Multi-agent system for managing distributed energy storage and electrical vehicles

2014 ◽  
Vol 9 (2) ◽  
pp. 181-190
Author(s):  
Thillainathan Logenthiran ◽  
Dipti Srinivasan
Keyword(s):  
Energy Storage ◽  
Multi Agent System ◽  
Distributed Energy ◽  
Agent System ◽  
Distributed Energy Storage ◽  
Electrical Vehicles ◽  
Multi Agent

Management of Distributed Energy Resources Using Intelligent Multi-Agent System

2010 ◽  
pp. 208-231 ◽  
Author(s):  
T. Logenthiran ◽  
Dipti Srinivasan
Keyword(s):  
Open Systems ◽  
Agent Technology ◽  
Energy Market ◽  
Multi Agent System ◽  
Distributed Energy ◽  
Agent System ◽  
Market Outcomes ◽  
Rule Based System ◽  
Multi Agent ◽  
Cross Platform

The technology of intelligent Multi-Agent System (MAS) has radically altered the way in which complex, distributed, open systems are conceptualized. This chapter presents the application of multi-agent technology to design and deployment of a distributed, cross platform, secure multi-agent framework to model a restructured energy market, where multi players dynamically interact with each other to achieve mutually satisfying outcomes. Apart from the security implementations, some of the best practices in Artificial Intelligence (AI) techniques were employed in the agent oriented programming to deliver customized, powerful, intelligent, distributed application software which simulates the new restructured energy market. The AI algorithm implemented as a rule-based system yielded accurate market outcomes.

Fuzzy-Based Multi-Agent System for Distributed Energy Management in Smart Grids

2016 ◽  
Vol 24 (05) ◽  
pp. 781-803 ◽  
Author(s):  
Bharat Menon Radhakrishnan ◽  
Dipti Srinivasan ◽  
Rahul Mehta
Keyword(s):  
Energy Management ◽  
Smart Grids ◽  
Test System ◽  
Small Scale ◽  
Multi Agent System ◽  
Distributed Energy ◽  
Agent System ◽  
Renewable Sources ◽  
Distributed Energy Management ◽  
Multi Agent

Energy Management Systems have become an imperative aspect of smart grids, owing to the enormous challenges imposed due to real-time pricing, distributed generation and integration of intermittent renewables. Due to the uncertainty associated with renewable sources and prominent fluctuations in the load demand, it is extremely important to maintain the overall energy balance in such grids. In this paper, the distributed energy management is achieved using a Multi-agent System which provides a flexible and reliable solution to control and manage smart grids. Adaptive fuzzy systems are designed to instill intelligent decision making capability in the agents of multi-agent system. When renewable sources are inadequate, the sustainability of the system is not guaranteed and multi-agent system is capable of deciding the mode of operation such that the system reliability and performance is not compromised. The proposed algorithm maintains power balance in the system and also sustains desired values for the State of Charge of storage units in order to guarantee extended battery life. The Energy management system also implements a cost optimization algorithm based on the Particle Swarm Optimization technique, to minimize operating costs and maximize profits earned by the grid. The proposed energy management algorithm is tested and validated on a practical test system which inherits most of the features of a small-scale smart grid.

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