def func_graphics_storage_results_1scenario(input_data):
# -------------------------------------------------------------------------
optimization_results_file_path = input_data[
"optimization_results_file_path"]
directory_output = input_data["directory_output"]
graphics_file_name = input_data["graphics_file_name"]
SAVE_FIGURES_TO_PDF = input_data["SAVE_FIGURES_TO_PDF"]
# -------------------------------------------------------------------------
# load the optimization assumptions and results
temp_dict = func_load_optimization_results(optimization_results_file_path)
model_inputs = temp_dict['model_inputs']
model_results = temp_dict['model_results']
# -------------------------------------------------------------------------
# Get the needed data and call the ploting function
demand = model_inputs['demand']
dispatched_storage_charge = model_results["dispatched_storage_charge"]
dispatched_storage_discharge = model_results[
"dispatched_storage_discharge"]
storage_energy_soc = model_results["storage_energy_soc"]
capacity_storage = model_results["capacity_storage"]
inputs = {
"storage_energy_soc": storage_energy_soc,
"capacity_storage": capacity_storage,
"dispatched_storage_discharge": dispatched_storage_discharge,
"dispatched_storage_charge": dispatched_storage_charge,
"demand": demand,
"directory_output": directory_output,
"graphics_file_name": graphics_file_name,
"SAVE_FIGURES_TO_PDF": SAVE_FIGURES_TO_PDF,
}
# Call function
#func_plotting_storage_static_1scenario(inputs)
# Here is an outpudated information, which would generate figures to show
# the same information as in func_plotting_storage_static_1scenario().
# But instead of using subplots to combine figures, this _1() function
# generated each figure seperately.
inputs["graphics_file_name"] = graphics_file_name + "_1"
func_plotting_storage_static_1scenario_1(inputs)
# # Deal with dictionary variables
#
# # First note that when you saved dict variables into .npz files and then retrieve
# # them back, they have data_type = object (more accurately, it is a numpy.ndarray
# # type), rather than dictionary, so you have to use them slightly differently.
# # However, model_results.item() is a dictionary type variable.
# # So, you access the files like these, model_results.item()['unmet_demand']
#
#elif DATA_TYPE == "pkl":
#
# import pickle
#
# file_path = "D:/M/\WorkCarnegie/Post_Processing/Results_Base/20180514/Without_NG/20180514_1e1/optimization_related.pkl"
#
# f = open(file_path, 'rb')
# model_inputs, model_results = pickle.load(f)
#%% Script
# optimization_results_data_type = 'pkl'
optimization_results_file_path = "D:/M/WorkCarnegie/Post_Processing/Results_Base/20180514/Without_NG/20180514_1e1/optimization_related.pkl"
#input_data = {
# 'optimization_results_data_type': optimization_results_data_type,
# 'optimization_results_file_path': optimization_results_file_path,
# }
load_optimization = func_load_optimization_results(
optimization_results_file_path)
model_inputs = load_optimization['model_inputs']
model_results = load_optimization['model_results']
def func_storage_analysis_alternative_size(input_data):
alternative_size_list = input_data['alternative_size_list']
optimization_results_file_path = input_data[
'optimization_results_file_path']
SAVE_FIGURES_TO_PDF = input_data['SAVE_FIGURES_TO_PDF']
directory_output = input_data['directory_output']
graphics_file_name = input_data['graphics_file_name']
scenario_name = input_data['scenario_name']
# -------------------------------------------------------------------------
# # DATA_TYPE = 'pkl'
#
# f = open(optimization_results_file_path, 'rb')
# model_inputs, model_results = pickle.load(f)
temp_dict = func_load_optimization_results(optimization_results_file_path)
model_inputs = temp_dict['model_inputs']
model_results = temp_dict['model_results']
# -------------------------------------------------------------------------
# Previously imported, but never used
# demand = model_inputs['demand']
# -------------------------------------------------------------------------
# Note that storage_optimization_charge and storage_optimization_discharge, are
# both defined on the grid side.
# so, from the storage perspective,
# discharge* = storage_optimization_discharge / one-way efficiency
# charge* = storage_optimization_charge * one-way efficiency
storage_optimization_charge = model_results["dispatched_storage_charge"]
storage_optimization_discharge = model_results[
"dispatched_storage_discharge"]
storage_optimization_capacity = model_results["capacity_storage"]
storage_optimization_soc = model_results["storage_energy_soc"]
storage_charging_efficiency = model_inputs["storage_charging_efficiency"]
# -------------------------------------------------------------------------
# Test:
# for any time step, discharging energy and charging energy cannot be both nonzero.
# In other word, the piece-by-piece product of the two variables should always be zero.
test_variables = storage_optimization_charge * storage_optimization_discharge
# print 'Test: for any time step, can both discharging power flow and charging power folw be nonzero?'
# print 'Results (sum {discharging * charging)): ', np.sum(test_variables)
if np.sum(test_variables) > 0:
raise ValueError("Test failed")
# -------------------------------------------------------------------------
input_data = {
# Core assumptions
"storage_optimization_charge": storage_optimization_charge,
"storage_optimization_discharge": storage_optimization_discharge,
"storage_optimization_soc": storage_optimization_soc,
"storage_optimization_capacity": storage_optimization_capacity,
"storage_charging_efficiency": storage_charging_efficiency,
"alternative_size": 1,
}
discharge_unmet_energy = np.zeros(alternative_size_list.shape)
discharge_unmet_hours = np.zeros(alternative_size_list.shape)
discharge_unmet_energy_percentage = np.zeros(alternative_size_list.shape)
discharge_unmet_hours_percentage = np.zeros(alternative_size_list.shape)
for i in xrange(alternative_size_list.size):
input_data['alternative_size'] = alternative_size_list[i]
# Call the function to do the analysis
outputs = func_alternative_storage_analysis(input_data)
discharge_unmet_energy[i] = outputs["discharge_unmet_energy"]
discharge_unmet_energy_percentage[i] = outputs[
"discharge_unmet_energy_percentage"]
discharge_unmet_hours[i] = outputs["discharge_unmet_hours"]
discharge_unmet_hours_percentage[i] = outputs[
"discharge_unmet_hours_percentage"]
# -----------------------
# some additional calculations
discharge_met_energy_percentage = (1 - discharge_unmet_energy_percentage)
# Calculate the derivative
discharge_met_energy_percentage_derivative = np.zeros(
discharge_met_energy_percentage.size - 1)
for i in xrange(discharge_met_energy_percentage_derivative.size):
discharge_met_energy_percentage_derivative[i] = (
(discharge_met_energy_percentage[i + 1] -
discharge_met_energy_percentage[i]) /
(alternative_size_list[i + 1] - alternative_size_list[i]))
# -------------------------------------------------------------------------
title_text = (scenario_name + "\n" + "capacity: {:0.0f}e6 kWh, ".format(
storage_optimization_capacity / 1e6) +
"discharged in {:0.1f} eq. cycles, ".format(
np.sum(storage_optimization_discharge) /
storage_optimization_capacity) + "over {} hours".format(
np.count_nonzero(storage_optimization_discharge)))
# Plotting
input_data = {
'storage_optimization_discharge': storage_optimization_discharge,
'storage_optimization_capacity': storage_optimization_capacity,
'alternative_size_list': alternative_size_list,
'discharge_unmet_energy_percentage': discharge_unmet_energy_percentage,
'discharge_unmet_hours': discharge_unmet_hours,
'discharge_met_energy_percentage': discharge_met_energy_percentage,
'discharge_met_energy_percentage_derivative':
discharge_met_energy_percentage_derivative,
'SAVE_FIGURES_TO_PDF': SAVE_FIGURES_TO_PDF,
'directory_output': directory_output,
'graphics_file_name': graphics_file_name,
'title_text': title_text,
}
# call the function to plot figures
func_alternative_storage_plotting(input_data)
storage_scenario_list = ['1e-2']
ng_scenario_list = ['without_NG']
# ------------------------------
# actually load the data
for ng_scenario, storage_scenario in product(ng_scenario_list,
storage_scenario_list):
optimization_results_file_path = directory_project + \
"Results_Base/20180514/{}/20180514_{}/optimization_related.pkl".format(ng_scenario, storage_scenario)
graphics_file_name = "Storage_analysis_alternative_size_" + storage_scenario + "_" + ng_scenario
scenario_name = "storage cost: " + storage_scenario + "$/kWh, " + ng_scenario
input_data = {
"alternative_size_list": alternative_size_list,
"optimization_results_file_path": optimization_results_file_path,
"SAVE_FIGURES_TO_PDF": SAVE_FIGURES_TO_PDF,
"directory_output": directory_output,
"graphics_file_name": graphics_file_name,
"scenario_name": scenario_name,
}
func_storage_analysis_alternative_size(input_data)
temp_dict = func_load_optimization_results(optimization_results_file_path)
model_inputs = temp_dict['model_inputs']
model_results = temp_dict['model_results']