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import numpy as np
import pandas as pd
import cvxpy as cp
import logging
from .battery import Battery
from .solution import DaySolution
from .day_input import DayInput
logger = logging.getLogger(__name__)
class DaySolver:
"""
Day-ahead co-optimization for a battery with perfect foresight over one day.
Energy buffer (tau) made configurable and properly tied to SoC bounds
FCR symmetry enforced with dedicated up/down headroom + SoC margin
Activation "average" constraints made dimensionally consistent and optionally applied per block
Prevent "free" reserve saturation by adding optional small reserve-holding penalty
"""
def __init__(self, battery: Battery, config: Dict[str, Any]) -> None:
self.battery = battery
self.config = config
act = (config.get("activation", {}) or {})
self.alpha_up = float(act.get("alpha_up", 0.10))
self.alpha_down = float(act.get("alpha_down", 0.10))
# Apply activation ratio constraint either:
# - "daily": over the full day
# - "block": per reserve price interval block
self.activation_mode = str(act.get("mode", "daily"))
self.reserve_price_interval_minutes = int(act.get("reserve_price_interval_minutes", 15))
tp = (config.get("throughput_penalty", {}) or {})
self.c_throughput_eur_per_mwh = float(tp.get("c_eur_per_mwh", 0.0))
# optional small penalty on holding reserves
rp = (config.get("reserve_penalty", {}) or {})
self.c_reserve_eur_per_mw = float(rp.get("c_eur_per_mw", 0.0))
# Energy buffer / deliverability horizon tau (hours)
eb = (config.get("energy_buffer", {}) or {})
self.use_energy_buffer = bool(eb.get("enabled", True))
self.tau_hours = float(eb.get("tau_hours", 0.25))
# enforce FCR symmetry explicitly (up and down headroom)
fcr_cfg = (config.get("fcr", {}) or {})
self.enforce_fcr_symmetry = bool(fcr_cfg.get("enforce_symmetry", True))
# Some operators derate FCR
self.fcr_derate = float(fcr_cfg.get("derate", 1.0))
eod = (config.get("end_of_day", {}) or {})
self.use_end_of_day = bool(eod.get("enabled", False))
self.end_of_day_min_soc_mwh = eod.get("min_soc_mwh", None)
solver_cfg = (config.get("solver", {}) or {})
self.solver_name = str(solver_cfg.get("name", "ECOS"))
self.solver_opts = dict(solver_cfg.get("options", {}) or {})
self._vars: Dict[str, cp.Expression] = {}
self._params: Dict[str, cp.Parameter] = {}
self._prob: Optional[cp.Problem] = None
def solve_day(self, day_input: DayInput) -> DaySolution:
T = int(day_input.T)
dt_hours = float(day_input.dt)
soc0 = float(day_input.soc0)
self._compile(T=T, dt_hours=dt_hours)
self._params["pi"].value = np.asarray(day_input.price_energy).reshape(T)
self._params["rho_fcr"].value = np.asarray(day_input.price_fcr).reshape(T)
self._params["rho_up"].value = np.asarray(day_input.price_afrr_up).reshape(T)
self._params["rho_down"].value = np.asarray(day_input.price_afrr_down).reshape(T)
self._params["soc0"].value = soc0
try:
self._prob.solve(solver=getattr(cp, self.solver_name), **self.solver_opts)
except Exception as e:
logger.error(f"Solver failed: {e}")
return DaySolution(
date=pd.Timestamp(day_input.index_ts[0].date(), tz="UTC"),
schedule=pd.DataFrame(index=day_input.index_ts),
status="failed",
solver=self.solver_name,
input=None,
)
logger.info(f"\tSolver status: {self._prob.status}")
v = self._vars
def clean_nonneg(x: np.ndarray, eps: float = 1e-8) -> np.ndarray:
x = np.asarray(x).reshape(-1)
x[np.abs(x) < eps] = 0.0
return np.maximum(x, 0.0)
res = pd.DataFrame(
{
"p_ch_mw": clean_nonneg(v["p_ch"].value),
"p_dis_mw": clean_nonneg(v["p_dis"].value),
"r_fcr_mw": clean_nonneg(v["r_fcr"].value),
"r_afrr_up_mw": clean_nonneg(v["r_up"].value),
"r_afrr_down_mw": clean_nonneg(v["r_down"].value),
"a_act_up_mw": clean_nonneg(v["a_up"].value),
"a_act_down_mw": clean_nonneg(v["a_down"].value),
"soc_mwh": np.asarray(v["soc"].value).reshape(-1)[:-1],
},
index=day_input.index_ts,
)
return DaySolution(
date=pd.Timestamp(day_input.index_ts[0].date(), tz="UTC"),
schedule=res,
status=str(self._prob.status),
solver=self.solver_name,
input=None,
)
def _compile(self, T: int, dt_hours: float) -> None:
b = self.battery
# Variables
p_ch = cp.Variable(T, nonneg=True)
p_dis = cp.Variable(T, nonneg=True)
r_fcr = cp.Variable(T, nonneg=True)
r_up = cp.Variable(T, nonneg=True)
r_down = cp.Variable(T, nonneg=True)
# Model activation as MW
a_up = cp.Variable(T, nonneg=True)
a_down = cp.Variable(T, nonneg=True)
soc = cp.Variable(T + 1)
# Parameters
pi = cp.Parameter(T)
rho_fcr = cp.Parameter(T)
rho_up = cp.Parameter(T)
rho_down = cp.Parameter(T)
soc0 = cp.Parameter()
# Bounds helpers
soc_min_mwh = b.soc_min * b.e_max_mwh
soc_max_mwh = b.soc_max * b.e_max_mwh
tau = float(self.tau_hours)
# Constraints
constraints = []
constraints += [soc[0] == soc0]
constraints += [soc >= soc_min_mwh, soc <= soc_max_mwh]
for t in range(T):
# Energy dynamics (MWh)
# Activation a_down means "extra charging", a_up means "extra discharging"
constraints += [
soc[t + 1] == soc[t]
+ b.eta_ch * (p_ch[t] + a_down[t]) * dt_hours
- (1.0 / b.eta_dis) * (p_dis[t] + a_up[t]) * dt_hours
]
# Headroom for planned dispatch + reserves
# aFRR is directional and uses the corresponding converter direction.
# FCR is symmetric -> needs headroom in BOTH directions .
constraints += [
p_dis[t] + r_up[t] <= b.p_dis_max_mw,
p_ch[t] + r_down[t] <= b.p_ch_max_mw,
]
# Activation must be within reserved capacity
constraints += [
a_up[t] <= r_up[t],
a_down[t] <= r_down[t],
]
# FCR symmetry (more realistic)
# If you offer r_fcr, you must be able to:
# - increase net injection by r_fcr (up direction)
# - decrease net injection by r_fcr (down direction)
# This implies converter headroom BOTH ways.
if self.enforce_fcr_symmetry:
f = self.fcr_derate * r_fcr[t]
constraints += [
# Upward response uses discharge capability (reduce charge or increase discharge)
p_dis[t] + r_up[t] + f <= b.p_dis_max_mw,
# Downward response uses charge capability (reduce discharge or increase charge)
p_ch[t] + r_down[t] + f <= b.p_ch_max_mw,
]
else:
# Fallback : share headroom with both directions
constraints += [
p_dis[t] + r_fcr[t] + r_up[t] <= b.p_dis_max_mw,
p_ch[t] + r_fcr[t] + r_down[t] <= b.p_ch_max_mw,
]
# Energy deliverability buffer
# To offer upward reserves (FCR+UP), you need energy above soc_min.
# To offer downward reserves (FCR+DOWN), you need empty space below soc_max.
if self.use_energy_buffer:
f = self.fcr_derate * r_fcr[t]
constraints += [
soc[t] >= soc_min_mwh + (f + r_up[t]) * tau,
soc[t] <= soc_max_mwh - (f + r_down[t]) * tau,
]
# Activation ratio constraints
# a_* and r_* are MW. Summing them over T gives "MW-steps". Ratio is fine without dt.
if self.activation_mode == "daily":
constraints += [
cp.sum(a_up) == self.alpha_up * cp.sum(r_up),
cp.sum(a_down) == self.alpha_down * cp.sum(r_down),
]
elif self.activation_mode == "block":
# Enforce the average activation ratio over blocks of reserve-price intervals.
# This avoids pathological "all activation in one step" while staying convex.
step_minutes = dt_hours * 60.0
block_len = int(round(self.reserve_price_interval_minutes / step_minutes))
block_len = max(block_len, 1)
for k0 in range(0, T, block_len):
k1 = min(T, k0 + block_len)
constraints += [
cp.sum(a_up[k0:k1]) == self.alpha_up * cp.sum(r_up[k0:k1]),
cp.sum(a_down[k0:k1]) == self.alpha_down * cp.sum(r_down[k0:k1]),
]
else:
raise ValueError("activation.mode must be 'daily' or 'block'")
# End-of-day constraint
if self.use_end_of_day:
if self.end_of_day_min_soc_mwh is not None:
constraints += [soc[T] >= float(self.end_of_day_min_soc_mwh)]
else:
constraints += [soc[T] >= soc0]
else:
constraints += [soc[T] >= soc0]
# Objective
# Energy revenue (€/MWh) * (MW) * dt (h)
rev_energy = cp.sum(cp.multiply(pi, (p_dis - p_ch + a_up - a_down)) * dt_hours)
# Reserve capacity revenue
rev_reserve = cp.sum(
cp.multiply(rho_fcr, r_fcr)
+ cp.multiply(rho_up, r_up)
+ cp.multiply(rho_down, r_down)
) * dt_hours
# Throughput penalty discourages simultaneous charge/discharge
cost_throughput = 0.0
if self.c_throughput_eur_per_mwh > 0:
cost_throughput = self.c_throughput_eur_per_mwh * cp.sum((p_ch + p_dis) * dt_hours)
# Optional reserve-holding penalty
cost_reserve = 0.0
if self.c_reserve_eur_per_mw > 0:
cost_reserve = self.c_reserve_eur_per_mw * cp.sum(r_fcr + r_up + r_down)
objective = cp.Maximize(rev_energy + rev_reserve - cost_throughput - cost_reserve)
self._prob = cp.Problem(objective, constraints)
self._params = {
"pi": pi,
"rho_fcr": rho_fcr,
"rho_up": rho_up,
"rho_down": rho_down,
"soc0": soc0,
}
self._vars = {
"p_ch": p_ch,
"p_dis": p_dis,
"r_fcr": r_fcr,
"r_up": r_up,
"r_down": r_down,
"a_up": a_up,
"a_down": a_down,
"soc": soc,
} |