Update app.py

app.py

@@ -522,7 +522,7 @@ def clear_market_network(df_units: pd.DataFrame,
         vom_u = float(vom_adders_map.get(tech_u, 0.0))
         for t in range(T):
             if row_u["is_renew"]:
-                # 再エネにも小さな可変費(運転費)を与えて価格ゼロ固定化を回避
+                # Give small variable O&M to renewables to avoid price pinning at 0
                 vc_ut = vom_u
             elif fuel_u == "nuclear":
                 vc_ut = float(nuc_vc[t]) + vom_u
@@ -673,22 +673,83 @@ with st.sidebar:
     uploaded = st.file_uploader("Time-varying overrides CSV（任意）", type=["csv"])
 
     st.header("スカラー既定（CSV欠損の補完に使用）")
-usd_jpy0 = st.number_input("USD/JPY (scalar)", value=148.21, step=0.5)
-lng_px0 = st.number_input("LNG (USD/MMBtu, scalar)", value=11.27, step=0.1)
-coal_px0 = st.number_input("Coal (USD/ton, scalar)", value=130.0, step=1.0)
-oil_px0 = st.number_input("Oil (USD/bbl, scalar)", value=80.0, step=1.0)
-nuc_vc0 = st.number_input("Nuclear var cost (JPY/MWh, scalar)", value=2300.0, step=100.0)
-rr0 = st.number_input("Reserve ratio (scalar)", value=0.03, step=0.005, min_value=0.0, max_value=0.2, format="%.3f")
-voll0 = st.number_input("VOLL (JPY/MWh, scalar)", value=300000.0, step=10000.0)
-# 新規: スピル罰金（オーバーサプライ時の限界価格を規定）
-spill_penalty_ui = st.number_input("Spill penalty (JPY/MWh)", value=1.0, step=1.0, min_value=0.0)
+    usd_jpy0 = st.number_input("USD/JPY (scalar)", value=148.21, step=0.5)
+    lng_px0 = st.number_input("LNG (USD/MMBtu, scalar)", value=11.27, step=0.1)
+    coal_px0 = st.number_input("Coal (USD/ton, scalar)", value=130.0, step=1.0)
+    oil_px0 = st.number_input("Oil (USD/bbl, scalar)", value=80.0, step=1.0)
+    nuc_vc0 = st.number_input("Nuclear var cost (JPY/MWh, scalar)", value=2300.0, step=100.0)
+    rr0 = st.number_input("Reserve ratio (scalar)", value=0.03, step=0.005, min_value=0.0, max_value=0.2, format="%.3f")
+    voll0 = st.number_input("VOLL (JPY/MWh, scalar)", value=300000.0, step=10000.0)
+    spill_penalty_ui = st.number_input("Spill penalty (JPY/MWh)", value=1.0, step=1.0, min_value=0.0)
 
     st.header("VOM adder [JPY/MWh]")
-vom_df = st.data_editor(pd.DataFrame({
-    "tech": ["lng","coal","oil","nuclear","solar","onshore_wind","offshore_wind","river"],
-    # 既定は小さめの値（0~1000JPY/MWh程度）: 実勢VOMは別途CSVで上書き可能
-    "VOM": [400.0, 600.0, 1000.0, 800.0, 50.0, 80.0, 120.0, 30.0]
-}), use_container_width=True)
+    vom_df = st.data_editor(pd.DataFrame({
+        "tech": ["lng","coal","oil","nuclear","solar","onshore_wind","offshore_wind","river"],
+        "VOM": [400.0, 600.0, 1000.0, 800.0, 50.0, 80.0, 120.0, 30.0]
+    }), use_container_width=True)
+
+    st.header("ユニット数（各エリア）")
+    units_df = st.data_editor(pd.DataFrame({
+        "region": regions,
+        "lng_units":[6,10,25,10,4,20,8,4,10,0],
+        "coal_units":[3,6,10,6,3,10,5,2,7,0],
+        "oil_units":[2,3,6,3,2,5,3,2,3,0],
+        "nuc_units":[0,2,4,2,0,3,1,0,2,0],
+        "solar_units":[20,30,60,30,12,40,20,12,30,0],
+        "on_units":[10,15,25,15,6,20,10,6,15,0],
+        "off_units":[2,3,6,3,1,4,2,1,3,0],
+        "river_units":[5,8,12,8,4,12,6,3,8,0]
+    }), use_container_width=True)
+
+    st.header("容量レンジ [MW/ユニット]")
+    cap_bounds = {
+        "lng": (200.0, 900.0), "coal": (300.0, 1000.0), "oil": (100.0, 700.0), "nuclear": (500.0, 1400.0)
+    }
+    ren_bounds = {
+        "solar": (10.0, 200.0), "onshore_wind": (20.0, 300.0),
+        "offshore_wind": (100.0, 600.0), "river": (10.0, 200.0)
+    }
+
+    st.header("熱率レンジ（GJ/MWh, 乱数生成）")
+    hr_bounds = {
+        "lng": (6.2, 6.9), "coal": (7.8, 9.0), "oil": (8.8, 10.0), "nuclear": (np.nan, np.nan)
+    }
+
+    st.header("最低出力（比率レンジ, 乱数生成）")
+    minout_cfg = {"lng": (0.0, 0.2), "coal": (0.3, 0.6), "oil": (0.0, 0.4), "nuclear": (0.6, 0.9)}
+
+    st.header("可用性（FOR, 平均停止時間[h]）")
+    for_map = st.data_editor(pd.DataFrame({
+        "fuel": ["lng","coal","oil","nuclear"], "FOR":[0.06,0.08,0.10,0.04], "mean_down_h":[24,48,24,120]
+    }), use_container_width=True)
+
+    st.header("ランプ比率（capの何倍/時）")
+    ramp_df = st.data_editor(pd.DataFrame({
+        "fuel": ["lng","coal","oil","nuclear"], "RU_frac":[0.50,0.20,0.30,0.05], "RD_frac":[0.50,0.20,0.30,0.05]
+    }), use_container_width=True)
+
+# Build config dicts
+counts_cfg = {}
+for _, row in units_df.iterrows():
+    rname = row["region"]
+    counts_cfg[(rname, "lng")] = int(row["lng_units"])
+    counts_cfg[(rname, "coal")] = int(row["coal_units"])
+    counts_cfg[(rname, "oil")] = int(row["oil_units"])
+    counts_cfg[(rname, "nuclear")] = int(row["nuc_units"])
+    counts_cfg[(rname, "solar")] = int(row["solar_units"])
+    counts_cfg[(rname, "onshore_wind")] = int(row["on_units"])
+    counts_cfg[(rname, "offshore_wind")] = int(row["off_units"])
+    counts_cfg[(rname, "river")] = int(row["river_units"])
+
+ramp_up_frac_map = {row["fuel"]: float(row["RU_frac"]) for _, row in ramp_df.iterrows()}
+ramp_down_frac_map = {row["fuel"]: float(row["RD_frac"]) for _, row in ramp_df.iterrows()}
+for_map_dict = {row["fuel"]: float(row["FOR"]) for _, row in for_map.iterrows()}
+mean_down_map = {row["fuel"]: float(row["mean_down_h"]) for _, row in for_map.iterrows()}
+
+# Time-varying series (override CSV or synthesized)
+if uploaded is not None:
+    ov = load_overrides_csv(uploaded, ts_slice.index)
+    usd_jpy_s = ov.get("usd_jpy", pd.Series(usd_jpy0, index=ts_slice.index))
     lng_s = ov.get("lng_usd_per_mmbtu", pd.Series(lng_px0, index=ts_slice.index))
     coal_s = ov.get("coal_usd_per_ton", pd.Series(coal_px0, index=ts_slice.index))
     oil_s = ov.get("oil_usd_per_bbl", pd.Series(oil_px0, index=ts_slice.index))
@@ -781,7 +842,7 @@ if st.button("シミュレーション実行（ネットワーク＆実務スケ
     st.plotly_chart(px.scatter(diag_df, x="Spill_MW", y="LMP", title=f"Spill vs LMP — {area}"), use_container_width=True)
     st.plotly_chart(px.scatter(diag_df, x="Shed_MW", y="LMP", title=f"Shed vs LMP — {area}"), use_container_width=True)
 
-    # 再エネ余裕(未使用キャパ)とLMPの関係
+    # Renewables slack vs LMP
     disp = res["dispatch_df"]
     units_ren = fleet_df[fleet_df["is_renew"]]
     disp_merge = disp.merge(units_ren[["unit_id","region","tech","cap_MW","cf_key"]], on=["unit_id","region"], how="inner")
@@ -796,7 +857,7 @@ if st.button("シミュレーション実行（ネットワーク＆実務スケ
     slack = disp_merge.groupby(["Time","region"], as_index=False)["slack"].sum().pivot(index="Time", columns="region", values="slack").reindex(columns=regions)
     st.plotly_chart(px.scatter(x=slack[area], y=lmp_df[area], labels={"x":"Renewable Slack (MW)", "y":"LMP"}, title=f"Renewable Slack vs LMP — {area}"), use_container_width=True)
 
-    # メトリクス
+    # Metrics
     eps = 1e-3
     spill_hours = int((spill_df[area] > 1.0).sum())
     near_spill_price_hours = int((((lmp_df[area] - spill_penalty_ui).abs() < eps) & (spill_df[area] > 1.0)).sum())