SunSolve P90 Analysis Client

A Python client library for connecting to the SunSolve P90 analysis service via gRPC. This package enables photovoltaic system P90 analysis with comprehensive uncertainty modeling and Monte Carlo simulations.

Features

• Direct gRPC Connection: Secure, efficient communication with SunSolve P90 service
• Uncertainty Analysis: Apply probability distributions to solar resource, system losses, and operational parameters
• Multi-Year Simulations: Support for both Typical Mean Year (TMY) and multi-year actual datasets
• Comprehensive Error Handling: Custom exception classes for different error scenarios
• Type Safety: Full type annotations for better development experience
• Weather Data Support: Load data from PVW files and CSV formats

Quick Start

Installation

pip install sunsolve-p90-client

Prerequisites

• Python 3.12+
• SunSolve account with P90 Analysis subscription (sign up here)
• Network access to SunSolve servers

Basic Usage

from pvl_p90_client.client.p90_client import P90Client
from pvl_p90_client.grpcclient.uncertaintyMessages_pb2 import DistributionInput
from pvl_p90_client.helpers import pvl_login
from pvl_p90_client.helpers.request_helpers import (
    build_distribution,
    build_gaussian_distribution,
    build_request,
    load_weather_data_from_pvw_file,
)

# Connect to the P90 analysis service
with P90Client() as client:
    # Authenticate with your SunSolve credentials (prompts for username/password)
    credentials = pvl_login.login(client=client)

    # Load weather data from a PVW file (Typical Mean Year format)
    weather_data = load_weather_data_from_pvw_file("../data/sydney.pvw") or []

    # Add uncertainty distribution for Global Horizontal Irradiance (GHI)
    # This adds ±5% variation to solar irradiance values
    distributions = [build_distribution(input=DistributionInput.GHI, sim_to_sim_distribution=build_gaussian_distribution(1.0, 0.05))]

    # Create a minimal request with weather data and basic uncertainty
    # This uses default system parameters and simulation settings
    request = build_request(time_step_data=weather_data, distributions=distributions)

    # Send the analysis request and receive results
    summary, used_inputs = client.send_request(request, credentials, timeout=90.0)

    # Display results
    if summary:
        print(f"Analysis complete! Generated {len(summary.YearlyPValue)} yearly P-values")
        for pvalue in summary.YearlyPValue:  # Show all years
            print(f"  Year {pvalue.Year}: P{pvalue.P} = {pvalue.P50Deviation:.4f}")
    else:
        print("No analysis results received")

What This Package Does

The SunSolve P90 Analysis Client enables you to:

• Run Monte Carlo Simulations: Calculate P-values (probability of exceedance) for photovoltaic energy production
• Apply Uncertainty Distributions: Model variability in solar resource, system performance, and operational factors
• Process Weather Data: Import and analyze weather datasets in multiple formats
• Get Probabilistic Results: Receive P5, P10, P50, P90, P95 confidence intervals for energy production

Uncertainty Modeling

Apply probability distributions to key parameters:

• Solar Resource: GHI, temperature, wind speed variations
• System Performance: Module mismatch, inverter efficiency, soiling losses
• Operational Factors: Degradation rates, availability, curtailment

Results

Get comprehensive probabilistic analysis including:

• P-Values: Probability of exceedance levels (P5, P10, P50, P90, P95)
• Yearly Projections: Multi-year degradation modeling
• Monte Carlo Statistics: Distribution analysis across thousands of simulations

Support & Documentation

• Documentation: docs.sunsolve.com/en/p90/
• Getting Started: P90 Analysis Guide
• Contact: sunsolve.info/contact/

Note: This package requires a SunSolve account with P90 Analysis subscription. Visit sunsolve.info to get started.