Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> Stochastic automata operating in an unknown random environment have been proposed earlier as models of learning. These automata update their action probabilities in accordance with the inputs received from the environment and can improve their own performance during operation. In this context they are referred to as learning automata. A survey of the available results in the area of learning automata has been attempted in this paper. Attention has been focused on the norms of behavior of learning automata, issues in the design of updating schemes, convergence of the action probabilities, and interaction of several automata. Utilization of learning automata in parameter optimization and hypothesis testing is discussed, and potential areas of application are suggested. <s> BIB001 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> Abstract The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems. <s> BIB002 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> Based on the set theory and the Prolog programming language, the paper develops methods by which the allocation of loads to the busbars of distribution substations may be determined automatically. Starting from the initial possible allocation of loads to the substation busbar sections, a formulation for load allocation is first developed using set theory. The `generate evaluate and test? technique and the depth-first search method are then applied to the solution-seeking process. The formulation is implemented and recursive search procedures are derived using the Prolog language. The Prolog software aid developed includes a complete solution generator from which all hypothetical solutions are generated, an evaluator which prunes invalid solutions, and a tester which produces feasible solutions by checking the load allocation constraints related to the firm capacity of the distribution substations. An application of the methods developed is given for two 11 kV interconnected substations which include 5 busbar sections and 10 ring loads. <s> BIB003 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> An algorithm to obtain an approximate optimal solution to the problem of large-scale radial distribution system planning is proposed. The distribution planning problem is formulated as a MIP (mixed integer programming) problem. The set of constraints is reduced to a set of continuous variable linear equations by using the fact that the basis of the simplex tableau consists of the power flow variables of radial branch. This linear problem is solved by pivot operations which correspond to a branch-exchange of the radial network. Numerical examples are presented to demonstrate the validity and effectiveness of the algorithm. > <s> BIB004 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> 1. Problem Independent Distributed Simulated Annealing and its Applications.- 2. On Simulating Thermodynamics.- 3. Solving the Quadratic Assignment Problem.- 4. A Computational Comparison of Simulated Annealing and Tabu Search Applied to the Quadratic Assignment Problem.- 5. School Timetables: A Case Study in Simulated Annealing.- 6. Using Simulated Annealing for Efficient Allocation of Students to Practical Classes.- 7. Timetabling by Simulated Annealing.- 8. Using Simulated Annealing to Solve Concentrator Location Problems in Telecommunication Networks.- 9. Design of a Teleprocessing Communication Network Using Simulated Annealing.- 10. Location of Civil Defence Sirens.- 11. Solving the Afforestation Problem.- 12. Algorithms for Nesting Problems.- 13. Balanced Grouping through Simulated Annealing.- 14. Optimal Partition of an Interval - The Discrete Version.- 15. Simulated Annealing in Image Processing.- 16. Optimal Pallet Capacity for a FMS.- 17. Final Remarks. <s> BIB005 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> Rural networks are characterized by a low load density and high specific investment costs. This results in comparatively long periods for the extension or reconstruction of networks. In order to optimize the planning process described in this paper, the planning rules are elaborated for a distribution network, which extends over a period of 30 years. Network structures and planning criteria are derived from this principle. This forms the basis for a knowledge based planning system aimed at cutting down the work expended on engineering in the course of annual planning. > <s> BIB006 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described. <s> BIB007 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs. <s> BIB008 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> This paper proposes an algorithm for solving a problem of multi-year distribution systems expansion planning in which severe fault cases (multi-stage) are taken into consideration. In the algorithm, the multistage branch exchange algorithm is applied to make the solution accurate, and the decomposition co-ordination method is introduced to make the algorithm efficient. An N-years distribution planning problem is decomposed into N single-year planning problems each of which is decomposed into several planning problems to find a feasible radial feeder configuration according to predetermined severe fault cases. So as to co-ordinate these decomposed problems, so-called forward/backward-path procedure is applied. The validity and accuracy of the proposed algorithm are demonstrated by several numerical examples through comparing the calculation result with the ones by conventional methods. <s> BIB009 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> The paper addresses the problem of distribution network expansion planning under uncertainty. The deterministic problem is itself a large-scale, complex, difficult-to-define one; the combinatorial nature of the decision making can be efficiently handled by a specific evolutionary approach, We develop, in the evolutionary optimization context, a hedging algorithm to deal with scenario representation of uncertainty; decision making results in a convergence process for the first stage naturally conflicting decisions. The process robustness is discussed and illustration is provided for a comprehensive example. <s> BIB010 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB011 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> Abstract In this paper, an attempt has been made to develop a new algorithm for distribution system planning. The proposed algorithm does not require prior knowledge of candidate substation location and can automatically select location of a substation, the optimal feeder configuration and the optimal sizes of branch conductors while satisfying constraints such as current capacity, voltage drop and heuristic rules. Several algorithms are proposed for distribution systems planning. A generalized algorithm is developed for obtaining the optimal feeder path and the optimal location of substation on minimum loss criterion. Heuristic rules are incorporated in the above algorithm. Another algorithm is for branch conductor optimizations. Modified load flow method is also presented in the paper, which can handle composite load models. The load flow algorithm is used for solving radial distribution networks (RDN) and branch conductor optimization algorithm. The load flow algorithm and branch conductor optimization techniques are used as subroutine in the generalized distribution systems planning algorithm. Through numerical example the validity of proposed method is verified. <s> BIB012 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> The planning problem of electrical power distribution networks, stated as a mixed nonlinear integer optimization problem, is solved using the ant colony system algorithm (ACS). The behavior of real ants has inspired the development of the ACS algorithm, an improved version of the ant system (AS) algorithm, which reproduces the technique used by ants to construct their food recollection routes from their nest, and where a set of artificial ants cooperate to find the best solution through the interchange of the information contained in the pheromone deposits of the different trajectories. This metaheuristic approach has proven to be very robust when applied to global optimization problems of a combinatorial nature, such as the traveling salesman and the quadratic assignment problem, and is favorably compared to other solution approaches such as genetic algorithms (GAs) and simulated annealing techniques. In this work, the ACS methodology is coupled with a conventional distribution system load-flow algorithm and adapted to solve the primary distribution system planning problem. The application of the proposed methodology to two real cases is presented: a 34.5-kV system with 23 nodes from the oil industry and a more complex 10-kV electrical distribution system with 201 nodes that feeds an urban area. The performance of the proposed approach outstands positively when compared to GAs, obtaining improved results with significant reductions in the solution time. The technique is shown as a flexible and powerful tool for the distribution system planning engineers. <s> BIB013 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> In this work, the planning of secondary distribution circuits is approached as a mixed integer nonlinear programming problem (MINLP). In order to solve this problem, a dedicated evolutionary algorithm (EA) is proposed. This algorithm uses a codification scheme, genetic operators, and control parameters, projected and managed to consider the specific characteristics of the secondary network planning. The codification scheme maps the possible solutions that satisfy the requirements in order to obtain an effective and low-cost projected system-the conductors' adequate dimensioning, load balancing among phases, and the transformer placed at the center of the secondary system loads. An effective algorithm for three-phase power flow is used as an auxiliary methodology of the EA for the calculation of the fitness function proposed for solutions of each topology. Results for two secondary distribution circuits are presented, whereas one presents radial topology and the other a weakly meshed topology. <s> BIB014 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> This paper addresses the problem of electric distribution network expansion under condition of uncertainty in the evolution of node loads in a time horizon. An immune-based evolutionary optimization algorithm is developed here, in order to find not only the optimal network, but also a set of suboptimal ones, for a given most probable scenario. A Monte-Carlo simulation of the future load conditions is performed, evaluating each such solution within a set of other possible scenarios. A dominance analysis is then performed in order to compare the candidate solutions, considering the objectives of: smaller infeasibility rate, smaller nominal cost, smaller mean cost and smaller fault cost. The design outcome is a network that has a satisfactory behavior under the considered scenarios. Simulation results show that the proposed approach leads to resulting networks that can be rather different from the networks that would be found via a conventional design procedure: reaching more robust performances under load evolution uncertainties <s> BIB015 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> Large scale distribution system planning is a relatively complex and reasonably difficult problem. This paper proposes the application of improved genetic algorithm (GA) for the optimal design of large scale distribution systems in order to provide optimal sizing and locating of the high and medium voltage (HV and MV) substations, as well as medium voltage (MV) feeders routing, using their corresponding fixed and variable costs associated with operational and optimization constraints. The novel approach presented in the paper solves hard satisfactory optimization problems with different constraints in large scale distribution networks. This paper presents a new concept based on loss characteristic matrix introduced for optimal locating of MV substations, followed by new methodology based on graph theory and GA for optimal locating of the HV substations and MV feeders routing in a real size distribution network. Minimum spanning tree algorithm is employed to generate set of feasible initial population. In the present article to reduce computational burden and avoid huge search space leading to infeasible solutions, special coding methods are generated for GA operators to solve optimal feeders routing. The proposed coding methods guarantee the validity of the solution during the progress of the genetic algorithm toward the global optimal solution. The developed GA-based software is tested in a real size large scale distribution system and the well satisfactory results are presented. <s> BIB016 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> This paper proposes a methodology to achieve integrated planning and projects for secondary distribution circuits. The planning model is formulated as a mixed integer nonlinear programming problem (MINLP). In order to resolve this problem, a tabu search (TS) algorithm is used, with a neighborhood structure developed to explore the physical characteristics of specific geographies included in the planning and expansion of secondary networks, thus obtaining effective solutions as well as low operating costs and investments. The project stage of secondary circuits consists of calculating the mechanical efforts to determine the support structures of the primary and secondary distribution systems and determining the types of structures that should be used in the system according to topological and electrical parameters of the network and, therefore, accurately assessing the costs involved in the construction and/or reform of secondary systems. A constructive heuristic based on information of the electrical and topological conditions between the medium voltage and low voltage systems is used to connect the primary systems and secondary circuits. The results obtained from planning and design simulations of a real secondary system of electric energy distribution are presented. <s> BIB017 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> This paper is the second of two and presents a planning methodology for low-voltage distribution network planning. Combined optimization of transformers and associated networks is applied, considering the street layout which connects the different consumers. In the first section, a planning zone division into smaller mini-zones is performed; the smaller zones are optimized independently. This second section develops macro-optimization methodologies that use the results and techniques of the previous stage, which allow performing a global analysis of the planning zone. Three methodologies are proposed, one based on Voronoi polygons to obtain irregular mini-zones that incorporate proximity between consumers. Another is based on network recombination, with the purpose of finding potential savings produced by combining neighboring networks. Lastly, the sequential application of Voronoi polygons and network recombination is performed. The methodology is applied to a zone having 20 215 consumers distributed in an area of 12.9 km2 , corresponding to a district in the city of Santiago, Chile, to subsequently apply this to the entire city, an area having a surface of 2118 km2 and approximately 1300 000 customers. <s> BIB018 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> This paper presents a new methodology using fuzzy and Particle Swarm Optimization (PSO) for the placement of Distributed Generators (DG) in the radial distribution systems to reduce the real power losses and to improve the voltage profile. A two-stage methodology is used for the optimal DG placement. In the first stage, fuzzy approach is used to find the optimal DG locations and in the second stage, PSO is used to find the size of the DGs corresponding to maximum loss reduction. The proposed method is tested on standard IEEE 33 bus test system and the results are presented and compared with an existing method. <s> BIB019 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB020 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB021 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB022 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB023 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB024 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB025 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> This paper presents a new method to solve the dynamic sub-transmission substation expansion planning (SSEP) in electric power distribution networks. The method employs a new algorithm based on learning automata for optimization process. The developed algorithm includes different electrical constraints such as voltage drops, thermal limits, power flow and radial flow constraints. Moreover, prevalent cost indices are taken into consideration. The proposed method is used to solve the dynamic SSEP for Birjand city, center of South-Khorasan province of Iran, up to the year 2024. In addition, effects of the dynamic and pseudo-dynamic planning models are investigated at the study region and the obtained results are compared to those of genetic algorithm. Detailed numerical results and comparisons presented in the paper show that the proposed approach leads to proper solutions and can be used as an effective tool for the dynamic sub-transmission substation expansion planning in an actual large scale distribution network. <s> BIB026 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB027 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> Increasing growing of electricity demand and environmental issues bring huge incentives to electric vehicles (EVs) market. High penetration of these vehicles leads to some challenges for the future power system. Specifically, utilizing them in an unscheduled manner will result in distribution network technical issues such as reliability issues, congestion and loss enhancement. In this paper, firstly, a probabilistic modeling of EVs' behavior is achieved. Secondly, reliability-constraint optimum allocation of EVs' parking lots is proposed. To achieve this purpose, a composite objective function is defined. This function contains three significant distribution network reliability indices and used to allocate parking lots by means of enhancing reliability. Finally, the proposed method is implemented on a standard distribution system for RBTS bus 2. <s> BIB028 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> Abstract The optimal expansion planning of electric power distribution network to meet system load growth and overcome to pseudo dynamic behavior of network parameters considering a large number of constraints is a hard satisfactory multiobjective optimization problem. This paper implements new developed Imperialist Competitive Algorithm (ICA) for the optimal expansion planning of distribution network. The topology of medium voltage (MV) distribution network as backbone of electric power distribution systems is designed by optimal sizing, siting and timing of medium voltage network components such as HV substation and MV feeders’ routes. A multistage expansion planning is proposed to consider dynamic behavior of the system parameters asset management and geographical constraints. In order to reach the global solution an efficient coding is developed for ICA parameters. The Greedy algorithm is used to solve the minimum spanning tree problem to construct a radial configuration of the mesh network. At each stage of the problem the results are fully illustrated either by figures or by tables. A sensitivity analysis is used to show the robustness of the results with respect to ICA parameters variation. The obtained results are compared with GA as well known heuristic optimization tool. The efficiency and capability of the methodology has been tested on an under developed relatively large-scale distribution network. <s> BIB029 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB030 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> This paper proposes a hybrid optimization method for optimal allocation of wind turbines (WTs) that combines a fast and elitist multiobjective genetic algorithm (MO-GA) and the market-based optimal power flow (OPF) to jointly minimize the total energy losses and maximize the net present value associated with the WT investment over a planning horizon. The method is conceived for distributed-generator-owning distribution network operators to find the optimal numbers and sizes of WTs among different potential combinations. MO-GA is used to select, among all the candidate buses, the optimal sites and sizes of WTs. A nondominated sorting GA II procedure is used for finding multiple Pareto-optimal solutions in a multiobjective optimization problem, while market-based OPF is used to simulate an electricity market session. The effectiveness of the method is demonstrated with an 84-bus 11.4-kV radial distribution system. <s> BIB031 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> This paper presents an enhanced evolutionary algorithm to solve the static distribution substation planning problem within large distribution networks. It is based on a deterministic heuristic algorithm to find the approximate substation service areas for each substation and an expert selection strategy that increases the convergence chance to a global optimal solution. The introduced algorithm takes different electrical constraints such as voltage drops, power flow, radial flow constraints, and all prevalent cost indices into consideration. In addition, effects of unreliability within network feeders and substations are investigated on the obtained layouts. The developed method is applied to four benchmark test systems and an actual large-scale distribution system with about 140 000 customers, followed by a discussion on results. <s> BIB032 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <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> BIB033 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> In this paper, a small remote area which is located in Nigeria has been considered as a model to be tested by a managing scheme for providing both electricity and water. In this strategy, the groundwater is pumped into a water tank which can be later used for supplying required irrigation and drinking water. A PAT (Pump as Turbine) is used as a hybrid system for supplying electricity and water as well as storing water in the water tank. Also, a PV (photovoltaic plant), a package of batteries (BAT) in addition to a diesel ICE (internal combustion engine) are used and optimized along with the best size of devices and managing system, with the purpose of obtaining the maximum economical operating strategy. In this paper, two cases are considered to assess the effectiveness of the system under study. Firstly, all of the mentioned devices are used to show how internal combustion engine system dominates all the other components due to the low cost of fuel. In the second case, all renewable resources have been exploited and optimized in order to make a 100% renewable system with least possible cost. Having about 53 variables makes this problem very complicated which requires to be solved by an algorithm with more accuracy. Therefore, a MGSA (Modified Gravitational Search Algorithm) with an adapted mutation tactic is used to find the best cost and management strategy. In the first case, although the cost of diesel oil is very low, by using the PAT, about 5% of diesel oil consumption is reduced. In the second case, in order to make a 100% renewable system, the size of PV is enlarged approximately 16 times in comparison with the first case. The hybrid PV-PAT storing structure is capable to deliver the water for irrigation and domestic requirements as well as 9% of the electricity needed for the rural community. <s> BIB034 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> Abstract This study relies on a dynamic reliability-based model for distributed energy resources (DER) planning in electric energy distribution networks (EEDN) with the aim of maximizing the profit of EEDN companies by increasing income and reducing costs. Load uncertainty is considered in the proposed planning model and the robust optimization (RO) approach is employed to cope with the uncertainty. The developed methodology is illustrated using real-world voltage-dependent load models, including residential, commercial and industrial types. These load models are used in evaluating the reliability cost and energy selling for customers. The reliability cost is calculated based on the total unsupplied load after an outage. Furthermore, a new modified harmony search algorithm is proposed to solve the formulated robust dynamic DER planning problem. The solution of the proposed optimization model provides the size, location, and power factor of DER. Furthermore, the need for transformers or lines upgrades and the best year for DER installation are other decision variables determined by the model. The effectiveness and capability of the developed model have been demonstrated with the aid of a case study based on a typical EEDN. The obtained results indicate that installing DER in EEDNs can relieve congestion on feeders; therefore, it can mitigate or defer upgrade investment. Moreover, if carefully planned, other benefits of DER integration such as reliability improvement and energy loss reduction can be achieved. <s> BIB035 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> C. HEURISTIC AND EVOLUTIONARY OPTIMIZATION <s> With the evolution of smart grids, penetration of distributed energy resources (DERs) in the distribution networks has become ever-increasing problem. To improve network reliability, the complexity of the two important aspects of adequacy and security must be well assessed. There is a trade-off between adequacy of DERs, and the distribution network security, i.e. improving the adequacy can reduce the security. In this study, enhancement of the distribution network adequacy and security is proposed. In this regard, capacity of simultaneous reconfiguration and DERs sizing are utilised to improve the adequacy and security of an active distribution network. In the reconfiguration process, graph theory concept is adopted to implement a fast reconfiguration method. Since DERs are active, a combined bus and line security index is used to overcome security concerns of their existence. The IEEE 33-bus distribution network as a widely used standard test system in reconfiguration studies, and a practical 83-bus distribution network of Taiwan Power Company (TPC) which is a part of a real distribution network, are used to test the performance of the proposed method. The simulation results demonstrate the performance of the proposed framework. <s> BIB036
Several researchers proposed heuristic EAs to optimise the complex optimisation problems directly. Besides the ease of implementation, we hardly can guarantee the global optimality of the solution. Meanwhile, the solution point is close enough to an optimum solution. Several researchers employed heuristic algorithms despite their inherent randomness BIB033 . The numerous heuristic evolutionary algorithms have been introduced including Genetic Algorithm (GA), fuzzy system, Artificial Immune Systems (AIS), Tabu Search (TS), Imperialistic Competition Algorithm (ICA), Particle Swarm Optimization (PSO), Learning Automata (LA), Simulated Annealing (SA), Ant Colony (AC), Harmony Search Algorithm (HSA) BIB035 , Artificial Bee Colony (ABC), expert systems (ES), Gravitational Search Algorithm (GSA), Human Behaviour-Based Optimization (HBBO) BIB036 , branch-exchange (BE) algorithm, etc.   BIB002   BIB007 BIB001 BIB005   , BIB012 ,  BIB003 BIB006 BIB004 BIB034 . GA is an evolutionary-based method, developed by Holland in the middle of 80s and inspired by the natural selection and regeneration law concepts  . Compared to classical optimization approaches, heuristic algorithms (such as GA) can optimize complex and large-scale problems. Furthermore, the implementation of different types of objective functions and constraints is easily possible (no matter of being continuous, discrete, linear or non-linear). Even though GA needs high computational process and cannot ensure finding a globally optimum solution, it is applicable in the planning problem. In fact, the aim of the planning problem is to search a good solution with an acceptable optimality gap and computational tractability rather than searching a global optimum point BIB023 , BIB028 . A major shortcoming of GA is that obtaining a global solution cannot be rigorously proven mathematically BIB020 , BIB025 , BIB016 . The concept of the fuzzy logic was proposed by Lotfi A Zadeh  to deal with uncertainty problems and soft computing. In fuzzy logic, fuzzy variables employ the membership functions to model the uncertainties by assigning a degree of membership (or truth which is between 0 and 1) BIB019 ,  . Researchers use fuzzy logic extensively to solve the optimisation problem in power systems. By inspiring from theoretical immunology, AIS introduced by Farmer, Packard and Perelson BIB002 . In comparison with other evolutionary methods, AIS can determine a single optimal solution, as well as delivering all the local optimum points that are stored through the optimisation process BIB015 . A heuristic method, TS, also proposed by Glover and Hansen  to solve the combinatorial problem within a reasonably short time. The main advantage of this method is the need for less iteration to achieve the desired results BIB017 , BIB018 . By inspiring from the imperialistic competition, Atashpaz-Gargari and Lucas introduced ICA  . In this algorithm, finding a global solution starts with empires competing where each empire rules by an imperialist and several colonies. The competition continues in a way that empires gradually lose their colonies, and a solution is found when a single empire survives. The corresponding colonies to this final empire gain the same cost as the imperialist of this empire  . The authors in BIB029 discussed an example of using ICA in DEP problem. PSO is a popular population-based heuristic algorithm inspired by bird flocking and a fish schooling that at first proposed by Kennedy and Eberhart BIB007 . The advantage of PSO is the ease of implementation, simple conceptual structure, and less computational bookkeeping BIB021 . LA is adaptive decision-making machines, introduced by Narendra and Thathachar BIB001 . LA performs its current action according to the previous experiences from a virtual environment. By learning the way to select the best action among several actions (by repeated interactions with the environment), the performance of the LA will be improved BIB026 . SA is among reliable optimizer algorithms that firstly surveyed by Kirkpatrick, Gelatt, and Vecchi BIB005 . Conceptually, the SA is based on the annealing of the solids. The objective function is considered as the energy function, and the optimization procedure is based on cooling strategy of a hightemperature material to freeze point and finding the global optima. Marco Dorigo proposed AC algorithm, which is inspired by the behaviour of ants to find the nearest path from the nest to the food sources using a chemical substance called pheromone  . The authors in BIB008 extend the AC to present the ant colony system algorithm (ACS). Compared to AC, ACS shows a more reliable solution in engineering problems BIB013 . ABC is a population-based method that was introduced by Karaboga  . It is based on food (called nectar) finding a strategy of real bees and sharing the information of food sources with other members of the hive BIB027 . The authors in BIB022 proposed Honey-bee mating (HBM) method inspired by the process of marriage in real honey-bee. The ES methods are also used in DEP problems and are based on human experience and knowledge BIB012 ,  BIB003 BIB006 . The GSA is a heuristic method proposed in 2009. It shows better results compared to GA and PSO, and it's a simple but powerful algorithm for optimization BIB034 . The authors in BIB004 developed a BE method to solve the single-period DEP, and it has been used in the past few years to cope with the complexity and time and optimal planning of MV networks BIB009 , BIB031 . Besides all the heuristic methods and evolutionary algorithm mentioned above, researchers proposed novel Evolutionary Algorithms (EA) BIB030 , BIB011 , BIB014 , BIB032 , BIB024 , BIB010 .