mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2026-08-31 12:46:38 +00:00
Improve genetic optimizer seeding
This commit is contained in:
Andreas
@@ -62,6 +62,9 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
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ETS forecasts. A median fallback is used when the available history is too short for ETS.
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### Changed
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- Seed genetic optimization runs with ten exact warm-start copies, twenty locally mutated
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warm-start neighbours, and diverse domain-informed battery, direct-marketing, EV, and flexible
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appliance schedules to improve early convergence without discarding the previous solution.
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- `max_home_appliances` is now purely an upper bound. No demo appliance is created when
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no `home_appliances` are configured, and the number is no longer used as an on/off switch.
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@@ -73,6 +76,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
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are deprecated in favour of `appliance_starts` and `result.home_appliance_energy_wh`.
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### Fixed
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- Re-simulate genetic candidates after removing EV charging genes from slots that begin at full
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SoC, keeping the repaired genome and its assigned fitness consistent.
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- FeedInTariffEnergyCharts no longer aborts the whole prediction/optimization when the
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Energy-Charts API is briefly unreachable: transient timeouts/connection errors are
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retried (with a (connect, read) timeout of (5, 60) s), and if a fetch still fails while
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@@ -536,6 +536,10 @@ class GeneticSimulation(PydanticBaseModel):
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class GeneticOptimization(OptimizationBase):
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"""GENETIC algorithm to solve energy optimization."""
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WARM_START_COPIES = 10
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WARM_START_MUTATIONS = 20
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EDUCATED_GUESS_EXPORT_QUANTILES = (0.60, 0.75, 0.90)
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# Slot-math helpers — single source of truth for the optimization grid.
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# At the default optimization interval of 3600 s, slot_duration_h is 1.0 and
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# total_slots equals prediction.hours, so the established hourly behaviour is
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@@ -1110,6 +1114,250 @@ class GeneticOptimization(OptimizationBase):
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return discharge_hours_bin, eautocharge_hours_index, appliance_gene_values
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def _repair_ev_charge_at_full_soc(
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self,
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individual: list[int],
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simulation_result: dict[str, Any],
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) -> bool:
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"""Remove EV charging genes in slots that begin at full SoC.
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The repair is deliberately separated from fitness calculation. Callers
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must re-simulate after a change so the individual's genome, simulation
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state and assigned fitness always describe the same schedule.
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"""
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if not self.optimize_ev or not self.ev_possible_charge_values:
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return False
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zero_charge_index = min(
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range(len(self.ev_possible_charge_values)),
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key=lambda index: abs(self.ev_possible_charge_values[index]),
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)
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if abs(self.ev_possible_charge_values[zero_charge_index]) > 1e-12:
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return False
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_, ev_charge_indices, _ = self.split_individual(individual)
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if ev_charge_indices is None:
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return False
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ev_soc = np.asarray(simulation_result.get("EAuto_SoC_pro_Stunde", []), dtype=float)
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start_slot = self._start_day_slot()
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result_slots = min(ev_soc.size, self.total_slots - start_slot)
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if result_slots <= 0:
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return False
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changed = False
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for offset in range(result_slots):
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slot = start_slot + offset
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charge_index = int(ev_charge_indices[slot])
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if (
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ev_soc[offset] >= 100.0 - 1e-9
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and self.ev_possible_charge_values[charge_index] > 0.0
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):
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ev_charge_indices[slot] = zero_charge_index
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changed = True
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if changed:
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battery_genes, _, appliance_genes = self.split_individual(individual)
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individual[:] = self.merge_individual(
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battery_genes,
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ev_charge_indices,
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appliance_genes,
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)
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return changed
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def _heuristic_ev_schedule(self, *, prefer_pv: bool) -> list[int]:
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"""Build a low-cost EV schedule that reaches the configured minimum SoC."""
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if not self.optimize_ev or not self.ev_possible_charge_values:
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return []
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zero_index = min(
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range(len(self.ev_possible_charge_values)),
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key=lambda index: abs(self.ev_possible_charge_values[index]),
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)
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schedule = [zero_index] * self.total_slots
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ev = self.simulation.ev
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if ev is None:
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return schedule
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required_stored_wh = max(
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ev.min_soc_wh
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- ev.capacity_wh * ev.initial_soc_percentage / 100.0,
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0.0,
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)
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if required_stored_wh <= 0.0:
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return schedule
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start_slot = self._start_day_slot()
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end_slot = max(start_slot, self.total_slots - self.fixed_eauto_hours)
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prices = np.asarray(self.simulation.elect_price_hourly, dtype=float)
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feed_in = np.asarray(self.simulation.elect_revenue_per_hour_arr, dtype=float)
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pv = np.asarray(self.simulation.pv_prediction_wh, dtype=float)
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load = np.asarray(self.simulation.load_energy_array, dtype=float)
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def marginal_cost(slot: int) -> tuple[float, float]:
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surplus = pv[slot] - load[slot]
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if prefer_pv and surplus > 0.0:
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return (float(feed_in[slot]), -float(surplus))
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return (float(prices[slot]), -float(surplus))
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candidates = sorted(range(start_slot, end_slot), key=marginal_cost)
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positive_rates = sorted(
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(
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(rate, index)
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for index, rate in enumerate(self.ev_possible_charge_values)
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if rate > 0.0
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),
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key=lambda item: item[0],
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)
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if not positive_rates:
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return schedule
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max_stored_wh = (
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ev.max_charge_power_w
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* self.slot_duration_h
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* ev.charging_efficiency
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)
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remaining_wh = required_stored_wh
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for slot in candidates:
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required_rate = remaining_wh / max(max_stored_wh, 1e-9)
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rate, rate_index = next(
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(item for item in positive_rates if item[0] >= required_rate),
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positive_rates[-1],
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)
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schedule[slot] = rate_index
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remaining_wh -= max_stored_wh * rate
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if remaining_wh <= 1e-9:
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break
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return schedule
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def _heuristic_appliance_genes(self) -> list[int]:
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"""Choose low-opportunity-cost starts for flexible appliances."""
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if self.appliance_layout.n_genes == 0:
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return []
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prices = np.asarray(self.simulation.elect_price_hourly, dtype=float)
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feed_in = np.asarray(self.simulation.elect_revenue_per_hour_arr, dtype=float)
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pv = np.asarray(self.simulation.pv_prediction_wh, dtype=float)
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load = np.asarray(self.simulation.load_energy_array, dtype=float)
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genes: list[int] = []
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for gene in self.appliance_layout.genes:
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def opportunity_cost(position: int) -> float:
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slot = gene.allowed_start_slots[position]
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return float(feed_in[slot] if pv[slot] > load[slot] else prices[slot])
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genes.append(min(range(len(gene.allowed_start_slots)), key=opportunity_cost))
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return genes
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def _educated_guess_individuals(self) -> list[list[int]]:
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"""Create diverse domain-informed candidates for the initial population."""
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slots = self.total_slots
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start_slot = self._start_day_slot()
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len_bat = len(self.bat_possible_charge_values)
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idle_state = 0
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discharge_state = len_bat
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ac_charge_state = 3 * len_bat - 1
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dc_allowed_state = 3 * len_bat + 1
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export_state = 3 * len_bat + (2 if self.optimize_dc_charge else 0)
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prices = np.asarray(self.simulation.elect_price_hourly, dtype=float)
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feed_in = np.asarray(self.simulation.elect_revenue_per_hour_arr, dtype=float)
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pv = np.asarray(self.simulation.pv_prediction_wh, dtype=float)
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load = np.asarray(self.simulation.load_energy_array, dtype=float)
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future = slice(start_slot, slots)
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future_prices = prices[future]
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future_feed_in = feed_in[future]
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high_import_price = float(np.quantile(future_prices, 0.70))
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low_import_price = float(np.quantile(future_prices, 0.25))
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ev_price = self._heuristic_ev_schedule(prefer_pv=False)
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ev_pv = self._heuristic_ev_schedule(prefer_pv=True)
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appliance_genes = self._heuristic_appliance_genes()
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def compose(battery_genes: list[int], ev_genes: list[int]) -> list[int]:
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individual = list(battery_genes)
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if self.optimize_ev:
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individual.extend(ev_genes)
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individual.extend(appliance_genes)
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return individual
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guesses: list[list[int]] = []
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# Baseline and self-consumption candidates are useful even without
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# direct marketing and anchor the population with feasible schedules.
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guesses.append(compose([idle_state] * slots, ev_price))
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self_consumption = [idle_state] * slots
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for slot in range(start_slot, slots):
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if self.optimize_dc_charge and pv[slot] > load[slot]:
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self_consumption[slot] = dc_allowed_state
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elif prices[slot] >= high_import_price and load[slot] > pv[slot]:
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self_consumption[slot] = discharge_state
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guesses.append(compose(self_consumption, ev_pv))
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# Direct marketing candidates export only in the relatively expensive
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# feed-in slots. At low tariffs PV is preferentially stored instead.
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if self.optimize_battery_grid_export and future_feed_in.size:
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feed_spread = float(np.ptp(future_feed_in))
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for quantile in self.EDUCATED_GUESS_EXPORT_QUANTILES:
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export_threshold = float(np.quantile(future_feed_in, quantile))
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direct_marketing = [idle_state] * slots
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for slot in range(start_slot, slots):
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high_feed_in = (
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feed_spread > 1e-12
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and feed_in[slot] > 0.0
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and feed_in[slot] >= export_threshold
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)
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if high_feed_in:
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direct_marketing[slot] = export_state
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elif self.optimize_dc_charge and pv[slot] > load[slot]:
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direct_marketing[slot] = dc_allowed_state
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elif prices[slot] >= high_import_price and load[slot] > pv[slot]:
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direct_marketing[slot] = discharge_state
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guesses.append(compose(direct_marketing, ev_pv))
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inverter = self.simulation.inverter
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if inverter is not None and (
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inverter.max_ac_charge_power_w is None or inverter.max_ac_charge_power_w > 0
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):
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price_arbitrage = [idle_state] * slots
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for slot in range(start_slot, slots):
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if prices[slot] <= low_import_price:
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price_arbitrage[slot] = ac_charge_state
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elif prices[slot] >= high_import_price:
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price_arbitrage[slot] = discharge_state
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guesses.append(compose(price_arbitrage, ev_price))
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unique: dict[tuple[int, ...], list[int]] = {}
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for guess in guesses:
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unique.setdefault(tuple(guess), guess)
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return list(unique.values())
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def _mutated_warm_start_neighbors(
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self,
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start_solution: list[float],
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count: int,
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) -> list[list[int]]:
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"""Create unique local variants while preserving already elapsed slots."""
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original = [int(value) for value in start_solution]
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start_slot = self._start_day_slot()
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seen = {tuple(original)}
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neighbors: list[list[int]] = []
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for _ in range(max(count * 10, 1)):
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neighbor = creator.Individual(original)
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self.mutate(neighbor)
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neighbor[:start_slot] = original[:start_slot]
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if self.optimize_ev:
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ev_start = self.total_slots
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neighbor[ev_start : ev_start + start_slot] = original[
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ev_start : ev_start + start_slot
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]
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key = tuple(int(value) for value in neighbor)
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if key in seen:
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continue
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seen.add(key)
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neighbors.append(list(key))
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if len(neighbors) >= count:
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break
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return neighbors
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def setup_deap_environment(self, opti_param: dict[str, Any], start_hour: int) -> None:
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"""Set up the DEAP environment with fitness and individual creation rules."""
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self.opti_param = opti_param
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@@ -1267,46 +1515,14 @@ class GeneticOptimization(OptimizationBase):
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"""
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try:
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simulation_result = self.evaluate_inner(individual)
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except Exception as e:
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if self._repair_ev_charge_at_full_soc(individual, simulation_result):
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simulation_result = self.evaluate_inner(individual)
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except Exception:
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# Return bad fitness score ("FitnessMin") in case of an exception
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return (100000.0,)
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gesamtbilanz = simulation_result["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0)
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# EV 100% & charge not allowed
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if self.optimize_ev:
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discharge_hours_bin, eautocharge_hours_index, appliance_gene_values = (
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self.split_individual(individual)
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)
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eauto_soc_per_hour = np.array(
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simulation_result.get("EAuto_SoC_pro_Stunde", [])
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) # Beispielkey
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if eauto_soc_per_hour is None or eautocharge_hours_index is None:
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raise ValueError("eauto_soc_per_hour or eautocharge_hours_index is None")
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min_length = min(eauto_soc_per_hour.size, eautocharge_hours_index.size)
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eauto_soc_per_hour_tail = eauto_soc_per_hour[-min_length:]
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eautocharge_hours_index_tail = eautocharge_hours_index[-min_length:]
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# Mask
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invalid_charge_mask = (eauto_soc_per_hour_tail == 100) & (
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eautocharge_hours_index_tail > 0
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)
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if np.any(invalid_charge_mask):
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invalid_indices = np.where(invalid_charge_mask)[0]
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if len(invalid_indices) > 1:
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eautocharge_hours_index_tail[invalid_indices] = 0
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eautocharge_hours_index[-min_length:] = eautocharge_hours_index_tail.tolist()
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adjusted_individual = self.merge_individual(
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discharge_hours_bin, eautocharge_hours_index, appliance_gene_values
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)
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individual[:] = adjusted_individual
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# New check: Activate discharge when battery SoC is 0
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# battery_soc_per_hour = np.array(
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# o.get("akku_soc_pro_stunde", [])
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@@ -1531,8 +1747,25 @@ class GeneticOptimization(OptimizationBase):
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"appliance layout."
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)
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else:
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for _ in range(10):
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for _ in range(self.WARM_START_COPIES):
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population.insert(0, creator.Individual(start_solution))
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warm_neighbors = self._mutated_warm_start_neighbors(
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start_solution,
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self.WARM_START_MUTATIONS,
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)
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population.extend(creator.Individual(neighbor) for neighbor in warm_neighbors)
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logger.info(
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"Seeded population with {} exact and {} mutated warm-start solutions.",
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self.WARM_START_COPIES,
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len(warm_neighbors),
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)
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educated_guesses = self._educated_guess_individuals()
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population.extend(creator.Individual(guess) for guess in educated_guesses)
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logger.info(
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"Seeded population with {} educated-guess solutions.",
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len(educated_guesses),
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)
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# Run the evolutionary algorithm
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pop, log = algorithms.eaMuPlusLambda(
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@@ -0,0 +1,113 @@
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from types import SimpleNamespace
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from unittest.mock import patch
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import numpy as np
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import pytest
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from deap import creator
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from akkudoktoreos.config.config import ConfigEOS
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from akkudoktoreos.core.coreabc import get_ems
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from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
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from akkudoktoreos.utils.datetimeutil import to_datetime
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def _configure_hourly_grid(config_eos: ConfigEOS, *, start_hour: int = 0) -> None:
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 48},
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"optimization": {"horizon_hours": 48, "interval": 3600},
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}
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)
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get_ems(init=True).set_start_datetime(to_datetime().set(hour=start_hour, minute=0))
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def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = True
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opt.ev_possible_charge_values = [0.0, 1.0]
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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individual = creator.Individual([0] * opt.total_slots + [1] * opt.total_slots)
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first_result = {
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"Gesamtbilanz_Euro": 10.0,
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"Gesamt_Verluste": 0.0,
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"EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
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}
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repaired_result = {
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"Gesamtbilanz_Euro": 1.0,
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"Gesamt_Verluste": 0.0,
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"EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
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}
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parameters = SimpleNamespace(
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ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
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eauto=None,
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)
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with patch.object(opt, "evaluate_inner", side_effect=[first_result, repaired_result]) as evaluate:
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fitness = opt.evaluate(individual, parameters, start_hour=0, worst_case=False) # type: ignore[arg-type]
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assert evaluate.call_count == 2
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assert fitness == pytest.approx((1.0,))
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assert individual[opt.total_slots :] == [0] * opt.total_slots
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def test_mutated_warm_start_neighbors_keep_elapsed_slots(config_eos: ConfigEOS):
|
||||
_configure_hourly_grid(config_eos, start_hour=10)
|
||||
opt = GeneticOptimization(fixed_seed=42)
|
||||
opt.optimize_ev = False
|
||||
opt.setup_deap_environment({"home_appliance": 0}, start_hour=10)
|
||||
start_solution = [0] * opt.total_slots
|
||||
|
||||
neighbors = opt._mutated_warm_start_neighbors(start_solution, count=5)
|
||||
|
||||
assert len(neighbors) == 5
|
||||
assert len({tuple(neighbor) for neighbor in neighbors}) == 5
|
||||
assert all(neighbor[:10] == start_solution[:10] for neighbor in neighbors)
|
||||
assert all(neighbor != start_solution for neighbor in neighbors)
|
||||
|
||||
|
||||
def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigEOS):
|
||||
_configure_hourly_grid(config_eos)
|
||||
opt = GeneticOptimization(fixed_seed=42)
|
||||
opt.optimize_ev = False
|
||||
opt.optimize_dc_charge = True
|
||||
opt.optimize_battery_grid_export = True
|
||||
opt.bat_possible_charge_values = [1.0]
|
||||
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
|
||||
|
||||
slots = opt.total_slots
|
||||
opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
|
||||
opt.simulation.elect_revenue_per_hour_arr = np.linspace(0.00001, 0.0003, slots)
|
||||
opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
|
||||
opt.simulation.load_energy_array = np.full(slots, 500.0)
|
||||
|
||||
guesses = opt._educated_guess_individuals()
|
||||
|
||||
dc_allowed_state = 4
|
||||
export_state = 5
|
||||
assert len(guesses) >= 4
|
||||
assert all(len(guess) == slots for guess in guesses)
|
||||
assert any(guess[0] == dc_allowed_state for guess in guesses)
|
||||
assert any(guess[-1] == export_state for guess in guesses)
|
||||
|
||||
|
||||
def test_flat_feed_in_tariff_does_not_seed_direct_marketing(config_eos: ConfigEOS):
|
||||
_configure_hourly_grid(config_eos)
|
||||
opt = GeneticOptimization(fixed_seed=42)
|
||||
opt.optimize_ev = False
|
||||
opt.optimize_dc_charge = True
|
||||
opt.optimize_battery_grid_export = True
|
||||
opt.bat_possible_charge_values = [1.0]
|
||||
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
|
||||
|
||||
slots = opt.total_slots
|
||||
opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
|
||||
opt.simulation.elect_revenue_per_hour_arr = np.full(slots, 0.00005)
|
||||
opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
|
||||
opt.simulation.load_energy_array = np.full(slots, 500.0)
|
||||
|
||||
guesses = opt._educated_guess_individuals()
|
||||
|
||||
export_state = 5
|
||||
assert all(export_state not in guess for guess in guesses)
|
||||
@@ -1,4 +1,5 @@
|
||||
import json
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
from unittest.mock import patch
|
||||
@@ -32,7 +33,10 @@ def compare_dict(actual: dict[str, Any], expected: dict[str, Any]):
|
||||
compare_dict(actual[key], value)
|
||||
elif isinstance(value, list):
|
||||
assert isinstance(actual[key], list)
|
||||
assert actual[key] == pytest.approx(value)
|
||||
if value and isinstance(value[0], datetime):
|
||||
assert actual[key] == value
|
||||
else:
|
||||
assert actual[key] == pytest.approx(value)
|
||||
else:
|
||||
assert actual[key] == pytest.approx(value)
|
||||
|
||||
@@ -149,7 +153,7 @@ def test_optimize(
|
||||
pass
|
||||
|
||||
# Fake energy management run start datetime
|
||||
ems_eos.set_start_datetime(to_datetime().set(hour=fixed_start_hour))
|
||||
ems_eos.set_start_datetime(to_datetime("2025-01-15T10:00:00+01:00"))
|
||||
|
||||
# Throw away any cached results of the last energy management run.
|
||||
CacheEnergyManagementStore().clear()
|
||||
|
||||
+13
-11
@@ -145,7 +145,7 @@
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
0
|
||||
],
|
||||
@@ -272,10 +272,10 @@
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"Gesamt_Verluste": 3425.4668727209255,
|
||||
"Gesamtbilanz_Euro": 0.9585224311392879,
|
||||
"Gesamt_Verluste": 3290.8745999936527,
|
||||
"Gesamtbilanz_Euro": 1.251565700139288,
|
||||
"Gesamteinnahmen_Euro": 1.1316277804018695,
|
||||
"Gesamtkosten_Euro": 2.0901502115411574,
|
||||
"Gesamtkosten_Euro": 2.3831934805411574,
|
||||
"Home_appliance_wh_per_hour": [
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -316,6 +316,7 @@
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"home_appliance_energy_wh": {},
|
||||
"Kosten_Euro_pro_Stunde": [
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -352,7 +353,7 @@
|
||||
0.0,
|
||||
0.17784012884918773,
|
||||
0.19011028252189552,
|
||||
0.0,
|
||||
0.293043269,
|
||||
0.0,
|
||||
0.16484566
|
||||
],
|
||||
@@ -392,7 +393,7 @@
|
||||
0.0,
|
||||
556.6201215937018,
|
||||
617.0408390843736,
|
||||
0.0,
|
||||
987.01,
|
||||
0.0,
|
||||
592.97
|
||||
],
|
||||
@@ -472,7 +473,7 @@
|
||||
81.2380484620021,
|
||||
0.0,
|
||||
0.0,
|
||||
134.59227272727276,
|
||||
0.0,
|
||||
100.08954545454549,
|
||||
0.0
|
||||
],
|
||||
@@ -513,8 +514,8 @@
|
||||
100.0,
|
||||
100.0,
|
||||
100.0,
|
||||
95.75150654269973,
|
||||
92.59211432506888
|
||||
100.0,
|
||||
96.84060778236915
|
||||
],
|
||||
"Electricity_price": [
|
||||
0.000228,
|
||||
@@ -753,9 +754,10 @@
|
||||
1.0,
|
||||
2.0,
|
||||
2.0,
|
||||
1.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0
|
||||
],
|
||||
"washingstart": null
|
||||
"washingstart": null,
|
||||
"appliance_starts": {}
|
||||
}
|
||||
+149
-147
@@ -14,13 +14,6 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
1.0,
|
||||
0.0,
|
||||
1.0,
|
||||
1.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -41,7 +34,14 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -110,9 +110,7 @@
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
@@ -122,17 +120,25 @@
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
@@ -140,14 +146,8 @@
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
1
|
||||
0
|
||||
],
|
||||
"battery_grid_export_allowed": [],
|
||||
"eautocharge_hours_float": null,
|
||||
@@ -157,7 +157,7 @@
|
||||
1063.91,
|
||||
1320.56,
|
||||
1132.03,
|
||||
1308.5200000000004,
|
||||
1163.67,
|
||||
1176.82,
|
||||
1216.22,
|
||||
1103.78,
|
||||
@@ -237,13 +237,11 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.21077535122459587,
|
||||
0.022582049506752234,
|
||||
0.3039575,
|
||||
0.19320652266312205,
|
||||
0.1358062627100041,
|
||||
0.0692592282596561,
|
||||
0.023370695807696434,
|
||||
0.0019327051509204403,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -262,20 +260,22 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.24478962017661132,
|
||||
0.15146384621553569,
|
||||
0.0,
|
||||
0.0013652045768039896,
|
||||
0.2577971476677123,
|
||||
0.15236390731688437,
|
||||
0.10316291465819699,
|
||||
0.05435777788576338,
|
||||
0.020928608511559126,
|
||||
0.003524558735906156,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"Gesamt_Verluste": 3110.842855499046,
|
||||
"Gesamtbilanz_Euro": 1.1850381627731374,
|
||||
"Gesamteinnahmen_Euro": 1.2125780919995675,
|
||||
"Gesamtkosten_Euro": 2.397616254772705,
|
||||
"Gesamt_Verluste": 2758.4157389677953,
|
||||
"Gesamtbilanz_Euro": 1.2690402398027016,
|
||||
"Gesamteinnahmen_Euro": 1.2938585152448954,
|
||||
"Gesamtkosten_Euro": 2.562898755047597,
|
||||
"Home_appliance_wh_per_hour": [
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -316,98 +316,97 @@
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"home_appliance_energy_wh": {},
|
||||
"Kosten_Euro_pro_Stunde": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.10013413727316416,
|
||||
0.09007999454232955,
|
||||
0.11436917344552285,
|
||||
0.07557452231671152,
|
||||
0.026623430000000066,
|
||||
0.0,
|
||||
4.55656845588237e-17,
|
||||
0.001414013162203277,
|
||||
0.005881449073870462,
|
||||
0.05258762370598476,
|
||||
0.1614775630079859,
|
||||
0.2338232746714084,
|
||||
0.0,
|
||||
0.0,
|
||||
0.29116746986009023,
|
||||
0.26650619799999997,
|
||||
0.19588158,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.182970359,
|
||||
0.162995926,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.25364873699864443,
|
||||
0.1306329312971816,
|
||||
0.07362195915902499,
|
||||
0.060401289430882174,
|
||||
0.009619897970888898,
|
||||
0.0,
|
||||
0.0,
|
||||
0.07029121023060134,
|
||||
4.179128154646605e-17,
|
||||
2.3325608707865465e-05,
|
||||
0.0,
|
||||
0.0021886029750169123,
|
||||
0.013012984677295973,
|
||||
0.08357424731947552,
|
||||
0.17784012884918773,
|
||||
0.19011028252189552,
|
||||
0.293043269,
|
||||
0.0,
|
||||
0.0
|
||||
0.0,
|
||||
0.293043269,
|
||||
0.214398479,
|
||||
0.16484566
|
||||
],
|
||||
"Netzbezug_Wh_pro_Stunde": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
439.1848126015972,
|
||||
407.23324838304495,
|
||||
546.436566868241,
|
||||
402.20607938643707,
|
||||
144.85000000000036,
|
||||
0.0,
|
||||
2.2737367544323206e-13,
|
||||
6.433180901743754,
|
||||
25.909467285772962,
|
||||
175.4675465665157,
|
||||
505.40708296709204,
|
||||
758.9200735845777,
|
||||
0.0,
|
||||
0.0,
|
||||
980.6920507244535,
|
||||
912.38,
|
||||
704.61,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
556.31,
|
||||
488.89,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
833.8222781020527,
|
||||
537.58407941227,
|
||||
322.9033296448464,
|
||||
273.0618871197205,
|
||||
45.96224544141853,
|
||||
0.0,
|
||||
0.0,
|
||||
374.088399311343,
|
||||
2.2737367544323206e-13,
|
||||
0.11639525303326081,
|
||||
0.0,
|
||||
9.957247384062384,
|
||||
57.32592368852852,
|
||||
278.85968408233407,
|
||||
556.6201215937018,
|
||||
617.0408390843736,
|
||||
987.01,
|
||||
0.0,
|
||||
0.0
|
||||
0.0,
|
||||
987.01,
|
||||
733.99,
|
||||
592.97
|
||||
],
|
||||
"Netzeinspeisung_Wh_pro_Stunde": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
3011.0764460656555,
|
||||
322.60070723931767,
|
||||
4342.25,
|
||||
2760.093180901744,
|
||||
1940.089467285773,
|
||||
989.4175465665157,
|
||||
333.86708296709196,
|
||||
27.61007358457772,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -426,95 +425,97 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
3496.9945739515906,
|
||||
2163.76923165051,
|
||||
0.0,
|
||||
19.502922525771282,
|
||||
3682.816395253033,
|
||||
2176.6272473840627,
|
||||
1473.7559236885286,
|
||||
776.5396840823341,
|
||||
298.9801215937018,
|
||||
50.35083908437366,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"Verluste_Pro_Stunde": [
|
||||
97.85621559422765,
|
||||
101.92059640365329,
|
||||
114.42806532440363,
|
||||
37.96737751219166,
|
||||
46.38878980596536,
|
||||
39.91398802418894,
|
||||
51.82392952637247,
|
||||
599.9999999999995,
|
||||
159.7408264721214,
|
||||
543.9059151312817,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
133.7321802766326,
|
||||
108.98319763338179,
|
||||
106.80230977350088,
|
||||
0.0014460869344160802,
|
||||
0.0,
|
||||
0.0,
|
||||
70.41409090909087,
|
||||
118.37045454545455,
|
||||
94.68272727272722,
|
||||
83.01681818181817,
|
||||
0.0,
|
||||
0.0,
|
||||
75.86045454545456,
|
||||
66.66681818181814,
|
||||
69.12409090909085,
|
||||
109.07302361034766,
|
||||
116.99491129525349,
|
||||
3.2918733722463145,
|
||||
22.312089529472388,
|
||||
54.18759955738153,
|
||||
86.6234264543665,
|
||||
165.15986945297027,
|
||||
116.9350623689079,
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"2025-01-15 16:00:00+01:00"
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93.5560747303571,
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||||
98.5560747303571,
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98.5560747303571,
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95.93559435845627,
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93.54100930335983,
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93.54100930335983,
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93.54100930335983,
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90.09811906241156,
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90.1895599894184,
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90.80934025412597,
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90.92464377010445,
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93.33085006050352,
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99.12651346349443,
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99.34747913633947,
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100.0,
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100.0,
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@@ -702,17 +702,17 @@
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0.875,
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0.0,
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0.75,
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0.625,
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0.375,
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@@ -795,112 +795,112 @@
|
||||
"capacity_wh": 60000,
|
||||
"charging_efficiency": 0.95,
|
||||
"max_charge_power_w": 11040,
|
||||
"soc_wh": 59110.8,
|
||||
"soc_wh": 59897.4,
|
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"initial_soc_percentage": 5
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},
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"start_solution": [
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],
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"washingstart": 14,
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"washingstart": 15,
|
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"appliance_starts": {
|
||||
"dishwasher1": [
|
||||
"2026-07-15 14:00:00+02:00"
|
||||
"2025-01-15 15:00:00+01:00"
|
||||
]
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user