Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Abstract This paper presents a new and efficient method to address the electrical power distribution system planning problem. The proposed model can cope with important features implicit in planning studies such as consideration of multiple and conflicting objectives, time-phased representation, piecewise linearization for representation of network losses and so forth. A variant of the basic Benders' decomposition algorithm is applied to the problem, since the formulation can be embedded under the framework of mixed integer linear programming. Special procedures are applied to solve the linear programming sub-problems and pure integer programming (master) problem involved in the decomposition solution procedure. The method determines non-dominated solutions and allows the decision maker (or planning engineer) to express his preferences and determine the best compromise solution. A case study illustrates the proposed methodology, when numerical results are presented. <s> BIB001 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a multiobjective optimization methodology, using an evolutionary algorithm, for finding out the best distribution network reliability while simultaneously minimizing the system expansion costs. A nonlinear mixed integer optimization model, achieving the optimal sizing and location of future feeders (reserve feeders and operation feeders) and substations, has been used. The proposed methodology has been tested intensively for distribution systems with dimensions that are significantly larger than the ones frequently found in the papers about this issue. Furthermore, this methodology is general since it is suitable for the multiobjective optimization of n objectives simultaneously. The algorithm can determine the set of optimal nondominated solutions, allowing the planner to obtain the optimal locations and sizes of the reserve feeders that achieve the best system reliability with the lowest expansion costs. The model and the algorithm have been applied intensively to real-life power systems showing its potential of applicability to large distribution networks in practice. <s> BIB002 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a new possibilistic (fuzzy) model for the multiobjective optimal planning of power distribution networks that finds out the nondominated multiobjective solutions corresponding to the simultaneous optimization of the fuzzy economic cost, level of fuzzy reliability, and exposure (optimization of robustness) of such networks, using an original and powerful meta-heuristic algorithm based on Tabu Search. This model determines the optimal location and size of the future feeders and substations in distribution networks with dimensions significantly larger than the ones usually presented in papers on the matter. The model also allows to determine the optimal reserve feeders (location and size) that provide the best distribution network reliability at the lowest cost for a given level of robustness (exposure). The model and the algorithm have been intensively tested in real distribution networks, which proves their practical application to large power distribution systems. <s> BIB003 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously accomplished to achieve the optimality of the power system development and operation. In many cases, these objectives contradict each other and cannot be handled by conventional single optimization techniques. In this paper, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed. The methodology adopted permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, and cost of energy required by the served customers. The implemented technique is based on a genetic algorithm and an /spl epsiv/-constrained method that allows obtaining a set of noninferior solutions. Application examples are presented to demonstrate the effectiveness of the proposed procedure. <s> BIB004 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> The growing demand for improved quality of service increases the importance of network automation, namely the investment in remote-controlled switch (RCS) devices. These allow improving the fault isolation and reconfiguration time and therefore increasing the system quality of service. The investment in switch devices comes at a cost and thus must be optimized. The problem of determining the optimal number of devices and their optimal location is a difficult problem: the solution space is combinatorial and the objective function is nonanalytical. We propose to address this problem in a two-stage decomposition approach. Results are presented to i) divide the solution space into independent subspaces, and then ii) solve the optimization problems in each subspace. The solution approach is illustrated for a real distribution network problem. <s> BIB005 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a multiobjective approach for the design of electrical distribution networks. The objectives are defined as a monetary cost index (including installation cost and energy losses cost) and a system failure index. The true Pareto-optimal solutions are found with a multiobjective genetic algorithm that employs an efficient variable encoding scheme and some problem-specific mutation and crossover operators. Results based on 21- and 100-bus systems are presented. The information gained from the Pareto-optimal solution set is shown to be useful for the decision-making stage of distribution network evolution planning. <s> BIB006 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a new multiobjective Tabu search (NMTS) algorithm to solve a multiobjective fuzzy model for optimal planning of distribution systems. This algorithm obtains multiobjective nondominated solutions to three objective functions: fuzzy economic cost, level of fuzzy reliability, and exposure (maximization of robustness), also including optimal size and location of reserve feeders to be built for maximizing the level of reliability at the lowest economic cost (for a given level of robustness). The main characteristics of the NMTS algorithm are: search of planning solutions using several objective functions simultaneously; partition of the space of solutions to diversify the search; intensification of the search by ranking lists of the best network nodes of the distribution system; and an elaborated Tabu list that stores visited network nodes, avoiding unwanted movements. The NMTS algorithm has been intensively tested in real distribution systems, proving its practical application in large power distribution systems. <s> BIB007 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents, for the first time, an application of two well-know multiobjective optimization techniques, namely, nondominated sorting genetic algorithm (NSGA) and strength Pareto evolutionary algorithm (SPEA), to the multiobjective design of power distribution systems. These algorithms have been applied to a multiobjective optimization problem with some technical constraints, minimizing the total costs while maximizing the reliability of the power distribution system. The NSGA uses a fitness sharing scheme to achieve diversity among the obtained solutions. In SPEA, it is necessary to apply a reduction procedure because of the number of solutions. For this purpose, a fuzzy c-means (FCM) clustering algorithm has been applied, with this being the first time that an FCM algorithm in the SPEA has been used. The obtained results from both techniques have been compared, concluding that both offer similar efficiency in order to solve the stated multiobjective optimization problem. The developed methodology is applicable to practical cases of design, allowing for additional requirements that the designer imposes <s> BIB008 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This is the second part of a two-part paper in which application methods to enhance distribution horizon planning for a 20+ year period are described. The optimal horizon planning model encompasses all distribution design requirements for primary and secondary systems. The model formulation is described in the companion paper Part I. The model makes use of a generalized feeder layout viewed as a tree-like circuit serving a circular sector of a round or hexagonal service area of uniform load density. The horizon model optimization objective cost function and constraint equation applications are described in this paper. The horizon planning model is validated with analysis of case studies and sensitivity evaluations for input and output variables. The case studies are assessed under various conditions such as changing consumer density, consumer characteristics, cost of energy, and economic assumptions. The application of the model is demonstrated using Snohomish PUD future expansion assumptions with case studies to examine impacts of horizon assumptions on future conductor sizes, primary voltage classes, voltage drop designs, reliability, and consumer cost of service <s> BIB009 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> A major goal for managers of electric power networks is maximum asset performance. Minimal life cycle cost and maintenance optimization becomes crucial in reaching this goal, while meeting demands from customers and regulators. This necessitates the determination of the optimal balance between preventive and corrective maintenance in order to obtain the lowest total cost. The approach of this paper is to study the problem of balance between preventive and corrective maintenance as a multiobjective optimization problem, with customer interruptions on one hand and the maintenance budget of the network operator on the other. The problem is solved with meta-heuristics developed for the specific problem, in conjunction with an evolutionary particle swarm optimization algorithm. The maintenance optimization is applied in a case study to an urban distribution system in Stockholm, Sweden. Despite a general decreased level of maintenance (lower total maintenance cost), better network performance can be offered to the customers. This is achieved by focusing the preventive maintenance on components with a high potential for improvements. Besides this, this paper displays the value of introducing more maintenance alternatives for every component and choosing the right level of maintenance for the components with respect to network performance. <s> BIB010 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a model for use in the problem of multistage planning of energy distribution systems including distributed generation. The expansion model allows alternatives to be considered for increasing the capacity of existing substations, for installing new ones, for using distributed generation, and for the possible change to feeders in terms of addition and removing feeders sections; combining, subdividing, and load transfer between feeders; and replacement of conductors. The objective function to be minimized is the present value of total installation costs (feeders and substations), of operating and maintaining the network, and of distributed generation. The model takes operational constraints on equipment capacities and voltage limits together into account with logical constraints, aimed at reducing the search space. This paper presents: (1) an extension to the linear disjunctive formulation to represent the inclusion, exclusion, and replacement of branches and (2) a generalization of constraints related to the creation of new paths which can be applied in more complex topologies. The resulting mixed integer linear model allows the optimal solution to be found using mathematical programming methods, such as the branch-and-bound algorithm. The validity and efficiency of the model are demonstrated in Part II of this paper. <s> BIB011 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents the computer simulation of the multistage model for distribution expansion planning with distributed generation, as described in Part I. The simulations deal with the planning of an electrical power distribution network in three stages, in five different situations: 1. each of the three stages planned independently; 2. multistage planning; 3. multistage planning with distributed generation; 4. multistage planning with distributed generation and constraints on investment; and 5. multistage planning with distributed generation considering three load levels. The influence of additional constraints is analyzed in terms of the computational effort required to find the optimum solution to the problem. <s> BIB012 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> In this paper, a technique based on nondominated sorting genetic algorithm-II (NSGA-II) is presented for solving the service restoration problem in an electric power distribution system. Due to the presence of various conflicting objective functions and constraints, the service restoration task is a multiobjective, multiconstraint optimization problem. In contrast to the conventional genetic-algorithm (GA)-based approach, this approach does not require weighting factors for the conversion of such a multiobjective optimization problem into an equivalent single objective function optimization problem. In this work, various practical distribution system operation issues, such as the presence of priority customers, presence of remotely controlled, as well as manually controlled switches, etc. have also been considered. Based on the simulation results on four different distribution systems, the performance of the NSGA-II-based scheme has been found to be significantly better than that of a conventional GA-based method. Besides, to reduce the software runtime of the NSGA-II algorithm, a faster version of NSGA-II has also been implemented. <s> BIB013 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Distributed generation expansion planning (DGEP) has been frequently reported in the literature around the world. In this scope, renewable technologies which are considered as a kind of distributed generations are developing due to their environmental benefits. However, only a few renewable energies have proven to be competitive so far, while their economic viability is also limited to certain regions of the world. In this paper, an encouraging mechanism is proposed in favor of clean technologies in the planning process. This mechanism is defined based on a grant function of emission not polluted which is paid to DG owners to promote renewable and clean technologies. In the planning process, a multi-objective optimization algorithm is applied to produce a Pareto set of optimal planning schemes by taking into account different objective functions (cost and grant functions). The best planning scheme among the Pareto set is chosen based on a composite utility which are obtained through a Monte Carlo simulation of uncertain situations. Distributed generation technologies which are considered in this paper are conventional and renewable technologies, namely photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), gas turbine (GT), and reciprocal engine (RE). To assess the ability of the proposed method, a typical distribution system is used for expansion planning under two environmental scenarios. <s> BIB014 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This work presents a model to evaluate the Distribution System Dynamic De-adaptation respecting its planning for a given period of Tariff Control. The starting point for modeling is brought about by the results from a multi-criteria method based on Fuzzy Dynamic Programming and on Analytic Hierarchy Processes applied in a mid/short-term horizon (stage 1). Then, the decision-making activities using the Hierarchy Analytical Processes will allow defining, for a Control of System De-adaptation (stage 2), a Vector to evaluate the System Dynamic Adaptation. It is directly associated to an eventual series of inbalances that take place during its evolution. <s> BIB015 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> In a deregulated power market, customers would have more choices for their power service and the improvement of service quality has become a challenge to power transmission and distribution companies. Distribution system reliability that was traditionally considered within the planning activities is now incorporated in the operational environment. This paper presents study results of a multiobjective feeder operation optimization problem that considers how to balance network efficiency, switching and reliability costs in a distribution network. The proposed method divides annual feeder load curve into multiperiods of load levels and optimizes the feeder configurations for different load levels in annual operation planning. Customer load profiles and seasonal varying data of feeder section failure rates and customer interruption costs are considered. Numerical simulations demonstrate the time-varying effects on the optimal distribution feeder configuration and operation costs. A binary particle swarm optimization (BPSO) search is adopted to determine the feeder switching schedule. Test results indicate that not considering time-varying effects and using only simplified fixed load and reliability parameters could underestimate the total loss to the utility and its customers. <s> BIB016 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> In this paper, the remuneration of fixed costs of distribution networks with distributed generation is evaluated by means of an efficient planning strategy that includes the concept of fuzzy robustness of a given solution plan. As a key contribution, it is included the possibility of choice among different conductor sizes in order to assess the deep costs by overcapacity introduced by distributed generation. A single-stage multicriteria nonlinear problem is stated using three different criteria: investment cost in lines and transformers or distributed generators, power losses cost and unserved energy cost. Pareto or efficient plans are identified using the epsiv-Constraint/Weighting method and solved by Benders decomposition algorithm. Uncertainty associated to load demand and power injections of distributed resources are integrated using a fuzzy power flow in order to obtain the robustness indexes of each Pareto solution. The annualized fixed charge rate (AFCR) associated to new and existing distribution lines and transformers or distributed generators is assessed through a with-without analysis permitting to compute the annual avoided charge rate (AACR) in order to send incentives or charges to distributed generation promoters by the avoided or added investment costs on the system. The model is programmed in GAMS mathematical modeling language. The effectiveness of the proposal is demonstrated through a real 201-node distribution network. <s> BIB017 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a dynamic multi-objective model for distribution network expansion, considering the distributed generations as non-wire solutions. The proposed model simultaneously optimizes two objectives namely, total costs and technical constraint satisfaction by finding the optimal schemes of sizing, placement and specially the dynamics (i.e., timing) of investments on DG units and/or network reinforcements over the planning period. An efficient heuristic search method is proposed to find non-dominated solutions of the formulated problem and a fuzzy satisfying method is used to choose the final solution. The effectiveness of the proposed model and search method are assessed and demonstrated by various studies on an actual distribution network. <s> BIB018 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Conventional distribution planning method based on peak load brings about large investment, high risk and low utilization efficiency. A distribution network planning method considering distributed generation (DG) for peak cutting is proposed in this paper. The new integrated distribution network planning method with DG implementation aims to minimize the sum of feeder investments, DG investments, energy loss cost and the additional cost of DG for peak cutting. Using the solution techniques combining genetic algorithm (GA) with the heuristic approach, the proposed model determines the optimal planning scheme including the feeder network and the siting and sizing of DG. The strategy for the site and size of DG, which is based on the radial structure characteristics of distribution network, reduces the complexity degree of solving the optimization model and eases the computational burden substantially. Furthermore, the operation schedule of DG at the different load level is also provided. <s> BIB019 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> It is widely accepted that renewable energy sources are the key to a sustainable energy supply infrastructure since they are both inexhaustible and nonpolluting. A number of renewable energy technologies are now commercially available, the most notable being wind power, photovoltaic, solar thermal systems, biomass, and various forms of hydraulic power. In this paper, a methodology has been proposed for optimally allocating different types of renewable distributed generation (DG) units in the distribution system so as to minimize annual energy loss. The methodology is based on generating a probabilistic generation-load model that combines all possible operating conditions of the renewable DG units with their probabilities, hence accommodating this model in a deterministic planning problem. The planning problem is formulated as mixed integer nonlinear programming (MINLP), with an objective function for minimizing the system's annual energy losses. The constraints include the voltage limits, the feeders' capacity, the maximum penetration limit, and the discrete size of the available DG units. This proposed technique has been applied to a typical rural distribution system with different scenarios, including all possible combinations of the renewable DG units. The results show that a significant reduction in annual energy losses is achieved for all the proposed scenarios. <s> BIB020 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> In this paper a multi-period planning problem, with arbitrarily defined planning goals, is formulated in terms of graph theory. The proposed formulation represents a multi-period planning problem as a weighted graph problem and thus decomposes original problem into a number of sequences (spanning paths) of static planning problems without loss of accuracy. This graph problem is solved using dynamic programming technique. The proposed dynamic programming algorithm guaranties that optimal solution of multi-period planning problems will be found efficiently, assuming that optimality of static planning problems is guarantied. Detailed numerical results and comparisons presented in the paper show that proposed approach could improve noticeably the quality of multi-period solutions. <s> BIB021 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> In this paper, a framework is presented to solve the problem of multistage distribution system expansion planning in which installation and/or reinforcement of substations, feeders and distributed generation units are taken into consideration as possible solutions for system capacity expansion. The proposed formulation considers investment, operation, and outage costs of the system. The expansion methodology is based on pseudo-dynamic procedure. A combined genetic algorithm (GA) and optimal power flow (OPF) is developed as an optimization tool to solve the problem. The performance of the proposed approach is assessed and illustrated by numerical studies on a typical distribution system. <s> BIB022 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Distribution system companies intend to supply electricity to its customers in an economical and reliable manner whereas customers in most distribution system are outspread and connect to distribution system with different type of equipments. These equipment usually have various types and resistance together, that produce highest loss and lowest reliability for distribution systems and customers that are not appreciated in networks. Distributed generations (DGs) are one of the best reliable solutions for these problems if they are allocated appropriately in the distribution system. This paper presents multi-objective function to determine the optimal locations to place DGs in distribution system to minimize power loss of the system and enhance reliability improvement and voltage profile. Time varying load is applied in this optimization to reach pragmatic results meanwhile all of the study and their requirement are based on cost/benefit forms. Finally to solve this multi-objective problem a novel approach based on dynamic programming is used. The proposed methodology is successfully applied to a study case and simulation results are reported to verify the proposed approach. <s> BIB023 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Electrical generators of renewable electricity resources are quiet, clean and reliable. Optimal placement of renewable electricity generators (REGs) results in reduction of objective functions like losses, costs of electrical generation and voltage deviation. Because of recent technology developments of photovoltaic units, wind turbine and fuel cell units, only these generators are considered in this paper. This work presents a multiobjective optimization algorithm for the siting and sizing of renewable electricity generators. The objectives consist of minimization of costs, emission and losses of distributed system and optimization of voltage profile. This multiobjective optimization is solved by the Improved honey bee mating optimization (HBMO) algorithm. In the proposed algorithm, an external repository is considered to save non-dominated (Pareto) solutions found during the search process. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository within the limits. This algorithm is executed on a typical 70-bus test system. Results of the case study show the proper siting and sizing of REGs are important to improve the voltage profile, reduce costs, emission and losses of distribution system. The main feature of the algorithm refers to its accuracy and calculation speed. <s> BIB024 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Summary form only given: This paper presents a model for active distribution systems expansion planning based on Genetic Algorithms, where Distributed Generation (DG) integration is considered together with conventional alternatives for expansion, such as, rewiring, network reconfiguration, installation of new protection devices, etc. The novel approach of planning DG integration together with network expansion is a requirement for the modern active distribution network. However, the uncertainties related to DG power generation and load response growth must be taken into account in order to plan a safe system at a minimum cost. Thus, two different methodologies for uncertainties incorporation through the use of multiple scenarios analysis are proposed and compared. The multiple objectives optimization algorithm applied in the model takes into account the costs of reliability, losses, power imported from transmission, and network investments. <s> BIB025 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Abstract A new technique for optimal planning of MV and LV segments of a distribution system is presented in this paper. The main goal is to find optimally distribution transformer and substation locations and ratings, as well as, the route and type of Medium Voltage (MV) and Low Voltage (LV) feeders. The proposed technique is applicable to both uniform and non-uniform load densities areas. In this method, the planning area is divided into regions with relatively uniform load density such as urban, semi-urban, sub-urban. Each of regions is divided into zones, called LV zone. Each LV zone is supplied by an MV/LV transformer. The dimensions of LV zones are found based on the average load of each region. The placement and rating of MV/LV transformers, the type and route of LV conductors in an LV zone all depend on its loads’ location and power. Regarding the placement and rating of MV/LV transformers in planning area and the space of regions, the dimensions of a zone which is supplied by a HV/MV transformer, called MV zone, is determined. Additionally, the location and rating of HV/MV transformers as well as the feeder's routes and types are calculated. Since the dimensions of an LV zone influence the associated length of MV feeder, the MV feeder cost needs to be included in the total cost associated with the LV zone. This requires the MV feeder type to be known to calculate the corresponding cost. However, the MV feeder type is determined as an output from MV zone planning. As a result, an iterative based method is proposed to consider this common element in computations to develop integrated planning of both LV and MV zones. It is observed that the iterative technique quickly converges to the same results as the exhaustive search method. Discrete particle swarm optimization (DPSO) method is employed for solving the planning problem. The results are compared with nonlinear programming, genetic algorithm and exhaustive search methods. It is observed that DPSO is as accurate as the exhaustive search method for integrated planning of MV–LV distribution systems while its computation time is significantly lower. <s> BIB026 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a model for solving the multistage planning problem of a distribution network. The objective function to be minimized is the net present value of the investment cost to add, reinforce or replace feeders and substations, losses cost, and operation and maintenance cost. The model considers three levels of load in each node and two investment alternatives for each resource to be added, reinforced or replaced. The nonlinear objective function is approximated by a piecewise linear function, resulting in a mixed integer linear model that is solved using standard mathematical programming. The model allows us to find multiple solutions in addition to the optimal one, helping the decision maker to analyze and choose from a pool of solutions. In addition to the optimization problem, reliability indexes and associated costs are computed for each solution, based on the regulation model used in Brazil. Numerical results and discussion are presented for an illustrative 27-node test network. <s> BIB027 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a comprehensive study on mono- and multi-objective approaches for electrical distribution network design using particle swarm optimization (PSO). Specifically, two distribution network design problems, i.e., static and expansion planning, are solved using PSO. The network planning involves optimization of both network topology and branch conductor sizes. Both the planning problems are used to illustrate mono- and multi-objective optimization of distribution networks. Firstly, three PSO variants, i.e., PSO with inertia weight (PSO-IW), PSO with constriction factor (PSO-CF), and comprehensive learning PSO, are evaluated on a mono-objective (minimization of total cost of installation and energy loss) static planning problem. A novel encoding/decoding technique is devised to represent the network as a particle in PSO. Also, a heuristics based branch conductor size selection algorithm has been developed and used. Statistical tests performed to compare the performances of the three PSO variants reveal that the PSO-CF exhibits relatively better performance. Subsequently, the PSO-CF is applied for mono-objective expansion planning and multi-objective static and expansion planning problems. In the multi-objective planning with two conflicting objectives (total cost of installation and energy loss, and total non-delivered energy), the Pareto-optimality principle based tradeoff is done using the strength Pareto evolutionary algorithm-2. The efficiency of PSO for distribution system planning problem, in general, is demonstrated through different examples. <s> BIB028 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a methodology for active distribution networks dynamic expansion planning based on Genetic Algorithms, where Distributed Generation integration is considered together with conventional alternatives for expansion, such as, rewiring, network reconfiguration, installation of new protection devices, etc. All aspects related to the expansion planning problem, such as multiple objective analysis, reliability constraints, modeling under uncertainties of demand and power supplied by Distributed Generation units and multistage planning, which are usually dealt with separately, are considered in an integrated model. Uncertainties are represented through the use of multiple scenario analysis. Multiple stages are incorporated by an algorithm based on the pseudo-dynamic programming theory. Results obtained with a test system and with an actual large scale system are presented and demonstrate the flexibility of applying the model for different purposes active network planning. <s> BIB029 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Deregulation in the power system industry and invention of new technologies for producing electrical energy have led to innovations in distribution system planning (DSP). Distributed generation (DG) is one of the most attractive technologies that brings different kinds of advantages to a wide range of entities, engaged in power systems. In this paper, a new model for considering DGs in the DSP problem is presented. In this model, an optimal power flow (OPF) is proposed to minimize capital costs for network upgrading, operation and maintenance costs, and the cost of losses for handling the load growth for the planning horizon. The term “dynamic” is used to refer to the planning over a specific period so that dynamic distribution system planning is, in fact, proposed. Besides, a modified genetic algorithm is used to find the optimal topology solution. The effectiveness of this method is demonstrated through examination on a radial distribution network. <s> BIB030 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Abstract This paper presents a novel approach for Integrated Generation and Primary–Secondary Distribution System Expansion Planning (IGDSEP) in the presence of wholesale and retail markets. The proposed method uses a model to explore the impacts of wholesale and retail market on IGDSEP procedure. This algorithm decomposes the IGDSEP problem into six subproblems to achieve an optimal expansion planning of a system, in which the investment and operational costs are minimized, while the reliability of the system is maximized. The proposed model of IGDSEP is a Mixed Integer Non Linear Programming (MINLP) problem and a Scenario Driven MINLP (SCMINLP) method is proposed for solving the problem. The algorithm was successfully tested for an urban distribution system. <s> BIB031 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Distributed generation (DG) systems are considered an integral part in future distribution system planning. The active and reactive power injections from DG units, typically installed close to the load centers, are seen as a cost-effective solution for distribution system voltage support, energy saving, and reliability improvement. This paper proposes a novel distribution system expansion planning strategy encompassing renewable DG systems with schedulable and intermittent power generation patterns. The reactive capability limits of different renewable DG systems covering wind, solar photovoltaic, and biomass-based generation units are included in the planning model and the system uncertainties such as load demand, wind speed, and solar radiation are also accounted using probabilistic models. The problem of distribution system planning with renewable DG is formulated as constrained mixed integer nonlinear programming, wherein the total cost will be minimized with optimal allocation of various renewable DG systems. A solution algorithm integrating TRIBE particle swarm optimization (TRIBE PSO) and ordinal optimization (OO) is developed to effectively obtain optimal and near-optimal solutions for system planners. TRIBE PSO, OO, and the proposed algorithm are applied to a practical test system and results are compared and presented. <s> BIB032 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Abstract A multi-objective planning approach for electrical distribution systems using particle swarm optimization is presented in this paper. In this planning, the number of feeders and their routes, number and locations of sectionalizing switches, and number and locations of tie-lines of a distribution system are optimized. The multiple objectives to determine optimal values for these planning variables are: (i) minimization of total installation and operational cost and (ii) maximization of network reliability. The planning optimization is performed in two steps. In the first step, the distribution network structure, i.e., number of feeders, their routes, and number and locations of sectionalizing switches are determined. In the second step, the optimum number and locations of tie-lines are determined. Both the objectives are minimized simultaneously to obtain a set of non-dominated solutions in the first step of optimization. The solution strategy used for the first step optimization is the Strength Pareto Evolutionary Algorithm-2 (SPEA2) based multi-objective particle swarm optimization (SPEA2–MOPSO). In the second step, the solutions/networks obtained from the previous step are further optimized by placement of tie-lines. SPEA2-based binary MOPSO (SPEA2–BMOPSO) is used in the second step of optimization. The proposed planning algorithm is tested and evaluated on different practical distribution systems. <s> BIB033 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> In this work, the planning problem of primary distribution networks is formulated as a multi-objective mixed-integer non-linear programming model (MINLP). The objective functions of this model are the expansion and operation investment costs of primary distribution networks as well as the system's reliability costs in the contingency events. The reliability costs are obtained by means of calculating the non-supplied energy because of the repairing and switching operations in the distribution network carried out to isolate and to redistribute loads in the affected sections by permanent faults. In order to solve the expansion problem of the primary network a Multi-objective Reactive Tabu Search algorithm (MO-RTS) is proposed. The concepts of dominance are used to obtain Pareto's optimal frontier in the MO-RTS. The problem of placement of sectionalising switches (automatic or manual) to restore the distribution network and to reduce non-supplied energy costs when permanent faults occur in the network is solved simultaneously with the network expansion planning problem by means of a dedicated genetic algorithm. For testing, the proposed methodology is used as a primary power distribution system, with 180 points of consumption, where 49 already exist and 131 can be added to the system during the planning process. <s> BIB034 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> The planning and development of distribution networks with a substantial penetration of microgrids connected to the medium voltage (MV) network form the main themes of this paper. The impact of microgrids is assessed in terms of their effect on optimal network topology, losses, reliability, reserve connections, network upgrade and expansion savings. The earning base of the distribution system operator also comes under scrutiny. A suburban MV cable network is planned using a network planning algorithm developed by the authors, first with optimal routing for demand-only nodes and then with a 33% penetration of randomly located microgrids. The network is then expanded to meet the requirements of a future planning horizon, in order to compare the expanded and upgraded optimum MV network topology with and without microgrids. Apart from visually depicting the topological differences, the savings such microgrids can give to the long term distribution network investment and running costs are quantified in terms of the investment costs, loss costs and interruption costs. When networks are planned with optimal rather than full backup, the introduction of microgrids is shown to have a considerable saving impact on all cost components except the cost per unit power transfer in the distribution network. <s> BIB035 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> In this paper, a mathematical model for stochastic simulation and optimization of small wind energy systems is presented. This model is able to consider the operation of the charge controller, the coulombic efficiency during charge and discharge processes, the influence of temperature on the battery bank capacity, the wind speed variability, and load uncertainty. The joint effect of charge controller operation, ambient temperature, and coulombic efficiency is analyzed in a system installed in Zaragoza (Spain), concluding that if the analysis without considering these factors is carried out, the reliability level of the physical system could be lower than expected, and an increment of 25% in the battery bank capacity would be required to reach a reliability level of 90% in the analyzed case. Also, the effect of the wind speed variability and load uncertainty in the system reliability is analyzed. <s> BIB036 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Much of the research has been focused on developing the optimization techniques, varying from classical to nontraditional soft computing techniques, to solve the distribution system planning problem. Most of the methods preserve the distinctions and niceties, but dependent on complex search algorithm with a lot of convergence related issues that require more time to reach a firm conclusion at planning stage. This paper proposes a simple direct solution that significantly reduces the inherent difficulties of finding the solution and ensures optimum solution at the same time. Moreover, the concept of principle of optimality is effectively used to make the proposed technique more computationally efficient and useful. The effectiveness of the developed planning technique has been verified with different test cases. <s> BIB037 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> In this study, a multistage expansion and unit commitment (UC) planning for distribution networks using artificial bee colony (ABC) is presented. A dynamic model for expansion considers installation of new distributed generation units, expansion of pre-installed generating units’ capacities, rewiring and addition of new load points. The expansion plane is carried out in three stages. In each stage, an hourly-day-ahead UC schedule is determined over the course of the stage period. The proposed algorithm uses the ABC to obtain a feasible commitment schedule for the generating units installed. An economical dispatch of these units along with load shedding is then carried out in each stage period to satisfy the system's equality and inequality constraints. The proposed algorithm is applied to different test systems to verify its efficiency. <s> BIB038 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Abstract Since the emerging of distributed generation (DG) technologies, their penetration into power systems has provided new options in the design and operation of electric networks. In this paper, DG units are considered as a novel alternative for supplying the load of sub-transmission system. Thus, the mathematical model of considering DG on the expansion planning of sub-transmission system is developed. Fix and variable costs of the plan and the related constraints are formulated in the proposed model. The proposed objective function and its constraint are converted to an optimization problem where the hybrid decimal codification genetic algorithm (DCGA) and linear programming (LP) technique are employed to solve it. Solution of the proposed method gives the optimal capacity of substations; optimal location and capacity of DGs as well as optimal configuration of the sub-transmission lines. To demonstrate the effectiveness of the proposed approach, it is applied on a realistic sub-transmission system of Zanjan Regional Electrical Company, Iran, and the results are evaluated. <s> BIB039 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper proposes a method to evaluate the worth of installing renewable distributed generation (DG) in distribution networks. Moreover, the work optimally allocates these DG units in the distribution network to maximize the worth of the connection to the local distribution company (LDC), as well as the customers connected to the system. The proposed methodology helps the LDC to better assess the benefits of the renewable DG units' proposed connections and to identify the optimal buses on which to connect these DG units. The benefits considered in this paper are deferral of upgrade investments, reduction of the cost of energy losses, and reliability improvement, which is represented by the interruption cost reduction. The proposed methodology takes into consideration the uncertainty and variability associated with the output power of renewable DG as well as the load variability. The planning problem of determining the optimal location and sizes of DG units is defined as multi-objective mixed integer programming. <s> BIB040 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> The main goal of this paper is to present a Multistage Distribution network Expansion Planning (MDEP) problem in the presence of Distributed Generations (DGs) in a multi-objective optimization framework. The proposed model simultaneously optimizes two objectives: minimization of investment and operation costs and maximization of reliability index. The proposed optimization model is solved subject to AC power flow constraints to obtain the optimal configuration of feeders (adding and removing lines) including the optimal size of branch conductor, replacement of conductor for reserve feeders, and generated power of DGs. To include reliability concerns in the proposed MDEP problem, an analytical approach on the basis of graph theory is implemented to evaluate the Energy-Not-Supplied (ENS) index as an extra objective function. Also, in this paper, in order to identify Pareto optimal solutions of the multi-objective MDEP problem, a hybrid Particle Swarm Optimization (PSO) and Shuffled Frog Leaping (SFL) algorithm is implemented. A synthetic distribution test system is considered for the MDEP problem in a 4-year planning horizon. The results of the hybrid PSO and SFL algorithm are compared with those of the classical PSO and SFL methods. <s> BIB041 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Abstract This paper presents a novel dynamic programming approach for multi-objective planning of electrical distribution systems. In this planning, the optimal feeder routes and branch conductor sizes of a distribution system are determined by simultaneous optimization of cost and reliability. The multiple planning objectives are minimization of: (i) installation and operational cost, and (ii) interruption cost. The first objective function consists of the installation cost of new feeder branches and substations, maintenance cost of the existing and new feeder branches, and the cost of energy losses. The second objective function measures the reliability of the distribution network in terms of the associated interruption costs for all the branches, which includes the cost of non-delivered energy, cost of repair, and the customer damage cost due to interruptions. A dynamic programming based planning algorithm for optimization of the feeder routes and branch conductor sizes is proposed. A set of Pareto solutions is obtained using a weighted aggregation of the two objectives with different weight settings. The proposed approach is evaluated on 21-, 54-, and 100-node distribution systems. The simulation test results are analyzed with various case studies and are compared with those of two existing planning approaches based on multi-objective evolutionary algorithm. <s> BIB042 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Multistage distribution network expansion because of load growth is a complex problem in distribution planning. The problem includes minimizing cost of objective function subject to technical constraints. The objective function consists of investment, operation and reliability costs. In this paper, HV/MV substations, main and reserve MV feeders, dispatchable DG sources and storage units are considered as possible solutions for multistage distribution expansion planning. A three-load level is used for variable load and some strategies are proposed for DG and storage units operation. A modified PSO algorithm is applied to solve the complex optimization problem. Numerical results of the case studies show the ability of the modification. Moreover, the proposed strategies improve the distribution network from both economical and reliability points of view compared with the other methods. <s> BIB043 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Investment in distributed generation (DG) is an attractive distribution planning option for adding flexibility to an expansion plan, mainly by deferring network reinforcements. In this first part of a two-part paper, a risk-based optimization approach is proposed to model a multistage distribution expansion planning problem that takes DG into account as a flexible option to temporarily defer large network investments. Five features of the installation of DG related to location, size, type, operation and timing are all optimized. The evolutionary particle swarm optimization (EPSO) method is applied to solve this mixed integer nonlinear problem. A return-per-risk index is proposed to assess expansion investments. This index achieves an efficient synergy between the expected return and the risk of investments by performing Monte Carlo simulations. In turn, the flexibility of network investment deferral is assessed through a real option valuation. Finally, in order to quantify the investment deferral benefit, the expected return from a traditional expansion plan (without DG) is compared to the return obtained from a flexible expansion plan (with DG). In the companion paper, the proposed approach is tested on a typical Latin American distribution network; implementation aspects and analysis of numerical results are presented. <s> BIB044 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Abstract This paper proposes a long-term planning method to maximize the benefits of network reconfiguration and distributed generation (DG) allocation in distribution networks. The proposed method handles long-term yearly load increase and seeks to define the reinforcements (i.e., line upgrades, network reconfiguration, and DG integration) and when and where they are required to meet the load rise with minimal cost and acceptable quality standards. The objectives considered in this paper are: economic (costs of line upgrades, energy losses, switching operations, and DG capital, operation and maintenance costs) and environmental (emissions from grid and DG units). The proposed method takes into consideration the uncertainty related to renewable DG output power and the load variability. The long-term planning problem is defined as multi-objective nonlinear mixed integer programming. The outcomes of the planning problem are the Pareto front, which represents different optimum operating system points. Finally, the local distribution company (LDC) can choose the system operating point based on its preferences. <s> BIB045 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Power intermittency and maintenance cost are the major challenges in harvesting wind energy. This paper proposes a multicriteria optimization model to design and operate a wind-based distributed generation (DG) system. The goal is to determine the equipment sizing, siting, and maintenance schedules so that the system cost is minimized while the turbine reliability is maximized. System cost comprises initial capital, operations, maintenance, downtime losses, and environmental penalty. The study makes an early attempt to incorporate the maintenance policy of generating units into the planning model. The moment method and the central limit theorem are used to characterize the power intermittency and the load uncertainty. A genetic algorithm is developed to search the nondominant solution set for the equipment siting, sizing, and maintenance intervals. The proposed model is demonstrated on the IEEE 37-node distribution network considering independent and correlated wind speed scenarios. <s> BIB046 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but also promote the popularization of environmentally friendly EVs. In this context, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems. In the developed model, the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs). Additionally, the user equilibrium based traffic assignment model (UETAM) is integrated to address the maximal traffic flow capturing problem. Subsequently, a decomposition based multi-objective evolutionary algorithm (MOEA/D) is employed to seek the non-dominated solutions, i.e., the Pareto frontier. Finally, collaborative planning results of two coupled distribution and transportation systems are presented to illustrate the performance of the proposed model and solution method. <s> BIB047 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Abstract Reconstruction in the power system and appearing of new technologies for generation capacity of electrical energy has led to significant innovation in Distribution Network Expansion Planning (DNEP). Distributed Generation (DG) includes the application of small/medium generation units located in power distribution networks and/or near the load centers. Appropriate utilization of DG can affect the various technical and operational indices of the distribution network such as the feeder loading, energy losses and voltage profile. In addition, application of DG in proper size is an essential tool to achieve the DG maximum potential benefits. In this paper, a time-based (dynamic) model for DNEP is proposed to determine the optimal size, location and installation year of DG in distribution system. Also, in this model, the Optimal Power Flow (OPF) is exerted to determine the optimal generation of DGs for every potential solution in order to minimize the investment and operation costs following the load growth in a specified planning period. Besides, the reinforcement requirements of existing distribution feeders are considered, simultaneously. The proposed optimization problem is solved by the combination of evolutionary methods of a new Binary Enhanced Particle Swarm Optimization (BEPSO) and Modified Differential Evolution (MDE) to find the optimal expansion strategy and solve OPF, respectively. The proposed planning approach is applied to two typical primary distribution networks and compared with several other methods. These comparisons illustrate the effectiveness of the proposed DNEP approach. <s> BIB048 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Distribution feeders and substations need to provide additional capacity to serve the growing electrical demand of customers without compromising the reliability of the electrical networks. Also, more control devices, such as DG (Distributed Generation) units are being integrated into distribution feeders. Distribution networks were not planned to host these intermittent generation units before construction of the systems. Therefore, additional distribution facilities are needed to be planned and prepared for the future growth of the electrical demand as well as the increase of network hosting capacity by DG units. This paper presents a multiobjective optimization algorithm for the MDEP (Multi-Stage Distribution Expansion Planning) in the presence of DGs using nonlinear formulations. The objective functions of the MDEP consist of minimization of costs, END (Energy-Not-Distributed), active power losses and voltage stability index based on SCC (Short Circuit Capacity). A MPSO (modified Particle Swarm Optimization) algorithm is developed and used for this multiobjective MDEP optimization. In the proposed MPSO algorithm, a new mutation method is implemented to improve the global searching ability and restrain the premature convergence to local minima. The effectiveness of the proposed method is tested on a typical 33-bus test system and results are presented. <s> BIB049 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> Abstract In this work, the problem of low voltage distribution system planning is solved using diversified peak demand to estimate nodal power injection from the number of users and the diversity curves in demand. The problem is formulated as a model of mixed integer nonlinear programming. To solve the problem, a tabu search algorithm is used. It considers location and design of new and existing elements, phase balance, and system losses. Additionally, it takes into account the relocation of distribution transformers considering existing, new, and in store ones, installation of new primary network to feed distribution transformers, and removing existing elements. The methodology is applied in a Colombian distribution system using three demand scenarios: average demand curves, cumulative maximum demand, and diversity in demand. The results support the validity of the methodology proposed in this article. <s> BIB050 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This study presents an integrated methodology that considers renewable distributed generation (RDG) and demand responses (DR) as options for planning distribution systems in a transition towards low-carbon sustainability. It is assumed that demand responsiveness is enabled by real-time pricing (RTP), and the problem has been formulated as a dynamic two-stage model. It co-optimizes the allocation of renewables [including wind and solar photovoltaic (PV)], non-renewable DG units (gas turbines) and smart metering (SM) simultaneously with network reinforcement for minimizing the total economic and carbon-emission costs over planning horizons. The behavior compliance to RTP is described through a nodal-based DR model, in which the fading effect attended during the load recovery is highlighted. Besides, uncertainties associated with renewable energy generation and price-responsiveness of customers are also taken into account and represented by multiple probabilistic scenarios. The proposed methodology is implemented by employing an efficient hybrid algorithm and applied to a typical distribution test system. The results demonstrate the effectiveness in improving the efficiency of RDG operations and mitigating CO2 footprint of distribution systems, when compared with the conventional planning paradigms. <s> BIB051 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents new analytical expressions to efficiently capture the optimal power factor of each Distributed Generation (DG) unit for reducing energy losses and enhancing voltage stability over a given planning horizon. These expressions are based on the derivation of a multi-objective index (IMO), which is formulated as a combination of active and reactive power loss indices. The decision for the optimal location, size and number of DG units is then obtained through a benefit–cost analysis. Here, the total benefit includes energy sales and additional benefits, namely energy loss reduction, network upgrade deferral and emission reduction. The total cost is a sum of capital, operation and maintenance costs. The methodology was applied to a 69-bus industrial distribution system. The results showed that the additional benefits are imperative. Inclusion of these in the analysis would yield faster DG investment recovery. <s> BIB052 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> B. MAIN OBJECTIVE TERMS <s> This paper presents a probabilistic multiobjective framework for optimal distributed energy resources (DERs) planning in the distribution electricity networks. The proposed model is from the distribution company (DISCO) viewpoint. The projected formulation is based on nonlinear programming (NLP) computation. The proposed design attempts to achieve a trade-off between minimizing the monetary cost and minimizing the emission of pollutants in presence of the electrical load as well as electricity market prices uncertainties. The monetary cost objective function consists of distributed generation (DG) investment and operation cost, payment toward loss compensation as well as payment for purchased power from the network. A hybrid fuzzy C-mean/Monte-Carlo simulation (FCM/MCS) model is used for scenario based modeling of the electricity prices and a combined roulette-wheel/Monte-Carlo simulation (RW/MCS) model is used for generation of the load scenarios. The proposed planning model considers six different types of DERs including wind turbine, photovoltaic, fuel cell, micro turbine, gas turbine and diesel engine. In order to demonstrate the performance of the proposed methodology, it is applied to a primary distribution network and using a fuzzified decision making approach, the best compromised solution among the Pareto optimal solutions is found. <s> BIB053
The most crucial objective function for the network expansion problem is the cost of investment and operation terms BIB009 . The investment cost (or fixed cost) is all the cost related to system upgrade (by installing a new instrument and enhancing the capacity of the existing facilities) BIB011 . The operation cost (or variable cost) is all the cost related to operational and maintenance costs of the system during the whole planning period, e.g. cost of power losses, cost of curtailed loads, and cost of DGs' power generation BIB004 , BIB018 , BIB014 , BIB019 , BIB020 , BIB011 , BIB012 BIB022 BIB023 BIB024 . To simplify planning, we convert fixed and variable costs to the annualized Net Present Value (NPV). Several studies proposed approaches to determine the decision variables of the DEP problem optimally and minimize various cost terms BIB029 , BIB025 , BIB004 , BIB018 , BIB014 , BIB030 BIB031 BIB038 BIB039 BIB047 BIB048 BIB032 . As both types of costs are monetary expenditures, they can be aggregated together by considering the discount rates to compute the present value of all the cost terms BIB006 . With discount rates, different time cost terms can be transformed to the current values and joined together to create a single cost function. Moreover, the cost of fault frequencies, i.e. number of faults ( BIB004 , BIB040 , BIB031 , BIB006 BIB015 BIB026 BIB027 ), and the cost of not-served energy, i.e. duration of the faults BIB031 , BIB026 BIB027 BIB028 BIB016 , BIB017 ) can be aggregated as the reliability cost function to be minimized to enhance the reliability level of the network. As discussed in the literature, the consideration of energy not-supplied (ENS) as a reliability index is essential  BIB041 BIB042 BIB033 , BIB029 , BIB025 , BIB043 , BIB020 , BIB011 , BIB012 BIB022 BIB023 , BIB038 , BIB032 , BIB001 BIB002 BIB003 BIB007 BIB008 BIB010 BIB021 BIB034 BIB035 BIB036 BIB037 BIB044 BIB049 BIB050 . By allocating the sectionalizing switches on the distribution feeders or installing new branches, the load points are fed with new routes. Hence, the load points could be de-energized if any permanent fault occurred or when maintenance services are planned to be executed BIB029 , BIB025 , BIB004 , BIB016 , BIB035 , BIB005 , BIB013 . Switching actions with sectionalizing switches can isolate some faults, and we can use tie-lines to maintain the supply to some feeders when faults occur in an upstream branch. Accordingly, in a reliability-driven DEP, the main goal is to minimize the unwanted load shedding in the case of an outage occurrence, based on the socio-economic priority of customers. System planners focus on environmental issues these days more than before and follow several rules and protocols, e.g. Kyoto protocol, to reduce the impact of hazardous environmental factors BIB014 . Researchers now consider emission rate as one of the objective functions in the DEP problems BIB045 , BIB014 , BIB024 , BIB032 , BIB046 BIB051 BIB052 BIB053 , and reducing the pollution rate by using clean energy resources is standard practice by network planners. Fig. 3 and 4 show the normalized versatility of objective functions used in the different references, and constraints and objective functions of distribution planning, respectively. Table 1 show the completeness of the references in covering various constraints and objective variables.