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Research on optimal schedule strategy for active distribution network using particle swarm optimization combined with bacterial foraging algorithm
- Source :
- International Journal of Electrical Power & Energy Systems. 78:637-646
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- Comparing with the traditional distribution network, a significant feature of the active distribution network (ADN) is that the performance of distributed generation (DG) units, energy storage units and micro-grid (MG) in the network is controllable for the distribution network operator. Considering the characteristics of the distributed power supply and micro-grid, and giving full play to the advantages of distributed generation technology in the economic, environmental and energy aspects, this paper highlights an environmental protection and energy saving optimal schedule model for ADN. The scheduling model focuses on the minimum network loss, minimum voltage deviation and minimum difference between peak and valley load. In addition, the two stage algorithm is presented to solve the proposed multi-objective scheduling model of ADN. First, a set of Pareto solutions are obtained by using the proposed particle swarm optimization combined with bacterial foraging algorithm (PSO-BFO) to solve multi-objective optimization problems, then the optimal schedule strategy of ADN is gained through evaluating the Pareto solutions with entropy weight decision-making method. To avoid the search falling into local optimal solution, the two-value crossover operator is introduced to exchange the information among subpopulations and update the position of related particles. Meanwhile, the adaptive adjusting inertia constant strategy is used to improve the algorithm convergence speed. Finally, the case study results demonstrate the rationality of the proposed optimal schedule model and the validity of its solution algorithm for ADN.
- Subjects :
- Mathematical optimization
Engineering
Optimization problem
business.industry
020209 energy
Crossover
Pareto principle
Energy Engineering and Power Technology
Distributed power
Particle swarm optimization
02 engineering and technology
Energy storage
Distributed generation
0202 electrical engineering, electronic engineering, information engineering
Entropy (information theory)
020201 artificial intelligence & image processing
Electrical and Electronic Engineering
business
Algorithm
Subjects
Details
- ISSN :
- 01420615
- Volume :
- 78
- Database :
- OpenAIRE
- Journal :
- International Journal of Electrical Power & Energy Systems
- Accession number :
- edsair.doi...........57698bff2c585f260e91cf0f192cf490