Fixed effect- three identifier variables and result interpretation

I have panel dataset where each observation is identified by three variables ( server#, Motherboard_tempreature, cpu_utlization) I built the fixed effect regression as follows:

Code:

egen id= group( server_num, CPUAAverageCoreTemperature)
xtset id CPUAUtilization

xtreg IPMIPower i.CPUAUtilization i.CPUAAverageCoreTemperature Fan1RPM , fe vce(robust)


Fixed-effects (within) regression               Number of obs     =      1,296
Group variable: id                              Number of groups  =        163

R-squared:                                      Obs per group:
     Within  = 0.9573                                         min =          1
     Between = 0.3868                                         avg =        8.0
     Overall = 0.8253                                         max =          9

                                                F(9,162)          =     882.55
corr(u_i, Xb) = 0.0615                          Prob > F          =     0.0000

                                                 (Std. err. adjusted for 163 clusters in id)
--------------------------------------------------------------------------------------------
                           |               Robust
                 IPMIPower | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
           CPUAUtilization |
                        1  |    45.0428   1.088814    41.37   0.000      42.8927     47.1929
                        2  |    65.8275   1.355795    48.55   0.000     63.15019    68.50481
                        3  |   81.43575   2.302044    35.38   0.000     76.88986    85.98163
                        4  |   99.49748   3.169925    31.39   0.000     93.23778    105.7572
                        5  |   101.0006   3.407896    29.64   0.000     94.27099    107.7302
                        6  |   104.4599   3.555344    29.38   0.000     97.43913    111.4807
                        7  |   106.5967   3.766806    28.30   0.000     99.15836    114.0351
                        8  |   107.0671   3.877293    27.61   0.000     99.41051    114.7236
                           |
CPUAAverageCoreTemperature |
                       72  |          0  (omitted)
                       74  |          0  (omitted)
                           |
                   Fan1RPM |   .0151772   .0019755     7.68   0.000     .0112762    .0190783
                     _cons |   28.71307   4.337745     6.62   0.000     20.14726    37.27889
---------------------------+----------------------------------------------------------------
                   sigma_u |  18.396744
                   sigma_e |  9.5483104
                       rho |  .78778394   (fraction of variance due to u_i)
--------------------------------------------------------------------------------------------

My questions:
1- can I use the model in this way to get the coefficient of CPUAUtilization- this variable is used in xtset- ? can I say that when the cpu utilization=2 then the IPMIPower increased by 65?
2- I want to see the coefficient of CPUAAverageCoreTemperature as well- used in xtset - but it is omitted? how to check the coefficent for this time invariant variable? should I switch to random effect?

To conclude, can I include the variables used in xtset as independent variable in the model and interrupt them as we interrupt other independent variable?