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large amount of the internal weighted factor can create a limitation to the algorithm to discover the best possible solution locally, the convergence can be achieved at a prolonged rate. The author has recommended enhancing the PSO technique which adjusts and decreases the weight factor linearly through iterations to solve the problem. Thus, the enhanced PSO can get the optimum solution universally without fixing at local minima. This method is utilized in an MSE for hybrid power sources. In some highly developed means, the chaotic sequence is used in place of arbitrary numbers to optimize the action.

The authors in Ref. [45] have suggested CBA (Chaotic Bat Algorithm) optimize the financial send-off of the system under study. To get better performance of the BA (Bat Algorithm) to get the universal optimal solution, the chaotic sequences is applied in the primary BA. Authors of Ref. [44] have suggested Ant Colony Optimization technology for actual time functioning of MSE with a new Multi-Layer technique (MACO) in standalone micro-grid. The MACO is an enhanced version of the basic Ant Colony Optimization (ACO) algorithm. In this algorithm, the numerical quantity of levels is same as that of variables in designing on the problem and quantity of nodes in every level is equal to the numeric quantity of satisfactory values of every variable. The author investigated an MSE in Ref. [55] using a two-layer predictive control, and the degradation cost of SSE is taken as the main consideration.In Ref. [56] the author has suggested a new technology to optimize the performance of fireworks algorithm (FA) as a novel crossbreed Multi-goal-based FA and Gravitational Search Operator (MFAGSO) to resolve the non-linear trouble with several variables. There are also multiple hybrid constraints. This recommended algorithm uses gravitational explorer to lead the flash into the collection area to swap position information with optimal solutions to reach the best results.

      Authors of Ref. [57] have explained an advanced algorithm called Improved Artificial Bee Colony algorithm (IABC) to get an optimized result in a hybrid grid-connected micro-grid. The author has rectified the basic ABC by generating the scrutinize bee using Gravitational Search Operator, which optimises the finding accuracy, so the universal best possible solution can be enhanced. The authors in ref. [58] have suggested a new algorithm such as Enhanced Bee Colony Optimization (EBCO), which gives a better performance of MSE for MGs with verity RESs and several SSE. EBCO operator is unlike the classical BCO, as it has the self- adaptation revulsion factor in the bee swarm, for getting the better performance of each bee swarm and that is why the finding accuracy enhances effectively in more dimensional problems.

      The authors in Ref. [59] described an MSE applying Artificial Bee Colony (ABC) algorithm for an isolated MG. In Ref. [60] it proposed an MSE, which is based on utilization of fuzzy logic controller in a micro-grid that employs around 25 sets of laws. The objective function is to lower the deviation of power with maintaining battery SoC. In Ref. [61] the MSE is for an interconnected system of micro-grid using an advanced algorithm based on fuzzy logic called Mamdani algorithm. The optimization is done with the scheme combination of fuzzy logic and genetic algorithms. The authors in Ref. [62] provide an algorithm for MSE based on game theory to maximize the gain available during consumption of energy. Ref. [63] represents an adaptable neural fuzzy interference system, with the help a predictor of the echo state network. In Ref. [64] the author proposed a new approach, a Stackelberg game approach for managing the flow of energy in MG. The author of Ref. [65] suggested an MSE model for a smart micro-grid using game theory, where maximization of profit to the overall cost and satisfactory power utilization is selected as the strategy. It is a distributed energy management model.

Figure 1.8 summarizes 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.

1.5 Conclusion

      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.

References

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