Distribution Network Quantum-Inspired Evolutioary Algorithm Planning Method

The distribution network planning is a class of combinatorial optimization problems in mathematics is an NP-Hard problem, NP-Hard problem solving is the field of computer science bottleneck one of the tasks, when the problem size increases, seek the global optimal solution is still a problem. Distribution network planning problem is to meet the network operation constraints and supply to consumers under the premise of seeking an optimal set of decision variables (such as the capacity and location of the substation, the capacity and location of the distributed power feeder path and size), making the net loss, operating, investment, loss of maintenance and reliability fees and minimum. The distribution network planning from the1960s caused widespread concern in the electricity workers, Since then, domestic and foreign scholars have proposed a number of methods to solve the problem of distribution network planning. Actual distribution network planning needs to consider many factors, it is a very complex engineering problems, many of which are difficult to determine with the characteristics of non-linear, discrete, and multi-objective. Therefore, the search the better optimization intelligent optimization algorithm is a subject worth exploring. The main work and results are as follows:1. Study Quantum-inspired Evolutionary Algorithm (QIEA) basic theory, on this basis, combined with the group statistical learning ideas and local search strategy and Tabu Search (TS) to further improve the QIEA optimization ability, given an Improved Quantum-inspired Evolutionary Algorithm (IQEA) and described in detail, using in the literature truncation selection, proportional selection and competitions to select three statistical methods for statistical and experimental verification by0-1knapsack problem, the results show that the Improved Quantum-inspired Evolutionary Algorithm (IQEA) optimization capability has been further improved, the performance is better than groups in the literature on Statistical Learning Quantum-inspired Evolutionary Algorithm (SLQEA).2. Analysis substation locating factors given substation optimization model for location and penalty factor in the geographic information is added to the model, after the model was finishing and simplification, a simplified model of the substation locating, using an Improved Quantum-inspired Evolutionary Algorithm (IQEA) to optimize the distribution network upgrade substation locating, and substation site selection of cases and multi-source single source substation locating operator example as an example the experimental simulation, simulation results show that the optimization results added to the geographic information penalty factor is indeed better than geographic information did not join the penalty factor is more reasonable. Planning of substation locating, Yuyao City substation site instances, for example experiment simulation, investigative study and professional and technical personnel of the power supply company in YuYao City, ultimately come from Yuyao City planning of substation locating results fully in line Yuyao City substation site planning, and thus prove the substation locating improved quantum evolutionary algorithm method is an effective and accurate method.3. Research of distributed generation (DG) on distribution network planning, distributed power is given locating and sizing optimization model, in the distributed power access standards require the use of Improved Quantum-inspired Evolutionary Algorithm (IQEA) distributed power locating and sizing optimization, for distributed power locating and sizing, standard IEEE30bus for example experimental simulation, finally achieved the desired optimization results, which proved distributed power locating and sizing Improved Quantum-inspired Evolutionary Algorithm (IQEA) method is an effective and accurate method.