Wind Energy Bitcoin Mining: How to Print Money While Saving the Planet (No Kidding)

# Mining Hardware Specifications
ASIC_MINERS = {
    'antminer_s19_pro': {
        'hashrate': '110 TH/s',
        'power_consumption': '3250W',
        'efficiency': '29.5 J/TH'
    },
    'whatsminer_m30s': {
        'hashrate': '88 TH/s',
        'power_consumption': '3344W',
        'efficiency': '38 J/TH'
    }
}

# Wind Turbine Requirements
WIND_TURBINE_SPECS = {
    'capacity': '2.5MW',
    'cut_in_speed': '3 m/s',
    'rated_speed': '12 m/s',
    'cut_out_speed': '25 m/s',
    'efficiency': '45%'
}

import numpy as np
from datetime import datetime

class WindMiningController:
    def __init__(self, max_miners=100, battery_capacity=500):
        self.max_miners = max_miners
        self.active_miners = 0
        self.battery_capacity = battery_capacity  # kWh
        self.current_charge = battery_capacity * 0.8
        
    def calculate_mining_allocation(self, wind_power, current_demand):
        """
        Dynamically adjust mining operations based on wind generation
        """
        # Reserve 20% for battery charging
        available_power = wind_power * 0.8
        
        # Each miner consumes ~3.25kW
        possible_miners = int(available_power / 3.25)
        
        # Scale miners based on available power
        target_miners = min(possible_miners, self.max_miners)
        
        # Gradual scaling to prevent power spikes
        if target_miners > self.active_miners:
            self.active_miners += min(5, target_miners - self.active_miners)
        elif target_miners < self.active_miners:
            self.active_miners -= min(5, self.active_miners - target_miners)
            
        return self.active_miners
    
    def optimize_yield(self, wind_forecast, btc_price):
        """
        Optimize mining operations for maximum sustainable yield
        """
        projected_earnings = []
        
        for hour, wind_speed in enumerate(wind_forecast):
            # Calculate wind power generation
            power_output = self.wind_power_curve(wind_speed)
            
            # Determine optimal mining allocation
            active_miners = self.calculate_mining_allocation(power_output, 0)
            
            # Calculate hourly earnings
            hashrate = active_miners * 110  # TH/s per miner
            btc_earned = (hashrate / 450_000_000) * 6.25  # Simplified calculation
            usd_earned = btc_earned * btc_price
            
            projected_earnings.append({
                'hour': hour,
                'wind_speed': wind_speed,
                'power_output': power_output,
                'active_miners': active_miners,
                'btc_earned': btc_earned,
                'usd_earned': usd_earned
            })
            
        return projected_earnings
    
    def wind_power_curve(self, wind_speed):
        """
        Calculate power output based on wind speed
        """
        if wind_speed < 3:  # Cut-in speed
            return 0
        elif wind_speed > 25:  # Cut-out speed
            return 0
        elif wind_speed >= 12:  # Rated speed
            return 2500  # kW
        else:
            # Linear interpolation between cut-in and rated
            return (wind_speed - 3) / (12 - 3) * 2500

# Infrastructure Configuration
power_system:
  wind_turbines:
    - model: "Vestas V150-4.2MW"
      quantity: 3
      total_capacity: 12.6MW
  
  battery_storage:
    - technology: "LiFePO4"
      capacity: 2MWh
      cycles: 6000
      efficiency: 95%
  
  mining_facility:
    - miners: 1000
      total_consumption: 3.25MW
      cooling_load: 0.8MW
      auxiliary_systems: 0.2MW
  
  grid_connection:
    - capacity: 5MW
    - export_capability: true
    - revenue_streams: ["excess_energy_sales", "grid_services"]

// Smart Pool Management
const miningPools = [
    {
        name: "Slush Pool",
        fee: 0.02,
        payout_threshold: 0.001,
        green_energy_bonus: 0.001,  // Additional rewards for renewable mining
        latency: 45
    },
    {
        name: "F2Pool", 
        fee: 0.025,
        payout_threshold: 0.005,
        geographic_optimization: true,
        latency: 38
    }
];

function selectOptimalPool(currentWindPower, btcPrice) {
    const efficiency = currentWindPower / 12600; // Percentage of max capacity
    
    // Switch to lower-fee pools during high efficiency periods
    if (efficiency > 0.8) {
        return miningPools.find(pool => pool.fee < 0.021);
    }
    
    // Prioritize low-latency pools during variable wind periods
    if (efficiency < 0.4) {
        return miningPools.reduce((best, current) => 
            current.latency < best.latency ? current : best
        );
    }
    
    return miningPools[0]; // Default to most reliable
}

class YieldOptimizer:
    def __init__(self):
        self.efficiency_targets = {
            'high_wind': 0.95,      # >80% turbine capacity
            'medium_wind': 0.85,    # 40-80% capacity  
            'low_wind': 0.75       # <40% capacity
        }
    
    def adaptive_frequency_scaling(self, wind_conditions):
        """
        Adjust miner frequencies based on available power
        """
        base_frequency = 550  # MHz
        
        if wind_conditions == 'high_wind':
            return base_frequency * 1.1  # Overclock during abundance
        elif wind_conditions == 'low_wind':
            return base_frequency * 0.8  # Underclock for efficiency
        else:
            return base_frequency
    
    def calculate_roi(self, investment, monthly_revenue, monthly_costs):
        """
        Calculate return on investment for wind mining operations
        """
        net_monthly = monthly_revenue - monthly_costs
        annual_net = net_monthly * 12
        roi_years = investment / annual_net
        
        return {
            'roi_period': roi_years,
            'annual_yield': (annual_net / investment) * 100,
            'break_even_months': investment / net_monthly
        }

#!/bin/bash
# Automated energy trading script

check_btc_profitability() {
    current_price=$(curl -s "https://api.coindesk.com/v1/bpi/currentprice.json" | jq '.bpi.USD.rate_float')
    difficulty=$(curl -s "https://blockstream.info/api/blocks/tip/height")
    
    # Calculate profitability threshold
    threshold=0.08  # USD per kWh
    
    if (( $(echo "$current_price < $threshold" | bc -l) )); then
        echo "SELL_ENERGY"
    else
        echo "MINE_CRYPTO"
    fi
}

# Execute trading decision
decision=$(check_btc_profitability)
if [ "$decision" = "SELL_ENERGY" ]; then
    # Redirect power to grid
    python3 energy_market_connector.py --action=sell --capacity=8000
else
    # Maximize mining operations
    python3 mining_controller.py --mode=maximum --efficiency=high
fi

# Real-time monitoring system
import matplotlib.pyplot as plt
import pandas as pd

def generate_performance_report(mining_data, wind_data):
    """
    Create comprehensive performance analysis
    """
    metrics = {
        'uptime': calculate_uptime(mining_data),
        'efficiency': calculate_mining_efficiency(wind_data, mining_data),
        'yield_per_mwh': calculate_btc_per_mwh(mining_data),
        'carbon_savings': calculate_carbon_offset(wind_data)
    }
    
    # Generate visualizations
    plt.figure(figsize=(15, 10))
    
    # Plot 1: Wind vs Mining Output
    plt.subplot(2, 2, 1)
    plt.plot(wind_data['timestamp'], wind_data['power_output'], label='Wind Power')
    plt.plot(mining_data['timestamp'], mining_data['power_consumption'], label='Mining Load')
    plt.title('Power Generation vs Consumption')
    plt.legend()
    
    # Plot 2: Cumulative Bitcoin Earned
    plt.subplot(2, 2, 2)
    plt.plot(mining_data['timestamp'], mining_data['cumulative_btc'])
    plt.title('Cumulative Bitcoin Yield')
    
    # Plot 3: Efficiency Over Time
    plt.subplot(2, 2, 3)
    plt.plot(mining_data['timestamp'], mining_data['efficiency'])
    plt.title('Mining Efficiency (%)')
    
    # Plot 4: Carbon Offset Progress
    plt.subplot(2, 2, 4)
    plt.plot(wind_data['timestamp'], wind_data['cumulative_co2_offset'])
    plt.title('Carbon Emissions Avoided (tons)')
    
    plt.tight_layout()
    plt.savefig('wind_mining_performance.png', dpi=300)
    
    return metrics

compliance_checklist:
  environmental:
    - noise_limits: "<45dB at property line"
    - bird_migration_assessment: "required"
    - environmental_impact_study: "completed"
  
  electrical:
    - grid_interconnection_agreement: "signed"
    - power_quality_standards: "IEEE 1547"
    - backup_systems: "N+1 redundancy"
  
  financial:
    - green_energy_certification: "REC tracking"
    - carbon_accounting: "verified annually"
    - sustainability_reporting: "quarterly"

def expansion_planning(current_capacity, target_roi):
    """
    Calculate optimal expansion timeline for wind mining operations
    """
    scenarios = []
    
    for expansion_factor in [1.5, 2.0, 3.0]:
        new_capacity = current_capacity * expansion_factor
        investment_required = calculate_expansion_cost(new_capacity)
        projected_revenue = estimate_revenue(new_capacity)
        
        scenario = {
            'expansion_factor': expansion_factor,
            'new_capacity_mw': new_capacity,
            'investment_usd': investment_required,
            'annual_revenue_usd': projected_revenue,
            'roi_years': investment_required / projected_revenue,
            'recommended': projected_revenue / investment_required > target_roi
        }
        
        scenarios.append(scenario)
    
    return scenarios