tesp_support.original package

Submodules

tesp_support.original.case_merge module

Combines GridLAB-D and agent files to run a multi-feeder TESP simulation

Public Functions:

merge_glm:

combines GridLAB-D input files

merge_glm_dict:

combines GridLAB-D metadata files

merge_agent_dict:

combines the substation agent configuration files

merge_substation_yaml:

combines the substation agent FNCS publish/subscribe files

merge_fncs_config:

combines GridLAB-D FNCS publish/subscribe files

merge_gld_msg:

combines GridLAB-D HELICS publish/subscribe configurations

merge_substation_msg:

combines the substation agent HELICS publish/subscribe configurations

tesp_support.original.case_merge.key_present(val, ary)

tesp_support.original.case_merge.merge_agent_dict(target, sources, xfmva)

Combines the substation agent configuration files into target/target.json. The source files must already exist.

Parameters:

• target (str) – the directory and root case name

• sources (list) – list of feeder names in the target directory to merge

tesp_support.original.case_merge.merge_fncs_config(target, sources)

Combines GridLAB-D input files into target/target.txt. The source feeders must already exist.

Parameters:

• target (str) – the directory and root case name

• sources (list) – list of feeder names in the target directory to merge

tesp_support.original.case_merge.merge_gld_msg(target, sources)

tesp_support.original.case_merge.merge_glm(target, sources, xfmva)

Combines GridLAB-D input files into target/target.glm. The source files must already exist.

Parameters:

• target (str) – the directory and root case name

• sources (list) – list of feeder names in the target directory to merge

tesp_support.original.case_merge.merge_glm_dict(target, sources, xfmva)

Combines GridLAB-D metadata files into target/target.json. The source files must already exist.

Each constituent feeder has a new ID constructed from the NamePrefix + original base_feeder, then every child object on that feeder will have its feeder_id, originally network_node, changed to match the new one.

Parameters:

• target (str) – the directory and root case name

• sources (list) – list of feeder names in the target directory to merge

• xfmva (int) –

tesp_support.original.case_merge.merge_substation_msg(target, sources)

tesp_support.original.case_merge.merge_substation_yaml(target, sources)

Combines GridLAB-D input files into target/target.yaml. The source files must already exist.

Parameters:

• target (str) – the directory and root case name

• sources (list) – list of feeder names in the target directory to merge

tesp_support.original.commercial_feeder_glm module

tesp_support.original.commercial_feeder_glm.create_comm_zones(bldg, comm_loads, key, op, batt_metadata, storage_percentage, ev_metadata, ev_percentage, solar_percentage, pv_rating_MW, solar_Q_player, case_type, metrics, metrics_interval, mode=None)

For large buildings, breaks building up into multiple zones.

For all buildings sends definition dictionary to function that writes out building definition to GLD file format.

Parameters:

• bldg (dict) – dictionary of building parameters for the building to be processed.

• comm_loads –

• key (str) – name of feeder node or meter being used

• op (any) – GLD output file

• batt_metadata –

• storage_percentage –

• ev_metadata –

• ev_percentage –

• solar_percentage –

• pv_rating_MW –

• solar_Q_player –

• case_type –

• metrics –

• metrics_interval –

• mode (str) – if ‘test’ will ensure that GLD output function does not set parent - allows buildings to be run in GLD without feeder info

tesp_support.original.commercial_feeder_glm.define_comm_bldg(bldg_metadata, dso_type, num_bldgs)

Randomly selects a set number of buildings by type and size (sq. ft.)

Parameters:

• bldg_metadata – dictionary of DSO+T specific building parameter data

• dso_type – ‘Urban’, ‘Suburban’, or ‘Rural’

• num_bldgs – scalar value of number of buildings to be selected

tesp_support.original.commercial_feeder_glm.define_comm_loads(bldg_type, bldg_size, dso_type, climate, bldg_metadata)

Determines building parameters based on building type, dso type, and (ASHRAE) climate zone

Parameters:

• bldg_type (str) – class of building (e.g. ‘strip_mall’, etc.)

• bldg_size – size of the building in sq. ft.

• dso_type (str) – whether the dso is ‘Urban’, ‘Suburban’, or ‘Rural’

• climate (str) – ASHRAE climate zone that the DSO resides in (e.g. ‘2A’)

• bldg_metadata – dictionary of DSO+T specific building parameter data

tesp_support.original.commercial_feeder_glm.find_envelope_prop(prop, age, env_data, climate)

Returns the envelope value for a given type of property based on the age and (ASHRAE) climate zone of the

building

Parameters:

• prop (str) – envelope material property of interest (e.g. ‘wood-framed’ or ‘u-windows’)

• age (int) – age of the building in question (typically between 1945 and present).

• env_data (dict) – Dictionary of envelope property data

• climate ('string') – ASHRAE climate zone of building (e.g. ‘2A’)

Returns:

property value - typically a U-value.

Return type:

val (float)

tesp_support.original.commercial_feeder_glm.normalize_dict_prob(name, diction)

Ensures that the probability distribution of values in a dictionary effectively sums to one

Parameters:

• name – name of dictionary to normalize

• diction – dictionary of elements and associated non-cumulative probabilities

tesp_support.original.commercial_feeder_glm.rand_bin_select(diction, probability)

Returns the element (bin) in a dictionary given a certain probability

Parameters:

• diction – dictionary of elements and associated non-cumulative probabilities

• probability – scalar value between 0 and 1

tesp_support.original.commercial_feeder_glm.sub_bin_select(_bin, _type, _prob)

Returns a scalar value within a bin range based on a uniform probability within that bin range

Parameters:

• _bin – name of bin

• _type – building parameter describing set of bins

• _prob – scalar value between 0 and 1

tesp_support.original.commercial_feeder_glm.write_one_commercial_zone(bldg, op, metrics, metrics_interval, mode=None)

Write one pre-configured commercial zone as a house

Parameters:

• bldg – dictionary of GridLAB-D house and zipload attributes

• op (file) – open file to write to

• metrics (list) –

• metrics_interval (int) –

• mode (str) – if in ‘test’ mode will not write out parent info.

tesp_support.original.copperplate_feeder_glm module

Replaces ZIP loads with houses, and optional storage and solar generation.

As this module populates the feeder backbone with houses and DER, it uses the Networkx package to perform graph-based capacity analysis, upgrading fuses, transformers and lines to serve the expected load. Transformers have a margin of 20% to avoid overloads, while fuses have a margin of 150% to avoid overloads. These can be changed by editing tables and variables in the source file.

There are two kinds of house populating methods implemented:

• Feeders with Service Transformers:

  This case applies to the full PNNL taxonomy feeders. Do not specify the taxchoice argument to populate_feeder. Each service transformer receiving houses will have a short service drop and a small number of houses attached.

• Feeders without Service Transformers:

  This applies to the reduced-order ERCOT feeders. To invoke this mode, specify the taxchoice argument to populate_feeder. Each primary load to receive houses will have a large service transformer, large service drop and large number of houses attached.

References

GridAPPS-D Feeder Models

Public Functions:

populate_feeder:

processes one GridLAB-D input file

Todo

• Verify the level zero mobile home thermal integrity properties; these were copied from the MATLAB feeder generator

• Populate commercial building loads

tesp_support.original.copperplate_feeder_glm.Find1PhaseXfmr(kva)

Select a standard 1-phase transformer size, with data

Standard sizes are 5, 10, 15, 25, 37.5, 50, 75, 100, 167, 250, 333 or 500 kVA

Parameters:

kva (float) – the minimum transformer rating

Returns:

the kva, %r, %x, %no-load loss, %magnetizing current

Return type:

[float,float,float,float,float]

tesp_support.original.copperplate_feeder_glm.Find1PhaseXfmrKva(kva)

Select a standard 1-phase transformer size, with some margin

Standard sizes are 5, 10, 15, 25, 37.5, 50, 75, 100, 167, 250, 333 or 500 kVA

Parameters:

kva (float) – the minimum transformer rating

Returns:

the kva size, or 0 if none found

Return type:

float

tesp_support.original.copperplate_feeder_glm.Find3PhaseXfmr(kva)

Select a standard 3-phase transformer size, with data

Standard sizes are 30, 45, 75, 112.5, 150, 225, 300, 500, 750, 1000, 1500, 2000, 2500, 3750, 5000, 7500 or 10000 kVA

Parameters:

kva (float) – the minimum transformer rating

Returns:

the kva, %r, %x, %no-load loss, %magnetizing current

Return type:

[float,float,float,float,float]

tesp_support.original.copperplate_feeder_glm.Find3PhaseXfmrKva(kva)

Select a standard 3-phase transformer size, with some margin

Standard sizes are 30, 45, 75, 112.5, 150, 225, 300, 500, 750, 1000, 1500, 2000, 2500, 3750, 5000, 7500 or 10000 kVA

Parameters:

kva (float) – the minimum transformer rating

Returns:

the kva size, or 0 if none found

Return type:

float

tesp_support.original.copperplate_feeder_glm.FindFuseLimit(amps)

Find a Fuse size that’s unlikely to melt during power flow

Will choose a fuse size of 40, 65, 100 or 200 Amps. If that’s not large enough, will choose a recloser size of 280, 400, 560, 630 or 800 Amps. If that’s not large enough, will choose a breaker size of 600 (skipped), 1200 or 2000 Amps. If that’s not large enough, will choose 999999.

Parameters:

amps (float) – the maximum load current expected; some margin will be added

Returns:

the GridLAB-D fuse size to insert

Return type:

float

tesp_support.original.copperplate_feeder_glm.ProcessTaxonomyFeeder(outname, rootname, vll, vln, avghouse, avgcommercial)

Parse and re-populate one backbone feeder, usually but not necessarily one of the PNNL taxonomy feeders

This function:

• reads and parses the backbone model from rootname.glm

• replaces loads with houses and DER

• upgrades transformers and fuses as needed, based on a radial graph analysis

• writes the repopulated feeder to outname.glm

Parameters:

• outname (str) – the output feeder model name

• rootname (str) – the input (usually taxonomy) feeder model name

• vll (float) – the feeder primary line-to-line voltage

• vln (float) – the feeder primary line-to-neutral voltage

• avghouse (float) – the average house load in kVA

• avgcommercial (float) – the average commercial load in kVA, not used

tesp_support.original.copperplate_feeder_glm.accumulate_load_kva(data)

Add up the total kva in a load-bearing object instance

Considers constant_power_A/B/C/1/2/12 and power_1/2/12 attributes

Parameters:

data (dict) – dictionary of data for a selected GridLAB-D instance

tesp_support.original.copperplate_feeder_glm.buildingTypeLabel(rgn, bldg, ti)

Formatted name of region, building type name and thermal integrity level

Parameters:

• rgn (int) – region number 1..5

• bldg (int) – 0 for single-family, 1 for apartment, 2 for mobile home

• ti (int) – thermal integrity level, 0..6 for single-family, only 0..2 valid for apartment or mobile home

tesp_support.original.copperplate_feeder_glm.checkResidentialBuildingTable()

Verify that the regional building parameter histograms sum to one

tesp_support.original.copperplate_feeder_glm.connect_ercot_commercial(op)

For the reduced-order ERCOT feeders, add a billing meter to the commercial load points, except small ZIPLOADs

Parameters:

op (file) – an open GridLAB-D input file

tesp_support.original.copperplate_feeder_glm.connect_ercot_houses(model, h, op, vln, vsec)

For the reduced-order ERCOT feeders, add houses and a large service transformer to the load points

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• op (file) – an open GridLAB-D input file

• vln (float) – the primary line-to-neutral voltage

• vsec (float) – the secondary line-to-neutral voltage

tesp_support.original.copperplate_feeder_glm.getDsoThermalTable(dso_type)

tesp_support.original.copperplate_feeder_glm.identify_ercot_houses(model, h, t, avgHouse, rgn)

For the reduced-order ERCOT feeders, scan each primary load to determine the number of houses it should have

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class name to scan

• avgHouse (float) – the average house load in kva

• rgn (int) – the region number, 1..5

tesp_support.original.copperplate_feeder_glm.identify_xfmr_houses(model, h, t, seg_loads, avgHouse, rgn)

For the full-order feeders, scan each service transformer to determine the number of houses it should have

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class name to scan

• seg_loads (dict) – dictionary of downstream load (kva) served by each GridLAB-D link

• avgHouse (float) – the average house load in kva

• rgn (int) – the region number, 1..5

tesp_support.original.copperplate_feeder_glm.is_edge_class(s)

Identify switch, fuse, recloser, regulator, transformer, overhead_line, underground_line and triplex_line instances

Edge class is networkx terminology. In GridLAB-D, edge classes are called links.

Parameters:

s (str) – the GridLAB-D class name

Returns:

True if an edge class, False otherwise

Return type:

bool

tesp_support.original.copperplate_feeder_glm.is_node_class(s)

Identify node, load, meter, triplex_node or triplex_meter instances

Parameters:

s (str) – the GridLAB-D class name

Returns:

True if a node class, False otherwise

Return type:

bool

tesp_support.original.copperplate_feeder_glm.log_model(model, h)

Prints the whole parsed model for debugging

Parameters:

• model (dict) – parsed GridLAB-D model

• h (dict) – object ID hash

tesp_support.original.copperplate_feeder_glm.obj(parent, model, line, itr, oidh, octr)

Store an object in the model structure

Parameters:

• parent (str) – name of parent object (used for nested object defs)

• model (dict) – dictionary model structure

• line (str) – glm line containing the object definition

• itr (iter) – iterator over the list of lines

• oidh (dict) – hash of object id’s to object names

• octr (int) – object counter

Returns:

the current line and updated octr

Return type:

str, int

tesp_support.original.copperplate_feeder_glm.populate_feeder(config=None)

Wrapper function that processes one feeder. One or two keyword arguments must be supplied.

Parameters:

config (dict) – dictionary of feeder population data already read in, mutually exclusive with configfile

tesp_support.original.copperplate_feeder_glm.replace_commercial_loads(model, h, t, avgBuilding)

For the full-order feeders, scan each load with load_class==C to determine the number of zones it should have

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class name to scan

• avgBuilding (float) – the average building in kva

tesp_support.original.copperplate_feeder_glm.selectResidentialBuilding(rgnTable, prob)

Selects the building with region and probability

Parameters:

• rgnTable –

• prob –

tesp_support.original.copperplate_feeder_glm.selectSetpointBins(bldg, rand)

Randomly choose a histogram row from the cooling and heating setpoints

The random number for the heating and cooling set points row is generated internally.

Parameters:

• bldg (int) – 0 for single-family, 1 for apartment, 2 for mobile home

• rand (float) – random number [0..1] for the cooling setpoint row

tesp_support.original.copperplate_feeder_glm.selectThermalProperties(bldgIdx, tiIdx)

Retrieve the building thermal properties for a given type and integrity level

Parameters:

• bldgIdx (int) – 0 for single-family, 1 for apartment, 2 for mobile home

• tiIdx (int) – 0..7 for single-family, apartment or mobile home

tesp_support.original.copperplate_feeder_glm.union_of_phases(phs1, phs2)

Collect all phases on both sides of a connection

Parameters:

• phs1 (str) – first phasing

• phs2 (str) – second phasing

Returns:

union of phs1 and phs2

Return type:

str

tesp_support.original.copperplate_feeder_glm.write_config_class(model, h, t, op)

Write a GridLAB-D configuration (i.e. not a link or node) class

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class

• op (file) – an open GridLAB-D input file

tesp_support.original.copperplate_feeder_glm.write_ercot_small_loads(basenode, op, vnom)

For the reduced-order ERCOT feeders, write loads that are too small for houses

Parameters:

• basenode (str) – the GridLAB-D node name

• op (file) – an open GridLAB-D input file

• vnom (float) – the primary line-to-neutral voltage

tesp_support.original.copperplate_feeder_glm.write_link_class(model, h, t, seg_loads, op)

Write a GridLAB-D link (i.e. edge) class

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class

• seg_loads (dict) – a dictionary of downstream loads for each link

• op (file) – an open GridLAB-D input file

tesp_support.original.copperplate_feeder_glm.write_local_triplex_configurations(op)

Write a 4/0 AA triplex configuration

Parameters:

op (file) – an open GridLAB-D input file

tesp_support.original.copperplate_feeder_glm.write_small_loads(basenode, op, vnom)

Write loads that are too small for a house, onto a node

Parameters:

• basenode (str) – GridLAB-D node name

• op (file) – open file to write to

• vnom (float) – nominal line-to-neutral voltage at basenode

tesp_support.original.copperplate_feeder_glm.write_solar_inv_settings(op)

Writes volt-var and volt-watt settings for solar inverters

Parameters:

op (file) – an open GridLAB-D input file

tesp_support.original.copperplate_feeder_glm.write_substation(op, name, phs, vnom, vll)

Write the substation swing node, transformer, metrics collector and fncs_msg object

Parameters:

• op (file) – an open GridLAB-D input file

• name (str) – node name of the primary (not transmission) substation bus

• phs (str) – primary phasing in the substation

• vnom (float) – not used

• vll (float) – feeder primary line-to-line voltage

tesp_support.original.copperplate_feeder_glm.write_tariff(op)

Writes tariff information to billing meters

Parameters:

op (file) – an open GridLAB-D input file

tesp_support.original.copperplate_feeder_glm.write_voltage_class(model, h, t, op, vprim, vll, secmtrnode)

Write GridLAB-D instances that have a primary nominal voltage, i.e., node, meter and load

Parameters:

• model (dict) – a parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class name to write

• op (file) – an open GridLAB-D input file

• vprim (float) – the primary nominal line-to-neutral voltage

• vll (float) – the primary nominal line-to-line voltage

• secmtrnode (dict) – key to [transfomer kva, phasing, nominal voltage] by secondary node name

tesp_support.original.copperplate_feeder_glm.write_xfmr_config(key, phs, kvat, vnom, vsec, install_type, vprimll, vprimln, op)

Write a transformer_configuration

Parameters:

• key (str) – name of the configuration

• phs (str) – primary phasing

• kvat (float) – transformer rating in kVA

• vnom (float) – primary voltage rating, not used any longer (see vprimll and vprimln)

• vsec (float) – secondary voltage rating, should be line-to-neutral for single-phase or line-to-line for three-phase

• install_type (str) – should be VAULT, PADMOUNT or POLETOP

• vprimll (float) – primary line-to-line voltage, used for three-phase transformers

• vprimln (float) – primary line-to-neutral voltage, used for single-phase transformers

• op (file) – an open GridLAB-D input file

tesp_support.original.curve module

Utility functions for use within tesp_support, including new agents.

class tesp_support.original.curve.ClearingType(value)

Bases: IntEnum

Describes the market clearing type

BUYER = 5

EXACT = 3

FAILURE = 1

NULL = 0

PRICE = 2

SELLER = 4

tesp_support.original.curve.aggregate_bid(crv)

Aggregates the buyer curve into a quadratic or straight-line fit with zero intercept

Parameters:

crv (curve) – the accumulated buyer bids

Returns:

Qunresp, Qmaxresp, degree, c2 and c1 scaled to MW instead of kW. c0 is always zero.

Return type:

[float, float, int, float, float]

class tesp_support.original.curve.curve

Bases: object

Accumulates a set of price, quantity bids for later aggregation. The default order is descending by price.

price

array of prices, in $/kWh

Type:

[float]

quantity

array of quantities, in kW

Type:

[float]

count

the number of collected bids

Type:

int

total

the total kW bidding

Type:

float

total_on

the total kW bidding that are currently on

Type:

float

total_off

the total kW bidding that are currently off

Type:

float

add_to_curve(price, quantity, is_on)

Add one point to the curve

Parameters:

• price (float) – the bid price, should be $/kWhr

• quantity (float) – the bid quantity, should be kW

• is_on (bool) – True if the load is currently on, False if not

set_curve_order(flag)

Set the curve order (by price) to ascending or descending

Parameters:

flag (str) – ‘ascending’ or ‘descending’

tesp_support.original.fncs module

Functions that provide access from Python to the FNCS library

Notes

Depending on the operating system, libfncs.dylib, libfncs.dll or libfncs.so must already be installed. Besides the defined Python wrapper functions, these pass-through library calls are always needed:

• fncs.finalize: call after the simulation completes

• fncs.time_request (long): request the next time step; blocks execution of this process until FNCS grants the requested time. Then, the process should check for messages from FNCS.

These pass-through calls are also available, but not used in TESP:

• fncs.route

• fncs.update_time_delta

• fncs.get_id

• fncs.get_simulator_count

• fncs.get_events_size

• fncs.get_keys_size

• fncs.die: stops FNCS and sends ‘die’ to other simulators

References

ctypes

FNCS

Examples

• under tesp_support, see substation.py, precool.py and tso_PYPOWER_f.py

• under examples, see loadshed/loadshed.py

tesp_support.original.fncs.agentGetEvents()

Retrieve FNCS agent messages

Returns:

concatenation of agent messages

Return type:

str

tesp_support.original.fncs.agentPublish(value)

Publish a value over FNCS, under the configured simulator name / agent name

Parameters:

value (str) – value

tesp_support.original.fncs.agentRegister(config=None)

Initialize the FNCS configuration for the agent interface

Parameters:

config (str) – a ZPL file. If None (default), provide YAML file in FNCS_CONFIG_FILE environment variable.

tesp_support.original.fncs.die()

Call FNCS die because of simulator error

tesp_support.original.fncs.finalize()

Call FNCS finalize to end connection with broker

tesp_support.original.fncs.get_event_at(i)

Retrieve FNCS message by index number after time_request returns

Returns:

one decoded FNCS event

Return type:

str

tesp_support.original.fncs.get_events()

Retrieve FNCS messages after time_request returns

Returns:

tuple of decoded FNCS events

Return type:

list

tesp_support.original.fncs.get_events_size()

Get the size of the event queue

tesp_support.original.fncs.get_id()

Find the FNCS ID

tesp_support.original.fncs.get_key_at(i)

Get the topic by index number

Parameters:

i (int) – the index number

Returns:

decoded topic name

Return type:

str

tesp_support.original.fncs.get_keys()

Find the list of topics

Returns:

decoded topic names

Return type:

[str]

tesp_support.original.fncs.get_keys_size()

Get the size of the keys

tesp_support.original.fncs.get_name()

Find the FNCS simulator name

Returns:

the name of this simulator as provided in the ZPL or YAML file

Return type:

str

tesp_support.original.fncs.get_simulator_count()

Find the FNCS simulator count

tesp_support.original.fncs.get_value(key)

Extract value from a FNCS message

Parameters:

key (str) – the topic

Returns:

decoded value

Return type:

str

tesp_support.original.fncs.get_value_at(key, i)

For list publications, get the value by index

Parameters:

• key (str) – the topic

• i (int) – the list index number

Returns:

decoded value

Return type:

str

tesp_support.original.fncs.get_values(key)

For list publications, get the list of values

Parameters:

key (str) – the topic

Returns:

decoded values

Return type:

[str]

tesp_support.original.fncs.get_values_size(key)

For list publications, find how many values were published

Parameters:

key (str) – the topic

Returns:

the number of values for this topic

Return type:

int

tesp_support.original.fncs.get_version()

Find the FNCS version

Returns:

major, minor and patch numbers

Return type:

int, int, int

tesp_support.original.fncs.initialize(config=None)

Initialize the FNCS configuration

Parameters:

config (str) – a ZPL file. If None (default), provide YAML file in FNCS_CONFIG_FILE environment variable.

tesp_support.original.fncs.is_initialized()

Determine whether the FNCS library has been initialized

Returns:

True if initialized, False if not.

Return type:

bool

tesp_support.original.fncs.publish(key, value)

Publish a value over FNCS, under the simulator name

Parameters:

• key (str) – topic under the simulator name

• value (str) – value

tesp_support.original.fncs.publish_anon(key, value)

Publish a value over FNCS, under the ‘anonymous’ simulator name

Parameters:

• key (str) – topic under ‘anonymous’

• value (str) – value

tesp_support.original.fncs.route(sender, receiver, key, value)

Route a value over FNCS from sender to receiver

Parameters:

• sender (str) – simulator routing the message

• receiver (str) – simulator to route the message to

• key (str) – topic under the simulator name

• value (str) – value

tesp_support.original.fncs.time_request(time)

FNCS time request

Parameters:

time (int) – requested time.

tesp_support.original.fncs.update_time_delta(delta)

Update simulator time delta value

Parameters:

delta (int) – time delta.

tesp_support.original.glm_dictionary module

Functions to create metadata from a GridLAB-D input (GLM) file

Metadata is written to a JSON file, for convenient loading into a Python dictionary. It can be used for agent configuration, e.g., to initialize a forecasting model based on some nominal data. It’s also used with metrics output in post-processing.

Public Functions:

glm_dict:

Writes the JSON metadata file.

tesp_support.original.glm_dictionary.append_include_file(lines, fname)

Parameters:

• lines (list<str>) –

• fname (string) –

tesp_support.original.glm_dictionary.ercotMeterName(objname)

Enforces the meter naming convention for ERCOT

Replaces anything after the last _ with mtr.

Parameters:

objname (str) – the GridLAB-D name of a house or inverter

Returns:

The GridLAB-D name of upstream meter

Return type:

str

tesp_support.original.glm_dictionary.glm_dict(name_root, ercot=False, te30=False)

Writes the JSON metadata file from a GLM file

This function reads name_root.glm and writes [name_root]_glm_dict.json The GLM file should have some meters and triplex_meters with the bill_mode attribute defined, which identifies them as billing meters that parent houses and inverters. If this is not the case, ERCOT naming rules can be applied to identify billing meters.

Parameters:

• name_root (str) – path and file name of the GLM file, without the extension

• ercot (bool) – request ERCOT billing meter naming. Defaults to false.

• te30 (bool) – request hierarchical meter handling in the 30-house test harness. Defaults to false.

tesp_support.original.glm_dictionary.isCommercialHouse(house_class)

tesp_support.original.glm_dictionary.ti_enumeration_string(tok)

if thermal_integrity_level is an integer, convert to a string for the metadata

tesp_support.original.hvac_agent module

Class that controls the responsive thermostat for one house.

Implements the ramp bidding method, with HVAC power as the bid quantity, and thermostat setting changes as the response mechanism.

class tesp_support.original.hvac_agent.hvac(hvac_dict, key, aucObj)

Bases: object

This agent manages thermostat setpoint and bidding for a house

Parameters:

• hvac_dict (dict) – dictionary row for this agent from the JSON configuration file

• key (str) – name of this agent, also key for its dictionary row

• aucObj (simple_auction) – the auction this agent bids into

name

name of this agent

Type:

str

control_mode

control mode from dict (CN_RAMP or CN_NONE, which still implements the setpoint schedule)

Type:

str

houseName

name of the corresponding house in GridLAB-D, from dict

Type:

str

meterName

name of the corresponding triplex_meter in GridLAB-D, from dict

Type:

str

period

market clearing period, in seconds, from dict

Type:

float

wakeup_start

hour of the day (0..24) for scheduled weekday wakeup period thermostat setpoint, from dict

Type:

float

daylight_start

hour of the day (0..24) for scheduled weekday daytime period thermostat setpoint, from dict

Type:

float

evening_start

hour of the day (0..24) for scheduled weekday evening (return home) period thermostat setpoint, from dict

Type:

float

night_start

hour of the day (0..24) for scheduled weekday nighttime period thermostat setpoint, from dict

Type:

float

wakeup_set

preferred thermostat setpoint for the weekday wakeup period, in deg F, from dict

Type:

float

daylight_set

preferred thermostat setpoint for the weekday daytime period, in deg F, from dict

Type:

float

evening_set

preferred thermostat setpoint for the weekday evening (return home) period, in deg F, from dict

Type:

float

night_set

preferred thermostat setpoint for the weekday nighttime period, in deg F, from dict

Type:

float

weekend_day_start

hour of the day (0..24) for scheduled weekend daytime period thermostat setpoint, from dict

Type:

float

weekend_day_set

preferred thermostat setpoint for the weekend daytime period, in deg F, from dict

Type:

float

weekend_night_start

hour of the day (0..24) for scheduled weekend nighttime period thermostat setpoint, from dict

Type:

float

weekend_night_set

preferred thermostat setpoint for the weekend nighttime period, in deg F, from dict

Type:

float

deadband

thermostat deadband in deg F, invariant, from dict

Type:

float

offset_limit

maximum allowed change from the time-scheduled setpoint, in deg F, from dict

Type:

float

ramp

bidding ramp denominator in multiples of the price standard deviation, from dict

Type:

float

price_cap

the highest allowed bid price in $/kwh, from dict

Type:

float

bid_delay

from dict, not implemented

Type:

float

use_predictive_bidding

from dict, not implemented

Type:

float

std_dev

standard deviation of expected price, determines the bidding ramp slope, initialized from aucObj

Type:

float

mean

mean of the expected price, determines the bidding ramp origin, initialized from aucObj

Type:

float

Trange

the allowed range of setpoint variation, bracketing the preferred time-scheduled setpoint

Type:

float

air_temp

current air temperature of the house in deg F

Type:

float

hvac_kw

most recent non-zero HVAC power in kW, this will be the bid quantity

Type:

float

mtr_v

current line-neutral voltage at the triplex meter

Type:

float

hvac_on

True if the house HVAC is currently running

Type:

bool

basepoint

the preferred time-scheduled thermostat setpoint in deg F

Type:

float

setpoint

the thermostat setpoint, including price response, in deg F

Type:

float

bid_price

the current bid price in $/kwh

Type:

float

cleared_price

the cleared market price in $/kwh

Type:

float

bid_accepted()

Update the thermostat setting if the last bid was accepted

The last bid is always “accepted”. If it wasn’t high enough, then the thermostat could be turned up.p

Returns:

True if the thermostat setting changes, False if not.

Return type:

bool

change_basepoint(hod, dow)

Updates the time-scheduled thermostat setting

Parameters:

• hod (float) – the hour of the day, from 0 to 24

• dow (int) – the day of the week, zero being Monday

Returns:

True if the setting changed, False if not

Return type:

bool

formulate_bid()

Bid to run the air conditioner through the next period

Returns:

bid price in $/kwh, bid quantity in kW and current HVAC on state, or None if not bidding

Return type:

[float, float, bool]

inform_bid(price)

Set the cleared_price attribute

Parameters:

price (float) – cleared price in $/kwh

set_air_temp_from_fncs_str(val)

Sets the air_temp attribute

Parameters:

val (str) – FNCS message with temperature in degrees Fahrenheit

set_air_temp_from_helics(val)

set_hvac_load_from_fncs_str(val)

Sets the hvac_load attribute, if greater than zero

Parameters:

val (str) – FNCS message with load in kW

set_hvac_load_from_helics(val)

set_hvac_state_from_fncs_str(val)

Sets the hvac_on attribute

Parameters:

val (str) – FNCS message with state, ON or OFF

set_hvac_state_from_helics(val)

set_voltage_from_fncs_str(val)

Sets the mtr_v attribute

Parameters:

val (str) – FNCS message with meter line-neutral voltage

set_voltage_from_helics(val)

tesp_support.original.parse_msout module

tesp_support.original.parse_msout.next_matrix(fp, var)

tesp_support.original.parse_msout.next_val(fp, var, bInteger=True)

tesp_support.original.parse_msout.read_most_solution(fname='msout.txt')

tesp_support.original.player_f module

tesp_support.original.player_f.load_player_loop_f(casename, keyName)

tesp_support.original.precool module

Classes for NIST TE Challenge 2 example

The precool_loop class manages time stepping and FNCS messages for the precooler agents, which adjust thermostat set points in response to time-of-use rates and over voltages. The precooler agents also estimate house equivalent thermal parameter (ETP) models based on total floor area, number of stories, number of exterior doors and estimated thermal integrity level. This ETP estimate serves as an example for other agent developers; it’s not actually used by the precooler agent.

Public Functions:

precooler_loop:

Initializes and runs the precooler agents.

tesp_support.original.precool.fncs_precool_loop(nhours, metrics_root, dict_root, response)

Function that supervises FNCS messages and time stepping for precooler agents

Opens metrics_root_agent_dict.json and metrics_root_glm_dict.json for configuration. Writes precool_metrics_root.json at completion.

Parameters:

• nhours (float) – number of hours to simulate

• metrics_root (str) – name of the case, without file extension

• dict_root (str) – repeat metrics_root, or the name of a shared case dictionary without file extension

• response (str) – combination of Price and/or Voltage

tesp_support.original.precool.helics_precool_loop(nhours, metrics_root, dict_root, response, helicsConfig)

Function that supervises FNCS messages and time stepping for precooler agents

Opens metrics_root_agent_dict.json and metrics_root_glm_dict.json for configuration. Writes precool_metrics_root.json at completion.

Parameters:

• nhours (float) – number of hours to simulate

• metrics_root (str) – name of the case, without file extension

• dict_root (str) – repeat metrics_root, or the name of a shared case dictionary without file extension

• response (str) – combination of Price and/or Voltage

• helicsConfig (str) – name for HELICS message file

tesp_support.original.precool.precool_loop(nhours, metrics_root, dict_root, response='PriceVoltage', helicsConfig=None)

Wrapper for inner_substation_loop

When inner_substation_loop finishes, timing and memory metrics will be printed for non-Windows platforms.

class tesp_support.original.precool.precooler(name, agentrow, gldrow, k, mean, stddev, lockout_time, precooling_quiet, precooling_off, bPrice, bVoltage)

Bases: object

This agent manages the house thermostat for time-of-use and overvoltage responses.

References

NIST TE Modeling and Simulation Challenge

Parameters:

• name (str) – name of this agent

• agentrow (dict) – row from the FNCS configuration dictionary for this agent

• gldrow (dict) – row from the GridLAB-D metadata dictionary for this agent’s house

• k (float) – bidding function denominator, in multiples of stddev

• mean (float) – mean of the price

• stddev (float) – standard deviation of the price

• lockout_time (float) – time in seconds between allowed changes due to voltage

• precooling_quiet (float) – time of day in seconds when precooling is allowed

• precooling_off (float) – time of day in seconds when overvoltage precooling is always turned off

name

name of this agent

Type:

str

meterName

name of the corresponding triplex_meter in GridLAB-D, from agentrow

Type:

str

night_set

preferred thermostat setpoint during nighttime hours, deg F, from agentrow

Type:

float

day_set

preferred thermostat setpoint during daytime hours, deg F, from agentrow

Type:

float

day_start_hour

hour of the day when daytime thermostat setting period begins, from agentrow

Type:

float

day_end_hour

hour of the day when daytime thermostat setting period ends, from agentrow

Type:

float

deadband

thermostat deadband in deg F, invariant, from agentrow, from agentrow

Type:

float

vthresh

meter line-to-neutral voltage that triggers precooling, from agentrow

Type:

float

toffset

temperature setpoint change for precooling, in deg F, from agentrow

Type:

float

k

bidding function denominator, in multiples of stddev

Type:

float

mean

mean of the price

Type:

float

stddev

standard deviation of the price

Type:

float

lockout_time

time in seconds between allowed changes due to voltage

Type:

float

precooling_quiet

time of day in seconds when precooling is allowed

Type:

float

precooling_off

time of day in seconds when overvoltage precooling is always turned off

Type:

float

air_temp

current air temperature of the house in deg F

Type:

float

mtr_v

current line-neutral voltage at the triplex meter

Type:

float

basepoint

the preferred time-scheduled thermostat setpoint in deg F

Type:

float

setpoint

the thermostat setpoint, including price response, in deg F

Type:

float

lastchange

time of day in seconds when the setpoint was last changed

Type:

float

precooling

True if the house is precooling, False if not

Type:

bool

ti

thermal integrity level, as enumerated for GridLAB-D, from gldrow

Type:

int

sqft (float

total floor area in square feet, from gldrow

stories

number of stories, from gldrow

Type:

int

doors

number of exterior doors, from gldrow

Type:

int

UA

heat loss coefficient

Type:

float

CA

total air thermal mass

Type:

float

HM

interior mass surface conductance

Type:

float

CM

total house thermal mass

Type:

float

check_setpoint_change(hour_of_day, price, time_seconds)

Update the setpoint for time of day and price

Parameters:

• hour_of_day (float) – the current time of day, 0..24

• price (float) – the current price in $/kwh

• time_seconds (long long) – the current FNCS time in seconds

Returns:

True if the setpoint changed, False if not

Return type:

bool

get_temperature_deviation()

For metrics, find the difference between air temperature and time-scheduled (preferred) setpoint

Returns:

absolute value of deviation

Return type:

float

make_etp_model()

Sets the ETP parameters from configuration data

References

Thermal Integrity Table Inputs and Defaults

set_air_temp(val)

Set the air_temp member variable

Parameters:

val (str) – FNCS/HELICS message with temperature in degrees Fahrenheit

set_voltage(val)

Sets the mtr_v attribute

Parameters:

val (str) – HELICS message with meter line-neutral voltage

set_voltage_f(val)

Sets the mtr_v attribute

Parameters:

val (str) – FNCS message with meter line-neutral voltage

tesp_support.original.prep_eplus module

tesp_support.original.prep_eplus.configure_eplus(caseConfig, template_dir)

tesp_support.original.prep_eplus.make_gld_eplus_case(fname, bGlmReady=False)

tesp_support.original.prep_eplus.prepare_bldg_dict(caseConfig)

tesp_support.original.prep_eplus.prepare_glm_dict(caseConfig)

tesp_support.original.prep_eplus.prepare_glm_file(caseConfig)

tesp_support.original.prep_eplus.prepare_glm_helics(caseConfig, fedMeters, fedLoadNames)

tesp_support.original.prep_eplus.prepare_run_script(caseConfig, fedMeters)

tesp_support.original.prep_eplus.writeGlmClass(theseLines, thisClass, op)

tesp_support.original.prep_precool module

Writes the precooling agent and GridLAB-D metadata for NIST TE Challenge 2 example

Public Functions:

prep_precool:

writes the JSON and YAML files

tesp_support.original.prep_precool.prep_precool(name_root, time_step=15)

Sets up agent configurations for the NIST TE Challenge 2 example

Reads the GridLAB-D data from name_root.glm; it should contain houses with thermal_integrity_level attributes. Writes:

• [name_root]_agent_dict.json, contains configuration data for the precooler agents

• [name_root]_precool.yaml, contains FNCS subscriptions for the precooler agents

• [name_root]_gridlabd.txt, a GridLAB-D include file with FNCS publications and subscriptions

Parameters:

• name_root (str) – the name of the GridLAB-D file, without extension

• time_step (int) – time step period

tesp_support.original.prep_substation module

Sets up the FNCS and agent configurations for te30 and sgip1 examples

This works for other TESP cases that have one GridLAB-D file, one EnergyPlus model, and one PYPOWER model. Use tesp_case or tesp_config modules to specify supplemental configuration data for these TESP cases, to be provided as the optional jsonfile argument to prep_substation.

Public Functions:

prep_substation:

processes a GridLAB-D file for one substation and one or more feeders

tesp_support.original.prep_substation.ProcessGLM(fileroot)

Helper function that processes one GridLAB-D file

Reads fileroot.glm and writes:

• [fileroot]_agent_dict.json, contains configuration data for the simple_auction and hvac agents

• [fileroot]_substation.yaml, contains FNCS subscriptions for the psimple_auction and hvac agents

• [fileroot]_gridlabd.txt, a GridLAB-D include file with FNCS publications and subscriptions

• [fileroot]_substation.json, contains HELICS subscriptions for the psimple_auction and hvac agents

• [fileroot]_gridlabd.json, a GridLAB-D include file with HELICS publications and subscriptions

Parameters:

fileroot (str) – path to and base file name for the GridLAB-D file, without an extension

tesp_support.original.prep_substation.prep_substation(gldfileroot, jsonfile='', bus_id=None)

Process a base GridLAB-D file with supplemental JSON configuration data

If provided, this function also reads jsonfile as created by tesp_config and used by tesp_case. This supplemental data includes time-scheduled thermostat set points (NB: do not use the scheduled set point feature within GridLAB-D, as the first FNCS messages will erase those schedules during simulation). The supplemental data also includes time step and market period, the load scaling factor to PYPOWER, ramp bidding function parameters and the EnergyPlus connection point. If not provided, the default values from te30 and sgip1 examples will be used.

Parameters:

• gldfileroot (str) – path to and base file name for the GridLAB-D file, without an extension

• jsonfile (str) – fully qualified path to an optional JSON configuration file (if not provided, an E+ connection to Eplus_load will be created)

• bus_id – substation bus identifier

tesp_support.original.process_agents module

Functions to plot data from GridLAB-D substation agents

Public Functions:

process_agents:

Reads the data and metadata, then makes the plots.

tesp_support.original.process_agents.plot_agents(diction, save_file=None, save_only=False)

tesp_support.original.process_agents.process_agents(name_root, diction_name='', save_file=None, save_only=False, print_dictionary=False)

Plots cleared price, plus bids from the first HVAC controller

This function reads auction_[name_root]_metrics.json and controller_[name_root]_metrics.json for the data; it reads [name_root]_glm_dict.json for the metadata. These must all exist in the current working directory. Makes one graph with 2 subplots:

1. Cleared price from the only auction, and bid price from the first controller

2. Bid quantity from the first controller

Parameters:

• name_root (str) – name of the TESP case, not necessarily the same as the GLM case, without the extension

• diction_name (str) – metafile name (with json extension) for a different GLM dictionary, if it’s not [name_root]_glm_dict.json. Defaults to empty.

• save_file (str) – name of a file to save plot, should include the png or pdf extension to determine type.

• save_only (bool) – set True with save_file to skip the display of the plot. Otherwise, script waits for user keypress.

• print_dictionary (bool) – set True to print dictionary.

tesp_support.original.process_agents.read_agent_metrics(path, name_root, diction_name='', print_dictionary=False)

tesp_support.original.residential_feeder_glm module

Replaces ZIP loads with houses, and optional storage and solar generation.

As this module populates the feeder backbone with houses and DER, it uses the Networkx package to perform graph-based capacity analysis, upgrading fuses, transformers and lines to serve the expected load. Transformers have a margin of 20% to avoid overloads, while fuses have a margin of 150% to avoid overloads. These can be changed by editing tables and variables in the source file.

There are two kinds of house populating methods implemented:

• Feeders with Service Transformers:

  This case applies to the full PNNL taxonomy feeders. Do not specify the taxchoice argument to populate_feeder. Each service transformer receiving houses will have a short service drop and a small number of houses attached.

• Feeders without Service Transformers:

  This applies to the reduced-order ERCOT feeders. To invoke this mode, specify the taxchoice argument to populate_feeder. Each primary load to receive houses will have a large service transformer, large service drop and large number of houses attached.

References

GridAPPS-D Feeder Models <https://github.com/GRIDAPPSD/Powergrid-Models>

Public Functions:

populate_feeder:

processes one GridLAB-D input file

Todo

• Verify the level zero mobile home thermal integrity properties; these were copied from the MATLAB feeder generator

tesp_support.original.residential_feeder_glm.Find1PhaseXfmr(kva)

Select a standard 1-phase transformer size, with data

Standard sizes are 5, 10, 15, 25, 37.5, 50, 75, 100, 167, 250, 333 or 500 kVA

Parameters:

kva (float) – the minimum transformer rating

Returns:

the kva, %r, %x, %no-load loss, %magnetizing current

Return type:

[float,float,float,float,float]

tesp_support.original.residential_feeder_glm.Find1PhaseXfmrKva(kva)

Select a standard 1-phase transformer size, with some margin

Standard sizes are 5, 10, 15, 25, 37.5, 50, 75, 100, 167, 250, 333 or 500 kVA

Parameters:

kva (float) – the minimum transformer rating

Returns:

the kva size, or 0 if none found

Return type:

float

tesp_support.original.residential_feeder_glm.Find3PhaseXfmr(kva)

Select a standard 3-phase transformer size, with data

Standard sizes are 30, 45, 75, 112.5, 150, 225, 300, 500, 750, 1000, 1500, 2000, 2500, 3750, 5000, 7500 or 10000 kVA

Parameters:

kva (float) – the minimum transformer rating

Returns:

the kva, %r, %x, %no-load loss, %magnetizing current

Return type:

[float,float,float,float,float]

tesp_support.original.residential_feeder_glm.Find3PhaseXfmrKva(kva)

Select a standard 3-phase transformer size, with some margin

Standard sizes are 30, 45, 75, 112.5, 150, 225, 300, 500, 750, 1000, 1500, 2000, 2500, 3750, 5000, 7500 or 10000 kVA

Parameters:

kva (float) – the minimum transformer rating

Returns:

the kva size, or 0 if none found

Return type:

float

tesp_support.original.residential_feeder_glm.FindFuseLimit(amps)

Find a Fuse size that’s unlikely to melt during power flow

Will choose a fuse size of 40, 65, 100 or 200 Amps. If that’s not large enough, will choose a recloser size of 280, 400, 560, 630 or 800 Amps. If that’s not large enough, will choose a breaker size of 600 (skipped), 1200 or 2000 Amps. If that’s not large enough, will choose 999999.

Parameters:

amps (float) – the maximum load current expected; some margin will be added

Returns:

the GridLAB-D fuse size to insert

Return type:

float

tesp_support.original.residential_feeder_glm.ProcessTaxonomyFeeder(outname, rootname, vll, vln, avghouse, avgcommercial)

Parse and re-populate one backbone feeder, usually but not necessarily one of the PNNL taxonomy feeders

This function:

• reads and parses the backbone model from rootname.glm

• replaces loads with houses and DER

• upgrades transformers and fuses as needed, based on a radial graph analysis

• writes the repopulated feeder to outname.glm

Parameters:

• outname (str) – the output feeder model name

• rootname (str) – the input (usually taxonomy) feeder model name

• vll (float) – the feeder primary line-to-line voltage

• vln (float) – the feeder primary line-to-neutral voltage

• avghouse (float) – the average house load in kVA

• avgcommercial (float) – the average commercial load in kVA, not used

tesp_support.original.residential_feeder_glm.accumulate_load_kva(data)

Add up the total kva in a load-bearing object instance

Considers constant_power_A/B/C/1/2/12 and power_1/2/12 attributes

Parameters:

data (dict) – dictionary of data for a selected GridLAB-D instance

tesp_support.original.residential_feeder_glm.buildingTypeLabel(rgn, bldg, ti)

Formatted name of region, building type name and thermal integrity level

Parameters:

• rgn (int) – region number 1..5

• bldg (int) – 0 for single-family, 1 for apartment, 2 for mobile home

• ti (int) – thermal integrity level, 0..6 for single-family, only 0..2 valid for apartment or mobile home

tesp_support.original.residential_feeder_glm.checkResidentialBuildingTable()

Verify that the regional building parameter histograms sum to one

tesp_support.original.residential_feeder_glm.connect_ercot_commercial(op)

For the reduced-order ERCOT feeders, add a billing meter to the commercial load points, except small ZIPLOADs

Parameters:

op (file) – an open GridLAB-D input file

tesp_support.original.residential_feeder_glm.connect_ercot_houses(model, h, op, vln, vsec)

For the reduced-order ERCOT feeders, add houses and a large service transformer to the load points

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• op (file) – an open GridLAB-D input file

• vln (float) – the primary line-to-neutral voltage

• vsec (float) – the secondary line-to-neutral voltage

tesp_support.original.residential_feeder_glm.identify_ercot_houses(model, h, t, avgHouse, rgn)

For the reduced-order ERCOT feeders, scan each primary load to determine the number of houses it should have

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class name to scan

• avgHouse (float) – the average house load in kva

• rgn (int) – the region number, 1..5

tesp_support.original.residential_feeder_glm.identify_xfmr_houses(model, h, t, seg_loads, avgHouse, rgn)

For the full-order feeders, scan each service transformer to determine the number of houses it should have

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class name to scan

• seg_loads (dict) – dictionary of downstream load (kva) served by each GridLAB-D link

• avgHouse (float) – the average house load in kva

• rgn (int) – the region number, 1..5

tesp_support.original.residential_feeder_glm.is_edge_class(s)

Identify switch, fuse, recloser, regulator, transformer, overhead_line, underground_line and triplex_line instances

Edge class is networkx terminology. In GridLAB-D, edge classes are called links.

Parameters:

s (str) – the GridLAB-D class name

Returns:

True if an edge class, False otherwise

Return type:

bool

tesp_support.original.residential_feeder_glm.is_node_class(s)

Identify node, load, meter, triplex_node or triplex_meter instances

Parameters:

s (str) – the GridLAB-D class name

Returns:

True if a node class, False otherwise

Return type:

bool

tesp_support.original.residential_feeder_glm.log_model(model, h)

Prints the whole parsed model for debugging

Parameters:

• model (dict) – parsed GridLAB-D model

• h (dict) – object ID hash

tesp_support.original.residential_feeder_glm.obj(parent, model, line, itr, oidh, octr)

Store an object in the model structure

Parameters:

• parent (str) – name of parent object (used for nested object defs)

• model (dict) – dictionary model structure

• line (str) – glm line containing the object definition

• itr (iter) – iterator over the list of lines

• oidh (dict) – hash of object id’s to object names

• octr (int) – object counter

Returns:

the current line and updated octr

Return type:

str, int

tesp_support.original.residential_feeder_glm.populate_all_feeders()

Wrapper function that batch processes all taxonomy feeders in the casefiles table (see source file)

tesp_support.original.residential_feeder_glm.populate_feeder(configfile=None, config=None, taxconfig=None)

Wrapper function that processes one feeder. One or two keyword arguments must be supplied.

Parameters:

• configfile (str) – JSON file name for the feeder population data, mutually exclusive with config

• config (dict) – dictionary of feeder population data already read in, mutually exclusive with configfile

• taxconfig (dict) – dictionary of custom taxonomy data for ERCOT processing

tesp_support.original.residential_feeder_glm.randomize_commercial_skew()

tesp_support.original.residential_feeder_glm.randomize_residential_skew()

tesp_support.original.residential_feeder_glm.randomize_skew(value, skew_max)

tesp_support.original.residential_feeder_glm.replace_commercial_loads(model, h, t, avgBuilding)

For the full-order feeders, scan each load with load_class==C to determine the number of zones it should have

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class name to scan

• avgBuilding (float) – the average building in kva

tesp_support.original.residential_feeder_glm.selectResidentialBuilding(rgnTable, prob)

Selects the building with region and probability

Parameters:

• rgnTable –

• prob –

tesp_support.original.residential_feeder_glm.selectSetpointBins(bldg, rand)

Randomly choose a histogram row from the cooling and heating setpoints

The random number for the heating and cooling set points row is generated internally.

Parameters:

• bldg (int) – 0 for single-family, 1 for apartment, 2 for mobile home

• rand (float) – random number [0..1] for the cooling setpoint row

tesp_support.original.residential_feeder_glm.selectThermalProperties(bldgIdx, tiIdx)

Retrieve the building thermal properties for a given type and integrity level

Parameters:

• bldgIdx (int) – 0 for single-family, 1 for apartment, 2 for mobile home

• tiIdx (int) – 0..6 for single-family, 0..2 for apartment or mobile home

tesp_support.original.residential_feeder_glm.union_of_phases(phs1, phs2)

Collect all phases on both sides of a connection

Parameters:

• phs1 (str) – first phasing

• phs2 (str) – second phasing

Returns:

union of phs1 and phs2

Return type:

str

tesp_support.original.residential_feeder_glm.write_commercial_loads(rgn, key, op)

Put commercial building zones and ZIP loads into the model

Parameters:

• rgn (int) – region 1..5 where the building is located

• key (str) – GridLAB-D load name that is being replaced

• op (file) – open file to write to

tesp_support.original.residential_feeder_glm.write_config_class(model, h, t, op)

Write a GridLAB-D configuration (i.e. not a link or node) class

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class

• op (file) – an open GridLAB-D input file

tesp_support.original.residential_feeder_glm.write_ercot_small_loads(basenode, op, vnom)

For the reduced-order ERCOT feeders, write loads that are too small for houses

Parameters:

• basenode (str) – the GridLAB-D node name

• op (file) – an open GridLAB-D input file

• vnom (float) – the primary line-to-neutral voltage

tesp_support.original.residential_feeder_glm.write_houses(basenode, op, vnom, bIgnoreThermostatSchedule=True, bWriteService=True, bTriplex=True, setpoint_offset=1.0)

Put houses, along with solar panels and batteries, onto a node

Parameters:

• basenode (str) – GridLAB-D node name

• op (file) – open file to write to

• vnom (float) – nominal line-to-neutral voltage at basenode

tesp_support.original.residential_feeder_glm.write_kersting_quadriplex(fp, kva)

Writes a quadriplex_line_configuration based on 1/0 AA example from Kersting’s book

The conductor capacity is 202 amps, so the number of triplex in parallel will be kva/sqrt(3)/0.208/202

tesp_support.original.residential_feeder_glm.write_kersting_triplex(fp, kva)

Writes a triplex_line_configuration based on 1/0 AA example from Kersting’s book

The conductor capacity is 202 amps, so the number of triplex in parallel will be kva/0.12/202

tesp_support.original.residential_feeder_glm.write_link_class(model, h, t, seg_loads, op, want_metrics=False)

Write a GridLAB-D link (i.e. edge) class

Parameters:

• model (dict) – the parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class

• seg_loads (dict) – a dictionary of downstream loads for each link

• op (file) – an open GridLAB-D input file

tesp_support.original.residential_feeder_glm.write_local_triplex_configurations(op)

Write a 4/0 AA triplex configuration

Parameters:

op (file) – an open GridLAB-D input file

tesp_support.original.residential_feeder_glm.write_node_house_configs(fp, xfkva, xfkvll, xfkvln, phs, want_inverter=False)

Writes transformers, inverter settings for GridLAB-D houses at a primary load point.

An aggregated single-phase triplex or three-phase quadriplex line configuration is also written, based on estimating enough parallel 1/0 AA to supply xfkva load. This function should only be called once for each combination of xfkva and phs to use, and it should be called before write_node_houses.

Parameters:

• fp (file) – Previously opened text file for writing; the caller closes it.

• xfkva (float) – the total transformer size to serve expected load; make this big enough to avoid overloads

• xfkvll (float) – line-to-line voltage [kV] on the primary. The secondary voltage will be 208 three-phase

• xfkvln (float) – line-to-neutral voltage [kV] on the primary. The secondary voltage will be 120/240 for split secondary

• phs (str) – either ‘ABC’ for three-phase, or concatenation of ‘A’, ‘B’, and/or ‘C’ with ‘S’ for single-phase to triplex

• want_inverter (bool) – True to write the IEEE 1547-2018 smarter inverter function setpoints

tesp_support.original.residential_feeder_glm.write_node_houses(fp, node, region, xfkva, phs, nh=None, loadkw=None, house_avg_kw=None, secondary_ft=None, storage_fraction=0.0, solar_fraction=0.0, electric_cooling_fraction=0.5, node_metrics_interval=None, random_seed=False)

Writes GridLAB-D houses to a primary load point.

One aggregate service transformer is included, plus an optional aggregate secondary service drop. Each house has a separate meter or triplex_meter, each with a common parent, either a node or triplex_node on either the transformer secondary, or the end of the service drop. The houses may be written per phase, i.e., unbalanced load, or as a balanced three-phase load. The houses should be #included into a master GridLAB-D file. Before using this function, call write_node_house_configs once, and only once, for each combination xfkva/phs that will be used.

Parameters:

• fp (file) – Previously opened text file for writing; the caller closes it.

• node (str) – the GridLAB-D primary node name

• region (int) – the taxonomy region for housing population, 1..6

• xfkva (float) – the total transformer size to serve expected load; make this big enough to avoid overloads

• phs (str) – ‘ABC’ for three-phase balanced distribution, ‘AS’, ‘BS’, or ‘CS’ for single-phase triplex

• nh (int) – directly specify the number of houses; an alternative to loadkw and house_avg_kw

• loadkw (float) – total load kW that the houses will represent; with house_avg_kw, an alternative to nh

• house_avg_kw (float) – average house load in kW; with loadkw, an alternative to nh

• secondary_ft (float) – if not None, the length of adequately sized secondary circuit from transformer to the meters

• electric_cooling_fraction (float) – fraction of houses to have air conditioners

• solar_fraction (float) – fraction of houses to have rooftop solar panels

• storage_fraction (float) – fraction of houses with solar panels that also have residential storage systems

• node_metrics_interval (int) – if not None, the metrics collection interval in seconds for houses, meters, solar and storage at this node

• random_seed (bool) – if True, reseed each function call. Default value False provides repeatability of output.

tesp_support.original.residential_feeder_glm.write_one_commercial_zone(bldg, op)

Write one pre-configured commercial zone as a house

Parameters:

• bldg – dictionary of GridLAB-D house and zipload attributes

• op (file) – open file to write to

tesp_support.original.residential_feeder_glm.write_small_loads(basenode, op, vnom)

Write loads that are too small for a house, onto a node

Parameters:

• basenode (str) – GridLAB-D node name

• op (file) – open file to write to

• vnom (float) – nominal line-to-neutral voltage at basenode

tesp_support.original.residential_feeder_glm.write_solar_inv_settings(op)

Writes volt-var and volt-watt settings for solar inverters

Parameters:

op (file) – an open GridLAB-D input file

tesp_support.original.residential_feeder_glm.write_substation(op, name, phs, vnom, vll)

Write the substation swing node, transformer, metrics collector and fncs_msg object

Parameters:

• op (file) – an open GridLAB-D input file

• name (str) – node name of the primary (not transmission) substation bus

• phs (str) – primary phasing in the substation

• vnom (float) – not used

• vll (float) – feeder primary line-to-line voltage

tesp_support.original.residential_feeder_glm.write_tariff(op)

Writes tariff information to billing meters

Parameters:

op (file) – an open GridLAB-D input file

tesp_support.original.residential_feeder_glm.write_voltage_class(model, h, t, op, vprim, vll, secmtrnode)

Write GridLAB-D instances that have a primary nominal voltage, i.e., node, meter and load

Parameters:

• model (dict) – a parsed GridLAB-D model

• h (dict) – the object ID hash

• t (str) – the GridLAB-D class name to write

• op (file) – an open GridLAB-D input file

• vprim (float) – the primary nominal line-to-neutral voltage

• vll (float) – the primary nominal line-to-line voltage

• secmtrnode (dict) – key to [transfomer kva, phasing, nominal voltage] by secondary node name

tesp_support.original.residential_feeder_glm.write_xfmr_config(key, phs, kvat, vnom, vsec, install_type, vprimll, vprimln, op)

Write a transformer_configuration

Parameters:

• key (str) – name of the configuration

• phs (str) – primary phasing

• kvat (float) – transformer rating in kVA

• vnom (float) – primary voltage rating, not used any longer (see vprimll and vprimln)

• vsec (float) – secondary voltage rating, should be line-to-neutral for single-phase or line-to-line for three-phase

• install_type (str) – should be VAULT, PADMOUNT or POLETOP

• vprimll (float) – primary line-to-line voltage, used for three-phase transformers

• vprimln (float) – primary line-to-neutral voltage, used for single-phase transformers

• op (file) – an open GridLAB-D input file

tesp_support.original.simple_auction module

Double-auction mechanism for the 5-minute markets in te30 and sgip1 examples

The substation_loop module manages one instance of this class per GridLAB-D substation.

Todo

• Initialize and update price history statistics

• Allow for adjustment of clearing_scalar

• Handle negative price bids from HVAC agents, currently they are discarded

• Distribute marginal quantities and fractions; these are not currently applied to HVACs

2021-10-29 TDH: Key assumptions we need to refactor out of this: - This is a retail market working under a wholesale market. We want something more general that can operate at any market level. - Load state is not necessary information to run a market. PNNL TSP has traditionally had the construct of responsive and unresponsive loads and I think the idea is crucial for being able to get good quadratic curve fits on the demand curve (by lopping off the unresponsive portion) but I think we should refactor this so that these assumptions are not so tightly integrated with the formulation.

class tesp_support.original.simple_auction.simple_auction(diction, key)

Bases: object

This class implements a simplified version of the double-auction market embedded in GridLAB-D.

References

Market Module Overview - Auction

Parameters:

• diction (diction) – a row from the agent configuration JSON file

• key (str) – the name of this agent, which is the market key from the agent configuration JSON file

name

the name of this auction, also the market key from the configuration JSON file

Type:

str

std_dev

the historical standard deviation of the price, in $/kwh, from diction

Type:

float

mean

the historical mean price in $/kwh, from diction

Type:

float

price_cap

the maximum allowed market clearing price, in $/kwh, from diction

Type:

float

max_capacity_reference_bid_quantity

this market’s maximum capacity, likely defined by a physical limitation in the circuit(s) being managed.

Type:

float

statistic_mode

always 1, not used, from diction

Type:

int

stat_mode

always ST_CURR, not used, from diction

Type:

str

stat_interval

always 86400 seconds, for one day, not used, from diction

Type:

str

stat_type

always mean and standard deviation, not used, from diction

Type:

str

stat_value

always zero, not used, from diction

Type:

str

curve_buyer

data structure to accumulate buyer bids

Type:

curve

curve_seller

data structure to accumulate seller bids

Type:

curve

refload

the latest substation load from GridLAB-D. This is initially assumed to be all unresponsive and using the load state parameter when adding demand bids (which are generally price_responsive) all loads that bid and are on are removed from the assumed unresponsive load value

Type:

float

lmp

the latest locational marginal price from the bulk system market

Type:

float

unresp

unresponsive load, i.e., total substation load less the bidding, running HVACs

Type:

float

agg_unresp

aggregated unresponsive load, i.e., total substation load less the bidding, running HVACs

Type:

float

agg_resp_max

total load of the bidding HVACs

Type:

float

agg_deg

degree of the aggregate bid curve polynomial, should be 0 (zero or one bids), 1 (2 bids) or 2 (more bids)

Type:

int

agg_c2

second-order coefficient of the aggregate bid curve

Type:

float

agg_c1

first-order coefficient of the aggregate bid curve

Type:

float

clearing_type

describes the solution type or boundary case for the latest market clearing

Type:

ClearingType

clearing_quantity

quantity at the last market clearing

Type:

float

clearing_price

price at the last market clearing

Type:

float

marginal_quantity

quantity of a partially accepted bid

Type:

float

marginal_frac

fraction of the bid quantity accepted from a marginal buyer or seller

Type:

float

clearing_scalar

used for interpolation at boundary cases, always 0.5

Type:

float

add_unresponsive_load(quantity)

aggregate_bids()

Aggregates the unresponsive load and responsive load bids for submission to the bulk system market

clear_bids()

Re-initializes curve_buyer and curve_seller, sets the unresponsive load estimate to the total substation load.

clear_market(tnext_clear=0, time_granted=0)

Solves for the market clearing price and quantity

Uses the current contents of curve_seller and curve_buyer. Updates clearing_price, clearing_quantity, clearing_type, marginal_quantity and marginal_frac.

Parameters:

• tnext_clear (int) – next clearing time in seconds, should be <= time_granted, for the log file only

• time_granted (int) – the current time in seconds, for the log file only

collect_bid(bid)

Gather HVAC bids into curve_buyer

Also adjusts the unresponsive load estimate, by subtracting the HVAC power if the HVAC is on.

Parameters:

bid ([float, float, bool]) – price in $/kwh, quantity in kW and the HVAC on state

initAuction()

Sets the clearing_price and lmp to the mean price

2021-10-29 TDH: TODO - Any reason we can’t put this in constructor?

set_lmp(lmp)

Sets the lmp attribute

Parameters:

lmp (float) – locational marginal price from the bulk system market

set_refload(kw)

Sets the refload attribute

Parameters:

kw (float) – GridLAB-D substation load in kw

supplier_bid(bid)

Gather supplier bids into curve_seller

Use this to enter curves in step-wise blocks.

Parameters:

bid ([float, float]) – price in $/kwh, quantity in kW

surplusCalculation(tnext_clear=0, time_granted=0)

Calculates consumer surplus (and its average) and supplier surplus.

This function goes through all the bids higher than clearing price from buyers to calculate consumer surplus, and also accumulates the quantities that will be cleared while doing so. Of the cleared quantities, the quantity for unresponsive loads are also collected. Then go through each seller to calculate supplier surplus. Part of the supplier surplus corresponds to unresponsive load are excluded and calculated separately.

Parameters:

• tnext_clear (int) – next clearing time in seconds, should be <= time_granted, for the log file only

• time_granted (int) – the current time in seconds, for the log file only

update_statistics()

Update price history statistics - not implemented

tesp_support.original.substation_f module

Manages the simple_auction and hvac agents for the te30 and sgip1 examples

Public Functions:

substation_loop_f:

initializes and runs the agents

Todo

• Getting an overflow error when killing process - investigate whether that happens if simulation runs to completion

• Allow changes in the starting date and time; now it’s always midnight on July 1, 2013

• Allow multiple markets per substation, e.g., 5-minute and day-ahead for the DSO+T study

tesp_support.original.tesp_case module

Creates and fills a subdirectory with files to run a TESP simulation

Use tesp_config to graphically edit the case configuration

Public Functions:

make_tesp_case:

sets up for a single-shot TESP case

make_monte_carlo_cases:

sets up for a Monte Carlo TESP case of up to 20 shots

first_tesp_feeder:

customization of make_tesp_case that will accept more feeders

add_tesp_feeder:

add another feeder to the case directory created by first_tesp_feeder

tesp_support.original.tesp_case.add_tesp_feeder(cfgfile)

Wrapper function to start a single TESP case configuration.

This function opens the JSON file, and calls write_tesp_case for just the GridLAB-D files. The subdirectory targetdir doesn’t have to match the case name in cfgfile, and it should be created first with make_tesp_case

Parameters:

cfgfile (str) – JSON file containing the TESP case configuration

tesp_support.original.tesp_case.make_monte_carlo_cases(cfgfile='test.json')

Writes up to 20 TESP simulation case setups to a directory for Monte Carlo simulations

Latin hypercube sampling is recommended; sample values may be specified via tesp_config

Parameters:

cfgfile (str) – JSON file containing the TESP case configuration

tesp_support.original.tesp_case.make_tesp_case(cfgfile='test.json')

Wrapper function for a single TESP case configuration.

This function opens the JSON file, and calls write_tesp_case

Parameters:

cfgfile (str) – JSON file containing the TESP case configuration

tesp_support.original.tesp_case.modify_mc_config(config, mcvar, band, sample)

Helper function that modifies the Monte Carlo configuration for a specific sample, i.e., shot

For variables that have a band associated, the agent preparation code will apply additional randomization. This applies to thermostat ramps, offset limits, and period starting or ending times. For those variables, the Monte Carlo sample value is a mean, and the agent preparation code will apply a uniform distribution to obtain the actual value for each house.

tesp_support.original.tesp_case.write_tesp_case(config, cfgfile, freshdir=True)

Writes the TESP case from data structure to JSON file

This function assumes one GridLAB-D, one EnergyPlus, one PYPOWER and one substation_loop federate will participate in the TESP simulation. See the DSO+T study functions, which are customized to ERCOT 8-bus and 200-bus models, for examples of other configurations.

The TESP support directories, working directory and case name are all specified in config. This function will create one directory as follows:

• workdir = config[‘SimulationConfig’][‘WorkingDirectory’]

• casename = config[‘SimulationConfig’][‘CaseName’]

• new directory created will be casedir = workdir/casename

This function will read or copy several files that are specified in the config. They should all exist. These include taxonomy feeders, GridLAB-D schedules, weather files, a base EnergyPlus model, a base PYPOWER model, and supporting scripts for the end user to invoke from the casedir. The user could add more base model files weather files or schedule files under the TESP support directory, where this tesp_case module will be able to find and use them.

This function will launch and wait for 6 subprocesses to assist in the case configuration. All must execute successfully:

• TMY3toTMY2_ansi, which converts the user-selected TMY3 file to TMY2

• tesp.convert_tmy2_to_epw, which converts the TMY2 file to EPW for EnergyPlus

• tesp.TMY3toCSV, which converts the TMY3 file to CSV for the weather agent

• tesp.populate_feeder, which populates the user-selected taxonomy feeder with houses and DER

• tesp.glm_dict, which creates metadata for the populated feeder

• tesp.prep_substation, which creates metadata and FNCS/HELICS configurations for the substation agents

As the configuration process finishes, several files are written to casedir:

• casename.glm: the GridLAB-D model, copied and modified from the TESP support directory

• casename_gridlabd.txt: FNCS subscriptions and publications, included by casename.glm

• casename_agent_dict.json: metadata for the simple_auction and hvac agents

• casename_glm_dict.json: metadata for casename.glm

• casename_pp.json: the PYPOWER model, copied and modified from the TESP support directory

• casename_substation.yaml: FNCS subscriptions and time step for the substation, which manages the simple_auction and hvac controllers

• NonGLDLoad.txt: non-responsive load data for the PYPOWER model buses, currently hard-wired for the 9-bus model. See the ERCOT case files for examples of expanded options.

• SchoolDualController.idf: the EnergyPlus model, copied and modified from the TESP support directory

• WA-Yakima_Air_Terminal.epw: the selected weather file for EnergyPlus, others can be selected

• WA-Yakima_Air_Terminal.tmy3: the selected weather file for GridLAB-D, others can be selected

• appliance_schedules.glm: time schedules for GridLAB-D

• clean.sh: Linux/Mac OS X helper to clean up simulation outputs

• commercial_schedules.glm: non-responsive non-responsive time schedules for GridLAB-D, invariant

• eplus.yaml: FNCS subscriptions and time step for EnergyPlus

• eplus_agent.yaml: FNCS subscriptions and time step for the EnergyPlus agent

• kill5570.sh: Linux/Mac OS X helper to kill all federates listening on port 5570

• launch_auction.py: helper script for the GUI solution monitor to launch the substation federate

• launch_pp.py: helper script for the GUI solution monitor to launch the PYPOWER federate

• monitor.py: helper to launch the GUI solution monitor (FNCS_CONFIG_FILE envar must be set for this process, see gui.sh under examples/te30)

• plots.py: helper script that will plot a selection of case outputs

• pypower.yaml: FNCS subscriptions and time step for PYPOWER

• run.sh: Linux/Mac OS X helper to launch the TESP simulation

• monitor.json: shell commands and other configuration data for the solution monitor GUI

• tesp_monitor.json: HELICS subscriptions and time step for the solution monitor GUI

• tesp_monitor.yaml: FNCS subscriptions and time step for the solution monitor GUI

• water_and_setpoint_schedule_v5.glm: non-responsive time schedules for GridLAB-D, invariant

• weather.dat: CSV file of temperature, pressure, humidity, solar direct, solar diffuse and wind speed

• casename_gridlabd.txt: HELICS subscriptions and publications, included by casename.glm

• casename_substation.json: HELICS subscriptions and time step for the substation, which manages the simple_auction and hvac controllers

• eplus.json: HELICS subscriptions and time step for EnergyPlus

• eplus_agent.json: HELICS subscriptions and time step for the EnergyPlus agent

• pypower.json: HELICS subscriptions and time step for PYPOWER

Parameters:

• config (dict) – the complete case data structure

• cfgfile (str) – the name of the JSON file that was read

• freshdir (bool) – flag to create the directory and base files anew

tesp_support.original.tesp_config module

Presents a GUI to configure and package TESP cases

Public Functions:

show_tesp_config:

Initializes and runs the GUI

References

Graphical User Interfaces with Tk

class tesp_support.original.tesp_config.TespConfigGUI(master)

Bases: object

Manages a seven-page GUI for case configuration

The GUI opens and saves a JSON file in the format used by tesp.tesp_config

Todo

• Possible data loss if the user updated the number of Monte Carlo cases, but didn’t click the Update button before saving the case configuration.

nb

the top-level GUI with tabbed pages

Type:

Notebook

f1

the page for date/time setup, along with T&D files, weather files and file paths

Type:

Frame

f2

the page for feeder generator setup

Type:

Frame

f3

the page for PYPOWER setup

Type:

Frame

f4

the page for EnergyPlus setup

Type:

Frame

f5

the page for simple_auction and hvac agent setup

Type:

Frame

f6

the page for time-scheduled thermostat settings

Type:

Frame

f7

the page for Monte Carlo setup

Type:

Frame

AttachFrame(tag, vars)

Creates a GUI page and loads it with data

Label, Combobox and Entry (i.e. edit) controls are automatically created for each row of data

Parameters:

• tag (str) – the name of the Frame, i.e., GUI page

• vars (dict) – the section of case configuration data to be loaded onto this new GUI page

InitializeMonteCarlo(n)

Makes default variable choices and then initializes the Monte Carlo GUI page

Parameters:

n (int) – the number of Monte Carlo shots

JsonToSection(jsn, vars)

Helper function that transfers a JSON file segment into GUI data structures

Parameters:

• jsn (dict) – the loaded JSON file

• vars (dict) – the local data structure

OpenConfig()

Opens a JSON case configuration; transfers its data to the GUI

ReadFrame(f, vars)

Helper function that reads values from gridded GUI controls into the local case configuration

Parameters:

• f (Frame) – the GUI page to read

• vars (dict) – the local data structure to update

ReadLatLong()

Updates the Latitude and Longitude from TMY3 file

ReloadFrame(f, vars)

Helper function to recreate the GUI page controls and load them with values

Parameters:

• f (Frame) – the GUI page to reload

• vars (dict) – the section of case configuration with values to be loaded

SaveConfig()

Updates the local case configuration from the GUI, queries user for a file name, and then saves case configuration to that file

SizeMonteCarlo(n)

Initializes the Monte Carlo data structures with variable choices and samples

Parameters:

n (int) – the number of Monte Carlo shots

SizeMonteCarloFrame(f)

Update the Monte Carlo page to match the number of shots and variables

Parameters:

f (Frame) – the Monte Carlo GUI page

UpdateEMS(event)

UpdateMonteCarloFrame()

Transfer data from the Monte Carlo page into the case configuration

mcBand(var)

Find the band size corresponding to each Monte Carlo variable choice

Parameters:

var (str) – one of ElectricCoolingParticipation, ThermostatRampMid, ThermostatOffsetLimitMid, WeekdayEveningStartMid or WeekdayEveningSetMid

mcSample(var)

Return an appropriate random value for each Monte Carlo variable choice

Parameters:

var (str) – one of ElectricCoolingParticipation, ThermostatRampMid, ThermostatOffsetLimitMid, WeekdayEveningStartMid or WeekdayEveningSetMid

update_entry(ctl, val)

tesp_support.original.tesp_config.show_tesp_config()

Runs the GUI. Reads and writes JSON case configuration files.

tesp_support.original.tesp_monitor module

Presents a GUI to launch a TESP simulation and monitor its progress

Public Functions:

show_tesp_monitor:

Initializes and runs the monitor GUI

References

Graphical User Interfaces with Tk

Matplotlib Animation

class tesp_support.original.tesp_monitor.TespMonitorGUI(master, HELICS=True)

Bases: object

Manages a GUI with 4 plotted variables, and buttons to stop TESP

The GUI reads a JSON file with scripted shell commands to launch other HELICS/FNCS federates, and a YAML file with HELICS/FNCS subscriptions to update the solution status. Both JSON and YAML files are written by tesp.tesp_config The plotted variables provide a sign-of-life and sign-of-stability indication for each of the major federates in the te30 or sgip1 examples, namely GridLAB-D, PYPOWER, EnergyPlus, and the substation_loop that manages a simple_auction with multiple hvac agents. If a solution appears to be unstable or must be stopped for any other reason, exiting the solution monitor will do so.

The plots are created and updated with animated and bit-blitted Matplotlib graphs hosted on a TkInter GUI. When the JSON and YAML files are loaded, the x axis is laid out to match the total TESP simulation time range.

Parameters:

master –

root

the TCL Tk toolkit instance

Type:

Tk

top

the top-level TCL Tk Window

Type:

Window

labelvar

used to display the monitor JSON configuration file path

Type:

StringVar

hrs

x-axis data array for time in hours, shared by all plots

Type:

[float]

y0

y-axis data array for PYPOWER bus voltage

Type:

[float]

y1

y-axis data array for EnergyPlus load

Type:

[float]

y2lmp

y-axis data array for PYPOWER LMP

Type:

[float]

y2auc

y-axis data array for simple_auction cleared_price

Type:

[float]

y3fncs

y-axis data array for GridLAB-D load via FNCS

Type:

[float]

y3gld

y-axis data array for sample-and-hold GridLAB-D load

Type:

[float]

gld_load

the most recent load published by GridLAB-D; due to the deadband, this value isn’t necessary published at every FNCS time step

Type:

float

y0min

the first y axis minimum value

Type:

float

y0max

the first y axis maximum value

Type:

float

y1min

the second y axis minimum value

Type:

float

y1max

the second y axis maximum value

Type:

float

y2min

the third y axis minimum value

Type:

float

y2max

the third y axis maximum value

Type:

float

y3min

the fourth y axis minimum value

Type:

float

y3max

the fourth y axis maximum value

Type:

float

hour_stop

the maximum x axis time value to plot

Type:

float

ln0

the plotted PYPOWER bus voltage, color GREEN

Type:

Line2D

ln1

the plotted EnergyPlus load, color RED

Type:

Line2D

ln2lmp

the plotted PYPOWER locational marginal price (LMP), color BLUE

Type:

Line2D

ln2auc

the plotted simple_auction cleared_price, color BLACK

Type:

Line2D

ln3gld

the plotted sample-and-hold GridLAB-D substation load, color MAGENTA

Type:

Line2D

ln3fncs

the plotted GridLAB-D substation load published via FNCS; may be zero if not published for the current animation frame, color CYAN

Type:

Line2D

fig

animated Matplotlib figure hosted on the GUI

Type:

Figure

ax

set of 4 xy axes to plot on

Type:

Axes

canvas

a TCL Tk canvas that can host Matplotlib

Type:

FigureCanvasTkAgg

bFNCSactive

True if a TESP simulation is running with other FNCS federates, False if not

Type:

bool

bHELICSactive

True if a TESP simulation is running with other HELICS federates, False if not

Type:

bool

OpenConfig()

Read the JSON configuration file for this monitor, and initialize the plot axes

Quit()

Shuts down this monitor, and also shuts down FNCS/HELICS if active

expand_limits(v, vmin, vmax)

Whenever a variable meets a vertical axis limit, expand the limits with 10% padding above and below

Parameters:

• v (float) – the out of range value

• vmin (float) – the current minimum vertical axis value

• vmax (float) – the current maximum vertical axis value

Returns:

the new vmin and vmax

Return type:

float, float

kill_all()

Shut down all FNCS/HELICS federates in TESP, except for this monitor

launch_all()

Launches the simulators, initializes HELICS and starts the animated plots

launch_all_f()

Launches the simulators, initializes FNCS and starts the animated plots

on_closing()

Verify whether the user wants to stop TESP simulations before exiting the monitor

This monitor is itself a FNCS federate, so it can not be shut down without shutting down all other FNCS federates in the TESP simulation.

reset_plot()

update_plots(i)

This function is called by Matplotlib for each animation frame

Each time called, collect HELICS messages until the next time to plot has been reached. Then update the plot quantities and return the Line2D objects that have been updated for plotting. Check for new data outside the plotted vertical range, which triggers a full re-draw of the axes. On the last frame, finalize HELICS.

Args:

i (int): the animation frame number

update_plots_f(i)

This function is called by Matplotlib for each animation frame

Each time called, collect FNCS messages until the next time to plot has been reached. Then update the plot quantities and return the Line2D objects that have been updated for plotting. Check for new data outside the plotted vertical range, which triggers a full re-draw of the axes. On the last frame, finalize FNCS.

Args:

i (int): the animation frame number

tesp_support.original.tesp_monitor.show_tesp_monitor(HELICS=True)

Creates and displays the monitor GUI

tesp_support.original.tesp_monitor_ercot module

Presents a GUI to launch a TESP simulation and monitor its progress

This version differs from the one in tesp_monitor, in that the user can select the FNCS federate and topic to plot. The number of monitored plots is still fixed at 4.

Public Functions:

show_tesp_monitor:

Initializes and runs the monitor GUI

References

Graphical User Interfaces with Tk

Matplotlib Animation

class tesp_support.original.tesp_monitor_ercot.ChoosablePlot(master, color='red', xLabel='', topicDict={}, **kwargs)

Bases: Frame

Hosts one Matplotlib animation with a selected variable to plot

Parameters:

• master –

• color (str) – Matplotlib color of the line to plot

• xLabel (str) – label for the x axis

• topicDict (dict) – dictionary of FNCS topic choices to plot

• kwargs – arbitrary keyword arguments

topicDict

listOfTopics

root

the TCL Tk toolkit instance

Type:

Tk

fig

animated Matplotlib figure hosted on the GUI

Type:

Figure

ax

set of 4 xy axes to plot on

Type:

Axes

xLabel

horizontal axis label

Type:

str

yLabel

vertical axis label

Type:

str

hrs

y

ymin

Type:

float

ymax

Type:

float

color

title

Type:

str

ln

Type:

Line2D

topicSelectionRow

topicLabel

combobox

voltageLabel

voltageBase

voltageBaseTextbox

onTopicSelected(event)

Change the GUI labels to match selected plot variable

Parameters:

event –

class tesp_support.original.tesp_monitor_ercot.TespMonitorGUI(master)

Bases: object

Version of the monitor GUI that hosts 4 choosable plots

Parameters:

master –

root

the TCL Tk toolkit instance

Type:

Tk

top

the top-level TCL Tk Window

Type:

Window

labelvar

used to display the monitor JSON configuration file path

Type:

StringVar

plot0

first plot

Type:

ChoosablePlot

plot1

second plot

Type:

ChoosablePlot

plot2

third plot

Type:

ChoosablePlot

plot3

fourth plot

Type:

ChoosablePlot

topicDict

plots

Type:

Frame

scrollbar

Type:

Scrollbar

frameInCanvas

Type:

Frame

canvas

a TCL Tk canvas that can host Matplotlib

Type:

FigureCanvasTkAgg

bFNCSactive

True if a TESP simulation is running with other FNCS federates, False if not

Type:

bool

CalculateValue(topic, value, plot)

Parses a value from FNCS to plot

Parameters:

• topic (str) – the FNCS topic

• value (str) – the FNCS value

• plot (ChoosablePlot) – the plot that will be updated; contains the voltage base if needed

Returns:

the parsed value

Return type:

float

OpenConfig()

Read the JSON configuration file for this monitor, and initialize the plot axes

Quit()

Shuts down this monitor, and also shuts down FNCS if active

kill_all()

Shut down all FNCS federates in TESP, except for this monitor

launch_all()

Launches the simulators, initializes FNCS and starts the animated plots

onFrameConfigure(event)

Reset the scroll region to encompass the inner frame

on_closing()

Verify whether the user wants to stop TESP simulations before exiting the monitor

This monitor is itself a FNCS federate, so it can not be shut down without shutting down all other FNCS federates in the TESP simulation.

update_plots(i)

This function is called by Matplotlib for each animation frame

Each time called, collect FNCS messages until the next time to plot has been reached. Then update the plot quantities and return the Line2D objects that have been updated for plotting. Check for new data outside the plotted vertical range, which triggers a full re-draw of the axes. On the last frame, finalize FNCS.

Args:

i (int): the animation frame number

tesp_support.original.tesp_monitor_ercot.show_tesp_monitor()

Creates and displays the monitor GUI

tesp_support.original.tso_PYPOWER_f module

PYPOWER solutions under control of FNCS or HELICS for te30 and dsot, sgip1 examples

Public Functions:

pypower_loop:

Initializes and runs the simulation.

tesp_support.original.tso_psst_f module

tesp_support.original.tso_psst_f.make_generator_plants(ppc, renewables)