Code Editor for app.py

import pandas as pd
import matplotlib.pyplot as plt
import math
import streamlit as st
from io import StringIO

# ==============================================
# Streamlit Dashboard for Thermal Storage Model
# ==============================================

st.title("Thermal Storage Simulation Dashboard")

st.sidebar.header("⚙️ Configuration")

# File uploader for CSV
uploaded_file = st.sidebar.file_uploader("Upload Input CSV", type="csv")

# Option to generate sample data
if st.sidebar.button("Generate Sample Input Data"):
    import numpy as np
    n = 8760
    hours = range(n)
    load = np.random.uniform(100, 400, n)
    cop = np.random.uniform(2.5, 4.0, n)
    temp = np.random.uniform(-5, 25, n)
    df_sample = pd.DataFrame({"Load_kW": load, "COP": cop, "Outdoor_Temp_C": temp})
    df_sample.to_csv("sample_input.csv", index=False)
    uploaded_file = "sample_input.csv"
    st.sidebar.success("Sample input generated and loaded.")

if uploaded_file is not None:
    if isinstance(uploaded_file, str):
        load_df = pd.read_csv(uploaded_file)
    else:
        load_df = pd.read_csv(uploaded_file)

    st.subheader("📊 Input Data Preview")
    st.write(load_df.head())

    # Sidebar parameters
    STORAGE_VOLUME_L = st.sidebar.number_input("Storage Volume (L)", 100, 100000, 1000)
    STORAGE_DT = st.sidebar.number_input("ΔT for Storage (K)", 1, 50, 10)
    PLANT_CAPACITY_KW = st.sidebar.number_input("Plant Capacity (kW)", 10, 2000, 500)
    OFFPEAK_THRESHOLD = st.sidebar.slider("Off-peak Threshold (% of avg peak)", 0.1, 1.0, 0.3)
    AVGPEAK_TOPFRACTION = st.sidebar.slider("Top fraction for avg peak load", 0.01, 0.2, 0.05)
    START_FULL = st.sidebar.checkbox("Start with full storage", True)

    # Physical constants
    WATER_CP = 4.186
    RHO_WATER = 1.0

    # Pre-calc Storage Capacity (kWh)
    storage_capacity_kWh = STORAGE_VOLUME_L * RHO_WATER * WATER_CP * STORAGE_DT / 3600.0
    soc = storage_capacity_kWh if START_FULL else 0.0

    # Compute average-peak load baseline
    n = len(load_df)
    top_count = max(1, int(math.ceil(n * AVGPEAK_TOPFRACTION)))
    avg_peak_load = load_df["Load_kW"].nlargest(top_count).mean()
    offpeak_cutoff = OFFPEAK_THRESHOLD * avg_peak_load

    rows = []
    for idx, row in load_df.iterrows():
        load = float(row['Load_kW'])
        cop_in = float(row['COP'])
        tout = float(row['Outdoor_Temp_C'])

        plant_to_load = 0.0
        storage_out = 0.0
        storage_in = 0.0

        discharge_possible = min(load, soc)
        storage_out = discharge_possible
        soc -= discharge_possible
        load_remaining = load - discharge_possible

        if load_remaining > 0:
            plant_to_load = min(load_remaining, PLANT_CAPACITY_KW)
            load_remaining -= plant_to_load

        spare_capacity = max(0.0, PLANT_CAPACITY_KW - plant_to_load)
        is_offpeak = load < offpeak_cutoff
        if spare_capacity > 0 and is_offpeak:
            charge_possible = min(spare_capacity, (storage_capacity_kWh - soc))
            soc += charge_possible
            storage_in = charge_possible

        plant_thermal_kW = plant_to_load + storage_in
        cop_result = max(0.1, cop_in)
        plant_elec_kW = plant_thermal_kW / max(0.1, cop_result)

        rows.append({
            "Load_kW": load,
            "COP_Input": cop_in,
            "Outdoor_Temp_C": tout,
            "Plant_Load_kW": plant_to_load,
            "Storage_Discharge_kW": storage_out,
            "Storage_Charge_kW": storage_in,
            "Plant_Thermal_kW": plant_thermal_kW,
            "Plant_Elec_kW": plant_elec_kW,
            "COP_Resulting": cop_result,
            "SOC_kWh": soc,
            "Unserved_Load_kW": max(0.0, load_remaining),
            "Is_Offpeak": bool(is_offpeak)
        })

    out_df = pd.DataFrame(rows)

    st.subheader("📈 Simulation Results Preview")
    st.write(out_df.head())

    # Download buttons
    csv = out_df.to_csv(index=False).encode('utf-8')
    st.download_button("📥 Download Full Results CSV", data=csv, file_name="thermal_storage_results.csv", mime="text/csv")

else:
    st.info("Please upload an input CSV or generate sample data from the sidebar.")