Photovoltaic power station design is a systematic project, and accurate power generation calculation is the key to investment decision-making and system optimization:
1. Core process of system design
Resource assessment: Analyze local meteorological data such as solar radiation (annual total solar irradiance H, kWh/m²), temperature, humidity, and wind speed
Capacity planning: Determine the installed capacity (P, kW) based on the investment budget and expected returns, typically calculated as 1 kW per 1000 kWh of annual demand
Component selection: Choose the appropriate component type (monocrystalline / polycrystalline / bifacial) and specifications based on resource conditions
Support design: Determine the fixation or tracking method, inclination angle, and azimuth angle, and optimize lighting
Inverter selection: Select a suitable inverter based on the component configuration, and determine the quantity and installation location
Electrical design: Designing DC/AC circuits, protective devices, and grounding systems
Energy storage configuration: Determine whether to configure storage and its capacity, which is usually 1-3 times the peak load, with a duration of 2-4 hours
2. Calculation method of power generation
Basic formula: E = H × P × η × 365/1000 (kWh / year)
H: Annual total irradiance at local level (kWh/m²), with a range of 1000-1800kWh/m² in most regions of China
P: Total power of the component (kW)
η: System comprehensive efficiency (typically 0.7-0.85), including:
Module conversion efficiency (15%-26%)
Inverter efficiency (95%-98%)
Line loss (3%-5%)
Temperature loss (5%-15%, region-dependent)
Dust obstruction (2%-5%)
Component matching loss (1%-3%)
Correction for double-sided power generation: E = E (single-sided) × (1 + β × γ)
β: Dual-sided efficiency (backside power generation / frontside power generation), with high-quality dual-sided modules achieving up to 85%
γ: Backside gain coefficient (0.1-0.3), related to ground reflectivity and inclination angle
Tracking system correction: Etracking = Efixed × (1 + α)
α: Tracking gain (15%-25% for single axis, 25%-40% for dual axis)
3. Key factors affecting power generation
Factor    Impact Level    Optimization Measures    Expected Improvement
Tilt / Azimuth: Extremely high (±10° variation affects 5%-15%) Optimal angle is precisely calculated, utilizing a tracking system 15%-40%
Module orientation: High (Poor roof orientation affects 10%-30%) Prefer south orientation, avoid shading 10%-30%
Temperature: Medium to High (Power decreases by 0.3%-0.5% for every 1℃ increase) Optimize ventilation and choose components with a low temperature coefficient. 3%-8%
Obstruction: Extremely high (local obstruction impact can reach 50%) Reasonable layout, reserved spacing, removal of obstacles 10%-50%
Dust / Snow Accumulation (Cumulative Impact 5%-15%) Regular Cleaning, Module Tilt ≥15° 5%-15%
Component matching: Medium (mismatch loss 3%-10%) - components from the same batch, grouped for MPPT (maximum power point tracking) - 3%-10%
4. Key points of economic evaluation
Initial investment: modules (40%-50%), inverters (10%-15%), supports (8%-12%), installation (10%-15%), and others (10%-20%)
Operating costs: maintenance (20,000-50,000 yuan/MW/year), insurance (0.1%-0.3%/year), monitoring (10,000-30,000 yuan/MW/year)
Return on investment:
Static payback period: total investment / annual net income, typically 5-8 years for photovoltaic projects
IRR (Internal Rate of Return): For high-quality projects, it can reach 10%-15%, subject to the influence of region, electricity price, and financing cost
Sensitivity analysis: Focus on the impact of changes in irradiation, electricity prices, and module prices on earnings, providing a basis for investment decisions
Design suggestion: During the resource assessment phase, historical meteorological data spanning at least 10 years should be utilized; the module configuration should incorporate an over-provisioning of 10%-20% to enhance system efficiency; the selection of inverters should be based on local irradiation characteristics, aiming to strike an optimal balance between cost and efficiency