Multi Objective Based Framework for Energy Management of Smart Micro-Grid

The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses. This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost ef...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.220302-220319
Main Authors: Haseeb, Muhammad, Kazmi, Syed Ali Abbas, Malik, M. Mahad, Ali, Sajid, Bukhari, Syed Basit Ali, Shin, Dong Ryeol
Format: Article
Language:English
Subjects:
Control agent
Controllers
Distributed generation
Economic conditions
Economics
Electrical loads
Energy industry
Energy management
energy market management controller
Heuristic methods
Home appliances
home energy management controller
local generation
market management
micro-grid
multi objective grey wolf optimization
Particle swarm optimization
Pricing
Priorities
renewable energy resources
Residential energy
Schedules
Smart grids
Task analysis
Wind turbines
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Summary:The increasing demand of energy in the traditional grids is getting more complex, less feasible, harmful, uneconomical and high in power losses. This paper presents an efficient energy management approach to mitigate such issues with smart micro grid (SMG) and aims at a solution that is both cost effective and eco-friendly, within energy market paradigm. Goals are achieved with the help of Home Energy Management Controller (HEMC), Energy Market Management Controller (EMMC) and Control Agent (CA). The individual load is managed in the presence of local generation, storage system, user comfort, DGs and Utility within energy market paradigm. Two level energy management approach is proposed to achieve concerned goals. First is to manage load and schedule storage with respect to individual local generation and market pricing. Second is to manage energy market with the help of four different types of priorities and control agent input. The problem is solved with a variant of meta-heuristic method, Multi Objective Grey Wolf Optimization (MOGWO), which gives more comprehensive solution by comparing with Particle Swarm Optimization (PSO). The proposed methodology is implemented on a SMG based-community test system. Homes within that community have different economic conditions and personal priorities. Simulation results demonstrates achievement of aimed goals in presented work.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3041473