scholarly journals Research on Economic Optimization of Microgrid Cluster Based on Chaos Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Peng Wang ◽  
Yu Zhang ◽  
Hongwan Yang
Keyword(s):  
Energy Storage ◽  
Transaction Cost ◽  
Power Distribution ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Population Diversity ◽  
Particle Swarm Algorithm ◽  
Economic Optimization ◽  
Chaotic Mapping ◽  
Cauchy Mutation

With the deepening of the power market reform on the retail side, it is of great significance to study the economic optimization of the microgrid cluster system. Aiming at the economics of the microgrid cluster, comprehensively considering the degradation cost of energy storage battery, the compensation cost of demand-side controllable loads dispatch, the electricity transaction cost between the microgrids, and the electricity transaction cost between the microgrid and the power distribution network of the microgrid cluster, we establish an optimal dispatch model for the microgrid cluster including wind turbines, photovoltaics, and energy storage batteries. At the same time, in view of the problem that the population diversity of the basic sparrow search algorithm decreases and it is easy to fall into local extremes in the later iterations of the basic sparrow search algorithm, a chaos sparrow search algorithm based on Bernoulli chaotic mapping, dynamic adaptive weighting, Cauchy mutation, and reverse learning is proposed, and different types of test functions are used to analyze the convergence effect of the algorithm, and the calculation effects of the sparrow algorithm, the particle swarm algorithm, the chaotic particle swarm, and the genetic algorithm are compared. The algorithm has higher convergence speed, higher accuracy, and better global optimization ability. Finally, through the calculation example, it is concluded that the benefit of the microgrid cluster is increased by nearly 20%, which verifies the effectiveness of the improvement.

scholarly journals A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for Cooperative Air Combat DWTA

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Guang Peng ◽  
Yangwang Fang ◽  
Shaohua Chen ◽  
Weishi Peng ◽  
Dandan Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Time Window ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Neighborhood Search ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Cauchy Mutation ◽  
Air Combat

A hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm is proposed to solve cooperative air combat dynamic weapon target assignment (DWTA). First, based on the threshold of damage probability and time window constraints, a new cooperative air combat DWTA multiobjective optimization model is presented, which employs the maximum of the target damage efficiency and minimum of ammunition consumption as two competitive objective functions. Second, in order to tackle the DWTA problem, a mixed MODPSO and neighborhood search algorithm is proposed. Furthermore, the repairing operator is introduced into the mixed algorithm, which not only can repair infeasible solutions but also can improve the quality of feasible solutions. Besides, the Cauchy mutation is adopted to keep the diversity of the Pareto optimal solutions. Finally, a typical two-stage DWTA scenario is performed by HMODPSO and compared with three other state-of-the-art algorithms. Simulation results verify the effectiveness of the new model and the superiority of the proposed algorithm.

Optimal Control of Hybrid Energy Storage System of New Energy Power Generation System Based on Improved Particle Swarm Algorithm

2022 ◽  
Author(s):  
Shangzhou Zhang
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Storage System ◽  
Particle Swarm ◽  
Energy Storage System ◽  
Particle Swarm Algorithm ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
Swarm Algorithm

In order to ensure the stability and reliability of power supply and realize day and night power generation, wind and solar complementary power generation systems are built in areas with abundant solar and wind energy resources. However, the system investment cost is too high. Because of this, there are wind, light intermittent, and non-intermittent power generation systems. For issues such as stability, an energy storage system needs to be configured to stabilize power fluctuations. This paper aims to study the optimization control of hybrid energy storage system of new energy power generation system based on improved particle swarm algorithm. In this paper, the application of particle swarm algorithm to power system reactive power optimization has been researched in two aspects. Through optimization methods, reasonable adjustment of control variables, full use of equipment resources of the power grid, to improve voltage quality and reduce system operation network to ensure the stability of the voltage system. In addition, this paper selects the IEEE30 node test system and simulation data analysis, takes the hybrid energy storage system as the optimization object, and optimizes the reactive power of the newly improved particle swarm algorithm. The experiments in this paper show that the improved algorithm has a good effect in reactive power optimization, increasing the performance of the hybrid energy storage system by 27.02%. MPSO algorithm is also better than basic PSO algorithm. It can be seen from the figure that in the PSO algorithm, the algorithm basically tends to be stable after more than 40 iterations, and finally the algorithm converges to 0.089.

scholarly journals Capacity Configuration Optimization of Micro-grid System basedon Quantum Particle Swarm Algorithm

2021 ◽  
Vol 252 ◽  
pp. 01021
Author(s):  
Zhang Yuqiong ◽  
Chen Ziwei ◽  
Shao Zhifang ◽  
Zhao Qiang ◽  
Han Chuyin
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Hydrogen Production ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Environmental Benefits ◽  
Particle Swarm Algorithm ◽  
Grid System ◽  
Micro Grid ◽  
Swarm Algorithm

The optimized configuration of wind/photovoltaic/storage micro-grid system capacity can realize multi-energy complementation and improve the stability and economy of grid-connected operation of power generation units. In this paper, the capacity of each core component of the micro-grid system under different combination paths such as Wind-PV power generation, battery energy storage, hydrogen production by electrolysis, and fuel cell power generation are optimized and economically analyzed. Taking the FCFF (Free Cash Flow of Firm) net present value maximization of the system running for 20 years as the objective function, considering the impact of energy shortage rate and dynamic electricity price, an operation research optimization model is established and intelligent algorithms are used to solve the model. The model can flexibly realize capacity optimization under different micro-grid combination paths, and it can prevent the solution result from falling into the local optimum through the design of quantum particle swarm algorithm. We analyzed the optimization results in terms of economic benefits, social benefits, and environmental benefits, and further analyzed the annual power generation status of the system and the operation status of the electrolysis hydrogen production system. The calculation example shows that under the current technical conditions, the micro-grid system composed of wind and solar power generation, electrochemical energy storage, and hydrogen production by electrolysis has better economic, social and environmental benefits than other models.

Software test data generation technology based on polymorphic particle swarm evolutionary algorithm

2021 ◽  
pp. 1-13
Author(s):  
Wenning Zhang ◽  
Qinglei Zhou
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Test Case Generation ◽  
Particle Swarm Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Learning Factor ◽  
Swarm Algorithm

Combinatorial testing is a statute-based software testing method that aims to select a small number of valid test cases from a large combinatorial space of software under test to generate a set of test cases with high coverage and strong error debunking ability. However, combinatorial test case generation is an NP-hard problem that requires solving the combinatorial problem in polynomial time, so a meta-heuristic search algorithm is needed to solve the problem. Compared with other meta-heuristic search algorithms, the particle swarm algorithm is more competitive in terms of coverage table generation scale and execution time. In this paper, we systematically review and summarize the existing research results on generating combinatorial test case sets using particle swarm algorithm, and propose a combinatorial test case generation method that can handle arbitrary coverage strengths by combining the improved one-test-at-a-time strategy and the adaptive particle swarm algorithm for the variable strength combinatorial test problem and the parameter selection problem of the particle swarm algorithm. To address the parameter configuration problem of the particle swarm algorithm, the four parameters of inertia weight, learning factor, population size and iteration number are reasonably set, which makes the particle swarm algorithm more suitable for the generation of coverage tables. For the inertia weights.

A Novel Particle Swarm Algorithm for Online Trading Customer Classification

2014 ◽  
Vol 644-650 ◽  
pp. 2181-2184
Author(s):  
Chen Chen
Keyword(s):  
Search Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Computation Method ◽  
Original Algorithm ◽  
Online Trading ◽  
Customer Classification ◽  
Swarm Algorithm ◽  
Improved Algorithm

Particle swarm algorithm is an efficient evolutionary computation method and wildly used in various disciplines. But as a random global search algorithm, particle swarm algorithm easily falls into the local optimal solution for its rapid propagation in populations and in order to overcome these shortcomings, a novel particle swarm algorithm is presented and used in classifying online trading customers. The corresponding improvements include improving the speed update formula of particles and improving the balance between the development and detection capability of original algorithm and redesigning the calculation flow of the improved algorithm. Finally after designing 21 customer classification indicators, the improved algorithm is realized for customer classification of a certain E-commerce enterprise and experimental results show that the algorithm can improve classification accuracy and decreases the square errors.

scholarly journals Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller

2017 ◽  
Vol 17 (1) ◽  
pp. 72-86 ◽  
Author(s):  
Hossein Azadi Kherabadi ◽  
Sepehr Ebrahimi Mood ◽  
Mohammad Masoud Javidi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fuzzy Controller ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Gravitational Search ◽  
Optimum Solution

Abstract Gravitational Search Algorithm (GSA) isanovel meta-heuristic algorithm. Despite it has high exploring ability, this algorithm faces premature convergence and gets trapped in some problems, therefore it has difficulty in finding the optimum solution for problems, which is considered as one of the disadvantages of GSA. In this paper, this problem has been solved through definingamutation function which uses fuzzy controller to control mutation parameter. The proposed method has been evaluated on standard benchmark functions including unimodal and multimodal functions; the obtained results have been compared with Standard Gravitational Search Algorithm (SGSA), Gravitational Particle Swarm algorithm (GPS), Particle Swarm Optimization algorithm (PSO), Clustered Gravitational Search Algorithm (CGSA) and Real Genetic Algorithm (RGA). The observed experiments indicate that the proposed approach yields better results than other algorithms compared with it.

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