WO2016148651A1 - Procédé d'exploitation de système de gestion d'environnement de bâtiment - Google Patents

Procédé d'exploitation de système de gestion d'environnement de bâtiment Download PDF

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Publication number
WO2016148651A1
WO2016148651A1 PCT/SG2016/050122 SG2016050122W WO2016148651A1 WO 2016148651 A1 WO2016148651 A1 WO 2016148651A1 SG 2016050122 W SG2016050122 W SG 2016050122W WO 2016148651 A1 WO2016148651 A1 WO 2016148651A1
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WIPO (PCT)
Prior art keywords
zone
cooling
bems
air
energy
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PCT/SG2016/050122
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English (en)
Inventor
Rong Su
Nikitha RADHAKRISHNAN
Yang Su
Kameshwar Poolla
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Nanyang Technological University
The Regents Of The University Of California
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Priority to SG11201706843RA priority Critical patent/SG11201706843RA/en
Publication of WO2016148651A1 publication Critical patent/WO2016148651A1/fr

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/56—Remote control
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
    • F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F3/00—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems
    • F24F3/044—Systems in which all treatment is given in the central station, i.e. all-air systems
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00—Systems controlled by a computer
    • G05B15/02—Systems controlled by a computer electric
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F3/00—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems
    • F24F3/044—Systems in which all treatment is given in the central station, i.e. all-air systems
    • F24F2003/0446—Systems in which all treatment is given in the central station, i.e. all-air systems with a single air duct for transporting treated air from the central station to the rooms
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/30—Velocity
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2140/00—Control inputs relating to system states
    • F24F2140/50—Load
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2140/00—Control inputs relating to system states
    • F24F2140/60—Energy consumption
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2614—HVAC, heating, ventillation, climate control

Definitions

  • the present invention relates to a method of operating a building environment management system (BEMS). Particularly, but not exclusively, the invention relates to the energy-efficient scheduling of a Heating, Ventilating and Air Conditioning (HVAC) system.
  • BEMS building environment management system
  • HVAC Heating, Ventilating and Air Conditioning
  • the building sector represents more than 40% of worldwide primary energy consumption, 72% of US electricity consumption, and 38% of carbon dioxide (C0 2 ) emissions.
  • C0 2 carbon dioxide
  • electricity comprises the single largest building operating expense with up to 60% of the energy consumed being used for air- conditioning. Much of this energy is wasted.
  • Environmental Protection Agency studies suggest that energy savings of 30% can be realised through improvements to facilities and facility management, while more aggressive measures promise even greater savings.
  • HVAC Heating, Ventilating and Air Conditioning
  • HVAC control methods are centralised, involve sophisticated optimal control methods, and aim to minimise the total energy consumption across all zones.
  • model predictive control is very widely used for its ability to handle complicated constrained multivariable problems combined with uncertainty.
  • Kelman and Borelli propose a MPC approach to minimise energy use and satisfy occupant constraints using a sequential quadratic programming algorithm (see reference 1 of the references listed at the end of the description). This technique provides a locally optimal solution that reproduces some known scheduling strategies like pre-cooling, but the computational complexity is unfavorable when applied to large buildings.
  • the power drawn by a chiller in an HVAC system is approximately linear under a cooling load in the simplest model.
  • the power drawn by HVAC fans is approximately cubic or quadratic in relation to a mass flow rate of air. Accordingly, large peaks in the total mass flow rate of air are undesirable as they result in greater energy consumption.
  • Pre-cooling zones in advance of their occupancy offers an attractive strategy for energy efficiency gains. However, the pre-cooling strategy must be carefully tuned. Conservative pre-cooling strategies fail to flatten the mass flow rate of air sufficiently. Conversely, pre-cooling well in advance of what is required increases energy consumption because the cumulative cooling load is larger than necessary. In essence, pre-cooling serves to shift loads by exploiting demand temporal flexibility and a natural thermal mass in zones.
  • HVAC operational efficiency includes demand shifting, occupancy-based scheduling, set-point selection, base-lining, aggressive duty cycling, and scheduling that accounts for time-of-use electricity pricing.
  • BEMS building environment management system
  • BEMS building environment management system
  • step e) calculating an air supply strategy comprising a cooling/heating air supply allocation for each zone to minimise energy consumption of the one or more components, while aiming to satisfy all zone set-point requirements; and f) controlling the BEMS to deliver the allocation of step e) to each zone.
  • embodiments of present invention provide a novel, computationally efficient and scalable air distribution scheduling and control approach.
  • the requests for a particular cooling/heating air supply rate can be considered to be requests for a particular number of tokens and the cooling/heating air supply allocation can be considered as a token allocation.
  • This approach utilises an original hierarchical architecture, featuring locally (among individual zones) generated cooling/heating service (or token) requests together with centralised service/token allocations by the scheduler. It is believed that this strategy would realise aggressive targets for energy efficiency in BEMS such as HVAC systems (including variable air volume (VAV) systems), while respecting human comfort and air quality constraints.
  • VAV variable air volume
  • this solution enables rapid and cost-effective deployment both in new buildings and for retrofits.
  • the method is adaptive to occupancy and environment changes and robust by supporting fault detection and isolation of individual zones.
  • the architecture proposed offers several key advantages over existing approaches. For example, the architecture is scalable to realistic buildings with more than 500 thermal zones as the computational burden both on individual zone modules and the scheduler is modest. Co-ordination of token requests for individual zones ensures the best energy saving in the BEMS. Zone modules naturally deliver robustness as local models can be adaptively tuned to non-stationary environments, zone requests can accommodate abrupt changes in projected occupancy, and local environmental sensor data can serve to detect and localise faults. Indoor environmental quality (IEQ) constraints can be explicitly handled as constraints on return-to-fresh air ratios. Finally, the modular nature of the architecture implies that deployment costs will be minimal.
  • the scheduler can be considered to be centralised in that it operates as a single entity to schedule air supply to each of the individual zones. In practice, the scheduler need not be located centrally within the BEMS or building.
  • the zone set-points may comprise pre-determined values or acceptable ranges for the environmental sensor data. These may be pre-programmed or determined by user input.
  • the one or more components of the BEMS may comprise an air handling unit. More specifically, the one or more components may comprise a fan and/or damper associated with the air handling unit. Additionally, or alternatively, the one or more components may comprise a chiller or heater.
  • the method may further comprise repeating steps a) to f) at pre-defined time- intervals and/or when the zone environmental sensor data reaches a pre-defined value.
  • the requests may convey an amount of cooling/heating required and an urgency of the request.
  • the request may convey the amount of cooling/heating required, by when and for how long.
  • the requests may be determined for multiple time periods (i.e. horizons).
  • the multiple time periods may comprise periods with a common start time and different end times.
  • the cooling/heating air supply rate required for the next 30 minutes, next hour, next 2 hours etc. may be calculated and communicated to the scheduler.
  • the scheduler may take a long or short term view of the cooling/heating requirement to try to flatten out the effects of any peak demand periods on the energy efficiency of the BEMS.
  • the scheduler may be configured to take into account a chiller/heater coefficient of performance and/or air duct network design constraints when minimising energy consumption.
  • the zone set-points may be determined on the basis of thermostat settings or may be input to the system through a user interface.
  • BEMS building environment management system
  • a) two or more zone modules configured to:
  • zone environmental sensor data and zone set-points i. obtain zone environmental sensor data and zone set-points; ii. compute a request for a minimum cooling/heating air supply rate to meet the zone set-points within each respective zone;
  • a scheduler configured to:
  • ii. calculate an air supply strategy comprising a cooling/heating air supply allocation for each zone to minimise energy consumption of the one or more components, while satisfying all zone set-point requirements; and iii. control the BEMS to deliver the allocation of step ii) to each zone.
  • the BEMS may further comprise zone sensors for obtaining the environmental sensor data which may comprise one or more of temperature, air pressure, carbon dioxide (C0 2 ) concentration, humidity, occupancy and condition or status of windows and/or doors (e.g. open or closed).
  • zone sensors for obtaining the environmental sensor data which may comprise one or more of temperature, air pressure, carbon dioxide (C0 2 ) concentration, humidity, occupancy and condition or status of windows and/or doors (e.g. open or closed).
  • the BEMS may comprise a communication network infrastructure for communication between each zone module and the scheduler.
  • the network may be wired but is preferably wireless for ease of installation.
  • the BEMS may be configured to adjust damper or fan settings to regulate a flow of cool/warm air to the zones.
  • Each zone module may comprise one or more local mathematical models configured for predicting environmental (e.g. thermal) conditions within the respective zone.
  • the local models may include forecasts of weather, cooling/heating load, zone set-points and occupancy.
  • a goal of the present scheduling approach is to minimise energy consumption for the BEMS, for example, through more efficient operation of the air handling unit AHU (which may comprise one or more fans and/or dampers) and/or the chiller/heater system, while providing for a user-specified zone comfort level.
  • the air handling unit AHU which may comprise one or more fans and/or dampers
  • the chiller/heater system while providing for a user-specified zone comfort level.
  • Stage 1 (relating to the request generation) takes place in each individual zone, which may be a room or an area within a room.
  • a zone module takes zone environmental sensor data and zone set-points (such as temperature, humidity and a fresh air/returned air ratio) determined by a (given) human input or comfort model or an Indoor Environment Quality model, and computes a request for the minimum cooling/heating air supply rate (e.g. in term of the number of tokens) that may meet the zone set-points, while having a potential of minimising the BEMS energy consumption.
  • zone environmental sensor data and zone set-points such as temperature, humidity and a fresh air/returned air ratio
  • zone set-points such as temperature, humidity and a fresh air/returned air ratio
  • a complex thermal dynamics model in the form of a local model for each zone, an ideal BEMS energy consumption model and a zone human comfort model may be considered in order to ensure that the minimum cooling/heating air supply rate requested is properly chosen, which can save energy for the BEMS while ensuring zone comfort.
  • Stage 2 (relating to the air supply rate allocation) takes place in a centralised scheduler (CS).
  • the CS takes all zone requests (i.e. the requested amount of cooling/heating air supply rate from each zone, as opposed to the amount of cooling/heating air itself), and its knowledge on actual BEMS component (e.g. air handling unit or chiller/heater) power efficiencies into account, and calculates a cooling/heating air supply strategy (i.e. token allocation) for each individual zone, which would lead to minimum energy consumption of the BEMS, while satisfying all zone set- point requirements.
  • zone requests i.e. the requested amount of cooling/heating air supply rate from each zone, as opposed to the amount of cooling/heating air itself
  • BEMS component e.g. air handling unit or chiller/heater
  • a cooling/heating air supply strategy i.e. token allocation
  • This two-stage decision-making procedure can solve an HVAC energy optimisation problem in a distributed and hierarchical manner.
  • This HVAC energy optimisation problem addresses the energy consumption of the in-building part of an HVAC system in a more computationally efficient manner than the prior art (e.g. example embodiments of the invention can handle more than 200 zones, while a conventional method can handle no more than 50 zones in low-level optimisation) due to the proposed two-stage, distributed, hierarchical, decision-making (or optimisation) procedure.
  • the two-stage decision-making procedure induces a simple hierarchical information architecture, which may be quickly deployed in a new or old building with an extremely low deployment cost. This simple hierarchical information architecture may render high flexibility, adaptability and robustness for an actual deployment.
  • Example embodiments of the invention address an optimal HVAC scheduling/control problem instead of merely a communication problem in a conventional method and focus on a novel scheduling/control strategy with the simple hierarchical information architecture described above, allowing a quick deployment.
  • the simple hierarchical information architecture proposed in example embodiments of the invention is not limited to use of a wireless communication network, but may comprise an integration of any suitable, cost-effective communication network and local/centralised decision modules for energy efficiency optimisation.
  • the cooling/heating requirement of a zone can be regarded as a service.
  • the provider of the service is the BEMS (which may be implemented by a HVAC system) and the customers are the different thermal zones in the building.
  • the central concept can be considered to be that of tokens. These represent requests for cooling/heating service in various time windows.
  • the tokens may be expressed in terms of a desired mass flow rate of cool/warm air that a zone might need in order to meet temperature constraints determined by occupants through thermostat settings.
  • the requested tokens may be provided by an air handling unit (AHU) by adjusting damper or fan settings that regulate a flow of cool/warm air to the zones.
  • AHU air handling unit
  • Each zone may be associated with a zone module, which maintains a local model for heat transfer within the zone, processes available measurements from local sensors (i.e. zone environmental sensor data), and responds to user-specified temperature requests (i.e. set-points).
  • the zone module may conduct the following operations in sequence:
  • ZM Information processing of forecasts of weather, cooling/heating load, zone set- points, and occupancy
  • the centralised scheduler balances the requests and allocates tokens to each zone for the next time period. Specifically, the centralised scheduler may conduct the following operations in sequence:
  • Zone Modules CS1 Gather (token) requests from all Zone Modules CS2. Interrogate system state: IEQ, chiller/heater efficiencies, AHU status (e.g. damper positions, fan speeds), duct pressures, etc.
  • CS5. Issue control commands to the chiller/heater and/or the AHU (e.g. dampers and fans) to provide the allocated tokens to each zone
  • the allocation of tokens by the Centralised Scheduler attempts to minimise total energy use across the BEMS (e.g. including the chiller/heater and AHU which may comprise fans, dampers and pumps).
  • BEMS e.g. including the chiller/heater and AHU which may comprise fans, dampers and pumps.
  • C0 2 sensors may be employed as a surrogate monitor for environment comfort, together with IEQ models.
  • Combining sensors and models allows computation of IEQ constraints on return-to-fresh air ratios for each AHU (e.g. in step CS3).
  • Chiller/heater coefficient-of-performance factors may be used from look-up tables to update energy cost functions. These may drive the token allocation optimisation of the Centralised Scheduler (e.g. in step CS4).
  • Token allocation may be realised by issuing control commands to BEMS components such as the dampers, fans, chillers/heaters in the BEMS (e.g. in step CS5).
  • Computation conducted by the Centralised Scheduler may be centralised.
  • the BEMS may implement a model predictive control (MPC) framework to mitigate modeling errors (e.g. in the energy consumption models), uncertainties (e.g. in forecasted cooling loads), and abrupt changes (e.g. due to faults or unexpected changes in occupancy).
  • MPC model predictive control
  • the Zone Modules may have access to an effect of a control action on local variables (e.g. temperature) as obtained from the sensors in each zone. This information (e.g. the zone environmental sensor data) may therefore be used to update local models based on a measured thermal response from allocated tokens (e.g. in step ZM4). Future token requests may then be re-computed for subsequent time periods in a MPC framework (e.g. in steps ZM1 , ZM2, ZM3).
  • FIG. 1A is a flow diagram illustrating a method of operating a building environment management system (BEMS) in accordance with an embodiment of the present invention
  • FIG. 1 B is a schematic diagram of a building environment management system (BEMS) in accordance with another embodiment of the invention.
  • FIG. 2 is a block diagram of a BEMS in accordance with an embodiment of the invention.
  • Figure 3A is a graph of cooling load against time for daily use of a chiller in a BEMS
  • Figure 3B is a graph of ambient temperature against time
  • Figure 3C is a graph of returned air ratio against time
  • Figure 4A is a graph of simulated zone temperature over time
  • Figure 4B is a graph of cool air mass flow rate for different zones over time
  • Figure 4C is a graph of cool air mass flow rate for a fan of a BEMS over time
  • Figure 4D is a graph of power consumption over time
  • Figure 5 is a graph of energy against time
  • Figure 6A is a graph of zone temperature over time
  • Figure 6B is a graph of cool air mass flow rate for different zones over time
  • Figure 6C is a graph of cool air mass flow rate for a fan of a BEMS over time
  • Figure 6D is a graph of power consumption over time
  • Figure 7A is a graph of zone temperature over time
  • Figure 7B is a graph of cool air mass flow rate for a fan of a BEMS over time
  • Figure 7C is a graph of power consumption over time
  • FIG. 8 is a schematic diagram of a building environment management system (BEMS) in accordance with another embodiment of the invention.
  • BEMS building environment management system
  • Figure 9 is a pneumatic diagram showing air supply from a fan to different zones in the BEMS of Figure 8;
  • Figure 10 is a graph of supply fan efficiency against percentage load
  • Figure 11 A is a graph of simulated zone temperature over time
  • Figure 11 B is a graph of cool air mass flow rate for different zones over time.
  • FIG. 11C is a graph of power consumption over time.
  • the method 500 comprises the following steps:
  • Step 502 obtain zone environmental sensor data and zone set-points for two or more zones in a building;
  • Step 504 compute, for each zone, a (token) request for a minimum cooling/heating air supply rate to meet the zone set-points;
  • Step 506 communicate the request for each zone to a scheduler
  • Step 508 receive, at the scheduler, the requests for each zone and energy efficiency data on one or more components of the BEMS;
  • Step 510 calculate an air supply strategy comprising a (token) cooling/heating air supply allocation for each zone to minimise energy consumption of the one or more components, while aiming to satisfy all zone set-point requirements;
  • Step 512 control the BEMS to deliver the allocation of step 510 to each zone.
  • a building environment management system (BEMS) 10 as illustrated in Figure 1 B, that is configured to carry out the method of Figure 1A. More specifically, the BEMS 10 is configured as a heating, ventilating and air-conditioning (HVAC) system that utilises a token-based approach for air distribution scheduling for efficient operation. Notably, in this embodiment coefficients of performance for components of the BEMS 10 are not considered.
  • HVAC heating, ventilating and air-conditioning
  • FIG. 1 B shows information flow within an architecture of the HVAC system.
  • the architecture comprises individual zone modules 12, a centralised scheduler 14 and a communication network 16.
  • the zone modules 12 each comprise a processor 18 and sensors 20.
  • the centralised scheduler 14 may be configured to control an existing HVAC system comprising a chiller or heater by providing appropriate input signals to an existing air handling unit (AHU) 22.
  • the AHU 22 is configured to operate one or more supply fans 24 and/or dampers 26 to control an air mass flow rate into each zone in order to control the environmental conditions therein.
  • AHU 22 is configured to operate one or more supply fans 24 and/or dampers 26 to control an air mass flow rate into each zone in order to control the environmental conditions therein.
  • the centralised scheduler 14 does not directly control a chiller/heater subsystem, but rather, indirectly minimises operational energy use in the building by regulating a cooling/heating load seen by the chiller/heater (which is implemented by control of the AHU 22). Importantly, this feature negates the need for costly interfacing with proprietary chiller/heater subsystems, detailed domain knowledge, and particular details of an installation base for the HVAC system.
  • cooling or heating requirement may be regarded as a service provided by an HVAC system where customers are constituted by individual zones in a building or home.
  • the service may be delivered by a mass flow rate of cool/warm air, which may be measured in tokens.
  • the zone modules 12 are configured to compute service (i.e. token) requests on the basis of zone environmental sensor data (e.g. temperature, humidity, insolation, occupancy) and zone set-points, which may comprise a user-defined comfort range. More specifically, the zone module 12 comprises a local (mathematical) model of the environment which takes as inputs, the zone environmental sensor data sensed by the sensors 20 and produces, as an output, a minimum cooling/heating air supply rate (in the form of a token request) to meet future zone set-points.
  • the token request is sent via the communication network 16 (e.g. a wired or wireless internet protocol IP connection) to the centralised scheduler 14.
  • the zone modules 12 and token requests are decentralised and this allows fast linear computation of a forecasted cooling/heating service requirement over predefined timescales.
  • the centralised scheduler 14 receives the token requests from two or more zone modules 12 over the communication network 16 and responds by allocating tokens (i.e. controlling an air supply rate through the HVAC system) to deliver a required heating/cooling service in each zone via a token allocation provided to each zone.
  • the centralised scheduler 14 uses model predictive control (MPC) to balance the token requests from each zone against an energy cost for operation of the HVAC system.
  • the allocated tokens are used by the centralised scheduler 14 to control the HVAC system (i.e. the AHU 22) to provide the required air supply rate in each zone.
  • this may comprise flattening an AHU's supply fan mass flow rate profile to minimise energy consumption.
  • the centralised scheduler 14 can employ fast quadratic computations.
  • each zone module 12 serves to maintain and update its local model through a feedback process. It should be understood that the updating of the local model may be carried out at pre-set time intervals or when a certain threshold is reached (e.g. when the zone environmental sensor data changes by a pre-defined amount).
  • this embodiment of the invention does not require direct modification of the chiller/heater control system. Instead, the HVAC system calculates optimal demand signals for control of the AHU's supply fan 24 and optimal damper 26 positions for each zone.
  • the HVAC system is easily scalable (e.g. it may take only 3-5 minutes for the HVAC system to deliver the token allocations to 150 or more zones), that there is low deployment cost, robustness to faults, enablement of fault detection and isolation (FDI), informed chiller staging and negligible energy performance loss when compared to a known non-linear optimisation strategy as will be explained in more detail below.
  • Embodiments of the invention may therefore be particularly, but not exclusively, suitable for use in commercial buildings.
  • the zone modules 12 use local models and sensor 20 measurements to compute requests for HVAC service over various future time windows. As explained above, these requests are expressed in terms of the heating/cooling service required which may be conceptually regarded as tokens.
  • the centralised scheduler 14 balances the token requests from the zone modules 12 and allocates tokens to each zone for a subsequent time-slot. This allocation attempts to minimise total energy consumption in the HVAC system, while respecting operational constraints.
  • the zone modules 12 update their local models based on measured thermal responses resulting from the allocated tokens, and re-compute future token requests.
  • VAV Variable Air Volume
  • the BEMS is an HVAC system that is constituted by a Variable Air Volume (VAV) air-conditioning system 30 as shown in Figure 2. Whilst such a VAV system is primarily concerned with chilling air to a comfortable temperature in a hot climate, it will be understood that the principles of the present embodiment may also be applied to other HVAC systems and BEMS, for example, those that concern heating as opposed to chilling.
  • VAV Variable Air Volume
  • a zone 34 may be regarded as an area inside a building that is controlled by a single thermostat. It could be a part of a large room or might comprise several small rooms.
  • the VAV system 30 in Figure 2 has a duct 32 servicing multiple of such zones 34 numbered 1 , 2...n.
  • the VAV system 30 includes a chiller 36 that comprises units of various capacities that produce chilled water 38 at a fixed temperature (typically 4-7°C) and with a fixed flow rate. These units are staged based on typical daily cooling load patterns that the building experiences.
  • the VAV system 30 further comprises a VAV Air Handling Unit 40 (VAV AHU) that receives chilled water 38 from the chiller 36 and fresh outside air 44 filtered through external duct 42.
  • VAV AHU VAV Air Handling Unit 40
  • air supplied to the VAV AHU 40 is a mix of the fresh outside air 44 plus re-circulated air 46.
  • the fresh outside air 44 is used to keep C0 2 levels within mandated levels, and re-circulated air 46 is used because it has lower humidity and is already cooled.
  • a heat exchanger comprising cooling coils (not shown) uses the chilled water 38 to cool the air supplied to the VAV AHU 40 to a pre-set temperature set-point (typically 12-14°C).
  • the VAV AHU's cool air output 48 is then forced by a supply fan 50 into the duct 32 in the building.
  • the supply fan 50 is responsible for creating sufficient pressure differences to ensure that the cool air output 48 is available at all zones 34 requesting a cooling service.
  • the VAV AHU's cool air output 48 is used to cool the various zones 34. Accordingly, a portion of the cool air output 48 mixes with existing warmer air to cool each zone 34, and flows back to the VAV AHU 40 through the duct 32. Mass flow rate of supplied cool air to each zone 34 is controlled by dampers 52 that can alter the cross-sectional area of the duct 32 opening to each one of the zones 34.
  • the VAV system 30 may also comprise a control module (not shown) that receives information from thermostats fitted in each zone 34 and controls the dampers 52 to meet user-specified zone temperature set-points.
  • An aim of embodiments of the present invention is in minimising the overall operational energy consumption in the HVAC system through improved scheduling and/or control.
  • the components with the most significant energy consumption are the chiller 36 and the supply fan 50 in the VAV AHU 40.
  • the chiller 36 consumes over 75% of the total energy used although efficiency gains could be realised through optimised chiller staging.
  • Energy consumption in other HVAC system components is less significant, and being relatively fixed, offers modest potential for efficiency gains through improved control.
  • the chiller 36 constitutes a single centralised chiller that serves the single VAV AHU 40 responsible for providing cool air to all zones 34.
  • a coefficient of performance of the chiller 36 is assumed to be constant across operating points and an air duct pressure distribution associated with allocated cool air mass flow rates is not considered in order to simplify the description of the present embodiment.
  • Example 2 described below, these assumptions are removed to illustrate a more general embodiment of the invention. In both examples, humidity effects are not considered, for simplicity of illustration. Furthermore, it is assumed that air mixing within any zone 34 is instantaneous.
  • Optimal control/scheduling of the VAV system 30 requires a model to capture the thermal dynamics of the zones 34 and their interactions within a structure of the building.
  • Many papers describe lumped electric circuit equivalent models for thermal zones. Others develop detailed prediction models that account for a wide variety of factors including physical building parameters, lighting, occupants and climate.
  • Equation (1 ) a simple bilinear thermal model illustrated in Equation (1 ) is employed as the local model.
  • Equation (1 ) uses a known profile of cooling load Q to represent a load due to thermal input from internal loads and occupants, as well as coupling with adjacent zones and a constant Rj that represents thermal input from the environment.
  • c t Ti m lCp T c - T + R t (T oa - Tt) + Q t ( 1 )
  • This local model decouples thermal dynamics across zones 34 because coupling effects are lumped into a disturbance process Q
  • this local model is nonlinear so as to capture mixing dynamics which appears as the product of a control input mass flow rate of cool air supplied to zone i, m t and the temperature of zone i, T.
  • This local model is discretised with sampling time ⁇ as shown in Equation (2):
  • An acceptable temperature range i.e. zone set-point for each zone 34 during occupied and unoccupied hours is specified in advance as:
  • the local model has access to forecasts of ambient temperature T oa and cooling load profiles Q lt which are used to predict a random process V j (/ ).
  • a model-predictive control strategy is employed, as explained in more detail below, to deal with both increasing forward uncertainty in the above forecasts, as well as abrupt changes in the acceptable temperature range (possibly from occupancy forecasts or zone environmental sensor data).
  • the chiller 36 is the principal component responsible for the cooling of the zones 34 in the building. In current practice, multiple chillers 36 coordinate to work at their best operating conditions to improve overall efficiency of the VAV system 30.
  • the function of the chiller 36 is to provide a continuous supply of chilled water 38 to cooling coils inside the VAV AHU 40. Warmer air passes over the cooling coils inside the VAV AHU 40 producing the cool air output 48 that serves to cool the zones 34.
  • Models of the chiller 36 performance are complex and depend on the particular technology used. Calibrated performance curves supplied by a manufacturer of the chiller 36 specify the chiller 36 energy efficiency at various operating points.
  • a detailed chiller 36 model based on first-principle heat transfer mechanisms with coefficients calibrated from the manufacturers' performance curves has been described in the prior art. However, such a detailed chiller 36 model is complex and has seen limited use in practical control applications.
  • power consumption by the chiller 36 is quadratic in relation to a cooling load.
  • Equation (4) a simple control-oriented chiller 36 model is used as described in Equation (4):
  • the power consumed by the supply fan 50 (which may be an axial or a centrifugal fan) depends principally on the mass flow rate of cool air and a pressure difference between an inlet and an outlet of the supply fan 50 as illustrated in the equation below.
  • Equation (2) subject to the zone 34 thermal dynamics of Equation (2), constraints on the acceptable temperature ranges as per Equation (3), and limits on the mass flow rates of cool air in Equation (6).
  • the zone modules 12 in each zone 34 maintain local models regarding heat transfer, process user-specified temperature requests (i.e. zone set-points), and process available sensor 20 measurements in the form of zone environmental sensor data.
  • the zone modules 12 receive forecasts of weather, cooling load, and occupancy. This information, together with the local models described above, is used to compute requests for cooling service over various future time periods. These requests are expressed in terms of the desired mass flow rate of cool air which is conceptually regarded as a token request.
  • the tokens are provided by the VAV AHU 40 by adjusting settings for the dampers 52 to regulate the mass flow rate of cool air to the zones 34.
  • the centralised scheduler 14 has the job of balancing the requests and allocating tokens to each zone 34 for the next time-slot in a manner that attempts to minimise total energy use while respecting operational constraints. As demonstrated below, an algorithm for token allocation can be reduced to quadratic programming.
  • Equation (2) for zone i is a bilinear dynamic model as the input m, multiplies the state T,. Forecasts of an exogenous noise process x? £ (/e) on forward time windows are used and a critical transformation of the input variables is introduced as per Equation (8).
  • Each zone 34 calculates its cooling energy requirements individually. These are computed over several future time horizons (or window) to obtain a cooling energy profile for each zone 34.
  • an associated zone module 12 solves Equation (10) subject to Equations (1 1 ), (12) and (13):
  • T t (k) is a linear function of the decision variables gi(s) ; s ⁇ k because the dynamics in Equation (1 1 ) are linear. This results in a linear programming problem which can be solved very quickly, and in parallel, in each zone 34.
  • J, (H p ) it is possible to interpret J, (H p ) as a minimum cooling energy or minimum number of tokens needed by zone i on the planning horizon H p to meet local temperature constraints.
  • J, (H P ) does not convey an urgency of the token request.
  • the token request profile J, (H p ) of computed requests captures the urgency of the cooling requirements for zone
  • the zone modules 12 translate token requests into mass flow rate of cool air requests. More precisely, for a fixed planning horizon H p , g t p (k) is the optimal cooling request that solves Equation (10). Using Equation (11 ), it is possible to compute an associated optimal temperature profile T° pt (k). Using Equation (8), it is possible to translate the token request into a mass flow rate of cool air request as below:
  • H P V and a corresponding minimum total mass flow rate of cool air profile over each planning horizon required for zone i is computed as:
  • this minimum mass flow rate of cool air profile 5 ⁇ (# ⁇ ) is transmitted to the centralised scheduler 14 using standard IP protocols over the communication network 16.
  • the minimum mass flow rate of cool air profile (also referred to as a request profile) supplies a lower bound on the total mass flow rate of cool air demanded by zone i on the planning horizon H p , as dictated by the local model for the zone 34 and acceptable temperature ranges (i.e. zone set-points).
  • the centralised scheduler 14 attempts to allocate the mass flow rate of cool air requested by each zone 34 while minimising total energy consumption in the VAV system 30. In this step, the thermal dynamics in each zone 34 and acceptable temperature ranges for each zone 34 are discarded, as they are captured by the request profile 5 ⁇ ( ⁇ ⁇ ). The centralised scheduler 14 therefore solves Equation (14) subject to Equations (15) and (16). m a ⁇ r l (k) ⁇ rri lh , l ⁇ i ⁇ n z , l ⁇ k ⁇ W (16)
  • the decision variables in this step are the mass flow rates of cool air m t (k).
  • the objective function is quadratic in the decision variables, and the constraints are linear inequalities. This is therefore a quadratic problem that can be efficiently solved with standard software tools.
  • the centralised scheduler 14 tries to low-pass filter the request profiles for each zone 34 to reduce operational energy consumption.
  • Simulations were conducted for a synthetic building with five zones 34.
  • the VAV system 30 comprised a single VAV AHU 40 servicing all five zones, and one centralised chiller 36 supplying chilled water 38 to cooling coils in the VAV AHU 40.
  • the simulated architecture was essentially as illustrated in Figure 2 and as described above.
  • the zone 34 service hours and desired zone (temperature) set-points employed are detailed in Table 1.
  • Figure 4A shows temperature profiles for each zone 34 and these reveal that the acceptable temperature ranges are respected for all zones 34 in the time periods concerned. The temperature profiles tend to follow the upper bound of the acceptable temperature ranges to expend minimal energy for cooling the zones 34.
  • Figure 4B shows the mass flow rate of cool air for each zone 34 and
  • Figure 4C shows the mass flow rate of cool air at the supply fan 50.
  • Figure 4D shows the power consumption for the entire VAV system 30, P, compared with the power consumption for the chiller 36, P c , and the power consumption for the supply fan 50, P f .
  • the zones 34 in this embodiment are typically pre-cooled 60 minutes in advance of occupancy. Longer pre-cooling requires larger energy consumption in the chiller 36 because of increased net external cooling load, while shorter pre-cooling times result in larger mass flow rates of cool air increasing the energy consumption in the supply fan 50. The present embodiment balances these effects to minimise overall energy consumption.
  • each zone module 12 generates token requests over various future time windows up to W hours.
  • Small sampling times result in peaks in the mass flow rate of cool air, with the supply fan 50 supplying a bulk of the token requests at the end of the pre-cooling period, which wastes energy in the supply fan 50.
  • Large sampling times result in long pre-cooling periods, which waste energy in the chiller 36.
  • Embodiments of the present invention solve the general scheduling problem described above in two stages: token requests and token allocation. It is possible to compare this strategy with a brute-force approach of single-stage centralised nonlinear optimisation. Such a nonlinear optimisation approach was applied to the same five zone setup as described above and the results are shown in Figure 6A, B, C and D, for comparison with the present embodiment.
  • Figure 6A shows the temperature profiles for each zone 34
  • Figure 6B shows the mass flow rate of cool air for each zone 34
  • Figure 6C shows the mass flow rate of cool air at the supply fan 50
  • Figure 6D shows the power consumption for entire VAV system 30, P, the chiller 36, P c , and the supply fan 50, P f .
  • the temperature profiles and mass flow rates of cool air in each zone 34 closely resemble those computed in the token-based approach according to the above embodiment of the invention, as shown in Figures 4A, B, C and D.
  • the total energy consumption with the token-based approach according to an embodiment of the present invention is only sub-optimal (compared to the nonlinear optimisation approach) by 1.6%.
  • the computation time for the token-based approach is significantly lower by a factor of 35 for only 5 zones.
  • Further simulations have revealed even more dramatic computational savings for buildings with 100 or more zones 34 and with still only modest sub-optimal performance in terms of total energy consumption.
  • the token-based approach according to embodiments of the present invention could comfortably accommodate the realistic case of 500 zones 34.
  • a legacy pre-cooling strategy for commercial buildings is to commence cooling at a fixed time each day.
  • an acceptable temperature range for all zones 34 is identical and assumes occupancy over the working day (9am to 6pm).
  • a pre-cooling period was 30 minutes in advance of the start of the working day at 9am.
  • This pre-cooling period was uniform across all zones 34 in the building and the mass flow rate of cool air to each zone 34 was also pre-set to be constant across the pre-cooling period.
  • This legacy pre-cooling strategy was simulated to offer a further point of comparison with embodiments of the present token-based approach as described above.
  • Figure 7A shows the temperature profile for a single zone 34
  • Figure 7B shows the mass flow rate of cool air at the supply fan 50
  • Figure 7C shows the power consumption for the VAV system 30, P, the chiller 36, P c , and the supply fan 50, P f under this legacy pre-cooling strategy.
  • the VAV system 30 shows the temperature profile for the VAV system 30, P, the chiller 36, P c , and the supply fan 50, P f under this legacy pre-cooling strategy.
  • COP Coefficient of Performance
  • Example 2 describes an elaborated scheduling problem similar to that of example 1 but with a consideration of chiller COP and feasibility of incorporating air duct pressure distribution.
  • FIG. 8 illustrates a further embodiment of a BEMS according to the present invention which also utilises a token-based approach similar to that described above, but taking chiller COP and air duct pressure distribution into account.
  • FIG 8 shows an in-building section of a Variable Air Volume (VAV) HVAC system 60 which is similar to that shown in Figure 2.
  • the HVAC system 60 comprises an Air Handling Unit (AHU) 62 that takes outside air and performs multiple functions like filtration, temperature control and humidity control, etc. After passing through filters 64, the outside air is mixed (into mixed air) in a mixing chamber 66 with return air from inside building zones. This process is vital for maintaining indoor air quality (e.g. in relation to C0 2 levels) for occupants.
  • the mixed air passes around cooling coils 68 that circulate chilled water supplied by chillers (not shown). A mass flow rate and temperature of the chilled water is controlled to ensure that the mixed air is cooled to a predetermined temperature forming cool air, suitable for cooling the building zones.
  • the mixed air incoming to the cooling coils 68 is relatively warm and exchanges heat with the chilled water in the cooling coils 68 within the AHU 62 as described previously.
  • the cool air outgoing from the cooling coils 68 is forced by supply fan 70 into the building's supply duct 72.
  • a pressure rise at the supply fan 70 depends on the mass flow rate of cool air which, in turn, is determined by a cooling demand of the building zones.
  • the zones 74 in this embodiment are conditioned spaces inside the building that are regulated by a single thermostat.
  • Each zone 74 has a duct opening fitted with a set of metal plates called dampers 76 that control a cross sectional area of the duct openings, affecting the mass flow rates of cool air entering the zones 74.
  • dampers 76 that control a cross sectional area of the duct openings, affecting the mass flow rates of cool air entering the zones 74.
  • the cool air incoming through the damper 76 mixes with existing air in the zone 74 reducing an overall zone 74 temperature.
  • a return duct 78 transports air from each zone 74 and vents a portion of the air in the return duct 78 to outside the building, while a remainder of the air in the return duct 78 is fed into the mixing chamber 66 for re-circulation.
  • the HVAC system 60 comprises zone modules 12 configured to compute token requests for minimum mass flow rates of cool air, which are communicated to a centralised scheduler 14 configured for token allocation and control of the HVAC system 60 components to deliver the required mass flow rates of cool air to each zone 74, in accordance with Figures 1A and 1 B.
  • the coefficient-of-performance functions for the chiller are taken into account by the centralised scheduler 14 when minimising energy consumption of the HVAC system 60.
  • the present embodiment concentrates on commercial buildings, where employees would have relatively fixed working hours and temperature set-points for zones 74 are stable and predictable. Humidity is not considered, air mixing inside building spaces is assumed to be instantaneous, and local weather forecasts are provided as inputs to the local models in each zone module 12.
  • zone temperature set- points for each zone 74 are specified in advance. Instead of a strict zone set-point, a range of temperatures that are within human comfort requirements such as those depicted in Table 1 are employed. As before, a model predictive control strategy is employed to deal with both increasing forward uncertainty in the weather forecasts, as well as abrupt changes in the zone temperature set-points (possibly from occupancy forecasts or zone environmental sensor 20 data).
  • the mass flow rate of cool air for each zone 74 is scheduled by the centralised scheduler 14 and the HVAC system 60 is operated to ensure that a pressure rise due to the supply fan 70 and damper 76 positions deliver the scheduled mass flow rate of cool air to each zone 74.
  • FIG. 9 A schematic of an air supply duct network 80 employed in the present embodiment is presented in Figure 9 and comprises a supply fan 82 coupled to a main duct 84 from which branch ducts 86 carry supplied air to each room in the building.
  • each branch duct 86 splits into two arms 88 to feed air into duct openings in each room through two separate dampers (not shown).
  • An area fed by each damper therefore constitutes a zone 90 in this embodiment.
  • a pressure in the duct network 80 is given by Equation (17):
  • A is the cross-sectional area of the duct network 80
  • m is the mass flow rate of the supplied air.
  • every zone 74 may initially be considered to be the same constant value p z .
  • a damper fitted in the present embodiment which alters the cross-sectional area A, of the duct network 80 in order to control the mass flow rate of cool air passing through it.
  • A is the minimum cross-sectional area of the duct opening and Ai ⁇ is maximum cross-sectional area of the duct opening.
  • the power consumed by the supply fan 70 depends mainly on the mass flow rate of the cool air passing though it and the pressure difference between an inlet and an outlet of the supply fan 70 as defined in Equation (23).
  • each supply fan 70 may also be considered in each application. Chiller
  • the chiller 36 is a key component of a building HVAC system, responsible for removing heat from the building spaces and, as described above in relation to Figure 2, provides a continuous supply of chilled water 38 to cooling coils in an AHU 50, which cools air passing over the cooling coils.
  • chiller 36 sequencing control determines thresholds according to a building instantaneous cooling load and a maximum chiller 36 cooling capacity, which is in principle the best approach for chiller 36 sequence control.
  • a coefficient of performance is the ratio of heating or cooling provided with respect to electrical energy consumed. Higher COPs equate to lower operating costs.
  • the COP of the chiller (not shown in Figure 8) is considered to be a piecewise constant function of a building cooling load.
  • ⁇ is denoted as a reciprocal of the COP, g, is a cooling energy provided to zone i, and Q ch is a constant that denotes various amounts of cooling loads.
  • the power consumption is not linear in the mass flow rate of cool air m ( as the zone temperature T, also depends on m t through the zone module's local model described above.
  • a capacity of the chiller 36 is another constraint that may be considered in embodiments of the invention.
  • Such a linear constraint that couples all zones 74 can be easily handled by using Lagrangian relaxation. However, for ease of illustration this constraint is not considered further in this example.
  • the present embodiment has an architecture similar to that depicted in Figure 1 B and described above.
  • the zone modules 12 operate in parallel to model the thermal dynamics of each zone 74 independently and to compute token requests (e.g. in the form of minimum energy cooling requirement profiles) for each zone 74 which are relayed to the centralised scheduler 14.
  • the centralised scheduler 14 receives the token requests and allocates them to each zone 74.
  • the work of the centralised scheduler 14 is in two steps.
  • the first step concerns only the coefficient of performance (COP) of the chiller 36.
  • An optimisation algorithm is configured to increase the cooling energy supplied to each zone 74 and improve the COP of the chiller 36, hence reducing overall energy consumption. This computation reduces to a mixed integer linear programming problem and the token requests are expressed as cool air mass flow rate tokens to be allocated by the centralised scheduler 4 to provide a token allocation for each zone 74.
  • the second step of the centralised scheduler 14 is to check damper 76 constraints and identify an optimal pressure profile for the duct network 80, such as that illustrated in Figure 9, for the next time-slot. Damper 76 settings are adjusted to regulate a flow of cool air to each of the zones 74. The power consumption of the supply fan 70 is also considered in this step.
  • the token allocation attempts to minimise total energy consumption while respecting operational constraints and reduces to a constrained quadratic programming problem.
  • Zone modules 12 update their local models based on measured thermal response (e.g. from sensors in each zone) as a result of the allocated tokens and re-compute forward token requests for subsequent time-slots in a Model Predictive Control framework, as before.
  • a huge reduction in computation time over the prior art can be expected from both parallel optimisation of the zone modules 12 and a hierarchical architecture concerning the token requests and the subsequent token allocation.
  • the reduced complexity ensures that the architecture is scalable to large buildings (e.g. containing 300-500 zones).
  • Boolean variables ⁇ are introduced for proper selection of ⁇ defined as:
  • the zone modules 12 receive zone environmental sensor data from the available sensors 20 in the zones 74. In addition to standard thermostats, this could include information from occupancy sensors, mass flow rate sensors, and damper 76 position sensors. The zone module 12 may also be informed about weather forecasts and occupancy predictions for its zone 74.
  • each zone module 12 solves the following optimisation problem for a fixed time planning horizon H p using Equation (34) subject to Equations (35) and (36):
  • T t ik is a linear function of the decision variables s ⁇ k because the dynamics in Equation (35) are linear.
  • the power consumption of the chiller 36 depends on the building cooling load as derived earlier in Equation (27). As explained above, most buildings have a set of more than one chiller 36 following a scheduling algorithm to attain maximum efficiency at any given cooling load. Energy savings in incorporating COP into the present embodiment may not be significant over a small time period like a day or a week, but annual savings may be significant. For a fixed window size W, the centralised scheduler 14 in the present embodiment solves a mixed integer linear programming problem as defined in Equation (37) subject to Equations (38) to (49).
  • Vk 1,2, ... rij - 1, ⁇ i 9t (k - Q chj ⁇ e + (it, - ⁇ ) ⁇ ⁇ (fc) (41 )
  • Vk.j 1,2, ... rij - 1, Dj(k ⁇ U8j(k) (42)
  • Vfc 1,2, ... rij - 1, Dj k) ⁇ u6j(k) (43)
  • Vfc.y 2, ... rij, Dj(k) ⁇ 1/(1 - (44)
  • Vfc.y 2, ... rij, Dj ⁇ k) ⁇ u(l - 5y_i)(fc) (45)
  • the planning horizon H w is fixed and g° pt (k) is an optimal cool air token request that solves the mixed integer linear Equation (37).
  • Equation (35) it is possible to compute an associated optimal temperature profile T° pt (k) .
  • Equation (8) it is possible to translate the token request into a mass flow rate request as below:
  • a final step of the present token based approach concerns the supply fan 82 (or supply fan 70), and the duct network 80.
  • a cost function is the power consumption of the supply fan 82 and the decision variables are p 0 , p, and m ( Vi.
  • the supply fan 82 has a power function that is non-linear (usually quadratic or cubic) in relation to the mass flow rate of supply air at the AHU 62 and flattening of the mass flow rate of supply air at the AHU 62 rri SA profile could lead to savings at the supply fan 82 level. However, this may not be a high priority as the power consumption of the chiller 36 is linear in m ' SA and much larger than the supply fan's 82 power consumption.
  • Feasibility of delivering the requested mass flow rate of cool air to each zone 74 may be considered with respect to pressure distribution in the duct network 80 through analysis of the equations (19), (21 ) and (22).
  • a convex approximation to a pressure model of a building is assumed and the dampers 76 at the duct openings of the zones 74 are designed to not completely close at any time. As long as the supply fan 82 is running, a small amount of cool air enters a zone 74 captured by a non-zero damper 76 opening in Equation (21 ).
  • Equation (52) As this is a non-convex equation, it is converted into a convex one by linearising the equation as it represents only a very small value of m t below 1 as given by Equation (52). Equations (19) and (22) represent further constraints in this step.
  • the duct network 80 shown in Figure 9 is considered and a distance between the duct openings of two zones 74 is assumed to be the same throughout. All zones 74 are assumed to be at the same pressure p z and the centralised scheduler 14 solves Equation (50) below subject to constraints (51 ) to (56). min /c ⁇ ⁇ )) 2 (50)
  • pre-cooling of building spaces began at a fixed time for all zones 34 before the expected arrival of the first occupants.
  • the mass flow rate of cool air supplied during the pre-cooling period was constant and the pre- cooling period was typically around 30-45 minutes. All zones 34 were pre-cooled at the same time.
  • the zone temperature demands were not handled individually.
  • This example was implemented in MATLAB for six zones to compare it with the token-based approach described above for embodiments of the present invention in accordance with example 1. For a better comparison, the token-based approach was implemented for zones with the same temperature requirements but with varying service times.
  • Figures 7A and 11 A show the difference in temperature trajectories between both approaches.
  • the token based approach only supplies enough cool air to satisfy a minimum cooling requirement whereas the legacy pre- cooling approach satisfies the temperature demands, but cools zones 34 that are not even in service.
  • the saving in terms of energy consumption for the present embodiment is 17%.
  • the following table shows more details about this comparison.
  • the amount of energy saving that can be expected for a given application will depend on the zone 34 service hours, temperature demands, and most importantly, the total number of zones 34 under consideration. It is expected that the greater the number of zones 34 under consideration, the higher the energy saving using embodiments of the present invention.

Abstract

L'invention concerne un procédé d'exploitation d'un système de gestion d'environnement de bâtiment (BEMS) et concerne également un système pour le mettre en œuvre. Le procédé consiste à : a) obtenir des données de capteur environnemental de zone et des points de consigne de zone pour au moins deux zones dans un bâtiment ; b) calculer, pour chaque zone, une demande de débit minimal d'alimentation en air de refroidissement/chauffage pour satisfaire les points de consigne de la zone; c) communiquer la demande de chaque zone à un planificateur ; d) recevoir, au niveau du planificateur, les demandes pour chaque zone et des données de rendement énergétique sur un ou plusieurs élément(s) du BEMS ; e) calculer une stratégie d'alimentation en air comprenant une affectation d'alimentation en air de refroidissement/chauffage pour chaque zone afin de réduire au minimum la consommation énergétique du ou des élément(s), tout en visant à satisfaire les exigences de point de consigne de toutes les zones ; et f) commander le BEMS à délivrer l'affectation de l'étape e) à chaque zone.
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