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This article systematically explains group control optimization strategies for multi-unit collaborative operation in building chiller and heat source systems, from load forecasting to energy-efficient optimal combination.&Detail_en=

In building total energy consumption, the chiller and heat source system typically accounts for 35%-50%, making it the undisputed “energy consumption giant.” Traditional control methods often adopt single-unit independent operation mode, which is not only inefficient but also prone to uneven equipment wear and high operating costs. This article systematically explains group control optimization strategies for multi-unit collaborative operation, helping building operators achieve high-efficiency, energy-saving, and stable operation of chiller and heat source systems.

I. Pain Points of Traditional Control

Traditional control systems commonly suffer from the following issues:

1. Fixed number of units with poor load matching: Using “one in use, one on standby” or fixed number of units cannot flexibly adjust according to actual load, resulting in low efficiency under partial load.

2. Rough switching logic: Unit startup and shutdown are based only on simple temperature thresholds, lacking consideration of overall system efficiency, and frequent startup and shutdown exacerbate equipment wear.

3. Uneven load distribution: When multiple units run in parallel, load distribution lacks optimization strategy, with some units running at high load and others at low load, preventing maximization of overall efficiency.

II. Core Principles of Group Control Optimization

The essence of group control optimization is to achieve optimal combination and operation scheduling of multiple units through intelligent algorithms while meeting building cooling and heating demands. Core principles include:

1. Load forecasting: Based on historical data, weather forecasts, and occupancy information, predict future 1-4 hour building load trends.

2. Energy efficiency curve modeling: Each unit’s COP (coefficient of performance) varies non-linearly with load rate, and an energy efficiency curve database is established through measured data.

3. Optimal combination search: Under predicted load conditions, enumerate all possible unit combination schemes, calculate total power consumption for each combination, and select the most energy-efficient operation plan.

III. Four Major Control Strategies

1. Number optimization control: Dynamically determine the number of running units based on real-time load, avoiding extreme situations of “big horse pulling small cart” or “small horse pulling big cart.”

2. Load equalization distribution: When multiple units run in parallel, distribute total load to each unit according to their energy efficiency characteristic curves to maximize overall COP.

3. Sequential startup and shutdown optimization: Establish scientific startup and shutdown priorities considering unit runtime, energy efficiency rating, and maintenance status to achieve “wear equalization.”

4. Linked optimization control: Link chiller and heat source group control with terminal air conditioning system, cooling tower, and water pump system to achieve full system collaborative optimization.

IV. Implementation Points

1. Sensor deployment: Real-time collection of key parameters such as chilled water supply and return temperature, flow rate, outdoor temperature and humidity, and unit operating status (current, power, COP).

2. Control platform selection: It is recommended to use BMS or dedicated group control optimization controllers that support standard protocols such as OPC UA and Modbus/BACnet.

3. Commissioning and optimization: After system commissioning, continuously collect operating data and use machine learning to% continuously optimize energy efficiency curve models and control strategy parameters.

V. Summary

Chiller and heat source group control optimization is a key technical path for achieving deep energy savings in buildings. Through load forecasting, energy efficiency modeling, and intelligent scheduling, the overall energy efficiency of chiller and heat source systems can be improved by 15%-25%, while extending equipment life and reducing O&M costs. For scenarios with large chiller and heat source scale such as large commercial buildings, data centers, and hospitals, the investment payback period for group control optimization is typically 2-4 years, delivering significant economic and environmental benefits.

Zhiyinghui provides full-link solutions from sensor deployment to group control system integration, helping building chiller and heat source systems achieve intelligent and energy-efficient upgrades.

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