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   "source": [
    "# Distribution Generation Curtailment with OPF\n",
    "This is an introduction on how to use the pandapower optimal power flow for calculation optimal distributed generation curtailment.\n",
    "\n",
    "## Example Network\n",
    "\n",
    "We use the four bus example network from the basic OPF tutorial:\n",
    "\n",
    "<img src=\"pics/example_opf.png\" width=\"50%\">\n",
    "\n",
    "We first create this network in pandapower:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandapower as pp\n",
    "from numpy import array\n",
    "net = pp.create_empty_network()\n",
    "\n",
    "#create buses\n",
    "bus1 = pp.create_bus(net, vn_kv=220., min_vm_pu=1.0, max_vm_pu=1.02)\n",
    "bus2 = pp.create_bus(net, vn_kv=110., min_vm_pu=1.0, max_vm_pu=1.02)\n",
    "bus3 = pp.create_bus(net, vn_kv=110., min_vm_pu=1.0, max_vm_pu=1.02)\n",
    "bus4 = pp.create_bus(net, vn_kv=110., min_vm_pu=1.0, max_vm_pu=1.02)\n",
    "\n",
    "#create 220/110 kV transformer\n",
    "pp.create_transformer(net, bus1, bus2, std_type=\"100 MVA 220/110 kV\", max_loading_percent=50)\n",
    "\n",
    "#create 110 kV lines\n",
    "pp.create_line(net, bus2, bus3, length_km=70., std_type='149-AL1/24-ST1A 110.0', max_loading_percent=50)\n",
    "pp.create_line(net, bus3, bus4, length_km=50., std_type='149-AL1/24-ST1A 110.0', max_loading_percent=50)\n",
    "pp.create_line(net, bus4, bus2, length_km=40., std_type='149-AL1/24-ST1A 110.0', max_loading_percent=50)\n",
    "\n",
    "#create loads\n",
    "pp.create_load(net, bus2, p_kw=60e3, controllable = False)\n",
    "pp.create_load(net, bus3, p_kw=70e3, controllable = False)\n",
    "pp.create_load(net, bus4, p_kw=10e3, controllable = False)\n",
    "\n",
    "#create generators\n",
    "eg = pp.create_ext_grid(net, bus1)\n",
    "g0 = pp.create_gen(net, bus3, p_kw=-80e3, min_p_kw=-80e3, max_p_kw=0., vm_pu=1.01, controllable=True)\n",
    "g1 = pp.create_gen(net, bus4, p_kw=-100e3, min_p_kw=-100e3, max_p_kw=0., vm_pu=1.01, controllable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PYPOWER Version 5.0.0, 29-May-2015 -- AC Optimal Power Flow\n",
      "Python Interior Point Solver - PIPS, Version 1.0, 07-Feb-2011\n",
      "Converged!\n"
     ]
    }
   ],
   "source": [
    "pp.create_polynomial_cost(net, 0, 'gen', array([-1e5, 0]))\n",
    "pp.create_polynomial_cost(net, 1, 'gen', array([-1e5, 0]))\n",
    "pp.runopp(net, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the negative costs, the OPF now maximizes power generation at the generators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "      <th></th>\n",
       "      <th>p_kw</th>\n",
       "      <th>q_kvar</th>\n",
       "      <th>va_degree</th>\n",
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     "metadata": {},
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>vm_pu</th>\n",
       "      <th>va_degree</th>\n",
       "      <th>p_kw</th>\n",
       "      <th>q_kvar</th>\n",
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       "      <td>-18913.037576</td>\n",
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       "   vm_pu  va_degree          p_kw        q_kvar\n",
       "0   1.00   0.000000 -11071.749595    153.958270\n",
       "1   1.00  -0.759444  60000.000000      0.000000\n",
       "2   1.02  -0.485066  23947.429812 -18913.037576\n",
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       "      <th>p_hv_kw</th>\n",
       "      <th>q_hv_kvar</th>\n",
       "      <th>p_lv_kw</th>\n",
       "      <th>q_lv_kvar</th>\n",
       "      <th>pl_kw</th>\n",
       "      <th>ql_kvar</th>\n",
       "      <th>i_hv_ka</th>\n",
       "      <th>i_lv_ka</th>\n",
       "      <th>loading_percent</th>\n",
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       "      <td>170.35026</td>\n",
       "      <td>0.029059</td>\n",
       "      <td>0.057831</td>\n",
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       "        p_hv_kw  q_hv_kvar       p_lv_kw  q_lv_kvar      pl_kw    ql_kvar  \\\n",
       "0  11071.749595 -153.95827 -11013.580692  324.30853  58.168903  170.35026   \n",
       "\n",
       "    i_hv_ka   i_lv_ka  loading_percent  \n",
       "0  0.029059  0.057831         11.07282  "
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     "metadata": {},
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    {
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       "      <th>p_from_kw</th>\n",
       "      <th>q_from_kvar</th>\n",
       "      <th>p_to_kw</th>\n",
       "      <th>q_to_kvar</th>\n",
       "      <th>pl_kw</th>\n",
       "      <th>ql_kvar</th>\n",
       "      <th>i_from_ka</th>\n",
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       "      <td>69.011368</td>\n",
       "      <td>-2229.496009</td>\n",
       "      <td>0.046064</td>\n",
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       "      <th>1</th>\n",
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       "      <td>29770.915374</td>\n",
       "      <td>-13899.350544</td>\n",
       "      <td>813.825772</td>\n",
       "      <td>-10.328003</td>\n",
       "      <td>0.165259</td>\n",
       "      <td>0.169067</td>\n",
       "      <td>0.169067</td>\n",
       "      <td>35.971608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45326.943919</td>\n",
       "      <td>-5578.908656</td>\n",
       "      <td>-44045.770886</td>\n",
       "      <td>6929.202512</td>\n",
       "      <td>1281.173032</td>\n",
       "      <td>1350.293856</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>0.234024</td>\n",
       "      <td>0.235000</td>\n",
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       "      p_from_kw   q_from_kvar       p_to_kw     q_to_kvar        pl_kw  \\\n",
       "0  -4940.648422  -7253.511043   5009.659789   5024.015034    69.011368   \n",
       "1 -28957.089601  13889.022542  29770.915374 -13899.350544   813.825772   \n",
       "2  45326.943919  -5578.908656 -44045.770886   6929.202512  1281.173032   \n",
       "\n",
       "       ql_kvar  i_from_ka   i_to_ka      i_ka  loading_percent  \n",
       "0 -2229.496009   0.046064  0.036508  0.046064         9.800771  \n",
       "1   -10.328003   0.165259  0.169067  0.169067        35.971608  \n",
       "2  1350.293856   0.235000  0.234024  0.235000        50.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
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    }
   ],
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    "net.res_line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously the voltage profile was the limiting factor for the generator feed-in. If we relax this constraint a little bit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net.bus[\"max_vm_pu\"] = 1.05\n",
    "pp.runopp(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see an increased feed-in of the generators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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    "collapsed": false
   },
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       "          p_kw        q_kvar  va_degree     vm_pu\n",
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       "      vm_pu  va_degree          p_kw        q_kvar\n",
       "0  1.000000   0.000000   4542.418041   8523.878212\n",
       "1  1.010373   0.298334  60000.000000      0.000000\n",
       "2  1.050000   2.328268 -10000.000000 -17252.534836\n",
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     "execution_count": 11,
     "metadata": {},
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    "net.res_bus"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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    "net.res_cost"
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