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ems/db/routines/R__033_fn_plan_current_bundle.sql
Dusan Vojacek 5b383e9028
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sjednoceni forecastu
2026-05-05 10:42:49 +02:00

222 lines
6.3 KiB
PL/PgSQL

create or replace function ems.fn_plan_current_bundle(p_site_id int)
returns jsonb
language plpgsql
stable
as $fn$
declare
v_run jsonb;
v_run_id int;
v_batt_wh float;
v_intervals jsonb;
v_total_cost numeric;
v_curtailed numeric;
v_charge bigint;
v_discharge bigint;
v_export bigint;
v_pv_kwh numeric;
v_cap numeric;
v_cov numeric;
v_scarcity numeric;
begin
select to_jsonb(pr)
into v_run
from ems.planning_run pr
where pr.site_id = p_site_id
and pr.status = 'active'
order by pr.created_at desc
limit 1;
if v_run is null then
return jsonb_build_object('error', 'no_active_plan');
end if;
v_run_id := (v_run->>'id')::int;
select coalesce(sum(ab.usable_capacity_wh), 0)::float
into v_batt_wh
from ems.asset_battery ab
where ab.site_id = p_site_id;
with fc_slot as (
-- Kanonický PV forecast pro UI = to, co solver používá (planning_interval.*_forecast_solver_w),
-- aby seděla bilance v tabulce slotů. Pro sloty mimo uložený plán doplníme forecast-only řádky.
select
c.interval_start,
(coalesce(c.pv_a_forecast_canonical_w, 0) + coalesce(c.pv_b_forecast_canonical_w, 0))::bigint as pv_forecast_total_w,
coalesce(c.pv_a_forecast_canonical_w, 0)::bigint as pv_a_forecast_solver_w,
coalesce(c.pv_b_forecast_canonical_w, 0)::bigint as pv_b_forecast_solver_w
from jsonb_to_recordset(
ems.fn_forecast_pv_slots_range_canonical_ab(
p_site_id,
(v_run->>'horizon_start')::timestamptz,
greatest((v_run->>'horizon_end')::timestamptz, (v_run->>'horizon_start')::timestamptz + interval '96 hours'),
now()
)
) as c(
interval_start timestamptz,
pv_a_forecast_canonical_w bigint,
pv_b_forecast_canonical_w bigint
)
),
joined as (
select
to_jsonb(pi.*)
|| jsonb_build_object(
'pv_power_w', ai.actual_pv_power_w,
'pv_forecast_total_w',
coalesce(pi.pv_a_forecast_solver_w, 0)
+ coalesce(pi.pv_b_forecast_solver_w, 0),
'pv_a_forecast_solver_w', pi.pv_a_forecast_solver_w,
'pv_b_forecast_solver_w', pi.pv_b_forecast_solver_w
) as j,
pi.interval_start,
pi.expected_cost_czk,
pi.pv_a_curtailed_w,
pi.battery_setpoint_w,
pi.grid_setpoint_w,
(coalesce(pi.pv_a_forecast_solver_w, 0) + coalesce(pi.pv_b_forecast_solver_w, 0))::bigint as pv_forecast_total_w
from ems.planning_interval pi
left join ems.audit_interval ai
on ai.site_id = p_site_id
and ai.interval_start = pi.interval_start
where pi.run_id = v_run_id
union all
select
jsonb_build_object(
'interval_start', fs.interval_start,
'battery_setpoint_w', null,
'battery_soc_target_pct', null,
'grid_setpoint_w', null,
'export_limit_w', null,
'export_mode', null,
'deye_physical_mode', null,
'ev1_setpoint_w', null,
'ev2_setpoint_w', null,
'heat_pump_enabled', null,
'pv_a_curtailed_w', null,
'expected_cost_czk', null,
'effective_buy_price', null,
'effective_sell_price', null,
'is_predicted_price', false,
'pv_power_w', null,
'pv_forecast_total_w', fs.pv_forecast_total_w,
'pv_a_forecast_solver_w', fs.pv_a_forecast_solver_w,
'pv_b_forecast_solver_w', fs.pv_b_forecast_solver_w,
'load_baseline_w', null
) as j,
fs.interval_start,
null::numeric as expected_cost_czk,
null::int as pv_a_curtailed_w,
null::int as battery_setpoint_w,
null::int as grid_setpoint_w,
fs.pv_forecast_total_w
from fc_slot fs
where fs.interval_start >= (v_run->>'horizon_start')::timestamptz
and fs.interval_start < greatest((v_run->>'horizon_end')::timestamptz, (v_run->>'horizon_start')::timestamptz + interval '96 hours')
and not exists (
select 1
from ems.planning_interval pi2
where pi2.run_id = v_run_id
and pi2.interval_start = fs.interval_start
)
),
agg as (
select
coalesce(jsonb_agg(j order by interval_start), '[]'::jsonb) as intervals,
coalesce(
sum(
case
when expected_cost_czk is not null then expected_cost_czk::numeric
else 0::numeric
end
),
0::numeric
) as total_cost,
coalesce(
sum(coalesce(pv_a_curtailed_w, 0)::numeric * 0.25 / 1000.0),
0::numeric
) as curtailed_kwh,
coalesce(
sum(
case
when battery_setpoint_w is not null and battery_setpoint_w > 0 then 1
else 0
end
),
0::bigint
) as charge_slots,
coalesce(
sum(
case
when battery_setpoint_w is not null and battery_setpoint_w < 0 then 1
else 0
end
),
0::bigint
) as discharge_slots,
coalesce(
sum(
case
when grid_setpoint_w is not null and grid_setpoint_w < 0 then 1
else 0
end
),
0::bigint
) as export_slots
from joined
),
pv96 as (
select coalesce(
sum(
greatest(0::numeric, coalesce(pv_forecast_total_w, 0)::numeric) * 0.25 / 1000.0
),
0::numeric
) as pv_kwh
from (
select pv_forecast_total_w
from joined
order by interval_start
limit 96
) z
)
select
a.intervals,
a.total_cost,
a.curtailed_kwh,
a.charge_slots,
a.discharge_slots,
a.export_slots,
p.pv_kwh
into strict
v_intervals,
v_total_cost,
v_curtailed,
v_charge,
v_discharge,
v_export,
v_pv_kwh
from agg a
cross join pv96 p;
v_cap := greatest(1::numeric, coalesce(v_batt_wh, 0::float)::numeric / 1000.0);
v_cov := least(1::numeric, greatest(0::numeric, coalesce(v_pv_kwh, 0) / v_cap));
v_scarcity := round(0.65::numeric + 0.35 * v_cov, 4);
return jsonb_build_object(
'run', v_run,
'intervals', v_intervals,
'summary', jsonb_build_object(
'total_expected_cost_czk', round(v_total_cost, 4),
'total_pv_curtailed_kwh', round(v_curtailed, 6),
'charge_slots', v_charge,
'discharge_slots', v_discharge,
'export_slots', v_export,
'pv_scarcity_factor', v_scarcity
)
);
end;
$fn$;
comment on function ems.fn_plan_current_bundle(int) is
'Aktivní planning_run + intervaly + souhrn (GET /plan/current).';