ENERFLUX

Adaptive Renewable Microgrid Optimization

Balance renewable generation, storage, and demand in real time.

GRID STATUS

STABLE

RENEWABLE SHARE

CURRENT LOAD

142 kW

Solar PV

82 kW

Wind

64 kW

Hydro

51 kW

Battery

+42 kW

GRID STRESS TEST

WEATHER

SYSTEM IMPACT

Renewable used

91.3%

CO2 avoided

128 kg

Operating cost

$127.42

Curtailment

4.8%

Battery degrade

$8.41

OPTIMIZATION MODE

24-HOUR MICROGRID FORECAST

Solar Wind Hydro Demand Battery SOC

WHY OPTIMIZATION MATTERS

Compare naive rule-based control against EnerFlux multi-objective constrained optimization.

NAIVE CONTROL

Solar then Wind then Hydro then Battery

Operating Cost

$184

CO2 Emissions

84 kg

Battery Cycles

3.8

Unserved Energy

11 kWh

VS

ENERFLUX

Multi-objective constrained optimization

Operating Cost

$127

CO2 Emissions

61 kg

Battery Cycles

1.9

Unserved Energy

0 kWh

HOW ENERFLUX THINKS

FORECAST

Solar / Wind / Hydro / Demand prediction

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CONSTRAINTS

Generation limits, battery limits, demand requirements

Gen ≤ 290kW · Batt 10-100% SOC · Discharge ≤ 100kW

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OPTIMIZATION

Minimize: Cost + CO2 + Battery wear + Curtailment

Weighted objective · Linear constraints · Deterministic solution

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DISPATCH

Optimal generation setpoints sent to each source

↓

GRID STABILITY

Generation = Demand, all constraints satisfied

Objective Function minimize Z = w1*C(gen) + w2*E(gen) + w3*D(batt) + w4*Q(curtailed) + w5*U(unmet)

Subject to sum(gen) + batt_discharge - batt_charge = demand 0 \leq gen_i \leq capacity_i for each source i SOC_min \leq SOC \leq SOC_max -P_discharge_max \leq P_batt \leq P_charge_max