## 1 Introduction  
The action plan for meeting the huge energy needs of developing countries includes the strategic design of small microgrids for areas that are not yet connected to the main grid due to technical and geographical limitations. Moreover, the global call for decarbonization of the current energy mix towards climate change mitigation has increased the call for a more sustainable energy supply system via renewable energy resources-based microgrid technologies [1]. A microgrid is a small power system consisting of a cluster of distributed energy generation resources, energy storage systems and aggregated loads that can operate in isolated mode or grid-connected mode for providing clean, reliable and affordable energy supply [2]. The technical aspect of the design of a microgrid's energy supply systems entails three interrelated aspects which include; the capacity sizing, time-based energy supply unit scheduling and point-to-point energy transaction strategy [3]. Most research activities on microgrid design consider these aspects in a segregated approach; hence, there is a tendency to miss the link between the long-term investment requirements and the short-term operational requirements. Achieving an optimum component sizing is highly essential for realising the long-term techno-economic goals of the microgrid system. However, the cost of short-term scheduling and energy transactions should be essentially factored in at the component sizing stage so as to achieve the best configuration in terms of overall cost benefits. More over, a system that considers only energy storage as a source of flexibility tends to be very expensive [4]; thus, there is a need to consider all possible resources that can contribute to overall power system flexibility enhancement [5; 6]. The implementation of the DSM programs has the potential of acting as an ancillary system to meet the net-load by load profile reshaping. This will minimize the storage capacity requirement and subsequently reduce the total cost of investment on the microgrid. Hence, the transition to a sustainable, reliable and fully renewable energy-based microgrid development dictates the incorporation of demand-side management (DSM) [7] and other flexibility measures some of which involves smart energy management and energy resource forecasting [8]. As the penetration level of wind and solar energy resources increases, system resilience decreases due to the stochastic and intermittent changes in their output due to the weather conditions [9]. Thus, incorporating an effective forecasting technology can be a crucial supporting tool for wind and solar power coordination for DSM implementation into the smart microgrid network [10]. The cost benefits of incorporating the short-term forecasts of solar irradiance, wind speed and load demand in microgrid system operation planning are highlighted in [11; 12]. A precise outlook of the expected generated power profile helped the system operator to achieve an efficient flexibility option for reliable operation planning by the utilities [13; 14]. This involves intelligently scheduling the BESS operation to charge during peak generation periods and supplement the grid deficiencies during low power generation hours [15]. Furthermore, the advancement of smart grid technologies has brought remarkable improvement in efficiency, economics, and reliability of operation, especially in power systems with high renewable energy penetration [16]. The evolution of the smart grid requires the integration of the end-user devices, smart meters, advanced monitoring systems, communication infrastructure, substations, transmission lines, transformers, energy storage, protection systems, and so on, for efficient monitoring and control [17]. This transition requires the adoption of internet infrastructure as an enabling platform for monitoring, data acquisition, and system control of the power grid infrastructure by the utility, customer information dispensation, and the management of the flexible demand-side resources [18].