TECHNICAL FIELD

The present invention relates to the determination of an operational parameter of a district energy system, such as a district heating system or a cooling distribution network.

BACKGROUND

In district energy systems a fluid is transported from an energy supply plant, e.g. a district heating plant, via a grid of fluid lines to a plurality of consumers. To this end, the district energy system comprises a network of supply lines and a plurality of service lines. The service lines branch off from the supply lines and connect the respective consumers with the supply line. The grid of fluid lines for distributing the fluid to a plurality of consumers will also be referred to as district energy grid.

A problem for the district energy grid operator is that to operate the grid efficiently the state of the medium in many locations of the supply lines must be known. To obtain this state of the medium, and in particular the temperature, grid measure points are typically used. Such grid measure points in the supply lines are expensive to establish and therefore the grid is often monitored and operated based on a few measurements from critical points located in the supply lines of the grid. Using such few critical point measurements is problematic when the district energy grid expands, e.g. due to the expansion of a city, because, what was a feasible location for placing a critical point measurement before the district energy grid expansion may not be a feasible location in the expanded district energy grid.

A further problem for the district energy grid operator is that the district energy grid shall be run with as high an efficiency as possible, and optimizing controllable variables such as, temperatures, pressure and flows to increase the district energy grid's efficiency is not possible without a sufficient number of grid measure points.

Being able to determine supply line temperatures in different parts of the district energy grid cheaply and, preferably, by using existing hardware, is therefore highly desirable. Having access to supply line temperatures in many areas of the district energy grid facilitates an optimal control of the district energy grid. Moreover, it would be desirable if the supply line temperatures are updated within a short time frame, in particular when they are used for control of the district energy grid.

Another problem for the district energy grid operator is that there are currently no good ways of inspecting the conditions of the service lines. For example, while modern energy grids are equipped with electrical detection systems for detecting leakage or other damages of the supply lines, the service lines typically have no such electrical detection system. Moreover, the service lines are typically located on private property, which creates difficulties when performing thermal inspections or other inspection types.

Knowing the condition of the service lines is important as there are large financial resources bound in a district energy grid, and the grid operator needs to plan maintenance in advance and avoid expensive ad hoc repairs of failed pipes, such as leaking service lines. A fault in a service line can for example be a service line where the insulation has deteriorated or, even worse, a service line leaking district energy water.

Therefore, it would be desirable if such an inspection can be done remotely. Moreover it would be desirable when faults in a service line can be detected early, in particular before leakage occurs. EP3531368 discloses a method for determining a flow rate and a temperature of a fluid at a selected position in a district heating or cooling utility distribution network, comprising a plurality of interconnected distribution lines and smart utility meters. The meters are arranged to register the energy delivered at consumer premises situated along the distribution lines, the method comprising the steps of: collecting meter data time series from the smart utility meters using an Advanced Metering Infrastructure, and calculating the temperature and the flow rate at the selected position. The calculations are based on flow and temperature information derived from the collected meter data time series, the topology of the utility distribution network, heat transfer coefficients and known lengths of the distribution lines. The meter data time series comprises the accumulated volume of fluid delivered to the consumer, and the integrated flow- temperature product calculated by the meter.

However, the above prior art method requires a detailed network model in terms of pipe diameters, lengths, insulation class, ambient temperature, and heat loss coefficients. For a utility provider with large resources to maintain this information in a GIS database, it may be possible to obtain such detailed information. However, district energy grids have often been in the ground for many years why dimensional and heat loss coefficient data may not always be available, or the district heating grid operator is not maintaining the information to the level needed.

SUMMARY

Thus, it remains desirable to provide a method and system for determining one or more operational parameters, in particular a supply line temperature and/or a heat loss in a service line, of a district energy system that solve one or more of the above problems and/or that have other benefits, or that at least provide an alternative to existing solutions. In particular, it is desirable to determine a supply line temperature in the district energy grid's supply lines and/or to determine heat loss in a service line of the energy grid without requiring detailed knowledge about the properties of the supply lines.

According to one aspect, disclosed herein are embodiments of a computer-implemented method for determining an operational parameter of a district energy grid, the district energy grid comprising one or more supply lines and a plurality of service lines for transporting a fluid to a plurality of consumers, each supply line feeding the fluid into a plurality of service lines and each service line connecting a consumer with one of the one or more supply lines, the method comprising:

- obtaining measured sensor data from one or more sensors associated with one or more selected service lines of the plurality of service lines, the selected service lines being connected to at least one selected supply line of the plurality of supply lines, the received sensor data being indicative of fluid temperatures and observed fluid flows of the transported fluid received at the respective consumers at respective points in time via respective ones of said one or more selected service lines;

-obtaining heat loss models of heat loss in the respective selected service lines, the heat loss models relating the observed fluid temperatures and the observed fluid flows to a supply line temperature of the fluid flowing from said at least one supply line into the respective selected service lines,

- computing, from said heat loss models and from the received sensor data, an estimate of an operational parameter of said selected supply line and/or of one or more of said selected service lines.

In particular, various embodiments disclosed herein may be used to determine a temperature in a supply line of a district energy grid and/or to determine heat loss in the district energy grid. Determining the heat loss may specifically include determining a thermal loss parameter indicative of a thermal loss of at least a first service line of said selected one or more service lines. Accordingly, the one or more operational parameters may include or consist of a temperature in a supply line of a district energy grid and/or one or more thermal loss parameters indicative of thermal losses of respective one or more service lines of said selected service lines.

Accordingly, in various embodiments of the method and system disclosed herein, the supply line temperature of a supply line of the district energy grid and/or thermal loss parameters of one or more service lines of the district energy grid can be calculated from sensor data obtainable from the consumers' heat meters or from other suitable logging devices. Embodiments of the method and system disclosed herein do not require detailed knowledge of a network model or the modelling of temperature and flow at respective nodes of the main grid. In particular, various embodiments of the method disclosed herein compute the estimate of the supply line temperature and/or thermal loss parameter without any input indicative of known thermal loss parameters of the plurality of supply lines and/or of the service lines. In some embodiments, computing comprises computing said estimate of the supply line temperature and/or said thermal loss parameters of the one or more service lines only from said heat loss models of the selected service lines and from the observed sensor data. Knowledge about the conditions, in particular the state or quality of the heat insulation, of the supply lines or the service lines is not required.

In some embodiments, each of the selected service lines has a logging device associated with it, such that the associated logging device is configured to record sensor data pertaining to the service line with which the logging device is associated. Accordingly, the process may be based on existing sensor infrastructure without the need for installing additional sensors, in particular additional sensors along the supply line.

It will be appreciated that the method may, alternatively or additionally to computing the estimated supply line temperature of one or more selected supply lines, be applied to determine thermal energy losses for all or for only some service lines of the district energy grid, e.g. for only a single selected group of service lines or for different selected groups of service lines of the energy grid. To this end, in some embodiments, the method comprises computing estimates of one or more of the thermal loss parameters of the plurality of the service lines.

In some embodiments, the heat loss models further include thermal loss parameters indicative of thermal losses of respective ones of said selected one or more service lines. The thermal loss parameters may be indicative of a quality of a thermal insulation of at least respective ones of the selected service lines. In some embodiments, the thermal loss parameters are indicative of a quality of a thermal insulation of the entire fluid conduit between the supply line and the location of the temperature sensor at the consumer location, which is used to obtain sensor data indicative of the fluid temperature of the transported fluid received at the respective consumers. The fluid conduit may, in some embodiments, include additional piping, e.g. inside the consumer's building, in addition to the service line that connects the consumer's installation with the supply line.

The supply line temperature and/or the thermal loss parameters of the selected service lines may be unknowns of the heat loss models, which may be determined from said heat loss models.

In some embodiments, the heat loss models are linear in the thermal loss parameters and/or linear in the supply line temperature, thus allowing for a computationally efficient modelling. In particular, in some embodiments, the heat loss models relate instant heat losses in the respective service lines to the fluid flow in the respective service lines and to a temperature difference between the observed fluid temperatures, observed for the respective service lines, and the supply line temperature. Embodiments of the method and system described herein provide the district energy grid operator with information about supply line temperatures in sub areas of the grid. Accordingly, the district grid operator can use that information to optimize controllable variables of the grid, such as the supply temperatures for a sub area of the grid, even without establishing a physical grid measure point in that sub area, and when only modest knowledge of the network is available. The inventors have realized that knowledge of supply line temperatures at specific points of the energy grid are typically not required for an effective control of the energy grid. In most situations it is sufficient to monitor temperature estimates indicative of the supply line temperatures in predetermined areas of the grid, e.g. in a supply line supplying a street with residential homes, in particular areas where the supply line temperature does not vary much along the supply line. In particular, this is the case for sections of the supply line without branching points other than the branching points at which service lines branch off from the supply line. At least some embodiments of the method disclosed herein allow for calculation of the supply line temperature even if some logging devices of a sub area are temporarily not delivering data while other logging devices in the same area do deliver data. Embodiments of the method and system disclosed herein allow an operator to easily set up virtual temperature measurement locations throughout the network. The definition of such virtual measurement locations does not require the installation of new hardware and only involves the selection of a group of existing logging devices associated with a section of supply line forwhich an operational parameter is to be monitired. Accordingly, an efficient and flexible monitoring of the energy grid is provided.

In some embodiments, computing comprises modelling a temporal evolution of at least the supply line temperature as a stochastic process, in particular a stochastic process including a diffusion term. In some embodiments, the stochastic process is a diffusion process, such as an Ito process. Accordingly, the temporal evolution of the supply line temperature may be monitored and this may be done in real-time or quasi real-time, thus facilitating an efficient monitoring or control of the energy grid. Moreover, the modelling the temporal evolution of the supply line temperature as a stochastic process facilitates an estimation of the supply line temperature without detailed knowledge of the conditions of the supply line. Moreover, embodiments of the process facilitates an efficient monitoring of variations in the supply line temperature occurring over relatively short time scales, thus providing a useful input for an efficient control of the energy grid.

In some embodiments, the stochastic process further models a temporal evolution of one or more thermal loss parameters and/or an ambient temperature, the one or more thermal loss parameters are indicative of thermal losses of respective ones of said selected one or more service lines. Accordingly, knowledge about the insulation state and other physical properties of the service lines is not required either. On the contrary, at least some embodiments of the method provide information about the current thermal losses in the service lines and can thus be used to schedule corrective maintenance or repair. The ambient temperature may be the temperature of the medium/material that surrounds the service line. For underground service lines, the ambient temperature may be a ground temperature while for above-ground service lines, the ambient temperature may be the air temperature of the air surrounding the service line.

In some embodiments the stochastic process models the temporal evolution of a system model having a state defined by at least the supply line temperature and the thermal loss parameters, such as a state defined by at least the supply line temperature, the ambient temperature and the thermal loss parameters. In some embodiments, computing comprises applying a recursive filter. The recursive filter is configured to output real time supply line temperature estimates, in particular in real-time or quasi real-time. Here the term quasi real-time is intended to refer to delays between the measurement of the sensor data and the computation of the supply line temperature estimates that is no larger than the typical timescales at which the supply line temperatures vary, in particular small enough to allow control of an energy grid to avoid undesired fluctuations of the supply line temperatures. In some embodiments, computing comprises applying an extended Kalman filter. The supply line temperature, one or more thermal loss parameters and, optionally, the ambient temperature may be state variables of the Kalman filter, wherein the one or more thermal loss parameters are indicative of thermal losses of respective ones of said selected one or more service lines.

In some embodiments, the heat loss models relate the observed fluid temperatures at respective service lines to the supply line temperature, the ambient temperature, the observed fluid flows in the respective service lines, and to the thermal loss parameter of the service line. Preferably, the heat loss models include respective heat loss models associated with different service lines, in particular different service lines branching off from a common supply line, thus allowing an accurate estimate of the supply line temperature of the common supply line that is less sensitive to measurement noise. The heat loss models of respective service lines may all have the same model structure, but they may in any event include one or more service-line-specific model parameters and/or variables, in particular service-line-specific thermal loss parameters, that may vary from service line to service line. In some embodiments the one or more selected service lines include a plurality of service lines, and each of the heat loss models relates a thermal loss parameter of a respective service line with an observed fluid temperature and an observed fluid flow associated with said respective service line. The selected service lines may be connected to a portion of the at least one supply line and the computed estimate of the supply line temperature is a uniform estimate of the supply line temperature for said entire portion of the at least one supply line. Accordingly a reliable estimate of a supply line temperature may be obtained for the selected portion of the supply line, while reducing measurement/estimation noise. Preferably, the selected plurality of service lines are all connected to a single supply line, in particular to a section of a single supply line, that does not include any branch points other than the service lines branching off to individual consumers. Alternatively, the selected plurality of service lines may be selected such that they are otherwise connected to one or more supply lines that may be assumed to have a reasonably uniform supply line temperature. The heat loss models associated with respective service lines may each include a common supply line temperature, the supply line temperature representing an unknown variable of the heat loss models, i.e. the computational model may exploit the fact that the selected service lines are all connected to the same supply line or otherwise such that the temperature in the supply line is substantially uniform across the selected service lines. The heat loss models associated with respective service lines may include respective thermal loss parameters, indicative of heat losses in individual service lines; as discussed herein, the thermal loss parameters may be unknown variables of the heat loss models.

In some embodiments, computing comprises computing an updated estimate of the supply line temperature and/or the one or more thermal loss parameters responsive to receiving updated observed sensor data from at least a subset of the one or more sensors associated with the selected service lines of the plurality of service lines, thereby facilitating real-time or quasi-real time computation of updated estimates of the supply line temperatures and/or thermal loss parameters. Accordingly, the measurements from the individual logging devices do not need to be synchronized, i.e. they do not need to be taken at the same time instances. At least some embodiments of the recursive filter, e.g. the Kalman filter, is configured to update the states of the model based on the available measurements at each update time. Hence, the use of sensor readings are optimized and ad-hoc data alignment procedures can be avoided.

The present disclosure relates to different aspects including the method described above and in the following, corresponding apparatus, systems, methods, and/or products, each yielding one or more of the benefits and advantages described in connection with one or more of the other aspects, and each having one or more embodiments corresponding to the embodiments described in connection with one or more of the other aspects and/or disclosed in the appended claims.

In particular, according to another aspect, a method for controlling operation of a district energy grid is disclosed. The district energy grid comprises one or more supply lines and a plurality of service lines for transporting a fluid to a plurality of consumers, each supply line feeding the fluid into a plurality of service lines and each service line connecting a consumer with one of the one or more supply lines. Embodiments of the method comprise:

- selecting one or more sets of service lines, each selected set of service lines being connected to at least a respective selected supply line of the plurality of supply lines.

- determining a temporal evolution of supply line temperatures in the respective selected supply lines by performing the steps of the method for determining the temperature in a supply line of a district energy grid as disclosed herein,

- controlling operation of the district energy grid based at least on the determined temporal evolution of supply line temperatures.

According to yet another aspect, disclosed herein are embodiments of a control system for controlling operation of a district energy grid, the control system being configured to perform the steps of the method for controlling operation of a district energy grid as disclosed herein.

Embodiments of the methods disclosed herein may be computer-implemented. Accordingly, disclosed herein are embodiments of a data processing system configured to perform the steps of one or more of the methods described herein. In particular, the data processing system may have stored thereon program code adapted to cause, when executed by the data processing system, the data processing system to perform the steps of one or more of the methods described herein. Embodiments of the control system and/or of the data processing system may be embodied as a single computer or other data processing device, or as a distributed system including multiple computers and/or other data processing devices, e.g. a client-server system, a cloud-based system, etc. The control system and/or the data processing system may include a data storage device for storing the computer program and sensor data. The control system and/or the data processing system may include a communications interface for receiving sensor data from consumer's heat meters or other suitable logging devices and/or sensors associated with the individual service lines. The control system and/or the data processing system may receive the sensor data from the heat meters and/or other logging devices and/or sensors via a suitable wired or wireless communicative connection, e.g. via a suitable communications network.

The control system and/or the data processing system may provide a user-interface for allowing a user to monitor the computed estimates of the supply line temperatures and/or other data associated with the conditions of the supply lines and/or of individual service lines. The data processing system may also issue warnings or alerts or other notifications responsive to detected conditions, e.g. audible or visual alerts, alerts communicated via e-mail, SMS, or other forms of notifications, and/or the like.

Another aspect relates to a district heating or cooling system. The district heating or cooling system comprises:

- a district energy grid, the district energy grid comprising one or more supply lines and a plurality of service lines for transporting a fluid to a plurality of consumers, each supply line feeding fluid into a plurality of service lines and each service line connecting a consumer with one of the one or more supply lines, - a plurality of sensors configured to output sensor data indicative of measured fluid temperatures and associated fluid flow of the transported fluid received at respective consumers via the respective service lines,

- a data processing system and or a control system as described above and in the following.

The sensor data indicative of the measured fluid temperatures and the associated fluid flow may represent the fluid temperatures and associated fluid flow directly. Alternatively, the fluid temperatures and the associated fluid flow may be derivable from the sensor data. For example, the sensor data indicative of the fluid flow may be sensor data directly representing the fluid flow or it may be sensor data from which the fluid flow can be derived, e.g. sensor data representing received volumes of fluid.

Yet another aspect disclosed herein relates to embodiments of a computer program configured to cause a data processing system to perform the acts of the method described above and in the following. A computer program may comprise program code means adapted to cause a data processing system to perform the acts of the method disclosed above and in the following when the program code means are executed on the data processing system. The computer program may be stored on a computer-readable storage medium, in particular a non-transient storage medium, or embodied as a data signal. The non-transient storage medium may comprise any suitable circuitry or device for storing data, such as a RAM, a ROM, an EPROM, EEPROM, flash memory, magnetic or optical storage device, such as a CD ROM, a DVD, a hard disk, and/or the like.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments will be described in more detail in connection with the appended drawings, where FIG. 1 schematically shows an embodiment of a district energy system.

FIG. 2 schematically shows another embodiment of a district energy system.

FIG. 3 shows a flow diagram of an example of method for determining an operational parameter of a district energy grid.

FIG. 4 shows a flow diagram of a method for initializing estimators for the supply line temperatures and thermal loss parameters of service lines in a district energy grid.

FIG. 5 shows a flow diagram of a method for updating the estimators for the supply line temperatures and thermal loss parameters of service lines in a district energy grid.

FIGs. 6A-B and 7A-B illustrate examples of measured fluid temperatures and flows and examples of results computed by an embodiment of the method described herein.

FIG. 8 illustrates an embodiment of a method for controlling operation of a district energy grid.

FIG. 9 shows a flow diagram of an alternative method for updating the estimators for the supply line temperatures and thermal loss parameters of service lines in a district energy grid.

DETAILED DECRIPTION

FIG. 1 schematically shows an embodiment of a district energy system, generally designated by reference numeral 1. Generally, the district energy system comprises a district energy grid 2, which comprises one or more supply lines 6 and a plurality of service lines 5 for transporting a fluid to a plurality of consumers 4. Each supply line 6 feeds the fluid into a plurality of service lines 5 and each service line 5 connects a consumer 4 with one of the one or more supply lines 6.

The supply lines 6 and/or the service lines may be pipes or other suitable fluid conduits, preferably thermally insulated pipes. The fluid may be water or another suitable medium facilitating heat transfer by advection of the medium through the district energy grid. The district energy system comprises an energy supply plant 3. In the example of FIG. 1, the district energy system is a district heating system and the energy supply plant is a district heating plant which feeds heated liquid into the district energy grid 2. It will be appreciated that other embodiments may relate to other types of energy systems, such as cooling systems.

The district energy grid may comprise a network of supply lines, also referred to as main grid, e.g. as illustrated in FIG. 2. Each consumer 4 is connected to the main grid by a service line 5, e.g. by an insulated service pipe.

Typically, the main grid has one or more supply lines 6 and one or more return lines 7. The supply lines form a supply path for feeding the fluid from the energy supply plant 3 to the individual consumers 4 and the return lines 7 define a return path for returning fluid from the consumers to the energy supply plant 3.

The district energy system further comprises a plurality of logging devices 8. The logging devices 8 are configured to provide temperature sensor data and flow sensor data. The temperature data may be indicative of the measured fluid temperature of the fluid arriving at the respective consumers, also referred to as the supply temperature, in particular, the service line has a receiving end and a consumer end, opposite the receiving end. The service line is fluidly connected to the supply line and receives fluid from the supply line at the receiving end. The consumer receives the fluid via the service line at the consumer end of the service line. The supply temperature represented by the sensor data is indicative of the fluid temperature of the fluid arriving at the consumer at the consumer end of the service line. The flow sensor data may be sensor data directly representing the fluid flow or it may be sensor data from which the fluid flow can be derived. For example, the flow sensor data may represent volume data indicative of the accumulated volume of fluid received by the consumer through the service line. The fluid flow may then be derived as a derivative of the volume data with respect to time. Typically, the logging devices are provided in the form of heat meters, that are installed at the respective consumers 4 and that are configured to log the supply temperature and the accumulated volume of fluid received by the respective consumers. It will be appreciated that, in alternative embodiments, the logging devices may include other types of sensors, e.g. they may be embodied as individual and separate sensors for measuring temperature and volume/flow, respectively.

The district energy system further comprises a data processing system 9, e.g. a server computer, a network of multiple computers, e.g. a client-server architecture, a distributed computing architecture, a cloud-based computing environment, and/or the like. The data processing system 9 may be located at the energy supply plant 3 or at a different location. The data processing system 9 receives logged sensor data from the logging devices 8. To this end, the logging devices 8 at the respective consumers are communicatively coupled to the data processing system 9, e.g. directly or indirectly. The logging devices 8 may communicate the logged sensor data to the data processing system via wired and/or wireless connections, e.g. via a suitable communications network, such as a cellular telecommunications network, via the internet or another suitable computer network. For example, many modern heat meters are configured for automatic communication of the logged data to a central data processing system. The logging devices may communicate the sensor data continuously or intermittently, e.g. one measurement at a time or as batches of multiple, time-stamped sets of sensor data. In some embodiments, the data processing system obtains the logged sensor data from a suitable data repository where the logged sensor data has previously been stored, e.g. by the heat meters, by an energy grid maintenance or surveillance system, or the like.

The data processing system 9 receives the logged sensor data and is programmed to process the received sensor data to determine supply line temperatures and/or thermal loss parameters in the district energy grid as described herein. Embodiments of a method implemented by the data processing system will be described in more detail below with reference to FIGs. 3 through 5. The data processing system may store the received sensor data and the results of the determination of the supply line temperatures and/or thermal loss parameters. Alternatively or additionally, the data processing system may implement functionality for presenting the sensor data and/or the results of the supply line temperature determination to a user of the data processing system, e.g. in the form of graphs, tables or the like. The data processing system may further have an interface for providing the determined supply line temperatures to one or more control systems that control the temperature and/or pressure at one or more locations in the district energy grid. Alternatively, the data processing system may be an integral part of such a control system.

FIG. 2 schematically shows another embodiment of a district energy system 1. The district energy system 1 of FIG. 2 is similar to the district energy system of FIG. 1 in that it comprises an energy supply plant 3, an energy grid 2 including supply lines and service lines for distributing a fluid from the energy supply plant to a plurality of consumers, a plurality of logging devices 8, e.g. heat meters or other types of sensors, installed at the respective consumers, and a data processing system 9, all as described in connection with FIG. 1. Even though not explicitly shown in FIG. 2, it will be appreciated that the energy grid 2 of FIG. 2 also includes return lines defining a return path as described in connection with FIG. 1. Similarly, even though not explicitly shown in FIG. 2, the logging devices 8 are preferably communicatively coupled to the data processing system 9 and configured to transmit logged sensor data to the data processing system. It will generally be appreciated that, alternatively to communicatively coupling the logging devices to the data processing system, sensor data may be read out and entered into the data processing system in a different manner, e.g. by storing the logged sensor data on a portable data carrier, and by reading the stored data by the data processing system from the portable data carrier. However, it will be appreciated that a communication of the logged sensor data via a suitable communications, link allows a more efficient and, optionally, even (quasi) real- time transmission and analysis of the logged sensor data.

FIG. 2 illustrates that the energy grid 2 typically includes more than a single supply line, in particular a network of supply lines 6 that are interconnected at nodes 20. The nodes 20 typically include valves, which allow the energy grid operator to direct the fluid flow along respective paths through the grid, e.g. so as to temporarily isolate certain parts of the grid from each other for maintenance, or the like. In the example of FIG. 2 open valves are designated V and closed valves are designated C. Accordingly, the energy grid 2 includes multiple subareas 2a - 2e where all consumers in a given subarea are connected to a single supply line. In each subarea of the grid, there are multiple logging devices 8.

Embodiments of a process for determining supply line temperatures and/or thermal loss parameters in a district energy grid will now be described with reference to FIGs. 3-5 and with continued reference to FIGs. 1 and 2. The process may be performed by the data processing system 9. For example, some embodiments of the method may be implemented by a cloud computing environment or another suitable data processing system. The determined supply line temperatures and/or thermal loss parameters may be calculated and presented in the form of a graphical indicator, e.g. as illustrated in FIGs. 7A-B. The graphical indicators and/or other representation of the computed results may be presented via an internet browser or another suitable user interface. The determined supply line temperatures may also be provided for a one or more control systems that control(s) the district energy grid.

The description of the following embodiments will mainly focus on the estimation of the supply line temperature in a supply line of the energy grid. In particular, the embodiment described below and other embodiments allow for a real-time or quasi-real time monitoring of the supply line temperatures without need for direct measurements of the supply line temperatures and without the need for any knowledge of the state or of the detailed structural properties of the supply lines. Accordingly, such embodiments are well suited for the control of an energy grid system. It will be appreciated from the description, however, that the method also provides estimates of the thermal loss parameters of the individual service lines. While a real-time monitoring of the thermal loss parameters in many situations is not required, because the thermal properties of the service lines only vary on a considerably longer timescale, the estimated thermal parameters nevertheless provide valuable information to the energy grid operator when planning maintenance and/or repair of parts of the energy grid. Accordingly, while the following description, for the sake of brevity, mainly refers to the used estimator as a supply line temperature estimator, it will be appreciated that the embodiment described in the following may also be applied to determine thermal losses in the service line and that the described estimator is also an estimator of the thermal loss parameters.

FIG. 3 shows a flow diagram of an example of method for determining an operational parameter of a district energy grid.

In initial step SI, the process sets up and initializes a suitable model of the district energy grid and initializes one or more estimators for estimating the temporal evolution of supply line temperatures and/or thermal loss parameters in one or more respective portions of the grid. An example of the initialization of the model and the estimators will be described in greater detail below with reference to FIG. 4.

In subsequent step S2, the process receives sensor data and updates the one or more estimators. An example of a process for updating the estimators will be described in greater detail below with reference to FIG. 5. Generally, various embodiments described herein are based on suitable heat loss models of the heat loss in the service pipes of the selected portion of the energy grid, e.g. a heat loss model relating the measured temperature and the measured fluid flow to at least the supply line temperature and to suitable thermal loss parameters of the service lines which are indicative of the quality of the service line.

FIG. 4 shows a flow diagram of an example of a computer-implemented initialization process for initiating estimators for determining supply line temperatures and/or thermal loss parameters in a district energy grid 2.

In initial step S41, the initialization process groups the service lines and the associated logging devices 8 of the district energy grid 2 into respective groups. To this end, the initialization process may set up a data structure representing the district energy grid and identifying respective selected groups of data logging devices. The grouping may be performed manually, automatically or in a semi-automatic, user-assisted manner. The grouping is typically only performed once or when changes to the structure of the energy grid occur.

An example embodiment of the initialization process uses data from a selected group of logging devices 8 within a geographically limited region of the energy grid 2 where the supply line temperature of the fluid fed from the main district energy grid into the service lines can be expected to be roughly the same for all service lines or where differences in the supply line temperature between users can easily be modelled. In the following, such a geographically limited region will also be referred to as a "similar-temperature area".

Groups of logging devices within the energy grid may be selected in a number of ways, e.g. based on existing information about the energy grid, such as: 1. Based on street name, consumer address coordinates and/or registered GPS coordinates of the respective logging devices.

2. Based on valve closing maps from the district energy grid operator's GIS data.

3. Based on a graph-theoretical representation of the energy grid.

It will be appreciated that the grouping may be based on each of the above information, individually or on a combination of two or even all three types of information. It will further be appreciated that the grouping may be performed in a different manner, including based on other types of information.

The grouping based on street name and/or consumer address coordinates is easy to implement but is also expected to be the most inaccurate. GPS location of the logging devices is sometimes registered by the grid operator upon installation of the logging device. If available, this data may provide valuable additional information for the grouping, thus increasing the accuracy of the grouping.

Some district energy grid operators maintain valve closing maps in their GIS data. Accordingly, if such data is available, it may also provide a useful basis for the grouping of logging devices. Moreover, such maps may give accurate information about the flow direction in the grid, and thus help identifying similar temperature areas. For example, in the example of FIG. 2, the logging devices in each of the sub-areas 2a - 2d may be selected as a respective group representing a similar temperature area, as all service pipes within each group are connected to a linear string of supply line without any branches. In some instances, depending on the grid topology, several of the sub-areas may be combined into a single group. Alternatively or additionally, a subarea may be divided into multiple groups, e.g. if the subarea includes a very long string of supply line.

In order to aid the grouping, a graph theoretical network model can be established from the district energy grid operator's GIS data. Even though the grid operator in many cases does not have detailed pipe dimensions and heat loss coefficients available they often do have a map of their pipes that may be transferred to a graph theoretical model. It is an advantage of the method disclosed herein, that it does not rely on detailed information about the energy grid, such as pipe dimensions, heat loss coefficients of the pipes etc. As can be appreciated from the above, even though access to GIS data or other grid topology data may increase the accuracy of the grouping, and thus the accuracy of the resulting estimates of the supply line temperatures and/or heat losses, the grouping of logging devices may even be performed without any topology information about the grid; it may entirely be based on postal addresses of the consumers.

In some embodiments, a user may be able to define virtual measurement locations at respective user-selected locations of the energy grid. The process may then select a group of logging devices in a proximity of the selected location, e.g. logging devices associated with a selected street for which monitoring is desired. The selection of the associated logging devices may be performed automatically, manually or in a user-assisted semi- automatic manner, e.g. based on one or more of the types of information described above. Accordingly users are able to easily and quickly define one or more virtual ad-hoc measurement locations, e.g. in an area of the grid in which control issues have been observed, where a monitoring, in particular a real-time monitoring, of the supply line temperature or another operational parameter is desired.

Once one or more groups of logging devices are set up, data structures and estimators for respective groups, e.g. all groups or only a selected subset of the groups, may be set up.

To this end, in subsequent step S42, the initialization process selects a group of logging devices of the district energy grid 2, each selected logging device is located at the consumer end of a service line, i.e. each selected logging device is associated with one of the service lines. Accordingly, an example embodiment of the method disclosed herein performs a determination of a supply line temperature associated for a thus selected group of logging devices, preferably a group of service lines connected to a single supply line or otherwise connected to the supply grid such that the supply lines of the selected group receive fluid of approximately the same or at least of similar temperature or where temperature differences between the points of the supply line where the service lines are connected can easily be modelled. Preferably, the selected group of logging devices includes between 5 and 50 logging devices, a typical group size is 10 to 30 logging devices. Larger groups of logging devices increase the statistical accuracy of the computation of the supply line temperature, but also increase the computational complexity. Larger groups of logging devices may increase the risk that the assumption of uniform temperature in the supply line is inaccurate, thus reducing the accuracy of the supply line temperature determination. Generally, when each logging device has a service line associated with it, the selected group of logging devices corresponds to a selected group of associated service lines. Alternatively or additionally, the process may be used to estimate thermal loss parameters of individual ones, such as of each, of the service lines associated with the selected group of logging devices.

In step S43, the initialization process determines the number of logging devices in the selected group. The number of logging devices in a group may, for example, be determined by counting the number of unique logging device identifications that are associated with the selected group, the logging device identifications, for example, being fetched from a database as strings or any other suitable identification. Alternatively, the number of logging devices may be determined from a lookup table including unique identification strings of different logging device groups and integers specifying the number of logging devices for the different groups. In step 44, the initialization process initializes a supply line temperature estimator for the selected group by setting up suitable data structures for representing the estimator. The process populates the data structure by at least the initial estimator states. The supply line temperature estimator may represent a stochastic model of the temporal evolution of at least the supply line temperature as a stochastic process, such as an Itô process.

For example, in one embodiment, the supply line temperature and the ambient temperature may be modelled by a discrete time Ito processes with a linear drift term, such that

T _M (t _k + δt) = T _M (t _k ) + v _M (t _k ) (la)

T _A (t _k + δt) = T _A (t _k ) + v _A (t _k ) , (lb)

Here T _M denotes the supply line temperature (which may also be referred to as the mains temperature) and T _A denotes the ambient temperature.

The stochastic terms v _M and v _A may be modelled as distributed according to respective normal distributions, e.g. as , where the variances σ _M and σ _A indicate how much the mains and the ambient temperatures are expected to change between samples, i.e. over the sample time δt.

The computation of the supply line temperature may be based on a suitable heat loss model of the heat losses in the respective service lines. To this end, the heat losses in a service line may be described in terms of the temperature T _M in the supply line that supplies the service line (i.e. the fluid temperature at the receiving end of the service line), by the measured consumer temperature T _C and the measured fluid flow q of the fluid in the service line arriving at the consumer, i.e. at the consumer end of the service line. Denoting the heat loss of a service line by , the heat loss may be expressed as where C _w is the volumetric heat capacity of the transported fluid, which is typically water. Accordingly, the parameter C _w is a constant known from water properties (or corresponding material properties in the case of another heat transport fluid). T _C and q can be obtained from the heat meter or another form of logging device associated with the service line. They may be obtained either directly or by suitable pre-processing, depending on the type of sensor data logged by the logging device.

The heat losses can also be described by a temperature loss model of the service pipe, e.g. as a mean temperature loss model

Here, B is the heat transfer coefficient, i.e. a thermal loss parameter associated with the service line, and T _A is the ambient temperature. In many cases the ambient temperature equals the ground temperature as most service lines are dug into the ground. However, it may also be the outdoor air temperature for service lines above ground. The above heat loss expressions may be used to obtain the following relation between the consumer temperature T _C , the supply line temperature T _M , and the ambient temperature T _A . For example, such a heat loss model may be of the form

It will be appreciated that other embodiments may use other heat loss models, e.g. another heat loss model relating the measured temperature T _C and the measured fluid flow q to at least the supply line temperature T _M and suitable thermal loss parameters of the service lines which are indicative of the quality of the service line. In the present embodiment, the quality of the service line may thus be described by the thermal loss parameter B and/or by a parameter derived therefrom. Hence, B and/or a parameter derived therefrom indicates the quality of the pipe isolation and, in some embodiments is an output parameter of interest and may thus be an output of the process. To ease the following description, a scaled thermal loss parameter 6 may be introduced for the service line: θ = B/(2C _w ).

Assuming the supply line temperature T _M and the ambient temperature T _A are uniform across the service lines in a sufficiently small section of the district energy grid, then the only parameter that differs between the individual consumers are the heat loss properties of the respective service lines as expressed by the thermal loss parameters of the respective service lines. These conditions are for example expected to be present in a small section of the network formed by a group one-family houses connected to the same main pipeline. For the purpose of the present description, the selected section of the network is assumed to comprise n logging devices. The individual consumer temperatures and flows are denoted where the index i, 1 ≤ i ≤ n, denotes the different logging devices.

With this notation, the above heat loss model at time t _k of the i'th user may be described by

(3) where and are the temperatures and flow, respectively, at time t _k . In this embodiment, the (scaled) heat loss parameter depends on k and thereby on time as well, thus allowing detection of changes in these parameters over time. With the above heat loss model, a common model for an entire selected part of the energy grid may be described by y _k =h _k (q _k ,Θ _k ) + + w _k (5)

Here, Θ _k is a state vector of the selected part of the grid. q _k and y _k are vectors that contain the subset of the measured flows q ^(l) (t _k ) and temperatures , respectively, which are available at the time t _k . That is, for the consumers where M is the set of consumer logging devices that have successfully communicated data to the process at time t _k . This means that y _k and q _k may change size between samples, where the size of and q _k equals the number of obtained measurements at time t _k . Accordingly, the process may determine an updated temperature estimate even though not all logging devices send measurement data at all times, or they send them at different times, thus allowing monitoring of the supply line temperature even in the absence of complete sensor data sets at all times. It will be appreciated that, in other embodiments, the process may ensure that there is measured data from each of the logging devices at each time t _k , e.g. by shifting or interpolating sensor data in time to align them to uniform measurement times.

The function h _k (q _k ,Θ _k ) is a vector function with the same size as y _k , hence has the same number of rows as the obtained number of measurements at time t _k . Each of the rows in _h k represents the right side of equation (3) for one of the logging devices. Finally, the parameter vector Θ _k represents the state of the system and may be given by

The vector Θ _k contains the unknown elements in the consumer temperature model of eqn. (5). The set of available measurement at time th is always a subset of the logging devices in the chosen area of the energy grid, hence . The time evolution of the supply line and ambient temperatures are described by (la) and (lb), whereas the parameters to are assumed to vary very slowly compared to the time at which the measurements are taken. Finally, w _k represents measurement noise. In one embodiment, the noise w _k may be modelled with covariance meaning that the same noise variance is expected on all the measured temperatures at the consumers. However, other embodiments may use consumer-specific variances.

A preferred data structure that is suitable for a supply line temperature estimator and/or for estimating the thermal loss parameters of the service lines may for example be formulated as an Extended Kalman Filter.

In the Extended Kalman filter approach the evolution of the unknowns may be defined by Θ _k+1 = FΘ _k + GT _A0 + v _k , (6) where F represents the drift, v _k represents the diffusion, and GT _A0 is an input term that is used to define the expected average ambient temperature T _A0 . The diffusion may be assumed to a Gaussian with . Here, Q contains the variance of the loss parameters as well as the diffusion terms in (1a) and (1b). For the purpose of the present description, the same variance is used for the evolution of the loss parameters for all the consumers. However, in other embodiments, consumer-specific variances may be used. Assuming a uniform variance, Q becomes

From eqn. (3) it will be appreciated that, when the loss parameter Θ ⁽ⁱ⁾ is small, which is the case with a well-insulated pipe, the ambient temperature T _A has very little influence on the costumer temperature T _C . Under these conditions, the ambient temperature is hard to estimate and can drift away. To ensure the robustness of the estimation, the diagonal element in F, which is related to ambient temperature, may be chosen slightly smaller than 1, and the corresponding term in G slightly larger than 0. The diagonal terms related to the loss parameters and the mains temperature may be set to 1. It will be appreciated that other choices are possible. The above choices lead to the following structure of F and G: where 0 < ∈ < 1 is a suitably chosen parameter.

In general, based on a suitably chosen drift model, e.g. the drift model of eqn. (6), and a suitable expression for the consumer temperatures, e.g. the expression according to eqn. (5), the Extended Kalman filter may be given by the following update equations that are executed at sample time k during the update process (as will described below with reference to FIG. 5): (7)

The vector function h _k (q _k , Θ _k|k _ ₁ ) contains the heat loss model of the service lines in the group, and y _k and q _k are the most recent measurements from the logging devices in the group where subscript k denotes the current time step; accordingly, the subscript k — 1 denotes the previous time step. Here, K _k is the so called Kalman gain and P _k|k is the variance of the state vector Θ _k|k . The first subscript of both P _k|k and Θ _k|k indicates the updated values at time k and the second subscript indicates the update is based on measurements at time k. Correspondingly, the subscripts of both and Θ _k|k -1 means the predicted values at time k based on measurements at time k-1. The state vector Θ _k|k at least contains the supply line temperature to be estimated but may also contain other estimated states such as loss coefficients of the service lines in the selected group, e.g. as described above. R is a diagonal matrix containing the expected covariances of y, e.g. as described above.

In the present embodiment, the i'th row in H _k of eqn. (7) becomes and whereby the supply line temperature can be estimated based on equations (7). The supply line temperature estimate at the current timestep is the T _{M, k} , which can be seen to be included in the state vector Θ _k .

Applying the Extended Kalman Filter as the supply line temperature estimator means that step 44 of the initialization process sets up the initial estimator states Θ ₀ and the initial covariance prediction P ₀ . The number of states and the size of the covariance prediction (a matrix) depends on the number of logging devices in the group.

It will be appreciated that the supply line temperature estimator may take on other forms of nonlinear observers than an Extended Kalman Filter. In step 45, the initialization process stores the initial states of the supply line temperature estimator that is associated with the selected group. The initial states are needed for later update of the supply line temperature estimator. When the supply line temperature estimator takes the form of an Extended Kalman Filter the initialization process also stores the initial covariance prediction for later retrieval and update.

In general, it will be appreciated that the initialization process sets up and populates the parts of the supply line temperature estimator that shall be updated recursively as new logging device measurements are obtained, and subsequently performs an initial storage of these recursively updated parts of the supply line temperature estimator.

After step 45 the initialization process checks if there are further logging device groups in the district energy grid in question that need processing/initialisation. If there are further logging device groups, steps 42 to 45 are repeated until no further logging device groups need processing.

The selection of logging device groups may for example be based on where additional grid measure points are desired in order to monitor and/or control the district energy grid in an optimal manner. A district energy grid operator skilled in the art will often now where to place the grid measure points. The logging devices around that location may then be added to a logging device group.

It is appreciated that the number of logging device groups that are processed may be a subset of the total number of logging device groups in a district energy grid. In other embodiments all logging device groups may be processed. Therefore, the supply line temperature estimators may be initialized for only some or for all logging device groups in a district energy grid, as desired. When there are no further logging device groups needing processing, the initialization process will hand over to an update process, e.g. the update process of FIG. 5, for running the supply line temperature estimators.

FIG. 5 shows a flow diagram of an example of a computer-implemented update process for running the supply line temperatures estimators.

In step S51, the update process selects a logging device group for which the supply line temperature estimator needs updating. The selection can for example be done consecutively by running through a list of supply line temperature estimators that have been initialized by the initialization process. It may also be that the update process receives from another entity, such as from another process, a logger group identification for which the supply line temperature estimator needs updating.

In subsequent step S52 the update process retrieves at least the latest stored estimator states and other parts of the supply line temperature estimator that needs recursive updating. During the initial iteration after initialization of the supply line estimator, it is the states and, if used, other parts for recursive update that were stored by the initialization process.

In general, it will be appreciated that the update process retrieves the most recent calculation results of the parts of the supply line temperature estimator that are recursively updated. In embodiments using the data structure of an Extended Kalman Filter it is the state prediction and the covariance prediction that are retrieved. These are described further below by equations (8) and (9).

If there are new measurements from the selected logging device group, the update process continues to step S53. If there are no new measurements since the last update, the process proceeds at step S56 instead. In step S53, the update process obtains logged sensor data from the logging devices in the selected group. In the following, the number of logging devices in the selected group will be denoted n (n>=l). The update process may obtain the sensor data in a variety of ways.

In some embodiments, the process may receive the measured temperatures and the measured fluid flows directly or indirectly from the respective logging devices. In other embodiments, the process derives these quantities from the sensor data received from the logging devices. For example, in some embodiments, some or all of the logging devices may only measure the accumulated volume and not the instantaneous flow, or the flow measurements received from the logging devices may be inaccurate.

Accordingly, optionally, the process may derive the fluid flow and/or the fluid temperature from the received sensor data. The update process may receive sensor data from an entity that transmits the sensor data to the update process, or the update process may request the sensor data from another entity, or retrieve the sensor data from a suitable data repository.

In embodiments where the received sensor data includes time-stamped volume data of the accumulated volume received at the logging device via the service line, the process may calculate the fluid flow as the gradient with respect to time of the accumulated volume using the actual time stamps of the received volume data. The used gradient method may be a central difference method, but backward or forward difference or other methods may be used instead.

It will be appreciated that other embodiments may receive other forms of temperature data and/or other forms of data indicative of the fluid flow. Accordingly, in such embodiments, the received sensor data may be pre-processed in a different manner in order to derive the fluid temperature and/or the fluid flow.

The logging devices may conveniently be heat meters. Heat meters normally log instant supply and return temperatures across a consumer installation, and/or flow weighted supply and return temperatures. The flow weighted temperature is sometimes referred to as the integrated flow temperature product (ITFP). For the purpose of the present description, the temperature data will simply be referred to either as supply temperature or commonly as temperature data. In addition to temperature data, accumulated volume and/or instant fluid flow rate is logged by the heat meter.

FIGs. 6A and 6B shows examples of time series of logged flow and temperature data, respectively, for a number of consumers in a selected part of an urban district energy grid. The data was obtained during normal operation of the district energy grid.

In subsequent step S54, the update process updates the supply line temperature estimator based on more recent logging device measurements than were used for previous updates. By the update, the data structure of the supply line temperature estimator is populated with the measurements from the logging devices obtained in step S53, and with the recursively calculated parts of the estimator retrieved in step S52; subsequently, the update process carries out calculations according to update equations for the estimator.

It is an interesting feature of various embodiments of the method disclosed herein that it may handle asynchronous measurements from the logging devices, i.e. the logging devices in the selected group might not all have delivered data before an update of the supply line temperature estimator. In that case the update of the supply line temperature estimator may be done based on measurements from a subset of the logging devices from the selected group of logging devices.

An optional step, which may be included in the update of the supply line temperature estimator associated with selected group, is that the measurements from multiple logging devices may be aligned in time before the data structure of the estimator is populated for the update.

In the preferred data structure of the Extended Kalman Filter, the update is for example performed by means equations (7), in particular in the listed order equations. In the case that only a subset of the logging devices in the group have delivered new data since the last update, only these parts of y _k and the vector function h _k q _k , Θ _k|k _ ₁ ) are populated.

Generally, both the setting up and populating of the data structure for the supply line temperature estimator by the initialization process, and the update of the supply line temperature estimator by the update process are based on a heat loss models of the service lines, e.g. a heat loss model of eqn. (2) above, or another suitable heat loss model.

It will be appreciated that stacking several equations according to the heat loss models into a system of equations, they may be conveniently solved by an Extended Kalman Filter as described herein. The supply line temperature can then be estimated based on equations (7). The supply line temperature estimate at the current timestep is the T _{M, k} , which is included in the state vector Θ _k . Similarly, the thermal loss parameter estimates may also be obtained as part of the state vector.

In step S55 the update process outputs the estimated supply line temperature T _{M, k} , which may be taken from the current state estimate Θ _k|k . The estimated supply line temperature may e.g. be output as a datapoint of a time series that may dynamically be displayed to a user or fed into another process that makes use of the estimated supply line temperature. Such a process may for example be a control loop that controls the supply temperature in the district heating grid and thus directly or indirectly for the meter group that was selected in step S51. It may also be output for a process presenting the supply line temperature on a web page, e.g. graphical. The output may even be for an application on a handheld device such as a smart phone, where the mobile device may display the supply line temperature at a given GPS location of the mobile device that is associated with a supply line in the district heating grid at that location. In alternative embodiments, the process may output estimates of the thermal loss parameters, e.g. for individual consumers, or parameters derived therefrom in addition to or alternatively to outputting the supply line temperature estimates. The loss parameter may be output as a time series of instantaneous loss parameters or used to compute an accumulated loss parameter, e.g. an average loss parameter, for an extended period of time.

FIG. 7A shows an example of a time series of estimated supply line temperatures in a portion of an urban district energy grid. The estimates were obtained by the embodiment of the process described herein and based on the sensor data illustrated in FIGs. 6A-B.

FIG. 7B shows an estimated thermal loss parameter 701 for the service pipe of one of the consumers for which sensor data is illustrated in FIG. 6A-B.

If the supply line temperature estimator, due to its structure, requires a prediction of the recursive parts before the next time step, this prediction may be included in the update process by the optional step S56. In case of the preferred embodiment of the Extended Kalman Filter both a covariance prediction and a state prediction may be needed before the next time step. They may be calculated as where F and Q. are constant and manually selected diagonal matrices, G is a constant and manually selected column vector, and T _A,0 is the initial temperature of the environment. Here the first subscript of both P _{k+1 |k} and Θ _{k+1 |k} indicates the prediction for time k+1, and the second subscript indicates that the prediction is based on measurements at time k. At times tk where no measurements are communicated, the process may simply skip the update of the state vector and base the prediction on the previously predicted values instead.

It is possible that, e.g. in step S56, the predicted estimator states may optionally be limited numerically if they exceed predefined numerical limits that are physically infeasible due to the heat loss model of the service line that the supply line temperature estimator is based on. This may sometimes give better tracking of the state vector if the data quality of the logging device measurements is poor.

In subsequent step S57 the update process stores the states of the supply line temperature estimator of the selected group that has been updated in step S54 or, if the prediction step S56 has been carried out, it stores the state predictions. When the supply line temperature estimator takes the form of an Extended Kalman Filter the update process stores the state prediction and the covariance prediction for later retrieval and update.

Optionally, the update process may check, whether there are further groups to process; if so, step S51 to S57 are repeated until there are no further groups to process.

If there are no further groups to process at the current time the update process waits until new measurements from the logging devices may be expected. The waiting time may be a predefined amount of time, or it may be a variable amount of time. The update process may for example be triggered by the receipt of new measurements to continue from step S57 to step S58. In the latter case, the time the update process waits is directly dependent on the arrival of new logger measurements. Preferable the waiting time may be fixed in the range of 1 to 5 minutes, but can be up to average one hour. In some embodiments, measurement results may arrive at more or less regular intervals from the individual logging devices, e.g. at intervals of between few minutes and about 1 h, or even longer intervals. Embodiments of the method disclosed herein can be used irrespective of the length of these intervals, and even if not all logging devices of a selected group provide measurement data at all times.

In the subsequent step S58 the update process increases the time, preferably by a fixed amount. In the example case of the Extended Kalman Filter it corresponds to increasing the counter k by 1 after a fixed amount of time has elapsed.

After increasing the time the update process is repeated beginning at step S51. The repetition of the update process may continue until it is manually stopped or, for example, until there is a timeout because no new data has been received within a certain time frame from the logging device groups that have been selected for setting up grid measure points.

It will be appreciated that some of the above steps may be omitted or performed in a different order. For example, concurrent update processes may be associated with each selected group of logging device and executed in separate computing threads. Thereby the step S51 may be omitted because the selection of logging device groups is intrinsically included in the setting up of the more than one update processes.

The embodiment of a process shown in FIG. 5 is particularly suitable for updating the state of the estimators in real-time or quasi real-time. It will be appreciated that the steps of the process may be reorganized, e.g. to process an already recorded time series of logged sensor data from the logging devices in a group. In that case, the process does not need to wait for new measurements to arrive but may simply increase the time along the entries in the recorded time series, and it may process the time series to the end before a new group is selected. Such and alternative process is shown in FIG. 9, where the individual steps S51 through S58 correspond to the corresponding steps of the process of FIG. 5.

As mentioned above, it is generally useful for an energy grid operator to identify service lines having a high thermal loss, as it may be useful to eliminate the cause of abnormally high thermal losses. Possible causes of high identified thermal losses may include defective or incorrectly mounted logging devices, insufficient or damaged thermal insulation, pipe leakages, etc. Accordingly, it will be appreciated that various embodiments of the method described herein may be used to estimate thermal losses in a district energy grid in addition to, or even alternatively to, the estimation of supply line temperatures. In particular, various embodiments of the method described herein may be used to estimate thermal loss parameters indicative of a thermal loss of respective service lines. The process may output the estimated thermal loss parameters instead of or in addition to the estimates of the supply line temperature. In particular, in the embodiment of FIGs. 4 and 5, the estimated thermal loss parameters may be derived from the state vector, which includes the parameters to \

In will further be appreciated that the supply line temperature and/or the thermal loss parameters may be estimated from the measured temperatures T _C and the measured fluid flows q in a different manner. For example, the supply line temperature and/or the thermal loss parameters may be computed by a computer-implemented method for determining thermal loss in a district energy grid, the district energy grid comprising one or more supply lines and a plurality of service lines for transporting a fluid to a plurality of consumers, each supply line feeding the fluid into a plurality of service lines and each service line connecting an consumer with one of the one or more supply lines, the method comprising:

- obtaining measured sensor data from one or more sensors associated with one or more selected service lines of the plurality of service lines;

- determining, from the received sensor data, respective observed fluid temperatures and observed fluid flows of the transported fluid received at respective points in time via respective ones of said one or more selected service lines;

- formulating a set of equations, the set of equations comprising a plurality of equations relating the observed fluid temperatures and the observed fluid flows with thermal loss parameters and a supply line temperature, the thermal loss parameters being indicative of thermal losses of respective ones of said selected one or more service lines,

- computing a solution of the set of equations and computing, from the computed solution of the set of equations, a result value of at least a supply line temperature and/or a first thermal loss parameter indicative of a thermal loss of a first service line of said selected one or more service lines.

An example of such a process is described in co-pending Danish patent application PA202270257, the entire contents of which are hereby incorporated herein by reference.

FIG. 8 illustrates an embodiment of a method for controlling operation of a district energy grid 2. In this embodiment, a monitoring process 801 monitors supply line temperatures in one or more parts of the energy grid, e.g. by performing the process of FIGs. 3-5. The monitored supply line temperatures are fed into a concurrently running control process 802, which controls the energy grid 2, i.a. based on the monitored supply line temperatures.

The various embodiments described herein are applicable to thermal energy carrying pipe networks where a group of consumers are supplied from a common supply line, and where temperature data of the fluid flowing out of the service line connecting the consumer to the supply line is available together with data about the amount of medium flowing in the service line. Examples of data relating to the amount of medium flowing in the service line are data of the accumulated volume or of the instantaneous flow rate. In such a case the thermal pipe loss or a supply line temperature and/or a thermal pipe loss related coefficient of the service line can be estimated by the various embodiments disclosed herein.

Generally, embodiments disclosed herein may be applied to energy grids of different types and sizes, including to energy grids located inside or outside of buildings.

In summary, various embodiments of the aspects disclosed herein may be summaries as follows:

Embodiment 1: A computer-implemented method for determining one or more operational parameters of a district energy grid, the district energy grid comprising one or more supply lines and a plurality of service lines for transporting a fluid to a plurality of consumers, each supply line feeding the fluid into a plurality of service lines and each service line connecting a consumer with one of the one or more supply lines, the method comprising:

- obtaining measured sensor data from one or more sensors associated with one or more selected service lines of the plurality of service lines, the selected service lines being connected to at least one selected supply line of the plurality of supply lines, the received sensor data being indicative of fluid temperatures and observed fluid flows of the transported fluid received at the respective consumers at respective points in time via respective ones of said one or more selected service lines;

- obtaining heat loss models of heat loss in the respective selected service lines, the heat loss models relating the observed fluid temperatures and the observed fluid flows to a supply line temperature of the fluid flowing from said at least one supply line into the respective selected service lines,

- computing, from said heat loss models and from the received sensor data, an estimate of the one or more operational parameters, in particular of the supply line temperature in said at least one selected supply line and/or of a heat loss parameter of one or more of the selected service lines.

Embodiment 2: The method according to embodiment 1, wherein computing comprises computing said estimate of the operational parameter only from said heat loss models of the selected service lines and from the observed sensor data.

Embodiment 3: The method according to any one of the preceding embodiments, wherein computing comprises computing the estimate of the operational parameter without any input indicative of known thermal loss parameters of the plurality of supply lines and/or of the service lines.

Embodiment 4: The method according to any one of the preceding embodiments, wherein computing comprises computing an estimate of the supply line temperature and of thermal loss parameters of one or more of the selected service lines.

Embodiment 5: The method according to any one of the preceding embodiments, wherein the heat loss models further include thermal loss parameters indicative of thermal losses of respective ones of said selected one or more service lines, in particular wherein the thermal loss parameters of the selected service lines are unknowns of the heat loss models. Embodiment 6: The method according to any one of the preceding embodiments, wherein computing comprises modelling a temporal evolution of at least the supply line temperature as a stochastic process.

Embodiment 7: The method according to embodiment 6, wherein the stochastic process is an Ito process.

Embodiment 8: The method according to embodiment 6 or 7, wherein the stochastic process further models a temporal evolution of one or more thermal loss parameters and/or an ambient temperature, the one or more thermal loss parameters being indicative of thermal losses of respective ones of said selected one or more service lines.

Embodiment 9: The method according to embodiment 8, wherein the stochastic process models the temporal evolution of a system model having a state defined by at least the supply line temperature and/or the thermal loss parameters.

Embodiment 10: The method according to embodiment 9, wherein the stochastic process models the temporal evolution of a system model having a state defined by at least the supply line temperature, the ambient temperature and the thermal loss parameters.

Embodiment 11: The method according to any one of the preceding embodiments, wherein computing comprises applying a recursive filter.

Embodiment 12: The method according to embodiment 11, wherein the recursive filter is configured to output real time supply line temperature estimates, in particular in real- time or quasi real-time. Embodiment 13: The method according to any one of embodiments 10 through 12, wherein computing comprises applying an extended Kalman filter, in particular wherein the supply line temperature, one or more thermal loss parameters and, optionally, the ambient temperature are state variables of the Kalman filter, the one or more thermal loss parameters being indicative of thermal losses of respective ones of said selected one or more service lines.

Embodiment 14: The method according to any one of the preceding embodiments, wherein the selected service lines are connected to a portion of the at least one supply line and wherein the computed estimate of the supply line temperature is a uniform estimate of the supply line temperature for said entire portion of the at least one supply line.

Embodiment 15: The method according to any one of the preceding embodiments, wherein the heat loss models relate the observed fluid temperatures at respective service lines to the supply line temperature, the ambient temperature, the observed fluid flows in the respective service lines, and to the thermal loss parameter of the service line.

Embodiment 16: The method according to any one of the preceding embodiments, further comprising computing estimates of one or more of the thermal loss parameters indicative of a quality of a thermal insulation of at least respective ones of the selected service lines.

Embodiment 17: The method according to any one of the preceding embodiments, wherein computing comprises computing an updated estimate of the supply line temperature responsive to receiving updated observed sensor data from at least a subset of the one or more sensors associated with the selected service lines of the plurality of service lines. Embodiment 18: A method for controlling operation of a district energy grid, the district energy grid comprising one or more supply lines and a plurality of service lines for transporting a fluid to a plurality of consumers, each supply line feeding the fluid into a plurality of service lines and each service line connecting a consumer with one of the one or more supply lines, the method comprising:

- selecting one or more sets of service lines, each selected set of service lines being connected to at least a respective selected supply line of the plurality of supply lines.

- determining a temporal evolution of supply line temperatures in the respective selected supply lines by performing the steps of the method according to any one of the preceding embodiments,

- controlling operation of the district energy grid based at least on the determined temporal evolution of supply line temperatures.

Embodiment 19: A computer-implemented method for determining a supply line temperature and/or thermal loss in a district energy grid, the district energy grid comprising one or more supply lines and a plurality of service lines for transporting a fluid to a plurality of consumers, each supply line feeding the fluid into a plurality of service lines and each service line connecting an consumer with one of the one or more supply lines, the method comprising:

- obtaining measured sensor data from one or more sensors associated with one or more selected service lines of the plurality of service lines;

- determining, from the received sensor data, respective observed fluid temperatures and observed fluid flows of the transported fluid received at respective points in time via respective ones of said one or more selected service lines;

- obtaining a set of equations, the set of equations comprising a plurality of equations relating the observed fluid temperatures and the observed fluid flows with thermal loss parameters and a supply line temperature, the thermal loss parameters being indicative of thermal losses of respective ones of said selected one or more service lines,

- computing a solution of the set of equations and computing, from the computed solution of the set of equations, a result value of at least a supply line temperature and/or a first thermal loss parameter indicative of a thermal loss of a first service line of said selected one or more service lines.

Embodiment 20: A method according to embodiment 19, wherein the thermal loss parameters represent unknown variables of the set of equations.

Embodiment 21: A method according to any one of embodiments 19 through 20, wherein the plurality of equations are linear in the thermal loss parameters.

Embodiment 22: A method according to any one of embodiments 19 through 21, wherein the set of equations is an overdetermined set of equations and wherein the solution of the set of equations is an approximate solution of the overdetermined set of equations.

Embodiment 23: A method according to embodiment 22, wherein computing the solution of the set of equations comprises computing the solution as a solution to a constrained optimization problem.

Embodiment 24: A method according to embodiment 22 or 23, wherein computing the solution of the set of equations comprises applying a least-square method for determining the approximate solution of the overdetermined set of equations.

Embodiment 25: A method according to any one of embodiments 22 through 24, wherein computing the solution of the set of equations comprises minimizing or maximizing a cost function over at least one additional temperature parameter indicative of a temperature and/or a temperature loss associated with the supply line.

Embodiment 26: A method according to any one of embodiments 22 through 25, wherein computing the solution of the set of equations comprises minimizing or maximizing a cost function over at least one ground temperature parameter that is associated with a ground temperature.

Embodiment 27: A method according to any one of embodiments 19 through 26, wherein the one or more selected service lines include a plurality of service lines, and wherein each of the plurality of equations relates a thermal loss parameter of a respective service line with an observed fluid temperature and an observed fluid flow associated with said respective service line.

Embodiment 28: A method according to any one of embodiments 19 through 27, wherein the set of equations comprises a first equation relating an observed fluid temperature and an observed fluid flow of the first service line at a first point in time with the first thermal loss parameter.

Embodiment 29: A method according to embodiment 28, wherein the plurality of equations further comprises another equation associated with a second point in time and relating an observed fluid temperature and an observed fluid flow of the first service line at said second point in time with said first thermal loss parameter, the second point in time being different from the first point in time.

Embodiment 30: A method according to embodiment 28 or 29, wherein the one or more selected service lines include a first and a second service line, different from the first service line, and wherein the plurality of equations comprises another equation associated with the second service line and relating an observed fluid temperature and an observed fluid flow of said second service line with a second thermal loss parameter indicative of a thermal loss of said second service line.

Embodiment 31: A method according to embodiment 30, wherein the first equation and the another equation associated with the second service line each include a temperature parameter associated with the supply line, the temperature parameter representing an unknown variable of the first equation and of the another equation associated with the second service line.

Embodiment 32: A method according to any one of the preceding embodiments, wherein the one or more selected service lines include a plurality of service lines all connected to a single supply line.

Embodiment 33: A control system for controlling operation of a district energy grid, the control system being configured to perform the steps of the method according to embodiment 18.

Embodiment 34: A data processing system configured to perform the steps of the method according to any one of embodiments 1 through 32.

Embodiment 35: A computer program comprising program code configured to cause, when executed on a data processing system, the data processing system to perform the steps of the method according to any one of embodiments 1 through 32.

Embodiment 36: A district heating or cooling system comprising:

- a district energy grid, the district energy grid comprising one or more supply lines and a plurality of service lines for transporting a fluid to a plurality of consumers, each supply line feeding fluid into a plurality of service lines and each service line connecting a consumer with one of the one or more supply lines,

- a plurality of sensors configured to output sensor data indicative of measured fluid temperatures and associated fluid flow of the transported fluid received at respective consumers via the respective service lines,

- a data processing system according to embodiment 34.

Embodiments of the method described herein may be computer-implemented. In particular, embodiments of the method may be implemented by means of hardware comprising several distinct elements, and/or at least in part by means of a suitably programmed data processing system. In the apparatus claims enumerating several means, several of these means can be embodied by one and the same element, component or item of hardware. The mere fact that certain measures are recited in mutually different dependent claims or described in different embodiments does not indicate that a combination of these measures cannot be used to advantage.

The term "obtaining a set of equations" as used in this disclosure, refers to the computer system automatically or semi-automatically, setting up the set of equations according to operational parameters of the district energy grid. For example, the computer system may take into account the size or number of users of the district energy grid, input data for one or more individual zones of the district energy grid, and/or any other relevant input parameters relating to the district energy grid, where the input data and/or other relevant input parameters may be obtained automatically and/or through user input. Likewise, the term "obtaining heat loss models" refers to the computer system automatically or semi- automatically calculating the heat loss models according to operational parameters of the district energy grid. For example, the computer may automatically or semi-automatically generate a heat loss model relating the measured temperature and the measured fluid flow to at least the supply line temperature and to suitable thermal loss parameters of the service lines.

It should be emphasized that the term "comprises/comprising" when used in this specification is taken to specify the presence of stated features, elements, steps or components but does not preclude the presence or addition of one or more other features, elements, steps, components or groups thereof.