onsset package

Submodules

onsset.gui_runner module

onsset.onsset module

class onsset.onsset.SettlementProcessor(path)

Bases: object

Processes the DataFrame and adds all the columns to determine the cheapest option and the final costs and summaries

apply_limitations(eleclimit, year, time_step, prioritization, auto_densification=0)

calc_summaries(df_summary, sumtechs, tech_codes, year)

The next section calculates the summaries for technology split, capacity added and total investment cost

static calc_wind_cfs(wind_vel)

calculate_investments_and_capacity(sa_diesel_investment, sa_diesel_capacity, sa_pv_investment, sa_pv_capacity, mg_diesel_investment, mg_diesel_capacity, mg_pv_investment, mg_pv_capacity, mg_wind_investment, mg_wind_capacity, mg_hydro_investment, mg_hydro_capacity, grid_investment, grid_capacity, year)

calculate_new_connections(year, time_step, start_year)

this method defines new connections for grid related purposes

Parameters:

• year (int) –

• time_step (int) –

• start_year (int) –

calculate_off_grid_lcoes(mg_hydro_calc, mg_wind_calc, mg_pv_calc, sa_pv_calc, mg_diesel_calc, sa_diesel_calc, year, end_year, time_step, techs, tech_codes, diesel_techs=0)

Calculate the LCOEs for all off-grid technologies

calculate_total_demand_per_settlement(year)

this method calculates total demand for each settlement per year

Parameters:

year (int) –

calibrate_current_pop_and_urban(pop_actual, urban_current)

The function calibrates population values and urban/rural split (as estimated from GIS layers) based on actual values provided by the user for the start year.

calibrate_elec_current(elec_actual, elec_actual_urban, elec_actual_rural, start_year, min_night_lights=0, min_pop=50, max_transformer_dist=2, max_mv_dist=2, max_hv_dist=5, buffer=True)

Calibrate the current electrification status

choose_minimum_off_grid_tech(year, mg_hydro_calc, techs, tech_codes)

Choose minimum LCOE off-grid technology

First step determines the off-grid technology with minimum LCOE Second step determines the value (number) of the selected minimum off-grid technology

Parameters:

• year (int) –

• mg_hydro_calc (dict) –

static classify_elevation(elevation)

Classify the elevation distance according to bins and labels

Parameters:

elevation (list) –

static classify_land_cover(column)

this is a different method employed to classify land cover and create new columns with the classification

Parameters:

column (series) –

Notes

0, 11 = 1 6, 8 = 2 1, 3, 5, 12, 13, 15 = 3 2, 4 = 4 7, 9, 10, 14, 16 = 5

static classify_road_distance(road_distance)

Classify the road distance according to bins and labels

Parameters:

road_distance (list) –

static classify_slope(slope)

Classify the slope according to bins and labels

Parameters:

slope (list) –

static classify_substation_distance(substation_distance)

Classify the substation distance according to bins and labels

Parameters:

substation_distance (list) –

closest_electrified_settlement(new_electrified, unelectrified, cell_path_real, grid_penalty_ratio, elecorder)

compute_diesel_cost(dataframe: DataFrame, sa_diesel_cost: Dict, mg_diesel_cost: Dict, year: int)

Calculate diesel fuel cost

Parameters:

• dataframe (pandas.DataFrame) –

• sa_diesel_cost (Dict) –

• mg_diesel_cost (Dict) –

• year (int) –

Return type:

pandas.DataFrame

condition_df()

Do any initial data conditioning that may be required.

current_mv_line_dist()

diesel_cost_columns(sa_diesel_cost: Dict[str, float], mg_diesel_cost: Dict[str, float], year: int)

Calculate diesel fuel cost based on TravelHours column

Parameters:

• sa_diesel_cost (Dict) –

• mg_diesel_cost (Dict) –

• year (int) –

static do_kdtree(combined_x_y_arrays, points)

elec_extension(grid_calc, max_dist, year, start_year, end_year, time_step, grid_capacity_limit, grid_connect_limit, new_investment, new_capacity, auto_intensification=0, prioritization=0)

Iterate through all electrified settlements and find which settlements can be economically connected to the grid Repeat with newly electrified settlements until no more are added

get_grid_lcoe(dist_adjusted, elecorder, additional_transformer, year, time_step, end_year, grid_calc)

grid_penalties(data_frame)

this method calculates the grid penalties in each settlement

First step classifies the parameters and creates new columns with classification

Second step adds the grid penalty to increase grid cost in areas that higher road distance, higher substation distance, unsuitable land cover, high slope angle or high elevation

static haversine_vector(lon1, lat1, lon2, lat2)

Calculate the great circle distance between two points on the earth (specified in decimal degrees)

limit_hydro_usage(mg_hydro_calc, year)

pre_electrification(grid_price, year, time_step, end_year, grid_calc, grid_capacity_limit, grid_connect_limit)

” …

prepare_wtf_tier_columns(num_people_per_hh_rural, num_people_per_hh_urban, tier_1, tier_2, tier_3, tier_4, tier_5)

Prepares the five Residential Demand Tier Targets based customized for each country

project_pop_and_urban(pop_future, urban_future, start_year, years_of_analysis)

This function projects population and urban/rural ratio for the different years of the analysis

results_columns(techs, tech_codes, year, time_step, prio, auto_intensification)

Calculate the capacity and investment requirements for each settlement

Once the grid extension algorithm has been run, determine the minimum overall option, and calculate the capacity and investment requirements for each settlement

Parameters:

year (int) –

set_residential_demand(rural_tier, urban_tier, num_people_per_hh_rural, num_people_per_hh_urban, productive_demand)

this method defines residential demand per tier level for each target year

Parameters:

• rural_tier (int) –

• urban_tier (int) –

• num_people_per_hh_rural (float) –

• num_people_per_hh_urban (float) –

• productive_demand (int) –

set_scenario_variables(year, num_people_per_hh_rural, num_people_per_hh_urban, time_step, start_year, urban_tier, rural_tier, end_year_pop, productive_demand)

this method determines some basic parameters required in LCOE calculation it sets the basic scenario parameters that differ based on urban/rural so that they are in the table and can be read directly to calculate LCOEs

Parameters:

• year (int) –

• num_people_per_hh_rural (float) –

• num_people_per_hh_urban (float) –

• time_step (int) –

• start_year (int) –

• urban_tier (int) –

• rural_tier (int) –

• end_year_pop (int) –

• productive_demand (int) –

update_grid_extension_info(grid_lcoe, dist, dist_adjusted, prev_dist, elecorder, new_elec_order, max_dist, new_lcoes, grid_capacity_limit, grid_connect_limit, cell_path_real, cell_path_adjusted, electrified, year, grid_calc, grid_investment, new_investment, grid_capacity, new_capacity)

class onsset.onsset.Technology(tech_life=0, base_to_peak_load_ratio=0, distribution_losses=0, connection_cost_per_hh=0, om_costs=0.0, capital_cost={inf: 0}, capacity_factor=0.9, grid_penalty_ratio=1, efficiency=1.0, diesel_price=0.0, grid_price=0.0, standalone=False, mini_grid=False, existing_grid_cost_ratio=0.1, grid_capacity_investment=0.0, diesel_truck_consumption=0, diesel_truck_volume=0, om_of_td_lines=0)

Bases: object

Used to define the parameters for each electricity access technology, and to calculate the LCOE depending on input parameters.

distribution_network(people, energy_per_cell, num_people_per_hh, grid_cell_area, productive_nodes=0)

This method calculates the required components for the distribution network This includes potentially MV lines, LV lines and service transformers

Parameters:

• people (float) – Number of people in settlement

• energy_per_cell (float) – Annual energy demand in settlement (kWh)

• num_people_per_hh (float) – Number of people per household in settlement

• grid_cell_area (float) – Area of settlement (km2)

• productive_nodes (int) – Additional connections (schools, health facilities, shops)

Notes

Based on: https://www.mdpi.com/1996-1073/12/7/1395

get_lcoe(energy_per_cell, people, num_people_per_hh, start_year, end_year, new_connections, total_energy_per_cell, prev_code, grid_cell_area, additional_mv_line_length=0.0, capacity_factor=0.9, grid_penalty_ratio=1, fuel_cost=0, elec_loop=0, productive_nodes=0, additional_transformer=0, penalty=1)

Calculates the LCOE depending on the parameters.

Parameters:

• people (float or pandas.Series) – Number of people in settlement

• new_connections (float or pandas.Series) – Number of new people in settlement to connect

• prev_code (int or pandas.Series) – Code representation of previous supply technology in settlement

• total_energy_per_cell (float or pandas.Series) – Total annual energy demand in cell, including already met demand

• energy_per_cell (float or pandas.Series) – Annual energy demand in cell, excluding already met demand

• num_people_per_hh (float or pandas.Series) – Number of people per household in settlement

• grid_cell_area (float or pandas.Series) – Area of settlement (km2)

• additional_mv_line_length (float or pandas.Series) – Distance to connect the settlement

• additional_transformer (int) – If a transformer is needed on other end to connect to HV line

• productive_nodes (int or pandas.Series) – Additional connections (schools, health facilities, shops)

• elec_loop (int or pandas.Series) – Round of extension in grid extension algorithm

• penalty (float or pandas.Series) – Cost penalty factor for T&D network, e.g. https://www.mdpi.com/2071-1050/12/3/777

• start_year (int) –

• end_year (int) –

• capacity_factor (float or pandas.Series) –

• grid_penalty_ratio (float or pandas.Series) –

• fuel_cost (float or pandas.Series) –

Return type:

lcoe or discounted investment cost

classmethod set_default_values(base_year, start_year, end_year, discount_rate, hv_line_type=69, hv_line_cost=53000, mv_line_type=33, mv_line_amperage_limit=8.0, mv_line_cost=7000, lv_line_type=0.24, lv_line_cost=4250, lv_line_max_length=0.5, service_transf_type=50, service_transf_cost=4250, max_nodes_per_serv_trans=300, mv_lv_sub_station_type=400, mv_lv_sub_station_cost=10000, mv_mv_sub_station_cost=10000, hv_lv_sub_station_type=1000, hv_lv_sub_station_cost=25000, hv_mv_sub_station_cost=25000, power_factor=0.9, load_moment=9643)

Initialises the class with parameter values common to all Technologies

td_network_cost(people, new_connections, prev_code, total_energy_per_cell, energy_per_cell, num_people_per_hh, grid_cell_area, additional_mv_line_length=0, additional_transformer=0, productive_nodes=0, elec_loop=0, penalty=1)

Calculates all the transmission and distribution network components

Parameters:

• people (float) – Number of people in settlement

• new_connections (float) – Number of new people in settlement to connect

• prev_code (int) – Code representation of previous supply technology in settlement

• total_energy_per_cell (float) – Total annual energy demand in cell, including already met demand

• energy_per_cell (float) – Annual energy demand in cell, excluding already met demand

• num_people_per_hh (float) – Number of people per household in settlement

• grid_cell_area (float) – Area of settlement (km2)

• additional_mv_line_length (float) – Distance to connect the settlement

• additional_transformer (int) – If a transformer is needed on other end to connect to HV line

• productive_nodes (int) – Additional connections (schools, health facilities, shops)

• elec_loop (int) – Round of extension in grid extension algorithm

• penalty (float) – Cost penalty factor for T&D network, e.g. https://www.mdpi.com/2071-1050/12/3/777

transmission_network(peak_load, additional_mv_line_length=0, additional_transformer=0, mv_distribution=False)

This method calculates the required components for connecting the settlement Settlements can be connected to grid or a hydropower source This includes potentially HV lines, MV lines and substations

Parameters:

• peak_load (float) – Peak load in the settlement (kW)

• additional_mv_line_length (float) – Distance to connect the settlement

• additional_transformer (int) – If a transformer is needed on other end to connect to HV line

• mv_distribution (bool) – True if distribution network in settlement contains MV lines

Notes

Based on: https://www.mdpi.com/1996-1073/12/7/1395

onsset.runner module

onsset.runner.calibration(specs_path, csv_path, specs_path_calib, calibrated_csv_path)

Parameters:

• specs_path –

• csv_path –

• specs_path_calib –

• calibrated_csv_path –

onsset.runner.scenario(specs_path, calibrated_csv_path, results_folder, summary_folder)

Parameters:

• specs_path (str) –

• calibrated_csv_path (str) –

• results_folder (str) –

• summary_folder (str) –

onsset.specs module

Columns in the specs file must match these exactly

Module contents

The onsset package contains the following modules:

• onsset.py : main functions of the model

• runner.py : runner is used to calibrate inputs and specify scenario runs