Several years, some simplifying methods are necessary to create its answer feasible, particularly when representing the intraday operation. To accomplish so, the present operate utilizes some in particular when representing the intraday operation. To perform so, the existing work utilizes some time-clustering assumptions. The very first step of this course of action is clustering a number of the months time-clustering assumptions. The first step of this method is clustering some of the months into seasons, which really should be defined based on rainy and dry periods along with the demand into seasons, which really should be defined based on rainy and dry periods and also the demand profiles. Once the seasons are defined, the representative days inside each and every of them have to profiles. When the seasons are defined, the representative days inside every of them should be estimated, here known as common days. be estimated, here known as common days.Energies 2021, 14, x FOR PEER REVIEWEnergies 2021, 14, 7281 PEER Overview x FOR8 ofof 21 8 8ofThis form of representation aims to lessen problem size, capturing the principle qualities inside every single prevalent day in each and every season. The operate developed in [43] makes use of This sort of representation aims to minimize problem size, capturing the key the main This kind of representation aims to lessen issue size, capturing charactera clustering idea to define the common days to be made use of by the proposed generation qualities inside eachday in each and every season. The operate created in [43] uses inclustering istics inside each typical typical day in every season. The Benzyl isothiocyanate Technical Information function created a [43] uses expansion model. For the modelling presented in this function, two common days had been defined a clustering concept typical days totypical daysthe proposed by the proposed generation notion to define the to define the be used by to be employed generation expansion model. for each with the 4 seasons. The definition of your seasons was determined by three-months expansion model. For the modelling presented in thisdays have been defined for each and every of defined For the modelling presented within this perform, two typical perform, two common days have been the 4 clusters. For every single season, the days have been separated into two groups: weekdays and for every The definition on the seasons was according to three-months clusters. For each season, seasons. with the 4 seasons. The definition from the seasons was determined by three-months weekends. Figure four Maresin 1 site summarizes the discussed clustering strategy. clusters. wereeach season, the days were separated into two groups: weekdays along with the days For separated into two groups: weekdays and weekends. Figure four summarizes weekends. Figure four summarizes the discussed clustering method. the discussed clustering tactic.Figure 4. Example of seasons and standard days clustering method (Supply: Authors’ elaboration). Figure four. Instance of seasons and typical days clustering approach (Supply: Authors’ elaboration). Figure four. Example of seasons and typical days clustering method (Supply: Authors’ elaboration).The optimization developed in this paper also contemplates the operating reserve The optimization developed in this paper also contemplates the operating reserve constraints as a variable with the decision method, which will depend on the generation The optimization created within this paper also contemplates the operating reserve constraintsof renewable energy sources. The endogenouswill depend on the generation variability as a variable of the choice method, which sizing on the spinning reserve constraints of.