Source code for technology.technology_a_model

"""
Template for technology model file.

Corresponds to Technology A in the template decision context.
"""

import numpy as np

[docs]def capital_cost(scale, parameter): """ Capital cost function. Parameters ---------- scale : float The scale of operation. parameter : array The technological parameterization. """ # Stack the capital cost values into a single array to return. return np.stack((parameter[0] * scale**0.6, parameter[1] * scale**0.6, parameter[2] * scale**0.6, parameter[3] * scale**0.6, parameter[4] * scale**0.6))
[docs]def fixed_cost(scale, parameter): """ Capital cost function. Parameters ---------- scale : float The scale of operation. parameter : array The technological parameterization. """ # Fixed costs are being modeled as a fraction of the # total capital cost # Stack the fixed cost values into a single array to return. return np.stack(0.1 * (parameter[0] + parameter[1] + parameter[2] + parameter[3] + parameter[4]) * scale)
[docs]def production(scale, capital, lifetime, fixed, input, parameter): """ Production function. Parameters ---------- scale : float The scale of operation. capital : array Capital costs. lifetime : float Technology lifetime. fixed : array Fixed costs. input : array Input quantities. parameter : array The technological parameterization. """ # Stack the calculated output values into a single array to return. return np.stack((scale * parameter[10] * parameter[11] * parameter[12] * parameter[13] * input[0], scale * parameter[13] * input[1], scale * parameter[13] * input[0]))
[docs]def metrics(scale, capital, lifetime, fixed, input_raw, input, input_price, output_raw, output, cost, parameter): """ Metrics function. Parameters ---------- scale : float The scale of operation. capital : array Capital costs. lifetime : float Technology lifetime. fixed : array Fixed costs. input_raw : array Raw input quantities (before losses). input : array Input quantities. output_raw : array Raw output quantities (before losses). output : array Output quantities. cost : array Costs. parameter : array The technological parameterization. """ _annualized_cost_per_unit_F = (capital[0] + capital[1] + capital[2] + capital[3] + capital[4] + fixed*lifetime[0] + input_price[0] * input_raw[0] + input_price[1] * input_raw[1] + input_price[2] * input_raw[2] - 12.0 * output[1] - 9.0 * output[2])/output[0] - parameter[15] _labor_per_unit_F = (parameter[8] * output_raw[0] + parameter[9] * (output_raw[1] + output_raw[2]))/output[0] - parameter[16] _impact_per_unit_F = (parameter[5] * input_raw[0] + parameter[6] * input_raw[1] + parameter[7] * input_raw[2]) / output[0] - parameter[17] _overall_efficiency = output[0] / (input_raw[0] + input_raw[1] + input_raw[2]) - parameter[18] # Stack the metric values into a single array to return. return np.stack((_annualized_cost_per_unit_F, _labor_per_unit_F, _impact_per_unit_F, _overall_efficiency))