recharging/read_me.html at main · helia94/recharging · GitHub

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<!DOCTYPE html>
<html>

<head>
  <meta charset="utf-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Welcome file.md</title>
  <link rel="stylesheet" href="https://stackedit.io/style.css" />
</head>

<body class="stackedit">
  <div class="stackedit__html"><h1 id="libraries-to-install">Libraries to install</h1>
<p>Please install:</p>
<ul>
<li>Python 3.7</li>
<li>Jupyter notebook</li>
<li>jupyter_contrib_nbextensions from <a href="https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/install.html">https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/install.html</a></li>
<li>jupyter_nbextensions_configurator from<br>
<a href="https://github.com/Jupyter-contrib/jupyter_nbextensions_configurator">https://github.com/Jupyter-contrib/jupyter_nbextensions_configurator</a><br>
Once you open jupyter hopepage, navigate to Nbextensions tab and enable the following features:</li>
<li>Table of Contents (2)</li>
<li>highlighter</li>
<li>Codefolding</li>
</ul>
<p>Other python libraries to install:<br>
Common libraries:<br>
[numpy, pandas, json, matplotlib, ast]<br>
More specific libraries<br>
[shapely,  geopandas, geopy, functools, requests, glob,  gurobipy (install from grouby installation folder), scipy, sklearn, imageio, seaborn]<br>
if the main directory of your gurobi installation does not have a python37 version for gurobipy:<br>
copy and replace the content of the folder “gurobipy installation” in the directory of the main gurobi installation, using the new <a href="http://setup.py">setup.py</a> you should be able to use gurobipy with python3.7 .</p>
<h1 id="files">Files</h1>
<p>The list of files you should have:<br>
From PTV:</p>
<ul>
<li>xserver</li>
<li>simulator (folder)</li>
<li>dispatcher_service.exe</li>
<li>ScenarioParameters.json<br>
(the fleet_size, vehicle_capacity, max_waiting_time can be set here)</li>
<li>start.bat</li>
</ul>
<p>For charging:</p>
<ul>
<li><a href="http://options.py">options.py</a></li>
<li>run_this_before_each_simulation.py</li>
<li><a href="http://charging.py">charging.py</a></li>
<li>charging_prams.py</li>
<li>scenarios folder, including a selection of scenarios with a description file</li>
<li>data (folder) including zones shapefile and OD matrix</li>
<li>GeneratingScenarios_MakingDailyChargingPlan_VisualizingResults.ipynb</li>
</ul>
<h1 id="run-ready-made-scenarios">Run ready made scenarios</h1>
<h3 id="running-the-simulation">Running the simulation</h3>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> start xsever:</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> make a high performance routing network for Barcelona (only once for all the runs) :
<ul>
<li>Go to xserver location on your browser, by default localhost:50000</li>
<li>Under administration, choose Raw Request Runner</li>
<li>choose the following options [XData/JSON/experimental/startCreateHighPerformanceRoutingNetwork]</li>
<li>Put in the following request, and send (this takes some time):</li>
</ul>
</li>
</ul>
<pre class=" language-java"><code class="prism  language-java">	  <span class="token punctuation">{</span><span class="token string">"label"</span><span class="token operator">:</span> <span class="token string">"barcelona"</span><span class="token punctuation">,</span>

		<span class="token string">"scope"</span><span class="token operator">:</span> <span class="token string">"barcelona"</span><span class="token punctuation">,</span>

		<span class="token string">"storedProfile"</span><span class="token operator">:</span> <span class="token string">"car"</span><span class="token punctuation">,</span>

		<span class="token string">"highPerformanceRoutingNetworkOptions"</span><span class="token operator">:</span> <span class="token punctuation">{</span>

		<span class="token string">"geographicRestrictions"</span><span class="token operator">:</span> <span class="token punctuation">{</span>

		<span class="token string">"allowedCountries"</span><span class="token operator">:</span> <span class="token punctuation">[</span>

		<span class="token string">"ES"</span><span class="token punctuation">]</span><span class="token punctuation">}</span><span class="token punctuation">}</span><span class="token punctuation">}</span>
</code></pre>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> start.bat, dispatcher.exe, ScenarioParameters.json, and simulator folder should be in the same directory (refered to as main directory from now on)</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> set the following in start.bat:</li>
<li>–energy_step_exe: Path to executable installed by your python distribution (e.g. “C:\Software\python.exe”)</li>
<li>–energy_step_script “<a href="http://charging.py">charging.py</a>”</li>
<li>–options_step_exe: Path to executable installed by your python distribution  (same as above)</li>
<li>–options_step_script “<a href="http://options.py">options.py</a>”</li>
<li>–kpi_file “KPI.json”</li>
<li>–log_file “simulation_log.txt”</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> choose scenario and copy its folder “example_scenario” to the main directory</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> from “example_scenario” folder copy TripRequests.json and Nodes.txt and paste them in the main directory</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> set the following in charging_prams.py :</li>
</ul>
<pre class=" language-python"><code class="prism  language-python">scenario_name<span class="token operator">=</span><span class="token string">'example_scenario'</span>
STAGE<span class="token operator">=</span><span class="token number">3</span>
LOG<span class="token operator">=</span><span class="token boolean">True</span><span class="token operator">/</span><span class="token boolean">False</span>
SoC_in_assignment<span class="token operator">=</span><span class="token boolean">True</span><span class="token operator">/</span><span class="token boolean">False</span>
lazy_charging<span class="token operator">=</span><span class="token boolean">True</span><span class="token operator">/</span><span class="token boolean">False</span>
</code></pre>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> set the following in ScenarioParameters.json, or just use the ScenarioParameters.json that came with the charging files :</li>
<li>“AcceptableWaitingTime”: 600</li>
<li>“MaximumDetourFactor”: 1.6</li>
<li>“FleetSize”: 150</li>
<li>“SeatCapacity”: 6</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> run run_this_before_each_simulation.py</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> run start.bat</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled="">  after the simulation is over, copy the vehicle%d.csv files to the directory ‘example_scenario/results/3/toure and trajectory’</li>
</ul>
<h3 id="visulizing-the-results">Visulizing the results</h3>
<p>To look at the results start the jupyter notebook GeneratingScenarios_MakingDailyChargingPlan_VisualizingResults.ipynb, which should be placed in the main directory. Further instructions follow in the notebook, in short:</p>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> Enter the name of the scenario</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> Make sure you see the table of content on the left (you should see 4 main sections)</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> Run every cell section 1</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> Run every cell in section 4</li>
</ul>
<p>You will see the plots, and KPI in the notebook, they will also be saved to the results folder (‘example_scenario/results/3/toure and trajectory’), location of the vehicles through the simulation is saved as Gif files and are not shown in the notebook.</p>
<h1 id="making-a-scenario">Making a scenario</h1>
<h3 id="general-scheme">General scheme:</h3>
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<h3 id="set-the-prameters-of-the-scenario">Set the prameters of the scenario:</h3>
<p>Here find where each parameter should be changed, first the relavent file is specified, and then the variable’s name:<br>
“notebook” is used as shortcut for “GeneratingScenarios_MakingDailyChargingPlan_VisualizingResults.ipynb”</p>
<p>Scenario name:</p>
<ul>
<li>charging_pram.py:  scenario_name</li>
<li>notebook: scenario_name</li>
</ul>
<p>Stage:</p>
<ul>
<li>charging_pram.py: STAGE</li>
<li>notebook: stage</li>
</ul>
<p>Charging method:</p>
<ul>
<li>charging_pram.py: [SoC_in_assignment, lazy_charging]</li>
<li>notebook: only applicable when visulizing (stage=4) [SoC_in_assignment, lazy_charging]</li>
</ul>
<hr>
<p>Operating hours:</p>
<ul>
<li>charging_pram.py: [<em>operation_start_time, operation_end_time</em>]</li>
<li>notebook: [<em>operation_start_time, operation_end_time</em>]</li>
</ul>
<p>Fleet size:</p>
<ul>
<li>ScenarioParameters.json: <em>FleetSize</em></li>
<li>notebook: <em>fleet_size</em></li>
</ul>
<p>Driving range:</p>
<ul>
<li>charging_pram.py: <em>DrivingRange</em></li>
<li>notebook: <em>DrivingRange</em></li>
</ul>
<p>Number of chargers:</p>
<ul>
<li>notebook: [<em>number_of_slow_plugs, number_of_slow_stations, number_of_fast_plugs, number_of_fast_stations</em>]</li>
</ul>
<p>Charging rate:</p>
<ul>
<li>charging_pram.py: <em>charging_time</em></li>
<li>notebook: <em>charging_time</em></li>
</ul>
<p>Seat Capacity:</p>
<ul>
<li>ScenarioParameters.json: <em>SeatCapacity</em></li>
</ul>
<p>Max wating time:</p>
<ul>
<li>ScenarioParameters.json: <em>AcceptableWaitingTime</em></li>
</ul>
<p>Max dtour:</p>
<ul>
<li>ScenarioParameters.json: <em>MaximumDetourfactor</em></li>
</ul>
<hr>
<p>Reoptimization frequency of charging:</p>
<ul>
<li>charging_pram.py: <em>time_step_online</em></li>
<li>strat.bat: <em>energy_time_step</em></li>
</ul>
<p>Objective weigths of algorithm A:</p>
<ul>
<li>notebook: <em>penalty_A</em></li>
</ul>
<p>Objective weigths of algorithm B, C, R:</p>
<ul>
<li>charging_pram.py: [<em>penalty_B, penalty_C, penalty_R</em>]</li>
</ul>
<p>Total expected demand:</p>
<ul>
<li>notebook: <em>es_total_demand</em></li>
</ul>
<h3 id="walk-through">Walk through:</h3>
<h4 id="stage-1">Stage 1</h4>
<p>To generate a scenario start with the jupyter notebook GeneratingScenarios_MakingDailyChargingPlan_VisualizingResults.ipynb, which should be placed in the main directory:<br>
Use Sections 1 and 2 to genarete a scenario the notebook includes a guid to choose number of zones, number of requests, and location of charger stations. Set white noise factor <span class="katex--inline"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>w</mi></mrow><annotation encoding="application/x-tex">w</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 0.43056em; vertical-align: 0em;"></span><span class="mord mathdefault" style="margin-right: 0.02691em;">w</span></span></span></span></span> to 0, set morning and evening bias factors <span class="katex--inline"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>b</mi><mi mathvariant="normal">_</mi><mi>m</mi><mi>o</mi><mi>r</mi><mi>n</mi><mi>i</mi><mi>n</mi><mi>g</mi></mrow><annotation encoding="application/x-tex">b\_morning</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 1.00444em; vertical-align: -0.31em;"></span><span class="mord mathdefault">b</span><span class="mord" style="margin-right: 0.02778em;">_</span><span class="mord mathdefault">m</span><span class="mord mathdefault">o</span><span class="mord mathdefault" style="margin-right: 0.02778em;">r</span><span class="mord mathdefault">n</span><span class="mord mathdefault">i</span><span class="mord mathdefault">n</span><span class="mord mathdefault" style="margin-right: 0.03588em;">g</span></span></span></span></span>, <span class="katex--inline"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mi>b</mi><mi mathvariant="normal">_</mi><mi>e</mi><mi>v</mi><mi>e</mi><mi>n</mi><mi>i</mi><mi>n</mi><mi>g</mi></mrow><annotation encoding="application/x-tex">b\_evening</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height: 1.00444em; vertical-align: -0.31em;"></span><span class="mord mathdefault">b</span><span class="mord" style="margin-right: 0.02778em;">_</span><span class="mord mathdefault">e</span><span class="mord mathdefault" style="margin-right: 0.03588em;">v</span><span class="mord mathdefault">e</span><span class="mord mathdefault">n</span><span class="mord mathdefault">i</span><span class="mord mathdefault">n</span><span class="mord mathdefault" style="margin-right: 0.03588em;">g</span></span></span></span></span> to 0 for stage=1.<br>
Once scenario is generated by running all relavent cells to stage=1 in the notebook, follow the checklist below to run the simulation with no relocation or charging:</p>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> from “new_scenario” folder copy TripRequests.json and Nodes.txt and paste them in the main directory</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> set the following in charging_prams.py :</li>
</ul>
<pre class=" language-python"><code class="prism  language-python">scenario_name<span class="token operator">=</span><span class="token string">'new_scenario'</span>
STAGE<span class="token operator">=</span><span class="token number">1</span>
LOG<span class="token operator">=</span><span class="token boolean">True</span><span class="token operator">/</span><span class="token boolean">False</span>
SoC_in_assignment<span class="token operator">=</span><span class="token boolean">False</span>
lazy_charging<span class="token operator">=</span><span class="token boolean">False</span>
</code></pre>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> set the value for fleet_size in ScenarioParameters.json</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> run run_this_before_each_simulation.py</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> run start.bat<br>
after the simulation is over, copy the vehicle%d.csv files to the directory ‘new_scenario/results/1/toure and trajectory’</li>
</ul>
<h4 id="stage-2">Stage 2</h4>
<p>go to GeneratingScenarios_MakingDailyChargingPlan_VisualizingResults.ipynb and use Sections 1 and 2 to genarete a scenario to for stage=2. Follow the checklist below to run the simulation with relocation but no charging:</p>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> from “new_scenario” folder copy TripRequests.json and Nodes.txt and paste them in the main directory</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> set the following in charging_prams.py :</li>
</ul>
<pre class=" language-python"><code class="prism  language-python">STAGE<span class="token operator">=</span><span class="token number">2</span>
</code></pre>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> run run_this_before_each_simulation.py</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> run start.bat<br>
after the simulation is over, copy the vehicle%d.csv files to the directory ‘new_scenario/results/2/toure and trajectory’, replacing the privous ones</li>
</ul>
<h4 id="stage-3">Stage 3</h4>
<p>go to GeneratingScenarios_MakingDailyChargingPlan_VisualizingResults.ipynb and use Sections 1, 2, and 3 to genarete a scenario to for stage 3.  In this stage you can set white noise factor, morning and evening bias factor for the demand.<br>
Section 3 in the jupyrter notebok will read trejectory data from stage 2 get travel time and distance from xroute for those trejectories. With this it will calculate the input to algorithm A. Then it will run algorithm A and write the solution to folder of the scenario. You will also observe the plan by algorithm A in section 3. Further guidline is included in the notebook.<br>
Follow the checklist below to run the simulation with relocation and charging:</p>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> from “new_scenario” folder copy TripRequests.json and Nodes.txt and paste them in the main directory</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> set the following in charging_prams.py :</li>
</ul>
<pre class=" language-python"><code class="prism  language-python">STAGE<span class="token operator">=</span><span class="token number">3</span>
LOG<span class="token operator">=</span><span class="token boolean">True</span><span class="token operator">/</span><span class="token boolean">False</span>
SoC_in_assignment<span class="token operator">=</span><span class="token boolean">True</span><span class="token operator">/</span><span class="token boolean">False</span>
lazy_charging<span class="token operator">=</span><span class="token boolean">True</span><span class="token operator">/</span><span class="token boolean">False</span>
</code></pre>
<ul>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> run run_this_before_each_simulation.py</li>
<li class="task-list-item"><input type="checkbox" class="task-list-item-checkbox" disabled=""> run start.bat</li>
</ul>
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