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Three Scenarios in Microgrid to Solve Management Problem for Residential Application Using Genetic Algorithms

Three Scenarios in Microgrid to Solve Management Problem for Residential Application Using Genetic Algorithms
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Author(s): Faisal A. Mohamed (Omar Al-Mukhtar University, Libya)
Copyright: 2014
Pages: 21
Source title: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
Source Author(s)/Editor(s): Pandian M. Vasant (Petronas University of Technology, Malaysia)
DOI: 10.4018/978-1-4666-4450-2.ch019

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Abstract

This chapter discusses online management of the MicroGrid components. A major challenge for all power utilities is not only to satisfy the consumer demand for power, but to do so at minimal cost and low emissions. Any given power system can be comprised of multiple generating units each of which has its own characteristic operating parameters. The operating cost and emission level of these generators usually correlate proportionally with their outputs, therefore the challenge for power utilities is to balance the total load among generators that are running as efficiently as possible. One of the important applications of the MicroGrid (MG) units is the utilization of small-modular residential or commercial units for onsite service. Genetic Algorithms (GA) optimization is well-suited to solve the environmental/economic problem of the MG. The proposed problem is first formulated as a nonlinear constrained optimization problem. Prior to the optimization, system model components from real industrial data are constructed. The model takes into consideration the operation and maintenance costs as well as the reduction in NOx, SO2, and CO2 emissions. The optimization is aimed at minimizing the cost function of the system while constraining it to meet the customer demand and safety of the system. The results ensure the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions in one single run.

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