.Sensitivity studies are conducted on the adopted IEEE

123-bus test system to gauge the effects of each factor onthe final optimum self-healing strategy. It is shown that the



weighting coefficients have significant impact on the plannedself-healing strategy. However, changes in the load and/orgeneration levels have minor effect on the optimum controlactions due to the probabilistic nature of the proposed problemformulation. Through consideration of different aspects of dis-tribution system’s operation during disturbances, the proposedstrategy for self-healing control actions can help in achieving amore intelligent and reliable smart grid.

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Seyed Ali Arefifar (S’06–M’10) was born inIsfahan, Iran. He received the B.Sc. and M.Sc.degrees with honors in electrical engineering, powersystems, from Isfahan University of Technology,Iran, in 2001 and 2004, respectively, and the Ph.D.degree in electrical engineering from the Universityof Alberta, Edmonton, AB, Canada, in 2010.He is currently an NSERC Visiting Fellow at Can-

metENERGY, Natural Resources Canada (NRCan).His research interests include optimizations in plan-ning and operation of smart grids, load modeling, and

power quality.

Yasser Abdel-Rady I. Mohamed (M’06–SM’011)was born in Cairo, Egypt, on November 25, 1977. Hereceived the B.Sc. (with honors) and M.Sc. degreesin electrical engineering from Ain Shams University,Cairo, in 2000 and 2004, respectively, and the Ph.D.degree in electrical engineering from the Universityof Waterloo, Waterloo, ON, Canada, in 2008.He is currently with the Department of Electrical

and Computer Engineering, University of Alberta,Edmonston, AB, Canada, as an Associate Professor.His research interests include dynamics and controls

of power converters; distributed and renewable generation; modeling, analysisand control of smart grids; and electric machines and motor drives.Dr. Mohamed is an Associate Editor of the IEEE TRANSACTIONS

ON INDUSTRIAL ELECTRONICS. He is also a Guest Editor of the IEEETRANSACTIONS ON INDUSTRIAL ELECTRONICS Special Section on “DistributedGeneration and Micro-grids”. His biography is listed in Marque’s Who’sWho in the World. He is a registered Professional Engineer in the Province ofAlberta.

Tarek H. M. EL-Fouly (S’97–M’07) received theB.Sc. and M.Sc. degrees in electrical engineeringfrom Ain Shams University, Cairo, Egypt, in 1996and 2002, respectively, and the Ph.D. degree in elec-trical engineering from the University of Waterloo,Waterloo, ON, Canada, in 2008.He joined CanmetENERGY, Natural Resources

Canada, in 2008, as a Transmission and DistributionResearch Engineer, where he is conducting and man-aging research activities related to active distributionnetworks, microgrids, and remote communities. In

2010, he was appointed as an Adjunct Assistant Professor at the Electrical andComputer Engineering Department at the University of Waterloo. His researchinterests include protection and coordination studies, integration of renewableenergy resources, smart microgrid, smart remote community applications,demand side management, and forecasting.

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