Repository: ettoreaquino/powersddp (language: python)

Issue:
Implement a Dispach method for the PowerSystem object
## Description

Once initialized a Power System can be optimally dispatched based on the minimization of an Objective Function.

## To Do
 - Create `PowerSystem.dispatch()` method to solve the Power System based on initial parameters of `v_i` and `inflow`
 - The output should be a dictionary and a `verbose` boolean parameter could be passed to allow the execution to run in a verbose mode printing the steps and outputs.


Discussion / hints:

Fix patch:
diff --git a/Notebook.ipynb b/Notebook.ipynb
index 18782e2..1eb6b54 100644
--- a/Notebook.ipynb
+++ b/Notebook.ipynb
@@ -2,27 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "9dd8e6a5-b075-404b-85eb-6131fcca2a2a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "System loaded from system.yml file\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from powersddp.system import PowerSystem\n",
-    "import cvxopt.modeling as model\n",
+    "import powersddp as psddp\n",
     "\n",
-    "data = {'load': [50, 50, 50],\n",
+    "\n",
+    "data = {'load': [100, 15, 50],\n",
     "        'discretizations': 3,\n",
     "        'stages': 3,\n",
     "        'scenarios': 2,\n",
-    "        'shedding_cost': 500,\n",
+    "        'outage_cost': 500,\n",
     "        'hydro-units': [{'name': 'HU1',\n",
     "                         'v_max': 100,\n",
     "                         'v_min': 20,\n",
@@ -32,7 +24,20 @@
     "        'thermal-units': [{'name': 'GT1', 'capacity': 15, 'cost': 10},\n",
     "                          {'name': 'GT2', 'capacity': 10, 'cost': 25}]}\n",
     "\n",
-    "TestSystem = PowerSystem(path='system.yml')"
+    "TestSystem = psddp.PowerSystem(data=data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0b41801a-dc45-4777-a2ec-1294c91890cc",
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "operation = TestSystem.dispatch()"
    ]
   },
   {
@@ -66,7 +71,7 @@
     "    \\begin{aligned}\n",
     "        \\min \\quad                    & C_1\\cdot g_{t_1} + C_2\\cdot g_{t_2} + C_{def}\\cdot def + 0.01\\cdot v_v\\\\\n",
     "        \\textrm{s.t.} \\quad           & \\\\\n",
-    "        \\textrm{hydro balance} \\quad  & v_f = v_i + afl - v_t \\\\\n",
+    "        \\textrm{hydro balance} \\quad  & v_f(i) = v_i(i) + afl(i) - v_t(i) \\\\\n",
     "        \\textrm{load supplying} \\quad & \\rho\\cdot v_t + g_{t_1} + g_{t_2} + def = \\textrm{load}\\\\\n",
     "        \\textrm{constraints} \\quad    & \\\\\n",
     "                                      & v_{f_{min}}\\leq v_f \\leq v_{f_{max}}\\\\\n",
@@ -83,15 +88,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "id": "ccbf3a14-01ed-4942-acb3-a4c64f47b6fd",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def dispatch(system, v_i, inflow):\n",
+    "import cvxopt.modeling as model\n",
+    "from cvxopt import solvers\n",
+    "\n",
+    "import pandas as pd\n",
+    "import plotly.graph_objects as go\n",
+    "from plotly.subplots import make_subplots\n",
+    "\n",
+    "solvers.options['glpk'] = dict(msg_lev='GLP_MSG_OFF')\n",
+    "\n",
+    "def dispatch(system, v_i, inflow, cuts, stage, verbose:bool=False):\n",
     "    n_tgu = len(system.data['thermal-units'])\n",
     "    n_hgu = len(system.data['hydro-units'])\n",
-    "\n",
+    "    solvers.options['show_progress'] = verbose\n",
     "\n",
     "    ## Initializing Model Variables\n",
     "    v_f = model.variable(n_hgu, \"Final Volume of the Hydro Unit\")\n",
@@ -99,6 +113,7 @@
     "    v_v = model.variable(n_hgu, \"Shed flow of the Hydro Unit\")\n",
     "    g_t = model.variable(n_tgu, \"Power generated by the Thermal Unit\")\n",
     "    deficit = model.variable(1, \"Power deficit\")\n",
+    "    alpha = model.variable(1, \"Future Cost\")\n",
     "\n",
     "    ## Objective Function\n",
     "    fob = 0\n",
@@ -107,14 +122,17 @@
     "    fob+=TestSystem.data['outage_cost']*deficit[0]\n",
     "    for i, _ in enumerate(system.data['hydro-units']):\n",
     "        fob += 0.01*v_v[i]\n",
+    "    fob += 1.0 * alpha[0]\n",
     "\n",
-    "    ## Hydro Balance\n",
+    "    ## Constraints\n",
+    "    \n",
+    "    ### Hydro Balance\n",
     "    constraints = []\n",
     "    for i, hgu in enumerate(system.data['hydro-units']):\n",
-    "        constraints.append( v_f[i] == v_i + inflow - v_t[i] - v_v[i] )\n",
+    "        constraints.append( v_f[i] == float(v_i[i]) + float(inflow[i]) - v_t[i] - v_v[i] )\n",
     "\n",
     "    supplying = 0\n",
-    "    ## Load Supplying\n",
+    "    ### Load Supply\n",
     "    for i, hgu in enumerate(system.data['hydro-units']):\n",
     "        supplying += hgu[\"prod\"] * v_t[i]\n",
     "\n",
@@ -123,9 +141,10 @@
     "\n",
     "    supplying += deficit[0]\n",
     "\n",
-    "    constraints.append(supplying == system.data['load'][2])\n",
+    "    constraints.append(supplying == system.data['load'][stage-2])\n",
+    "    \n",
     "\n",
-    "    ## Constraints\n",
+    "    ### Bounds\n",
     "    for i, hgu in enumerate(system.data['hydro-units']):\n",
     "        constraints.append(v_f[i] >= hgu[\"v_min\"])\n",
     "        constraints.append(v_f[i] <= hgu[\"v_max\"])\n",
@@ -138,152 +157,1341 @@
     "        constraints.append(g_t[i] <= tgu[\"capacity\"])\n",
     "\n",
     "    constraints.append(deficit[0] >= 0)\n",
+    "    constraints.append(alpha[0] >= 0)\n",
     "    \n",
+    "    ### Cut constraint (Future cost function of forward stage)\n",
+    "    for cut in cuts:\n",
+    "        if cut['stage'] == stage:\n",
+    "            equation = 0\n",
+    "            for hgu in range(n_hgu):\n",
+    "                equation += float(cut['coefs'][hgu])*v_f[hgu]\n",
+    "            equation += float(cut['coef_b'])\n",
+    "            constraints.append(alpha[0] >= equation)\n",
+    "    \n",
+    "    ## Solving\n",
     "    opt_problem = model.op(objective=fob, constraints=constraints)\n",
     "    opt_problem.solve(format='dense',solver='glpk')\n",
     "\n",
-    "    print(\"Total Cost: {}\".format(fob.value()))\n",
+    "    ## Print\n",
+    "    if verbose:\n",
+    "        print(\"Total Cost: {}\".format(fob.value()))\n",
     "\n",
-    "    for i, hgu in enumerate(system.data['hydro-units']):\n",
-    "        print(\"{} {} is {} hm3\".format(v_f.name,i,v_f[i].value()))\n",
-    "        print(\"{} {} is {} hm3\".format(v_t.name,i,v_t[i].value()))\n",
-    "        print(\"{} {} is {} hm3\".format(v_v.name,i,v_v[i].value()))\n",
+    "        for i, hgu in enumerate(system.data['hydro-units']):\n",
+    "            print(\"{} {} is {} hm3\".format(v_f.name,i,v_f[i].value()))\n",
+    "            print(\"{} {} is {} hm3\".format(v_t.name,i,v_t[i].value()))\n",
+    "            print(\"{} {} is {} hm3\".format(v_v.name,i,v_v[i].value()))\n",
     "\n",
-    "    for i, tgu in enumerate(system.data['thermal-units']):\n",
-    "        print(\"{} {} is {} MWmed\".format(g_t.name,i,g_t[i].value()))\n",
+    "        for i, tgu in enumerate(system.data['thermal-units']):\n",
+    "            print(\"{} {} is {} MWmed\".format(g_t.name,i,g_t[i].value()))\n",
     "\n",
-    "    print(\"{} is {} MWmed\".format(deficit.name,deficit[0].value()))\n",
+    "        print(\"{} is {} MWmed\".format(deficit.name,deficit[0].value()))\n",
     "\n",
-    "    for i, hgu in enumerate(system.data['hydro-units']):\n",
-    "        print(\"The cost of water at Hydro Unit {} is {} hm3\".format(i,constraints[i].multiplier.value))\n",
+    "        for i, hgu in enumerate(system.data['hydro-units']):\n",
+    "            print(\"The cost of water at Hydro Unit {} is {} hm3\".format(i,constraints[i].multiplier.value))\n",
     "\n",
-    "    print(\"The Marginal Cost is: {}\".format(constraints[n_hgu].multiplier.value))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "17fb0a0c-5a87-41fb-a645-1434a65c02d4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total Cost: [ 7.67e+03]\n",
-      "\n",
-      "Final Volume of the Hydro Unit 0 is [ 2.00e+01]\n",
-      " hm3\n",
-      "Turbined Flow of the Hydro Unit 0 is [ 1.10e+01]\n",
-      " hm3\n",
-      "Shed flow of the Hydro Unit 0 is [ 0.00e+00]\n",
-      " hm3\n",
-      "Power generated by the Thermal Unit 0 is [ 1.50e+01]\n",
-      " MWmed\n",
-      "Power generated by the Thermal Unit 1 is [ 1.00e+01]\n",
-      " MWmed\n",
-      "Power deficit is [ 1.45e+01]\n",
-      " MWmed\n",
-      "The cost of water at Hydro Unit 0 is [ 4.75e+02]\n",
-      " hm3\n",
-      "The Marginal Cost is: [-5.00e+02]\n",
-      "\n",
-      "GLPK Simplex Optimizer, v4.65\n",
-      "12 rows, 6 columns, 17 non-zeros\n",
-      "      0: obj =   0.000000000e+00 inf =   1.010e+02 (3)\n",
-      "      5: obj =   7.675000000e+03 inf =   0.000e+00 (0)\n",
-      "*     6: obj =   7.675000000e+03 inf =   0.000e+00 (0)\n",
-      "OPTIMAL LP SOLUTION FOUND\n"
-     ]
-    }
-   ],
-   "source": [
-    "system = TestSystem\n",
-    "v_i = 20\n",
-    "inflow = 11\n",
+    "        print(\"The Marginal Cost is: {}\".format(constraints[n_hgu].multiplier.value))\n",
+    "        \n",
+    "    return {\n",
+    "        \"deficit\": deficit[0].value()[0],\n",
+    "        \"operational_marginal_cost\": constraints[n_hgu].multiplier.value[0],\n",
+    "        \"total_cost\": fob.value()[0],\n",
+    "        \"future_cost\": alpha[0].value()[0],\n",
+    "        \"hydro_units\": [{\n",
+    "            \"v_f\": v_f[i].value()[0],\n",
+    "            \"v_t\": v_t[i].value()[0],\n",
+    "            \"v_v\": v_v[i].value()[0],\n",
+    "            \"water_marginal_cost\": constraints[i].multiplier.value[0]} for i in range(n_hgu)],\n",
+    "        \"thermal_units\": [{\"g_t\": g_t[i].value()[0]} for i in range(n_tgu)]\n",
+    "    }\n",
+    "\n",
+    "def plot_future_cost_function(operation: pd.DataFrame):\n",
+    "    \n",
+    "    n_stages = len(operation['stage'].unique())\n",
+    "    \n",
+    "    fig = make_subplots(rows=n_stages, cols=1)\n",
+    "\n",
+    "    i = 1\n",
+    "    for stage in operation['stage'].unique():\n",
+    "        stage_df = operation.loc[operation['stage'] == stage] \n",
+    "        fig.add_trace(go.Scatter(x=stage_df[\"v_i\"],\n",
+    "                                 y=stage_df['average_cost'],\n",
+    "                                 mode='lines',\n",
+    "                                 name=\"Stage {}\".format(i)), row=stage, col=1)\n",
+    "        i+=1\n",
     "\n",
-    "dispatch(system=system, v_i=v_i, inflow=inflow)"
+    "    fig.update_xaxes(title_text=\"Final Volume [hm3]\")\n",
+    "    fig.update_yaxes(title_text=\"$/MW\")\n",
+    "\n",
+    "    fig.update_layout(height=300*TestSystem.data['stages'], title_text=\"Future Cost Function\")\n",
+    "    fig.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "108fdcb5-9ce2-49db-b487-eeaf90ec0e24",
-   "metadata": {},
+   "execution_count": 17,
+   "id": "2b718ada-dd91-4cb6-a31c-f36b61674065",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "System Load: [50, 50, 50]\n",
-      "Number of HGUs: 1\n",
-      "Number of TGUs: 2\n"
-     ]
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
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",
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+       "                            \n",
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+       "var x = new MutationObserver(function (mutations, observer) {{\n",
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+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "import powersddp\n",
+    "from itertools import product\n",
+    "import numpy as np\n",
+    "\n",
+    "n_hgu = len(TestSystem.data['hydro-units'])\n",
+    "n_tgu = len(TestSystem.data['thermal-units'])\n",
+    "\n",
+    "step = 100/(TestSystem.data['discretizations']-1)\n",
+    "discretizations = list(product(np.arange(0,100+step,step), repeat=n_hgu))\n",
     "\n",
-    "system = powersddp.PowerSystem(path='system.yml')\n",
+    "cuts = []\n",
+    "operation = []\n",
+    "for stage in range(TestSystem.data['stages'],0,-1):\n",
+    "    for discretization in discretizations:\n",
+    "        \n",
+    "        v_i = []\n",
+    "        # For Every Hydro Unit\n",
+    "        for i, hgu in enumerate(TestSystem.data['hydro-units']):\n",
+    "            v_i.append(hgu['v_min'] + (hgu['v_max']-hgu['v_min'])*discretization[i]/100)\n",
+    "        \n",
+    "        # For Every Scenario\n",
+    "        average = 0.\n",
+    "        avg_water_marginal_cost = [0 for _ in TestSystem.data[\"hydro-units\"]]\n",
+    "        for scenario in range(TestSystem.data['scenarios']):\n",
+    "            inflow = []\n",
+    "            for i, hgu in enumerate(TestSystem.data['hydro-units']):\n",
+    "                inflow.append(hgu['inflow_scenarios'][stage-1][scenario])\n",
+    "            \n",
+    "            result = dispatch(TestSystem, v_i, inflow, cuts, stage+1)\n",
+    "            average += result[\"total_cost\"]\n",
+    "            for i, hgu in enumerate(result[\"hydro_units\"]):\n",
+    "                avg_water_marginal_cost[i] += hgu[\"water_marginal_cost\"]\n",
     "\n",
-    "print(\"System Load: {}\\n\"\n",
-    "      \"Number of HGUs: {}\\n\"\n",
-    "      \"Number of TGUs: {}\".format(system.data['load'],\n",
-    "                                  len(system.data['hydro-units']),\n",
-    "                                  len(system.data['thermal-units'])))"
+    "        # Calculating the average of the scenarios\n",
+    "        average = average/TestSystem.data['scenarios']\n",
+    "        coef_b = average\n",
+    "        for i, hgu in enumerate(result[\"hydro_units\"]):\n",
+    "            # ! Invert the coeficient because of the minimization problem inverts the signal\n",
+    "            avg_water_marginal_cost[i] = - avg_water_marginal_cost[i]/TestSystem.data['scenarios']\n",
+    "            coef_b -= v_i[i]*avg_water_marginal_cost[i]\n",
+    "            \n",
+    "        cuts.append({\"stage\": stage, \"coef_b\": coef_b, \"coefs\": avg_water_marginal_cost})\n",
+    "        operation.append({'stage': stage, 'discretization': discretization[i], 'v_i': v_i[0], 'average_cost': round(average,2)})\n",
+    "operation_df = pd.DataFrame(operation)\n",
+    "\n",
+    "if n_hgu == 1:\n",
+    "    plot_future_cost_function(operation=operation_df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "07837a84-da91-47bf-a749-d4292fde6d57",
+   "execution_count": 13,
+   "id": "87285cd1-3bb7-4c72-bb43-4f2baf6e4077",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "System Load: [50, 50, 50]\n",
-      "Number of HGUs: 1\n",
-      "Number of TGUs: 2\n"
-     ]
+     "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>stage</th>\n",
+       "      <th>discretization</th>\n",
+       "      <th>v_i</th>\n",
+       "      <th>average_cost</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>6725.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>7.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>-0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>11787.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>226.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.62</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15425.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>576.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>161.68</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   stage  discretization    v_i  average_cost\n",
+       "0      3             0.0   20.0       6725.00\n",
+       "1      3            50.0   60.0          7.75\n",
+       "2      3           100.0  100.0         -0.00\n",
+       "3      2             0.0   20.0      11787.50\n",
+       "4      2            50.0   60.0        226.93\n",
+       "5      2           100.0  100.0          0.62\n",
+       "6      1             0.0   20.0      15425.00\n",
+       "7      1            50.0   60.0        576.31\n",
+       "8      1           100.0  100.0        161.68"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "import powersddp\n",
-    "\n",
-    "payload = {'load': [50, 50, 50],\n",
-    "           'discretizations': 3,\n",
-    "           'stages': 3,\n",
-    "           'scenarios': 2,\n",
-    "           'outage_cost': 500,\n",
-    "           'hydro-units': [{'name': 'HU1',\n",
-    "                            'v_max': 100,\n",
-    "                            'v_min': 20,\n",
-    "                            'prod': 0.95,\n",
-    "                            'flow_max': 60,\n",
-    "                            'inflow_scenarios': [[23, 16],\n",
-    "                                                 [19, 14],\n",
-    "                                                 [15, 11]]}],\n",
-    "          'thermal-units': [{'name': 'GT1', 'capacity': 15, 'cost': 10},\n",
-    "                            {'name': 'GT2', 'capacity': 10, 'cost': 25}]}\n",
-    "\n",
-    "system = powersddp.PowerSystem(data=payload)\n",
-    "\n",
-    "print(\"System Load: {}\\n\"\n",
-    "      \"Number of HGUs: {}\\n\"\n",
-    "      \"Number of TGUs: {}\".format(system.data['load'],\n",
-    "                                  len(system.data['hydro-units']),\n",
-    "                                  len(system.data['thermal-units'])))"
+    "operation_df"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db680eea-a46e-4f24-a739-aae4117bb19e",
+   "cell_type": "markdown",
+   "id": "613592a2-e7e0-4b0a-81b9-a5d0126dde67",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Considering the Future Cost Function\n",
+    "\n",
+    "### Modelling the cost of water\n",
+    "\n",
+    "Now, let's consider the Future Cost Function to back propagate the solutions. By back propagating we assume that the future cost function of the \"stage ahead\" is used as input for the previous stage solution.\n",
+    "\n",
+    "Assuming that any Future Cost Function is aproximated by a series of straigh line discretizations. Any given point can be identified by a straight line, which is mathematically represented by:\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation}\n",
+    "    \\begin{aligned}\n",
+    "        \\alpha = a \\cdot v_f + b\n",
+    "    \\end{aligned}\n",
+    "\\end{equation}\n",
+    "$$\n",
+    "\n",
+    "Where $\\alpha$ is cost at a given point of final volume. Where shall find the coeficients $a$ and $b$ \n",
+    "\n",
+    "- $a$: Is the marginal cost of the water, which comes from the solution of the minimization problem.\n",
+    "\n",
+    "If we assume $\\alpha = 75$ and $v_f = 60$ which means a cost of $\\$60.00$ at Final Volume $60 hm^3$, that gives us:\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation}\n",
+    "    \\begin{aligned}\n",
+    "        b = \\alpha - a \\cdot v_f\n",
+    "    \\end{aligned}\n",
+    "\\end{equation}\n",
+    "$$\n",
+    "\n",
+    "> Naturaly, this process is repeated for every discretization used in the problem.\n",
+    "\n",
+    "> $a$ is given by the average value of every scenario considered when calculating the marginal cost of the water.\n",
+    "\n",
+    "If we evaluate for multiple Hydro Units, naturaly:\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation}\n",
+    "    \\begin{aligned}\n",
+    "        \\alpha =b + \\sum_{i=1}^{n} a_i \\cdot v_{i}\n",
+    "    \\end{aligned}\n",
+    "\\end{equation}\n",
+    "$$\n",
+    "\n",
+    "Where $n$ = number of Hydro units\n",
+    "\n",
+    "### Considering the cost function in the back propagation\n",
+    "\n",
+    "In the previos stage (back propagating from the end to the beggining) we have the objetive function:\n",
+    "\n",
+    "$$\n",
+    "\\begin{equation}\n",
+    "    \\begin{aligned}\n",
+    "        \\min \\quad                                   & C_1\\cdot g_{t_1} + C_2\\cdot g_{t_2} + C_{def}\\cdot def + 0.01\\cdot v_v + \\alpha\\\\\n",
+    "        \\textrm{s.t.} \\quad                          & \\\\\n",
+    "        \\textrm{hydro balance} \\quad                 & v_f(i) = v_i(i) + afl(i) - v_t(i) - v_v(i) \\\\\n",
+    "        \\textrm{load supplying} \\quad                & \\rho\\cdot v_t(i) + g_{t_1} + g_{t_2} + def = \\textrm{load}\\\\\n",
+    "        \\textrm{considering the forward state}\\quad  & \\\\\n",
+    "        \\textrm{for every scenario} `s` \\quad          & \\alpha \\geq a^{s} \\cdot v_f(i) + b^{s}\\\\\n",
+    "                \\textrm{constraints} \\quad           & \\\\\n",
+    "                                                     & v_{f_{min}}\\leq v_f(i) \\leq v_{f_{max}}\\\\\n",
+    "                                                     & v_{t_{min}}\\leq v_t(i)  \\leq v_{t_{max}}\\\\\n",
+    "                                                     & v_{v_{min}}\\leq v_v(i)  \\leq v_{v_{max}}\\\\\n",
+    "                                                     & g_{t_{min}}\\leq g_t^\\ast \\leq g_{t_{max}}\\\\\n",
+    "        ^\\ast \\textrm{for each TGU}& \n",
+    "    \\end{aligned}\n",
+    "\\end{equation}\n",
+    "$$"
+   ]
   }
  ],
  "metadata": {
diff --git a/README.md b/README.md
index 9294885..e375b86 100644
--- a/README.md
+++ b/README.md
@@ -27,9 +27,9 @@ There are two ways of initializing a `Power System`. Either by providing a `.yml
 
 ### Initializing a `PowerSystem`
 ```Python
-import powersddp
+import powersddp as psddp
 
-system = powersddp.PowerSystem(path='system.yml')
+system = psddp.PowerSystem(path='system.yml')
 
 print("System Load: {}\n"
       "Number of HGUs: {}\n"
@@ -39,25 +39,23 @@ print("System Load: {}\n"
 ```
 
 ```Python
-import powersddp
-
-payload = {'load': [50, 50, 50],
-           'discretizations': 3,
-           'stages': 3,
-           'scenarios': 2,
-           'outage_cost': 500,
-           'hydro-units': [{'name': 'HU1',
-                            'v_max': 100,
-                            'v_min': 20,
-                            'prod': 0.95,
-                            'flow_max': 60,
-                            'inflow_scenarios': [[23, 16],
-                                                 [19, 14],
-                                                 [15, 11]]}],
-          'thermal-units': [{'name': 'GT1', 'capacity': 15, 'cost': 10},
-                            {'name': 'GT2', 'capacity': 10, 'cost': 25}]}
-
-system = powersddp.PowerSystem(data=payload)
+import powersddp as psddp
+
+data = {'load': [50, 50, 50],
+        'discretizations': 3,
+        'stages': 3,
+        'scenarios': 2,
+        'outage_cost': 500,
+        'hydro-units': [{'name': 'HU1',
+                         'v_max': 100,
+                         'v_min': 20,
+                         'prod': 0.95,
+                         'flow_max': 60,
+                         'inflow_scenarios': [[23, 16], [19, 14], [15, 11]]}],
+        'thermal-units': [{'name': 'GT1', 'capacity': 15, 'cost': 10},
+                          {'name': 'GT2', 'capacity': 10, 'cost': 25}]}
+
+PowerSystem = psddp.PowerSystem(data=data)
 
 print("System Load: {}\n"
       "Number of HGUs: {}\n"
@@ -66,4 +64,37 @@ print("System Load: {}\n"
                                   len(system.data['thermal-units'])))
 ```
 
+### Dispatching a `PowerSystem`
+
+#### **dispatch()** accepts the following arguments:
+
+- `verbose : bool, optional defaults to False`
+  - Displays the PDDE solution for every stage of the execution. Use with care, solutions of complex systems with too many stages and scenarios might overflow the console.
+
+- `plot : bool, optional, defaults to False`
+  - Displays a sequence of plots showing the future cost function for every stage of the execution. 
+
+
+```Python
+import powersddp as psddp
+
+data = {'load': [50, 50, 50],
+        'discretizations': 3,
+        'stages': 3,
+        'scenarios': 2,
+        'outage_cost': 500,
+        'hydro-units': [{'name': 'HU1',
+                         'v_max': 100,
+                         'v_min': 20,
+                         'prod': 0.95,
+                         'flow_max': 60,
+                         'inflow_scenarios': [[23, 16], [19, 14], [15, 11]]}],
+        'thermal-units': [{'name': 'GT1', 'capacity': 15, 'cost': 10},
+                          {'name': 'GT2', 'capacity': 10, 'cost': 25}]}
+
+PowerSystem = psddp.PowerSystem(data=data)
+operation = PowerSystem.dispatch()
+
+print(operation)
+```
 <!-- <img src="https://render.githubusercontent.com/render/math?math=e^{i \pi} = -1"> -->
diff --git a/poetry.lock b/poetry.lock
index 7944ec3..a74fecf 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -683,6 +683,14 @@ docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "m
 json-logging = ["json-logging"]
 test = ["pytest", "coverage", "requests", "nbval", "selenium", "pytest-cov", "requests-unixsocket"]
 
+[[package]]
+name = "numpy"
+version = "1.21.1"
+description = "NumPy is the fundamental package for array computing with Python."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "packaging"
 version = "21.0"
@@ -694,6 +702,22 @@ python-versions = ">=3.6"
 [package.dependencies]
 pyparsing = ">=2.0.2"
 
+[[package]]
+name = "pandas"
+version = "1.3.2"
+description = "Powerful data structures for data analysis, time series, and statistics"
+category = "main"
+optional = false
+python-versions = ">=3.7.1"
+
+[package.dependencies]
+numpy = ">=1.17.3"
+python-dateutil = ">=2.7.3"
+pytz = ">=2017.3"
+
+[package.extras]
+test = ["hypothesis (>=3.58)", "pytest (>=6.0)", "pytest-xdist"]
+
 [[package]]
 name = "pandocfilters"
 version = "1.4.3"
@@ -741,6 +765,18 @@ category = "dev"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "plotly"
+version = "5.2.1"
+description = "An open-source, interactive data visualization library for Python"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+six = "*"
+tenacity = ">=6.2.0"
+
 [[package]]
 name = "pluggy"
 version = "0.13.1"
@@ -863,7 +899,7 @@ testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xm
 name = "python-dateutil"
 version = "2.8.2"
 description = "Extensions to the standard Python datetime module"
-category = "dev"
+category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 
@@ -874,7 +910,7 @@ six = ">=1.5"
 name = "pytz"
 version = "2021.1"
 description = "World timezone definitions, modern and historical"
-category = "dev"
+category = "main"
 optional = false
 python-versions = "*"
 
@@ -969,7 +1005,7 @@ win32 = ["pywin32"]
 name = "six"
 version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 
@@ -981,6 +1017,17 @@ category = "dev"
 optional = false
 python-versions = ">=3.5"
 
+[[package]]
+name = "tenacity"
+version = "8.0.1"
+description = "Retry code until it succeeds"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+doc = ["reno", "sphinx", "tornado (>=4.5)"]
+
 [[package]]
 name = "terminado"
 version = "0.11.0"
@@ -1114,7 +1161,7 @@ python-versions = "*"
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8"
-content-hash = "a80ab2d038ebd23b2a35fddb2e703975351608f10eedf2519677c49d97feb518"
+content-hash = "cae2aa10dea3acba3f146c7781ed78b2fb59bceac409a73911ca7d08dbffb158"
 
 [metadata.files]
 anyio = [
@@ -1531,10 +1578,61 @@ notebook = [
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     {file = "notebook-6.4.3.tar.gz", hash = "sha256:e6b6dfed36b00cf950f63c0d42e947c101d4258aec21624de62b9e0c11ed5c0d"},
 ]
+numpy = [
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 pandocfilters = [
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@@ -1554,6 +1652,10 @@ pickleshare = [
     {file = "pickleshare-0.7.5-py2.py3-none-any.whl", hash = "sha256:9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56"},
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 ]
+plotly = [
+    {file = "plotly-5.2.1-py2.py3-none-any.whl", hash = "sha256:bf7c8123541a2c6579c309561a8e1058c129434c67419651efbdc4922b11da8f"},
+    {file = "plotly-5.2.1.tar.gz", hash = "sha256:1575c34f87313818fc109a3d3326f2b91363d049c1e80cbf68561c8df24fb47c"},
+]
 pluggy = [
     {file = "pluggy-0.13.1-py2.py3-none-any.whl", hash = "sha256:966c145cd83c96502c3c3868f50408687b38434af77734af1e9ca461a4081d2d"},
     {file = "pluggy-0.13.1.tar.gz", hash = "sha256:15b2acde666561e1298d71b523007ed7364de07029219b604cf808bfa1c765b0"},
@@ -1769,6 +1871,10 @@ sniffio = [
     {file = "sniffio-1.2.0-py3-none-any.whl", hash = "sha256:471b71698eac1c2112a40ce2752bb2f4a4814c22a54a3eed3676bc0f5ca9f663"},
     {file = "sniffio-1.2.0.tar.gz", hash = "sha256:c4666eecec1d3f50960c6bdf61ab7bc350648da6c126e3cf6898d8cd4ddcd3de"},
 ]
+tenacity = [
+    {file = "tenacity-8.0.1-py3-none-any.whl", hash = "sha256:f78f4ea81b0fabc06728c11dc2a8c01277bfc5181b321a4770471902e3eb844a"},
+    {file = "tenacity-8.0.1.tar.gz", hash = "sha256:43242a20e3e73291a28bcbcacfd6e000b02d3857a9a9fff56b297a27afdc932f"},
+]
 terminado = [
     {file = "terminado-0.11.0-py3-none-any.whl", hash = "sha256:221eef83e6a504894842f7dccfa971ca2e98ec22a8a9118577e5257527674b42"},
     {file = "terminado-0.11.0.tar.gz", hash = "sha256:1e01183885f64c1bba3cf89a5a995ad4acfed4e5f00aebcce1bf7f089b0825a1"},
diff --git a/powersddp/core/system.py b/powersddp/core/system.py
index d8e72df..69a4181 100644
--- a/powersddp/core/system.py
+++ b/powersddp/core/system.py
@@ -1,59 +1,208 @@
 """Module to handle classes and methods related to a selected Power System.
-This module should follow a systems.json file standar:
-{
-  "{name}": {
-    "shedding_cost": float,
-    "load": [float, float, float],
-    "n_disc": int,
-    "n_est": int,
-    "n_cen": int,
-    "generation_units": [
-      {"type": "hydro",
-       "name": "str",
-       "v_max": float,
-       "v_min": float,
-       "prod": float,
-       "flow_max": float,
-       "inflow_scenarios":[<list>]},
-      {"type": "thermal", "name": "str", "capacity": "float", "cost": float},
-      ...
-    ]
-  }
-}
-Where {name} should be changed to whatever name you may choose to your system.
-For example, 'Test01'. Check README.md file.
+This module should follow a systems.yml file standard:
+
+# system.yml
+load: [float,float,float]
+discretizations: int
+stages: int
+scenarios: int
+outage_cost: float
+hydro-units: !include system-hydro.yml
+thermal-units: !include system-thermal.yml
+
+# system-hydro.yml
+-
+  name: str
+  v_max: float
+  v_min: float
+  prod: float
+  flow_max: float
+  inflow_scenarios:
+    - list<float>
+    - list<float>
+    - list<float>
+
+# system-thermal.yml
+-
+  name: str
+  capacity: float
+  cost: float
 """
 
-from abc import ABC, abstractclassmethod
+from abc import ABC, abstractmethod
+from itertools import product
+import numpy as np
+import pandas as pd
 import yaml
 
-from powersddp.util._yml import YmlLoader
-
-YmlLoader.add_constructor("!include", YmlLoader.include)
+from powersddp.utils._yml import YmlLoader
+from powersddp.utils._solver import sdp, plot_future_cost_function
 
 
 class PowerSystemInterface(ABC):
-    @abstractclassmethod
+    @abstractmethod
     def load_system(self):
         raise NotImplementedError
 
+    @abstractmethod
+    def dispatch(self):
+        raise NotImplementedError
+
 
 class PowerSystem(PowerSystemInterface):
-    def __init__(self, verbose: bool = False, **kwargs):
-        self.__verbose = verbose
-        self.__dict__.update(kwargs)
-        self.load_system()
+    """Singleton Class to instantiate a Power System.
 
-    def load_system(self):
-        if "path" in self.__dict__:
-            with open(self.path, "r") as f:
-                data = yaml.load(f, YmlLoader)
-
-                self.data = data
-                if self.__verbose:
-                    print("System loaded from {} file".format(self.path))
-        elif "data" in self.__dict__:
-            if self.__verbose:
-                print("System loaded from 'data' payload")
+    A Power System is defined based on set of parameters, including the systems parameters
+    and all the generation units. Both thermal and hydro.
+
+    Note: Either initialize a Power System by providing the path to a systems.yml file or
+    by providing a dictionary containing all the necessary data.
+
+    Attributes
+    ----------
+    path : str, optional
+        Path to the systems.yml file
+    data : dict, optional
+        Dictionary containing all of the power system parameters, including the generation units.
+
+    """
+
+    def __init__(self, path: str = None, data: dict = None):
+        """__init__ method.
+
+        Parameters
+        ----------
+        path : str, optional
+            Path to the systems.yml file.
+        data : :obj:`dict`, optional
+            Description of `param2`. Multiple
+            lines are supported.
+        param3 : :obj:`int`, optional
+            Dictionary containing all of the power system parameters, including the generation units.
+
+        """
+
+        self.load_system(path=path, data=data)
+
+    def load_system(self, *, path: str = None, data: dict = None):
+        """Loads a Power System from file or dictionary payload
+
+        A Power System data be loaded from both file or dictionary payload. In case both
+        positional parameters are suplied the file path will have priority and data payload
+        will be ignored.
+
+        PowerSystem loads the data by default during initialization, but can be reloaded ad-hoc.
+
+        Parameters
+        ----------
+        path : str, optional
+            Path to the .yml file containing the system data.
+        data : dict, optional
+            Dictionary containing the structured data of the system.
+
+        """
+        if path:
+            with open(path, "r") as f:
+                self.data = yaml.load(f, YmlLoader)
+        elif data:
+            self.data = data
         else:
-            raise NotImplementedError
+            raise ValueError(
+                "load_system() should receive path=str or data=dict as arguments"
+            )
+
+    def dispatch(
+        self, *, solver: str = "sdp", plot: bool = False, verbose: bool = False
+    ) -> pd.DataFrame:
+        """Solves a financial dispatch of a Power System class
+
+        Once instantiated a Power System can deploy the generation units based on the
+        minimization of an objective function. This method iterates over every stage
+        and scenario of the Power System, finding the optimal solution of the problem
+        using the Dual Stochastic Dynamic Programming technique.
+
+        Parameters
+        ----------
+        plot : bool, optional
+            Boolean to plot the future cost function of every stage.
+        verbose : bool, optional
+            Dictionary containing the structured data of the system.
+
+        Returns
+        -------
+        operation : pandas.DataFrame
+            A Dataframe containing the operation on every stage and scenario.
+
+        """
+
+        n_hgu = len(self.data["hydro-units"])
+
+        step = 100 / (self.data["discretizations"] - 1)
+        discretizations = list(product(np.arange(0, 100 + step, step), repeat=n_hgu))
+
+        operation = []
+        cuts = []  # type: ignore
+        for stage in range(self.data["stages"], 0, -1):
+            for discretization in discretizations:
+
+                v_i = []
+                # For Every Hydro Unit
+                for i, hgu in enumerate(self.data["hydro-units"]):
+                    v_i.append(
+                        hgu["v_min"]
+                        + (hgu["v_max"] - hgu["v_min"]) * discretization[i] / 100
+                    )
+
+                # For Every Scenario
+                average = 0.0
+                avg_water_marginal_cost = [0 for _ in self.data["hydro-units"]]
+                for scenario in range(self.data["scenarios"]):
+                    inflow = []
+                    for i, hgu in enumerate(self.data["hydro-units"]):
+                        inflow.append(hgu["inflow_scenarios"][stage - 1][scenario])
+
+                    if verbose:
+                        print(
+                            "STAGE: {} | DISC.: {}% | SCENARIO: {}".format(
+                                stage, int(discretization[0]), scenario
+                            )
+                        )
+                    result = sdp(
+                        system_data=self.data,
+                        v_i=v_i,
+                        inflow=inflow,
+                        cuts=cuts,
+                        stage=stage + 1,
+                        verbose=verbose,
+                    )
+                    average += result["total_cost"]
+                    for i, hgu in enumerate(result["hydro_units"]):
+                        avg_water_marginal_cost[i] += hgu["water_marginal_cost"]
+
+                # Calculating the average of the scenarios
+                average = average / self.data["scenarios"]
+                coef_b = average
+                for i, hgu in enumerate(result["hydro_units"]):
+                    # ! Invert the coefficient because of the minimization problem inverts the signal
+                    avg_water_marginal_cost[i] = (
+                        -avg_water_marginal_cost[i] / self.data["scenarios"]
+                    )
+                    coef_b -= v_i[i] * avg_water_marginal_cost[i]
+
+                cuts.append(
+                    {"stage": stage, "coef_b": coef_b, "coefs": avg_water_marginal_cost}
+                )
+                operation.append(
+                    {
+                        "stage": stage,
+                        "storage_percentage": "{}%".format(int(discretization[i])),
+                        "initial_volume": v_i[0],
+                        "average_cost": round(average, 2),
+                    }
+                )
+        operation_df = pd.DataFrame(operation)
+
+        if n_hgu == 1 and plot:
+            plot_future_cost_function(operation=operation_df)
+
+        return operation_df
diff --git a/powersddp/util/__init__.py b/powersddp/utils/__init__.py
similarity index 100%
rename from powersddp/util/__init__.py
rename to powersddp/utils/__init__.py
diff --git a/powersddp/utils/_solver.py b/powersddp/utils/_solver.py
new file mode 100644
index 0000000..a2d597f
--- /dev/null
+++ b/powersddp/utils/_solver.py
@@ -0,0 +1,215 @@
+"""Utilitarian module to solve power systems.
+"""
+
+
+import cvxopt.modeling as model
+import pandas as pd
+import plotly.graph_objects as go
+
+from cvxopt import solvers
+from plotly.subplots import make_subplots
+
+solvers.options["glpk"] = dict(msg_lev="GLP_MSG_OFF")
+
+
+# Unique Linear Programming
+def ulp(
+    system_data: dict,
+    v_i: list,
+    inflow: list,
+    cuts: list,
+    stage: int,
+    verbose: bool = False,
+):
+    """Unique Linear Programming Solver
+
+    Parameters
+    ----------
+    system_data : dict,
+        Dict containing data structured as used to instantiate a PowerSystem.
+    v_i : list
+        List containing the initial volume of the Hydro Units, for each
+    v_i : list
+        List containing the inflow to the Hydro Units
+    verbose : bool, optional
+        Dictionary containing the structured data of the system.
+    """
+
+    return None
+
+
+# Stochastic Dual Programming
+def sdp(
+    system_data: dict,
+    v_i: list,
+    inflow: list,
+    cuts: list,
+    stage: int,
+    verbose: bool = False,
+):
+    """Stochastic Dual Programming Solver
+
+    Method to abstract the Dual Stochastic Programming solver applied to the power system
+    problem.
+
+    Parameters
+    ----------
+    system_data : dict,
+        Dict containing data structured as used to instantiate a PowerSystem.
+    v_i : list
+        List containing the initial volume of the Hydro Units, for each
+    v_i : list
+        List containing the inflow to the Hydro Units
+    verbose : bool, optional
+        Dictionary containing the structured data of the system.
+
+    Returns
+    -------
+    operation : dict
+        A dictionary representing the operation
+    """
+
+    n_tgu = len(system_data["thermal-units"])
+    n_hgu = len(system_data["hydro-units"])
+
+    ## Initializing Model Variables
+    v_f = model.variable(n_hgu, "Final Volume")
+    v_t = model.variable(n_hgu, "Turbined Flow")
+    v_v = model.variable(n_hgu, "Shed Flow")
+    g_t = model.variable(n_tgu, "Power Generated")
+    shortage = model.variable(1, "Power Shortage")
+    alpha = model.variable(1, "Future Cost")
+
+    ## Objective Function
+    objective_function = 0
+    for i, tgu in enumerate(system_data["thermal-units"]):
+        objective_function += tgu["cost"] * g_t[i]
+    objective_function += system_data["outage_cost"] * shortage[0]
+    for i, _ in enumerate(system_data["hydro-units"]):
+        objective_function += 0.01 * v_v[i]
+    objective_function += 1.0 * alpha[0]
+
+    ## Constraints
+    ### Hydro Balance
+    constraints = []
+    for i, hgu in enumerate(system_data["hydro-units"]):
+        constraints.append(v_f[i] == float(v_i[i]) + float(inflow[i]) - v_t[i] - v_v[i])
+
+    ### Load Supply
+    supplying = 0
+    for i, hgu in enumerate(system_data["hydro-units"]):
+        supplying += hgu["prod"] * v_t[i]
+
+    for i, tgu in enumerate(system_data["thermal-units"]):
+        supplying += g_t[i]
+
+    supplying += shortage[0]
+
+    constraints.append(supplying == system_data["load"][stage - 2])
+
+    ### Bounds
+    for i, hgu in enumerate(system_data["hydro-units"]):
+        constraints.append(v_f[i] >= hgu["v_min"])
+        constraints.append(v_f[i] <= hgu["v_max"])
+        constraints.append(v_t[i] >= 0)
+        constraints.append(v_t[i] <= hgu["flow_max"])
+        constraints.append(v_v[i] >= 0)
+
+    for i, tgu in enumerate(system_data["thermal-units"]):
+        constraints.append(g_t[i] >= 0)
+        constraints.append(g_t[i] <= tgu["capacity"])
+
+    constraints.append(shortage[0] >= 0)
+    constraints.append(alpha[0] >= 0)
+
+    ### Cut constraint (Future cost function of forward stage)
+    for cut in cuts:
+        if cut["stage"] == stage:
+            equation = 0
+            for hgu in range(n_hgu):
+                equation += float(cut["coefs"][hgu]) * v_f[hgu]
+            equation += float(cut["coef_b"])  # type: ignore
+            constraints.append(alpha[0] >= equation)
+
+    ## Solving
+    opt_problem = model.op(objective=objective_function, constraints=constraints)
+    opt_problem.solve(format="dense", solver="glpk")
+
+    ## Print
+    if verbose:
+        print("--------------------------------------")
+        print("Total Cost: ${}".format(round(objective_function.value()[0], 2)))  # type: ignore
+        print("Future Cost: ${}".format(round(alpha[0].value()[0], 2)))
+        print("--------------------------------------")
+        for i, hgu in enumerate(system_data["hydro-units"]):
+            print(
+                "HGU {} | {:>15s}: {:>7.2f} hm3".format(i, v_f.name, v_f[i].value()[0])
+            )
+            print(
+                "HGU {} | {:>15s}: {:>7.2f} hm3".format(i, v_t.name, v_t[i].value()[0])
+            )
+            print(
+                "HGU {} | {:>15s}: {:>7.2f} hm3".format(i, v_v.name, v_v[i].value()[0])
+            )
+            print(
+                "HGU {} | {:>15s}: {:>7.2f} $/hm3".format(
+                    i, "Water Cost", constraints[i].multiplier.value[0]
+                )
+            )
+            print("--------------------------------------")
+
+        for i, tgu in enumerate(system_data["thermal-units"]):
+            print("TGU {} | {}: {:>7.2f} MWmed".format(i, g_t.name, g_t[i].value()[0]))
+            print("--------------------------------------")
+
+        print(
+            """{}: {:.2f} MWmed\nMarginal Cost: {:.2f}\n======================================\n
+        """.format(
+                shortage.name,
+                shortage[0].value()[0],
+                constraints[n_hgu].multiplier.value[0],
+            )
+        )
+
+    return {
+        "shortage": shortage[0].value()[0],
+        "operational_marginal_cost": constraints[n_hgu].multiplier.value[0],
+        "total_cost": objective_function.value()[0],  # type: ignore
+        "future_cost": alpha[0].value()[0],
+        "hydro_units": [
+            {
+                "v_f": v_f[i].value()[0],
+                "v_t": v_t[i].value()[0],
+                "v_v": v_v[i].value()[0],
+                "water_marginal_cost": constraints[i].multiplier.value[0],
+            }
+            for i in range(n_hgu)
+        ],
+        "thermal_units": [{"g_t": g_t[i].value()[0]} for i in range(n_tgu)],
+    }
+
+
+def plot_future_cost_function(operation: pd.DataFrame):
+
+    n_stages = len(operation["stage"].unique())
+
+    fig = make_subplots(rows=n_stages, cols=1)
+
+    for i, stage in enumerate(operation["stage"].unique()):
+        stage_df = operation.loc[operation["stage"] == stage]
+        fig.add_trace(
+            go.Scatter(
+                x=stage_df["initial_volume"],
+                y=stage_df["average_cost"],
+                mode="lines",
+                name="Stage {}".format(stage),
+            ),
+            row=i + 1,
+            col=1,
+        )
+
+    fig.update_xaxes(title_text="Final Volume [hm3]")
+    fig.update_yaxes(title_text="$/MW")
+
+    fig.update_layout(height=300 * n_stages, title_text="Future Cost Function")
+    fig.show()
diff --git a/powersddp/util/_yml.py b/powersddp/utils/_yml.py
similarity index 62%
rename from powersddp/util/_yml.py
rename to powersddp/utils/_yml.py
index b0b21b8..72f1c7a 100644
--- a/powersddp/util/_yml.py
+++ b/powersddp/utils/_yml.py
@@ -1,8 +1,17 @@
+"""Utilitarian module to deal with .yml files 
+"""
+
 import yaml
 import os
 
 
 class YmlLoader(yaml.SafeLoader):
+    """Class to extend yaml loader capabilities
+
+    Attributes
+    ----------
+    """
+
     def __init__(self, stream):
 
         self._root = os.path.split(stream.name)[0]
@@ -11,7 +20,10 @@ def __init__(self, stream):
 
     def include(self, node):
 
-        filename = os.path.join(self._root, self.construct_scalar(node))
+        filename = os.path.join(self._root, self.construct_scalar(node))  # type: ignore
 
         with open(filename, "r") as f:
             return yaml.load(f, YmlLoader)
+
+
+YmlLoader.add_constructor("!include", YmlLoader.include)
diff --git a/pyproject.toml b/pyproject.toml
index 31928aa..df3a553 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,6 +12,9 @@ exclude = ["Makefile","README.rst","Notebook.ipynb"]
 python = "^3.8"
 PyYAML = "^5.4.1"
 cvxopt = "^1.2.6"
+numpy = "^1.21.1"
+pandas = "^1.3.2"
+plotly = "^5.2.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^5.2"


Test patch:
diff --git a/tests/test_system.py b/tests/test_system.py
index e0860c2..59e99a8 100644
--- a/tests/test_system.py
+++ b/tests/test_system.py
@@ -2,6 +2,8 @@
 
 from powersddp import PowerSystem
 
+import pandas as pd
+
 
 class TestSystem(TestCase):
     def test_PowerSystem_should_load_from_file(self):
@@ -46,3 +48,38 @@ def test_PowerSystem_should_load_from_payload(self):
             # Content
             self.assertTrue(System.data["load"] == [50, 50, 50])
             self.assertTrue(System.data["hydro-units"][0]["name"] == "HU1")
+
+    def test_PowerSystem_should_dispatch(self):
+        with self.subTest():
+            payload = {
+                "load": [50, 50, 50],
+                "discretizations": 3,
+                "stages": 3,
+                "scenarios": 2,
+                "outage_cost": 500,
+                "hydro-units": [
+                    {
+                        "name": "HU1",
+                        "v_max": 100,
+                        "v_min": 20,
+                        "prod": 0.95,
+                        "flow_max": 60,
+                        "inflow_scenarios": [[23, 16], [19, 14], [15, 11]],
+                    }
+                ],
+                "thermal-units": [
+                    {"name": "GT1", "capacity": 15, "cost": 10},
+                    {"name": "GT2", "capacity": 10, "cost": 25},
+                ],
+            }
+
+            System = PowerSystem(data=payload)
+
+            # Dispatching
+            operation = System.dispatch()
+
+            # Assert Structure
+            self.assertEqual(type(operation), pd.DataFrame)
+
+            # Assert Values
+            self.assertEqual(operation.iloc[0].average_cost, 6725.0)