Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: 35, Pages: 1-15
Seyed Morteza Moghimi1 *, Seyed Hossein Hosseinian2 , Abolfazl Elahimanesh3 and Gholamreza Sarlak4
1 Department of Electrical Engineering, Jasb Branch, Islamic Azad University, Jasb, Iran; [email protected]
2 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran; [email protected]
3 Department of Electrical, Qom Branch, Jame Elmi Karbordi University, Qom, Iran; [email protected]
4 Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran; [email protected]
Background/Objectives: Design of smart energy planning and management as a Smart Energy Management System (SEMS). Methods/Statistical Analysis: Approach used in this paper is that instead of using a mathematical modeling system based on statistical analysis, a kind of intelligent modeling has been adopted. The methodology ventures to achieve the variety of processes which produce the desired objective function being monitored, that is the simulation results obtained by GAMS.Software GAMS (v. 24.1.2) as software has been utilized for solving the given problem of optimization in this paper that is considered as one of problems of Mixed Integer Linear Programming (MILP) and it is linked by MATLAB software (v. 22.214.171.124) to display its graphic results. Findings: In order to achieve the aforementioned objective a sample Micro-Grid (MG) including a wide range of sources scattered as well as smart ways to manage and save energy are adopted. Furthermore, the paper includes a SEMS in order to optimize the operation of smart sample network, generation planning and energy saving design. Since, functions such as optimization are dependent on unit power generation and renewable resources units output, SEMS becomes essential for energy generation forecasts. Hence, applying a suitable stochastic predictive algorithm to the output products, which is primarily distributed based on wind and solar energy, is considered for short periods and hourly forecasts. Finally, the predicted input values, as the optimization necessitates, are applied. Based on the data output of predictive models representing the generation power and climatic conditions in the coming hours, the intelligent optimization of optimal operation patterns to suit the user have been chosen as objectives and are implemented. Applications/Improvements: Predicted input values, as the optimization necessitates, are applied. Based on the data output of predictive models representing generation power and climatic conditions in the coming hours, SEMS are implemented.
Keywords: Distributed Generation, Energy Storage, Intelligent Energy Management System, Micro Grid, Multi-Objective Optimization
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