Application of evolutionary algorithms for the planning of urban distribution networks of medium voltage

A new framework for large distribution system optimal planning in a competitive electricity market

“…However, there is no guarantee that an optimum solution can be found. Branchexchange algorithms [10], algorithms based on evolutionary computation [11], ant colony [12], genetic algorithm [13], simulated annealing [14], and tabu search [15] are notable heuristic methods that have been used to solve optimization problem.…”

Allocation and sizing of distribution transformers and feeders for optimal planning of MV/LV distribution networks using optimal integrated biogeography based optimization method

“…So, MV feeders cost is a common component in the objective function associated with both MV and LV networks planning which highlights the importance of simultaneous planning of these networks, where the separate planning decreases the solution accuracy. However; most of the researches in the field of distribution systems planning are focused on MV networks planning [2][3][4][5][6][7][8][9][10] rather than LV networks [11][12][13][14][15][16] and only a few researches present integrated planning of both networks simultaneously [1,[17][18][19]. In [17][18][19], linear and non-linear programming techniques are employed for solving the planning problem; however it is well known that these techniques, as analytical methods, have their own disadvantages of convergence problem, algorithm complexity, as well as the problem associated with handling mixed integer variables.…”

“…In addition to that, NLP techniques suffer from algorithm complexity as another associated difficulty [35]. All of the above has resulted in introducing another category of optimization techniques which is so called "heuristic techniques" [3][4][5][6]36]. These techniques include, for instance, genetic algorithm (GA) [37], particle swarm optimization (PSO) [38,39], and biogeography based optimization (BBO).…”

Distribution network planning using a constructive heuristic algorithm

“…However the size of the search space of the DNP problem leads to a big computational effort. Techniques based in metaheuristics like genetic algorithms [8], [9], simulated annealing [10], [11], tabu search [12], ant colony system [13], and evolutive algorithms [14], [15] are also present in the literature. The majority of the proposed methods solve a load flow problem to calculate the operation point of the network and to verify the viability of each investment proposal.…”