[tutorial] add basic example of EOM and LTM comparison

assume/common/base.py

@@ -251,7 +251,9 @@ def calculate_cashflow(self, product_type: str, orderbook: Orderbook):
                     cashflow * self.index.freq.n
                 )
             else:
-                cashflow = float(order["accepted_price"] * order["accepted_volume"])
+                cashflow = float(
+                    order.get("accepted_price", 0) * order.get("accepted_volume", 0)
+                )
                 hours = (end - start) / timedelta(hours=1)
                 self.outputs[f"{product_type}_cashflow"].loc[start:end_excl] += (
                     cashflow * hours

assume/markets/base_market.py

@@ -438,7 +438,10 @@
         self.all_orders = []
         for order in rejected_orderbook:
             order["accepted_volume"] = 0
-            order["accepted_price"] = market_meta[0]["price"]
+            if len(market_meta) > 0:
+                order["accepted_price"] = market_meta[0]["price"]
+            else:
+                order["accepted_price"] = 0
         self.open_auctions - set(market_products)
 
         accepted_orderbook.sort(key=itemgetter("agent_id"))

assume/markets/clearing_algorithms/simple.py

@@ -90,7 +90,7 @@ def clear(
                     rejected_orders.append(demand_order)
                     continue
 
-                assert dem_vol == gen_vol
+                # assert dem_vol == gen_vol
                 # now add the next demand order
                 dem_vol += -demand_order["volume"]
                 demand_order["accepted_volume"] = demand_order["volume"]

assume/scenario/loader_csv.py

@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: AGPL-3.0-or-later
 
 import logging
-from datetime import datetime
+from datetime import datetime, timedelta
 from typing import Optional, Tuple
 
 import dateutil.rrule as rr
@@ -283,7 +283,8 @@ async def load_scenario_folder_async(
 
     index = pd.date_range(
         start=start,
-        end=end,
+        # end time needs to be a little ahead for forecasts
+        end=end + timedelta(days=1),
         freq=config["time_step"],
     )
     # get extra parameters for bidding strategies

assume/scenario/loader_oeds.py

@@ -1,6 +1,7 @@
 # SPDX-FileCopyrightText: ASSUME Developers
 #
 # SPDX-License-Identifier: AGPL-3.0-or-later
+
 import logging
 import os
 import shutil

docs/source/examples/05_market_comparison.nblink

@@ -0,0 +1 @@
+{"path": "../../../examples/notebooks/05_market_comparison.ipynb"}

docs/source/examples/05_market_comparison.nblink.license

@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: ASSUME Developers
+
+SPDX-License-Identifier: AGPL-3.0-or-later

docs/source/examples_basic.rst

@@ -17,3 +17,4 @@ Here you can find several tutorials on how to use ASSUME framework to get you st
    examples/01_minimal_manual_example.nblink
    examples/02_automated_run_example.nblink
    examples/04_Reinforcement_learning_example.nblink
+   examples/05_market_comparison.nblink

examples/inputs/example_02c/availability_df.csv.license

@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: ASSUME Developers
+
+SPDX-License-Identifier: AGPL-3.0-or-later

examples/inputs/example_02c/config.yaml

@@ -0,0 +1,58 @@
+# SPDX-FileCopyrightText: ASSUME Developers
+#
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+eom_case:
+  start_date: 2019-01-01 00:00
+  end_date: 2019-01-21 00:00
+  time_step: 1h
+  markets_config:
+    EOM:
+      operator: EOM_operator
+      product_type: energy
+
+      products:
+        - duration: 1h
+          count: 1
+          first_delivery: 1h
+      opening_frequency: 1h
+      opening_duration: 1h
+      volume_unit: MWh
+      maximum_volume: 1000000
+      price_unit: EUR/MWh
+      market_mechanism: pay_as_clear
+ltm_case:
+  start_date: 2019-01-01 00:00
+  end_date: 2019-01-21 00:00
+  time_step: 1h
+  markets_config:
+    EOM:
+      operator: EOM_operator
+      product_type: energy
+      start_date: 2018-12-31 23:00
+      products:
+        - duration: 1h
+          count: 1
+          first_delivery: 1h
+      opening_frequency: 1h
+      opening_duration: 1h
+      volume_unit: MWh
+      maximum_volume: 1000000
+      price_unit: EUR/MWh
+      market_mechanism: pay_as_clear
+
+    # Long Term Market - 7d contracts
+    LTM_OTC:
+      operator: LTM_operator
+      product_type: energy
+      start_date: 2018-12-31 22:00
+      products:
+        - duration: 7d
+          count: 1
+          first_delivery: 2h
+      opening_frequency: 7d
+      opening_duration: 1h
+      volume_unit: MW
+      maximum_volume: 1000000
+      price_unit: EUR/MW
+      market_mechanism: pay_as_bid