{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "# Tutorial 3 - Modeling an Array Power with PVWatts\n", "\n", "This notebook shows how to use pvlib to model an array's output power given POA irradiance and cell temperature.\n", "\n", "The physics of a solar cell are fairly complex. Models of a cell's response to light and temperature range from high-level approximations that need only one or two coefficients, to electrical circuit models that require more detailed cell characteristics, to full physics-based semiconductor models. The level of detail should match the application -- first-order approximations can be surprisingly accurate but probably shouldn't be used for financial decisions, while physics-based semiconductor models are rarely practical outside of a laboratory setting.\n", "\n", "We will model a system similar to one you might have or want to place on your house:\n", "\n", "\n", "\n", "Photo from Mark's Grandma's House\n", "\n", "\n", "\n", "## PVWatts\n", "\n", "Here we will demonstrate one of the more basic PV models implemented by pvlib. The PVWatts module model requires only two array parameters -- the array size (nameplate capacity) and the array's efficiency change with cell temperature. Typical array sizes range from a few kW for residential arrays to hundreds of MW for large utility-scale systems. The cell temperature response parameter, often called the module's temperature coefficient, determines the efficiency loss for a temperature increase. For example, a module that produces 100W at test conditions but only 95W when the cell temperature is increased by 10 degrees has a temperature coefficient of -0.5%/°C. Typical temperature coefficients range from -0.5%/°C to -0.2%/°C.\n", "\n", "We'll read in weather data, run the irradiance transposition, and model cell temperature as in previous tutorials.\n", "\n", "\n", "## STEPS:\n", "