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RTORPA Example

import generation_models as gt
from tyba_client.client import Client
import numpy as np
import pandas as pd
import os

client = Client(personal_access_token=os.environ["TYBA_PAT"])

model = gt.StandaloneStorageModel(
    energy_prices=gt.DARTPrices(
        dam=np.random.random(48).tolist(),
        rtm=np.random.random(48).tolist(),
        rtorpa=np.random.random(48).tolist(),
    ),
    project_term=48,
    project_term_units="hours",
    storage_inputs=gt.MultiStorageInputs(
        batteries=[
            gt.BatteryParams(
                power_capacity=1000.0,
                energy_capacity=1000.0 * 2,
                charge_efficiency=0.95,
                discharge_efficiency=0.95,
                degradation_rate=0.0,
            )
        ],
    ),
)

resp = client.schedule(model)
resp.raise_for_status()
res = client.wait_on_result(resp.json()["id"])
df = pd.DataFrame(res)

# two new columns
df[["rtorpa", "soe_mean"]]

	rtorpa	soe_mean
0	0.459418	475.000000
1	0.698615	1000.000000
2	0.814061	1525.000000
3	0.426796	2000.000000
4	0.660763	1473.684211
5	0.426009	736.184211
6	0.733494	1000.000000
7	0.991707	1737.500000
8	0.816683	1576.315789
9	0.040184	626.315789
10	0.862928	575.000000
11	0.967568	1525.000000
12	0.528905	1968.534483
13	0.320486	1410.753176
14	0.179744	756.034483
15	0.506928	1102.631579
16	0.184159	1051.315789
17	0.758873	1000.000000
18	0.773333	1737.500000
19	0.427789	1473.684211
20	0.568534	473.684211
21	0.038720	50.000000
22	0.406480	575.000000
23	0.467345	1525.000000
24	0.176373	1525.000000
25	0.489898	1525.000000
26	0.294734	2000.000000
27	0.096778	1473.684211
28	0.047591	473.684211
29	0.199256	475.000000
30	0.028051	1263.815789
31	0.402718	1051.315789
32	0.273838	1000.000000
33	0.517905	1737.500000
34	0.550950	2000.000000
35	0.059592	2000.000000
36	0.037408	1473.684211
37	0.083896	1422.368421
38	0.208502	1374.977314
39	0.552403	688.793103
40	0.530890	1000.000000
41	0.328186	1051.315789
42	0.720418	1102.631579
43	0.503628	1051.315789
44	0.096158	1000.000000
45	0.879919	1737.500000
46	0.244617	1473.684211
47	0.264351	473.684211