Merge pull request #8 from gilbertgong/ggong/consumption-prep

preparation for consumption - slight refactor, prepare for tests, tweak graphs

README.md

@@ -19,8 +19,6 @@ pip install solar-and-storage
 Import the packages
 ```python
 import numpy as np
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
 
 from solar_and_storage.solar_and_storage import SolarAndStorage
 
@@ -52,14 +50,15 @@ result_df = solar_and_storage.get_results()
 
 Now plot the data
 ```python
-fig = solar_and_storage.get_fig()
+fig = solar_and_storage.get_figure()
 
 fig.show(rendered="browser")
 ```
 
 
-![Example1](https://raw.githubusercontent.com/openclimatefix/solar-and-storage/main/examples/solar_and_storage.png)
-The first plot shows the solar profile, the second shows the prices that day. The third shows the battery profile.
+![Example1](examples/images/battery_solar.png)
+
+The first plot shows the solar profile, the second shows the prices that day. The third shows the battery profile.  Finally the fourth shows profit.
 You can see that the battery charged from the solar site at the end of the solar maximum
 
 
@@ -94,4 +93,4 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
 
 <!-- ALL-CONTRIBUTORS-LIST:END -->
 
-This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!
+This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!

TODO.md

@@ -0,0 +1,5 @@
+- add tests
+- refactor to use net_transfer_to_grid
+- incorporate consumption
+  - example with 0 solar + 0 battery
+  - build from here

examples/battery_only.py

@@ -3,35 +3,29 @@
 import numpy as np
 
 from solar_and_storage.solar_and_storage import SolarAndStorage
+from solar_and_storage.example import prices, no_solar
 
-HTML_OUTPUT = "examples/battery_only.html"  # Change this to your desired path or leave as an empty string
+HTML_OUTPUT = "" # set path or empty will skip writing HTML
+PNG_OUTPUT = "examples/images/battery_only.png" # empty will skip writing PNG
 
-hours_per_day = 24
-
-
-# make prices
-prices = np.zeros(hours_per_day) + 30
-prices[6:19] = 40
-prices[9] = 50
-prices[12:14] = 30
-prices[16:18] = 50
-prices[17] = 60
-
-# make zero solar profile
-solar = np.zeros(hours_per_day)
-
-solar_and_storage = SolarAndStorage(prices=prices, solar_generation=list(solar))
-solar_and_storage.run_optimization()
+# use example prices and no generation profile
+solar_and_storage = SolarAndStorage(prices=prices, solar_generation=list(no_solar))
 result_df = solar_and_storage.get_results()
 
 # data is available for direct access
 power = result_df["power"]
 e_soc = result_df["e_soc"]
 solar_power_to_grid = result_df["solar_power_to_grid"]
+profit = result_df["profit"]
 
 # plot
-fig = solar_and_storage.get_fig()
+fig = solar_and_storage.get_figure()
 
 fig.show(rendered="browser")
 if HTML_OUTPUT:
     fig.write_html(HTML_OUTPUT)
+if PNG_OUTPUT:
+    fig.write_image(PNG_OUTPUT, format="png")
+print(result_df.attrs["message"])
+total_profit = solar_and_storage.get_total_profit()
+print(f'total profit: {total_profit}')

examples/battery_solar.py

@@ -4,37 +4,29 @@
 import os
 
 from solar_and_storage.solar_and_storage import SolarAndStorage
+from solar_and_storage.example import prices, with_solar
 
-HTML_OUTPUT = "examples/battery_solar.html"  # Change this to your desired path or leave as an empty string
+HTML_OUTPUT = "" # set path or empty will skip writing HTML
+PNG_OUTPUT = "examples/images/battery_solar.png" # empty will skip writing PNG
 
-hours_per_day = 24
-
-
-# make prices
-prices = np.zeros(hours_per_day) + 30
-prices[6:19] = 40
-prices[9] = 50
-prices[12:14] = 30
-prices[16:18] = 50
-prices[17] = 60
-
-# make solar profile
-solar = np.zeros(hours_per_day)
-solar[8:16] = 2.0
-solar[10:14] = 4.0
-
-solar_and_storage = SolarAndStorage(prices=prices, solar_generation=list(solar))
-solar_and_storage.run_optimization()
+# use example prices and solar generation profile
+solar_and_storage = SolarAndStorage(prices=prices, solar_generation=list(with_solar))
 result_df = solar_and_storage.get_results()
 
 # data is available for direct access
 power = result_df["power"]
 e_soc = result_df["e_soc"]
 solar_power_to_grid = result_df["solar_power_to_grid"]
+profit = result_df["profit"]
 
 # plot
-fig = solar_and_storage.get_fig()
+fig = solar_and_storage.get_figure()
 
 fig.show(rendered="browser")
 if HTML_OUTPUT:
     fig.write_html(HTML_OUTPUT)
+if PNG_OUTPUT:
+    fig.write_image(PNG_OUTPUT, format="png")
+print(result_df.attrs["message"])
+total_profit = solar_and_storage.get_total_profit()
+print(f'total profit: {total_profit}')

requirements.txt

@@ -3,3 +3,4 @@ scipy==1.8.1
 cvxopt
 cvxpy==1.2.2
 plotly
+kaleido

solar_and_storage/example.py

@@ -0,0 +1,25 @@
+""" These are used in the /examples directory as well as (planned) tests """
+
+import numpy as np
+
+# examples use 24 hour day for analysis period
+# any granularity should be supported but only 24 hour day has been tested
+hours_per_day = 24
+
+prices = np.zeros(hours_per_day) + 30
+prices[6:19] = 40
+prices[9] = 50
+prices[12:14] = 30
+prices[16:18] = 50
+prices[17] = 60
+
+no_solar = np.zeros(hours_per_day)
+
+with_solar = np.zeros(hours_per_day)
+with_solar[8:16] = 2.0
+with_solar[10:14] = 4.0
+
+solar_generation = {
+    "no_solar": no_solar,
+    "with_solar": with_solar,
+}

solar_and_storage/solar_and_storage.py

@@ -40,6 +40,7 @@ def __init__(
             should be between 0 and 1.
         :param grid_connection_capacity: the amount of power that can be delivered to the grid
         """
+        self.prob = None
         self.battery_soc_min = battery_soc_min
         self.battery_soc_max = battery_soc_max
         self.battery_capacity = battery_capacity
@@ -132,6 +133,13 @@ def __init__(
         self.constraints = constraints
         self.objective_function = objective_function
 
+    def get_status(self) -> str:
+        """Runs optimization if not already run, and returns status"""
+
+        if self.prob is None:
+            self.run_optimization()
+        return self.prob.status
+
     def run_optimization(self):
         """
         Run optimization problem
@@ -145,6 +153,16 @@ def run_optimization(self):
 
     def get_results(self) -> pd.DataFrame:
         """Get optimization results (after running)"""
+
+        status = self.get_status()
+
+        if status != "optimal":
+            # Return an empty DataFrame with metadata for non-optimal cases
+            result_df = pd.DataFrame()
+            result_df.attrs["status"] = status
+            result_df.attrs["message"] = message
+            return result_df
+
         # run plot resutls
         power = np.round(
             self.battery_power_charge_cp_variable.value - self.power_discharge_cp_variable.value, 2
@@ -157,26 +175,69 @@ def get_results(self) -> pd.DataFrame:
 
         data = np.array([power, e_soc[:HOURS_PER_DAY], solar_power_to_grid, profit]).transpose()
 
-        return pd.DataFrame(
+        result_df = pd.DataFrame(
             data=data,
             columns=["power", "e_soc", "solar_power_to_grid", "profit"],
         )
+        result_df.attrs["status"] = status
+        result_df.attrs["message"] = "Optimization successful"
+
+        return result_df
+
+    def get_total_profit(self) -> float:
+        results = self.get_results()
+        if results.attrs["status"] != "optimal":
+            raise ValueError(f"Cannot calculate total profit: {results.attrs['message']}")
+        return sum(results["profit"])
+
+    def get_figure(self) -> go.Figure:
+        """Generate figure on successful optimization"""
+
+        status = self.get_status()
+
+        if status != "optimal":
+            fig = go.Figure()
+            fig.update_layout(
+                title=f"Optimization Failed: {status.capitalize()}",
+                title_x=0.5,
+            )
+            return fig
 
-    def get_fig(self) -> go.Figure:
         result_df = self.get_results()
+        total_profit = self.get_total_profit()
 
         # run plot resutls
         power = result_df["power"]
         e_soc = result_df["e_soc"]
         solar_power_to_grid = result_df["solar_power_to_grid"]
+        profit = result_df["profit"]
 
         # plot
-        fig = make_subplots(rows=3, cols=1, subplot_titles=["Solar profile", "Price", "SOC"])
+        fig = make_subplots(rows=4, cols=1, subplot_titles=["Solar profile", "Price", "SOC", "Profit"])
         fig.add_trace(go.Scatter(y=e_soc[:24], name="SOC"), row=3, col=1)
         fig.add_trace(go.Scatter(y=self.solar_generation, name="solar", line_shape="hv"), row=1, col=1)
         fig.add_trace(
             go.Scatter(y=solar_power_to_grid, name="solar to gird", line_shape="hv"), row=1, col=1
         )
         fig.add_trace(go.Scatter(y=self.prices, name="price", line_shape="hv"), row=2, col=1)
+        fig.add_trace(go.Scatter(y=profit, name="profit", line_shape="hv"), row=4, col=1)
+
+        # Add title
+        fig.update_layout(
+            title="Solar and Storage Optimization Results",
+            title_x=0.5,
+        )
+
+        # Add total profit as an annotation below the chart
+        fig.update_layout(
+            annotations=[
+                dict(
+                    text=f"Total Profit: {total_profit:.2f}",
+                    yref="paper",
+                    y=-0.2,  # Position below the chart
+                    font=dict(size=14)
+                )
+            ]
+        )
 
         return fig