diff --git a/SZ/auswertung/c.ipynb b/SZ/auswertung/c.ipynb
index cc02f06..9d0a6ed 100644
--- a/SZ/auswertung/c.ipynb
+++ b/SZ/auswertung/c.ipynb
@@ -782,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 82,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -795,20 +795,17 @@
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>desc</th>\n      <th>curve</th>\n      <th>area</th>\n      <th>j_c</th>\n      <th>u_cc</th>\n      <th>ff</th>\n      <th>eta</th>\n      <th>p_mlp</th>\n      <th>u_mlp</th>\n      <th>i_mlp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3x3_hell</th>\n      <td>3x3_hell</td>\n      <td>[[-1.5, -0.5417189], [-1.4825, -0.5438931], [-...</td>\n      <td>4056</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>-0.4693348714285935</td>\n    </tr>\n    <tr>\n      <th>3x3_schaltung_1</th>\n      <td>3x3_schaltung_1</td>\n      <td>[[-1.5, -0.4987553], [-1.4825, -0.4996429], [-...</td>\n      <td>4056</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>-0.20828160714286892</td>\n    </tr>\n    <tr>\n      <th>3x3_schaltung_2</th>\n      <td>3x3_schaltung_2</td>\n      <td>[[-1.5, -0.5053194], [-1.4825, -0.5066954], [-...</td>\n      <td>4056</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>-0.20258660000002998</td>\n    </tr>\n    <tr>\n      <th>3x3_schaltung_3</th>\n      <td>3x3_schaltung_3</td>\n      <td>[[-1.5, -0.0006112836], [-1.4825, -0.000607707...</td>\n      <td>4056</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>-0.00015580096971469002</td>\n    </tr>\n    <tr>\n      <th>3x3_schaltung_4</th>\n      <td>3x3_schaltung_4</td>\n      <td>[[-1.5, -0.2743212], [-1.4825, -0.2720917], [-...</td>\n      <td>4056</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>-0.07911919453640125</td>\n    </tr>\n    <tr>\n      <th>3x3_verschattung_1</th>\n      <td>3x3_verschattung_1</td>\n      <td>[[-1.0, -0.006214225], [-0.97, -0.006181907], ...</td>\n      <td>4056</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>-0.0039047434763039633</td>\n    </tr>\n    <tr>\n      <th>3x3_verschattung_2</th>\n      <td>3x3_verschattung_2</td>\n      <td>[[-1.0, -0.2602277], [-0.98, -0.2602689], [-0....</td>\n      <td>4056</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>-0.22754981766503463</td>\n    </tr>\n    <tr>\n      <th>3x3_verschattung_3</th>\n      <td>3x3_verschattung_3</td>\n      <td>[[-1.0, -0.01750273], [-0.98, -0.01755973], [-...</td>\n      <td>4056</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>-0.014582063482794307</td>\n    </tr>\n    <tr>\n      <th>huge_hell</th>\n      <td>huge_hell</td>\n      <td>[[-1.0, -0.1947314], [-0.9775, -0.1942969], [-...</td>\n      <td>8788</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>-0.1654561994540209</td>\n    </tr>\n    <tr>\n      <th>huge_verbraucher</th>\n      <td>huge_verbraucher</td>\n      <td>[[-1.0, -0.2670641], [-0.9775, -0.258778], [-0...</td>\n      <td>8788</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>-0.10818945820239775</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-      ],
       "text/plain": [
-       "                                  desc  \\\n3x3_hell                      3x3_hell   \n3x3_schaltung_1        3x3_schaltung_1   \n3x3_schaltung_2        3x3_schaltung_2   \n3x3_schaltung_3        3x3_schaltung_3   \n3x3_schaltung_4        3x3_schaltung_4   \n3x3_verschattung_1  3x3_verschattung_1   \n3x3_verschattung_2  3x3_verschattung_2   \n3x3_verschattung_3  3x3_verschattung_3   \nhuge_hell                    huge_hell   \nhuge_verbraucher      huge_verbraucher   \n\n                                                                curve  area  \\\n3x3_hell            [[-1.5, -0.5417189], [-1.4825, -0.5438931], [-...  4056   \n3x3_schaltung_1     [[-1.5, -0.4987553], [-1.4825, -0.4996429], [-...  4056   \n3x3_schaltung_2     [[-1.5, -0.5053194], [-1.4825, -0.5066954], [-...  4056   \n3x3_schaltung_3     [[-1.5, -0.0006112836], [-1.4825, -0.000607707...  4056   \n3x3_schaltung_4     [[-1.5, -0.2743212], [-1.4825, -0.2720917], [-...  4056   \n3x3_verschattung_1  [[-1.0, -0.006214225], [-0.97, -0.006181907], ...  4056   \n3x3_verschattung_2  [[-1.0, -0.2602277], [-0.98, -0.2602689], [-0....  4056   \n3x3_verschattung_3  [[-1.0, -0.01750273], [-0.98, -0.01755973], [-...  4056   \nhuge_hell           [[-1.0, -0.1947314], [-0.9775, -0.1942969], [-...  8788   \nhuge_verbraucher    [[-1.0, -0.2670641], [-0.9775, -0.258778], [-0...  8788   \n\n                    j_c  u_cc   ff  eta  p_mlp  u_mlp                    i_mlp  \n3x3_hell            0.0   2.0  1.0  0.0    1.0    1.0      -0.4693348714285935  \n3x3_schaltung_1     0.0   2.0  0.0  0.0    0.0    1.0     -0.20828160714286892  \n3x3_schaltung_2     0.0   2.0  0.0  0.0    0.0    1.0     -0.20258660000002998  \n3x3_schaltung_3     0.0   2.0  0.0  0.0    0.0    1.0  -0.00015580096971469002  \n3x3_schaltung_4     0.0   1.0  0.0  0.0    0.0    1.0     -0.07911919453640125  \n3x3_verschattung_1  0.0   1.0  1.0  0.0    0.0    1.0   -0.0039047434763039633  \n3x3_verschattung_2  0.0   2.0  1.0  0.0    0.0    1.0     -0.22754981766503463  \n3x3_verschattung_3  0.0   1.0  1.0  0.0    0.0    1.0    -0.014582063482794307  \nhuge_hell           0.0   7.0  1.0  0.0    1.0    5.0      -0.1654561994540209  \nhuge_verbraucher    0.0   6.0  0.0  0.0    0.0    4.0     -0.10818945820239775  "
+       "3x3_hell              0.533266\n3x3_schaltung_2       0.168064\n3x3_schaltung_3       0.000118\n3x3_schaltung_4       0.058548\n3x3_verschattung_1    0.004647\n3x3_verschattung_2    0.277610\n3x3_verschattung_3    0.018373\nhuge_hell             0.880641\nhuge_verbraucher      0.410309\nName: p_mlp, dtype: float64"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ccurves.round()"
+    "ccurves['p_mlp']"
    ]
   }
  ],
diff --git a/SZ/auswertung/d.ipynb b/SZ/auswertung/d.ipynb
index d683f3c..a81888f 100644
--- a/SZ/auswertung/d.ipynb
+++ b/SZ/auswertung/d.ipynb
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 3,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -66,10 +66,12 @@
     {
      "data": {
       "text/plain": [
-       "30    0.001202\n65    0.001254\nName: j_c, dtype: float64"
+       "30    0.031243\n",
+       "65    0.032597\n",
+       "Name: j_c, dtype: float64"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -80,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -98,7 +100,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 31,
+   "metadata": {
+    "autoscroll": false,
+    "collapsed": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "I_300 = 9.56e-8  # A\n",
+    "e_g = 1.12  # eV\n",
+    "kb = 8.617333262145e-5  # eV/K\n",
+    "T = 273.15 + 32   # K\n",
+    "a = 1.42\n",
+    "I0 = I_300 * np.exp(e_g/(T*kb))\n",
+    "I_p = 0.682171 # A\n",
+    "U = SecondaryValue('ln(ip/i0*exp(e_g/(k*T)))*T*k*a', defaults=dict(k=kb, e_g=e_g, ip=I_p, a=a, i0=I0))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
    "metadata": {
     "autoscroll": false,
     "collapsed": false,
@@ -112,7 +138,7 @@
     {
      "data": {
       "image/png": [
-       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\n"
+       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\n"
       ],
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
@@ -123,10 +149,13 @@
     }
    ],
    "source": [
-    "plt.plot(*ucs)\n",
+    "plt.plot(*ucs, label='Messung', linestyle='None', marker='x')\n",
+    "plt.plot(ucs[0], U(T=(273.15+ucs[0],))*1000, label='Theorie')\n",
     "plt.xlabel(r'Temparatur [$^\\circ$C]')\n",
-    "plt.ylabel(r'$U_{CC}$ [mV]')\n",
-    "plt.grid()"
+    "plt.ylabel(r'$V_{OC}$ [mV]')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "save_fig(plt.gcf(), 'D/ucc.pdf')"
    ]
   },
   {
@@ -171,9 +200,7 @@
     },
     {
      "data": {
-      "image/png": [
-       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\n"
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+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -211,9 +238,7 @@
     },
     {
      "data": {
-      "image/png": [
-       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\n"
-      ],
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -252,9 +277,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": [
-       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\n"
-      ],
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -293,7 +316,10 @@
     {
      "data": {
       "text/plain": [
-       "(array([1.        , 0.98611111, 0.97569444, 0.90972222, 0.82638889,\n        0.64236111, 0.61805556, 0.51388889, 0.36111111, 0.20486111]),\n array([0.04910464, 0.04876483, 0.04851155, 0.04694047, 0.04504422,\n        0.04126876, 0.04081881, 0.03903868, 0.03691678, 0.03544335]))"
+       "(array([1.        , 0.98611111, 0.97569444, 0.90972222, 0.82638889,\n",
+       "        0.64236111, 0.61805556, 0.51388889, 0.36111111, 0.20486111]),\n",
+       " array([0.04910464, 0.04876483, 0.04851155, 0.04694047, 0.04504422,\n",
+       "        0.04126876, 0.04081881, 0.03903868, 0.03691678, 0.03544335]))"
       ]
      },
      "execution_count": 70,
@@ -304,6 +330,75 @@
    "source": [
     "relangles"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "autoscroll": false,
+    "collapsed": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fde4095de80>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": [
+       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\n"
+      ],
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ts = ucs[0]\n",
+    "plt.plot(ts, U(T=(273.15+ts,)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "autoscroll": false,
+    "collapsed": false,
+    "ein.hycell": false,
+    "ein.tags": "worksheet-0",
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "300668347958.8219"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "I0"
+   ]
   }
  ],
  "metadata": {
diff --git a/SZ/auswertung/figs/D/ucc.pdf b/SZ/auswertung/figs/D/ucc.pdf
new file mode 100644
index 0000000..ee28268
Binary files /dev/null and b/SZ/auswertung/figs/D/ucc.pdf differ
diff --git a/SZ/auswertung/utility.py b/SZ/auswertung/utility.py
index a8b82e2..5970168 100644
--- a/SZ/auswertung/utility.py
+++ b/SZ/auswertung/utility.py
@@ -56,7 +56,8 @@ def plot_ccurve_line(ax, ccurve, area=None, marker='.', compliance=.99,
         if mlp:
             mlp[1] = mlp[1]/area
 
-    ax.errorbar(v, c, linestyle='None', marker=marker, markersize=2, alpha=1, label=label,
+    ax.errorbar(v, c, linestyle='None', marker=marker, markersize=2, alpha=1,
+                label=label,
                 **pyplot_args)
     ax.set_xlabel("Spannung U [V]")
     ax.set_ylabel("Stromstaerke I [A]" \
diff --git a/SZ/protokoll/figs/python b/SZ/protokoll/figs/python
new file mode 120000
index 0000000..2444e0a
--- /dev/null
+++ b/SZ/protokoll/figs/python
@@ -0,0 +1 @@
+/home/hiro/Documents/Projects/UNI/Prakt/FP/SZ/auswertung/figs/
\ No newline at end of file
diff --git a/SZ/protokoll/protokoll.bib b/SZ/protokoll/protokoll.bib
index 5e106f8..ab18709 100644
--- a/SZ/protokoll/protokoll.bib
+++ b/SZ/protokoll/protokoll.bib
@@ -1 +1,11 @@
 @misc{wikipedia_2019, title={Wikipedia, Shockley-Gleichung}, url={https://de.wikipedia.org/wiki/Diode#Ideale_Diode_/_Shockley-Gleichung}, journal={Wikipedia}, publisher={Wikimedia Foundation}, year={2019}, month={Apr}}
+
+@BOOK{Demtröder2018,
+        AUTHOR = {Demtröder, Wolfgang},
+        YEAR = {2018},
+        TITLE = {Experimentalphysik 2 - Elektrizität und Optik},
+        EDITION = {7. Aufl.},
+        ISBN = {978-3-662-55790-7},
+        PUBLISHER = {Springer-Verlag},
+        ADDRESS = {Berlin Heidelberg New York},
+}
diff --git a/SZ/protokoll/protokoll.tex b/SZ/protokoll/protokoll.tex
index cb83924..185ec71 100644
--- a/SZ/protokoll/protokoll.tex
+++ b/SZ/protokoll/protokoll.tex
@@ -563,6 +563,12 @@ Ein kleiner Ventilator wurde als
 Verbraucher mit dem Solarmodul in Reihe geschaltet und eine weitere
 Kennlinie wurde aufgenommen. Auch der Laststrom und die Lastspannung
 wurden mit dem Multimeter gemessen.
+
+\begin{align}
+  \label{eq:last}
+  U_V = \SI{5.25}{\volt}\\
+  I_V = \SI{143}{\milli\ampere}
+\end{align}
 \footnote{Dr. D\"orr macht den besten Kartoffelsalat.}
 
 \subsection{Temperatureinfluss}
@@ -665,7 +671,7 @@ Der Anstieg der Geraden gibt den Parameter
   \centering
   \begin{tabular}{l|SSS}
     \toprule
-    Zelle & {\(R_S\) [\si{ohm}]} & {\(\isc\) [\si{\A}]} & {\(a\)} \\
+    Zelle & {\(R_S\) [\si{ohm}]} & {\(I_\text{S}}\) [\si{\A}]} & {\(a\)} \\
     \midrule
     A8 & .34 & 9.56e-8 & 1.49 \\
     \"ubliche Werte \footcite{wikipedia_2019} & {-} &
@@ -914,6 +920,8 @@ da bei Verschattung/Ausfall einer Zelle, diese den Stromfluss nicht
 behindert. Dementsprechend w\"ahre auch eine Reihenschaltung von
 jeweils drei parallelgeschaltenen Modulen m\"oglich gewesen.
 
+Die Beleuchtungsintensität betrug \sun{1/3}.
+
 \subsubsection{Analyse der Kennlinien}
 
 Die Werte in~\ref{tab:verschtab} aufgelisteten Werte wurden wie
@@ -934,8 +942,8 @@ Plots der Kennlinien finden sich im Anhang:~\ref{sec:plotsc}
     6er Modul, Verschattung~\ref{fig:schatt1} &  0.001228 &  1.43 &  0.65 &  0.000894 \\
     6er Modul, Verschattung~\ref{fig:schatt2} &  0.063305 &  1.57 &  0.69 &  0.053387 \\
     6er Modul, Verschattung~\ref{fig:schatt3} &  0.004057 &  1.48 &  0.76 &  0.003533 \\
-    6er Modul, Hell &  0.021841 &  7.02 &  0.65 &  0.078163 \\
-    6er Modul mit Verbraucher &  0.026106 &  6.11 &  0.29 &  0.036418 \\
+    13er Modul, Hell &  0.021841 &  7.02 &  0.65 &  0.078163 \\
+    13er Modul mit Verbraucher &  0.026106 &  6.11 &  0.29 &  0.036418 \\
   \end{tabular}
   \caption{Charakteristische Kenngr\"o\ss{}en der betrachteten Solarmodule.}
   \label{tab:verschtab}
@@ -956,34 +964,99 @@ Plots der Kennlinien finden sich im Anhang:~\ref{sec:plotsc}
   \label{tab:verschwd}
 \end{table}
 
+\subsubsection{Verschaltung mit Widerst\"anden}
+\label{sec:verschanal}
 
 \ref{tab:verschwd} speist sich aus den Fits f\"ur gro\ss{}e \(I>0\)
 (gibt \(R_S\)) und gro\ss{}en \(I<0\) (gibt \(R_S+R_P\)), wobei
 letztere Fits aufgrund der form der Kennlinien (\ref{fig:hellkennfit})
-wenig Aussagekraft besitzen. Die Werte
-\(R_G=\SI{4.99}{\kilo\ohm}\) und \(R_K=\SI{3.3}{\ohm}\) erkennt man in
-den Werten f\"ur \(R_S\) in allen Schaltungen in Korrespondenz mit den
-Erwartungen wieder. F\"ur \(R_K\) als \(R_S\) in Schaltungen 1,3
-ergeben sich im Fit gr\"o\ss{}ere Werte, da hier der Widerstand des
-Solarmoduls an mehr ins Gewicht f\"allt. Bei n\"aherer Betrachtung
-von~\ref{fig:hellkenn} und~ kann man erkennen, dass sich die f
+wenig Aussagekraft besitzen. Die Werte \(R_G=\SI{4.99}{\kilo\ohm}\)
+und \(R_K=\SI{3.3}{\ohm}\) erkennt man in den Werten f\"ur \(R_S\) in
+allen Schaltungen in Korrespondenz mit den Erwartungen wieder, so als
+wenn man die Widerst\"ande im Ersatzschaltbild direkt anpasste. F\"ur
+\(R_K\) als \(R_S\) in Schaltungen 1,3 ergeben sich im Fit
+gr\"o\ss{}ere Werte, da hier der Widerstand des Solarmoduls an mehr
+ins Gewicht f\"allt. Bei n\"aherer Betrachtung von~\ref{fig:hellkenn}
+und~\ref{tab:verschtab} kann man erkennen, dass sich durch hinzunahme
+von Widerst\"anden die Kennlinien vom Ideal entfernt (FF und \(\eta\)
+sinken). Ist \(R_K\) gro\ss{} und \(R_S\) klein, so ist der Effekt
+gering (Schaltung 1). Verstauscht man die Verh\"altnisse (Schaltung
+2), so erh\"alt man den geringsten F\"ullfaktor und eine sehr geringe
+effizienz. Die kennlinien wird zu einer verschobenen geraden. Im falle
+kleiner, gleichartiger Widerst\"ande (Schaltung 3) \"uberwiegt der
+Effekt des Parallelwiderstandes (siehe \(U\rightarrow \SI{-1}{\volt}\))
+und auch hier wird die Effizienz beintr\"achtigt, wenn auch nich so
+stark, wie in der vorherigen Situation.
 
+Diese Betrachtungen spiegeln verschiedene Grade der nichtidealit\"at
+der Solarzelle wieder. Idealer weise sollte also \(R_S\) klein und
+\(R_P\) gro\ss{} sein.
 
+In einer realen Solarzelle entsteht \(R_S\) durch den inneren
+Widerstand des Halbleiters und durch den Widerstand an den Kontakten.
+\(R_P\) wird warscheinlich durch fehler im p-n-\"Ubergang
+hervorgerufen \todo{cite} durch die getrennte Ladungen in die falsche
+Richtung zur\"uckflie\ss{}en.
 
 
 \subsubsection{Verhalten bei Verschattung}
 \label{sec:verschattung}
-\todo{diagramme einfügen}
 
-An den Diagrammen kann man erkennen, dass der Stromfluss eines gesamten Solarmoduls
-zum Erliegen kommt sobald ein in Reihe geschaltetes Teilmodul komplett verschattet wird.
-Im Realen ist dies natürlich ein nicht hinnehmbarer Zustand, da es zum Beispiel bei
-Bewölkung immer wieder zu Teilverschattung kommt und dies somit den Stromfluss der gesamten
-Anlage stark beeinflussen kann.
-Dies umgeht man, in dem man zu jedem einzelnen Teilmodul eine so genannte \emph{Freilaufdiode}
-antiparallel schaltet, da diese den Stromfluss bei Verschattung eines in Reihe geschalteten
-Moduls um dieses herumleitet und damit eine solche Verschattung nicht das gesamte
-Solarmodul beeinflusst.
+
+An der Kennlinie in~\ref{diag:verschattung1} kann man erkennen, dass
+der Stromfluss eines gesamten Solarmoduls stark verringert wird sobald
+ein in Reihe geschaltetes Teilmodul komplett verschattet wird
+(Reihenschaltung eines gro\ss{}en Widerstandes \(R_S\)).  Im Realen
+ist dies natürlich ein nicht hinnehmbarer Zustand, da es zum Beispiel
+bei Bewölkung immer wieder zu Teilverschattung kommt und dies somit
+den Stromfluss der gesamten Anlage stark beeinflussen kann.  Dies
+umgeht man, in dem man zu jedem einzelnen Teilmodul eine so genannte
+\emph{Freilaufdiode}\todo{Quelle} antiparallel schaltet, da diese den
+Stromfluss bei Verschattung eines in Reihe geschalteten Moduls um
+dieses herumleitet und damit eine solche Verschattung nicht das
+gesamte Solarmodul beeinflusst.
+
+Verdeckt man jeweils nur eine H\"alfte der Parallelschaltungen
+(\ref{diag:verschattung2}) so verringert sich zwar der
+Kurzzschlussstrom und die Effizienz halbiert sich, aber der Effekt ist
+im Ganzen nur die Parallelschaltung eines zus\"atzlichen (grossen)
+Wiederstandes \(R_P\).
+
+Die dritte Situation \"ahnelt einer Reihen und Parallelschaltung von
+gro\ss{}en Widerst\"anden zum Modul und stellt somit das Mittel der
+beiden ersten Situationen dar.
+
+H\"atte man f\"ur Schaltung zwei gro\ss{}e Widerst\"ande gew\"ahlt, so
+h\"atten sich f\"ur alle Verschattungssituationen korrespondenzen
+ergeben. (Hier gilt Schalt. 1 zu Verschatt. 2; Schalt. 2 zu Verschatt. 1)
+
+\subsubsection{Solarmodul mit Verbraucher}
+\label{sec:analyseverbr}
+
+Die Leistung des Verbrauchers am gemessenen Arbeitspunkt betr\"agt
+(siehe auch~\ref{eq:last}): \[P_V=\SI{.75}{\watt}\]. Die Leistung am
+MPP des Solarmoduls betr\"agt: \[P_{MPP}=\SI{.88}{\watt}\]
+
+Der Verbracher nutzt also ca. \SI{85}{\percent} der Maximal
+verf\"ugbaren Leistung. Diese Ausnutzung kann vergrößert werden, indem
+man \(R_P\) des Moduls m\"oglichst mit \(R_S+R_V\) abstimmt, wobei
+\(R_V\) der innere Widerstand des Verbrauchers ist. Zur Herleitung
+dieses Zusammenh\"ange siehe (aus Zeitgr\"unden):
+\cite[154]{Demtröder2018}.
+
+\subsection{Der Einfluss der Temperatur}
+\label{sec:analysetemp}
+\begin{figure}[H]\centering
+        \includegraphics[width=.5\columnwidth]{figs/python/D/ucc.pdf}
+        \caption{Temperaturabh\"angigkei von \(\voc\).}
+        \label{fig:winkel}
+\end{figure}
+
+Bei konstanter Intensit\"at sinkt \(\voc\). Das ist zu erwarten, da
+mit steigender Temperatur der Diffusionsstrom zunimmt und damit die
+Eingbaute Spannung verringert.
+
+D
 
 \subsection{Winkelabhängigkeit des Stromflusses vom einfallenden Licht}
 \label{sec:winkel}