Overview of Electric Energy Distribution Networks Expansion Planning <s> D. HYBRID ALGORITHM <s> A multi-product two-stage production distribution network design problem (PDND) is considered in this paper. The network consists of capacitated plants, capacitated distribution centers and customers. The known demand for multiple products at the customers is satisfied by shipping via distribution centers, and each customer demand is supplied via a single distribution center. A relatively efficient hybrid algorithm of tabu search and Benders decomposition is proposed to determine where distribution centers are located and by which distribution center each customer is served exclusively. In Benders decomposition framework, a tabu search algorithm is used to solve the master problem to obtain quickly approximate-optimal solutions. At the same time, three kinds of neighbourhood structure are used in tabu search. The computational experiments show that the hybrid algorithm saves tremendous computation time in comparison with conventional Benders decomposition. <s> BIB001 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> D. HYBRID ALGORITHM <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> BIB002 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> D. HYBRID ALGORITHM <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> BIB003 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> D. HYBRID ALGORITHM <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> BIB004 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> D. HYBRID ALGORITHM <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> BIB005 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> D. HYBRID ALGORITHM <s> Abstract This paper presents a hybrid simulated annealing (SA) and mixed integer linear programming (MILP) approach for static expansion planning of radial distribution networks with distributed generators (DGs). The expansion planning problem is first modeled as MILP optimization problem with the goal of minimizing the investment cost, cost of losses, cost of customer interruptions due to failures at the branches and at DGs and the cost of lost DG production due to failures at branches. In order to reduce the complexity of planning problems the decomposition of the original problem is proposed into a number of sequences of sub-problems (local networks) that are solved using the MILP model. The decomposition and solution process is iteratively guided and controlled by the proposed SA algorithm that employs the proper intensification and diversification mechanism to obtain the minimum total cost solution. <s> BIB006 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> D. HYBRID ALGORITHM <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> BIB007
Hybrid algorithms are also proposed to solve the DEP problem by combining different techniques (PSO and OO, PSO and SFL, etc.) that could achieve better solutions with less computational burden BIB004 , BIB003 . In BIB002 , a Multi-Objective (MO) hybrid technique is proposed by using Immune GA (IGA) with the combination of a fuzzy algorithm. Fuzzy algorithm is implemented to select the best solution among several Pareto solutions. The authors in BIB006 present a hybrid SA and MINLP method to solve the DEP problem by considering the presence of DG units. A hybrid TS and Benders decomposition method is proposed in BIB001 with the advantage of combining mathematical programming and meta-heuristics techniques, and less computation time. A hybrid Decimal Codification Genetic Algorithm (DCGA) and LP method are proposed in BIB005 to optimize the planning of the sub-transition system. The authors in BIB007 also proposed a hybrid multi-objective GA and primal and dual interior point algorithm to solve a wind turbine allocation problem. Fig. 7 shows a complete list of single and multi-objective optimization methods and their citations.