Repository: ettoreaquino/powersddp (language: python)

Issue:
Multiple HGUs are now shown in the operation output
## Comments

> Closes #13 

Multiple HGUs now working




Discussion / hints:

Fix patch:
diff --git a/CITATION.cff b/CITATION.cff
index d98da6d..3da8f7d 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -15,8 +15,8 @@ authors:
 
 title: "PPEE_210092: Power System Stochastic Dual Dynamic Programming Library"
 
-version: 0.0.1
+version: 0.0.2
 
-date-released: 2021-08-22
+date-released: 2021-08-25
 
 url: "https://github.com/ettoreaquino/powersddp"
diff --git a/Notebook.ipynb b/Notebook.ipynb
index 1eb6b54..14a0d62 100644
--- a/Notebook.ipynb
+++ b/Notebook.ipynb
@@ -2,34 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "9dd8e6a5-b075-404b-85eb-6131fcca2a2a",
    "metadata": {},
    "outputs": [],
    "source": [
     "import powersddp as psddp\n",
     "\n",
-    "\n",
-    "data = {'load': [100, 15, 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], [19, 14], [15, 11]]}],\n",
-    "        'thermal-units': [{'name': 'GT1', 'capacity': 15, 'cost': 10},\n",
-    "                          {'name': 'GT2', 'capacity': 10, 'cost': 25}]}\n",
-    "\n",
-    "TestSystem = psddp.PowerSystem(data=data)"
+    "TestSystem = psddp.PowerSystem(path='system.yml')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "0b41801a-dc45-4777-a2ec-1294c91890cc",
    "metadata": {
     "scrolled": true,
@@ -37,7 +22,7 @@
    },
    "outputs": [],
    "source": [
-    "operation = TestSystem.dispatch()"
+    "operation = TestSystem.dispatch(solver='ulp', scenario=1, plot=True)"
    ]
   },
   {
@@ -88,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "ccbf3a14-01ed-4942-acb3-a4c64f47b6fd",
    "metadata": {},
    "outputs": [],
@@ -228,1024 +213,12 @@
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+   "execution_count": null,
    "id": "2b718ada-dd91-4cb6-a31c-f36b61674065",
    "metadata": {
     "tags": []
    },
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",
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-       "        var display = window.getComputedStyle(gd).display;\n",
-       "        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",
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-       "\n",
-       "// Listen for the clearing of the current output cell\n",
-       "var outputEl = gd.closest('.output');\n",
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-       "\n",
-       "                        })                };                });            </script>        </div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from itertools import product\n",
     "import numpy as np\n",
@@ -1297,123 +270,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "87285cd1-3bb7-4c72-bb43-4f2baf6e4077",
    "metadata": {},
-   "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>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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "operation_df"
    ]
@@ -1510,7 +370,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
diff --git a/README.md b/README.md
index f2d7ecb..b3a32d4 100644
--- a/README.md
+++ b/README.md
@@ -74,12 +74,19 @@ print("System Load: {}\n"
 
 #### **dispatch()** accepts the following arguments:
 
+- `solver : str, optional defaults to 'sdp'`
+  - Selects the solver option for the minimization objective function.
+
+- `scenario : int, optional defaults to 0`
+  - Chooses either a specific scenario to investigate (`scenario>1`) or all scenarios to evaluate (`scenario= 0`). Starting from 0 to the number of declared scenarios in the `hydro_units['inflow_scenarios']` parameter.
+
 - `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. 
 
+The following example executes the Power System dispatch using the Unique Linear Programming method for the first scenario (id = 1) and outputs the optimization steps.
 
 ```Python
 import powersddp as psddp
@@ -100,7 +107,7 @@ data = {'load': [50, 50, 50],
                           {'name': 'GT2', 'capacity': 10, 'cost': 25}]}
 
 PowerSystem = psddp.PowerSystem(data=data)
-operation = PowerSystem.dispatch()
+operation = PowerSystem.dispatch(solver='ulp', scenario=1, verbose=True)
 
 print(operation)
 ```
diff --git a/powersddp/core/system.py b/powersddp/core/system.py
index 38119d2..b25a00c 100644
--- a/powersddp/core/system.py
+++ b/powersddp/core/system.py
@@ -208,21 +208,25 @@ def dispatch(
                         )
                         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),
-                        }
-                    )
+                        cuts.append(
+                            {
+                                "stage": stage,
+                                "coef_b": coef_b,
+                                "coefs": avg_water_marginal_cost,
+                            }
+                        )
+                        operation.append(
+                            {
+                                "stage": stage,
+                                "name": self.data["hydro_units"][i]["name"],
+                                "storage_percentage": "{}%".format(
+                                    int(discretization[i])
+                                ),
+                                "initial_volume": v_i[i],
+                                "average_cost": round(average, 2),
+                            }
+                        )
+            self.cuts = cuts
             operation_df = pd.DataFrame(operation)
 
             if n_hgu == 1 and plot:
@@ -244,13 +248,17 @@ def dispatch(
                             gu_operation=result["hydro_units"],
                             yaxis_column="vf",
                             yaxis_title="HGU Volume (hm3)",
-                            plot_title="HGU Stored Volume on Scenario {}".format(scn + 1),
+                            plot_title="HGU Stored Volume on Scenario {}".format(
+                                scn + 1
+                            ),
                         )
                         plot_ulp(
                             gu_operation=result["thermal_units"],
                             yaxis_column="gt",
                             yaxis_title="Power Generation (MWmed)",
-                            plot_title="TGU Power Generation on Scenario {}".format(scn + 1),
+                            plot_title="TGU Power Generation on Scenario {}".format(
+                                scn + 1
+                            ),
                         )
             else:
                 result = ulp(
@@ -274,3 +282,5 @@ def dispatch(
                             scenario
                         ),
                     )
+
+            return result
diff --git a/pyproject.toml b/pyproject.toml
index 20c24e8..a8e194a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,7 +2,9 @@
 name = "powersddp"
 version = "0.0.2"
 description = "A Stochastic Dual Dynamic Programmimg library to solve economical dispach of power systems."
-authors = ["Ettore Aquino <ettore@ettoreaquino.com>", "João Pedro Peters <jppbrbs@gmail.com>"]
+authors = ["Ettore Aquino <ettore@ettoreaquino.com>",
+           "João Pedro Peters <jppbrbs@gmail.com>",
+           "Pedro Henrique Peters <pedro.peters@engenharia.ufjf.br>"]
 
 repository = "https://github.com/ettoreaquino/powersddp.git"
 
diff --git a/system-2hgu-hydro.yml b/system-2hgu-hydro.yml
new file mode 100644
index 0000000..21c0075
--- /dev/null
+++ b/system-2hgu-hydro.yml
@@ -0,0 +1,22 @@
+-
+  name: HU1
+  v_max: 100
+  v_min: 20
+  v_ini: 100
+  prod: 0.95
+  flow_max: 60
+  inflow_scenarios:
+    - [23,16]
+    - [19,14]
+    - [15,11]
+-
+  name: HU2
+  v_max: 200
+  v_min: 40
+  v_ini: 200
+  prod: 0.85
+  flow_max: 100
+  inflow_scenarios:
+    - [46,32]
+    - [38,28]
+    - [30,22]
\ No newline at end of file
diff --git a/system-2hgu.yml b/system-2hgu.yml
new file mode 100644
index 0000000..e513d7c
--- /dev/null
+++ b/system-2hgu.yml
@@ -0,0 +1,7 @@
+load: [150,150,150]
+discretizations: 3
+stages: 3
+scenarios: 2
+outage_cost: 500
+hydro_units: !include system-2hgu-hydro.yml
+thermal_units: !include system-thermal.yml
\ No newline at end of file


Test patch:
diff --git a/tests/test_system.py b/tests/test_system.py
index a0307d8..962f118 100644
--- a/tests/test_system.py
+++ b/tests/test_system.py
@@ -76,7 +76,7 @@ def test_PowerSystem_should_dispatch_sdp(self):
             System = PowerSystem(data=payload)
 
             # Dispatching
-            operation = System.dispatch(solver="sdp", verbose=True)
+            operation = System.dispatch(solver="sdp")
             # Assert Structure
             self.assertEqual(type(operation), pd.DataFrame)
 
@@ -111,12 +111,29 @@ def test_PowerSystem_should_dispatch_ulp(self):
             System = PowerSystem(data=payload)
 
             # Dispatching
-            operation = System.dispatch(solver="ulp", verbose=True)
+            operation = System.dispatch(solver="ulp", scenario=1)
 
             # Assert Structure
             self.assertEqual(type(operation), dict)
-            self.assertEqual(type(operation["hydro_units"]), list)
-            self.assertEqual(type(operation["thermal_units"]), list)
+            self.assertEqual(type(operation["hydro_units"]), pd.DataFrame)
+            self.assertEqual(type(operation["thermal_units"]), pd.DataFrame)
 
             # Assert Values
             self.assertEqual(operation["total_cost"], 198.49999999999972)
+
+    def test_PowerSystem_should_dispatch_two_hgus_using_ulp(self):
+        with self.subTest():
+            System = PowerSystem(path="system-2hgu.yml")
+
+            # Dispatching
+            operation = System.dispatch(solver="ulp", scenario=1)
+
+            # Structure
+            self.assertEqual(type(System.data), dict)
+
+            # Content
+            self.assertTrue(System.data["load"] == [150, 150, 150])
+            
+            # Results
+            self.assertEqual(operation["total_cost"], 7175.000000000012)
+