Critical server equipment requires main and reserve cooling systems are operated during all year
Annual carbon emissions – 33 000 tons
Total cost of the system Euro 450 000
Installation and configuring the system – 30 days
Payback <1 year
EXAMPLE OF A TYPICAL CASE
Solar PV
4 000 sensors that monitor energy consumption of all significant power load, thermal comfort air quality, equipment parameters
Integrated control of the BMS system
Control of all significant power consuming devices – chillers, pumps dry coolers, fan coils, heat curtains, ventilation, lighting, refrigerators, kitchen and office equipment
External data sources that provide data on energy prices and weather forecast
A digital twin
Microgrid
IoT Equipment control, sensors
Power loads, DR, emissions
Big Data
Analysis/visualization
Microgrid generation and energy flow control
Smart Energy Storage
Mesh optimization: Digital Twin + Machine Learning
Smart allocation of storage & DR among applications
Aggregation of small-scale distributed resources
The Energy Router:
IoT-based, end device microgrid model predictive control, energy mgt & DR based on digital twin
Agnostic smart thing mesh connection
Two-level optimization: AI-enabled real-time control + insights for structural efficiency investments
Electricity consumption down 35%
Heat consumption down 45%
Carbon emissions down 38%
Additional reserve margin that allows to install fast EV charging without very costly grid connection charge
Increased reliability and power quality for critical application
Generating an additional stream of revenues in the demand response market
SYSTEM INSTALLATION RESULT AND SOLVED PROBLEMS
Base period actual and reference electricity consumption based on digital twin. After the microgrid is installed, actual consumption starts to converge to the reference load.
The microgrid also will be supplemented with ice storage that will
Allow to stop standby chiller operation that is currently required to maintain a reserve source of cooling for servers
Improve chiller efficiency
Earn more revenues in the demand response market
Further decrease peak load to connect more EV charging
HOW DOES IOE HELP SAVE ENERGY?
A sophisticated control model predictive (digital twin based) algorithm that prevented conflicting heating and cooling
Optimized the total energy consumption of all elements of the system – Dry coolers, pumps, chillers, fan coils
AI based algorithms that determine the actual need in resources (are people present in the area? How long would they stay? Which resources do they need?)
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HOW IS RESULT REACHED?
EnZu Energie Zukunft GmbH
DE320942794 +49 302 061 6200 Friedrichstr, 95 10117 Berlin