Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <s> In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search. <s> BIB001 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> F. ARTIFICIAL INTELLIGENT TECHNIQUES <s> The goal of this chapter is to give fundamental knowledge on solving multi-objective optimization problems. The focus is on the intelligent metaheuristic approaches (evolutionary algorithms or swarm-based techniques). The focus is on techniques for efficient generation of the Pareto frontier. A general formulation of MO optimization is given in this chapter, the Pareto optimality concepts introduced, and solution approaches with examples of MO problems in the power systems field are given <s> BIB003 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB005 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB006 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB008 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <s> This study proposes and applies an evolutionary-based approach for multiobjective reconfiguration in electrical power distribution networks. In this model, two types of indicators of power quality are minimised: (i) power system's losses and (ii) reliability indices. Four types of reliability indices are considered. A microgenetic algorithm ('GA) is used to handle the reconfiguration problem as a multiobjective optimisation problem with competing and non-commensurable objectives. In this context, experiments have been conducted on two standard test systems and a real network. Such problems characterise typical distribution systems taking into consideration several factors associated with the practical operation of medium voltage electrical power networks. The results show the ability of the proposed approach to generate well-distributed Pareto optimal solutions to the multiobjective reconfiguration problem. In the systems adopted for assessment purposes, our proposed approach was able to find the entire Pareto front. Furthermore, better performance indexes were found in comparison to the Pareto envelope-based selection algorithm 2 (PESA 2) technique, which is another well-known multiobjective evolutionary algorithm available in the specialised literature. From a practical point of view, the results established, in general, that a compact trade-off region exists between the power losses and the reliability indices. This means that the proposed approach can recommend to the decision maker a small set of possible solutions in order to select from them the most suitable radial topology. <s> BIB009 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <s> This paper presents a multiobjective performance index-based size and location determination of distributed generation in distribution systems with different load models. Normally, a constant power (real and reactive) load model is assumed in most of the studies made in the literature. It is shown that load models can significantly affect the optimal location and sizing of distributed generation (DG) resources in distribution systems. The simulation technique based on genetic algorithms is studied. The studies have been carried out on 16-bus and 37-bus distribution systems. <s> BIB010 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB011 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB012 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB013 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <s> Modern approach to distribution systems planning requires developing tools able to interpret and analyze the stochastic nature of large power system problems. At the same time, it has to be able to optimize multiple, conflicting goals that appear in today’s open market environment. The presented evolutionary algorithm is based on a Multiple Vehicle Routing Problem adjusted for solving active electric distribution networks. An original approach to fuzzy number modeling is used for active power sources, consumption substations and distributed generation. This model, besides shorter computational time and less memory usage, is applicable to any form of fuzzy number shape required by the planner in modeling the stochastic nature of elements. An idea of defuzzification using pessimism is presented and compared to the solutions obtained without pessimism included. Application of the original universal fuzzy number modeling, using FER-fuzzy modeling, is demonstrated on multi-objective evolutionary algorithm and applied to real Croatian distribution network. <s> BIB014 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB015 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB016 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB017 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB018 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB019 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <s> This paper presents a new multi-objective model for distributed generation planning in distribution networks. The proposed model minimizes three objective functions, namely, voltage dissatisfaction, costs and environmental emissions. The short-circuit level of network is considered as a constraint which should not be exceeded by the integration of DG units. Pareto optimal solutions of multi-objective DG planning are found using a particle swarm optimization and a fuzzy method is applied to select the final solution. The proposed method is tested on a 33-bus network and results show the effectiveness of the proposed model. <s> BIB020 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB021 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <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> BIB022 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. ARTIFICIAL INTELLIGENT TECHNIQUES <s> Abstract This paper coordinates the reconfiguration of distribution systems with the expansion problem while the potential of demand response (DR) programs and distributed generation (DG) units are modeled in the active distribution expansion planning. The concept of system of systems (SoS) is proposed to model the expansion of DGs that are owned by private investors. SoS is an efficient system consisting of some autonomous and heterogeneous systems with distinct objective functions. According to the concept of SoS, a decision-making paradigm is developed to determine the location, size, and time of DG investment made by a commercial agent, as well as the price of generated power. From the distribution company (DISCO) viewpoint, the proposed model is a multi-objective (MO) optimization problem. The first objective function is the net present value of the total investment and operation costs related to the network. The second objective function is a reliability index, i.e. the expected energy not-supplied (EENS). The uncertainty of load growth in future years is handled by using a scenario-based approach. The introduced problem is solved by using multi-objective particle swarm optimization (MOPSO) algorithm empowered with an innovative three-layer procedure that is provided to better manage the space of the decision variables. The first layer is based on PSO particles while the second and third layers are based on a sensitivity analysis. Finally, a standard 33-bus distribution system is utilized to obtain the simulation results that show the performance and advantages of the proposed method. <s> BIB023
Some intelligent algorithms can directly find the Pareto solutions by simultaneously optimizing the individual objective functions. One of the advantages of the population-based methods is to evaluate multiple potential solutions in a single iteration. Moreover, such methods provide more flexibility for the decision-maker, especially if there is no information about the priorities of the decision-maker. However, one of the key challenges is the search of the optimum Pareto-front and maintain the diversity of the populations (to prevent premature convergence) BIB001 . Evolutionary computing is primarily proposed for the simulation of biological evolution procedures. A population is a set of individual solutions that are generated to solve the optimization problem. In each iteration, the fittest individuals are selected, and then by using some operations, the next generation (new solutions) is generated. EAs have been successfully applied to cases with complicated objectives such as discrete, non-differentiable objectives or objectives for which, no standard analytical solution exists BIB003 ). The authors in BIB017 , BIB022 , BIB002 , BIB014 use Pareto-ranking to determine the probability of proliferation of an individual and to find the set of non-dominated or nadir individuals in the population. They use a niching mechanism to avoid the convergence of the optimization procedure to a single region of the Pareto front. In an iterative process, the next generation of the population is generated based on mutation and crossover rules. Multi-Objective GA (MOGA) is a simple and efficient method implemented by Fonseca and Fleming (1993) BIB004 , BIB005 , BIB009 , BIB018 , BIB010 . Non-dominated Sorting Genetic Algorithm (NSGA), is another method that utilizes a layered classification technique BIB006 . The NSGA introduced by Srinivas and Deb (1994) and its improved version, NSGA-II, used in BIB012 , BIB011 , BIB008 . Zitzler and Thiele's suggested Strength Pareto Evolutionary Algorithm (SPEA) approach with the combination of elitism and the concept of nondomination. SPEA stores non-dominated point during the iterations using an external archive. The authors in BIB006 investigate the performance of NSGA and SPEA in the planning problem of distribution systems. SPEA-II, however, incorporates density information to assign a fine-grained fitness function BIB013 . In the Multi-objective PSO (MOPSO) method, the optimum solutions obtained by the particles in different iterations create a set of non-dominated solutions. Since the DEP is a MO problem; numerous strategies have been introduced to specify the Pareto solutions (such as SPEA-II based MOPSO, hybrid PSO, and SFL method) BIB019 , BIB015 , BIB023 , BIB020 . In BIB021 , the ABC algorithm is used to solve the DEP and unit commitment problems. TS algorithm improves the searching strategy using a memory mechanism which benefits historical records. The Multi-Objective Tabu Search (MOTS) method is presented in BIB007 , BIB016 to optimize the multi-objective DEP.