Monte carlo Improvement

config.default.yaml

@@ -341,18 +341,36 @@ costs:
 
 
 monte_carlo:
+    # Uniform: https://chaospy.readthedocs.io/en/master/api/chaospy.Uniform.html
+    # Normal: https://chaospy.readthedocs.io/en/master/api/chaospy.Normal.html
+    # LogNormal: https://chaospy.readthedocs.io/en/master/api/chaospy.LogNormal.html
+    # Triange: https://chaospy.readthedocs.io/en/master/api/chaospy.Triangle.html
+    # Beta: https://chaospy.readthedocs.io/en/master/api/chaospy.Beta.html
+    # Gamma: https://chaospy.readthedocs.io/en/master/api/chaospy.Gamma.html
+    # [mean, std] for Normal and LogNormal
+    # [lower_bound, upper_bound] for Uniform
+    # [lower_bound, midpoint, upper_bound] for Triangle
+    # [alpha, beta] for Beta
+    # [shape, scale] for Gamma
+    # TODO: Support inputs to simulate outages biggest lines "lines.p_nom_opt.max(): [-3000MW 0MW]""
+    # TODO: Support inputs to simulate outages of biggest power plant "generators.p_nom.max()": [-1000MW 0MW]
+    # ... user can add flexibly more features for the Monte-Carlo sampling
+    # as key: add below the pypsa object for the monte_carlo sampling, "network" is only allowed for filtering!
+    # as value: currently supported format [l_bound, u_bound] or empty [], represent multiplication factors for the object
   options:
     add_to_snakefile: false
-    samples: 7    # number of optimizations
-    sampling_strategy: "chaospy"    # "pydoe2", "chaospy", "scipy", packages that are supported
-  pypsa_standard:
-      # User can add here flexibly more features for the Monte-Carlo sampling.
-      # Given as "key: value" format
-      # Key: add below the pypsa object for the monte_carlo sampling, "network" is only allowed for filtering!
-      # Value: currently supported format [l_bound, u_bound] or empty [], represent multiplication factors for the object
-    loads_t.p_set: [0.9, 1.1]
-    # generators_t.p_max_pu.loc[:, n.generators.carrier == "wind"]: [0.9, 1.1]
-    # generators_t.p_max_pu.loc[:, n.generators.carrier == "solar"]: [0.9, 1.1]
+    samples: 9 # number of optimizations. Note that number of samples when using scipy has to be the square of a prime number
+    sampling_strategy: "chaospy"  # "pydoe2", "chaospy", "scipy", packages that are supported
+  uncertainties:
+    loads_t.p_set:
+      type: Uniform
+      args: [0, 1]
+    generators_t.p_max_pu.loc[:, n.generators.carrier == "wind"]:
+      type: LogNormal
+      args: [0, 2]
+    generators_t.p_min_pu.loc[:, n.generators.carrier == "wind"]:
+      type: Beta
+      args: [0.5, 2]
 
 
 solving:

config.tutorial.yaml

@@ -337,18 +337,36 @@ costs:
 
 
 monte_carlo:
+  # Uniform: https://chaospy.readthedocs.io/en/master/api/chaospy.Uniform.html
+  # Normal: https://chaospy.readthedocs.io/en/master/api/chaospy.Normal.html
+  # LogNormal: https://chaospy.readthedocs.io/en/master/api/chaospy.LogNormal.html
+  # Triange: https://chaospy.readthedocs.io/en/master/api/chaospy.Triangle.html
+  # Beta: https://chaospy.readthedocs.io/en/master/api/chaospy.Beta.html
+  # Gamma: https://chaospy.readthedocs.io/en/master/api/chaospy.Gamma.html
+  # [mean, std] for Normal and LogNormal
+  # [lower_bound, upper_bound] for Uniform
+  # [lower_bound, midpoint, upper_bound] for Triangle
+  # [alpha, beta] for Beta
+  # [shape, scale] for Gamma
+  # TODO: Support inputs to simulate outages biggest lines "lines.p_nom_opt.max(): [-3000MW 0MW]""
+  # TODO: Support inputs to simulate outages of biggest power plant "generators.p_nom.max()": [-1000MW 0MW]
+  # ... user can add flexibly more features for the Monte-Carlo sampling
+  # as key: add below the pypsa object for the monte_carlo sampling, "network" is only allowed for filtering!
+  # as value: currently supported format [l_bound, u_bound] or empty [], represent multiplication factors for the object
   options:
     add_to_snakefile: false
-    samples: 7    # number of optimizations
-    sampling_strategy: "chaospy"    # "pydoe2", "chaospy", "scipy", packages that are supported
-  pypsa_standard:
-      # User can add here flexibly more features for the Monte-Carlo sampling.
-      # Given as "key: value" format
-      # Key: add below the pypsa object for the monte_carlo sampling, "network" is only allowed for filtering!
-      # Value: currently supported format [l_bound, u_bound] or empty [], represent multiplication factors for the object
-    loads_t.p_set: [0.9, 1.1]
-    # generators_t.p_max_pu.loc[:, n.generators.carrier == "wind"]: [0.9, 1.1]
-    # generators_t.p_max_pu.loc[:, n.generators.carrier == "solar"]: [0.9, 1.1]
+    samples: 9 # number of optimizations. Note that number of samples when using scipy has to be the square of a prime number
+    sampling_strategy: "chaospy"  # "pydoe2", "chaospy", "scipy", packages that are supported
+  uncertainties:
+    loads_t.p_set:
+      type: Uniform
+      args: [0, 1]
+    generators_t.p_max_pu.loc[:, n.generators.carrier == "wind"]:
+      type: LogNormal
+      args: [0, 2]
+    generators_t.p_min_pu.loc[:, n.generators.carrier == "wind"]:
+      type: Beta
+      args: [0.5, 2]
 
 
 solving: