Recent reports from international energy agencies indicate that more than a billion of the population in the world is deprived of basic electricity provisions, confined mainly to the remote communities of developing nations. Microgrids are promoted as a potential technology for electricity provisions to off-grid rural communities, but have failed to reach their value proposition in the context of rural electrification access. In view of the rampant rural electrification issues, the objective of this paper is to furnish an understanding of, and advance the knowledge into, methods to facilitate the design and development of microgrid systems for remote communities in developing countries. The methodology involves an integrative review process of an annotated bibliography to summarise past empirical or theoretical literature. As such, this research is based on evaluation attributes, and identifies the challenges and barriers for remote microgrids through an analysis of 19 case studies. The paper concludes by proposing key aspects that need to be considered for developing a framework to improve the sustainability of electricity provisions for off-grid rural communities in developing countries.
This paper proposes a novel modeling approach for optimal sizing of the components of an islanded micro-grid subject to satisfying a reliability index for meeting the loads. The proposed micro-grid incorporates photovoltaic arrays, wind turbines, a battery bank, an inverter, and an electric vehicle (EV) charging station. A demand-side management mechanism based on a deferrable load program is implemented and a model reduction technique is also utilized to mitigate the computational cost. Three different optimization algorithms, namely the whale optimization algorithm (WOA), particle swarm optimization (PSO), and the genetic algorithm (GA) are considered in this study to minimize the total cost of the system. The simulation studies have shown that although the WOA reduces the computational burden and requires much lower iterations compared with PSO and GA, it converges to sub-optimal solutions; therefore, it is not a good option for micro-grid planning purposes. Moreover, the results demonstrate that by charging coordination of EVs and deferring a pre-determined portion of the residential loads, overloading can be avoided and available components can be utilized better, which in turn reduces the sizes of the components and total cost of the system.
Optimal sizing of renewable and sustainable energy systems should consider the uncertainties associated with various input data to ensure the financial sustainability of developing such systems, especially in the case of stand-alone systems. This paper proposes a novel stochastic modelling framework for the optimal sizing of micro-grids subject to satisfying a reliability index for supplying the loads. The proposed framework incorporates a model reduction technique, a state-of-the-art meta-heuristic optimization algorithm (i.e. moth-flame optimization algorithm), as well as an uncertainty analysis technique using Monte Carlo simulations based on a new scenario reduction process. It also preserves the computational tractability. A micro-grid test system incorporating photovoltaic panels, wind turbines, battery packs, a DC/AC inverter, and an electric vehicle fast charging station is used to assess the validity and effectiveness of the proposed stochastic framework. Accordingly, the impact of uncertainties associated with renewable power generation and load demand on the sizes of the considered micro-grid components are evaluated. The numerical simulation results for the considered micro-grid test system are presented and compared with those generated by a deterministic model, which have demonstrated the effectiveness of the proposed stochastic modelling framework.
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