Seyed Morteza Moghimi ¹, Seyed Hossein Hosseinian ², Abolfazl Elahimanesh ³, Gholamreza Sarlak ⁴

Affiliations
1 Department of Electrical Engineering, Jasb Branch, Islamic Azad University, Jasb, IR
2 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IR
3 Department of Electrical, Qom Branch, Jame Elmi Karbordi University, Qom, IR
4 Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IR

Abstract

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. 15.0.0.0) 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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Abolfazl Elahimanesh ¹, Seyed Morteza Moghimi ², Mohammad Reza Aghamohammadi ³

Affiliations
1 Department of Electrical Engineering, University of Applied Science and Technology (UAST) of Qom, Qom, Iran
2 Department of Electrical Engineering, Jasb Branch, Islamic Azad University, Jasb, Iran
3 Department of Electrical Engineering, Power and Water University of Technology, Tehran, Iran

Abstract

Background/Objectives: Reactive power sources, network structure and active generation algorithm of power network buses are effective factors on voltage stability and static loadability extent of standard power network. Methods/Statistical analysis: Simulation programming for the paper has been done through FORTRAN which is a strong programming language in solving mathematic issues. By creating different status of generation and investigating results of 50 generation patterns in this network, it has been observed that increment in generation of some controlled existing buses improve loadability limit in comparison to other buses (3 and 6 buses to 2 and 8 buses) Findings: In this paper, the effect of Distributed Generation (DG) placement on voltage stability trend and static loadability extent of IEEE 14-bus and Iran 34-bus test standard power networks has been investigated. In order to determine places of distributed generations for improving loading extent and voltage stability, at first equivalent reactance of Thevenin buses has been recognized; Then, weak buses of the network has been identified. It should be mentioned, this research has indicated that placing DGs on these buses has an effective role on improving static loadability extent and voltage stability. Three different generation algorithms have been created in this paper in order to observe research mentioned aims. Then, static loadability extent and voltage stability have been investigated in these three cases, the most desirable and most optimum one have been chosen from them. Application/Improvements: By investigating three different states in creating generation pattern, it has been observed that placing DG on weak load buses is more proper than other ways for increasing loadability limit and voltage stability improvement.

Keywords

Distributed Generation Optimum Placement, Generation Pattern, IEEE 14-Bus Network, 34-Bus Network, Power Network Structure

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