A microgrids energy management model based on multi-agent system using adaptive weight and chaotic search particle swarm optimization considering demand response

2020 ◽  
Vol 262 ◽  
pp. 121247 ◽  
Author(s):  
Cunbin Li ◽  
Xuefeng Jia ◽  
Ying Zhou ◽  
Xiaopeng Li
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Management ◽  
Demand Response ◽  
Management Model ◽  
Multi Agent System ◽  
Agent System ◽  
Swarm Optimization ◽  
Chaotic Search ◽  
Adaptive Weight ◽  
Multi Agent

scholarly journals A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3286
Author(s):  
Jicheng Liu ◽  
Fangqiu Xu ◽  
Shuaishuai Lin ◽  
Hua Cai ◽  
Suli Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Demand Response ◽  
Optimization Model ◽  
Particle Swarm ◽  
Multi Agent System ◽  
Agent System ◽  
Swarm Optimization ◽  
Chaotic Search ◽  
Multi Agent

The optimal operation of microgrids is a comprehensive and complex energy utilization and management problem. In order to guarantee the efficient and economic operation of microgrids, a three-layer multi-agent system including distributed management system agent, microgrid central control agent and microgrid control element agent is proposed considering energy storage units and demand response. Then, based on this multi-agent system and with the objective of cost minimization, an operation optimization model for microgrids is constructed from three aspects: operation cost, environmental impact and security. To solve this model, dynamic guiding chaotic search particle swarm optimization is adopted and three scenarios including basic scenario, energy storage participation and demand response participation are simulated and analyzed. The results show that both energy storage unit and demand response can effectively reduce the cost of microgrid, improve the operation and management level and ensure the safety and stability of power supply and utilization.

scholarly journals IMPLEMENTASI ALGORITMA PARTICLE SWARM OPTIMIZATION UNTUK PENENTUAN POSISI STRATEGIS AGENT PADA SIMULASI ROBOT SEPAK BOLA DUA DIMENSI

2012 ◽  
Vol 1 (2) ◽  
Author(s):  
Galih Hermawan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Multi Agent System ◽  
Agent System ◽  
Swarm Optimization ◽  
Multi Agent

Robot sepak bola merupakan perpaduan antara olah raga, teknologi robotika, dan multi agent system. Untuk mencapai tujuan, selain membutuhkan kecerdasan individu, juga menuntut kemampuan kerja sama antar individu. Posisi robot ketika bermain mempengaruhi kemampuan robot dalam bekerja sama dan memilih aksi yang sesuai. Dalam tulisan ini akan disajikan hasil penelitian kami dalam penerapan algoritma particle swarm optimization (PSO) untuk menentukan posisi robot ketika bermain sepak bola. Hasil pengujian pada simulasi RoboCup Soccer dua dimensi menunjukkan bahwa tim robot sepak bola yang menggunakan algoritma PSO memiliki performa bermain lebih baik ketimbang tim sebelum menggunakan algoritma PSO.

Multi-Task Coalition Generation of Multi-Agent System with Limited Resource

2014 ◽  
Vol 971-973 ◽  
pp. 1655-1658
Author(s):  
Ning Qiang ◽  
Feng Ju Kang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Limited Resource ◽  
Multi Agent System ◽  
Discrete Particle Swarm Optimization ◽  
Agent System ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Agent ◽  
Simulation Results

A new fitness function is introduced in order to maximize the number of task served by the multi-agent system (MAS) with limited resource, while the tasks information remains unknown until the system found them one by one. The new fitness function not only considers to maximize the profit of the system which can be seen as to maximize the remaining resource of the system in the case of the MAS with limited resource, but also takes the balance of remaining resource in to account and it can makes a compromise between them. This paper uses an improved discrete particle swarm optimization to optimize the coalition of MAS. In order to improve the performance of the algorithm we redefine the particle velocity and position update formula. The simulation results show the effectiveness and superiority of the proposed fitness function and optimization algorithm.

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