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The optimal objective of Scenario 2 is minimum CO2 emission.
The optimal objective of Scenario 3 is minimum energy consumption.
Accordingly, the optimal objective of (12) will be reached when the MCCs of the ESSs in the ADS converge to a common value (rho_{C}^), as in [27, 28].
Similar(57)
The optimal objectives of exhaust hood optimization are the limitation concentration of emission at the exit and the minimum deposition on the exhaust hood walls of cut tobacco.
The optimal objectives of the model also influence the development of energy demand structure.
The optimal objectives of Scenario 1 are Pareto Optimality of minimum total energy consumption and minimum total CO2 emission.
Based on the above analysis, the optimal objective functions of the revenue optimization model of PV-BESS power plants in typical scenario can be expressed as: mathit{operatorname{Max}}kern0.5em {R}_{sum}={R}_{elc}+{R}_{ass}+{R}_{tou} (11 where, the R sum is the revenue of the PV-BESS power plants.
In addition, the works in [15, 39, 40] have shown that it is equivalent to looking up a value of α such that the optimal objective value of the following optimization problem (29) equals to zero.
Making use of (30) and (31), the optimal objective value of sub-problem (26) can be written as max | δ f i | ≤ ε, i = 1, ⋯, R w H R ( δ f i ) w = w H R ~ w. (32).
As a result, the optimal objective value of (P2) will be slightly less than that of (P1).
Let τmax denote the optimal objective value of (57).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com