Microgrid Technologies

Figure 1.8summarizes the energy management technologies for micro-grids. Among them, some methodologies are classical techniques such as MILP, linear programming and non-linear programming. These programmings may be an excellent move towards optimization depending on the goal function and limitations. But the artificial intelligence (AI) processes are dedicated to approaches towards the situation while the classical methods come into unsatisfactory results.

Figure 1.8 Energy management methodology.

This chapter discusses the amalgamation of the management system of energy in different ranges of micro-grid with numerous mechanism and diverse load type, optimizing the complete system, achieving the definite goals considering system constraints. This chapter comprehensively presents the different novelties in the area of integration of the electric car as energy supply, the setting up of thermal and power combination systems to generate simultaneously the heat and electricity to supply thermal as well as electrical requirements. And it represents the accomplishment of crossbreed optimization operators which are the excellent substitute than a single algorithm.

This literature review emphasizes on the approaches for energy management in micro-grid: islanded and connected with grid network approaches. In a further approach, optimization is done using the available information. Coordination has to be done with the grid parameters. In islanded mode, the optimization can be done with incomplete information or making a strategy to coordinate the micro-grid participants or components. Each participant optimizes its own settings. Grid-connected or centralized energy management is mostly done with metaheuristic methods. Multi-agent methods can be implemented for islanded or decentralized micro-grid.

An MSE model of a microgrid consists of a data acquisition system, monitoring and data analysis of system parameters, supervised control, and human–machine interface.

Here the review represented the methods for management depending on short term and foresight basis. The choices of grid-connected or not ensure that the designer of MG understands the balancing between cost and gain. The decentralized energy management allows greater flexibility and reliability and safety of system operation have to be considered.

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