## 4 Optimization Techniques
### Optimization Techniques Regarding Uncertainty  
One of the major problems in a microgrid is to track the output fluctuation of the distributed generation by means of mechanical and electromagnetic regulation. The real culprit behind this problem is the volatility and randomness in renewable energy resources. The massive integration of renewable energy resources further leads to the issue of frequency offset and voltage fluctuation in a power system, lowering its quality. Although many electronic devices deal with this problem, they increase the system complexity and stability and are also limited in response speed and accuracy. Further, the cost of the objective function increases if we try to curb the uncertainties, as shown in Figure 4. The AC-DC hybrid system also allows users to participate in energy management and trading actively. The users make different decisions based on energy prices, which adds to uncertainty in energy production and consumption. The main methods employed to deal with  
Figure 3: Power flow general algorithm. uncertainty are stochastic programming and robust programming. As robust programming does not deal with interrelationships between uncertainties, the trend shifts in favor of stochastic programming to resolve this issue. In [43], the authors proposed a novel framework with day-ahead scheduling based on a distributional robust optimization model and intra-day multiple time scale (MTS) rolling optimal dispatch. This DRO model is reduced using the mixed-integer linear programming (MILP) approach, employing column and constraint generation algorithm, and achieves the optimal dispatch dealing with the uncertainties. It is compared with the existing adaptive robust optimization (ARO) model and shows better performance and results, reducing the load shedding and source abandoning in a hybrid microgrid. The impact of time interval needs to be further explored in this model. In [44], the authors presented a two-stage robust optimal dispatch model dealing with the state variables of distributed generation cost, schedule of power exchange, charging and discharging of batteries, and the power involved with interlink converters. It also employs the MILP approach to reduce the model and achieves optimal dispatch in light of uncertainties using column and constraint generation algorithm. In [45], the authors propose a stochastic load flow model based on unscented transform and modified crow search algorithm (MCSA) for uncertainty in variables of active and reactive loads, wind and solar power generation, and market power price for the power exchange among the sources and between sources and the grid. A new two-stage crow search algorithm is applied to enhance the ability of the existing algorithm to avoid premature convergence. It shows that model based on MCSA delivers better performance, but the cost of the system is higher in order to minimize the uncertainties in the variables mentioned above. In [46], the authors compare two approaches using Petri nets and fuzzy logic to design and implement a system for the schedule of power dispatch of a real-time hybrid microgrid. The constraints of cost optimization, grid supply limitation, load demand, and power losses are taken under study. It shows that fuzzy logic manages the constraints of the hybrid microgrid in a superior manner and hails out as a better approach. A new supervision system is suggested to manage the data regarding random variables of weather, speed of the wind, solar irradiance, and temperature in remote areas for hybrid power generation. In [47], the authors suggest a stochastic model employing cloud theory based on fuzzy logic to deal with the uncertainties related to wind and solar power, load demand, and market price. A proposed modified flower pollination algorithm (MFPA) is compared with the existing optimization algorithms in light of different scenarios. MPFA employed with a stochastic model, based on cloud theory of fuzzy logic and probability, shows better optimization with minimum deviation in all scenarios comparatively.