Multi-Agent Based Energy Management in Microgrids Using MACSimJX

Excessive growth in electricity consumption has been experienced over the past few years due to an increase in population around the world.This tends to increase the use of renewable energy and randomness of the load. So it is important to improve the traditional methodologies and techniques applied on microgrid to make it more intelligent. In this paper, multi agent system is employed over autonomous microgrid framework to endorse its intelligence. The Multi-Agent system is simulated in Java Agent Development Environment (JADE) environment and matlab toolbox Simulink is used for the implementation of the microgrid model. Further, MACSimJX is used to communicate between the micro grid and agent system. This paper shows the communication between the agents and the microgrid model and how they process the data through MACSimJX to make intelligent decisions

A Cost-Effective Multi-Verse Optimization Algorithm for Efficient Power Generation in a Microgrid

Renewable energy sources (RESs) are a great source of power generation for microgrids with expeditious urbanization and increase in demand in the energy sector. One of the significant challenges in deploying RESs with microgrids is efficient energy management. Optimizing the power allocation among various available generation units to serve the load is the best way to achieve efficient energy management. This paper proposes a cost-effective multi-verse optimizer algorithm (CMVO) to solve this optimization problem. CMVO focuses on the optimal sharing of generated power in a microgrid between different available sources to reduce the generation cost. The proposed algorithm is analyzed for two different scale microgrids (IEEE 37-node test system and IEEE 141-node test system) using IEEE test feeder standards to assess its performance. The results show that CMVO outperforms multi-verse optimizer (MVO), particle swarm optimization (PSO), artificial hummingbird algorithm (AHA), and genetic algorithm (GA). The simulation results emphasize the cost reduction and execution time improvement in both IEEE test systems compared with other meta-heuristic algorithms

Threshold Based Load Handling Mechanism for Multi-Agent Micro Grid Using Cloud Computing

Micro grid is an automated power system that incorporates advanced modern communication techniques and control methodologies into power grid. Its smartness lies in the intelligent and efficient decision making layer which can be enhanced using multi agent system. In this paper, multi agent system has been used in cloud computing where task from the consumer nodes for computing are handled by agents in accordance with set up threshold value to make the system more intelligent. The consumer nodes tasks are categorized as under loaded, lightly loaded, normally loaded, overloaded and highly overloaded while execution. The idea of threshold policy has been introduced in the multi agent based cloud computing to ensure the improvement in performance. This concept of threshold mechanism for task execution in the multi agent system deployed in cloud computing results in reduction of waiting time, improved processing time and effective resource utilization