| With the increasing depletion of fossil fuels and the environmental problems around the world, all countries are sparing no effort to develop renewable energy-based distributed power supply technology. Distributed generation (DG) has many advantages,such as reducing the pollution to environment, lowing charge to users, improving quality of the electric energy and security of the power supply, easy traveling of power system to the load's change and being able to meet the requirements of sustainable energy development, so supplement and corresponding with the big electric network, distributed generation is the best way to supply the reliable and high-quality electric power to users by making good use of existed resource and equipment, which also proposed a new distributed power supply planning challenges.This paper constructed a multi-objective optimal model of distributed generation, which including the maximum environmental benefits, the minimum investment costs, the minimum power loss of distribution network and the minimum outage cost when islanding. In terms of environmental benefits, the model takes environmental values of the pollutant into account, imposing fines to pollutant emissions. The genetic algorithm has some disadvantages when resolving the planning model such as slow convergence and easy to premature. In order to overcome the shortcomings of genetic algorithm, a new Improved Particle Swarm Optimization (IPSO) was proposed in this paper. The algorithm improves the quality of the initial particles through the simplex method and then adjusts the parameters self-adaptively and dynamically through fuzzy controller based on the idea of fuzzy reasoning. The simulated annealing algorithm is adopted to evaluate the new position of particle after its fitness adjustment. Finally, a 33-bus distribution system is taken as an example of DG multi-objective planning. The results verify the rationality of the proposed model, as well as the feasibility and robustness of IPSO.
|
PDF Full Text Request