Research Of Micro Grid Systems Optimization Based On Biological Behavior Algorithms

The development of Microgrid technology can greatly promote the usage of renewable energy,and improve energy efficiency. Microgrid power supply is a potent complementary to the current used centralized power supply. It is also an effective way to improve the energy structure in our country.The research of Optimal Microgrid Energy Scheduling(OMES) is still in its initial stage,and the optimal problem is dealing with presents a lot of difficulties such as the complexness of the model, the existence of multi-variables and multi-objectives, dynamic and large-scale complex optimized, thus putting forward a very high request on the optimal method as well as the research and application of related theories.Our project is aiming at designing a whole set of efficient and reliable intelligent optimization algorithm through researching into general scalable optimal microgrid energy scheduling models. The emphasis of our research is to solve the problem of optimal microgrid energy scheduling so that the operation efficiency of micro-grids and the utilization of renewable energy can be improved.The innovative points of our research is to solve the problem of optimal microgrid energy scheduling using an optimization method that is based on biological behavior. The outcome of our research will become an important base of the application of optimal micro-grid energy scheduling and bio-inspired computing, which will certainly deepen and enrich the existing intelligent computing theories, and promote the popularization and application of microgrid technologies.The intelligence computational method is the selective algorithm for solving the complex microgrid system optimization problems. As an emerging field, the development of intelligent computation has aroused many researchers’ interests in many fields. The intelligent computation has already become the research focus and the front field of the interdisciplinary studies such as artificial intelligence, the economics, and the biology interdisciplinary studies.Each kind of algorithm of the intelligent computation has already demonstrated excellent and the huge development potential in many practical application fields such as the traditional solution of NP-hard problems.This article gives research about the microgrid system optimization problems, constructs the optimization models according to the microgrid practical application demands, and designs the newintelligent optimization algorithms to solve these complex models effectively based on the intelligent computation methodology that includes:First, the microgrid energy scheduling problem is studied. On the basis of analysis of the conflict types and causes of the microgrid, this paper establishes the scheduling optimization model of microgrid that considering the conflict restraint of the microgrid networks. This scheduling model is in order to minimize the channel quantity, the time slot assignment, and total processing time of the microgrid system. From the perspective of biology, an improved PSO algorithm is proposed- the Particle Swarm Optimizer based on Predator-prey Co-evolution(PSOPPC). This algorithm is designed to get over the shortcoming that the PSO algorithm falls into local optima with loss of the diversity in the later optimization period. The solution algorithm of microgrid energy scheduling models is designed based on PSOPPC algorithm which compared to canonical PSO algorithm, show that the PSOPPC obtains superior microgrid energy scheduling solutions than PSO methods in terms of convergence speed.Second, the microgrid Networks Planning based on self Adjustment Bacterial Foraging Optimization(ABFO) is studied. The microgrid networks planning mathematical model’s objective functions are the maximizing the cover rate of microgrid tags, minimizing the micro powers conflict, maximizing the economic benefit of microgrid system, load balancing of micro powers,and the combined measurement, respectively. In order to improve the BFO’s performance on complex optimization problems with high dimensionality, we apply two natural foraging strategies,namely the producer-scrounger foraging and the area concentrated search, to the original BFO,resulting in two new self Adjustment Bacterial Foraging Optimization(ABFO) models, namely ABFO1 and ABFO0. Instead the simple description of chemotactic behavior in BFO, the proposed algorithms can also adaptively strike a balance between the exploitation of the search space and the exploration during the bacteria evolution, by which the significant improvement can be gained.Through the simulation experiment based on the ABFO algorithm, the example solution and analysis about the five objective functions of microgrid network planning models are given, and the comparative analysis between the test result and standard PSO algorithm, the genetic algorithm is given.Third, we constructed a two-level microgrid group control model based on distributive decision-making method. According to the complexity of this two-level DDM model that based on System Intelligence(SI) is proposed to solve it. The simulation experiment indicates that the new SImethod enhances the capacities including the adaptability, the robustness and the balance of the exploration and exploitation remarkably.