GB2472385A - Controlling power demand using a building management system - Google Patents

Abstract

A method of controlling power demand comprises: using a building management system to determine parameters of a plurality or devices in an environment; means for determining whether a reduced power demand is required; and means for adjusting, through the building management system, the parameters of one or more devices to reduce power demand when necessary. A building 10 has a number of devices 11 A-D including heating, cooling, ventilation or other building services. Sensors are provided to determine various parameters associated with the devices. All the plant is controlled by a building management system (BMS). A building interface (BI) 15 attached to the BMS is used to build up a database of the devices and their parameters and to control demand response within the building. The BI is connected to an external application server 16. The BI may have a "learning" function allowing it to learn where plant is located and what parameters it is able to change to achieve demand response.

Description

Energy Management System and Method This invention relates to an energy management system and method.

In particular, but not exclusively, it relates to a system and method for controlling the timing of demand for electricity to suit electricity grid suppliers or operators.

Countries or regions have a network of power stations and other power generation means (wind turbines, water turbines and so on) which feed into a power supply chain (generally known as the national grid in the UK). This power is used by businesses, domestic consumers and so on. The demand for such power, of course, varies both throughout the day and year and also with particular events and circumstances. For example, it is well known that if a high profile football match is being shown on national television, power demand surges at halftime when perhaps many millions of consumers will turn on kettles or other heating appliance to make or tea or other beverages and water utility company pumps are operated at high demand. This therefore produces a spike in the power demand requirement.

The operators of the national grid currently accommodate such spikes by having certain facilities which are arranged to not generate their full power normally but only to generate full or higher power when spikes are anticipated or encountered. Alternatively, arrangements may be made to buy in power from other sources (for example the UK may buy in extra power from France or other European countries when a spike is anticipated or encountered). However, there is a limit to how much this can be done and with ever increasing average and peak power requirements, there becomes more of a risk that the demand cannot be satisfied in this. Also, running power supply means in this way can be very inefficient and wasteful of power and money.

Furthermore, there is an ever-increasing chance, with increased average and peak power requirements, of the system simply not being able to cope and therefore a greater likelihood of sudden or planned power outages (power cuts). With increasing use of renewable energy, such as wind, such unpredicted changes may occur more often.

In addition, there is an ever increasing need to carefully monitor energy usage and demand in a building in order to maximise efficiency and minimise environmental cost.

An alternative method is controlling demand in buildings or other environments so that when a nation or region at large (or the national grid') requires an increased power supply the power demand in one or more, probably many, buildings or other environments can be temporarily reduced or deferred to accommodate this increased demand.

Figures 1 to 3 explain the principle used in very broad terms. Essentially, items in a building which require power, such as heating, cooling and similar items have a certain thermal inertia. That, if the output of components heating a room are reduced, the temperature in that room will not suddenly cool down; it will gradually cool down and may maintain a temperature which is not ideal but is certainly acceptable for a long time. For example, if a room is required to be kept at 22° and the heating is suddenly turned off, it may be 30 minutes or much longer before the temperature within the room cools down to 22° or lower. It will generally be perfectly acceptable for users who may wish to work in an ideal temperature of 23° to work at a temperature of 22°, but perhaps not lower than this.

This inherent lag is known as thermal inertia'. Similar considerations of course arise with cooling systems, air conditioning systems and so on where a person or equipment requiring a particular cool temperature may be able to cope perfectly satisfactory with a slightly higher temperature for a period of time. The hatched areas in Figures 1 to 3 indicate deferred energy. That is, in Figure 1 for example which shows the effects of a single building where the power demand (top D) is lower at point Dl. This may equate to, for example, the turning down or off of heaters, coolers, etc. At this point even though power demand reduces substantially and energy is saved (the deferred' energy E). In order to restore the various rooms, etc in the building at the initially desired temperature after the period Ti of reduced demand, it will be necessary for the components to be powered to a higher level for a while, over a period T2. At the end of period T2, the power demand is equal to the energy levels required. Thus, by reducing the power demand within the single building, various parameters of room temperature, etc with a building may be maintained fairly constant and this will have a negligible effect on the heating, ventilation or air conditioning or other building services for example, while deferring' energy. However, aggregated across many buildings this will provide a significant resource and significant savings.

Consider the case of Figure 2 where spike shaving' is used to reduce demand to avoid a maximum load. This may be the situation referred to above during a football match for example where a peak demand is expected during halftime. Demand rises at a level D until point Dl where each of a large number of buildings are instructed to reduce their demand, by reducing the power to various building services components. If this is reduced at Dl, then the total power gain over the several buildings will be considerable and this will be enough to accommodate the spike S. The national demand will rise of course from it lowest level D3 to a sub-peak D4 due to the rise in consumer demand, for example, but will then reduce down to level D5. At the end of this time period T the building services in the plurality of buildings are increased, as described with reference to Figure 1 to a level greater than before to accommodate the small loss that has incurred in temperatures, etc, within each of the buildings, but capacity will be available for this as the spike has been passed.

As described, this turn down event might result in a 10 rise in temperature, for example, in a particular room in a building, as shown in Figure 3.

The present invention arose in an attempt to provide an improved method of power demand management and response.

According to the present invention, in a first aspect, there is provided a method of controlling power demand comprising using means which interface with a Building Management System to determine parameters of a plurality of devices in an environment; determining the capability of the plurality of devices to provide demand response, determining when a change in power demand is required and adjusting, through the Building Management System, the parameters of one or more devices to vary power demand when necessary.

A Building Management System is a network of controllers (or microcontrollers), sensors and other equipment which control a building's various heating and cooling devices.

In a preferred embodiment, the method comprises a learning step in which the ability of devices to change one or more of characteristics, capabilities of the devices to providing demand response, and/or potential impact on occupant comfort of said plurality of devices are learnt.

The method may then comprise a demand response step in which, following a notification the power demand of one or more devices is reduced.

Most preferably, the parameters of the devices which are learnt include one or more of what devices are present, what parameters can be claimed, speed of demand response, amount of electrical change and speed of change in environmental conditions.

In one aspect, a degree-minute acceptable deviation is agreed with a client or building occupant for example and parameters and changes which achieve this are learnt.

Thus, for example, if a room is normally cooled to a particular temperature T1 °C but can function at T1 +1°C for up an half an hour the parameters which are learnt for devices are ones which enable the power demand to be reduced so that a temperature of at T1 +1°C is maintained for half an hour.

The learning process may comprise making changes to one or more parameters or one or more devices and monitoring the effect of that change.

In a further aspect, the invention provides an apparatus for changing power demand of a building which is retrofittable to a Building Management System to alter the power demand of the environment influenced by that Building Management System.

The invention further provides an apparatus adapted to perform any of the methods disclosed herein.

In a yet further aspect the invention provides a method of accommodating variations in demand of power from a national or regional power supply system, comprising indicating to one or more buildings or local environments that a national or regional power turndown is required and causing a control system controlling power demand in the building or local environment to reduce the power demand of one or more devices controlled or influenced thereby, thereby reducing the power demand in said one or more buildings or other environments and so releasing power for other uses by the national or regional supplier.

Embodiments of the invention will now be described, by way of example only, with reference to the accompanying schematic drawings, in which: Figure 1 shows a chart of electrical demand against time in a single building; Figure 2 shows a chart of demand against time on a national scale; Figure 3 shows a chart of temperature against time in a turn down event; Figure 4 shows a system for controlling electrical demand; Figure 5 shows examples of data that may be obtained from various devices in a building; Figure 6 shows a graphical display illustrating the efficiency of various fictitious zones on a floor of a building; and Figure 7 shows schematically the benefits of using embodiments of the invention.

In embodiments of the invention an acceptable change in parameters of a building, such as temperature, maximumlminimum values, etc (ventilation levels and so on) are determined (eg by agreement with a building owner or occupant). Parameters are then measured to be able to determine the amount of turn down or demand adjustment that can achieved from a particular building. Over a large number of buildings, significant savings in national power demand can be made by making relatively small savings at each particular building.

Figure 4 shows schematically a building demand response controlling systems. A building 10 is shown which will typically be a large building having many floors, rooms, etc. Inside the building, is a plurality of devices or plants 1 1A, 1 1B, 1 1C, 1 1D, for example. This may be heating, cooling, ventilation or other building services/apparatus which are adapted to heat, cool, ventilate or other affect the environmental conditions of one or more rooms, zones or areas within the building. Sensors (existing sensors, being part of the BMS) will also be provided as part of this plant, which sensors will be arranged to sense the temperature, humidity or other environmental parameters of regions which are affected by various building services, devices. All the plant is controlled by a building management system (BMS) which is shown as being distributed in a BMS WAN (wide area network) 12. Building management system may be a distributed system like this or may be a single entity, such as a computer server, positioned within the building and are well known per se. A number of BMS LANS (local area networks) 13, 14 for example may each be used to manage the number of different items of plant. For example, LAN 13 is associated with plant 1 1A and 1 lB and LAN 14 is associated with plant 1 1C and 1 1D.

A building interface device 15 is attached or interfaced with the BMS WAN 12.

This is used to build up a database of the various devices in the building and their properties and parameters and to orchestrate demand response within the building. The building interface (BI) 15 is connected to an external enterprise (application) server 16.

The BI 15 is the component within the building that is added, in embodiments of the invention to a standard or pre-existing BMS.

The BMS may include a network of micro-controllers distributed throughout the building which control all the heating, ventilation and air conditioning systems. These are present in nearly all large commercial buildings and use a common protocol to exchange information amongst themselves, such as room temperatures and the status of different machinery.

Thus, the BI 15 and server 16 are not part of the BMS.

In some embodiments, the building interface 15 and enterprise server 16 will be operated by a single service provider or group of service providers. The server 16 receives input from the national grid or other organisation representative of requests for service turn down 17.

The building interface 15 may connect to the enterprise server 16 by any convenient type of mechanism such as Internet, or by telecommunication means such as GPRS, 3 G telephone connections, and so on.

The enterprise application server 6 is connected to a web server 17 and this can link to a number of users 18 shown within the building, over HTTP or any other connection.

The web server can provide users with detail as to the way that the control systems in the building management system 12 are operated and estimating building energy usage so that the users can attain full analysis and statistics of various devices and their efficiency in various operating modes. Data is presented to various users at a level of complexity appropriate to their role.

The BI 15 is the only device (in addition to the Standard BMS and plant/devices) installed in the building. Its job is to communicate with the BMS, do some local processing, and communicate with the application server. The BIs first job is to extract all the points information from the BMS, learn what plant is what, and where it all is in the building (at least which floors and major areas). Tt learns what parameters it is able to change in order to achieve demand response, and it learns what to monitor to achieve the energy monitoring and system fault analysis functions. It also estimates the magnitude of power that each device requires under full load.

Once learnt, its job is to continuously monitor the system, report back to the application server, and listen for instructions to orchestrate a demand turndown. Once instructed to turn down, the BI 15 has to use all the plant at its disposal, and has to carefully time things so that the overall result is a neat reduction in demand lasting for a specified amount of time. Some plant can react fast, but not for very long, and other plant takes longer to react but can last longer. This is why they need orchestrating.

A BI is preferably provided in each building. They communicate (via GPRS) for example to a single application server 16. This is a central server that communicates with a plurality of buildings. It handles sending out instructions for demand response and ensuring that the buildings provide enough etc. It stores all the long term data.

The web server 17 serves web pages to users. The main purpose is to provide all the energy monitoring and system analysis displays. It receives requests for pages from users, and it asks the application server for all the data it needs to construct these pages.

The application server and web server may in fact be implemented on the same or different machine. A single BI physical device is installed in each building and a central server provided which communicates with them all.

Embodiments of the invention involve methods for using the Building Management System alone to orchestrate demand response, without requiring any reprogramming of existing BMS controllers. This can be by using different electrical devices in the building, such as chillers, pumps, fans, etc and combining their response to create a fast acting turn down' event from the national grid or other national or regional requirements.

The invention also extends to methods for estimating building energy usage, down to zonal or room level, using only information gained from the building management system.

Furthermore, methods are used for analysing ways in which the control systems in the BMS are operated and how energy might be saved and most efficiently used. The building interface 15 interfaces directly with the BMS1 1-14. It is used to manage and monitor the building's energy-using assets in such a way as to reduce carbon emission and reduce costs. It does this by identifying building controls related and/or mechanically related energy saving opportunities. Examples are given below.

The building interface firstly has a learning cycle upon installation. It is adapted to read all the points (devices, plant, etc) in the system from the BMS using the BMS's native protocol. Typical protocols adopted by BMS systems are BAC NET (Building Automation Control Network), MOD BUS, PROFIBUS (developed by Modicon), EIB (European Installation Bus) or other open protocols. The most widespread protocol used in the UK is TREND. In these protocols, each of the devices is available to the BMS and information regarding the device is known. In the learning cycle, the building interface 15 uses the text and/or BMS address stored in the BMS point to determine the type of point (sensor, driver, etc) and/or what plant it is associated with (fan coil unit, chiller, etc) and/or which floor and/or building area it is associated with. Bayesian probability or other Artificial Intelligence methods may be used to deduce what type of device (plant) are preset, which zones they belong to and where they connect in the system from text identifiers' that are available through connecting with the BMS. Features may be extracted by finding words' separated by spaces and/or letter pairs and/or letter triplets.

Known industry norms tend to be used for the labelling of points. In this way, a database is built up at the building interface and Figure 5 shows part of such a database -10 -illustrating a number of points on the first floor of a building. This provides data as to whether each point is a system input, output or involves user-adjustable parameters, etc and the various parameters (such as temperature, rated power, estimated errors, and so on) together with energy information and electrical loads. In this way, an internal model of the building is obtained that relates BMS points to plant types and floors.

Figure 7 shows a typical display that a user at a terminal such as 18 may be presented with. Note that these terminals need not be in the same building 10, they may be other buildings or other remote locations anywhere in the world. The figure shows on the first floor of a particular building there are four zones, which four zones Z 1 which fall into a particular energy rating A (the most efficient), five zones Z2 falling into energy rating B and so on. The professor's office, zone Z3, is in energy rating F and is therefore poor.

However, the comfort level is good as it is determined that the average temperature is around 2 1.5°C. The average zone on the floor is in energy rating C and therefore the floor overall is rated C, for energy efficiency. An indication may be given of various types of useful information such as that if the six worse zones on the floor were improved then a B energy certificate might be gained and this might save a certain amount of money per year (1,340 in this hypothetical example). This is all done by being able to measure the precise parameters and characteristics of each zone using the interface.

The interface is connected directly to the building management system and obtains all the database information directly from this. II also determines the control strategy for turn down or other events and can control all the various plant devices 1 1A, etc through the BMS directly.

In an initial learning stage, temporary changes are made to each or selected points and the affects of these are monitored in order to help build up a suitable database to determine what changes can be made to save what amounts of energy. Gentle changes are made and the responses are learnt, including details of what can and cannot be changed and speed of response. In this way, during the learning step the building interface can learn what plant does what process, what can be changed, what cannot be changed, the speed of response and other parameters.

The system has several main functions which enable it to manage and monitor a building's energy-using assets in such a way as to reduce carbon emissions and running costs, including, intcr alia: 1. finding energy-wasting faults in controls and mechanical system 2. identifying human behaviour related energy saving opportunities 3. providing a real-time estimate of available demand turn-down 4. orchestrating a turn-down across many plant-types on request 5. being simple to install (plug and play) 6. requiring no reprogramming of BMS controllers Examples are as follows: A Finding energy-wasting faults in controls and mechanical system The device can identify building controls-related and/or mechanically-related energy savings opportunities (ie it can find faults which, if put right, save lots of energy).

The faults, and how it detects them, are as follows: i) Simultaneous heating and cooling It can calculate where this is occurring and/or which zones are affected. It does this by deducing if a change in cooling energy results in a change in heating energy requirement or vice-versa. (If, when you turn the air conditioning off, you suddenly require less heating, this indicates the fault was present and both systems were fighting each other.) To calculate this, it takes a feed from a main gas meter and a main electricity meter and carries out correlation analysis. If generally they were both required at similar times, there could be problems. Additionally, it triggers a temporary test-deload of either the -12 -heating or cooling system and monitors the effect on the other system. The test deload is done by instructing the BMS to temporarily alter the cooling and/or heating set points.

During the test, all the cooling and heating valve positions are summed at regular intervals and correlation analysis is done on the two series.

Additionally, simultaneous heating and cooling will be detected by finding plant that is heating in close proximity to plant that is cooling. It does this by estimating which plant items are neighbours based on BMS point information such as sequential numbers or common text attributes.

ii) Saturation of control loops It can calculate the extent to which saturation of control loops is occurring and/or which zones are affected and/or most affected. It does this by monitoring control loops and looking for situations where driver positions are either at maximum or minimum and where the controlled variable is still outside the dead-band iii) Hunting It can calculate the extent to which hunting of control loops is occurring and/or which zones are affected and/or most affected. It does this by conducting intensive monitoring of one control loop at a time and calculating the root-mean-square of the incremental changes in driver position as sampled regularly.

iv) Non-correlation between sensors and drivers (eg temperatures are not being affected by valves) It can calculate the extent to which non-correlation between sensors and drivers in control loops is occurring and/or which zones are affected and/or most affected. It does this by sampling sensors and drivers over time and conducting correlation analysis on the time series.

-13 -v) Communications failures It can analyse the extent to which communication failures are occurring and/or which zones are affected and/or most affected. It does this by registering whenever a corns error is reported by the BMS after attempting to read a point value.

vi) Deteriorating performance of the chiller It can estimate the coefficient of performance of the chilled water system, and/or the way it changes over time. It does this by disaggregating the chiller electrical demand from the building electrical demand through an input from a current transducer attached to the chiller electrical supply. It then compares the known electrical demand from the chiller with the estimated thermal delivery through virtual thermal metering (see below).

vii) Mismatch between plant on-times and occupancy times It can calculate the extent to which there is time mismatch between plant on-times and occupancy times. It does this in one of two ways: -by estimating the thermal energy delivered during times of occupancy and comparing this with energy delivered during times of non-occupancy -by keeping a record of when a room's related plant is on according to the BMS and comparing this with when it should be on according to user input.

The first one is achieved through virtual thermal metering (see below).

The second one is achieved in three alternative ways: -interrogating BMS timers -providing a data entry input for end users to enter any zones ideal occupancy times -infers terminal unit on-times from driver output (eg fan) B Identifying human behaviour related energy saving opportunities It can use the BMS information to identify human behaviour-related energy savings opportunities. Virtual thermal metering can be implemented using BMS data to estimate -14 -the total heating load, H, and the total cooling load, C. It can do this by estimating the thermal energy supplied through each terminal unit, for example by using BMS data to ascertain heating and/or cooling driver positions associated with terminal units.

Initial estimates may be used for the rated power of the terminal units. The system estimates the rated power of terminal units from the building footprint size and usage-type (entered by installer) by calculating the average floor space served by each unit and using industry rules of thumb to estimate the nominal cooling and heating power of each terminal unit and its fan power consumption.

The system allows continuous improvement of accuracy by providing the facility for a user to input known terminal unit sizes and/or makes and models and/or or actual floor areas at any time.

It does this by assuming a linear relationship between thermal power delivered and driver position.

The thermal energy delivered to a zone is estimated using kW = driverposition * ratedpower compounded over time.

Zone set-points can be regularly sampled, and standard deviation for all zones or particular zones calculated, or the number of set-point changes can be counted that caused a net increase in thermal energy and the number that caused a decrease.

C Providing a real-time estimate of available demand turn-down This system can provide a real-time estimate of a building's ability to reduce its demand if requested to do so by an external signal. It estimates the zone-control cooling turndown capacity using the equation MW(turndown) = (Kz / COP) * C * ADB where Kz -15 -is the zone control response constant, C is the current estimated cooling load and ADB is the amount by which zone dead-bands are to be increased.

C, the current cooling load, is estimated using virtual thermal metering (see above).

Kz, the zone control response constant, is estimated by conducting a turn-down test, and monitoring the building's response to a change in zone set-points (ADB). C will be known through thermal virtual metering.

Any dependency of Kz upon other variables is handled by using machine learning to continually estimate Kz given outside air temperature, time of day, season, solar irradiance and other likely factors.

The system may use an artificial neural network trained using past turn-down tests to achieve this.

It can estimate the total amount of zone-control turndown (MWh) available using MWh = (Kz / COP) * C * DMmax / 60. Where DMmax is the maximum degree-minute deviation allowed for zones agreed in advance with the building users, Kz is the zone-control constant, C is the total current thermal cooling load estimated using virtual thermal meting and COP is the chiller coefficient of performance.

The system's ability to attain desired levels of comfort/occupant satisfactionl service delivery can be evaluated by sampling BMS points that are "learned" to be related to zone temperatures.

For each time period, the error between the measured environmental variable (eg temperature) and the user's chosen (inputtable setpoint) may be stored.

For any time period, the system is capable of providing the integrated error that lies during periods of expected occupation. This allows for comparisons between zones and with other similar buildings. -16-

This information can be used to create an overall comfort index, CL This may be done by using the total thermal energy delivered (from thermal metering) to provide an overall energy and performance index. EPT = CI / (W/m2). This will be used in conjunction with a standard W/m2 index. Weather information (degree days) may be used to create a Normalised Energy Performance Index (NEPI) that can be used to compare performance under different conditions or in different seasons.

This can be achieved by sampling BMS points that are "learned" to be related to humidity D Orchestrating a turn-down across many plant-types on request The interface with the BMS can be used to orchestrate demand-response turn-down events involving one or more plant types such as chillers, pumps and fans Thus, a basic zone-control turn-down can be implemented by issuing a temporary change in dead-band (heating and cooling setpoints). An appropriate change to deadbands (ADB) can be calculated using ADB = MW / (Kz * C) where MW is the target turndown amount, Kz is the zone-control response constant, and C the total cooling load. The dead bands can be changed by automatically issuing a global command in the native BMS protocol. A running total may be kept of dead-band deviations integrated over time in order to ensure the turn-down is ceased once the total reaches DMmax. Chiller set-point control can be combined with zone-control to achieve a faster response. A ramp-shaped change may be implemented to the chilled water set point so that it smoothly hands over to zone control.

This may be done by implementing an immediate change to the cold water set-point of magnitude, m degrees C, holding the change for a duration, d seconds, and then ramping out the change at a rate r degrees per second.

The ability to smoothly cross between chiller setpoint control and zone control over time may be improved using machine learning. This may be done by using a hill-climbing algorithm to improve smoothness. Each time a turn-down is required, it alters one or more -17 -of m, d or r by a small amount in order to ascertain which direction to alter the parameter.

Parameters are turned to minimise the area between the desired and actual MW difference against time.

As described, a turn-down may be initiated in response to an external signal provided by a central server as requested by, for example, the grid operator. A network failsafe for the power grid may be provided by underwriting turn-downs with low-frequency control. This ensures, even should a network failure occur, the device will proceed with a turn-down autonomously should the AC frequency of the grid fall below a predetermined level or fall faster than a predetermined rate.

For each terminal unit (TU) in the BMS, the following procedure is follows: 1. A DMmax for the TU has been defined in advance with the client as above.

2. Tf a bespoke sensitivity (Kz) has been defined for the TU, this is used, otherwise it is set to the global Kz.

3. The total available deferment of thermal energy for the TU is calculated by MWh thermal = DMmax * 4. The desired duration of the turndown, or this TU's maximum duration, deltaDBmax, (whichever is the shorter) is then used to calculate this unit's maximum possible change in deadband (delta_DB_fiill): delta DB full = DMmax / duration S. Then a request for the turndown is sent. The request takes the form of a portion of maximum available turndown (ie if the request signal is 0.5, this means each TU should change its deadband to 0.5 * delta_DB_max is calculated above) At the building level, the following procedure is followed: Each TU's maximum thermal deferment are summed together to give a total MWh (thermal) deferment. This is divided by the known COP (coefficient of performance) of the system to give a building maximum electrical energy deferment. The maximum possible power turndown is calculated by dividing the energy by the desired duration. This gives -18 -this building's maximum turndown possible for the duration specified. This figure is sent to the aggregator.

Similar procedures are followed by non-zone-control methods and added to this amount.

At the aggregator level (ie by an off-site server that is in contact with several buildings), the following procedure is followed: 1. Send out a desired duration to all buildings.

2. Collect back each building's associated MW maximum turn-down 3. Send out a Utilisation signal, U, request a proportion of the maximum available turndown, such that U = Desired MW / Total available MW 4. Ask each building for it's estimated actual achieved turndown.

5. Sum these together to give an actual achieved turndown.

6. Conduct a global control loop to tune the actual turndown to become the desired turndown.

7. Save the gain of the loop to use at the start of the next event.

Each building acts upon the Utilisation signal by conducting building-level control of its own Utilisation signal, thus compensating for changes to plant characteristics.

In the event of a network outage during a turndown, each building will be designed to continue to control its turndown to the last known. Utilisation signal for the agreed duration.

The above description refers only to zone control. Frequency control to the power grid may be provided by instructing fast-acting plant (such as pumps and fans) to de-load if the AC mains frequency falls below a predetermined level or falls faster than a predetermined rate. -19-

Specifically, not and cold water secondary circulation pumps may be used to provide frequency control by de-loading for a predetermined amount of time should the AC mains frequency fall below a predetermined level.

Where variable speed pumps and fans are installed to maintain a controlled pressure differential, the device provides continuous frequency control by regularly adjusting the pressure set-point and hence altering the average loading of the pumps.

It does this by regularly adjusting the pressure setpoint according to the formula: ASP AHZ * Kp, where Kp is a constant and AHZ is the difference between the AC mains frequency and the nominal AC mains frequency (eg 50 Hz in the UK).

E The device is simple to install (plug and play) It automatically learns the system, by reading in all the points from the BMS using the BMS native protocol. The text and/or BMS address stored in the BMS points are used to deduce what point type (sensor, driver, etc.) and/or what plant it is associate with (fan coil unit, chiller, etc) and/or which floor or building area it is associated with. Bayesian probability can be used, based on previous buildings and/or previous points in the current building, features gleaned from the BMS point information. Bayesian "features' from the identifiers or text strings associated with the point and/or the device that the point belongs to, are extracted, as are features by finding "words" separated by spaces and/or letter pairs and/or letter triplets, known industry norms are used for labelling of points.

An internal model of the building that relates BMS points to plant types and floors is thus created.

F Embodiments require no reprogramming of BMS controllers The building interface interfaces with the BMS using the same or compatible protocols. Thus, it is retrofittable onto a BMS by using standard connections, for example, -20 -and can in effect be clipped on' without any extra work being needed or additional components.

Figure 6 shows schematically how certain financial advantages can be obtained.

Within the client building 10 is the building interface 15, the eventual customer 20 (being the party responsible for facilities of energy management), a plurality of occupants 21 and the web application 22. When the national grid requires a turn-down the signal is sent to the enterprise server, which then interfaces with the building interface 50 to control plant behaviour and instigate the demand turn down. In addition to this, the occupants 21 of building and the customer are presented with information relating to energy usage and efficiency and they can immediately use this information to save energy by reducing consumption where appropriate and are given figures as to how this can save them money.

Thus, the occupant enjoys savings from behaviour changes, the customer enjoys saving from immediate improvement. The customer pays a monthly fee to the service provider 16 who operates the building interface 15 and perhaps a one off fee to a contractor 23 who controls the building management system 12, who may then provide savings through deep improvement. Since the national grid is saving money by not having to generate any more power than is necessary, financial models may be put in place by which balancing payments are made to the service provider 16.

Claims (32)

1. -21 -Claims 1. A method of controlling power demand comprising using a Building Management System to determine parameters of a plurality of devices in an environment; determining the capability of the devices to provide demand response; determining when a reduced power demand is required and adjusting, through the building management system, the parameters of one or more devices to reduce power demand when necessary.
2. 2. A method as claimed in Claim 1, comprising a learning step in which the ability of devices to change one or more of characteristics, capabilities of the devices to providing demand response, and/or potential impact on occupant comfort of said plurality of devices are learnt.
3. 3. A method as claimed in Claim 1 or Claim 2, wherein the parameters of the devices which are learnt include one or more of what devices are present, what parameters can be claimed, speed of demand response, amount of electrical change and speed of change in environmental conditions.
4. 4. A method as claimed in any preceding claim, comprising a learning step in which changes are made to one or more parameters on one or more devices and the effect of this on one or more sensed parameters are monitored.
5. 5. A method as claimed in any preceding claim, wherein the requirement for reduced power demand is established by a national or regional provider of power in order that that provider may use the energy thus saved for other purposes.
6. 6. A method as claimed in any preceding claim, wherein the parameters of a device include parameters relating to operation of the device, and the effect on the environment affected by the device of changing said parameters.

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7. 7. A method as claimed in any preceding claim, comprising initiating reduced power demand when requested to do so by an external signal.
8. 8. A method as claimed in any preceding claim, wherein a zone control demand reduction is implemented by changing heating and cooling set points.
9. 9. A method as claimed in Claim 8, wherein power demand reduction includes a step in which the changes made in a dead-band relating to heating and cooling set points.
10. 10. A method as claimed in Claim 9, wherein the change is conducted in accordance with a formula ADB = MW / (Kz * C), where ADB is the difference between heating and cooling set points, MW is a target power reduction amount, Kz is a zone control response constant and C is a total cooling load.
11. 11. A method as claimed in Claim 10, wherein demand reduction is ceased once a predetermined value has been reached.
12. 12. A method as claimed in any of Claims 8 to 11, wherein chiller set point control is used in combination with said zone control.
13. 13. A method as claimed in Claim 12, wherein a chilled water set point is altered in a ramped manner.
14. 14. A method as claimed in any of Claim 8 to 13, including a hill climbing algorithm.
15. 15. A method as claimed in any preceding claim, wherein a power supply is applied to a building upon which the system is operated, and including a step of proceeding with a power reduction automatically if an AC frequency of the power supplied to the building falls below a predetermined level or falls faster than a predetermined rate.

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16. 16. A method as claimed in any preceding claim, including providing frequency control by reducing power to certain devices for a predetermined amount of time should the frequency of the power supply supplied to the building fall below a predetermined level.
17. 17. A method as claimed in Claim 16, including adjusting a pressure set point ASP according to a formula ASP = \Hz * Kp, where ASP is a difference between predetermined minimum and maximum pressures, Kp is a constant and AHz is the difference between the frequency of the applied power and a nominal AC frequency.
18. 18. A method as claimed in Claim 17, where the constant Kp relates to a desired sensitivity to power supply frequency.
19. 19. A method as claimed in Claim 17 or 18, wherein Kp is measured in Pa/Hz.
20. 20. A method as claimed in any preceding claim, including virtual thermal metering using data from the building management system to estimate a total heating load and total cooling load.
21. 21. A method as claimed in Claim 18, including attributing a portion of the load to particular zones.
22. 22. A method as claimed in any preceding claim, wherein the devices are arranged in a number of zones and each zone has a predefined minimum or maximum parameter to maintain in the event of power turndown.
23. 23. Apparatus for controlling power usage of one or more devices within a building, adapted for using a method as claimed in any preceding claim.
24. 24. Apparatus as claimed in Claim 23, including a device retrofitted to a building management system.

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25. 25. A method of accommodating variations in demand of power from a national or regional power supply system, comprising indicating to one or more buildings or local environments that a national or regional power turndown is required and causing a control system controlling power demand in building or local environment to reduce the power demand of one or more devices controlled or influenced thereby, thereby reducing the power demand in said one or more buildings or other environments and so releasing power for other uses by the national or regional supplier.
26. 26. A method as claimed in Claim 25, including establishing a minimum or maximum necessary value of parameters related to the use of one or more devices and orchestrating power reduction in such a way that these minimum or maximum values are not exceeded.
27. 27. A method as claimed in any of the preceding claims including estimating a capacity of power reduction according to a formula MW (Turndown) = (Kz/COP*C*ADB) wherein Kz is a zone control response constant, C is a current estimated cooling load and ADB is the amount by which zone dead-bands are to be increased.
28. 28. A method as claimed in any preceding claims, comprising estimating a total amount of power reduction available (MWh) according to the formula MWh = (Kz/COP) * (*DMmax / 60), where DMmax is a maximum degree -minute deviation allowed for a particular zone or area, Kz is zone control constant, C is total current thermal cooling load and COP is a is a chiller coefficient of performance.
29. 29. Apparatus for changing power demand of a building, comprising means which is retrofitted to a building management system to alter the power demand of the environment influenced by that Building Management System.
30. 30. Apparatus as claimed in Claim 29, adapted to use the method of any of Claims 1 to 28.

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31. 31. A method of orchestrating power demands/values substantially as hereinbefore described with reference to, and as illustrated by, any of the accompanying drawings.
32. 32. Apparatus for orchestrating power demand substantially as hereinbefore described, with reference to, and as illustrated by, any of the accompanying drawings.