You can convert your recursion into a more linear recursion using the following technique:
$$T(n) = k~T(n-1) + n~d$$
$$\frac 1d ~ T(n) - \frac 1d~k~T(n-1) = n \tag{A}$$
$$\frac 1d ~ T(n - 1) - \frac 1d~k~T(n-2) = n - 1 \tag{B}$$
$$\frac 1d ~ T(n) + \left(-k~\frac 1d - \frac 1d\right)~ T(n - 1) + \frac 1d~k~T(n-2) = 1 \tag{C}$$
$$T(n) = (k + 1)~ T(n - 1) - k~T(n-2) + d \tag{D}$$
(A) is just a rewrite, (B) is the equation for $n-1$, (C) = (A) - (B), (D) is just a rewrite.
(D) is pretty standard form, my preferred approach to finishing it is matrices:
$$
\begin{bmatrix}T(n + 2) \\ T(n + 1) \\ 1\end{bmatrix} =
\begin{bmatrix}
k+1 & -k & d \\
1 & 0 & 0 \\
0 & 0 & 1 \end{bmatrix}
\begin{bmatrix} T(n + 1) \\ T(n) \\ 1\end{bmatrix}$$
$$
\begin{bmatrix}T(n + 2) \\ T(n + 1) \\ 1\end{bmatrix} =
\begin{bmatrix}
k+1 & -k & d \\
1 & 0 & 0 \\
0 & 0 & 1 \end{bmatrix} ^n
\begin{bmatrix} T(1) \\ T(0) \\ 1\end{bmatrix}$$
Jordan Decomp:
$$P = \begin{bmatrix}
1 & 1 & 1 \\
\frac 1k & 1 & 0 \\
0 & 0 & \frac{1-k}d
\end{bmatrix}, D = \begin{bmatrix}
k & 0 & 0 \\
0 & 1 & 1 \\
0 & 0 & 1
\end{bmatrix}$$
$$
\begin{bmatrix}T(n + 2) \\ T(n + 1) \\ 1\end{bmatrix} = \left(P~D~P^{-1}\right)^n
\begin{bmatrix} T(1) \\ T(0) \\ 1\end{bmatrix} = \left(P~D^n~P^{-1}\right)
\begin{bmatrix} T(1) \\ T(0) \\ 1\end{bmatrix}$$
Since $D^n = \begin{bmatrix}
k^n & 0 & 0 \\
0 & 1 & 1 \\
0 & 0 & 1 \end{bmatrix}$, you get:
$$
\begin{bmatrix}T(n + 2) \\ T(n + 1) \\ 1\end{bmatrix} =
\begin{bmatrix}\frac{{k}^{n+1}-1}{k-1} & -\frac{k\,\left( {k}^{n}-1\right) }{k-1} & \frac{d\,\left( {k}^{n+1}-2\,k+1\right) }{{\left( k-1\right) }^{2}} \\ \frac{{k}^{n}-1}{k-1} & -\frac{{k}^{n}-k}{k-1} & \frac{d\,\left( {k}^{n}-k\right) }{{\left( k-1\right) }^{2}} \\ 0 & 0 & 1\end{bmatrix}
\begin{bmatrix} T(1) \\ T(0) \\ 1\end{bmatrix}$$
Finally:
$$T(n+1) = \frac{{k}^{n}-1}{k-1} ~T(1) -\frac{{k}^{n}-k}{k-1} ~T(0) + \frac{d\,\left( {k}^{n}-k\right) }{{\left( k-1\right) }^{2}}$$
I know this isn't the approach that you started with, but it is a very general approach to solving even non-linear recurrences. Hope it helps you.