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ENWL flexibility tenders

A map of Autumn 2024 flexibility tender areas from Electricity North West.

Find out more about ENWL’s Autumn 2024 flex requirement.

Use it online

Visit https://mysociety.github.io/enwl-flexibility-tenders.

All modern browsers are supported. Internet Explorer is not. See .browserlistrc for details.

Running locally

Requirements:

Install all dependencies and get a local server running immediately, in one command:

script/server

The site will be available at both http://localhost:4000 and http://0.0.0.0:4000.

If you want to serve locally over SSL (recommended) then generate self-signed SSL certificates with:

script/generate-ssl-certificates

Once the SSL certificates are in place, script/server will serve the site over HTTPS, at both https://localhost:4000 and https://0.0.0.0:4000. (You will need to tell your web browser to accept the self-signed certificate.)

You can build the site to _site (without serving it) with:

script/build

Regenerating data

Regenerating tenders.csv

static/data/tenders.csv is a simplified copy of the tender requirements CSV provided by ENWL.

To recreate it, you’ll need to:

Then use csvfilter to pick out just the columns we need, into static/data/tenders.csv:

csvfilter -f 2,3,5,6,7,8,9,10,11,12,17,18,19,20,21,22,23 /path/to/enwl-flexibility-tender-site-requirements.csv > static/data/tenders.csv

Regenerating tender-areas.geojson

static/data/tender-areas.json is a GeoJSON FeatureCollection of areas with ENWL flexibility tender requirements that we’re interested in, in the Greater Manchester area.

ENWL provides a GeoJSON file of their tender requirements, but it includes duplicate polygons (the same area can have multiple tenders of different types at different times). For simplicity, we deduplicate these polygons, with script/generate-tender-areas.

To regenerate tender-areas.geojson, you will need to:

Then run script/generate-tender-areas to output just the tender areas we care about, to static/data/tender-areas.geojson:

script/generate-tender-areas /path/to/enwl-flexibility-tender-site-requirements.geojson

NOTE: If you change the list of tender areas that you’re interested in, you’ll also want to change the hand-picked postcode units in script/generate-postcode-units (see a comment there for a SQL query that will come in useful), and the where tender_area clause in the dumb-meters.csv SQL below.

Regenerating postcode-units.geojson

static/data/postcode-units.geojson contains the boundary polygon data for all postcode units (eg: M15 5DD) inside the ENWL tender areas (see tender-areas.geojson above), based on postcode boundaries from the wonderful Mark Longair.

To regenerate them, you’ll first need to:

Then, with these things in place, you can run script/generate-postcode-units, passing in the path to the units directory inside Mark’s data, to output just the boundaries we need to static/data/postcode-units.geojson:

script/generate-postcode-units /path/to/gb-postcodes-v5/units

Note that script/generate-postcode-units removes overlaps when exporting, with mapshaper’s -clean operation. We do this to reduce filesize and prevent duplicate/identical polygons from appearing in the output (and being stacked on top of one another on the map). But it is not really how postcodes work in the real world, and it means some postcodes with useful data might end up missing from our map (eg: multiple postcodes covering different floors in a single block of flats).

Regenerating dumb-meters.csv

static/data/dumb-meters.csv is a CSV of data about electricity and gas meters and consumption, per postcode in ENWL tender areas.

To recreate it, you’ll need to download:

I used the Python program csvfilter to extract just the columns I needed from enwl-flexibility-tender-postcode-data.csv (but annoyingly, an outstanding bug means you need to install a third-party branch to make it work in this specific situation):

pipx install "git+https://github.com/lk-jeffpeck/csvfilter.git@ec433f14330fbbf5d41f56febfeedac22868a949"
csvfilter -f 0,1 enwl-flexibility-tender-postcode-data.csv > enwl-postcodes.csv

I then created a SQLite3 database to munge the data together into:

sqlite3 dumb-meters.sqlite

From here on, everything is done from inside the SQLite command prompt. First, creating tables and indexes:

create table electricity_all(outcode, postcode, num_meters, total_cons_kwh, mean_cons_kwh, median_cons_kwh);
create table electricity_standard(outcode, postcode, num_meters, total_cons_kwh, mean_cons_kwh, median_cons_kwh);
create table electricity_economy7(outcode, postcode, num_meters, total_cons_kwh, mean_cons_kwh, median_cons_kwh);
create table gas(outcode, postcode, num_meters, total_cons_kwh, mean_cons_kwh, median_cons_kwh);
create table postcodes(postcode, tender_area);

create index idx_electricity_all_postcode on electricity_all (postcode);
create index idx_electricity_standard_postcode on electricity_standard (postcode);
create index idx_electricity_economy7_postcode on electricity_economy7 (postcode);
create index idx_gas_postcode on gas (postcode);
create index idx_postcodes_postcode on postcodes (postcode);

Then importing the data:

.mode csv
.import --skip 1 Postcode_level_all_meters_electricity_2022.csv electricity_all
.import --skip 1 Postcode_level_standard_electricity_2022.csv electricity_standard
.import --skip 1 Postcode_level_economy_7_electricity_2022.csv electricity_economy7
.import --skip 1 Postcode_level_gas_2022.csv gas
.import --skip 1 enwl-postcodes.csv postcodes
.mode columns

Then removing some rows we don’t need (technically this is optional, but it makes things simpler later on):

delete from gas where postcode='All postcodes';
delete from electricity_all where postcode='All postcodes';
delete from electricity_standard where postcode='All postcodes';
delete from electricity_economy7 where postcode='All postcodes';

Then adding a "cleaned" postcode column, pc, that’s standardised across all tables:

alter table electricity_all add column pc;
alter table electricity_standard add column pc;
alter table electricity_economy7 add column pc;
alter table gas add column pc;
alter table postcodes add column pc;

create index idx_electricity_all_pc on electricity_all (pc);
create index idx_electricity_standard_pc on electricity_standard (pc);
create index idx_electricity_economy7_pc on electricity_economy7 (pc);
create index idx_gas_pc on gas (pc);
create index idx_postcodes_pc on postcodes (pc);

update electricity_all set pc = upper(replace(postcode, ' ', ''));
update electricity_standard set pc = upper(replace(postcode, ' ', ''));
update electricity_economy7 set pc = upper(replace(postcode, ' ', ''));
update gas set pc = upper(replace(postcode, ' ', ''));
update postcodes set pc = upper(replace(postcode, ' ', ''));

Then creating a SQL view that joins all the tables together, picking just location data, the numbers of meters, and the total consumptions:

create view
    combined
as
    select
        postcodes.postcode,
        postcodes.tender_area,
        electricity_all.num_meters as "electricity_all_meters",
        electricity_all.total_cons_kwh as "electricity_all_consumption_kwh",
        electricity_all.mean_cons_kwh as "electricity_all_mean_cons_kwh",
        electricity_all.median_cons_kwh as "electricity_all_median_cons_kwh",
        electricity_standard.num_meters as "electricity_standard_meters",
        electricity_standard.total_cons_kwh as "electricity_standard_consumption_kwh",
        electricity_standard.mean_cons_kwh as "electricity_standard_mean_cons_kwh",
        electricity_standard.median_cons_kwh as "electricity_standard_median_cons_kwh",
        electricity_economy7.num_meters as "electricity_economy7_meters",
        electricity_economy7.total_cons_kwh as "electricity_economy7_consumption_kwh",
        electricity_economy7.mean_cons_kwh as "electricity_economy7_mean_cons_kwh",
        electricity_economy7.median_cons_kwh as "electricity_economy7_median_cons_kwh",
        gas.num_meters as "gas_meters",
        gas.total_cons_kwh as "gas_consumption_kwh",
        gas.mean_cons_kwh as "gas_mean_cons_kwh",
        gas.median_cons_kwh as "gas_median_cons_kwh"
    from
        postcodes
    left join
        electricity_all on postcodes.pc = electricity_all.pc
    left join
        electricity_standard on electricity_all.pc = electricity_standard.pc
    left join
        electricity_economy7 on electricity_all.pc = electricity_economy7.pc
    left join
        gas on electricity_all.pc = gas.pc;

Then exporting data from just the required tender areas, to a CSV:

.mode csv
.output ../static/data/dumb-meters.csv
select
    postcode,
    tender_area,
    electricity_all_meters,
    cast(round(electricity_all_consumption_kwh) as int) as "electricity_all_consumption_kwh",
    cast(round(electricity_all_mean_cons_kwh) as int) as "electricity_all_mean_cons_kwh",
    cast(round(electricity_all_median_cons_kwh) as int) as "electricity_all_median_cons_kwh",
    electricity_standard_meters,
    cast(round(electricity_standard_consumption_kwh) as int) as "electricity_standard_consumption_kwh",
    cast(round(electricity_standard_mean_cons_kwh) as int) as "electricity_standard_mean_cons_kwh",
    cast(round(electricity_standard_median_cons_kwh) as int) as "electricity_standard_median_cons_kwh",
    electricity_economy7_meters,
    cast(round(electricity_economy7_consumption_kwh) as int) as "electricity_economy7_consumption_kwh",
    cast(round(electricity_economy7_mean_cons_kwh) as int) as "electricity_economy7_mean_cons_kwh",
    cast(round(electricity_economy7_median_cons_kwh) as int) as "electricity_economy7_median_cons_kwh",
    gas_meters,
    cast(round(gas_consumption_kwh) as int) as "gas_consumption_kwh",
    cast(round(gas_mean_cons_kwh) as int) as "gas_mean_cons_kwh",
    cast(round(gas_median_cons_kwh) as int) as "gas_median_cons_kwh"
from
    combined
where
    lower(tender_area) in ('ardwick', 'bolton by bowland', 'catterall waterworks', 'coniston', 'frederick rd bsp', 'marple', 'moss side (longsight)', 'settle');
.output stdout
.mode columns

Regenerating warm-homes-local-grant-postcodes.csv

static/data/warm-homes-local-grant-postcodes.csv is a CSV of the postcodes and local authorities in DESNZ’s Excel spreadsheet of postcodes elligible for the December 2024 round of Warm Homes Local Grants and also within the ENWL tender areas.

To create it, I exported a CSV of the "Postcode" and "Local authority" columns from the DESNZ spreadsheet, and then imported it into a SQLite database:

sqlite3 warm-home-local-grant.sqlite

From here on, everything was done from inside the SQLite command prompt. First, creating tables and indexes:

create table grants(postcode, local_authority);
create table postcodes(postcode, tender_area);

create index idx_grants_postcode on grants (postcode);
create index idx_postcodes_postcode on postcodes (postcode);

Then importing the data:

.mode csv
.import --skip 1 warm-homes-local-grant.csv grants
.import --skip 1 enwl-postcodes.csv postcodes
.mode columns

Then adding a "cleaned" postcode column, pc, that’s standardised across both tables:

alter table grants add column pc;
alter table postcodes add column pc;

create index idx_grants_pc on grants (pc);
create index idx_postcodes_pc on postcodes (pc);

update grants set pc = upper(replace(postcode, ' ', ''));
update postcodes set pc = upper(replace(postcode, ' ', ''));

Then creating a SQL view that joins the two tables together:

create view
    combined
as
    select
        postcodes.postcode,
        postcodes.tender_area,
        grants.local_authority
    from
        postcodes
    inner join
        grants on postcodes.pc = grants.pc;

Then exporting data from just the required tender areas, to a CSV:

.mode csv
.output ../static/data/warm-homes-local-grant-postcodes.csv
select
    postcode,
    tender_area,
    local_authority
from
    combined
where
    lower(tender_area) in ('ardwick', 'bolton by bowland', 'catterall waterworks', 'coniston', 'frederick rd bsp', 'marple', 'moss side (longsight)', 'settle');
.output stdout
.mode columns

Regenerating smart-meters.geojson

static/data/smart-meters.geojson is a GeoJSON file of points representing smart meters, in the ENWL tender areas we’re interested in.

To recreate it, you’ll need to:

Then, with these things in place, you can run script/generate-smart-meters, passing in the path to the smart-meter-installation.geojson file, to output just the points we need to static/data/smart-meters.geojson:

script/generate-smart-meters /path/to/smart-meter-installation.geojson