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Electric Vehicle

This example demonstrates how to model heating and cooling flexibility in a building using EnergyCommunity.jl using the example provided by the tool named "energycommunitymodel_flexibility". The example includes 3 users:

  • User 1: User with a PV system, an electric load and an adjustable load representing the user's recharging station with an electric vehicle with 53kWh battery
  • User 2: User with a PV, battery system and an electric load
  • User 3: User with a PV, wind, battery system

This example showcase how to load and optimize an energy community model with the flexibility offered by a single electric vehicle.

In particular, the electric vehicle is modeled as an adjustable load with the following quantities retrieved from the file "data/flexibility_resources.csv":

  • energy_exchange: vector of energy exchange towards the recharging station: when positive with value X, it denotes a new EV to be connected with state of charge X; when negative with value -Y, it denotes an EV to be disconnected with state of charge Y
  • max_supply: maximum discharging power of the recharging station, depending on the available EVs; if no power-to-grid is allowed, this value must be 0
  • max_withdrawal: maximum charging power of the recharging station, depending on the available EVs
  • min_energy: minimum state of charge of the recharging station, accounting for all connected EVs
  • max_energy: maximum state of charge of the recharging station, accounting for all connected EVs

Initialization

Import the needed packages

using EnergyCommunity, JuMP
using HiGHS, Plots

Create a base Energy Community example in the data folder; use the default configuration.

folder = joinpath(@__DIR__, "data")
create_example_data(folder, config_name="default")

Input file to load the structure of the energy community based on a yaml file.

input_file = joinpath(@__DIR__, "data/energy_community_model_flexibility.yml");

Output path of the plots

output_plot_isolated = joinpath(@__DIR__, "./results/Img/plot_user_{:s}_CO.png");

define optimizer and options

optimizer = optimizer_with_attributes(HiGHS.Optimizer, "ipm_optimality_tolerance"=>1e-6)
MathOptInterface.OptimizerWithAttributes(HiGHS.Optimizer, Pair{MathOptInterface.AbstractOptimizerAttribute, Any}[MathOptInterface.RawOptimizerAttribute("ipm_optimality_tolerance") => 1.0e-6])

Create, build and optimize the model

Define the Cooperative model

EV_Model = ModelEC(input_file, EnergyCommunity.GroupCO(), optimizer)
An Energy Community Model
Energy Community problem for a Cooperative Model
User set: ["user1", "user2", "user3"]

Build the mathematical model

build_model!(EV_Model)
An Energy Community Model
Energy Community problem for a Cooperative Model
User set: ["user1", "user2", "user3"]

Optimize the model

optimize!(EV_Model)
An Energy Community Model
Energy Community problem for a Cooperative Model
User set: ["user1", "user2", "user3"]

Results

get objective value

objective_value(EV_Model)
-1.2383685123610068e6

Plots of dispatch

Create plots of the dispatch of resources by user and save them to disk

all_plots = plot(EV_Model, output_plot_isolated)
user_to_plot = 3  # select user to plot
plot(all_plots[user_to_plot, 3])  # show the plot of user 3 (top: power dispatch, bottom: battery storage)
Example block output

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