diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
index f561a110..994daf9f 100644
--- a/.github/workflows/python-app.yml
+++ b/.github/workflows/python-app.yml
@@ -16,10 +16,14 @@ jobs:
 
     steps:
     - uses: actions/checkout@v3
-    - name: Set up Python 3.10
+    - name: Install CBC solver
+      run: |
+        sudo apt-get update
+        sudo apt-get install -y coinor-cbc
+    - name: Set up Python 3.8
       uses: actions/setup-python@v3
       with:
-        python-version: "3.10"
+        python-version: "3.8"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
@@ -34,3 +38,6 @@ jobs:
     - name: Run match_market.py
       run: |
         python match_market.py projects/example_projects/example_project
+    - name: Run match_market.py EV optimisation
+      run: |
+        python match_market.py projects/example_projects/example_project_ev_opt
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 7d338f69..95952edc 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -3,7 +3,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "3.10"
+    python: "3.8"
 
 python:
   install:
diff --git a/README.rst b/README.rst
index ebba5854..247be39b 100644
--- a/README.rst
+++ b/README.rst
@@ -44,6 +44,9 @@ After cloning the repository, a virtual environment is created (e.g. using virtu
     # create virtual environment
     virtualenv venv --python=python3.8
     source venv/bin/activate
+    # install cbc-solver to be able to use optimization with pyomo
+    sudo apt-get update
+    sudo apt-get install -y coinor-cbc
 
 Then there are two options to use simply:
 
diff --git a/doc/requirements.txt b/doc/requirements.txt
index 70649cbc..05cfd7b8 100644
--- a/doc/requirements.txt
+++ b/doc/requirements.txt
@@ -3,4 +3,5 @@ sphinx_rtd_theme
 networkx
 matplotlib
 pandas
-pytest
\ No newline at end of file
+pytest
+pyomo
\ No newline at end of file
diff --git a/doc/source/actor_strategies.rst b/doc/source/actor_strategies.rst
index 08b97032..20c268c8 100644
--- a/doc/source/actor_strategies.rst
+++ b/doc/source/actor_strategies.rst
@@ -11,8 +11,8 @@ actor corresponds to the price of the market maker at the time of electricity tr
 constraints, energy has to be drawn from or fed into the grid, the actor sends an order with a price that corresponds to the
 market maker prices (incl. grid fees). This way a successful trade is guaranteed.
 
-There are four different actor strategies. Strategy 0 represents the simplest case, which is only based on the market maker's
-electricity price of the current time slot. The other three strategies represent forecast-based,
+There are five different actor strategies. Strategy 0 represents the simplest case, which is only based on the market maker's
+electricity price of the current time slot. The other four strategies represent forecast-based,
 market-oriented alternatives: The market maker's price time series as well as the actor's demand and supply of
 electrical energy are considered for the near future given the configured horizon. Thus, the timing of
 electricity trading based on future market maker prices permits the actor to
@@ -23,21 +23,21 @@ the selected matching algorithm, applied grid fees and additional pricing strate
 (cf. :ref:`pricing_strategies` and :ref:`matching_algorithms`). As a result, this allows the actor - within its constraints - to
 purchase energy at a lower price or sell energy at a higher price with respect to the guaranteed trading with the market maker.
 
-The four actor strategies build on each other and are characterized by different features:
+The first four actor strategies (0-3) build on each other and are characterized by different features:
 
-+--------------------------+--------------+--------------+--------------+--------------+
-|                          | Strategy 0   | Strategy 1   | Strategy 2   | Strategy 3   |
-+==========================+==============+==============+==============+==============+
-| Forecast based purchase  |              | x            | x            | x            |
-+--------------------------+--------------+--------------+--------------+--------------+
-| Forecast based sale      |              |              | x            | x            |
-+--------------------------+--------------+--------------+--------------+--------------+
-| Time arbitrage           |              |              |              | x            |
-+--------------------------+--------------+--------------+--------------+--------------+
++--------------------------+--------------+--------------+--------------+--------------+--------------+
+|                          | Strategy 0   | Strategy 1   | Strategy 2   | Strategy 3   | Strategy 4   |
++==========================+==============+==============+==============+==============+==============+
+| Forecast based purchase  |              | x            | x            | x            | x            |
++--------------------------+--------------+--------------+--------------+--------------+--------------+
+| Forecast based sale      |              |              | x            | x            | x            |
++--------------------------+--------------+--------------+--------------+--------------+--------------+
+| Time arbitrage           |              |              |              | x            | x            |
++--------------------------+--------------+--------------+--------------+--------------+--------------+
 
 From left to right, the strategies gain in economic advantage for the actor, but also in complexity. While an actor in
-strategy 0 can only directly use or feed in energy from its own generation, in strategies 1 - 3 an actor can also have a
-battery storage system, which enables it to temporarily store energy that is not used by the household or, for example,
+strategy 0 can only directly use or feed in energy from its own generation, in strategies 1 - 4 an actor can also use a
+battery storage system, which enables it to temporarily store energy that is not used by the household loads or, for example,
 an electric vehicle. In this way, the strategies serve self-consumption. By also selling electrical energy from the battery
 in strategy 2 und 3 the economic efficiency of the generator and the battery storage system is increased.
 
@@ -70,7 +70,6 @@ causes the positive deviation from the SOC of 1 can be sold the optimal time for
 energy can be sold it is checked if later time windows with a SOC > 1 can be served and if that would lead to maximum
 profit.
 
-
 Strategy 3
 ==========
 
@@ -78,5 +77,13 @@ Strategy 3 uses strategy 2 and considers time arbitrage to better exploit the ma
 If price fluctuations are strong enough to make it profitable to buy and later sell, the remaining capacity of the
 battery is used to conduct this trade.
 
+Strategy 4
+==========
+
+Strategy 4 is independent of strategies 0-3 and optimizes for the prediction horizon a mathematical optimization
+problem to profit from the market maker's dynamic prices, while considering the constraints of the local system, e.g.
+the availability of an electric vehicle, power constraints or the capacity of the stationary battery.
+Comparable to strategy 3 price for strong fluctuations energy can be actively bought to be sold at a higher price later.
+
 
 
diff --git a/projects/example_projects/example_project_ev_opt/actors_config.json b/projects/example_projects/example_project_ev_opt/actors_config.json
new file mode 100644
index 00000000..3b3b599f
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/actors_config.json
@@ -0,0 +1,58 @@
+[
+    {
+        "comment": "An example of a residential prosumer with load and pv data specified by their 'deviceID'",
+        "prosumerName": "residential_1",
+        "prosumerType": "residential",
+        "gridLocation": "N01",
+        "devices": [
+            {
+                "deviceType": "load",
+                "deviceID": "CHH10_sample.csv"
+            },
+            {
+                "deviceType": "solar",
+                "deviceID": "generated_pv.csv"
+            }
+        ]
+    },
+    {
+        "comment": "An example of a residential prosumer with load data specified by their 'deviceID' but without pv",
+        "prosumerName": "residential_2",
+        "prosumerType": "residential",
+        "gridLocation": "N02",
+        "devices": [
+            {
+                "deviceType": "load",
+                "deviceID": "CHH9_sample.csv"
+            }
+        ],
+        "strategy": 4
+    },
+    {
+        "comment": "An example of a residential prosumer with load, pv and electric vehicle data specified by their 'deviceID' and a battery",
+        "prosumerName": "residential_3",
+        "prosumerType": "residential",
+        "gridLocation": "N02",
+        "devices": [
+            {
+                "deviceType": "load",
+                "deviceID": "CHH16_sample.csv"
+            },
+	        {
+                "deviceType": "solar",
+                "deviceID": "generated_pv.csv"
+            },
+            {
+                "deviceType": "battery",
+                "capacityKwh": 3,
+                "initialSOC": 0.5
+            },
+            {
+                "deviceType": "ev",
+                "deviceID": "ev_0000.csv"
+            }
+        ],
+        "strategy": 4,
+        "pricing_strategy": {"name": "linear", "param": [0.01]}
+    }
+]
diff --git a/projects/example_projects/example_project_ev_opt/config.cfg b/projects/example_projects/example_project_ev_opt/config.cfg
new file mode 100644
index 00000000..9660c3c6
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/config.cfg
@@ -0,0 +1,42 @@
+#--------------------------
+# scenario
+#--------------------------
+# load existing scenario
+load_scenario = True
+# possible data_formats: json, csv
+data_format = csv
+
+#--------------------------
+# market
+#--------------------------
+# start timestep
+start = 0
+# number of timesteps in simulation
+nb_ts = 13
+horizon = 10
+# interval between simulation timesteps
+ts_per_hour = 4
+
+# which market type to use? fair, pac
+market_type = fair
+# reset market after each interval (discard unmatched orders)
+reset_market = True
+# size of energy units to be traded individually
+energy_unit = 0.01
+# factor describing the relation of grid fee to cumulative power network edge weights
+weight_factor = 0.03
+# default grid_fee to be used by market maker
+default_grid_fee = 0.09
+# local grid fee applied for trades within a cluster
+local_grid_fee = 0.02
+
+#--------------------------
+# output
+#--------------------------
+# print debug info to console
+show_prints = true
+# show various plots
+show_plots = true
+# save orders and matching results to csv files
+save_csv = true
+#--------------------------
diff --git a/projects/example_projects/example_project_ev_opt/network_config.json b/projects/example_projects/example_project_ev_opt/network_config.json
new file mode 100644
index 00000000..103926f4
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/network_config.json
@@ -0,0 +1,22 @@
+{
+  "example_network": {
+    "directed": false,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+      {
+        "id":  "N01"
+      },
+      {
+        "id":  "N02"
+      }
+    ],
+    "links": [
+      {
+        "weight": 1,
+        "source": "N01",
+        "target": "N02"
+      }
+    ]
+  }
+}
diff --git a/projects/example_projects/example_project_ev_opt/scenario/MarketMaker.csv b/projects/example_projects/example_project_ev_opt/scenario/MarketMaker.csv
new file mode 100644
index 00000000..102ff4e9
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario/MarketMaker.csv
@@ -0,0 +1,25 @@
+,all_buy_prices,all_sell_prices
+0,0.05087,0.05087
+1,0.05087,0.05087
+2,0.05087,0.05087
+3,0.05087,0.05087
+4,0.04819,0.04819
+5,0.04819,0.04819
+6,0.04819,0.04819
+7,0.04819,0.04819
+8,0.04468,0.04468
+9,0.04468,0.04468
+10,0.04468,0.04468
+11,0.04468,0.04468
+12,0.04292,0.04292
+13,0.04292,0.04292
+14,0.04292,0.04292
+15,0.04292,0.04292
+16,0.04039,0.04039
+17,0.04039,0.04039
+18,0.04039,0.04039
+19,0.04039,0.04039
+20,0.0402,0.0402
+21,0.0402,0.0402
+22,0.0402,0.0402
+23,0.0402,0.0402
diff --git a/projects/example_projects/example_project_ev_opt/scenario/_meta.inf b/projects/example_projects/example_project_ev_opt/scenario/_meta.inf
new file mode 100644
index 00000000..90cbab30
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario/_meta.inf
@@ -0,0 +1,3 @@
+{
+  "rng_seed": 1963995559
+}
\ No newline at end of file
diff --git a/projects/example_projects/example_project_ev_opt/scenario/actor_residential_1.csv b/projects/example_projects/example_project_ev_opt/scenario/actor_residential_1.csv
new file mode 100644
index 00000000..c312570c
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario/actor_residential_1.csv
@@ -0,0 +1,25 @@
+Time,load,pv,schedule
+2016-01-01 00:00:00,0.011886246327641098,7.04546466156181e-05,-0.01181579168102548
+2016-01-01 00:15:00,0.0410388447933982,0.0002472798864404,-0.040791564906957796
+2016-01-01 00:30:00,0.0406771589865564,0.0002556439361969,-0.0404215150503595
+2016-01-01 00:45:00,0.0405878697548583,0.00035547628083399996,-0.0402323934740243
+2016-01-01 01:00:00,0.0408237818838657,0.0010430721723367,-0.039780709711529
+2016-01-01 01:15:00,0.0407177271612198,0.0010594459560612,-0.039658281205158595
+2016-01-01 01:30:00,0.0155266774334944,0.0021104014895109,-0.013416275943983499
+2016-01-01 01:45:00,0.0163007931439657,0.0012627368346117,-0.015038056309354002
+2016-01-01 02:00:00,0.0322130119232581,0.0034005784433971996,-0.028812433479860903
+2016-01-01 02:15:00,0.0091297259677855,0.0034302420258755994,-0.0056994839419099
+2016-01-01 02:30:00,0.0193711826356853,0.0044974952377916,-0.0148736873978937
+2016-01-01 02:45:00,0.028851808922693505,0.0087416387113377,-0.020110170211355805
+2016-01-01 03:00:00,0.0092306359903934,0.0054444566014102,-0.0037861793889832004
+2016-01-01 03:15:00,0.021129578178791,0.013092584681101398,-0.0080369934976896
+2016-01-01 03:30:00,0.0261416596035054,0.0143581217010604,-0.011783537902444999
+2016-01-01 03:45:00,0.009300330445916,0.0010009727768025,-0.0082993576691135
+2016-01-01 04:00:00,0.0264843050659245,0.0190323954720175,-0.007451909593907003
+2016-01-01 04:15:00,0.0405926330346124,0.0020486715257943,-0.0385439615088181
+2016-01-01 04:30:00,0.040739211280267,0.0437830919236495,0.003043880643382499
+2016-01-01 04:45:00,0.0405072188688263,0.0031536962879483,-0.037353522580878
+2016-01-01 05:00:00,0.0364741472516811,0.0083302330939972,-0.0281439141576839
+2016-01-01 05:15:00,0.0093203889536746,0.0306331420625005,0.0213127531088259
+2016-01-01 05:30:00,0.0255923848829077,0.0366225507371767,0.011030165854268997
+2016-01-01 05:45:00,0.022564346575841502,0.0050192915366731,-0.0175450550391684
diff --git a/projects/example_projects/example_project_ev_opt/scenario/actor_residential_2.csv b/projects/example_projects/example_project_ev_opt/scenario/actor_residential_2.csv
new file mode 100644
index 00000000..d26b374f
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario/actor_residential_2.csv
@@ -0,0 +1,25 @@
+Time,load,pv,schedule
+2016-01-01 00:00:00,0.0542322728238491,0,-0.0542322728238491
+2016-01-01 00:15:00,0.033030005643254305,0,-0.033030005643254305
+2016-01-01 00:30:00,0.0142862986715691,0,-0.0142862986715691
+2016-01-01 00:45:00,0.0141467792373111,0,-0.0141467792373111
+2016-01-01 01:00:00,0.0142478747577574,0,-0.0142478747577574
+2016-01-01 01:15:00,0.0142467892610824,0,-0.0142467892610824
+2016-01-01 01:30:00,0.0258862073719723,0,-0.0258862073719723
+2016-01-01 01:45:00,0.0275837814486352,0,-0.0275837814486352
+2016-01-01 02:00:00,0.0143721215284459,0,-0.0143721215284459
+2016-01-01 02:15:00,0.0142922358318904,0,-0.0142922358318904
+2016-01-01 02:30:00,0.0309747101252188,0,-0.0309747101252188
+2016-01-01 02:45:00,0.0174783232826058,0,-0.0174783232826058
+2016-01-01 03:00:00,0.0144721663610269,0,-0.0144721663610269
+2016-01-01 03:15:00,0.0142060443537789,0,-0.0142060443537789
+2016-01-01 03:30:00,0.0140319164885409,0,-0.0140319164885409
+2016-01-01 03:45:00,0.0832210665762793,0,-0.0832210665762793
+2016-01-01 04:00:00,0.2529950562998062,0,-0.2529950562998062
+2016-01-01 04:15:00,0.11682297414734,0,-0.11682297414734
+2016-01-01 04:30:00,0.1520900997528187,0,-0.1520900997528187
+2016-01-01 04:45:00,0.1552827818895501,0,-0.1552827818895501
+2016-01-01 05:00:00,0.1564985586562892,0,-0.1564985586562892
+2016-01-01 05:15:00,0.0976440743721818,0,-0.0976440743721818
+2016-01-01 05:30:00,0.0298138002225059,0,-0.0298138002225059
+2016-01-01 05:45:00,0.0143183628313887,0,-0.0143183628313887
diff --git a/projects/example_projects/example_project_ev_opt/scenario/actor_residential_3.csv b/projects/example_projects/example_project_ev_opt/scenario/actor_residential_3.csv
new file mode 100644
index 00000000..4309c164
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario/actor_residential_3.csv
@@ -0,0 +1,25 @@
+Time,load,pv,schedule,ev_avail,ev_demand
+2016-01-01 00:00:00,0.0183501135461672,7.04546466156181e-05,-0.01827965889955158,1.0,0.0
+2016-01-01 00:15:00,0.0183276402096313,0.0002472798864404,-0.0180803603231909,1.0,0.0
+2016-01-01 00:30:00,0.0186881603411869,0.0002556439361969,-0.01843251640499,1.0,0.0
+2016-01-01 00:45:00,0.0184871298585762,0.00035547628083399996,-0.018131653577742202,1.0,0.0
+2016-01-01 01:00:00,0.0179609033250519,0.0010430721723367,-0.0169178311527152,1.0,0.0
+2016-01-01 01:15:00,0.0183569591857021,0.0010594459560612,-0.017297513229640897,1.0,0.0
+2016-01-01 01:30:00,0.0429906796923723,0.0021104014895109,-0.0408802782028614,1.0,0.0
+2016-01-01 01:45:00,0.0468752880631146,0.0012627368346117,-0.0456125512285029,1.0,0.0
+2016-01-01 02:00:00,0.04839955195562141,0.0034005784433971996,-0.044998973512224205,1.0,0.0
+2016-01-01 02:15:00,0.04895941513644,0.0034302420258755994,-0.0455291731105644,1.0,0.0
+2016-01-01 02:30:00,0.0448443403712756,0.0044974952377916,-0.040346845133483994,1.0,0.0
+2016-01-01 02:45:00,0.0243064739511402,0.0087416387113377,-0.0155648352398025,1.0,0.0
+2016-01-01 03:00:00,0.0495014864417933,0.0054444566014102,-0.0440570298403831,1.0,0.0
+2016-01-01 03:15:00,0.0493275564676503,0.013092584681101398,-0.0362349717865489,1.0,0.0
+2016-01-01 03:30:00,0.0503633213311156,0.0143581217010604,-0.036005199630055204,1.0,0.0
+2016-01-01 03:45:00,0.0499530098865384,0.0010009727768025,-0.0489520371097359,0.0,15.3136
+2016-01-01 04:00:00,0.0291384623307321,0.0190323954720175,-0.0101060668587146,0.0,0.0
+2016-01-01 04:15:00,0.0181229042056159,0.0020486715257943,-0.016074232679821603,0.0,0.0
+2016-01-01 04:30:00,0.0184295811072935,0.0437830919236495,0.025353510816356,0.0,0.0
+2016-01-01 04:45:00,0.0184305987280745,0.0031536962879483,-0.0152769024401262,0.0,0.0
+2016-01-01 05:00:00,0.0185237682294309,0.0083302330939972,-0.0101935351354337,0.0,0.0
+2016-01-01 05:15:00,0.0185859507615892,0.0306331420625005,0.012047191300911302,0.0,0.0
+2016-01-01 05:30:00,0.0183005206430141,0.0366225507371767,0.018322030094162597,0.0,0.0
+2016-01-01 05:45:00,0.0855216764172982,0.0050192915366731,-0.0805023848806251,0.0,0.0
diff --git a/projects/example_projects/example_project_ev_opt/scenario/actors.json b/projects/example_projects/example_project_ev_opt/scenario/actors.json
new file mode 100644
index 00000000..8372d153
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario/actors.json
@@ -0,0 +1,101 @@
+{
+  "MarketMaker": {
+    "id": "MarketMaker",
+    "sell_prices": [
+      0.05087,
+      0.05087,
+      0.05087,
+      0.05087,
+      0.04819,
+      0.04819,
+      0.04819,
+      0.04819,
+      0.04468,
+      0.04468,
+      0.04468,
+      0.04468,
+      0.04292,
+      0.04292,
+      0.04292,
+      0.04292,
+      0.04039,
+      0.04039,
+      0.04039,
+      0.04039,
+      0.0402,
+      0.0402,
+      0.0402,
+      0.0402
+    ],
+    "buy_prices": [
+      0.05087,
+      0.05087,
+      0.05087,
+      0.05087,
+      0.04819,
+      0.04819,
+      0.04819,
+      0.04819,
+      0.04468,
+      0.04468,
+      0.04468,
+      0.04468,
+      0.04292,
+      0.04292,
+      0.04292,
+      0.04292,
+      0.04039,
+      0.04039,
+      0.04039,
+      0.04039,
+      0.0402,
+      0.0402,
+      0.0402,
+      0.0402
+    ]
+  },
+  "residential_1": {
+    "id": "residential_1",
+    "df": {},
+    "csv": "actor_residential_1.csv",
+    "ls": 1,
+    "ps": 1,
+    "pm": {},
+    "battery_cap": 0.02,
+    "battery_initial_soc": 0.5,
+    "strategy": 0,
+    "pricing_strategy": NaN
+  },
+  "residential_2": {
+    "id": "residential_2",
+    "df": {},
+    "csv": "actor_residential_2.csv",
+    "ls": 1,
+    "ps": 1,
+    "pm": {},
+    "battery_cap": 0.02,
+    "battery_initial_soc": 0.5,
+    "strategy": 4.0,
+    "pricing_strategy": NaN
+  },
+  "residential_3": {
+    "id": "residential_3",
+    "df": {},
+    "csv": "actor_residential_3.csv",
+    "ls": 1,
+    "ps": 1,
+    "pm": {},
+    "battery_cap": 3,
+    "battery_initial_soc": 0.5,
+    "strategy": 4.0,
+    "pricing_strategy": {
+      "name": "linear",
+      "param": [
+        0.01
+      ]
+    },
+    "ev_cap": 18.37632,
+    "ev_initial_soc": 0.5,
+    "ev_available": false
+  }
+}
\ No newline at end of file
diff --git a/projects/example_projects/example_project_ev_opt/scenario/example_network.png b/projects/example_projects/example_project_ev_opt/scenario/example_network.png
new file mode 100644
index 00000000..9f3bcb25
Binary files /dev/null and b/projects/example_projects/example_project_ev_opt/scenario/example_network.png differ
diff --git a/projects/example_projects/example_project_ev_opt/scenario/map_actors.json b/projects/example_projects/example_project_ev_opt/scenario/map_actors.json
new file mode 100644
index 00000000..9e26dfee
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario/map_actors.json
@@ -0,0 +1 @@
+{}
\ No newline at end of file
diff --git a/projects/example_projects/example_project_ev_opt/scenario/network.json b/projects/example_projects/example_project_ev_opt/scenario/network.json
new file mode 100644
index 00000000..03776a39
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario/network.json
@@ -0,0 +1,46 @@
+{
+  "example_network": {
+    "directed": false,
+    "multigraph": false,
+    "graph": {},
+    "nodes": [
+      {
+        "id": "N01"
+      },
+      {
+        "id": "N02"
+      },
+      {
+        "id": "residential_1"
+      },
+      {
+        "id": "residential_2"
+      },
+      {
+        "id": "residential_3"
+      }
+    ],
+    "links": [
+      {
+        "weight": 1,
+        "source": "N01",
+        "target": "N02"
+      },
+      {
+        "weight": 0,
+        "source": "N01",
+        "target": "residential_1"
+      },
+      {
+        "weight": 0,
+        "source": "N02",
+        "target": "residential_2"
+      },
+      {
+        "weight": 0,
+        "source": "N02",
+        "target": "residential_3"
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/projects/example_projects/example_project_ev_opt/scenario_inputs/ev/ev_0000.csv b/projects/example_projects/example_project_ev_opt/scenario_inputs/ev/ev_0000.csv
new file mode 100644
index 00000000..70c7aa4f
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario_inputs/ev/ev_0000.csv
@@ -0,0 +1,49 @@
+Time,availability,consumption
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diff --git a/projects/example_projects/example_project_ev_opt/scenario_inputs/load/CHH10_sample.csv b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/CHH10_sample.csv
new file mode 100644
index 00000000..439735ce
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/CHH10_sample.csv
@@ -0,0 +1,49 @@
+Time,load
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diff --git a/projects/example_projects/example_project_ev_opt/scenario_inputs/load/CHH16_sample.csv b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/CHH16_sample.csv
new file mode 100644
index 00000000..8f543459
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/CHH16_sample.csv
@@ -0,0 +1,49 @@
+Time,Sum [kWh]
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diff --git a/projects/example_projects/example_project_ev_opt/scenario_inputs/load/CHH9_sample.csv b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/CHH9_sample.csv
new file mode 100644
index 00000000..38c66e8b
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/CHH9_sample.csv
@@ -0,0 +1,49 @@
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diff --git a/projects/example_projects/example_project_ev_opt/scenario_inputs/load/market_maker_buy.csv b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/market_maker_buy.csv
new file mode 100644
index 00000000..38dee330
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/market_maker_buy.csv
@@ -0,0 +1,35085 @@
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diff --git a/projects/example_projects/example_project_ev_opt/scenario_inputs/load/market_maker_sell.csv b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/market_maker_sell.csv
new file mode 100644
index 00000000..5ec5ce9b
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario_inputs/load/market_maker_sell.csv
@@ -0,0 +1,35086 @@
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diff --git a/projects/example_projects/example_project_ev_opt/scenario_inputs/loads_dir.csv b/projects/example_projects/example_project_ev_opt/scenario_inputs/loads_dir.csv
new file mode 100644
index 00000000..9ea619a7
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario_inputs/loads_dir.csv
@@ -0,0 +1,68 @@
+Filename,Type
+CHH0_sample.csv,residential
+CHH10_sample.csv,business
+CHH11_sample.csv,residential
+CHH12_sample.csv,residential
+CHH13_sample.csv,residential
+CHH14_sample.csv,residential
+CHH15_sample.csv,residential
+CHH16_sample.csv,residential
+CHH17_sample.csv,residential
+CHH18_sample.csv,residential
+CHH19_sample.csv,residential
+CHH1_sample.csv,residential
+CHH20_sample.csv,residential
+CHH21_sample.csv,residential
+CHH22_sample.csv,residential
+CHH23_sample.csv,residential
+CHH24_sample.csv,residential
+CHH25_sample.csv,residential
+CHH26_sample.csv,residential
+CHH27_sample.csv,residential
+CHH28_sample.csv,residential
+CHH29_sample.csv,residential
+CHH2_sample.csv,residential
+CHH30_sample.csv,residential
+CHH31_sample.csv,residential
+CHH32_sample.csv,residential
+CHH33_sample.csv,residential
+CHH34_sample.csv,residential
+CHH35_sample.csv,residential
+CHH36_sample.csv,residential
+CHH37_sample.csv,residential
+CHH38_sample.csv,residential
+CHH39_sample.csv,residential
+CHH3_sample.csv,residential
+CHH40_sample.csv,residential
+CHH41_sample.csv,residential
+CHH42_sample.csv,residential
+CHH43_sample.csv,residential
+CHH44_sample.csv,residential
+CHH45_sample.csv,residential
+CHH46_sample.csv,residential
+CHH47_sample.csv,residential
+CHH48_sample.csv,residential
+CHH49_sample.csv,residential
+CHH4_sample.csv,residential
+CHH50_sample.csv,residential
+CHH51_sample.csv,residential
+CHH52_sample.csv,residential
+CHH53_sample.csv,residential
+CHH54_sample.csv,residential
+CHH55_sample.csv,residential
+CHH56_sample.csv,residential
+CHH57_sample.csv,residential
+CHH58_sample.csv,residential
+CHH59_sample.csv,residential
+CHH5_sample.csv,residential
+CHH60_sample.csv,residential
+CHH61_sample.csv,residential
+CHH62_sample.csv,residential
+CHH6_sample.csv,residential
+CHH7_sample.csv,residential
+CHH8_sample.csv,residential
+CHH9_sample.csv,residential
+CHH3_sample.csv,industrial
+CHH6_sample.csv,business
+market_maker_sell.csv,market_maker_sell
+market_maker_buy.csv,market_maker_buy
diff --git a/projects/example_projects/example_project_ev_opt/scenario_inputs/price/basic_prices.csv b/projects/example_projects/example_project_ev_opt/scenario_inputs/price/basic_prices.csv
new file mode 100644
index 00000000..db279f67
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario_inputs/price/basic_prices.csv
@@ -0,0 +1,49 @@
+Time,prices
+2016-01-01 00:00:00,0.05087
+2016-01-01 00:15:00,0.05087
+2016-01-01 00:30:00,0.05087
+2016-01-01 00:45:00,0.05087
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diff --git a/projects/example_projects/example_project_ev_opt/scenario_inputs/pv/generated_pv.csv b/projects/example_projects/example_project_ev_opt/scenario_inputs/pv/generated_pv.csv
new file mode 100644
index 00000000..2a175872
--- /dev/null
+++ b/projects/example_projects/example_project_ev_opt/scenario_inputs/pv/generated_pv.csv
@@ -0,0 +1,49 @@
+Time,pv
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+2016-01-01 00:30:00,0.00025564393619692104
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+2016-01-01 04:00:00,0.019032395472017547
+2016-01-01 04:15:00,0.0020486715257943995
+2016-01-01 04:30:00,0.043783091923649556
+2016-01-01 04:45:00,0.0031536962879483927
+2016-01-01 05:00:00,0.008330233093997264
+2016-01-01 05:15:00,0.030633142062500535
+2016-01-01 05:30:00,0.03662255073717676
+2016-01-01 05:45:00,0.005019291536673174
+2016-01-01 06:00:00,0.025659428673072974
+2016-01-01 06:15:00,0.016974513114374694
+2016-01-01 06:30:00,0.00802235428217836
+2016-01-01 06:45:00,0.13606315022883653
+2016-01-01 07:00:00,0.2435515259676452
+2016-01-01 07:15:00,0.03616231546158113
+2016-01-01 07:30:00,0.22392277483552436
+2016-01-01 07:45:00,0.3160955533410713
+2016-01-01 08:00:00,0.015615314274961923
+2016-01-01 08:15:00,0.44557309902112185
+2016-01-01 08:30:00,0.08506386888004847
+2016-01-01 08:45:00,0.04345310282355447
+2016-01-01 09:00:00,0.42535841522379647
+2016-01-01 09:15:00,0.07862163419588185
+2016-01-01 09:30:00,0.4197207700313765
+2016-01-01 09:45:00,0.0908501242487132
+2016-01-01 10:00:00,0.09690992272584505
+2016-01-01 10:15:00,0.4996638183418846
+2016-01-01 10:30:00,0.05134605890938506
+2016-01-01 10:45:00,0.4980464977682207
+2016-01-01 11:00:00,0.31266085787936426
+2016-01-01 11:15:00,0.3071389902900542
+2016-01-01 11:30:00,0.38274618182590575
+2016-01-01 11:45:00,0.6306734712446767
diff --git a/requirements.txt b/requirements.txt
index 9995fde7..b4108e0a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,3 +5,4 @@ pytest
 pathlib
 numpy
 pydot
+pyomo
diff --git a/simply/actor.py b/simply/actor.py
index e9d14679..12a582bb 100644
--- a/simply/actor.py
+++ b/simply/actor.py
@@ -10,6 +10,7 @@
 from simply.battery import Battery, VariableBattery
 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__ = """
@@ -51,11 +52,20 @@ class Actor:
     :param dict pm: (optional) Prediction multiplier used to manipulate prediction time series based
         on the data time series
     :param int cluster: cluster in which actor is located
-    :param int strategy: Number for strategy [0-3]
+    :param int strategy: Number for strategy [0-4]
     :param float battery_cap: Battery capacity used to create battery object.
         Only applied, if battery parameter is None (default: 0)
-    :param float battery_initial_soc: Initial state of charge of newly created battery object,
+    :param float battery_initial_soc: Initial state of charge of newly created Battery object,
         Only applied, if battery parameter is None (default: 0.5)
+    :param float ev_cap: Variable Battery capacity used to create VariableBattery object
+        (default: 0)
+    :param float ev_initial_soc: Initial state of charge of newly created variable battery object,
+        (default: 0.5)
+    :param float ev_available: initial availability status and if not strategy 4 override
+        availability series and send a warning (default: 0)
+    :param float ev_max_c_rate: maximum c-rate (default: 1)
+    :param float ev_max_power: maximum ev charger power (default: 11)
+    :param float grid_connection_capacity: maximum in/out flowing power at grid connection
 
     Members:
 
@@ -116,7 +126,7 @@ class Actor:
     def __init__(self, id, df, environment=None, battery=None, csv=None, ls=1, ps=1, pm={},
                  cluster=None, strategy: int = 0, pricing_strategy=None,
                  battery_cap=0, battery_initial_soc=0.5, ev_cap=0, ev_initial_soc=1.0,
-                 ev_available=0, ev_max_c_rate=1, ev_max_power=11):
+                 ev_available=0, ev_max_c_rate=1, ev_max_power=11, grid_connection_capacity=20):
         """
         Actor Constructor that defines an ID, and extracts resource time series from the given
          DataFrame scaled by respective factors as well as the schedule on which basis orders
@@ -124,6 +134,7 @@ def __init__(self, id, df, environment=None, battery=None, csv=None, ls=1, ps=1,
         """
         self.id = id
         self.grid_id = None
+        self.grid_connection_capacity = grid_connection_capacity
         self.cluster = cluster
 
         self.horizon = cfg.config.horizon
@@ -159,6 +170,7 @@ def __init__(self, id, df, environment=None, battery=None, csv=None, ls=1, ps=1,
             self.csv_file = csv
         else:
             self.csv_file = f'actor_{id}.csv'
+
         # ToDo remove schedule from input or only allow either (load and pv) OR (schedule)
         for column, scale in [("load", ls), ("pv", ps), ("schedule", 1)]:
             self.data[column] = scale * df[column]
@@ -190,22 +202,74 @@ def __init__(self, id, df, environment=None, battery=None, csv=None, ls=1, ps=1,
         self.args = {"id": id, "df": df.reset_index().to_json(), "csv": csv, "ls": ls, "ps": ps,
                      "pm": pm}
 
+    def strategy_with_optimisation(self):
+        """ Return the optimized market schedule
+
+        An energy need with negative sign in the
+        schedule is met with buying energy in the market_schedule which has a positive sign
+
+        :return: default market schedule
+        """
+
+        # Use the optimization library to implement the new strategy
+        objective, df_results = optimize_schedule(
+            df_actor=self.pred,
+            buy_prices=self.mm_sell_prices,  # buy at MarketMaker sell prices incl. grid fee
+            sell_prices=self.mm_buy_prices,
+            capacity=self.battery.capacity,
+            max_c_rate=self.battery.max_c_rate,
+            soc_initial=self.battery.soc,
+            ev_capacity=self.var_battery.capacity,
+            ev_max_c_rate=self.var_battery.max_c_rate,
+            ev_soc_initial=self.var_battery.soc,
+            ts_per_hour=cfg.config.ts_per_hour,
+            end_min_soc=0.6,
+            grid_connection_capacity=self.grid_connection_capacity
+        )
+        if cfg.config.debug:
+            from simply.optimisation import plot_optimization_results
+            plot_optimization_results(df_results)
+        # Process the results as needed
+        market_schedule = (df_results["from_grid"] - df_results["to_grid"]) / cfg.config.ts_per_hour
+
+        return market_schedule
+
     def set_var_battery(self, capacity, soc_initial, df, available=0, max_c_rate=4,
-                        refresh=True):
+                        refresh=True, min_soc=0.1):
         """
-        available: initial availability status
+        Set a VariableBattery with a capacity and initial state of charge, availability to the
+        local actor energy system, and a c-rate.
+
+        Changing availability only applicable for strategy 4, otherwise warning is printed.
+
+        :param capacity: capacity of variable battery if available
+        :param soc_initial: initial state of charge
+        :param df: pd.DataFrame containing columns 'ev_demand' and 'ev_avail'
+            for driving energy consumption and availability time series
+        :param available: initial availability status and if not strategy 4 override availability
+            series and send a warning (default 0)
+        :param max_c_rate: c-rate of variable battery (default 4)
+        :param refresh: update prediction (default True)
+        :param min_soc: minimal soc attribute of battery, which is not automatically enforced
         """
-        # TODO: WIP availability changes not working properly
-        available = 1  # TODO remove line when done
+        if self.strategy != 4 and capacity != 0:
+            # TODO Implement changing availability for strategy 0-3
+            warnings.warn(
+                f"Actor {self.id} with strategy {self.strategy}: Set EV always available, "
+                f"as changes only fully working for strategy 4.")
+            available = 1
         self.var_battery = VariableBattery(
-            capacity=capacity, soc_initial=soc_initial, available=available, max_c_rate=max_c_rate)
+            capacity=capacity, soc_initial=soc_initial, available=available,
+            max_c_rate=max_c_rate, min_soc=min_soc
+        )
         # If EV should be included expect necessary time series in actor DataFrame
         if capacity > 0:
             for column in ["ev_avail", "ev_demand"]:
                 self.data[column] = df[column]
                 self.pm[column] = 0
-            # TODO: WIP availability not working properly
-            self.data["ev_avail"] = available  # TODO remove line when done
+            if self.strategy != 4:
+                # WIP availability not working properly
+                self.data["ev_avail"] = available
 
             if refresh:
                 self.create_prediction()
@@ -265,7 +329,7 @@ def get_market_schedule(self, strategy=None):
         :type strategy: int
         :return: market_schedule with planed amounts of energy buying/selling per time step
         """
-        possible_choices = [0, 1, 2, 3]
+        possible_choices = [0, 1, 2, 3, 4]
         if strategy is None:
             strategy = self.strategy
         if strategy not in possible_choices:
@@ -282,6 +346,10 @@ 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
@@ -298,6 +366,8 @@ def get_market_schedule(self, strategy=None):
         self.market_schedule = self.plan_selling_strategy()
         if strategy == 2:
             return self.market_schedule
+
+        # implicit strategy == 3
         self.market_schedule = self.plan_global_trading()
         return self.market_schedule
 
@@ -688,12 +758,17 @@ def update_battery(self, _cache=dict()):
 
         if self.var_battery.capacity > 0:
             # EV demand consumption is possible while not available (other than `charge`)
-            self.var_battery.consume(self.pred.ev_demand[0], constrain=True)
-
-            missing = self.pred.ev_demand[1] + self.var_battery.min_soc * self.var_battery.capacity\
+            self.var_battery.consume(self.pred.ev_demand[0], constrain=False)
+            missing = max(self.pred.ev_demand[:])\
+                + self.var_battery.min_soc * self.var_battery.capacity\
                 - self.var_battery.energy()
-            # Favor charging into variable battery if necessary soc for the subsequent drive is not
+            # Favor charging into variable battery if necessary soc for the subsequent trips are not
             # reached yet.
+            # - First try to charge the missing driving energy plus buffer energy
+            # - If traded energy does not suffice use stationary battery to balance as much as
+            #   possible
+            # - ultimately undo charged energy in variable battery as scheduled energy and
+            #   stationary battery could not provide it
             if missing > 0:
                 diff, _, _ = self.var_battery.charge(missing, constrain=True)
                 if cfg.config.debug:
@@ -705,8 +780,9 @@ def update_battery(self, _cache=dict()):
         diff, _, _ = self.battery.charge(charge_energy, constrain=True)
         if cfg.config.debug:
             print(f"Bat: charge {charge_energy} / diff {diff}")
-        # If stationary battery is full, try to charge the variable battery further
-        if self.var_battery.capacity > 0 and diff > missing:
+        # If stationary battery cannot fulfill the update,
+        # try to balance the difference with it with the variable battery further
+        if self.var_battery.capacity > 0:
             charge_energy = diff
             diff, _, _ = self.var_battery.charge(charge_energy, constrain=True)
             if cfg.config.debug:
@@ -839,15 +915,26 @@ def adjust_energy(self, energy, index=0):
         if energy > 0:
             # rounding to the next energy unit can lead to unfulfilled schedules or below 0 socs.
             # In these cases increase the order by one energy unit, i.e. buy more energy
-            if (self.battery.energy() + self.pred.schedule[0:index].sum() +
-                    ((energy + cfg.config.EPS) // cfg.config.energy_unit *
-                     cfg.config.energy_unit) < 0):
+            # Enforce: floor divided bought energy > Current battery energy + scheduled energy + EPS
+            #  - floor division e.g. for energy = 1.09 and energy_unit = 0.1:
+            #    yields 1.09 // 0.1 * 0.1 = 1
+            #    hence raise bought energy by + energy unit
+            #  - if var_battery has free available battery capacity always round up
+            if (self.var_battery.capacity - self.var_battery.energy() > cfg.config.energy_unit) or (
+                    self.battery.energy() + self.pred.schedule[0:index].sum() +
+                    ((energy + cfg.config.EPS) // cfg.config.energy_unit * cfg.config.energy_unit)
+                    < 0
+            ):
                 energy += cfg.config.energy_unit
 
         # selling energy
         elif energy < 0:
             # rounding to the next energy unit can lead to unfulfilled schedules or over 1 socs.
             # In these cases decrease the order by one energy unit, i.e. sell more energy
+            # Enforce: sold energy > Free battery energy volume - scheduled energy + EPS
+            #  - floor division e.g. for energy = 1 and energy_unit = 0.1:
+            #    yields 1 // 0.1 = 9
+            #    hence the + 1
             if self.battery.energy() + self.pred.schedule[0:index].sum() + (
                     ((energy+cfg.config.EPS) // cfg.config.energy_unit+1) *
                     cfg.config.energy_unit) > self.battery.capacity:
@@ -914,8 +1001,9 @@ def receive_market_results(self, time, sign, energy, price):
                 # unexpected behaviour or self defined orders might be the reason. In this case give
                 # warning and do not adjust market_schedule
                 warnings.warn("Matched energy does not match planned energy.")
-                print(f"Actor {self.id}' last order {self.orders[-1]}")
-                print(f"Actor {self.id}' last order {self.orders[-1]}")
+                if cfg.config.verbose:
+                    print(f"Actor {self.id}': planned: {self.market_schedule[i]};"
+                          f" delta energy {delta_energy}")
                 return
             planned_energy = self.market_schedule[i]
             if planned_energy == 0:
@@ -926,6 +1014,9 @@ def receive_market_results(self, time, sign, energy, price):
             if (not np.sign(delta_energy) == np.sign(planned_energy)
                     and abs(planned_energy) > 2 * cfg.config.energy_unit):
                 warnings.warn("Matched energy does not match planned energy.")
+                if cfg.config.verbose:
+                    print(f"Actor {self.id}': planned (i={i}): {planned_energy}; "
+                          f"delta energy {delta_energy}")
             self.market_schedule[i] -= sign*min(abs(delta_energy), abs(planned_energy))
             delta_energy -= planned_energy
 
@@ -1000,7 +1091,7 @@ def save_actor_result(self, dirpath=None):
         order_iter = iter(self.orders)
         o = next(order_iter, None)
         for i in range(len(self.traded_energy)):
-            if o is not None and i == o.time:
+            if o is not None and simulated_range_ts[i] == o.time:
                 save_df.loc[simulated_range_ts[i], "ordered_energy"] = o.energy * o.type
                 save_df.loc[simulated_range_ts[i], "ordered_price"] = o.price
                 o = next(order_iter, None)
diff --git a/simply/market_maker.py b/simply/market_maker.py
index f40aade8..e6e48e2a 100644
--- a/simply/market_maker.py
+++ b/simply/market_maker.py
@@ -145,8 +145,10 @@ def get_buy_prices(self):
     buy_prices = property(get_buy_prices)
 
     def create_prediction(self):
-        """Adjust predicted prices to the current time step"""
-        self.pred["buy_prices"] = self.all_buy_prices[self.t_step: self.t_step + self.horizon]
+        """Adjust predicted prices to the current time step and
+         add grid fee for orders and actor information"""
+        self.pred["buy_prices"] = self.all_buy_prices[self.t_step: self.t_step + self.horizon] + \
+            cfg.config.default_grid_fee
         self.pred["sell_prices"] = self.all_sell_prices[self.t_step: self.t_step + self.horizon]
 
     def generate_orders(self):
diff --git a/simply/optimisation.py b/simply/optimisation.py
new file mode 100644
index 00000000..8db89c0f
--- /dev/null
+++ b/simply/optimisation.py
@@ -0,0 +1,411 @@
+# 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,
+                      ev_capacity=0, ev_max_c_rate=1, ev_soc_initial=0, charger_max_power=11,
+                      ts_per_hour=1, end_min_soc=0.6, grid_connection_capacity=20):
+    """
+    Optimizes load, pv time series with battery and electric vehicle flexibility based on buying and
+    selling price time series.
+
+    Parametrisation of battery (no prefix) and electric vehicle (prefix: ev_*).
+
+    :param df_actor: pd.DataFrame with energy values (not power) with columns 'load', 'pv'
+        and optionally 'ev_avail' and 'ev_demand'
+    :param buy_prices: price series at which energy can be bought including fees
+    :param sell_prices: price series at which energy can be sold
+    :param capacity: maximum energy that can be stored (battery parameter) default=10
+    :param max_c_rate: How many times the capacity can be (dis-)charged within an hour
+        (battery parameter) default=1
+    :param soc_initial: initial state of charge (battery parameter) default=0.5
+    :param ev_capacity: maximum energy that can be stored (electric vehicle parameter) default=10
+    :param ev_max_c_rate: How many times the capacity can be (dis-)charged within an hour
+        (electric vehicle parameter) default=1
+    :param ev_soc_initial: initial state of charge  (electric vehicle parameter) default=0.5
+    :param ts_per_hour: time steps per hour;  default=1
+    :param end_min_soc: regarding the prediction horizon, the minimal end soc is a fix point
+        in order to promote a tendency to not extremely drain batteries at the end of the horizon
+    :param grid_connection_capacity: maximum power drawn from or fed into grid; default=20)
+    """
+    # TODO battery-efficiency ?
+
+    # single time series vectors
+    t = list(range(len(df_actor)))
+    # convert from dataFrame to lists and from energy to power
+    load = df_actor.loc[:, "load"].mul(ts_per_hour).to_list()
+    pv = df_actor.loc[:, "pv"].mul(ts_per_hour).to_list()
+    if isinstance(buy_prices, pd.Series):
+        buy_prices = buy_prices.to_list()
+        sell_prices = sell_prices.to_list()
+
+    # Add electric vehicle
+    # ev charging/discharing is limited by either the charger or the battery c-rate
+    ev_max_power = ev_capacity * ev_max_c_rate
+    ev_max_charger = min(charger_max_power, ev_max_power)
+    if ev_capacity != 0:
+        ev_avail = df_actor.loc[:, "ev_avail"].to_list()
+        ev_demand = df_actor.loc[:, "ev_demand"].mul(ts_per_hour).to_list()
+        consumption = 0
+        for i in range(len(ev_demand)):
+            if ev_avail[i] == 0:
+                if ev_demand[i] != 0:
+                    # save consumption energy and discharge with max power
+                    # until the consumption energy left is below max power
+                    consumption = consumption + ev_demand[i] / ts_per_hour
+                if consumption > 0:
+                    # consumption cannot exceed maximum battery power
+                    dischargable = min(consumption * ts_per_hour, ev_max_power)
+                    # update demand and carry along the rest
+                    ev_demand[i] = dischargable
+                    consumption -= dischargable / ts_per_hour
+            else:
+                # If consumption variable is not 0 => the availability ended to be 0 (driving)
+                # before it was possible to discharge with max power according to c-rate
+                assert consumption == 0, "EV consumed more energy than c-rate allows it during" \
+                                         " non-availability"
+    else:
+        ev_avail = [1] * len(df_actor)
+        ev_demand = [0] * len(df_actor)
+
+    # PYOMO OPTIMISATION MODEL
+    model = pyo.ConcreteModel()
+    # Stationary Battery
+    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))
+    # EV
+    model.ev_charging_power = pyo.Var(t, bounds=(0, ev_max_power))
+    model.ev_discharging_power = pyo.Var(t, bounds=(0, ev_max_power))
+    model.ev_stored_energy = pyo.Var(t, bounds=(0, ev_capacity))
+    # 0/ 1 for distinguishing between charging / discharging
+    model.bi_charge = pyo.Var(t, within=pyo.Binary)
+    model.ev_bi_charge = pyo.Var(t, within=pyo.Binary)
+    # Exchange with grid
+    # - if no upper bound is provided the problem turns out to be unbounded
+    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] + model.ev_discharging_power[i]
+            - load[i] - model.power_to_grid[i] - ev_demand[i]
+            - model.charging_power[i] - model.ev_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]) / ts_per_hour)
+
+    model.start_storage = pyo.Constraint(
+        expr=model.stored_energy[0] ==
+        soc_initial * capacity)
+
+    # optional: equal soc at first and last time step
+    # (0) no constraint for last time step
+    # (1) equal soc at first and last time step:
+    #      expr=model.stored_energy[len(t)-1] == model.stored_energy[0]
+    # (2) last time step above specific soc
+    model.start_end_storage = pyo.Constraint(
+        expr=model.stored_energy[len(t)-1] >= capacity * end_min_soc)
+
+    # 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)
+
+    ############################
+    # component mobile storage #
+    ############################
+    model.ev_energy_balance_storage = pyo.ConstraintList()
+    for i in range(len(t) - 1):
+        # constraint
+        # ev demand directly incorporated in ev internal balance
+        model.ev_energy_balance_storage.add(
+            model.ev_stored_energy[i + 1] - model.ev_stored_energy[i] ==
+            (model.ev_charging_power[i] - model.ev_discharging_power[i])
+            / ts_per_hour)
+
+        # The energy to be consumed during the succeeding time slot (while not available) has to be
+        # stored at the end of the current time slot, i.e. at the beginning of the next time slot
+        # try to keep a minimal soc after trip
+        # - ev_demand was converted to a power above
+        if ev_demand[i + 1] != 0:
+            model.ev_energy_balance_storage.add(
+                model.ev_stored_energy[i + 1] >=
+                # max(ev_capacity * ev_target_soc, ev_demand[i + 1] + 0.1 * ev_capacity)
+                ev_demand[i + 1] / ts_per_hour + 0.1 * ev_capacity
+            )
+
+    model.ev_start_storage = pyo.Constraint(
+        expr=model.ev_stored_energy[0]
+        == ev_soc_initial * ev_capacity)
+
+    # optional:
+    # (0) no constraint for last time step
+    # (1) equal soc at first and last time step:
+    #      expr = model.ev_stored_energy[len(t) - 1] == model.ev_stored_energy[0]
+    # (2) last time step above specific soc
+    #     Infeasible, if last timeslot has availability = 0
+    #     TODO Multiple timeslots might be necessary to get to a specific soc
+    #      or ev_capacity >= max(ev_demand)/ / ts_per_hour / (1 - end_min_soc)
+    # model.ev_start_end_storage = pyo.Constraint(
+    #    expr=model.ev_stored_energy[len(t) - 1] >= ev_capacity-max(ev_demand) / ts_per_hour
+    # )
+
+    # needed in order to not have a discharge that affects the timestep after the last considered
+    model.ev_end_no_discharge_storage = pyo.Constraint(
+        expr=0 == model.ev_discharging_power[len(t) - 1] - ev_demand[len(t) - 1])
+
+    # binary variable to separate charging and discharging timesteps in order to
+    # exclude having both at the same time
+    model.ev_binary_charge_storage = pyo.ConstraintList()
+    model.ev_binary_discharge_storage = pyo.ConstraintList()
+    for i in t:
+        if ev_avail[i] == 1:
+            model.ev_binary_charge_storage.add(
+                model.ev_charging_power[i] <= model.ev_bi_charge[i] * ev_max_charger)
+        elif ev_avail[i] == 0:
+            # no local charging possible
+            # Only discharging possible during away-time (i.e. not available)
+            model.ev_binary_charge_storage.add(model.ev_charging_power[i] == 0)
+            # save consumption energy and discharge with max power
+            # until the consumption energy left is below max power
+            assert ev_demand[i] <= ev_max_power
+            model.ev_binary_discharge_storage.add(
+                model.ev_discharging_power[i] == ev_demand[i])
+
+    for i in t:
+        # different discharging limits depending on availability i.e. charger or battery constraint
+        if ev_avail[i] == 1:
+            model.ev_binary_discharge_storage.add(
+                model.ev_discharging_power[i] <=
+                (1 - model.ev_bi_charge[i]) * ev_max_charger)
+        elif ev_avail[i] == 0:
+            model.ev_binary_discharge_storage.add(
+                model.ev_discharging_power[i] <=
+                (1 - model.ev_bi_charge[i]) * ev_max_power)
+
+    ##########################################
+    # 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]
+                - buy_prices[i] * model.power_from_grid[i]
+             ) / ts_per_hour)
+
+    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'] = 1   # solver option for CBC: timelimit in seconds
+    # opt.options['tol'] = 0.0001      # solver option for CBC: tolerance
+    opt.options['threads'] = 4
+    # opt.options['ratio'] = 0.01
+    # solve the problem
+    _ = opt.solve(
+        model,
+        tee=False  # suppress solver output
+    )
+    # model.pprint()                          # print results in run terminal
+
+    # RESULTS
+    # calculate objective for result output
+    try:
+        objective = sum(model.cash_flow[i].value for i in t)
+    except TypeError:
+        print({
+            "df_actor": df_actor.to_dict(),
+            "buy_prices": buy_prices,
+            "sell_prices": sell_prices,
+            "capacity": capacity,
+            "max_c_rate": max_c_rate,
+            "soc_initial": soc_initial,
+            "ev_capacity": ev_capacity,
+            "ev_max_c_rate": ev_max_c_rate,
+            "ev_soc_initial": ev_soc_initial,
+            "charger_max_power": charger_max_power,
+            "ts_per_hour": ts_per_hour,
+            "end_min_soc": end_min_soc,
+            "grid_connection_capacity": grid_connection_capacity
+        })
+        print(f"init soc: {ev_soc_initial}, demand_max: {max(ev_demand)/ts_per_hour/ev_capacity}")
+
+    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],
+        "ev_charge": [model.ev_charging_power[i].value for i in t],
+        "ev_discharge": [model.ev_discharging_power[i].value for i in t],
+        "ev_availability": [ev_avail[i] for i in t],
+        "ev_demand": [ev_demand[i] for i in t],
+        "ev_soc": [0 if ev_capacity == 0 else model.ev_stored_energy[i].value / ev_capacity
+                   for i in t],
+    })
+
+
+def plot_optimization_results(df_results):
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    # Define color scheme for different energy components
+    color_scheme = {
+        "load": "red",  # Energy consumption (outflow)
+        "ev_demand": "black",  # Energy consumption from EV (outflow)
+        "pv": "yellow",  # Energy generation (inflow)
+        "from_grid": "blue",  # Energy imported (inflow)
+        "to_grid": "purple",  # Energy exported (outflow)
+        "charge": "orange",  # Energy going into storage (outflow)
+        "discharge": "orange",  # Energy coming out of storage (inflow)
+        "ev_charge": "cyan",  # Energy going into storage (outflow)
+        "ev_discharge": "cyan",  # Energy coming out of storage (inflow)
+    }
+
+    # Assuming df_results is already loaded with your data
+    # energy_cols
+    # = ["pv", "from_grid", "charge", "ev_charge", "load", "to_grid", "discharge", "ev_discharge"]
+    bat_cols = ["soc", "ev_soc"]
+    price_cols = ["buy_prices", "sell_prices"]
+
+    # Adjust dataframe for directional flow
+    df_adjusted = df_results.copy()
+    df_adjusted["load"] = -df_adjusted["load"]  # Negative for visualization
+    df_adjusted["ev_demand"] = -df_adjusted["ev_demand"]  # Negative for visualization
+    df_adjusted["to_grid"] = -df_adjusted["to_grid"]  # Negative for visualization
+    df_adjusted["charge"] = -df_adjusted["charge"]  # Negative for visualization
+    df_adjusted["ev_charge"] = -df_adjusted["ev_charge"]  # Negative for visualization
+
+    # Create figure with three subplots
+    f, axs = plt.subplots(3, 1, figsize=(12, 18))
+
+    # Inflows and outflows
+    inflows = ["pv", "from_grid", "discharge", "ev_discharge"]
+    outflows = ["load", "to_grid", "charge", "ev_charge", "ev_demand"]
+    cumulative_inflow = np.zeros(len(df_adjusted))
+    cumulative_outflow = np.zeros(len(df_adjusted))
+
+    # Plot inflows and outflows using step and fill_between
+    for col in inflows:
+        axs[0].step(df_adjusted.index, cumulative_inflow + df_adjusted[col], where='post',
+                    label=col, color=color_scheme[col])
+        axs[0].fill_between(df_adjusted.index, cumulative_inflow,
+                            cumulative_inflow + df_adjusted[col], step='post',
+                            color=color_scheme[col])
+        cumulative_inflow += df_adjusted[col]
+
+    for col in outflows:
+        axs[0].step(df_adjusted.index, cumulative_outflow + df_adjusted[col], where='post',
+                    label=col, color=color_scheme[col])
+        axs[0].fill_between(df_adjusted.index, cumulative_outflow,
+                            cumulative_outflow + df_adjusted[col], step='post',
+                            color=color_scheme[col])
+        cumulative_outflow += df_adjusted[col]
+
+    axs[0].set_title('Household (Inflows & Outflows)')
+    axs[0].set_ylabel('Energy (kWh)')
+    axs[0].legend(loc='upper right')
+
+    # Plotting the state of charge of the battery
+    df_results[bat_cols].mul(100).plot(ax=axs[1], color=['orange', 'cyan'], linewidth=2)
+    axs[1].set_title('Battery State of Charge')
+    axs[1].set_ylabel('State of Charge (%)')
+    axs[1].legend(loc='upper right')
+
+    axs[1].fill_between(
+        df_results.index, 0, 100,
+        where=(df_results.iloc[df_results.index]["ev_availability"] == 1) | (
+                    df_results.iloc[df_results.index]["ev_availability"].diff() == -1),
+        interpolate=False, step='pre', color='C2', alpha=0.3)
+
+    # Plotting the buying prices of energy using step
+    axs[2].step(df_results.index, df_results[price_cols], where='post', color='green', linewidth=2)
+    axs[2].set_title('Energy Purchase Prices')
+    axs[2].set_ylabel('Price (EUR/kWh)')
+    axs[2].legend(loc='upper right')
+
+    # Adjust layout
+    plt.tight_layout()
+    plt.show()
+
+
+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(
+        '../projects/example_projects/example_project/scenario/actor_residential_1.csv')
+    df_input_data_prices = pd.read_csv(
+        '../projects/example_projects/example_project/scenario/MarketMaker.csv')
+    # include grid fee for buying electricity, see config.cfg
+    grid_fee = 0.09
+    ev_capacity = max(df_input_data_actor.get("ev_demand", [0]))
+
+    buy_prices = df_input_data_prices.loc[:, "all_buy_prices"] + grid_fee
+    sell_prices = df_input_data_prices.loc[:, "all_sell_prices"]
+
+    """
+    # For debugging:
+    parameter_dict =
+
+    parameter_dict["df_actor"] = pd.DataFrame(parameter_dict["df_actor"])
+    print(parameter_dict["df_actor"])
+    objective, df_results = optimize_schedule(
+        **parameter_dict)
+    #"""
+
+    objective, df_results = optimize_schedule(
+        df_input_data_actor, buy_prices, sell_prices,
+        capacity=3, max_c_rate=1, soc_initial=0.5,
+        ev_capacity=ev_capacity,
+        ev_max_c_rate=1,
+        ev_soc_initial=0.5)
+
+    # PRINTS
+    print("RESULTS:")
+    print("Objective:", objective)
+    pd.set_option('display.max_columns', None)
+    print(df_results)
+    plot_optimization_results(df_results)
diff --git a/simply/scenario.py b/simply/scenario.py
index 1ab91ed5..779e0693 100644
--- a/simply/scenario.py
+++ b/simply/scenario.py
@@ -1,12 +1,13 @@
 import json
 import warnings
 from typing import Sized, Iterable
-
 from networkx.readwrite import json_graph
 import pandas as pd
 import numpy as np
 import random
+import matplotlib
 import matplotlib.pyplot as plt
+
 import simply.config as cfg
 from simply import actor, market_maker
 from simply import power_network
@@ -16,6 +17,10 @@
 from simply.actor import Actor
 from simply.market import Market
 
+try:
+    matplotlib.use('TkAgg')
+except ImportError as e:
+    print(e)
 debug_actor = None  # 'residential_3'
 
 
@@ -51,16 +56,18 @@ def __init__(self, steps_per_hour, add_actor_to_scenario, time_range=None, **kwa
         self.steps_per_hour = steps_per_hour
         if time_range is None:
             self.time_range = range(cfg.config.start + cfg.config.nb_ts + 1)
-        else:
+        elif isinstance(time_range, pd.DatetimeIndex):
             # Check for correct datetime frequency
             if time_range.freq is None:
                 time_range.freq = pd.infer_freq(time_range)
-            if isinstance(time_range, pd.DatetimeIndex) and time_range.freq != "{}T".format(
-                    60 / steps_per_hour):
+            print(f"Found date time index starting at {time_range[0]} "
+                  f"with freq {time_range.freq}")
+            if time_range.freq != "{}T".format(60 / steps_per_hour):
                 warnings.warn(f"Time Index of data frequency {str(time_range.freq)} "
                               f"does not match the configured steps_per_hour: {steps_per_hour}")
             self.time_range = time_range
-        print(f"Time range {time_range}")
+        else:
+            self.time_range = time_range
         self.add_actor_to_scenario = add_actor_to_scenario
         # Get grid fee method of market to make grid fees accessible for actors. Will be overwritten
         # when market is added to scenario
diff --git a/tests/test_market_maker.py b/tests/test_market_maker.py
index ecd49194..8f208dfd 100644
--- a/tests/test_market_maker.py
+++ b/tests/test_market_maker.py
@@ -94,22 +94,27 @@ def test_order_generation(self):
         grid_fee = 0.5
         cfg.config.default_grid_fee = grid_fee
         market_maker = MarketMaker(buy_prices=self.buy_prices, environment=self.env)
+        self.scenario.add_market(Market())
 
         ask_order, bid_order = self.generate_mm_order(market_maker)
         # Is the market_maker using the correct data
         assert self.buy_prices[env.time_step] == market_maker.all_sell_prices[env.time_step]
-        assert self.buy_prices[env.time_step] == market_maker.all_sell_prices[env.time_step]
+        assert self.buy_prices[env.time_step] == market_maker.all_buy_prices[env.time_step]
 
         # Do the orders use the correct market_maker data ?
-        assert bid_order.price == market_maker.all_buy_prices[env.time_step]
+        # The market maker includes the virtual grid fees in its bids
+        assert bid_order.price == market_maker.all_buy_prices[env.time_step] + grid_fee
         assert ask_order.price == market_maker.all_sell_prices[env.time_step]
 
         # test grid fee adjustment
         actor = create_random("test_actor")
         self.scenario.add_participant(actor)
-        # Actor accessed the market maker prices which differ due to the grid_fee
+        # Actor accessed the market maker prices which differ due to the grid_fee assumptions
+        # - actors are able to sell energy to MM at less then MM bid order price without grid fees
+        # - actors are able to buy energy from MM at more then MM ask order price with grid fees
+        # as selling/ask participants do not have to pay grid fees
         assert actor.get_mm_buy_prices()[0] == bid_order.price - grid_fee
-        assert actor.get_mm_sell_prices()[0] == bid_order.price + grid_fee
+        assert actor.get_mm_sell_prices()[0] == ask_order.price + grid_fee
 
         # test if the energy amount is correct
         assert bid_order.energy == MARKET_MAKER_THRESHOLD
@@ -119,7 +124,7 @@ def test_order_generation(self):
         env.time_step = 5
         market_maker.create_prediction()
         ask_order, bid_order = self.generate_mm_order(market_maker)
-        assert bid_order.price == self.buy_prices[env.time_step]
+        assert bid_order.price == self.buy_prices[env.time_step] + grid_fee
         assert ask_order.price == self.buy_prices[env.time_step]
 
         assert actor.get_mm_buy_prices()[0] == bid_order.price - grid_fee
@@ -143,7 +148,7 @@ def test_order_generation(self):
             env.time_step = 0
             market_maker.create_prediction()
             ask_order, bid_order = self.generate_mm_order(market_maker)
-            assert bid_order.price * 2 == ask_order.price
+            assert (bid_order.price - grid_fee) * 2 == ask_order.price, kw
 
         # if only function is given, function is used
         kwarg = dict(buy_to_sell_function=lambda x: x + 1.5)
@@ -151,7 +156,7 @@ def test_order_generation(self):
         env.time_step = 0
         market_maker.create_prediction()
         ask_order, bid_order = self.generate_mm_order(market_maker)
-        assert bid_order.price + 1.5 == ask_order.price
+        assert (bid_order.price - grid_fee) + 1.5 == ask_order.price
 
     def generate_mm_order(self, market_maker):
         orders = market_maker.generate_orders()