Combined version of optimisation task and comparison of the strategies.

projects/example_projects/example_project/scenario/_meta.inf

@@ -1,3 +1,3 @@
 {
-  "rng_seed": 622414180
+  "rng_seed": 278718870
 }

projects/example_projects/example_project/scenario/actor_residential_1.csv

@@ -1,29 +1,29 @@
 Time,load,pv,schedule
-2016-01-01 00:00:00,0.011886246327641098,0.000517808433048365,-0.011368437894592734
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-2016-01-01 03:00:00,0.0092306359903934,0.04001418894280702,0.03078355295241362
-2016-01-01 03:15:00,0.021129578178791,0.09622432421329186,0.07509474603450086
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+2016-01-01 06:45:00,0.0095341104278976,0.1360631502288365,0.1265290398009389

projects/example_projects/example_project/scenario/actor_residential_3.csv

@@ -1,29 +1,29 @@
 Time,load,pv,schedule
-2016-01-01 00:00:00,0.0183501135461672,0.000517808433048365,-0.017832305113118833
-2016-01-01 00:15:00,0.0183276402096313,0.0018173905721315046,-0.016510249637499797
-2016-01-01 00:30:00,0.0186881603411869,0.0018788623941673238,-0.016809297947019578
-2016-01-01 00:45:00,0.0184871298585762,0.0026125830560011706,-0.01587454680257503
-2016-01-01 01:00:00,0.0179609033250519,0.007666088655028339,-0.01029481467002356
-2016-01-01 01:15:00,0.0183569591857021,0.007786428245115456,-0.010570530940586641
-2016-01-01 01:30:00,0.0429906796923723,0.015510455887296022,-0.02748022380507628
-2016-01-01 01:45:00,0.0468752880631146,0.009280520350204874,-0.037594767712909725
-2016-01-01 02:00:00,0.0483995519556214,0.024992648176070963,-0.023406903779550438
-2016-01-01 02:15:00,0.04895941513644,0.025210661520819418,-0.02374875361562058
-2016-01-01 02:30:00,0.0448443403712756,0.03305446941532318,-0.011789870955952415
-2016-01-01 02:45:00,0.0243064739511402,0.06424692281955591,0.039940448868415704
-2016-01-01 03:00:00,0.0495014864417933,0.04001418894280702,-0.009487297498986282
-2016-01-01 03:15:00,0.0493275564676503,0.09622432421329186,0.046896767745641554
-2016-01-01 03:30:00,0.0503633213311156,0.10552542460550364,0.05516210327438804
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-2016-01-01 04:30:00,0.0184295811072935,0.3217850817801391,0.3033555006728456
-2016-01-01 04:45:00,0.0184305987280745,0.02317818073919563,0.004747582011121132
-2016-01-01 05:00:00,0.0185237682294309,0.06122328551108127,0.04269951728165037
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-2016-01-01 06:45:00,0.041905529810521,1.0,0.958094470189479
+2016-01-01 00:00:00,0.0183501135461672,7.04546466156181e-05,-0.01827965889955158
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requirements.txt

@@ -5,3 +5,4 @@ pytest
 pathlib
 numpy
 pydot
+pyomo

simply/actor.py

@@ -11,6 +11,8 @@
 from simply.util import daily, gaussian_pv
 import simply.config as cfg
 
+from simply.optimisation import optimize_schedule
+
 Order = namedtuple("Order", ("type", "time", "actor_id", "cluster", "energy", "price"))
 Order.__doc__ = """
 Struct to hold order
@@ -188,6 +190,20 @@ def __init__(self, id, df, environment=None, battery=None, csv=None, ls=1, ps=1,
         self.args = {"id": id, "df": df.to_json(), "csv": csv, "ls": ls, "ps": ps,
                      "pm": pm}
 
+    def strategy_with_optimisation(self):#change the name of the func
+        # Use the optimization library to implement the new strategy
+
+        objective, df_results = optimize_schedule(self.pred, self.mm_buy_prices, self.mm_sell_prices)
+
+        # Process the results as needed
+        # For example:
+        print("Optimization objective:", objective)
+        print("Optimization results:")
+        print(df_results)
+
+        market_schedule = df_results["to_grid"] 
+        return market_schedule
+
     def set_var_battery(self, capacity, soc_initial, df, available=0, max_c_rate=4,
                         refresh=True):
         """
@@ -274,6 +290,10 @@ def get_market_schedule(self, strategy=None):
                     f"without planning instead.")
                 strategy = self.strategy
 
+        if strategy == 4:
+            self.market_schedule = self.strategy_with_optimisation()
+            return self.market_schedule
+
         if strategy == 0:
             self.market_schedule = self.get_default_market_schedule()
             # overwrite the current value of the market schedule if the soc would surpass 1

simply/optimisation.py

@@ -0,0 +1,148 @@
+# REQUIREMENTS# pyomo package needs to be installed
+# a solver needs to be installed: CBC or GLPK are open source; for CBC
+# coppy cbc_solver in the directory where the repo clone is
+
+import pyomo.environ as pyo
+import pandas as pd
+
+
+def optimize_schedule(df_actor, buy_prices, sell_prices, capacity=10, max_c_rate=1, soc_initial=0.5):
+    # parametrisation of battery specified in simply/battery.py
+    # capacity=10, max_c_rate=1, soc_initial=0.5
+    # TODO battery-efficiency ?
+    # TODO electric vehicle parameter
+
+    # single time series vectors
+    t = list(range(len(df_actor)))
+    load = df_actor[["load"]].to_list()
+    pv = df_actor[["pv"]].to_list()
+    buy_prices = buy_prices.to_list() 
+    sell_prices = sell_prices.to_list() 
+
+    # TODO add electric vehicle
+    ev_avail = [1] * len(buy_prices)
+    ev_demand = [0] * len(buy_prices)
+
+    # other parameters
+    # TODO minutes per time step can be obtained using datetime functions from input csv-files
+    time_interval = 15  # minutes per time step
+    # TODO grid fee for buying electricity, see config.cfg
+    grid_fee = 0.0  # 0.09
+    # TODO if no upper bound is provided the problem turns out to be unbounded
+    grid_connection_capacity = 20
+
+    # PYOMO OPTIMISATION MODEL
+    model = pyo.ConcreteModel()
+    model.charging_power = pyo.Var(t, bounds=(0, capacity * max_c_rate))
+    model.discharging_power = pyo.Var(t, bounds=(0, capacity * max_c_rate))
+    model.stored_energy = pyo.Var(t, bounds=(0, capacity))
+    # 0/ 1 for distinguishing between charging / discharging
+    model.bi_charge = pyo.Var(t, within=pyo.Binary)
+    model.power_from_grid = pyo.Var(t, bounds=(0, grid_connection_capacity))
+    model.power_to_grid = pyo.Var(t, bounds=(0, grid_connection_capacity))
+    model.cash_flow = pyo.Var(t)
+
+    # energy balance for the system
+    model.energy_balance_system = pyo.ConstraintList()
+    for i in range(len(t) - 1):
+        model.energy_balance_system.add(
+            0 ==
+            model.power_from_grid[i] + pv[i] + model.discharging_power[i]
+            - load[i] - model.power_to_grid[i] - model.charging_power[i])
+
+    # component energy storage
+    model.energy_balance_storage = pyo.ConstraintList()
+    for i in range(len(t) - 1):
+        # constraint
+        model.energy_balance_storage.add(
+            model.stored_energy[i+1] - model.stored_energy[i] ==
+            (model.charging_power[i] - model.discharging_power[i]) * time_interval/60)
+
+    model.start_storage = pyo.Constraint(
+        expr=model.stored_energy[0] ==
+        soc_initial * capacity)
+
+    # optional: equal soc at first and last time step
+    model.start_end_storage = pyo.Constraint(
+        expr=model.stored_energy[0] == model.stored_energy[len(t)-1])
+
+    # needed in order to not have a discharge that affects the timestep after the last considered
+    model.end_no_discharge_storage = pyo.Constraint(
+        expr=0 == model.discharging_power[len(t)-1])
+
+    # binary variable to separate charging and discharging timesteps in order to
+    # exclude having both at the same time
+    model.binary_charge_storage = pyo.ConstraintList()
+    for i in t:
+        model.binary_charge_storage.add(
+            model.charging_power[i] <= model.bi_charge[i] * capacity * max_c_rate)
+
+    model.binary_discharge_storage = pyo.ConstraintList()
+    for i in t:
+        model.binary_discharge_storage.add(
+            model.discharging_power[i] <= (1 - model.bi_charge[i]) * capacity * max_c_rate)
+
+    # costs to be used in objective function
+    model.cash_flow_equation = pyo.ConstraintList()
+    for i in t:
+        model.cash_flow_equation.add(
+            model.cash_flow[i] ==
+            sell_prices[i] * model.power_to_grid[i] * time_interval/60
+            - (buy_prices[i] + grid_fee) * model.power_from_grid[i] * time_interval/60)
+
+    model.obj = pyo.Objective(
+        expr=sum(model.cash_flow[i] for i in t), sense=pyo.maximize)
+
+    # chose solver and solver-specific options
+    # opt = pyo.SolverFactory('glpk')
+    # opt.options['mipgap'] = 1e-3    # solver option for GLPK: relative gap, default: 0.0
+    # opt.options['tmlim'] = 60*30    # solver option for GLPK: timelimit in seconds
+    opt = pyo.SolverFactory('cbc')
+    opt.options['seconds'] = 60*30   # solver option for CBC: timelimit in seconds
+
+    _ = opt.solve(model, tee=True)  # solve the problem
+    # model.pprint()                          # print results in run terminal
+
+    # RESULTS
+    # calculate objective for result output
+    objective = sum(model.cash_flow[i].value for i in t)
+    return objective, pd.DataFrame({
+        "Time": [df_actor.iat[i, 0] for i in t],
+        "load": load,
+        "pv": pv,
+        "sell_prices": sell_prices,
+        "buy_prices": buy_prices,
+        "from_grid": [model.power_from_grid[i].value for i in t],
+        "to_grid": [model.power_to_grid[i].value for i in t],
+        "charge": [model.charging_power[i].value for i in t],
+        "discharge": [model.discharging_power[i].value for i in t],
+        "soc": [model.stored_energy[i].value / capacity for i in t],
+    })
+
+
+if __name__ == "__main__":
+    # INPUT DATA
+    # read input data from csv nad write to df
+    # just as one example
+    df_input_data_actor = pd.read_csv('/home/bedirhan/simply/projects/example_projects/example_project/scenario/actor_residential_1.csv')
+    load = df_input_data_actor.loc[:, "load"]
+    pv = df_input_data_actor.loc[:, "pv"]
+    df_input_data_prices = pd.read_csv('/home/bedirhan/simply/projects/example_projects/example_project/scenario/MarketMaker.csv')
+    buy_prices = df_input_data_prices.loc[:, "all_buy_prices"]
+    sell_prices = df_input_data_prices.loc[:, "all_sell_prices"]
+    objective, df_results = optimize_schedule(df_input_data_actor, load, pv, buy_prices, sell_prices)
+    # PRINTS
+    print("RESULTS:")
+    print("Objective:", objective)
+    pd.set_option('display.max_columns', None)
+    print(df_results)
+
+    import matplotlib.pyplot as plt
+    f, axs = plt.subplots(3)
+    energy_cols = ["load", "pv", "from_grid", "to_grid", "charge", "discharge"]
+    df_results[energy_cols].plot(ax=axs[0])
+    bat_cols = ["soc"]
+    df_results[bat_cols].plot(ax=axs[1])
+    price_cols = ["buy_prices"]
+    df_results[price_cols].plot(ax=axs[2])
+    plt.show()

simply/scenario.py

@@ -6,7 +6,11 @@
 import pandas as pd
 import numpy as np
 import random
+#import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use('TkAgg')
 import matplotlib.pyplot as plt
+
 import simply.config as cfg
 from simply import actor, market_maker
 from simply import power_network