Control & Learning
Fault injection, priority logic, and guided scenarios for testing control behavior against the live microgrid state.
Faults
0
Active events
SoC
75.6%
Battery buffer
Algorithm
MPPT
Active tracking
Load Shedding Priority
Automatic shedding when SoC < 20%
EV Charger Priority
Hospital Load Priority
Low Battery Mode
Peak Load Management
MPPT Auto-Tracking
Night-Mode Scheduling
Awaiting toggle input
Fault Simulation0 ACTIVE
Inject or resolve hardware fault scenarios
No faults injected. System nominal.
Active Alerts0
All systems nominal
Event LogLatest 12
[TWIN] Digital twin sync established โ 1s interval
[SYS] CAN-Bus handshake: SOLAR_50W ยท WIND_30W ยท HYDRO_20W
[CTL] MPPT P&O active โ Vmp=18.2V Pmax=38.4W
[NET] MQTT Broker connected โ QoS 1
Scenario-Based Learning Challenges
Maximize System Efficiency
Adjust source allocation and load priorities to achieve system efficiency above 90%. Monitor real-time ฮท and optimize the generation-consumption balance.
MPPT FOCUS30 MIN
Start Challenge
Net Zero Challenge
Configure the microgrid to achieve net-zero energy over a 5-minute window. Generation must closely match consumption using load scheduling and source balancing.
BALANCE5 MIN
Start Challenge
Low Battery Response
Battery SoC is falling. Activate emergency protocols, shed non-critical loads, and prevent SoC from dropping below 15% while keeping the core system stable.
CRITICALTIMED
Start Challenge
Peak Load Management
A demand surge hits at peak hours. Use priority rules to prevent overload while maintaining service to high-priority loads and documenting the response protocol.
SHEDDING10 MIN
Start Challenge
Digital Twin System Status
ESP32 Controller
240MHz ยท 4MB flash
MQTT + CAN Bus active
ONLINEMQTT Broker
QoS 1 ยท Topic: assl/#
Latency: <12ms
CONNECTEDFirebase Realtime DB
assl-twin.firebaseio.com
Sync: 1s interval
SYNCEDDigital Twin
Tick: 1
Gen: 75.8W
LIVE