Update app.py

app.py

@@ -522,7 +522,8 @@ 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"]:
-                vc_ut = 0.0
+                # 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
+                vc_ut = vom_u
             elif fuel_u == "nuclear":
                 vc_ut = float(nuc_vc[t]) + vom_u
             else:
@@ -622,7 +623,17 @@ def clear_market_network(df_units: pd.DataFrame,
             })
     flows_df = pd.DataFrame(flow_rows)
 
-    return {"lmp_df": lmp_df, "res_price_df": res_df, "dispatch_df": dispatch_df, "flows_df": flows_df}
+    # Build spill/shed DataFrames
+    spill_mat = np.zeros((T, len(regions_list)))
+    shed_mat = np.zeros((T, len(regions_list)))
+    for j, r_name in enumerate(regions_list):
+        for t in range(T):
+            spill_mat[t, j] = spill[(r_name, int(t))].value()
+            shed_mat[t, j] = shed[(r_name, int(t))].value()
+    spill_df = pd.DataFrame(spill_mat, index=times, columns=regions_list)
+    shed_df = pd.DataFrame(shed_mat, index=times, columns=regions_list)
+
+    return {"lmp_df": lmp_df, "res_price_df": res_df, "dispatch_df": dispatch_df, "flows_df": flows_df, "spill_df": spill_df, "shed_df": shed_df}
 
 
 # -----------------------------
@@ -662,82 +673,22 @@ 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)
+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)
 
     st.header("VOM adder [JPY/MWh]")
-    vom_df = st.data_editor(pd.DataFrame({
-        "tech": ["lng","coal","oil","nuclear"],
-        "VOM": [400.0, 600.0, 1000.0, 800.0]
-    }), 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": (0.2, 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))
+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)
     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))
@@ -788,7 +739,7 @@ if st.button("シミュレーション実行（ネットワーク＆実務スケ
         ramp_down_frac_map=ramp_down_frac_map,
         intertie_caps_mw=intertie_caps,
         vom_adders_map=vom_adders_map,
-        spill_penalty=1e-6
+        spill_penalty=spill_penalty_ui
     )
 
     st.subheader("LMP（JPY/MWh）")
@@ -817,6 +768,40 @@ if st.button("シミュレーション実行（ネットワーク＆実務スケ
     st.subheader("平均連系潮流（MW）")
     st.dataframe(avg_flow)
 
+    # Diagnostics: 低LMPの原因解析
+    st.subheader("Diagnostics: LMPが極端に低い/ゼロになる要因")
+    spill_df = res["spill_df"]; shed_df = res["shed_df"]; lmp_df = res["lmp_df"]
+    area = st.selectbox("エリア選択（診断）", regions, index=2)
+
+    diag_df = pd.DataFrame({
+        "LMP": lmp_df[area],
+        "Spill_MW": spill_df[area],
+        "Shed_MW": shed_df[area]
+    })
+    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
+    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")
+    def _cf_lookup(r):
+        try:
+            return float(ts_slice.loc[r["Time"], r["cf_key"]])
+        except Exception:
+            return 0.0
+    disp_merge["cf"] = disp_merge.apply(_cf_lookup, axis=1)
+    disp_merge["avail"] = disp_merge["cap_MW"] * disp_merge["cf"]
+    disp_merge["slack"] = (disp_merge["avail"] - disp_merge["g_MW"]).clip(lower=0.0)
+    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
+    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())
+    st.info(f"{area}: spill>1MW 時間数 = {spill_hours} / {len(spill_df)}、 かつ |LMP - spill_penalty|< {eps} の時間数 = {near_spill_price_hours}")
+
     # Downloads
     csv1 = StringIO(); fleet_df.to_csv(csv1, index=False, encoding="utf-8")
     st.download_button("ユニット一覧CSV", data=csv1.getvalue(), file_name="fleet_units.csv", mime="text/csv")