## 1 Introduction  
Under the consequences of one of the biggest energy crises, all countries around the globe are exploring viable solutions for alternatives to fossil fuels and especially natural gas. In the light of polluting emissions and climate change, the necessity of renewable energy sources (RES) grew exponentially during the last two years, under the shadow of a war that is still plaguing European countries and states around the world. Apart from the public concerns relating to the upcoming living conditions, a huge uncertainty has been added to the price of essential products that are either directly or indirectly affected by the dependency on imported natural gas [1]. As more stakeholders seek greater clarity and confidence in long-term investments and their respective opportunities in the forthcoming years, the impact of a potential integration of renewable and storage systems needs to be evaluated. To mitigate the environmental concerns around fossil fuels exploitation, the benefit list of a transition towards sectors electrification, de-carbonization and sustainability must be strengthened, facilitated by efficient tools that are able to consolidate the real-world constraints. Recent research activities on renewable sources and storage have been concentrated successfully on single operations and objectives. New objectives have recently appeared, targeting the co-optimization of cost, emissions, security and reliability within the power systems. These targets can be examined in depth, considering resources hybridization, multi-sectoral energy satisfaction and smart grid consolidation. As the missing link between intermittent renewable power and constant reliability, the various energy storage technologies can be compared, accounting for microgrid and virtual power plant formations. The current state of research carefully reviews and assesses various approaches in optimal unit commitment (UC), which forms one of the most important tasks of the modern electric industry. Empirical methods simply consider the satisfaction of load by adjusting electricity production to demand during normal conditions [2; 3]. In the case of generation deficits, load shedding takes place, while during excess generation, the electricity from RES is curtailed. More evolutionary methods aim at the co-optimization of load shedding and RES curtailment, following a priority with respect to generating the incremental cost of the participating units [4]. In this direction, priority-list schemes [5], Benders decomposition [6], Branch-and-Bound [7] and Lagrange relaxation (LR) [8] are some representative techniques that are able to offer only near-optimal solutions. Their relevant advantages rely on the mathematical process, which facilitates both the traceable transitions towards the final recommendation and the computational complexity irrespective of the number of examined generators. On the other hand, the obtained duality gap cannot be eliminated due to the imposed, real-world constraints that plague the generation activity today. These refer to the complicating constraints of power balance and spinning reserve in order to deal with the intermittent and stochastic behaviour of RES [9]. To offer adaptive mechanisms that are able to satisfy the required ramping capability, unit capacity and operating times, a heuristic search has been proposed in several research works. These approaches rely on physical- or biological-based algorithms and involve genetic algorithms [10], particle swarm optimization [11], simulated annealing [12], ant colony [13], tabu search [14] and so on. Although flexible, these techniques cannot guarantee optimality, especially in large systems where the magnitude of their sub-optimality cannot be evaluated [15]. As a result, the most recent version of UC solvers includes meta-heuristic alternatives.