4. Input parameters

• ASAM simulations require a set of input data.
• Most simulation data is provided in a .xlsx file with several tabs. Here you can find a template.
• The input parameters are described per .xlsx tab below.
• Next to the .xlsx file, there maybe a need for a .h5 file, which contains pdf kernels of the imbalance price. This file is required when the imbalance price determined based on a random draw from a conditional probability density function. Please consult this publication for more information. Here you can find a template.

Simulation task

parameter	value	description
simulation_name	string	name of simulation
start_day	integer	start day of simulation
start_MTU	integer {>0, <= 96}	start MTU of simulation
number_steps	integer {>0, <= 96}	number of simulation steps
run_RDM[y/n]	string {y, n}	include redispatch in simulation (yes/no)
run_IDM[y/n]	string {y, n}	include intra-day market in simulation (yes/no)
run_DAM[y/n]	string {y, n}	include day-ahead in simulation (yes/no)
run_IBM[y/n]	string {y, n}	include imbalance in simulation (yes/no)
run_BEM[y/n]	string {y, n}	include balancing energy market in simulation (yes/no)
seed	{None, integer}	if not none, the randomness is hold steady to make simulations better comparable
forecast_errors	string {exogenious, from_scenario}	exogenious: forecast errors are provided in excel tab 'forecast errors from scenario: forecast errors are included in da_residual_load time series dataframe
congestions	string {exogenious, from_scenario}	exogenious: congestions are provided in excel tab congestions from scenario: congestions are included in da_residual_load time series dataframe
residual_load_scenario	string {scenario name of da_residual_load tab,flat_resload_profile, 24h_residual_load_profile}	scenario name of da_residual_load tab: residual load time series used flat_resload_profile: 80% system generation capacity used as constant day-ahead residual load. 24h_residual_load_profile: hard-coded profile between 60 and 90% of system generation capacity
save_intermediate_results[y/n]	string {y, n}	if yes: save all market statistics of intermediate simulation steps
plots_during_simulation	string {at_end, every_step, every_change}	When (and how often) to plot simulation results
solver_name	string {cbc, glpk, gurobi, cplex}	solver name supported by PyPSA and installed on your machine or server

Portfolios

parameter	value	description
asset_name	string	e.g. asset_1
asset_owner	string	e.g. market_party_1
energy_source	string	e.g. Fossil Gas / B04
Type	string	e.g. CCGT
pmax	integer	MW
pmin	integer	MW
location	string	name of the grid area (e.g. North, Rotterdam West, Poland)
srmc	integer	EUR/MWh
ramp_limit_up	float	p.u. pmax, Maximum active power increase from one step to the next, per unit of the nominal power. Ignored if NaN
ramp_limit_down	float	p.u. pmax, Maximum active power decrease from one step to the next, per unit of the nominal power. Ignored if NaN
ramp_limit_start_up	float	p.u. pmax, Maximum active power increase from one step to the next, per unit of the nominal power. Ignored if NaN
ramp_limit_shut_down	float	p.u. pmax, Maximum active power decrease from one step to the next, per unit of the nominal power. Ignored if NaN
min_down_time	integer	minimum number of steps down time, when shut down
min_up_time	integer	minimum number of steps up time, when started
start_up_cost	integer	Fix cost per start (EUR)
shut_down_cost	integer	Fix cost per stop (EUR)

Agent strategies

parameter	value	description
agent	string{All, asset_owner}	If all, same strategies for all agents. Otherwise strategies per agents must be provided in several rows.
DAM_quantity	string	for options see wiki page Agent Strategies
IDM_quantity	string	for options see wiki page Agent Strategies
RDM_quantity	string	for options see wiki page Agent Strategies
DAM_pricing	string	for options see wiki page Agent Strategies
IDM_pricing	string	for options see wiki page Agent Strategies
RDM_pricing	string	for options see wiki page Agent Strategies
IDM_timing	string	for options see wiki page Agent Strategies
RDM_timing	string	for options see wiki page Agent Strategies
IBM_quantity	string	for options see wiki page Agent Strategies
IBM_pricing	string	for options see wiki page Agent Strategies
IBM_timing	string	for options see wiki page Agent Strategies
BEM_quantity	string	for options see wiki page Agent Strategies
BEM_pricing	string	for options see wiki page Agent Strategies
BEM_timing	string	for options see wiki page Agent Strategies
ramp_limits	string	for options see wiki page Agent Strategies
start_stop_costs	string	for options see wiki page Agent Strategies
min_up_down_time	string	for options see wiki page Agent Strategies

Market rules

parameter	value	description
gate_opening_time	string	for options see wiki page Market Rules
gate_closure_time	string	for options see wiki page Market Rules
acquisition_method	string	for options see wiki page Market Rules
pricing_method	string	for options see wiki page Market Rules
order_types	string	for options see wiki page Market Rules
provider_accreditation	string	for options see wiki page Market Rules

Forecast Errors

parameter	value	description
identification_day	integer	moment (day) of identification of forecast error by agent
identification_MTU	integer {>0, <= 96}	moment (MTU) of identification of forecast error by agent
error_start_day	integer	start day of forecast error
error_start_time	integer {>0, <= 96}	start MTU of forecast error
error_end_day	integer	end day of forecast error
error_end_time	integer {>0, <= 96}	end MTU of forecast error
error_magnitude_pu	float {>0 ,< 1}	p.u. agent generation capacity
who	string {All, asset_owner, system_e_randomly_distributed}	All: same forecast error for all agents asset_owner: forecast error for specific agent system_e_randomly_distributed: a system forecast error is randomly distributed among agents

Congestions

parameter	value	description
identification_day	integer	moment (day) of identification of congestion by grid operator
identification_MTU	integer {>0, <= 96}	moment (MTU) of identification of congestion by grid operator
congestion_start_day	integer	start day of congestion
congestion_start_time	integer {>0, <= 96}	start MTU of congestion
congestion_end_day	integer	end day of congestion
congestion_end_time	integer {>0, <= 96}	end MTU of congestion
redispatch_quantity	integer	MW
down_area	string	grid area name
up_area	string	grid area name

Day-ahead residual load

parameter	value	description
scenario	string	scenario name provided as residual_load_scenario in simulation_task
delivery_day	integer	day of delivery
delivery_hour	integer {>0, <= 24}	hour of delivery
delivery_time	integer {>0, <= 96}	MTU of delivery
residual_load_DA	float {>=0 ,< 1}	day-ahead residual load (load minus expected renewable energies generation) in p.u. of system generation capacity
load_DA_cor	float {>=0 ,< 1}	day-ahead load (load minus expected renewable energies generation) in p.u. of system generation capacity
asset_availability	float {>=0 ,< 1}	Optionally assets_name may be added here with their availability between 0 and 1 p.u. (pmax). However, this is only needed for assets which have unavailabilities within the scenario.

Opportunity_costs

parameter	value	description
price_data	string {IB_price_short, IB_price_long}	name of the imabalance price
DAP_left_bin	integer	day-ahead price bin, lower bin edge
DAP_right_bin(excl)	integer	day-ahead price bin, upper bin edge (excluded)
K-value	integer	K-value is the fundamental cost (typically srmc) of an asset
Opp_costs_for_K	float	opportunity cost (EUR/MWh) of asset with cost K and day-ahead price within DAP bin
bin_exp_value	float	expected value of imbalance price (EUR/MWh) for given DAP bin