Enexa
Simulation Overview
  • Input SummaryAll configuration parameters
  • Simulation AlgorithmsReactive vs Smart EMS explained
  • Result OverviewSide-by-side comparison
Configuration
  • Location SetupEquipment specs & constraints
  • FinancialsCosts, income & margins
  • Solar ProductionPV generation profile
  • Charging SessionsEV demand profile
  • Not ModeledKnown gaps & limitations
Reactive BMS
  • BMS AlgorithmHow the reactive BMS works
  • BMS ReactiveRule-based simulation results
SmartEMS
  • SmartEMS AlgorithmHow the 2-layer optimizer works
  • SmartEMS ConfigPlanner tuning parameters
  • SmartEMS ResultsOptimized simulation output
  • SmartEMS DispatchingGate logic & energy flow rules
Solution Overview
  • Solution OverviewArchitecture & responsibilities
  • Comm ArchitectureAPI integration patterns
  • Middleware APITelemetry & command schema
  • Onboarding & ConfigMaster data & config API
  • Exception HandlingFallbacks & failure scenarios
Prototype
  • Site MonitoringReal-time telemetry dashboard
  • Dispatch LogsCommand execution & verification
  • Impact DashboardSavings & value demonstration
  • What-If ScenariosScenario analysis & comparison

SmartEMS Configuration

Tune all planner parameters. Each setting has a direct effect on the SOC target curve, grid import gating, and PV routing decisions.

Risk Factor
Controls the balance between cost savings and energy readiness. Higher risk = more aggressive price optimization but lower SOC buffers. All downstream parameters are modulated by this setting.
Conservative
50
Aggressive
0255075100

Effective at risk = 50:

Zone Thresholds:

Cheap: ≤20% of prices

Moderate: 20-43%

Expensive: 43-73%

Peak: >73% of prices

Gate Rates:

Moderate gate: 50% of idle rate

Expensive gate: 15% of idle rate

Other:

Base SOC shift: -12.5 pct pts

Emergency override: 20.0%

Demand Readiness
How aggressively the planner pre-charges the battery before expected EV arrivals. Higher values = more readiness buffer but higher electricity cost.

How far into the future the planner scans for probable EV arrivals. Longer windows smooth out the readiness curve but make it less responsive to sudden demand changes. 45 minutes covers the longest typical session.

min

Maximum SOC percentage points added to the base target when demand readiness is at its peak (score = 1.0). At 25%, during peak shopping hours the SOC target can be up to 25 percentage points above the base midpoint.

%
Price Signal
How the planner responds to electricity price variations. Cheap slots trigger opportunistic pre-charging + arbitrage. Expensive/peak slots reduce target and gate imports. Moderate slots are now throttled (above-median cost).

Base SOC target increase during bottom-25% price slots. In v2, this is combined with opportunistic pre-charging (which pushes target toward ceiling) and price spread arbitrage (6-hour look-ahead). The actual target in cheap slots will be significantly higher than this value alone.

%

SOC target decrease when electricity is in the 50-75th percentile. The planner avoids buying during these slots, letting SOC drift lower. Capped by the 50% readiness safety rule during high-demand hours.

%

SOC target decrease when electricity is in the top 25% of the day's prices. Grid imports are fully blocked during these slots (unless emergency override). This is the strongest cost avoidance signal.

%
Safety Overrides
Hard limits that override price optimization to prevent battery depletion. These ensure the system can always serve unexpected EV arrivals with a minimum buffer.

Below this SOC level, all price gates are ignored and the grid charges the battery at normal idle rate regardless of electricity cost. This is the absolute safety floor. Set it high enough that the remaining energy can serve at least one typical 20 kWh session.

%
Hourly Arrival Probability
Probability (0-100%) of an EV arriving during each hour. Based on typical Saturday HPC traffic at German supermarket sites. Adjust to match your location's pattern. The readiness score is derived from this curve via the look-ahead convolution.

Values are percentages (0-100). A value of 85 at 11:00 means there is an 85% probability that at least one EV will arrive during the 11:00-11:59 window.