Yes, adding controls can increase the power of your statistical tests and make standard errors smaller. To see this, consider the following two regressions for comparison:
$$
\begin{align}
Y_i &= \alpha + \beta D_i + X'_i\gamma +e_i \newline
Y_i &= \mu + \pi D_i + u_i
\end{align}
$$
Assume that $X,D$ are uncorrelated with the error terms $e$ and $u$ and that we have homoscedasticity. Then you can show that:
$$
\begin{align}
\sqrt{n}(\widehat{\beta} - \beta) &\stackrel{d}\rightarrow N\left(0, \frac{E(e^2)}{\text{Var}(D_i)(1-R^2_{D,X})} \right) \newline
\sqrt{n}(\widehat{\mu} - \mu) &\stackrel{d}\rightarrow N\left( 0,\frac{E(u^2)}{\text{Var}(D_i)} \right)
\end{align}
$$
where $\stackrel{d}\rightarrow$ denotes convergence in distribution and $R^2_{D,X}$ is the $R^2$ from the regression of $D_i$ on $X_i$. I'm not going to prove this unless you explicitly request it because the main point of interest is the next result which uses the variances from these two distributions. The ratio of the two asymptotic variances is:
$$
\frac{1-R^2_{Y,(D,X)}}{1-R^2_{Y,D}}\cdot \frac{1}{1-R^2_{D,X}}
$$
where again $R^2_{Y,(D,X)}$ and $R^2_{Y,D}$ are the $R^2$s from the first and the second regression, respectively.
What does this ratio tell you?
- It shows the trade-off in asymptotic variances when going from the short to the long regression. The first term is smaller than (or equal to) one since $R^2$ increases when you add $X_i$ to the regression. It will be much smaller than one if $X_i$ explains a lot of the variation in $Y_i$. So this is how your standard errors decrease.
- The second term will be larger than (or equal to) one depending on the correlation between $D_i$ and $X_i$. If the two are strongly correlated, then the $R^2$ from the regression of $D_i$ on $X_i$ will be large and hence this second term will be large which is why your standard errors increase in this case.
If $D_i$ and $X_i$ are uncorrelated (e.g. if $D_i$ comes from a randomized experiment), then $R^2_{D,X} = 0$. This is the case when adding control variables is very preferable because they soak up the residual variance and increase the power of your statistical tests on $D_i$ which is great if this is your variable of interest.
So why don't your standard errors change? It's probably because the two counteracting effects from adding controls to your regression balance each other.
