420 lines
15 KiB
Python
420 lines
15 KiB
Python
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"""能源策略优化服务 - 峰谷电价策略、谷电蓄热、负荷转移、光伏自消纳"""
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from datetime import datetime, date, timedelta, timezone
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, func, and_
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from app.models.energy_strategy import (
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TouPricing, TouPricingPeriod, EnergyStrategy, StrategyExecution, MonthlyCostReport,
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)
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from app.models.energy import EnergyData, EnergyDailySummary
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# Beijing TZ offset
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BJT = timezone(timedelta(hours=8))
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# Default Beijing industrial TOU pricing
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DEFAULT_PERIODS = [
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{"period_type": "sharp_peak", "start_time": "10:00", "end_time": "15:00", "price": 1.3761},
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{"period_type": "sharp_peak", "start_time": "18:00", "end_time": "21:00", "price": 1.3761},
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{"period_type": "peak", "start_time": "08:00", "end_time": "10:00", "price": 1.1883},
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{"period_type": "peak", "start_time": "15:00", "end_time": "18:00", "price": 1.1883},
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{"period_type": "peak", "start_time": "21:00", "end_time": "23:00", "price": 1.1883},
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{"period_type": "flat", "start_time": "07:00", "end_time": "08:00", "price": 0.7467},
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{"period_type": "valley", "start_time": "23:00", "end_time": "07:00", "price": 0.3048},
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]
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PERIOD_LABELS = {
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"sharp_peak": "尖峰",
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"peak": "高峰",
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"flat": "平段",
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"valley": "低谷",
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}
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def parse_month_range(month_range: str | None) -> list[int] | None:
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"""Parse month range string like '1-3,11-12' into list of month ints."""
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if not month_range:
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return None
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months = []
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for part in month_range.split(","):
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part = part.strip()
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if "-" in part:
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start, end = part.split("-")
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months.extend(range(int(start), int(end) + 1))
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else:
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months.append(int(part))
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return months
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def get_period_for_hour(periods: list[TouPricingPeriod], hour: int, month: int | None = None) -> TouPricingPeriod | None:
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"""Determine which TOU period an hour falls into."""
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hour_str = f"{hour:02d}:00"
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for p in periods:
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if month is not None and p.month_range:
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applicable = parse_month_range(p.month_range)
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if applicable and month not in applicable:
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continue
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start = p.start_time
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end = p.end_time
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if start <= end:
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if start <= hour_str < end:
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return p
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else: # crosses midnight
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if hour_str >= start or hour_str < end:
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return p
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return periods[0] if periods else None
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async def get_active_tou_pricing(db: AsyncSession, target_date: date | None = None) -> TouPricing | None:
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"""Get active TOU pricing plan."""
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q = select(TouPricing).where(TouPricing.is_active == True)
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if target_date:
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q = q.where(
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and_(
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(TouPricing.effective_date == None) | (TouPricing.effective_date <= target_date),
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(TouPricing.end_date == None) | (TouPricing.end_date >= target_date),
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)
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)
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q = q.order_by(TouPricing.created_at.desc()).limit(1)
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result = await db.execute(q)
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return result.scalar_one_or_none()
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async def get_tou_periods(db: AsyncSession, pricing_id: int) -> list[TouPricingPeriod]:
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"""Get pricing periods for a TOU plan."""
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result = await db.execute(
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select(TouPricingPeriod).where(TouPricingPeriod.pricing_id == pricing_id)
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)
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return list(result.scalars().all())
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async def calculate_hourly_cost(
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db: AsyncSession, target_date: date, periods: list[TouPricingPeriod],
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) -> dict:
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"""Calculate hourly electricity cost for a specific date."""
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day_start = datetime(target_date.year, target_date.month, target_date.day, tzinfo=BJT)
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hourly_data = []
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total_cost = 0.0
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total_kwh = 0.0
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for hour in range(24):
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hour_start = day_start + timedelta(hours=hour)
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hour_end = hour_start + timedelta(hours=1)
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q = select(func.sum(EnergyData.value)).where(
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and_(
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EnergyData.timestamp >= hour_start,
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EnergyData.timestamp < hour_end,
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EnergyData.data_type == "energy",
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)
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)
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result = await db.execute(q)
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hour_kwh = result.scalar() or 0.0
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period = get_period_for_hour(periods, hour, target_date.month)
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price = period.price_yuan_per_kwh if period else 0.7467
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period_type = period.period_type if period else "flat"
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cost = hour_kwh * price
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total_cost += cost
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total_kwh += hour_kwh
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hourly_data.append({
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"hour": hour,
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"consumption_kwh": round(hour_kwh, 2),
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"price": price,
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"cost": round(cost, 2),
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"period_type": period_type,
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"period_label": PERIOD_LABELS.get(period_type, period_type),
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})
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return {
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"date": str(target_date),
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"hourly": hourly_data,
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"total_cost": round(total_cost, 2),
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"total_kwh": round(total_kwh, 2),
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}
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async def calculate_monthly_cost_breakdown(
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db: AsyncSession, year: int, month: int,
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) -> dict:
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"""Calculate monthly cost breakdown by TOU period type."""
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pricing = await get_active_tou_pricing(db, date(year, month, 1))
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if not pricing:
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return _empty_cost_breakdown(year, month)
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periods = await get_tou_periods(db, pricing.id)
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if not periods:
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return _empty_cost_breakdown(year, month)
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# Build hour -> period mapping
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period_stats = {pt: {"kwh": 0.0, "cost": 0.0, "hours": 0}
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for pt in ["sharp_peak", "peak", "flat", "valley"]}
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for hour in range(24):
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period = get_period_for_hour(periods, hour, month)
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if not period:
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continue
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pt = period.period_type
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if pt not in period_stats:
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period_stats[pt] = {"kwh": 0.0, "cost": 0.0, "hours": 0}
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period_stats[pt]["hours"] += 1
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# Get daily summaries for the month
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month_start = date(year, month, 1)
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if month == 12:
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month_end = date(year + 1, 1, 1)
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else:
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month_end = date(year, month + 1, 1)
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q = select(
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func.sum(EnergyDailySummary.total_consumption),
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).where(
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and_(
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EnergyDailySummary.date >= datetime(month_start.year, month_start.month, month_start.day),
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EnergyDailySummary.date < datetime(month_end.year, month_end.month, month_end.day),
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EnergyDailySummary.energy_type == "electricity",
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)
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)
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result = await db.execute(q)
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total_monthly_kwh = result.scalar() or 0.0
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# Distribute by hour proportion
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total_hours = sum(ps["hours"] for ps in period_stats.values())
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for pt, ps in period_stats.items():
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proportion = ps["hours"] / total_hours if total_hours > 0 else 0
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ps["kwh"] = total_monthly_kwh * proportion
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period_obj = next((p for p in periods if p.period_type == pt), None)
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price = period_obj.price_yuan_per_kwh if period_obj else 0
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ps["cost"] = ps["kwh"] * price
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total_cost = sum(ps["cost"] for ps in period_stats.values())
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breakdown = []
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for pt, ps in period_stats.items():
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if ps["hours"] == 0:
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continue
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breakdown.append({
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"period_type": pt,
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"period_label": PERIOD_LABELS.get(pt, pt),
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"consumption_kwh": round(ps["kwh"], 2),
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"cost_yuan": round(ps["cost"], 2),
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"hours_per_day": ps["hours"],
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"proportion": round(ps["kwh"] / total_monthly_kwh * 100, 1) if total_monthly_kwh > 0 else 0,
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})
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return {
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"year_month": f"{year}-{month:02d}",
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"total_consumption_kwh": round(total_monthly_kwh, 2),
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"total_cost_yuan": round(total_cost, 2),
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"breakdown": breakdown,
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"pricing_name": pricing.name,
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}
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def _empty_cost_breakdown(year: int, month: int) -> dict:
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return {
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"year_month": f"{year}-{month:02d}",
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"total_consumption_kwh": 0,
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"total_cost_yuan": 0,
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"breakdown": [],
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"pricing_name": "未配置",
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}
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def calculate_heat_storage_savings(
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daily_kwh: float, periods: list[TouPricingPeriod], shift_ratio: float = 0.3,
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) -> dict:
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"""Calculate savings from valley-electricity heat storage strategy (谷电蓄热).
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Assumes shift_ratio of heat pump load can be moved from peak/sharp_peak to valley hours.
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"""
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peak_prices = []
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valley_price = 0.3048
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for p in periods:
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if p.period_type in ("sharp_peak", "peak"):
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peak_prices.append(p.price_yuan_per_kwh)
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elif p.period_type == "valley":
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valley_price = p.price_yuan_per_kwh
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avg_peak_price = sum(peak_prices) / len(peak_prices) if peak_prices else 1.2
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shifted_kwh = daily_kwh * shift_ratio
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savings_per_day = shifted_kwh * (avg_peak_price - valley_price)
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return {
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"shifted_kwh": round(shifted_kwh, 2),
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"avg_peak_price": round(avg_peak_price, 4),
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"valley_price": round(valley_price, 4),
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"savings_per_day": round(savings_per_day, 2),
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"savings_per_month": round(savings_per_day * 30, 2),
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"savings_per_year": round(savings_per_day * 365, 2),
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"strategy": "谷电蓄热",
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"description": f"将{shift_ratio*100:.0f}%的热泵负荷从尖峰/高峰时段转移至低谷时段(23:00-7:00)预热水箱",
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}
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def calculate_pv_priority_savings(
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pv_daily_kwh: float, grid_price: float = 0.7467, feed_in_price: float = 0.3548,
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) -> dict:
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"""Calculate savings from PV self-consumption priority strategy."""
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self_consume_value = pv_daily_kwh * grid_price
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feed_in_value = pv_daily_kwh * feed_in_price
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savings_per_day = self_consume_value - feed_in_value
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return {
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"pv_daily_kwh": round(pv_daily_kwh, 2),
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"self_consume_value": round(self_consume_value, 2),
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"feed_in_value": round(feed_in_value, 2),
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"savings_per_day": round(savings_per_day, 2),
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"savings_per_month": round(savings_per_day * 30, 2),
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"strategy": "光伏自消纳优先",
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"description": "优先使用光伏发电供给园区负荷,减少向电网购电",
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}
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def simulate_strategy_impact(
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daily_consumption_kwh: float,
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pv_daily_kwh: float,
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periods: list[TouPricingPeriod],
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strategies: list[str],
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) -> dict:
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"""Simulate impact of enabling various strategies."""
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baseline_cost = 0.0
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optimized_cost = 0.0
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# Calculate baseline cost (proportional by hours)
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period_hours = {}
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for p in periods:
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start_h = int(p.start_time.split(":")[0])
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end_h = int(p.end_time.split(":")[0])
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if start_h < end_h:
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hours = end_h - start_h
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else:
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hours = (24 - start_h) + end_h
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period_hours[p.period_type] = period_hours.get(p.period_type, 0) + hours
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total_hours = sum(period_hours.values()) or 24
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for p in periods:
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start_h = int(p.start_time.split(":")[0])
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end_h = int(p.end_time.split(":")[0])
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hours = end_h - start_h if start_h < end_h else (24 - start_h) + end_h
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proportion = hours / total_hours
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kwh = daily_consumption_kwh * proportion
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baseline_cost += kwh * p.price_yuan_per_kwh
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optimized_cost = baseline_cost
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savings_details = []
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if "heat_storage" in strategies:
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hs = calculate_heat_storage_savings(daily_consumption_kwh * 0.4, periods, 0.3)
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optimized_cost -= hs["savings_per_day"]
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savings_details.append(hs)
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if "pv_priority" in strategies:
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pv = calculate_pv_priority_savings(pv_daily_kwh)
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optimized_cost -= pv["savings_per_day"]
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savings_details.append(pv)
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if "load_shift" in strategies:
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# Shift 15% of peak load to flat/valley
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valley_p = next((p for p in periods if p.period_type == "valley"), None)
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peak_p = next((p for p in periods if p.period_type == "sharp_peak"), None)
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if valley_p and peak_p:
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shift_kwh = daily_consumption_kwh * 0.15
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saved = shift_kwh * (peak_p.price_yuan_per_kwh - valley_p.price_yuan_per_kwh)
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optimized_cost -= saved
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savings_details.append({
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"strategy": "负荷转移",
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"savings_per_day": round(saved, 2),
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"savings_per_month": round(saved * 30, 2),
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"description": "将15%的尖峰时段负荷转移至低谷时段",
|
||
|
|
})
|
||
|
|
|
||
|
|
return {
|
||
|
|
"baseline_cost_per_day": round(baseline_cost, 2),
|
||
|
|
"optimized_cost_per_day": round(max(0, optimized_cost), 2),
|
||
|
|
"total_savings_per_day": round(baseline_cost - max(0, optimized_cost), 2),
|
||
|
|
"total_savings_per_month": round((baseline_cost - max(0, optimized_cost)) * 30, 2),
|
||
|
|
"total_savings_per_year": round((baseline_cost - max(0, optimized_cost)) * 365, 2),
|
||
|
|
"savings_percentage": round((1 - max(0, optimized_cost) / baseline_cost) * 100, 1) if baseline_cost > 0 else 0,
|
||
|
|
"details": savings_details,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
async def get_recommendations(db: AsyncSession) -> list[dict]:
|
||
|
|
"""Generate current strategy recommendations based on data."""
|
||
|
|
recommendations = []
|
||
|
|
|
||
|
|
# Always recommend valley heat storage for heating season
|
||
|
|
now = datetime.now(BJT)
|
||
|
|
month = now.month
|
||
|
|
if month in (11, 12, 1, 2, 3):
|
||
|
|
recommendations.append({
|
||
|
|
"type": "heat_storage",
|
||
|
|
"title": "谷电蓄热策略",
|
||
|
|
"description": "当前为采暖季,建议在低谷时段(23:00-7:00)预热水箱,减少尖峰时段热泵运行",
|
||
|
|
"priority": "high",
|
||
|
|
"estimated_savings": "每月可节约约3000-5000元",
|
||
|
|
})
|
||
|
|
|
||
|
|
# PV priority during daytime
|
||
|
|
recommendations.append({
|
||
|
|
"type": "pv_priority",
|
||
|
|
"title": "光伏自消纳优先",
|
||
|
|
"description": "优先使用屋顶光伏发电满足园区负荷,减少购电成本",
|
||
|
|
"priority": "medium",
|
||
|
|
"estimated_savings": "每月可节约约1500-2500元",
|
||
|
|
})
|
||
|
|
|
||
|
|
# Load shifting
|
||
|
|
hour = now.hour
|
||
|
|
if 10 <= hour <= 15 or 18 <= hour <= 21:
|
||
|
|
recommendations.append({
|
||
|
|
"type": "load_shift",
|
||
|
|
"title": "当前处于尖峰时段",
|
||
|
|
"description": "建议减少非必要大功率设备运行,可延迟至低谷时段执行",
|
||
|
|
"priority": "high",
|
||
|
|
"estimated_savings": "尖峰电价1.3761元/kWh,低谷电价0.3048元/kWh",
|
||
|
|
})
|
||
|
|
|
||
|
|
return recommendations
|
||
|
|
|
||
|
|
|
||
|
|
async def get_savings_report(db: AsyncSession, year: int) -> dict:
|
||
|
|
"""Generate yearly savings report."""
|
||
|
|
reports = []
|
||
|
|
total_savings = 0.0
|
||
|
|
total_baseline = 0.0
|
||
|
|
total_optimized = 0.0
|
||
|
|
|
||
|
|
result = await db.execute(
|
||
|
|
select(MonthlyCostReport).where(
|
||
|
|
MonthlyCostReport.year_month.like(f"{year}-%")
|
||
|
|
).order_by(MonthlyCostReport.year_month)
|
||
|
|
)
|
||
|
|
monthly_reports = result.scalars().all()
|
||
|
|
|
||
|
|
for r in monthly_reports:
|
||
|
|
reports.append({
|
||
|
|
"year_month": r.year_month,
|
||
|
|
"total_consumption_kwh": r.total_consumption_kwh,
|
||
|
|
"total_cost_yuan": r.total_cost_yuan,
|
||
|
|
"baseline_cost": r.baseline_cost,
|
||
|
|
"optimized_cost": r.optimized_cost,
|
||
|
|
"savings_yuan": r.savings_yuan,
|
||
|
|
})
|
||
|
|
total_savings += r.savings_yuan
|
||
|
|
total_baseline += r.baseline_cost
|
||
|
|
total_optimized += r.optimized_cost
|
||
|
|
|
||
|
|
return {
|
||
|
|
"year": year,
|
||
|
|
"monthly_reports": reports,
|
||
|
|
"total_savings_yuan": round(total_savings, 2),
|
||
|
|
"total_baseline_cost": round(total_baseline, 2),
|
||
|
|
"total_optimized_cost": round(total_optimized, 2),
|
||
|
|
"savings_percentage": round(total_savings / total_baseline * 100, 1) if total_baseline > 0 else 0,
|
||
|
|
}
|