Quick Optimization

Get instant optimal solar parameters without running a full optimization job. Ideal for rapid assessments and interactive applications.

Endpoint

POST /solar/optimization/quick

Request

{
  "site": {
    "latitude": 35.0,
    "longitude": -110.0,
    "capacity_kw": 1000
  },
  "objective": "energy"
}

Parameters

Field	Type	Required	Default	Description
site.latitude	float	Yes	-	Site latitude (-90 to 90)
site.longitude	float	Yes	-	Site longitude (-180 to 180)
site.capacity_kw	float	Yes	-	System DC capacity in kW
objective	string	No	energy	Optimization objective

Objectives

Value	Description
energy	Maximize annual energy production (MWh)
lcoe	Minimize Levelized Cost of Energy (¢/kWh)
npv	Maximize Net Present Value ($)
irr	Maximize Internal Rate of Return (%)

Response

{
  "optimal_parameters": {
    "tilt": 32.5,
    "azimuth": 180.0,
    "dc_ac_ratio": 1.25,
    "gcr": 0.35
  },
  "energy_metrics": {
    "annual_energy_mwh": 1850.5,
    "capacity_factor": 0.211,
    "specific_yield": 1850.5,
    "performance_ratio": 0.82
  },
  "financial_metrics": {
    "lcoe_cents_per_kwh": 3.45,
    "npv_dollars": 1250000,
    "irr_percent": 14.5,
    "payback_years": 6.2,
    "total_capex": 850000
  },
  "solar_resource": {
    "annual_ghi": 2150.5,
    "annual_dni": 2450.3,
    "avg_temp": 18.5,
    "data_source": "NASA_POWER"
  },
  "optimization_time_ms": 245
}

Code Examples

PythonJavaScriptcURL

import requests

response = requests.post(
    "https://api.tessellaterenewables.com/solar/optimization/quick",
    json={
        "site": {
            "latitude": 35.0,
            "longitude": -110.0,
            "capacity_kw": 1000
        },
        "objective": "energy"
    }
)

result = response.json()

print("Optimal Parameters:")
print(f"  Tilt: {result['optimal_parameters']['tilt']}°")
print(f"  Azimuth: {result['optimal_parameters']['azimuth']}°")
print(f"  DC/AC Ratio: {result['optimal_parameters']['dc_ac_ratio']}")
print(f"  GCR: {result['optimal_parameters']['gcr']}")

print("\nEnergy Performance:")
print(f"  Annual Energy: {result['energy_metrics']['annual_energy_mwh']:,.0f} MWh")
print(f"  Capacity Factor: {result['energy_metrics']['capacity_factor']:.1%}")

print("\nFinancial Performance:")
print(f"  LCOE: {result['financial_metrics']['lcoe_cents_per_kwh']:.2f} ¢/kWh")
print(f"  NPV: ${result['financial_metrics']['npv_dollars']:,.0f}")
print(f"  IRR: {result['financial_metrics']['irr_percent']:.1f}%")

async function optimizeSolar(lat, lon, capacityKw, objective = 'energy') {
  const response = await fetch(
    'https://api.tessellaterenewables.com/solar/optimization/quick',
    {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        site: {
          latitude: lat,
          longitude: lon,
          capacity_kw: capacityKw
        },
        objective: objective
      })
    }
  );

  return response.json();
}

// Usage
const result = await optimizeSolar(35.0, -110.0, 1000, 'energy');
console.log(`Optimal tilt: ${result.optimal_parameters.tilt}°`);
console.log(`Annual energy: ${result.energy_metrics.annual_energy_mwh} MWh`);

curl -X POST https://api.tessellaterenewables.com/solar/optimization/quick \
  -H "Content-Type: application/json" \
  -d '{
    "site": {
      "latitude": 35.0,
      "longitude": -110.0,
      "capacity_kw": 1000
    },
    "objective": "energy"
  }'

Comparing Objectives

Different objectives yield different optimal parameters:

objectives = ['energy', 'lcoe', 'npv', 'irr']

for obj in objectives:
    result = requests.post(
        "https://api.tessellaterenewables.com/solar/optimization/quick",
        json={
            "site": {"latitude": 35.0, "longitude": -110.0, "capacity_kw": 1000},
            "objective": obj
        }
    ).json()

    params = result['optimal_parameters']
    print(f"{obj.upper()}: Tilt={params['tilt']:.1f}°, GCR={params['gcr']:.2f}")

When to Use Quick vs Full Optimization

Use Case	Recommended Endpoint
Interactive web apps	Quick Optimization
Initial site screening	Quick Optimization
Detailed feasibility study	Full Optimization
Custom parameter bounds	Full Optimization
Tradeoff analysis	Multi-Objective

Performance

• Response time: ~200-500ms
• No job polling required: Results returned immediately
• Caching: Solar resource data cached for faster repeated queries