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#morePVs v2 model morePVs v2 Copyright (C) 2018/2019

Mike B Roberts
Centre for Energy & Environmental Markets
UNSW Sydney

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/. Contact: [email protected]

#multi-occupancy residential electricity with PV and storage model

Running the script

Written in Python 3.7
Script requires the following input parameters. These can be set using default parameters (allocated after if __name__ == "__main__": in script) or using switches as shown in the table.

Parameter description default variable switch
base_path root directory for all data default_base_path -b
project subfolder containing data for multiple studies default_project -p
study Name of study Input parameters for every scenario are in 'study_xxxx.csv' where 'xxxx' is the stady name default_study -s
DST region identifier for DST time difference and dates 'nsw' -d
override_output Flag for use when running on UNSW HPC facility to divert output False -o

Sample Directory Structure

DATA_EN_6
|
|-  load_profiles
|   |-  siteA
|   |       siteA_load_profiles.csv
|   |
|   |-  siteB
|   |       siteB_load_profiles_v1.csv
|   |       siteB_load_profiles_v1.csv
|   |       siteB_load_profiles_v1.csv
|   |
|   |-  anotherFolderOfLoadFiles
|           LoadFile.csv
|    
|-  pv_profiles
|       pv_profiles_siteA.csv
|       pv_profiles_siteB_v1.csv
|       pv_profiles_siteB_v2.csv
| 
|-  reference
|       battery_control_strategies.csv
|       battery_lookup.csv
|       capex_en_lookup.csv
|       capex_pv_lookup.csv
|       dst_lookup.csv
|       tariff_lookup.csv
|       
|-  studies
|   |-  my_energy_project
|       |-  inputs
|       |      study_demo2.csv  
|       |      study_demo3.csv
|       |
|       |-  outputs
|              |-  study_demo2
|              |   |   demo2_customer_results.csv
|              |   |   demo1_results.csv
|              |   |   demo1_results_std_dev.csv
|              |   |
|              |   |-  saved_tariffs
|              |   |-  scenarios
|              |          demo2_001.csv
|              |          demo2_002.csv
|              |          demo2_003.csv
|              |          demo2_004.csv
|              |     
|              |- study_demo3

Notes:

  • All the files shown in reference folder are required
  • Load profiles, pv profiles and study files are user specified
  • studies/my_project/ must contain folders for inputs and outputs

Setup Instructions

All input parameters for each study are contained or referenced in study_xxxx.csv file,
located at base_path/studies/'project_name'/inputs

File contains one header row and 1 or more additional rows, each specifying a unique scenario,
with pointers to load profiles, PV profiles, tariffs, battery specs and cost settings. Column headers and required content are specified below. Columns in [square brackets] are optional.

scenario This column contains a unique (integer) Scenario identifier
All parameters are given per scenario i.e. per line of .csv file, except
[output_type] This column lists output formats required, applied to the whole study, not individual scenarios.

TECHNICAL ARRANGEMENTS

The column arrangement contains a tag indicating the network arrangement for the scenario.

Arrangement Description
bau Business as Usual. i.e. all customers on-market with their own retail arrangements. No PV. No BESS
bau_bat All customers on-market, no PV, individual BESS for some or all customers
en Embedded Network with no PV
en_pv Embedded Network, with central PV and/or individual PV for some or all customers.
cp_only All customers on-market. PV (and optional BESS) applied to CP load only
btm_i_u All customers on-market. Individual PV systems for some or all customers (not CP)
btm_i_c All customers on-market. Individual PV systems for some or all customers AND CP
btm_s_u All customers on-market. Central BTM PV shared between all customers (not CP)
btm_s_u All customers on-market. Central BTM PV shared between all customers (not CP)
btm_s_c All customers on-market. Central BTM PV shared between all customers AND CP)
btm_p_u All customers on-market. Central BTM PV with SOLAR PPA shared between all customers (not CP)
btm_p_c All customers on-market. Central BTM PV with SOLAR PPA shared between all customers AND CP)

(See also 'BATTERIES' below)

LOADS

load_folder contains the name of sub-folder within base_path\load_profiles containing the load profile(s) for the scenario.
The folder can contain a single load file or multiple files.

Load files contain:

  • timestamp (first column) in format d/mm/yyyy h:mm
  • 30 minute timestamps assumed. Up to 1 year (17520) but can be less
  • NB: timestamp refers to start of time period
    i.e 1/01/2013 00:00 timestamp is attached to the period 00:00 - 00:30. this is to align with the PV output files produced by NREL SAM.
    It is likely that interval data will need adjusting to align with this.
  • Customer load columns (in kWh). Headers identify customers (e.g. apartment residents)
  • 'cp' (optional) common property load (kWh)

If there are multiple loads for each scenario (ie multiple load files within the load folder)
they must all have the same list of customers within the folder.
This allows for a stochastic approach, modeling networks with multiple different load profiles and
assessing average outcomes as well as the variability.

BUT each scenario can have different number of residents, etc. i.e. if different load folders are used

PV:

pv_filename points to a file within base_path\pv_profiles which contains one or more PV profiles.

  • The first column, headed timestamp has format d/mm/yyyy h:mm and must match the timeseries of the load profile

  • For cp_only arrangement, pv file has single column, headed 'cp'

  • For btm_i_u : btm individual for units only: file has either:

    • a column for each unit, or
    • a single 'total' column that is split equally between units
      i.e. the single profile is used to determine the output of multiple PV systems

  • For btm_i_c : en has either:

    • a column for each unit and cp. Or,
    • a single 'total' column: cp gets share according to cp ratio; units get equal share of remainder
      i.e. the single profile is used to determine the output of multiple PV systems

  • For Shared btm inc cp: btm_s_c A single total column that is shared according to instantaneous load between all units AND cp

  • For Shared btm Units only:btm_s_u single total column that is split according to instantaneous load between all units EXCLUDING cp

  • btm_p_c and btm_p_u are similar to btm_s_u and btm_s_c,
    but generation is paid for under a ppa to a solar retailer

  • For en_pv:

    • a single column indicates (labelled 'central') indicates a single central PV system
      connected between parent and child meters
    • multiple columns are also possible, for 'cp', 'central' and each unit
      (with headings the same as the unit identifiers in the load profile)

For en scenario, if the ENO is the strata body, set the tariff for cp = TIDNULL.
Otherwise cp tariff != TIDNULL and ENO is an external party

###Scaleable PV: pv_scaleable is an optional flag (default = False) to indicate that the PV Profile in pv_filename column is for a 1kWp system,
and the system is scaled to the value given by pv_kW_peak

N.B. This is a legacy feature and is not compatible with the following:

  • Embedded network containing individual PV systems
  • "Price Point" capex settings (see CAPEX below)

CAPEX

The model only provides calculations for a single year of operation, but capex costs are included by amortizing\ costs over a user-selected period.

  • amortization a_term (years) and a_rate (%) are defined in study_....csv file and apply to all capex (PV, EN and BESS)
  • a_rate is decimal e.g. 0.06 NOT 6% or 6

###PV Capex The study_...csv file contains a column for pv_capex_id which must be defined for every scenario with PV. This references a row in reference file reference\pv_capex_lookup.csv

In pv_capex_lookup.csv, PV capex can be defined in two ways:

  1. (preferable method) As a series of $/W values which are applied to each PV system in the scenario
  2. (legacy method) as a single total system cost which includes all the PV in the scenario.

For both methods, values are given for the system cost (*including array, inverter, installation cost *), after rebates and including GST, and for replacement of the inverter after a period specified by inverter_life The replacement inverter cost is only required if inverter_life > a_term

###PV Capex Preferable Method (1) This method is preferable because it allows different $/W capex values for differently sized systems and must be used where there are multiple PV systems within an EN.

  • pv_capex_id must contain the text pricepoint or price_point
  • study_...csv must contain columns headed xxx_kWp containing numerical values of peak system size for every system defined in the scenario, where xxx corresponds to a column in the pv_profile, and xxx can be a customer / resident identifier (= column header in load profile) or cp or central
  • columns must be provided (in pv_capex_lookup.csv) for the total ($/W) system and inverter replacement costs for ranges of system size. e.g. sys_10_20 and inv_10_20 contain $/W costs for system sizes >10kWp and <= 20kWp
  • Ranges are exclusive, except for bound (e.g. sys_0_10 and sys_10_20 do not overlap) and must include all systems in the scenario as there is currently no validation to deal with overlapping or missing ranges.

###PV Capex Legacy Method (2) Here, pv_capex_lookup.csv contains pv_capex (*including inverter cost *, after rebates and including GST) and inverter_cost (only required if inverter_life > amortization period). Both refer to the total amount of PV in the scenario.

  • For en_pv or btm_s this is capex of central, shared PV system (and no individual PV systems are allowed)

  • For btm_i_, this is total capex of all PV systems, which is allocated to individual customers in the same proportion as the allocation of PV generation

  • if pv_scaleable == True and pv_capex_scaleable is True or absent (ie default setting = True): the $ values are taken as $/kW and scaled by pv_kW_peak (NB - use of this method should be restricted to system sizes with equal $/kWp capex). if pv_scaleable == True and pv_capex_scaleable is False then pv_cap_id has absolute capex values

  • N.B. Different pv_capex_id ids required for individual systems (btm_i_u and btm_i_c) to allow for higher $/W costs for smaller systems:

###EN Capex

  • The study_...csv file contains a column for en_capex_id which must be defined for every scenario with PV. This references a row in reference file reference\en_capex_lookup.csv
  • NB if capex_en_lookup has duplicate en_capex_ids, it all goes to cock. (read_csv returns a series instead of single value).

###BESS Capex

  • TBC....

TARIFFS

  • All tariffs are defined in reference/tariff_lookup.csv with tariff id's defined in study_....csv
  • Each customer, including cp can have their (bundled retail and network) tariff defined in a column headed by the same
    identifier as is used in the load_profile file.
    • For en and en_pv, this is the internal EN tariff;
    • for bau, btm_i, btm_s, cp_only, etc., this is the on-market retail tariff.
    • for btm_p it includes an element for the solar PPA
  • If all_residents has a tariff id defined, it applies to all households (not cp); this overrides individual tariffs
  • All tariffs can include a flat-rate Feed-in rate fit_flat_rate

Tariffs are treated according to their tariff_type:

  • 'Static tariffs' include Flat_Rate, Zero_Rate, TOU
    and are calculated independent of energy flows;\
  • 'Dynamic' tariffs (identified by having Block or Dynamic in the the tariff_type) are calculated iteratively
    for each timestep dependent on network status (e.g. on cumulative load, pv generation, battery state).

###Demand tariff Any tariff with Demand in the tariff_type has an additional demand_tariff component based on the peak demand
(kW or kVA according to demand_type) in the demand_period of the 12-month period (or whole timeseries, whichever is longer).
demand_period runs from demand_start to demand_end for demand_week = end, day or both

###'cp' tariff: In en scenarios, If ENO is the strata body, cp tariff = TIDNULL,
If ENO is not the strata cp tariff is what strata pays ENO for CP load

###Discount % discount is applied to fixed and volumetric charges

###Block Tariffs Tariff type: block_dailyhas upto three rates for daily usage. Parameters are:\

  • block_rate_1,high_1,
  • block_rate_2,high_2,
  • block_rate_3
    high_1 and high_2 are daily thresholds in kWh. If high_1 is provided, block_rate_2 is required.

Tariff type: block_quarterly:

  • as above but quarterly thresholds, also in kWh.
  • 1st Quarter starts at start of time period.

Solar TOU Tariffs

Any solar TOU tariff can be added, e.g. STS_xx Solar TOU Tariff based on peak, shoulder and off-peak solar periods with rates at xx% discount from EASO TOU rates STC_xx Solar TOU Combined tariff based on EASO TOU periods, with additional off-peak solar period and xx% off EASO TOU rates

###Solar PPA Tariffs For btm_p_u and btm_p_c arrangements: SIT_xx Solar Instantaneous TOU tariff :

  • tariff_type = Solar_Instantaneous

  • Based on underlying (Flat_Rate or TOU) tariff which is paid to primary retailer.

  • Additional solar instantaneous rate and period

  • customers allocated % of instantaneous generation at that timestamp after cp load has been satisfied

  • If name_x is solar_sc:

    • solar used by customer charged at solar_rate (paid to solar retailler)
    • solar exported charged at FiT (paid to solar retailer)

  • else: name_x is solar:

    • All solar charged at solar_rate

  • Solar rate must have details for solar period (even if it is 00:00 to 23:59)

  • This is not a block tariff.

  • local_import is calculated statically,

  • So doesn't allow for individual batteries.

###Legacy btm_s for btm_s (eg upfront). btm capex and opex are treated as en capex and opex and shared between customers. PV is treated as if owned individually, with instantaneous generation allocated equally

###EN Parent tariff parent is Tariff paid at the parent meter for en arrangement.(Bundled network and retail tariff) For Non EN scenarios (bau, btm, cp_only, etc.), parent tariff must be TIDNULL

##Daylight Savings All load and generation profiles are given in local standard time (no DST)
Most tariffs have adjustment for DST
(i.e. periods are fixed but applied to DST-shifted times)
Instudy_.....csv file :

  • Set dst = NSW (e.g.)
  • NB Default is nsw
  • NB dst must be the same for the whole study

Then reference/dst_lookup.csv has start and end timestamps (nsw_start and nsw_end ) for each year. NB Timestamps are given as local standard time (e.g 2am for start and end)

BATTERIES

N.B. (all _ids require _strategy too.)

###For central Battery

  • In study_xxxxxxx.csv file, battery is identified by central_battery_id and battery control strategy by central_battery_strategy
  • If central_battery_id and central_battery_strategy are in the headers, then both must be supplied;
  • If central_battery_id and central_battery_strategy are NOT BOTH in the headers, then battery is not included.

###For Individual Batteries

  • Batteries are identified by x_battery_id and battery control strategy by x_battery_strategy
    where x = customer id from load file
  • OR all_battery_id and all_battery_strategy for all households having the same battery arrangement,
    plus cp_battery_id and cp_battery_stratagey (this overrides individual settings).
Arrangement Battery Set-up
bau No battery, by definition
bau_bat - no pv, individual bats all_battery_id (or multiple x_bat....)cp_battery_id
en or en_pv - with central battery central_battery_id
This isn't allowed currently:
en or en_pv with individual batteries		 all_battery_id (or multiple x_bat....)cp_battery_id
en... with central and individual ?? central_ and all_ and cp_ ??
cp_only - cp bat only cp_battery_id
btm_i_c btm_i_u- only ind batteries all_battery_id (or multiple x_bat....)cp_battery_id
btm_s_c btm_s_u - only ind batteries all_battery_id (or multiple x_bat....)cp_battery_id

Battery Characteristics

All battery technical data is kept in reference\battery_lookup.csv

  • battery_id - identifier unique to battery characteristics
  • capacity_kWh - Single capacity figure: Useful discharge energy
  • efficiency_cycle - for charge and discharge (MAX = 1.0) (default 0.95)
  • charge_kW - for charge and discharge. Max charge rate constrained by inverter power and/or max ~0.8C for charging. Defaults to 0.5C
  • maxDOD (default 0.8
  • maxSOC (default 0.9)
  • max_cycles(default 2000)
  • battery_cost: Installed battery cost excluding inverter (if inverter cost is given) , including GST
  • battery_inv_cost : Installed cost of battery inverter, inc GST (If = zero, inverter and battery treated as single unit).

N.B. If battery_capex_per_kWh is given in the study_ parameter file, it overrides capital costs given in battery_lookup.csv

life_bat_inv : lifetime of battery inverter (years)) (Defaults to capital amortization period a_term )

###Scalable Battery If battery_id includes scale and x.....battery_capacity_kWh is in the study....csv file
(for x = central or cp or all or customer_id)
then this capacity is used to scale the capacity and max_charge_kW in the battery_lookup.csv file

Battery Control Strategies

kept in reference/battery_control_strategies\

Discharge periods:

  • Discharge only allowed between - discharge_start1 and discharge_end1 (optional) or
    between discharge_start2 and discharge_end2.\
  • discharge_day1 , discharge_day2 = week, end or both specify days to discharge
  • If there is no period, specified, battery is discharged whenever load > generation.

Charge Periods

  • Battery is charged whenever generation > load,
  • plus Optional additional grid-charging periods
    (charge_start1 to charge_end1) and (charge_start2 tocharge_end2 )
    charge_day1 and charge_day2 = week, end or both : days to charge from grid
  • If there is no charge period specified, battery is never charged from grid.

Seasonal Strategy

  • If seasonal_strategy = True then the model shifts all charge and discharge periods an hour EARLIER in summer.\
  • If False then winter and summer periods are the same.

Charge / Discharge Rate_

  • charge_c_rate and discharge_c_rate given in battery_control_strategies.csv
    as C-rate fraction of capacity to charge / discharge in 1 hour.
    e.g 0.5C takes 2 hours to charge / discharge
  • i.e. charging power = battery.charge_c_rate * battery.capacity_kWh
  • If omitted, charge and discharge rate default to max_charge_kW in battery_lookup.csv

Priority Charging

  • If prioritise_battery is True: PV generation is applied to charge battery before applying to net load.

Peak demand Strategy

  • If peak_demand_percentage is present (0 <= x <=100), it is applied as a percentage
    to the maximum 30-minute demand in the timeseries to calculate a peak_demand_threshold.
  • Battery is discharged only if net import is above this threshold and timestep is within discharge_period.\
  • Default is 0 - i.e. discharge is regardless of load value.
    i.e. to discharge battery to meet peak demand, regardless of time, discharge_period should be set to 24 hours.

OUTPUT TYPES

Column output_type in 'study_...csv

output_types Fields
log_timeseries_brief timeseries .csv for each scenario and load profile total load , total building import, sum_of_customer_imports
log_timeseries_detailed timeseries .csv for each scenario and load profile total load , total building import, total building export,total generation,battery saved charge, etc.

the following are no longer functional:

| -------------------- | ---------------------------------------------------- | | csv_total_vs_type | Summary .csv | scenario_label,load_folder, arrangement, number_of_households,total$_building_costs_mean,cp_ratio_mean,pv_ratio_mean | | csv_total_vs_bat | summary .csv | scenario_label,load_folder, arrangement, number_of_households,total$_building_costs_mean,self-consumption_mean,pv_ratio_mean, battery_id battery_strategy | | | | | | | | |

OUTPUT RESULTS

Fields output in /outouts/xxxx/xxxx_results.csv

TBC......

Name Description
scenario
scenario_label
arrangement
number_of_households
load_folder
en_opex
en_capex_repayment
pv_capex_repayment
average_hh_bill$
average_hh_solar_bill$
average_hh_total$
central_battery_SOH_mean
central_battery_capacity_kWh_mean
central_battery_cycles_mean
checksum_total_payments$_mean
cp_ratio_mean
cust_bill_cp_mean
cust_solar_bill_cp_mean
cust_total$_cp_mean
eno$_bat_capex_repay_mean
eno$_demand_charge_mean
eno$_energy_bill_mean
eno$_npv_building_mean
eno$_receipts_from_residents_mean
eno$_total_payment_mean
eno_net$_mean
export_kWh_mean
import_kWh_mean
pv_ratio_mean
retailer_bill$_mean
retailer_receipt$_mean
self-consumption_mean
self-consumption_OLD_mean
self-sufficiency_mean
self-sufficiency_OLD_mean
solar_retailer_profit_mean
total$_building_costs_mean
total_battery_losses_mean
total_building_load_mean

REFERENCES

The following publications are based on research conducted using this model and may be useful in
explaining terms used and contextualising the settings described above:

Roberts, M.B., A. Bruce and I. MacGill, Impact of shared battery energy storage systems on photovoltaic\
 self-consumption and electricity bills in apartment buildings. Applied Energy, 2019. 245: p. 78-95.
Roberts, M.B., A. Bruce, I. MacGill, J. Copper and N. Haghdadi. Photovoltaics on Apartment Buildings \
- Project Report. 2019; Available from: http://ceem.unsw.edu.au/sites/default/files/documents/Solar_Apartments_Final_Report_2019_4_3.pdf.
Roberts, M.B., A. Bruce and I. MacGill, A comparison of arrangements for increasing self-consumption and\ 
maximising the value of distributed photovoltaics on apartment buildings. Solar Energy, under review.