HyDesign

https://topfarm.pages.windenergy.dtu.dk/hydesign
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HyDesign is a DTU tool for design, control, and optimization of utility-scale hybrid power and...
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### Install Libraries for PyWake P2X Example

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/PyWake_P2X_Example

Installs the 'hydesign' and 'py_wake' libraries from GitLab if they are not already found. These libraries are essential for running the hybrid power plant simulation.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git

# Install pywake if needed
if not importlib.util.find_spec("py_wake"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake.git
```

--------------------------------

### Simple Installation of HyDesign from PyPI

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/installation.rst

Installs the latest official release of HyDesign from PyPI. This is the simplest method for users who want a stable release.

```bash
pip install hydesign
```

--------------------------------

### Install and Import Libraries for Hybrid Power Plant Simulation

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/PyWake_P2X_Example.ipynb

Installs the 'hydesign' and 'py_wake' libraries if they are not already present. These libraries are essential for setting up and running the hybrid power plant simulation.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git
```

```python
# Install pywake if needed
if not importlib.util.find_spec("py_wake"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake.git   
```

--------------------------------

### Install and Import hydesign Libraries

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/constant_output

Installs the hydesign library if not already present and imports necessary modules for plant sizing and evaluation. This includes pandas for data manipulation, numpy for numerical operations, the HPP model assembly class, and plotting utilities.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git

import pandas as pd
import numpy as np
from hydesign.assembly.hpp_assembly_constantoutput import hpp_model_constant_output as hpp_model
from hydesign.examples import examples_filepath
import matplotlib.pyplot as plt
```

--------------------------------

### Install and Import Hydesign Library

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/Hybridize.ipynb

This snippet demonstrates how to install the hydesign library if it's not already present and imports necessary components like time, pandas, and example file paths. It uses `importlib` to check for the library's existence before attempting installation via pip.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git
```

```python
import time
import pandas as pd
from hydesign.examples import examples_filepath
```

--------------------------------

### Install HyDesign from GitLab

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/installation.rst

Installs the latest version of HyDesign directly from the GitLab repository. This method includes any recent updates not yet released on PyPI.

```bash
pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git
```

--------------------------------

### Initialize HyDesign P2X Model with Example Data

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/break_even_price_and_PPA

Loads example site data, extracts relevant parameters (longitude, latitude, altitude, file paths), and initializes the HyDesign P2X model. This setup is crucial for running simulations with predefined or custom site configurations.

```python
%%capture
import pandas as pd
from hydesign.assembly.hpp_assembly import hpp_model
from hydesign.assembly.hpp_assembly_P2X import hpp_model_P2X
from hydesign.examples import examples_filepath

examples_sites = pd.read_csv(f'{examples_filepath}examples_sites.csv', index_col=0, sep=';')
name = 'France_good_wind'
ex_site = examples_sites.loc[examples_sites.name == name]
longitude = ex_site['longitude'].values[0]
latitude = ex_site['latitude'].values[0]
altitude = ex_site['altitude'].values[0]
input_ts_fn = examples_filepath+ex_site['input_ts_fn'].values[0]
sim_pars_fn = examples_filepath+ex_site['sim_pars_fn'].values[0]
H2_demand_fn = examples_filepath+ex_site['H2_demand_col'].values[0]
hpp = hpp_model_P2X(
    latitude=latitude,
    longitude=longitude,
    altitude=altitude,
    num_batteries = 1,
    work_dir = './',
    sim_pars_fn = sim_pars_fn,
    input_ts_fn = input_ts_fn,
    H2_demand_fn=H2_demand_fn)
  

```

--------------------------------

### Setup Hybrid Power Plant Model with PyWake

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/PyWake_P2X_Example

Configures and initializes the hybrid power plant model using PyWake. This involves setting site parameters, wind turbine specifications, farm layout, and simulation parameters for a comprehensive evaluation.

```python
name = 'Denmark_good_wind'
examples_sites = pd.read_csv(f'{examples_filepath}examples_sites.csv', index_col=0, sep=';')
ex_site = examples_sites.loc[examples_sites.name == name]

longitude = ex_site['longitude'].values[0]
latitude = ex_site['latitude'].values[0]
altitude = ex_site['altitude'].values[0]
sim_pars_fn = examples_filepath + ex_site['sim_pars_fn'].values[0]
input_ts_fn = examples_filepath + ex_site['input_ts_fn'].values[0]

intervals_per_hour = 1
n_wt = 40
n_loads = 4
wt = DTU10MW_1WT_Surrogate()
d = wt.diameter()
site = Hornsrev1Site()
sx = 4 * d
sy = 5 * d
x, y = regular_generic_layout(n_wt, sx, sy, stagger=0, rotation=0)
N_ws = 365 * 24 * intervals_per_hour
time_stamp = np.arange(N_ws) / 6 / 24
farm = NOJ(site, wt, turbulenceModel=STF2017TurbulenceModel(),
    deflectionModel=JimenezWakeDeflection(), superpositionModel=LinearSum())
yaw = 30 * np.sin(np.arange(N_ws) / 100)
tilt = np.zeros(N_ws)

hpp = hpp_model(latitude=latitude,
                longitude=longitude,
                altitude=altitude,
                sim_pars_fn=sim_pars_fn,
                input_ts_fn=input_ts_fn,
                intervals_per_hour=intervals_per_hour,
                farm=farm,
                x=x,
                y=y,
                tilt=tilt,
                time_stamp=time_stamp,
                n_loads=n_loads,
                H2_demand_fn=6000,
               Nwt=n_wt)
```

--------------------------------

### Editable Basic Installation of HyDesign

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/installation.rst

Installs HyDesign in editable mode from a local clone of the GitLab repository. This is useful for development as changes to the source code are immediately reflected without reinstallation.

```bash
git clone https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git
cd hydesign
pip install -e .
```

--------------------------------

### Install and Import HyDesign and Libraries

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/HPP_evaluation_P2MeOH

Installs the HyDesign library if not already present and imports essential Python libraries for data manipulation, time series analysis, and HPP modeling. It also includes a utility to find example file paths.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git

import os
import time
import yaml
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from hydesign.assembly.hpp_assembly_P2MeOH_bidirectional import hpp_model_P2MeOH_bidirectional as hpp_model

from hydesign.examples import examples_filepath
```

--------------------------------

### Load Example Site Data with Pandas

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/HPP_evaluation_P2X

Loads example site data from a CSV file into a pandas DataFrame. This data includes geographical coordinates and file paths for input time series and simulation parameters. The CSV is semicolon-separated and uses the first column as the index.

```python
examples_sites = pd.read_csv(f'{examples_filepath}examples_sites.csv', index_col=0, sep=';')
print(examples_sites)
```

--------------------------------

### Install and Import hydesign Library

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/Example_ISO_prob.ipynb

This code snippet installs the hydesign library from a Git repository if it's not already found. It then imports necessary libraries including numpy, pandas, scipy, and specific modules from statsmodels and hydesign for weather transformations and examples.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git   

import numpy as np
from numpy import newaxis as na
import pandas as pd
import scipy
from statsmodels.distributions.empirical_distribution import ECDF, monotone_fn_inverter
from hydesign.weather.weather import isoprob_transfrom
from hydesign.examples import examples_filepath
```

--------------------------------

### Install HyDesign Library

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/Example_ISO_prob

This code snippet installs the HyDesign library from a Git repository if it's not already found. It uses `importlib` to check for the library and `pip` to install it if necessary. This is a prerequisite for using the library's functionalities.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git

```

--------------------------------

### Install and Import Hydesign and Libraries

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/Hybridize

Installs the hydesign library if not already present and imports essential Python libraries including pandas for data manipulation and time for performance measurement.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git

import time
import pandas as pd
from hydesign.examples import examples_filepath
```

--------------------------------

### Configure Optimization Inputs for Hydesign

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/Simple_Sizing_Example.ipynb

Sets up the input parameters for the EfficientGlobalOptimizationDriver in Hydesign. This includes defining fixed and design variables for battery capacity, duration, and cost ratios.

```python
inputs = {
    'b_P [MW]': {
        'var_type': 'fixed',
        'value': 50
    },
    'b_E_h [h]': {
        'var_type': 'fixed',
        'value': 6
    },
    'cost_of_battery_P_fluct_in_peak_price_ratio': {
        'var_type': 'fixed',
        'value': 10
    }
}
EGOD = EfficientGlobalOptimizationDriver(**inputs)
```

--------------------------------

### Import Libraries for PyWake P2X Example

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/PyWake_P2X_Example

Imports necessary libraries for the PyWake P2X example, including numpy, pandas, hydesign components, py_wake models, and matplotlib for plotting. These are fundamental for setting up and running the simulation.

```python
import numpy as np
import pandas as pd
from hydesign.assembly.hpp_pywake_p2x import hpp_model
from hydesign.examples import examples_filepath
from py_wake.examples.data.hornsrev1 import Hornsrev1Site
from py_wake.turbulence_models.stf import STF2017TurbulenceModel
from py_wake import NOJ
from py_wake.deflection_models import JimenezWakeDeflection
from py_wake.examples.data.dtu10mw_surrogate import DTU10MW_1WT_Surrogate
from py_wake.superposition_models import LinearSum
from topfarm.utils import regular_generic_layout
from scipy.spatial import ConvexHull
import matplotlib.pyplot as plt
```

--------------------------------

### Install and Import Hydesign Library

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/evaluate_with_reliability

Installs the hydesign library from a Git repository if it's not already found. It then imports necessary libraries for the simulation.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git

```

```python
import time
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt

from hydesign.assembly.hpp_assembly import hpp_model
from hydesign.examples import examples_filepath

```

--------------------------------

### Select Site Coordinates and Parameters

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/HPP_evaluation_P2X

Selects a specific site from the loaded example site data based on its name. It then extracts the longitude, latitude, and altitude for the chosen site, which can be used for further simulations or analysis.

```python
name = 'Denmark_good_wind'

ex_site = examples_sites.loc[examples_sites.name == name]

longitude = ex_site['longitude'].values[0]
latitude = ex_site['latitude'].values[0]
altitude = ex_site['altitude'].values[0]
```

--------------------------------

### Install and Import HyDesign and Libraries (Python)

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/HPP_evaluation_P2MeOH.ipynb

Installs the HyDesign library if not already present and imports essential Python libraries for data manipulation, simulation, and plotting. This includes pandas for data handling, yaml for configuration files, numpy for numerical operations, and specific HyDesign modules for hybrid power plant assembly.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git   

import os
import time
import yaml
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from hydesign.assembly.hpp_assembly_P2MeOH_bidirectional import hpp_model_P2MeOH_bidirectional as hpp_model

from hydesign.examples import examples_filepath
```

--------------------------------

### Developer Installation of HyDesign

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/installation.rst

Installs HyDesign in editable mode with all optional dependencies included. This provides a complete setup for development, testing, and advanced features.

```bash
git clone https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git
cd hydesign
pip install -e .[all]
```

--------------------------------

### Install and Import HyDesign and Libraries

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/HPP_evaluation_BM

Installs the HyDesign library if not already present and imports necessary Python libraries for data manipulation, time series analysis, and HPP modeling. This setup is crucial for running the subsequent code examples.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git

import os
import time
import yaml
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from hydesign.assembly.hpp_assembly_BM import hpp_model
from hydesign.examples import examples_filepath
```

--------------------------------

### Install CPLEX Community Edition

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/installation.rst

Installs the community edition of CPLEX using pip. CPLEX is a prerequisite for HyDesign and is recommended to be installed before HyDesign.

```bash
pip install cplex
```

--------------------------------

### Load Site Data and Define Optimization Inputs

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/Simple_Sizing_P2X_Example.ipynb

Loads site-specific data from a CSV file and defines the input parameters for the hybrid power plant optimization. This includes geographical information, time-series data file paths, and simulation parameters.

```python
examples_sites = pd.read_csv(f'{examples_filepath}examples_sites.csv', index_col=0, sep=';')
ex_site = examples_sites.loc[4]
longitude = ex_site['longitude']
latitude = ex_site['latitude']
altitude = ex_site['altitude']
input_ts_fn = examples_filepath+ex_site['input_ts_fn']
sim_pars_fn = examples_filepath+ex_site['sim_pars_fn']
H2_demand_fn = examples_filepath+ex_site['H2_demand_col']

inputs = {
    'name': ex_site['name'],
    'longitude': longitude,
    'latitude': latitude,
    'altitude': altitude,
    'input_ts_fn': input_ts_fn,
    'sim_pars_fn': sim_pars_fn,
    'H2_demand_fn': H2_demand_fn,

    'opt_var': "NPV_over_CAPEX",
    'num_batteries': 10,
    'n_procs': n_procs,
    'n_doe': n_doe,
    'n_clusters': n_procs,
    'n_seed': 0,
    'max_iter': 10,
    'final_design_fn': 'hydesign_design_0.csv',
    'npred': 3e4,
    'tol': 1e-6,
    'min_conv_iter': 3,
    'work_dir': './',
    'hpp_model': hpp_model,
    'variables': {
    'clearance [m]':
        {'var_type':'design',
          'limits':[10, 60],
          'types':'int'
          },
        # {'var_type':'fixed',
        #   'value': 35
        #   },
     'sp [W/m2]':
        {'var_type':'design',
         'limits':[200, 360],
         'types':'int'
         },
    'p_rated [MW]':
        {'var_type':'design',
          'limits':[1, 10],
          'types':'int'
          },
        # {'var_type':'fixed',
        #  'value': 6
         # },
    'Nwt':
        {'var_type':'design',
          'limits':[0, 400],
          'types':'int'
          },
        # {'var_type':'fixed',
        #   'value': 200
        #   },
    'wind_MW_per_km2 [MW/km2]':
        # {'var_type':'design',
        #   'limits':[5, 9],
        #   'types':'float'
        #   },
        {'var_type':'fixed',
          'value': 7
          },
    'solar_MW [MW]':
        # {'var_type':'design',
        #   'limits':[0, 400],
        #   'types':'int'
        #   },
        {'var_type':'fixed',
          'value': 200
          },
    'surface_tilt [deg]':
        # {'var_type':'design',
        #   'limits':[0, 50],
        #   'types':'float'
        #   },
        {'var_type':'fixed',
          'value': 25
          },
    'surface_azimuth [deg]':
        # {'var_type':'design',
        #   'limits':[150, 210],
        #   'types':'float'
        #   },
        {'var_type':'fixed',
          'value': 180
          },
    'DC_AC_ratio':
        # {'var_type':'design',
        #   'limits':[1, 2.0],
        #   'types':'float'
        #   },

```

--------------------------------

### Update Anaconda Environment

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/installation.rst

Updates all packages in the root Anaconda environment. This command ensures that all installed packages are up-to-date.

```bash
conda update --all
```

--------------------------------

### Initialize HPP Model with Parameters

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/Quickstart

Initializes the HPP model with geographical coordinates, turbine and PV parameters, battery configuration, and input file paths. This sets up the model for simulation.

```python
hpp = hpp_model(
        latitude=latitude,
        longitude=longitude,
        altitude=altitude,
        rotor_diameter_m = rotor_diameter_m,
        hub_height_m = hub_height_m,
        wt_rated_power_MW = wt_rated_power_MW,
        surface_tilt_deg = surface_tilt_deg,
        surface_azimuth_deg = surface_azimuth_deg,
        DC_AC_ratio = DC_AC_ratio,
        num_batteries = 5,
        work_dir = './',
        sim_pars_fn = sim_pars_fn,
        input_ts_fn = input_ts_fn,
)
```

--------------------------------

### Configure Site and Input Parameters for Optimization

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/constant_output

Loads site-specific data (longitude, latitude, altitude) and input file paths for time series and simulation parameters. It also specifies the hydrogen demand file and sets up the optimization variables and their configurations.

```python
ex_site = examples_sites.loc[9]
longitude = ex_site['longitude']
latitude = ex_site['latitude']
altitude = ex_site['altitude']
input_ts_fn = examples_filepath+ex_site['input_ts_fn']
sim_pars_fn = examples_filepath+ex_site['sim_pars_fn'].replace('.yml','_benchmark.yml')
H2_demand_fn = examples_filepath+ex_site['H2_demand_col']
inputs = {
    'name': ex_site['name'],
    'longitude': longitude,
    'latitude': latitude,
    'altitude': altitude,
    'input_ts_fn': input_ts_fn,
    'sim_pars_fn': sim_pars_fn,
    'H2_demand_fn': H2_demand_fn,

    'opt_var': "NPV_over_CAPEX",
    'num_batteries': 10,
    'n_procs': n_procs - 1,
    'n_doe': 20,
    'n_clusters': 5,
    'n_seed': 0,
    'max_iter': 2,
    'final_design_fn': 'hydesign_design_0.csv',
    'npred': 3e4,
    'tol': 1e-6,
    'min_conv_iter': 3,
    'work_dir': './',
    'hpp_model': hpp_model,
    'PPA_price': 40,
    'load_min': 3, # MW
    'variables': {
    'clearance [m]':
        #{'var_type':'design',
        #  'limits':[10, 60],
        #  'types':'int'
        #  },
        {'var_type':'fixed',
           'value': 35
           },
    'sp [W/m2]':
        #{'var_type':'design',
        # 'limits':[200, 359],
        # 'types':'int'
        # },
        {'var_type':'fixed',
           'value': 300
           },
    'p_rated [MW]':
        {'var_type':'design',
          'limits':[1, 10],
          'types':'int'
          },
        # {'var_type':'fixed',
        #  'value': 6
         # },
    'Nwt':
        {'var_type':'design',
          'limits':[1, 400],
          'types':'int'
          },
        # {'var_type':'fixed',
        #   'value': 200
        #   },
    'wind_MW_per_km2 [MW/km2]':
        #{'var_type':'design',
        #  'limits':[5, 9],
        #  'types':'float'
        #  },
         {'var_type':'fixed',
           'value': 7
           },
    'solar_MW [MW]':
        {'var_type':'design',
           'limits':[1, 400],
           'types':'int'
          },
        #{'var_type':'fixed',
        #  'value': 20
        #  },
    'surface_tilt [deg]':
        # {'var_type':'design',
        #   'limits':[0, 50],
        #   'types':'float'
        #   },
        {'var_type':'fixed',
          'value': 25
          },
    'surface_azimuth [deg]':
        # {'var_type':'design',
        #   'limits':[150, 210],
        #   'types':'float'
        #   },
        {'var_type':'fixed',
          'value': 180
          },
    'DC_AC_ratio':
        # {'var_type':'design',
        #   'limits':[1, 2.0],
        #   'types':'float'
        #   },
        {'var_type':'fixed',
          'value':1.6,
          },
    'b_P [MW]':
        {'var_type':'design',
           'limits':[0, 100],
           'types':'int'
          },
        #{'var_type':'fixed',
        #  'value': 50
        #  },
    'b_E_h [h]':
        {'var_type':'design',
           'limits':[1, 10],
           'types':'int'
          },
        #{'var_type':'fixed',
        #  'value': 6
        #  },
    'cost_of_battery_P_fluct_in_peak_price_ratio':
        {'var_type':'design',
          'limits':[0, 20],
          'types':'float'
          },
#         {'var_type':'fixed',

```

--------------------------------

### Activate Anaconda Root Environment

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/installation.rst

Activates the root Anaconda environment. This command is used to switch to the base environment where Anaconda is installed.

```bash
activate
```

--------------------------------

### Create and Activate HyDesign Conda Environment

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/installation.rst

Clones the HyDesign repository, navigates into the directory, creates a new Conda environment from 'environment.yml', and activates it. This is the recommended method for installing HyDesign to avoid dependency conflicts.

```bash
git clone https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git
cd hydesign
conda env create --file environment.yml
conda activate hydesign
```

--------------------------------

### Import Optimization Driver and OS Module

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/constant_output

Imports the necessary EfficientGlobalOptimizationDriver from the hydesign library and the os module for system operations. This is a prerequisite for setting up the optimization process.

```python
from hydesign.Parallel_EGO import EfficientGlobalOptimizationDriver
import os
```

--------------------------------

### Display Project Parameters and Results

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/Simple_Sizing_Example.ipynb

This snippet displays a table containing various parameters and results for a wind energy project. It includes site conditions, system capacities, cost breakdowns, financial metrics, and optimization outcomes. The data is presented for a specific scenario, 'France_good_wind'.

```python
import pandas as pd

data = {
    'surface_azimuth [deg]': [180.000],
    'DC_AC_ratio': [1.000],
    'b_P [MW]': [50.000],
    'b_E_h [h]': [6.000],
    'cost_of_battery_P_fluct_in_peak_price_ratio': [10.000],
    'NPV_over_CAPEX': [0.843],
    'NPV [MEuro]': [407.749],
    'IRR': [0.123],
    'LCOE [Euro/MWh]': [34.895],
    'Revenues [MEuro]': [50.812],
    'CAPEX [MEuro]': [483.739],
    'OPEX [MEuro]': [8.286],
    'Wind CAPEX [MEuro]': [342.383],
    'Wind OPEX [MEuro]': [7.386],
    'PV CAPEX [MEuro]': [46.000],
    'PV OPEX [MEuro]': [0.900],
    'Batt CAPEX [MEuro]': [25.774],
    'Batt OPEX [MEuro]': [0.000],
    'Shared CAPEX [MEuro]': [69.582],
    'Shared OPEX [MEuro]': [0.000],
    'penalty lifetime [MEuro]': [0.000],
    'AEP [GWh]': [1281.154],
    'GUF': [0.488],
    'grid [MW]': [300.000],
    'wind [MW]': [310.000],
    'solar [MW]': [200.000],
    'Battery Energy [MWh]': [300.000],
    'Battery Power [MW]': [50.000],
    'Total curtailment [GWh]': [725.295],
    'Total curtailment with deg [GWh]': [280.323],
    'Awpp [km2]': [62.000],
    'Apvp [km2]': [2.452],
    'Plant area [km2]': [62.000],
    'Rotor diam [m]': [210.627],
    'Hub height [m]': [165.314],
    'Number of batteries used in lifetime': [2.000],
    'Break-even PPA price [Euro/MWh]': [23.340],
    'Capacity factor wind [-]': [0.415]
}

df = pd.DataFrame(data)

# Assuming the table display is handled by a Jupyter environment or similar
# For demonstration, we'll print the DataFrame
print(df.to_string())

# The original context suggests this might be part of a larger output, possibly from a tool like EGOD
# print("Optimization with 9 iterations and 141 model evaluations took 16.19 minutes")

```

--------------------------------

### Load and Select Example Site Data with Pandas

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/Hybridize.ipynb

This snippet demonstrates how to load site data from a CSV file using pandas and select a specific site by name. It reads 'examples_sites.csv' and filters for the 'Denmark_hybridization_solar_Langelinie' site.

```python
import pandas as pd

examples_filepath = './'
examples_sites = pd.read_csv(f'{examples_filepath}examples_sites.csv', index_col=0, sep=';')
name = 'Denmark_hybridization_solar_Langelinie'
ex_site = examples_sites.loc[examples_sites.name == name]
ex_site
```

--------------------------------

### Load Site Data and Define Optimization Inputs

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/Simple_Sizing_Example.ipynb

Loads site-specific data from a CSV file and defines the input parameters for the hybrid power plant optimization. This includes geographical coordinates, file paths for time-series data, simulation parameters, and hydrogen demand. It also sets up optimization variables with their types and limits.

```python
examples_sites = pd.read_csv(f'{examples_filepath}examples_sites.csv', index_col=0, sep=';')
ex_site = examples_sites.loc[4]
longitude = ex_site['longitude']
latitude = ex_site['latitude']
altitude = ex_site['altitude']
input_ts_fn = examples_filepath+ex_site['input_ts_fn']
sim_pars_fn = examples_filepath+ex_site['sim_pars_fn']
H2_demand_fn = examples_filepath+ex_site['H2_demand_col']
inputs = {
    'name': ex_site['name'],
    'longitude': longitude,
    'latitude': latitude,
    'altitude': altitude,
    'input_ts_fn': input_ts_fn,
    'sim_pars_fn': sim_pars_fn,
    'H2_demand_fn': H2_demand_fn,

    'opt_var': "NPV_over_CAPEX",
    'num_batteries': 10,
    'n_procs': n_procs,
    'n_doe': n_doe,
    'n_clusters': n_procs,
    'n_seed': 0,
    'max_iter': 10,
    'final_design_fn': 'hydesign_design_0.csv',
    'npred': 3e4,
    'tol': 1e-6,
    'min_conv_iter': 3,
    'work_dir': './',
    'hpp_model': hpp_model,
 'variables': {
    'clearance [m]':
        {'var_type':'design',
          'limits':[10, 60],
          'types':'int'
          },
        # {'var_type':'fixed',
        #   'value': 35
        #   },
     'sp [W/m2]':
        {'var_type':'design',
         'limits':[200, 360],
         'types':'int'
         },
    'p_rated [MW]':
        {'var_type':'design',
          'limits':[1, 10],
          'types':'int'
          },
        # {'var_type':'fixed',
        #  'value': 6
         # },
    'Nwt':
        {'var_type':'design',
          'limits':[0, 400],
          'types':'int'
          },
        # {'var_type':'fixed',
        #   'value': 200
        #   },
    'wind_MW_per_km2 [MW/km2]':
        {'var_type':'design',
          'limits':[5, 9],
          'types':'float'
          },
        # {'var_type':'fixed',
        #   'value': 7
        #   },
    'solar_MW [MW]':
        # {'var_type':'design',
        #   'limits':[0, 400],
        #   'types':'int'
        #   },
        {'var_type':'fixed',
          'value': 200
          },
    'surface_tilt [deg]':
        # {'var_type':'design',
        #   'limits':[0, 50],
        #   'types':'float'
        #   },
        {'var_type':'fixed',
          'value': 25
          },
    'surface_azimuth [deg]':
        # {'var_type':'design',
        #   'limits':[150, 210],
        #   'types':'float'
        #   },
        {'var_type':'fixed',
          'value': 180
          },
    'DC_AC_ratio':
        # {'var_type':'design',
        #   'limits':[1, 2.0],
        #   'types':'float'
        #   },
        {'var_type':'fixed',
          'value': 1.5
          }
    }
}
```

--------------------------------

### Install and Import HyDesign Libraries

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/constant_output.ipynb

This snippet shows how to install the hydesign library if it's not already present and then imports essential Python libraries including pandas, numpy, the HPP model assembly class, and utilities for accessing example file paths. It also imports matplotlib for plotting.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git   

import pandas as pd
import numpy as np
from hydesign.assembly.hpp_assembly_constantoutput import hpp_model_constant_output as hpp_model
from hydesign.examples import examples_filepath
import matplotlib.pyplot as plt
```

--------------------------------

### Display Optimized Project Parameters Table

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/Simple_Sizing_Example.ipynb

This snippet renders an HTML table summarizing the optimized parameters for a wind energy project. It includes detailed metrics such as clearance, solar irradiance, rated power, wind turbine count, land use, battery specifications, and financial indicators like Break-even PPA price. The table also shows optimization details like objective function, time, and number of model evaluations.

```python
import pandas as pd

# This is a representation of the data that would generate the HTML table.
# The actual generation is typically done by libraries like pandas in a Jupyter environment.
data = {
    'clearance [m]': [60.0],
    'sp [W/m2]': [287.0],
    'p_rated [MW]': [10.0],
    'Nwt': [31.0],
    'wind_MW_per_km2 [MW/km2]': [5.0],
    'solar_MW [MW]': [200.0],
    'surface_tilt [deg]': [25.0],
    'surface_azimuth [deg]': [180.0],
    'DC_AC_ratio': [1.0],
    'b_P [MW]': [50.0],
    'Apvp [km2]': [2.452],
    'Plant area [km2]': [62.0],
    'Rotor diam [m]': [210.627033],
    'Hub height [m]': [165.313517],
    'Number of batteries used in lifetime': [2.0],
    'Break-even PPA price [Euro/MWh]': [23.339817],
    'Capacity factor wind [-]': [0.41521],
    'design obj': ['NPV_over_CAPEX'],
    'opt time [min]': [16.19],
    'n_model_evals': [141]
}

df_optimized = pd.DataFrame(data, index=['France_good_wind'])

# In a Jupyter Notebook, simply having df_optimized as the last line would render the HTML table.
# For a script, you might use df_optimized.to_html()

# Example of how the HTML might be generated (simplified):
html_output = """
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border=\"1\" class=\"dataframe\">
  <thead>
    <tr style=\"text-align: right;\">
      <th></th>
      <th>clearance [m]</th>
      <th>sp [W/m2]</th>
      <th>p_rated [MW]</th>
      <th>Nwt</th>
      <th>wind_MW_per_km2 [MW/km2]</th>
      <th>solar_MW [MW]</th>
      <th>surface_tilt [deg]</th>
      <th>surface_azimuth [deg]</th>
      <th>DC_AC_ratio</th>
      <th>b_P [MW]</th>
      <th>Apvp [km2]</th>
      <th>Plant area [km2]</th>
      <th>Rotor diam [m]</th>
      <th>Hub height [m]</th>
      <th>Number of batteries used in lifetime</th>
      <th>Break-even PPA price [Euro/MWh]</th>
      <th>Capacity factor wind [-]</th>
      <th>design obj</th>
      <th>opt time [min]</th>
      <th>n_model_evals</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>France_good_wind</th>
      <td>60.0</td>
      <td>287.0</td>
      <td>10.0</td>
      <td>31.0</td>
      <td>5.0</td>
      <td>200.0</td>
      <td>25.0</td>
      <td>180.0</td>
      <td>1.0</td>
      <td>50.0</td>
      <td>2.452</td>
      <td>62.0</td>
      <td>210.627033</td>
      <td>165.313517</td>
      <td>2.0</td>
      <td>23.339817</td>
      <td>0.41521</td>
      <td>NPV_over_CAPEX</td>
      <td>16.19</td>
      <td>141</td>
    </tr>
  </tbody>
</table>
<p>1 rows × 20 columns</p>
</div>
"""
print(html_output)

```

--------------------------------

### Install HyDesign and Import Libraries

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/HPP_evaluation_BM.ipynb

Installs the HyDesign library if not already present and imports essential Python libraries for data manipulation, simulation, and plotting. This includes `os`, `time`, `yaml`, `numpy`, `pandas`, `matplotlib.pyplot`, and specific HyDesign modules for HPP assembly and example file paths.

```python
# Install hydesign if needed
import importlib
if not importlib.util.find_spec("hydesign"):
    !pip install git+https://gitlab.windenergy.dtu.dk/TOPFARM/hydesign.git    

import os
import time
import yaml
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from hydesign.assembly.hpp_assembly_BM import hpp_model
from hydesign.examples import examples_filepath
```

--------------------------------

### Evaluate Plant Configuration with hydesign

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/notebooks/constant_output

Evaluates a specific plant configuration using the hpp_model. It loads site-specific data, sets parameters like PPA price and minimum load, and then calls the evaluate method. The minimum load constraint is handled as a penalty.

```python
examples_sites = pd.read_csv(f'{examples_filepath}examples_sites.csv', index_col=0, sep=';')
name = 'France_good_wind'
ex_site = examples_sites.loc[examples_sites.name == name]
longitude = ex_site['longitude'].values[0]
latitude = ex_site['latitude'].values[0]
altitude = ex_site['altitude'].values[0]
input_ts_fn = examples_filepath+ex_site['input_ts_fn'].values[0]
sim_pars_fn = examples_filepath+ex_site['sim_pars_fn'].values[0].replace('.yml','_benchmark.yml')
H2_demand_fn = examples_filepath+ex_site['H2_demand_col'].values[0]

PPA = 40 # Euro/MWh
hpp = hpp_model(
    latitude=latitude,
    longitude=longitude,
    altitude=altitude,
    num_batteries = 10,
    work_dir = './',
    sim_pars_fn = sim_pars_fn,
    input_ts_fn = input_ts_fn,
    ppa_price = PPA,
    load_min = 3, #MW
    load_min_penalty = 100, #MEuro
    battery_deg = True,
)
x=[35.0, 300.0, 10.0, 3, 7.0, 30, 25.0, 180.0, 1.0, 30, 7, 10.0]

outs = hpp.evaluate(*x)
hpp.print_design(x, outs)
```

--------------------------------

### Display Optimization Results Log

Source: https://topfarm.pages.windenergy.dtu.dk/hydesign/_sources/notebooks/Simple_Sizing_P2X_Example.ipynb

This snippet displays the raw output log from an optimization process, showing intermediate results like NPV/CAPEX, number of model evaluations, and iteration times. It provides a detailed, step-by-step view of the optimization's progress.

```text
  Current solution -NPV_over_CAPEX = -1.403E+00
  Current No. model evals: 180
  rel_yopt_change = -0.00E+00
Iteration 10 took 2.29 minutes


Design:
---------------
clearance [m]: 42.000
sp [W/m2]: 285.000
p_rated [MW]: 4.000
Nwt: 83.000
wind_MW_per_km2 [MW/km2]: 7.000
solar_MW [MW]: 200.000
surface_tilt [deg]: 25.000
surface_azimuth [deg]: 180.000
DC_AC_ratio: 1.000
b_P [MW]: 50.000
b_E_h [h]: 6.000
cost_of_battery_P_fluct_in_peak_price_ratio: 10.000
ptg_MW [MW]: 50.000
HSS_kg [kg]: 0.000


NPV_over_CAPEX: 1.403
NPV [MEuro]: 697.279
IRR: 0.154
LCOE [Euro/MWh]: 30.975
LCOH [Euro/kg]: 5.762
Revenue [MEuro]: 1678.490
CAPEX [MEuro]: 496.981
OPEX [MEuro]: 7.464
penalty lifetime [MEuro]: 0.000
AEP [GWh]: 983.368
GUF: 0.374
annual_H2 [tons]: 5574.228
annual_P_ptg [GWh]: 375.740
grid [MW]: 300.000
wind [MW]: 332.000
solar [MW]: 200.000
PtG [MW]: 50.000
HSS [kg]: 0.000
Battery Energy [MWh]: 300.000
Battery Power [MW]: 50.000
Total curtailment [GWh]: 181.184
Awpp [km2]: 47.429
Apvp [km2]: 2.452
Rotor diam [m]: 133.679
Hub height [m]: 108.839
Number of batteries used in lifetime: 1.000
Break-even H2 price [Euro/kg]: -1.595
Break-even PPA price [Euro/MWh]: 2.551
Capacity factor wind [-]: 0.403

Optimization with 10 iterations and 180 model evaluations took 28.99 minutes


```
Tokens:
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