EP3435321A1 - Gestion d'énergie d'un bâtiment - Google Patents

Gestion d'énergie d'un bâtiment Download PDF

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Publication number
EP3435321A1
EP3435321A1 EP17183776.8A EP17183776A EP3435321A1 EP 3435321 A1 EP3435321 A1 EP 3435321A1 EP 17183776 A EP17183776 A EP 17183776A EP 3435321 A1 EP3435321 A1 EP 3435321A1
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EP
European Patent Office
Prior art keywords
infrastructure
model
energy
component
plan
Prior art date
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Withdrawn
Application number
EP17183776.8A
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German (de)
English (en)
Inventor
Rudolf Sollacher
Thomas Baumgärtner
Mike Pichler
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Siemens AG
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Siemens AG
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Application filed by Siemens AG filed Critical Siemens AG
Priority to EP17183776.8A priority Critical patent/EP3435321A1/fr
Publication of EP3435321A1 publication Critical patent/EP3435321A1/fr
Withdrawn legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply

Definitions

  • the present invention relates to the energy management of a building.
  • the invention relates to the planning of temporal operating phases of the infrastructure.
  • a building has a technical infrastructure comprising several systems or devices whose operation is required for use of the building, such as a heater, elevator or lighting system.
  • the operation of the devices requires energy.
  • the operation of the devices is usually part of a guaranteed service.
  • the required energy can be obtained from an external supplier or provided by itself, for example by means of a combined heat and power plant or a photovoltaic system.
  • the availability and price of the energy can vary greatly over time.
  • the operation of certain devices of the building can be scheduled in time, for example, to avoid that heavy consumers are operated at times of high energy prices. This requires a prediction of parameters such as the price of energy.
  • Differently long forecast periods typically require different approaches to the creation of an operational plan, such that, for example, a long-term operational plan established for a period of several days and a short-term operational plan established for a period of hours are based on different heuristics. Determining the short-term operational plan based on the long-term operational plan can be costly and different assumptions in the heuristics may ultimately lead to suboptimal provisions. The quality of the particular operating plan, for example with regard to required energy costs, is therefore generally well below a theoretically achievable optimum.
  • An object underlying the present invention is to provide an improved technique for controlling the energy balance of an infrastructure of a building.
  • the invention solves this problem by means of the subjects of the independent claims. Subclaims give preferred embodiments again.
  • a method of controlling a technical infrastructure of a building includes steps of capturing a description of the technical infrastructure in terms of its energy intake or output; determining a mathematical model for the infrastructure based on the description; providing an operating plan for the infrastructure based on the model and a long-term prediction for an external parameter; adjusting the operating plan based on a short-term prediction for the parameter and the model; and controlling the infrastructure based on the adjusted operational plan.
  • the infrastructure usually comprises at least one device whose operation receives energy, such as a ventilation system, and / or a device whose operation provides energy, such as a wind turbine.
  • the operating plan is the result of a determination in which time sequence the devices can be operated in order to ensure on the one hand predetermined processes or offers in the building, and on the other hand to proceed as economically as possible. In particular, the operation of the devices should cause the lowest possible energy costs.
  • short-term scheduling may be followed by a shorter forecast period of, for example, several hours, which, based on a now better available prediction of the parameter, makes adjustments to the particular operating plan to improve the result.
  • One or more further forecasts, each with a shorter forecast time, can be added in stages.
  • control of the infrastructure or at least one of its devices may be based on the latest operational plan and the current parameter.
  • three predictions are determined with time horizons of the coming day, the current day and the next hour. Underlying forecasts have correspondingly similar time horizons.
  • the operating schedule with the shortest prediction time can then be used for the actual control, which, for example, specifies target values to which certain parameters of the building can be controlled.
  • each of the plans and optionally also the controller is based on the same model for the operation of the infrastructure.
  • the one- or multi-level adaptation of an already determined operating plan can thus cause reduced handover problems. Numerical or logical incompatibilities can be reduced. Unwanted side effects between different models can be avoided.
  • the models of all forecasts can be checked at once by means of a validation.
  • the mathematical model preferably includes a system of differential equations. This makes it easier to verify the model in general, using a variety of known methods and procedures. Relationships between parameters can be recognizable or comprehensible by a person skilled in the art even without the use of an analysis tool.
  • the mathematical model may include one or more different types of equations, which are described in more detail below. In this case, each of the equations of different types described by way of example may each comprise a plurality of mathematical models.
  • a first equation may express a temporal evolution of an operating point of the infrastructure.
  • the equation may describe how one or more operating parameters, which together constitute the operating state, evolve over time.
  • linear or polynomial relationships can be specified.
  • a second equation may include a constraint on the technical infrastructure.
  • a constraint may include a technical indication given by the type of infrastructure or its connection to another system, for example maximum electrical power that can be exchanged over a connection to a power grid.
  • a constraint may also affect a constraint that should not be violated for other reasons in the operation of the infrastructure. For example, a maximum operating time of a combined heat and power plant per year can be predetermined.
  • Such constraints are usually results of considerations that are made in the planning and / or sizing of elements of the infrastructure.
  • Such constraints may be, for example may also be changed based on a decision of an operator or a maintenance condition of a component. The change can be provided in the mathematical model as a parameter or carried out by redetermining the model.
  • a third equation may include a cost function relating to total energy received or delivered by the infrastructure.
  • This cost function can include, for example, a current energy price, a maintenance status or an operating status of a component.
  • Providing the operational plan typically involves optimizing an operating point of the infrastructure with respect to one or more parameters.
  • One of these parameters usually relates to energy costs, so that the operating plan is preferably determined so that the building can be operated as cheaply as possible.
  • Other optimizations can also be applied.
  • the operating point is essentially formed by a number of parameters of the infrastructure. The optimization may be performed by any known technique, such as a search method, a probabilistic approach, or a neural network.
  • a device for determining an operational plan for a technical infrastructure of a building is set up to determine a mathematical model for the infrastructure based on a description of the technical infrastructure with regard to its energy absorption or energy output.
  • the device is preferably configured to perform at least part of the method described above.
  • the device may comprise a programmable microcomputer or microcontroller.
  • Parts of the method may be present as computer program products with program code means.
  • Features or advantages of the method may be related to the device and vice versa.
  • the description may include a document in XMLNS format.
  • a namespace can be specified for an XML language so that a vocabulary of an XML document can be uniquely identified. This may allow to mix several XML languages in one document.
  • the description can be easily generated from other sources, such as an infrastructure planning tool or a manufacturer description of a component.
  • the mathematical model can be determined on the basis of a document in XSLT format. This allows the mathematical model to be easily exchanged between different components.
  • XSLT is expressed in structured text form so that the description can be read by a person skilled in the art.
  • a system for controlling a technical infrastructure of a building includes a modeling component that may be implemented by the apparatus described above; a first scheduling component configured to provide an infrastructure operating plan based on the model and a long term external parameter prediction; a second scheduling component adapted to adjust the operational plan based on a short-term prediction of the parameter and the model; and a engine configured to control the infrastructure based on the customized operational plan.
  • the system can be verified more easily by using the unitary mathematical model as a whole. System errors, incompatibilities or divergent assumptions between individual components of the system can be reduced. Interconnected issues can be more easily identifiable or remediable.
  • the planning components may be logical and / or physical units of parts of a computer-aided processing or control system.
  • the control component can be implemented in the same way logically and / or physically.
  • FIG. 1 shows a system 100 for controlling a technical infrastructure of a building.
  • the system 100 includes a number of components that may be implemented both as physical devices and as logical components.
  • the device aspect will be discussed below, a transfer to corresponding logical components, in particular services or programs of a computer, is done by a person skilled in the art without any effort.
  • the system 100 includes a modeling component 105 that is configured to generate a mathematical model 115 based on a description 110. Furthermore, the system 100 comprises at least a first planning component 120 and a second planning component 125. Preferably, at least one third planning component 130 is provided. An optional control component 135 may also be included in the system 100.
  • the scheduling components 120-130 provide a step-by-step operational plan 140. To do so, a long-term operational plan 140 is first established based on long-term predictions, which is then progressively refined on the basis of increasingly short-term predictions - which are usually more accurate or more reliable - until there is a version that can be used to control an infrastructure 145 ,
  • the infrastructure 145 includes components that receive energy, such as an electrical utility, a lighting system or a cleaning system, and / or components that emit energy, such as a photovoltaic system or a combined heat and power plant.
  • the components 145 are usually present in great variety and can be combined in subsystems.
  • the operation of one subsystem can influence that of another subsystem; For example, a blind control and a heater may track the same or opposite control objectives.
  • the operation of some of the components can be timed to avoid peak loads or balance positive and negative energy needs.
  • the operation of a consumer may be scheduled to advantageously fall into a time when the energy required for its operation is favorable.
  • an energy-providing component can advantageously be operated as possible at times of high energy prices.
  • the first scheduling component 120 operates on the mathematical model 105.
  • the second scheduling component operates on the mathematical model 105 and the operational plan provided by the first scheduling component 120, and the third scheduling component 130 operates on that on the mathematical model 105 and the second scheduling component 125 provided operating plan 140.
  • the mathematical model 105 is always the same. Instead of the illustrated three-stage processing by means of the planning components 120-130, a two-stage or multi-stage processing can also be provided.
  • the first planning component 120 may also be called “day ahead planning", the second planning component 120 "intraday planning” today planning, and the third planning component 130 "load management”.
  • the scheduling components 120-130 are each configured to provide an operational plan 140 indicating at what time which component of the infrastructure 145 of the building is to operate in what way.
  • the scheduling components 120-130 have decreasing scheduling horizons in the order of processing.
  • the first planning component 120 may include a planning horizon of 24 hours of a following day
  • the second planning component 125 may include a planning horizon of a remaining current day - the length of the planning horizon may be variable - and the third planning component 130 may have a planning horizon of the next 60 minutes respectively.
  • a time resolution of the first scheduling component 120 may be 15 minutes or greater.
  • the time resolution of the second scheduling component 125 may be similar or slightly smaller.
  • the time resolution of the third planning component 130 is usually much smaller, for example, about 1 minute. Electrical components, which are usually operated shorter than the respective planning horizon, can be neglected by a planning component 120-130.
  • the scheduling components 120, 132 may each provide an operational plan 140 that must be met by the scheduling component (s) 125, 130.
  • the operating plan 140 can thereby be progressively refined become.
  • the scheduling components 120-130 may be activated at different times; For example, the first planning component 120 can determine an operating schedule 140 every hour and the third planning component 130 every minute.
  • the first scheduling component 120 preferably prepares an operational plan 140 for one or more subsequent days. For this purpose, it usually processes current state variables of the monitoring device 170, a current operating plan 140 from the second planning component 125, a current energy price forecast and / or a current forecast of the demand forecast 155.
  • the created operating plan 140 relates to selected control variables and attempts to calculate the total costs in which all relevant Tariffs are to be received, minimized, whereby all relevant restrictions for states and manipulated variables are to be adhered to.
  • the second planning component 125 addresses in particular two applications.
  • energy purchasing is to be optimized by optimally planning the operation of controllable loads, storage (typically electrical and thermal storage), and self-generated energy (i.e., demand reactive power and active power control).
  • Results of (optimized) planning are preferred: (a) the planned nominal load profile of the building at the grid connection point and / or (b) the maximum flexibility that can be used to deviate from the nominal load profile in a controlled manner if required.
  • the operating plan 140 is determined or refined by the second planning component 120 taking into account new measured values and forecasts. If the first planning component 120 has created a mandatory operating plan 140, it is the task of the second planning component 125 to comply with this operating plan 140 as well as possible with changed forecasts. If there is no obligatory plant timetable, the second planning component 120 preferably tries to create a cost-optimal operating plan 140.
  • the second planning component 120 typically processes current state variables of the monitoring device 170, possibly a current operating plan 140 from the first planning component 120, current energy price forecasts from one or more tariff servers and / or current forecasts of the demand forecast 155.
  • the created operating plan 140 relates to selected manipulated variables and attempts to either to minimize the total cost involved in all relevant tariffs, or to comply with a mandatory operating plan 140 of the first planning component 120. All relevant restrictions for states and manipulated variables should be observed.
  • the third planning component 130 usually has the task of meeting the specifications for energy consumption and / or (storage) states and / or manipulated variables of the second planning component 125 within a predetermined accounting interval (typically, for example, 15 minutes). For this purpose, it usually processes current state variables and manipulated variables from the monitoring device 170, a current operating plan 140 of the second planning component 125, current energy price forecasts from one or more tariff servers and / or a current forecast of the demand forecast 155. All relevant restrictions for states and manipulated variables should be adhered to , The optimization is usually model-based as in the first planning component 120 or the second planning component 125.
  • the scheduling components 120-130 may differ in which costs they optimize.
  • the scheduling components 120-130 may preferably deal with components of different average transmitted power or connected load, where the first scheduling component 120 may be large powers, the second scheduling component 125 may be medium powers, and the third scheduling component 130 may be small powers.
  • the size of the services can be distinguished on the basis of predetermined threshold values which can be selected as a function of an existing installation.
  • the powers can be correlated with usual turn-on times or minimum turn-on times of the components.
  • the determinations of the scheduling components 120-130 are preferably made based on predictions for one or more parameters affecting the operation of the infrastructure 145.
  • a parameter can determine an energy price due for energy sourced externally for the building.
  • a similar parameter can indicate what compensation is to be expected when the infrastructure provides energy to the outside world.
  • the energy price forecast may be provided by a tariff prediction 150, which may, for example, be in the form of an internet service.
  • the quality of a prediction can be judged on its accuracy or reliability.
  • the gradual shortening of the forecast period may facilitate the determination or optimization of the operating plan and support consideration of different rapidly changing parameters.
  • Each operating plan 140 is based on an optimization that was carried out by the respective planning component 120-130.
  • Another prediction that relates to an excess or need for energy through the infrastructure 145 may be be determined by a demand forecast 155. This is determined in the illustrated embodiment by way of example on the basis of calendar entries of planned uses of components of the infrastructure 145 by a calendar service 160 and / or a weather forecast of a weather service 165.
  • a demand forecast 155 preferably provides forecasts for influencing variables that can not be influenced by the system 100. These are z. B. a maximum expected energy production of a photovoltaic system and a power consumption, hot water consumption or heating demand in the building in future time intervals, but also an outside temperature or solar irradiation.
  • the forecasts provided by demand forecast 155 are based on e.g. B. learned models and / or physical models.
  • a monitor 175 may be provided to monitor the power supply.
  • the monitoring device 175 can, for example, current measured values of a sensor, for. B. a state of charge of a battery, a temperature of the outside air or a thermal storage, or feedback from an actuator of the infrastructure 145 with their current manipulated variable values include.
  • An optional observer 175 has the task of estimating unmeasured state variables of the infrastructure 145. These can z. Example, a temperature profile of a thermal stratified storage tank or a reservoir or the mass flows in piping belong.
  • the observer 175 can obtain current measured values from sensors or current manipulated variable values from actuators from the monitoring device 170.
  • the observer 175 determines estimates for the unmeasured state variables to the monitoring device 175. For this estimation, a suitable plant model is usually used. The estimate is made z. With an extended Kalman filter.
  • the control component 135 preferably converts the information provided by the last scheduling component 130 in the destination chain, which can be determined with a high temporal resolution of, for example, one minute, into default values that are driven by the components of the infrastructure 145. All relevant restrictions for states and manipulated variables should be observed.
  • the optimization is usually model-based as for planning component 120 or 125.
  • the last scheduling component 130 does not provide a schedule 140 but provides setpoints to which the control component 135 then controls the individual components.
  • the control component 135 can serve as an interface to a sensor or an actuator or its low-level controllers (eg PID controllers) of the infrastructure 145.
  • the control component 135 may receive specifications for selected manipulated variables from the third planning component 135, current measured values of a sensor of the infrastructure 145 from the monitoring device 170 and / or a current manipulated variable value of an actuator from the monitoring device 170.
  • the control component 135 preferably sets specifications for manipulated variables to a low-level controller, for. B. a PID controller of the infrastructure 145 ready.
  • the data exchange between the illustrated components and their configuration preferably takes place via a service-oriented interface, in particular a web service.
  • This interface describes all relevant electrical and thermal components and their interconnection based on Information that can be found, for example, from data sheets and plant plans of the infrastructure 145.
  • FIG. 12 shows a flow diagram of a method 200 for controlling a technical infrastructure 145 of a building, by way of example the system 100 of FIG. 1 is based on.
  • a description of the infrastructure 145 is captured.
  • a mathematical model 115 is determined that represents the infrastructure.
  • a long-term operational plan 140 is determined in the example assumed three-stage prediction in a step 215 on the basis of long-term prediction parameters 220.
  • the long-term operating plan is adjusted on the basis of medium-term parameters 230 into a medium-term operating plan 140.
  • the medium-term operating plan 140 is adjusted to a short-term operating plan 140 based on short-term forecasting parameters 240. More or less than three levels are also possible.
  • control parameters from the short-term operating plan 140 are determined. Based on the control parameters, the infrastructure 145 can be controlled.
  • FIG. 3 shows an exemplary determination of a mathematical model 115.
  • a description 110 of the infrastructure 145 of a building with respect to information of the energy consumption and / or the energy output of components as well as relationships between components is analyzed.
  • a mathematical model 115 is determined which, as a system of differential equations, preferably dynamically describes the behavior of the infrastructure 145.
  • the model 115 can then the function of the planning components 120-130 are used.
  • a third part describes costs c (x (t), u (t), uext (t), t) as a function of state variables x (t) and manipulated variables u (t).
  • the mathematical model for the infrastructure 145 is preferably generated only if something changes at the infrastructure 145.
  • the modeling component 105 preferably provides code having the function values f (x (t), u (t), uext (t), t), g (x (t), u (t), uext (t), t ), h (x (t), u (t), uext (t), t) and c (x (t), u (t), uext (t), t) and their derivatives according to the state variables x (t ) and manipulated variables u (t) are calculated.
  • the modeling component 105 z. From a concrete data object describing the infrastructure 145 (eg of the type "building") all devices and their parameters as well as their coupling, e.g.
  • heating circuits read out and thus models f (x (t), u (t), uext (t), t), g (x (t), u (t), uext (t), t) and h (x (t), u (t), uext (t), t) for the dynamics of the system which can be interpreted by the optimizer, in particular the third scheduling component 130.
  • Additional data objects regarding applicable energy prices (type "PriceData”) and possibly further configuration parameters from a configuration optimization (Type "ConfigOptimizer”) lead to a concrete realization of the cost term c (x (t), u (t), uext (t), t).
  • Fig. 4 shows an exemplary portion of a description 110 of an exemplary infrastructure of a building. Shown is a structured XML document from which certain parts are hidden or collapsed. Hidden parts are indicated by a line beginning with "+”; lines beginning with "-" are expanded.
  • the data structure shown is of the type "Building".
  • a corresponding data object contains information about the components of the infrastructure 145, such as energy converters, energy storage, energy transport systems and low-level controllers. With this object also current states of the system 100 can be transferred.
  • the illustrated data structure includes exemplary information about the maximum connected load (“MaximumGridConnectionPower”), to battery systems (“BatterySystems”), a photovoltaic system (“PVSystem”).
  • MaximumGridConnectionPower to battery systems
  • PVSystem a photovoltaic system
  • An electrical base load (“ElectricBaseLoadForecast”)
  • An ambient temperature (“AmbientTemperatureForecast”) are given.

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EP17183776.8A 2017-07-28 2017-07-28 Gestion d'énergie d'un bâtiment Withdrawn EP3435321A1 (fr)

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EP17183776.8A EP3435321A1 (fr) 2017-07-28 2017-07-28 Gestion d'énergie d'un bâtiment

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003085798A2 (fr) * 2002-04-01 2003-10-16 Battelle Memorial Institute Systeme de gestion d'energie
US20120065783A1 (en) * 2010-09-14 2012-03-15 Nest Labs, Inc. Thermodynamic modeling for enclosures
US20130085616A1 (en) * 2011-09-30 2013-04-04 Johnson Controls Technology Company Systems and methods for controlling energy use in a building management system using energy budgets
US20150112497A1 (en) * 2012-05-04 2015-04-23 Viridity Energy, Inc. Facilitating revenue generation from wholesale electricity markets using an engineering-based energy asset model
US20150378381A1 (en) * 2014-06-30 2015-12-31 Qualcomm Incorporated Systems and methods for energy cost optimization
US20160091904A1 (en) * 2014-09-29 2016-03-31 International Business Machines Corporation Hvac system control integrated with demand response, on-site energy storage system and on-site energy generation system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2003085798A2 (fr) * 2002-04-01 2003-10-16 Battelle Memorial Institute Systeme de gestion d'energie
US20120065783A1 (en) * 2010-09-14 2012-03-15 Nest Labs, Inc. Thermodynamic modeling for enclosures
US20130085616A1 (en) * 2011-09-30 2013-04-04 Johnson Controls Technology Company Systems and methods for controlling energy use in a building management system using energy budgets
US20150112497A1 (en) * 2012-05-04 2015-04-23 Viridity Energy, Inc. Facilitating revenue generation from wholesale electricity markets using an engineering-based energy asset model
US20150378381A1 (en) * 2014-06-30 2015-12-31 Qualcomm Incorporated Systems and methods for energy cost optimization
US20160091904A1 (en) * 2014-09-29 2016-03-31 International Business Machines Corporation Hvac system control integrated with demand response, on-site energy storage system and on-site energy generation system

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