**1. Introduction**

The quest for provision of affordable, clean, and reliable electricity supply is the key aspiration of many nations globally. These aspirations are portrayed by the commitment of most countries to the formulation and chartering of strategic policies that are targeted towards attaining 100% green energy transition in the near future [1]. Many countries have embarked on different sustainable energy pathways; for example Germany [2] and Sweden [3] aims to attain 100% renewable energy by 2050 while Hawaii in the United States has set 2045 as a target [4]. Several African countries have also taken significant steps and shown visible commitment towards massive green energy uptakes mainly by wind and solar energy. Countries such as Kenya [5], Ghana [6], Mauritius [7], Nigeria [8], Egypt, and

South Africa [9] are currently making efforts in the integration of renewable energy technologies on both small and large scale. However, the incorporation of variable renewable energy resources (VREs) such as wind and solar energy increases the flexibility needs of a power system. Hence, to achieve an acceptable level of power system operation reliability, dynamic and vibrant control strategies need to be devised to balance the demand and supply using efficient flexibility mechanism [10]. Flexibility providers are, usually, adaptable resources needed to address the short-term mismatches between the instantaneous generated power and the load demand [11].

Most of the classical power system planning strategies comprises of segregated optimization models for power system design. These models generally comprise of three features which are component sizing to determine the optimal capacity configuration, unit commitment model to determine the optimal operation strategy, and electricity market strategies to evaluate optimal point-to-point energy transactions [12]. However, the segregated approach is not sufficient for achieving an optimally reliable system design; this is because the operational efficiency of power system relies highly on the time-based dispatchability and controllability of the system generating resources. Furthermore, with the increasing penetration of VREs, the controllability and dispatchability of power system generation sources becomes more complex. Hence, the planning for the transition towards a high VREs-based energy system requires an integrated system planning that involves the cost of component sizing and system flexibility [13]. A comprehensive investigation of the economic viability of different kinds of flexibility providers available for power systems is discussed in [14]. The cost of flexibility is defined as the additional cost required to integrate additional adaptable resources to address the intermittency of VREs integration. There are many sources of flexibility provider options; these includes system interconnection, demand-side management, supply-side management, storage technologies, etc. [15]. From the generation planning perspective, flexibility is investigated based on the ramping capability of the generators, the minimum possible attainable generation, increased cycles of shutdowns and startups for hybrid configuration as outlined in [16].

The idea of hybrid-energy system has also shown some significant growing interest as a valuable and efficient flexibility provider towards 100% VREs generation as shown in much recent research. The authors in [14] performed and provided a comprehensive framework for techno-economic flexibility analysis based on MILP optimization model by combining complimentary distribution generation alternatives such as thermal storage, heat pump, and cogeneration. The importance of the appropriate selection of complementary generating technologies coupled with energy storage system (ESS), with an improved optimal operation strategy, as a cost-effective path towards ensuring power system flexibility is highlighted in [17]. Electricity storage has played a valuable and significant central role in power system in many aspects [18]. Energy storage has the advantage to time-shift the electrical energy supply thus it acts as an ideal mechanism for moderating the consequences of fluctuating output of VREs on the power system. There are many well-known types of ESS in many works of literature varying in terms of technical and economic specification as summarized in [19]. Many studies have evaluated and demonstrated the cost–benefits of appropriate selection and application of different ESS technologies incorporation into the power system planning. The common ESS ranges from pumped hydro [20], hydrogen storage [21], BESS [22], compressed air energy storage, etc. [23]. The inclusion of demand-side managemen<sup>t</sup> into optimal component sizing that involves energy storage (ESS) facilities is proposed in [24]. The final outcomes show that using the demand-side managemen<sup>t</sup> (DSM) increases the system flexibility and offers an economical planning option with reduced ESS capacity requirement.

There are two main categories of end-user electrical demand namely the flexible load demand (FDRs) and inflexible/static load demand. The flexible load demand (FDR) are assumed to be those appliances whose time of use can be transferred from one period to another. FDRs include heat pump, room heater, washing machines, etc. They are also referred to as the shiftable appliances because their usage can be delayed during the period of peak demand or shortage of electricity supply and activated later during the period of over-generation. Non-shiftable load demands, on the other hand, are appliances that are static in terms of the time of use, they have a fixed time of period as to

when to use, such as illumination loads. DSM has recently received heightened attention in terms of flexibility provision capability using flexible demand resources (FDRs) to achieve the controllability of the customer load demand pattern [25]. In general, the FDRs provide allowances and capacity for time-shifting in terms of their energy requirements. A proper schedule of the FDRs can guarantee mitigation of the gap that exists between demand and system generation for power systems with very high renewable energy fraction as addressed in [26]. A demand-side flexibility approach using the controllability of FDRs has been developed with a detailed implementation framework for commercial and residential smart building in [27]. Demand response program (DRP) is a subset of DSM designed to influences the consumer's behaviors in terms of the time of usage of the FDR through motivations such as incentive payments and lucrative electricity prices to improve the overall system efficiency [28]. The concepts of DRP have been adequately covered in the literature with much focus on optimizing the electricity market design. The commonly featured types of DRPs found in the literature includes and not limited to; real-time pricing, day-ahead pricing, time of use, interrupted curtailable, direct load control, critical peak pricing, etc. [29]. Successful implementation of DRP should take into account the current and the forecasted future power system status to fully exploit the market flexibility [30] and captures the VREs generation uncertainties [31,32].

However, an accurate and reliable VREs output forecasting can serve as a core and vital component of energy managemen<sup>t</sup> systems (EMS) implementation [33]. The role of forecasting also has significant value in the implementation of pricing schemes in the power markets to decrease the rate of market volatility [34]. Hence, power forecasting plays a pivotal role in flexibility planning for integrating and addressing the uncertainty of the VREs in hybrid power systems. Accurate power forecasting provides critical information of the anticipated status (power shortages and surplus) of the power system ahead of time before the actual occurrence. Hence, a good foresight of the time-ahead generation profile provides an opportunity to plan for future uncertainties adequately and cost-effectively. The ability of a system to meet and handle the growing ramping requirements and volatile residual demand is a significant concern of system operators as the share of wind and solar increases. The economic benefit of accurate solar forecasting in minimizing the generation cost, as well as managing power curtailment was investigated and illustrated by Martinez-Anido et al. [32]. A detailed approach has been adopted for wind power forecast application in [35] and in [36] considering several power market scenarios.
