pv-sizing 1.1.2

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PV-sizing

Please read the instructions carefully for the correct operation of the library. It is in an early stage of development and needs some improvements. However, its correct use gives very good results. Any help for the improvement and maintenance of the library is welcome!

Introduction

Photovoltaic sizing

This library allows the sizing of photovoltaic panels for any building and load curve provided. The photovoltaic producition was calculated using the "IDAE: Pliego de Condiciones Técnicas de Instalaciones Conectadas a Red" with the following equation:

$$ E_{\mathrm{p}}=\frac{G_{\mathrm{dm}}(\alpha, \beta) P_{\mathrm{mp}} P R}{G_{\mathrm{CEM}}} $$

By simply entering the number of panels (and their parameters from the datasheet provided by the manufacturer), the hourly irradiance (from https://re.jrc.ec.europa.eu/pvg_tools/en/), and the hourly load over a year it is possible to obtain the accumulated cahsflow taking into account the inflation over a period of 25 years (typical duration of a photovoltaic project). It is also necessary to provide the electricity purchase tariff, and also the price for compensation in case of grid feed-in.

It also allows the sizing of the battery bank for N days of autonomy based on the daily energy consumed.

The calculations for the initial investment have been calculated with typical market values. They can also be varied depending on the actual initial investment.

In the image you can see a representation of the final result obtained. Also in the table below the accumulated cashflow over the liftime of the project is shown.

Year	Initial inversion	Operation and maintenence	Savings	Cashflow	Accumulated cashflow
0	3565.1725	71.303450	527.383444	-3109.092506	-3109.092506
1	0.0000	72.729519	548.478782	475.749263	-2633.343243
2	0.0000	74.184109	570.417933	496.233824	-2137.109419
3	0.0000	75.667792	593.234651	517.566859	-1619.542560
4	0.0000	77.181147	616.964037	539.782889	-1079.759671
5	0.0000	78.724770	641.642598	562.917828	-516.841843
6	0.0000	80.299266	667.308302	587.009036	70.167193
7	0.0000	81.905251	694.000634	612.095383	682.262576
8	0.0000	83.543356	721.760659	638.217303	1320.479879
9	0.0000	85.214223	750.631086	665.416863	1985.896742
10	0.0000	86.918508	780.656329	693.737822	2679.634563
11	0.0000	88.656878	811.882582	723.225705	3402.860268
12	0.0000	90.430015	844.357886	753.927870	4156.788138
13	0.0000	92.238616	878.132201	785.893585	4942.681724
14	0.0000	94.083388	913.257489	819.174101	5761.855825
15	0.0000	95.965056	949.787789	853.822733	6615.678558
16	0.0000	97.884357	987.779300	889.894943	7505.573501
17	0.0000	99.842044	1027.290472	927.448428	8433.021929
18	0.0000	101.838885	1068.382091	966.543206	9399.565136
19	0.0000	103.875663	1111.117375	1007.241712	10406.806848
20	0.0000	105.953176	1155.562070	1049.608894	11456.415742
21	0.0000	108.072239	1201.784553	1093.712313	12550.128055
22	0.0000	110.233684	1249.855935	1139.622251	13689.750306
23	0.0000	112.438358	1299.850172	1187.411814	14877.162120
24	0.0000	114.687125	1351.844179	1237.157054	16114.319174

Data structure

Irradiance and load must be hourly and must contain a minimum of one full year of data. If the data contains more than one year, the library automatically averages the available data over the year.

Load data

time	Load (kWh)
2020-07-01 01:00:00	0.206
2020-07-01 02:00:00	0.204
2020-07-01 03:00:00	0.197
2020-07-01 04:00:00	0.205
2020-07-01 05:00:00	0.196
2020-07-01 06:00:00	0.198
2020-07-01 07:00:00	0.196
2020-07-01 08:00:00	0.418
2020-07-01 09:00:00	0.187
2020-07-01 10:00:00	0.189
2020-07-01 11:00:00	0.19
2020-07-01 12:00:00	0.203
2020-07-01 13:00:00	0.19
2020-07-01 14:00:00	0.193
2020-07-01 15:00:00	0.191
2020-07-01 16:00:00	0.235
2020-07-01 17:00:00	0.197
2020-07-01 18:00:00	0.194
2020-07-01 19:00:00	0.189
2020-07-01 20:00:00	0.189
2020-07-01 21:00:00	0.207
2020-07-01 22:00:00	0.292
2020-07-01 23:00:00	0.298

Irradiation data

time	Gb(i)	Gd(i)	Gr(i)	H_sun	T2m	WS10m	Int
2005-01-01 00:09:00	0.0	0.0	0.0	0.0	7.97	1.66	0.0
2005-01-01 01:09:00	0.0	0.0	0.0	0.0	6.42	1.79	0.0
2005-01-01 02:09:00	0.0	0.0	0.0	0.0	6.37	1.93	0.0
2005-01-01 03:09:00	0.0	0.0	0.0	0.0	6.35	2.0	0.0
2005-01-01 04:09:00	0.0	0.0	0.0	0.0	6.31	2.0	0.0
2005-01-01 05:09:00	0.0	0.0	0.0	0.0	6.12	1.86	0.0
2005-01-01 06:09:00	0.0	0.0	0.0	0.0	5.79	1.72	0.0
2005-01-01 07:09:00	0.0	0.0	0.0	0.0	5.75	1.93	0.0
2005-01-01 08:09:00	0.0	0.0	0.0	0.0	5.57	1.86	0.0
2005-01-01 09:09:00	181.33	57.09	1.71	12.55	6.39	1.86	0.0
2005-01-01 10:09:00	400.78	113.83	4.3	22.66	8.11	2.07	0.0
2005-01-01 11:09:00	611.53	135.72	6.6	30.96	9.2	2.28	0.0
2005-01-01 12:09:00	660.96	192.72	7.89	36.57	9.85	2.28	0.0
2005-01-01 13:09:00	710.3	211.02	8.59	38.61	10.18	2.14	0.0
2005-01-01 14:09:00	650.11	235.03	8.25	36.65	10.25	2.07	0.0
2005-01-01 15:09:00	670.37	184.76	7.57	31.1	10.12	2.07	0.0
2005-01-01 16:09:00	485.18	184.65	5.6	22.85	9.75	2.14	0.0
2005-01-01 17:09:00	0.0	72.22	1.09	12.76	9.08	2.21	0.0
2005-01-01 18:09:00	0.0	24.97	0.38	1.48	7.98	2.21	0.0
2005-01-01 19:09:00	0.0	0.0	0.0	0.0	7.19	2.41	0.0
2005-01-01 20:09:00	0.0	0.0	0.0	0.0	6.67	2.62	0.0
2005-01-01 21:09:00	0.0	0.0	0.0	0.0	6.29	2.76	0.0
2005-01-01 22:09:00	0.0	0.0	0.0	0.0	6.03	2.97	0.0
2005-01-01 23:09:00	0.0	0.0	0.0	0.0	5.82	3.17	0.0

Gb(i): Beam (direct) irradiance on the inclined plane (plane of the array) (W/m2) Gd(i): Diffuse irradiance on the inclined plane (plane of the array) (W/m2) Gr(i): Reflected irradiance on the inclined plane (plane of the array) (W/m2) H_sun: Sun height (degree) T2m: 2-m air temperature (degree Celsius) WS10m: 10-m total wind speed (m/s) Int: 1 means solar radiation values are reconstructed

This CSV file can be obtained with the PVGIS class explained below.

Electricity price

For electricity price data you can provide a fixed price, or a format as shown in the table below. It needs to be a DataFrame with the index of type DatetimeIndex in order to automatically match the first hour of the load DataFrame and the electricity price DataFrame. Only the prices for 24 hours must be provided.

Hour	€/kWh
2022-04-09 00:00:00	0.33419
2022-04-09 01:00:00	0.33143
2022-04-09 02:00:00	0.33193
2022-04-09 03:00:00	0.34965
2022-04-09 04:00:00	0.35213
2022-04-09 05:00:00	0.34960
2022-04-09 06:00:00	0.36944
2022-04-09 07:00:00	0.37212
2022-04-09 08:00:00	0.36155
2022-04-09 09:00:00	0.29125
2022-04-09 10:00:00	0.17853
2022-04-09 11:00:00	0.14803
2022-04-09 12:00:00	0.13411
2022-04-09 13:00:00	0.13434
2022-04-09 14:00:00	0.11547
2022-04-09 15:00:00	0.07960
2022-04-09 16:00:00	0.06471
2022-04-09 17:00:00	0.05458
2022-04-09 18:00:00	0.13163
2022-04-09 19:00:00	0.19826
2022-04-09 20:00:00	0.29412
2022-04-09 21:00:00	0.34978
2022-04-09 22:00:00	0.35915
2022-04-09 23:00:00	0.35213

Install

PV-sizing depends on the following libraries:

• pandas
• numpy
• numpy-financial

Optional libraries:

• pvlib
• selenium
• plotly
• dash

To install the library you can simply use the pip command as follows:

pip install pv-sizing

Example photovoltaic production

from pv_sizing.dimension.pv import PVProduction

from pv_sizing.utils.load_example import example_irr, example_load

from pv_sizing.utils.constants import fresnel_fixed
from pv_sizing.utils.pv_utils import init_inv

from pv_sizing.web_scrapping.electricity_price import ElectricityPrice

import pandas as pd


pv_costs = {"num_panel": 5,
            "price_panel": 260,
            "price_inverter" : 1300,
            "additional_cost" : 500,
            "installation_cost_perc" : 0.15}

initial_investment = init_inv(**pv_costs)

electricity_price = ElectricityPrice().extract_data()

pv = PVProduction(irr_data=example_irr, load=example_load, tnoct=42, gamma=-0.36, 
                    panel_power=450, num_panel=num_panel, fresnel_eff=fresnel_fixed)

cashflow, van, tir = pv.economic_analysis(initial_investment, sell_price=0.06, buy_price=electricity_price)

pv.plot(cashflow['Accumulated cashflow'])

Example battery sizing

from pv_sizing.dimension.battery import BatterySizing
from pv_sizing.utils.load_example import example_irr, example_load

days_auto = 0.5

bat = BatterySizing(irr_data=example_irr, load=example_load, tnoct=42, gamma=-0.36, 
                    panel_power=450, num_panel=num_panel,fresnel_eff=fresnel_fixed,
                    amb_temp_multiplier=1.163, days_auto=days_auto, dod=0.95,
                    amp_hour_rating= 2400 / 48, batt_volt=48, inversor_eff=0.85)

total_battery_capacity, n_bat_paralell, n_bat_series = bat.battery_sizing()

Example PVGIS scrapping

from pv_sizing.web_scrapping.irradiance import PVGIS
import os

pvgis = PVGIS(lat = 28.242, lon = -16.647, azimuth = 0, elevation = 30, absolute_path = os.getcwd())
pvgis.interact_with_page()

*Some other files may be necessary for the correct functioning of this class. Currently it is necessary to use Chrome with ChromeDriver 103.0.5060.134. The location in the machine is no relevant since the webdriver_manager is used in the script.

Example of electricity price scrapping

This class allows the extraction of hourly energy prices. The prices have been obtained from the Spanish Electricity Grid (REE) for the day of execution of the script.

from pv_sizing.web_scrapping.electricity_price import ElectricityPrice

electricity_price = ElectricityPrice().extract_data()

*Some other files may be necessary for the correct functioning of this class. Currently it is necessary to use Chrome with ChromeDriver 103.0.5060.134. The location in the machine is no relevant since the webdriver_manager is used in the script.

Example interactive plot

from pv_sizing.app_layout.dashboard import interactive_plot
from pv_sizing.app_layout.applayout import app

interactive_plot()

if __name__ == "__main__":
    app.run_server(debug=True)

When this code has been run, the following message will appear in the terminal:

Dash is running on http://127.0.0.1:8050/

• Serving Flask app 'pv_sizing.app_layout.applayout'
• Debug mode: on

Open http://127.0.0.1:8050/ on your terminal and try the interactive sizing mode.

In this version, only the example data can be used for the interactive plot. In future versions it will be possible to drag the load and irradiance .CSV files directly into the browser.