import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from datetime import datetime, timedelta import math import os class BifacialSolarSimulation: def __init__(self): # Panel specifications (Hanersun N-TOPCon 700W) self.panel_power = 700 # watts self.panel_efficiency = 0.225 # 22.5% self.bifaciality_factor = 0.80 # Average of 75-85% self.panel_area = 2.384 * 1.303 # m² # Location: Lago Brown, Chile self.latitude = -47.3919449 self.longitude = -72.3174973 self.elevation = 165 # meters # Simplified solar irradiance for Chilean Patagonia (kWh/m²/day) # Based on Global Solar Atlas data for southern Chile self.monthly_ghi = { 1: 6.5, # January 2: 5.8, # February 3: 4.2, # March 4: 2.8, # April 5: 1.8, # May 6: 1.3, # June 7: 1.5, # July 8: 2.2, # August 9: 3.5, # September 10: 4.8, # October 11: 6.0, # November 12: 6.8 # December } # Ground albedo (reflection factor) self.ground_albedo = 0.2 # Standard for grass/vegetation def solar_position(self, day_of_year, hour): """Calculate solar position for given day and hour""" # Solar declination declination = 23.45 * math.sin(math.radians(360 * (284 + day_of_year) / 365)) # Hour angle hour_angle = 15 * (hour - 12) # Solar elevation angle elevation = math.asin( math.sin(math.radians(declination)) * math.sin(math.radians(self.latitude)) + math.cos(math.radians(declination)) * math.cos(math.radians(self.latitude)) * math.cos(math.radians(hour_angle)) ) # Solar azimuth angle azimuth = math.atan2( math.sin(math.radians(hour_angle)), math.cos(math.radians(hour_angle)) * math.sin(math.radians(self.latitude)) - math.tan(math.radians(declination)) * math.cos(math.radians(self.latitude)) ) return math.degrees(elevation), math.degrees(azimuth) def calculate_irradiance_on_tilted_surface(self, ghi, solar_elevation, solar_azimuth, panel_tilt, panel_azimuth): """Calculate irradiance on tilted surface""" if solar_elevation <= 0: return 0, 0 # Convert to radians se_rad = math.radians(solar_elevation) sa_rad = math.radians(solar_azimuth) pt_rad = math.radians(panel_tilt) pa_rad = math.radians(panel_azimuth) # Angle of incidence cos_incidence = ( math.sin(se_rad) * math.cos(pt_rad) + math.cos(se_rad) * math.sin(pt_rad) * math.cos(sa_rad - pa_rad) ) if cos_incidence < 0: cos_incidence = 0 # Direct normal irradiance (simplified) dni = ghi * 0.8 if solar_elevation > 0 else 0 # Direct irradiance on panel direct_irradiance = dni * cos_incidence # Diffuse irradiance (simplified sky model) diffuse_irradiance = ghi * 0.2 * (1 + math.cos(pt_rad)) / 2 # Ground reflected irradiance ground_reflected = ghi * self.ground_albedo * (1 - math.cos(pt_rad)) / 2 # Front side irradiance front_irradiance = direct_irradiance + diffuse_irradiance + ground_reflected # Back side irradiance (mainly ground reflected and diffuse) back_irradiance = (ground_reflected + diffuse_irradiance * 0.3) * self.bifaciality_factor return front_irradiance, back_irradiance def calculate_monthly_production(self, panel_tilt, panel_azimuth): """Calculate monthly energy production for given panel configuration""" monthly_production = {} for month in range(1, 13): # Get representative day of month day_of_year = month * 30 - 15 # Approximate middle of month daily_production = 0 hourly_data = [] for hour in range(6, 19): # Daylight hours solar_elev, solar_az = self.solar_position(day_of_year, hour) if solar_elev > 0: # Get hourly irradiance hourly_ghi = self.monthly_ghi[month] / 10 # Simplified hourly distribution front_irr, back_irr = self.calculate_irradiance_on_tilted_surface( hourly_ghi, solar_elev, solar_az, panel_tilt, panel_azimuth ) total_irradiance = front_irr + back_irr # Power calculation power = (total_irradiance / 1000) * self.panel_power * self.panel_efficiency daily_production += power hourly_data.append({ 'hour': hour, 'solar_elevation': solar_elev, 'front_irradiance': front_irr, 'back_irradiance': back_irr, 'total_irradiance': total_irradiance, 'power': power }) # Days in month days_in_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31][month-1] monthly_energy = daily_production * days_in_month / 1000 # kWh monthly_production[month] = { 'energy_kwh': monthly_energy, 'daily_production': daily_production, 'hourly_data': hourly_data } return monthly_production def run_simulation(self): """Run simulation for all configurations""" configurations = [ {'name': 'Vertical North-South', 'tilt': 90, 'azimuth': 180}, {'name': 'Vertical East-West', 'tilt': 90, 'azimuth': 90}, {'name': 'Tilt 20°', 'tilt': 20, 'azimuth': 180}, {'name': 'Tilt 30°', 'tilt': 30, 'azimuth': 180}, {'name': 'Tilt 45°', 'tilt': 45, 'azimuth': 180}, {'name': 'Tilt 60°', 'tilt': 60, 'azimuth': 180}, {'name': 'Tilt 70°', 'tilt': 70, 'azimuth': 180} ] results = {} for config in configurations: production = self.calculate_monthly_production(config['tilt'], config['azimuth']) results[config['name']] = production return results def create_visualizations(self, results): """Create charts and visualizations""" plt.style.use('seaborn-v0_8') # Monthly energy production comparison fig, axes = plt.subplots(2, 2, figsize=(15, 12)) # 1. Monthly energy production by configuration months = list(range(1, 13)) month_names = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] ax1 = axes[0, 0] for config_name, data in results.items(): monthly_energy = [data[month]['energy_kwh'] for month in months] ax1.plot(month_names, monthly_energy, marker='o', label=config_name, linewidth=2) ax1.set_title('Monthly Energy Production Comparison', fontsize=14, fontweight='bold') ax1.set_xlabel('Month') ax1.set_ylabel('Energy Production (kWh)') ax1.legend(bbox_to_anchor=(1.05, 1), loc='upper left') ax1.grid(True, alpha=0.3) # 2. Annual total comparison ax2 = axes[0, 1] annual_totals = {} for config_name, data in results.items(): annual_total = sum(data[month]['energy_kwh'] for month in months) annual_totals[config_name] = annual_total bars = ax2.bar(range(len(annual_totals)), list(annual_totals.values()), color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7', '#DDA0DD', '#98D8C8']) ax2.set_title('Annual Energy Production Comparison', fontsize=14, fontweight='bold') ax2.set_xlabel('Configuration') ax2.set_ylabel('Annual Energy (kWh)') ax2.set_xticks(range(len(annual_totals))) ax2.set_xticklabels(list(annual_totals.keys()), rotation=45, ha='right') # Add value labels on bars for bar, value in zip(bars, annual_totals.values()): ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 5, f'{value:.0f}', ha='center', va='bottom') # 3. Seasonal comparison ax3 = axes[1, 0] seasons = { 'Summer': [12, 1, 2], 'Autumn': [3, 4, 5], 'Winter': [6, 7, 8], 'Spring': [9, 10, 11] } seasonal_data = {} for config_name, data in results.items(): seasonal_energy = {} for season, season_months in seasons.items(): seasonal_energy[season] = sum(data[month]['energy_kwh'] for month in season_months) seasonal_data[config_name] = seasonal_energy x = np.arange(len(seasons)) width = 0.1 colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7', '#DDA0DD', '#98D8C8'] for i, (config_name, season_energy) in enumerate(seasonal_data.items()): values = list(season_energy.values()) ax3.bar(x + i*width, values, width, label=config_name, color=colors[i]) ax3.set_title('Seasonal Energy Production Comparison', fontsize=14, fontweight='bold') ax3.set_xlabel('Season') ax3.set_ylabel('Energy Production (kWh)') ax3.set_xticks(x + width * 3) ax3.set_xticklabels(list(seasons.keys())) ax3.legend(bbox_to_anchor=(1.05, 1), loc='upper left') # 4. Hourly production example (January 15th) ax4 = axes[1, 1] jan_15_data = {} for config_name, data in results.items(): hourly_powers = [h['power'] for h in data[1]['hourly_data']] hours = [h['hour'] for h in data[1]['hourly_data']] jan_15_data[config_name] = (hours, hourly_powers) for config_name, (hours, powers) in jan_15_data.items(): ax4.plot(hours, powers, marker='o', label=config_name, linewidth=2) ax4.set_title('Hourly Power Production (January 15th)', fontsize=14, fontweight='bold') ax4.set_xlabel('Hour of Day') ax4.set_ylabel('Power Output (W)') ax4.legend() ax4.grid(True, alpha=0.3) plt.tight_layout() plt.savefig('/home/www/claudev.cz/orb3/solar_panel_comparison.png', dpi=300, bbox_inches='tight') plt.close() return annual_totals def create_data_tables(self, results): """Create data tables for the report""" months = list(range(1, 13)) month_names = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] # Monthly production table monthly_table = pd.DataFrame() monthly_table['Month'] = month_names for config_name, data in results.items(): monthly_energy = [data[month]['energy_kwh'] for month in months] monthly_table[config_name] = [f"{energy:.1f}" for energy in monthly_energy] # Annual totals annual_row = ['ANNUAL TOTAL'] for config_name, data in results.items(): annual_total = sum(data[month]['energy_kwh'] for month in months) annual_row.append(f"{annual_total:.1f}") monthly_table.loc[len(monthly_table)] = annual_row # Hourly production example tables for 15th of each month hourly_tables = {} for month in months: hourly_df = pd.DataFrame() # Get first configuration's hourly data as reference for hours first_config = list(results.keys())[0] hours = [h['hour'] for h in results[first_config][month]['hourly_data']] hourly_df['Hour'] = hours for config_name, data in results.items(): powers = [h['power'] for h in data[month]['hourly_data']] hourly_df[config_name] = [f"{power:.1f}" for power in powers] hourly_tables[month_names[month-1]] = hourly_df return monthly_table, hourly_tables def main(): simulation = BifacialSolarSimulation() print("Starting bifacial solar panel simulation for Lago Brown, Chile...") print(f"Panel: Hanersun N-TOPCon 700W Bifacial") print(f"Location: {simulation.latitude}°, {simulation.longitude}°") print("Configurations: Vertical N-S, Vertical E-W, Tilted 20°-70°") print() # Run simulation results = simulation.run_simulation() # Create visualizations annual_totals = simulation.create_visualizations(results) # Create data tables monthly_table, hourly_tables = simulation.create_data_tables(results) print("Simulation completed!") print("\nAnnual Energy Production Summary:") for config, total in annual_totals.items(): print(f"{config}: {total:.1f} kWh/year") return results, monthly_table, hourly_tables, annual_totals if __name__ == "__main__": results, monthly_table, hourly_tables, annual_totals = main()