Procedures for operating an energy management system for buildings

Abstract

The invention relates to a method for operating an energy management system for buildings, wherein the energy management system (1) comprises a thermal system (2), an electrical system (3) and a system model (4) with a thermal model (5) and an electrical model (6), comprising the steps: predicting boundary conditions (7), disturbances (8) and costs (9) for the system model (4), determining the control variables (10) of the system model (4) using the predicted boundary conditions (7), disturbances (8) and costs (9) with regard to overall energy cost reduction, and operating the thermal system (2) and the electrical system (3) using the determined control variables (10) of the system model (4).

Description

[0001] The invention relates to a method for operating an energy management system for buildings and an energy management system.

[0002] From DE102123213465 a method for calculating a shortfall, which includes a difference between the predicted amount of electricity consumed and the predicted amount of electricity generated, is known.

[0003] The inventive method comprises a method for operating an energy management system for buildings, wherein the energy management system comprises a thermal system, an electrical system and a system model with a thermal model and an electrical model as well as a prediction model, comprising the steps - Predicting boundary conditions, disturbances and costs for the system model, - Determining the control variables of the system model using the predicted boundary conditions, disturbances and costs with regard to overall energy cost reduction, - Operating the thermal system and the electrical system using the specific control variables of the system model.

[0004] Boundary conditions are defined limits within the system. The boundary conditions preferably include, for the electrical model, a time-based target state of charge for an electric vehicle storage system, a time-based availability of an electric vehicle storage system, and / or a time-based state of charge of a stationary storage system. The boundary conditions for the thermal model preferably include at least one time-based limit for an interior temperature and / or at least one time-based limit for a hot water temperature.

[0005] Disturbances are influences on the system that cannot be directly controlled. These disturbances typically include non-controllable electrical consumption, the actual production of a photovoltaic system, heat losses to the surrounding area, heat radiation from the surrounding area, and / or hot water consumption.

[0006] In this context, non-controllable electrical appliances include, for example, a stove or a refrigerator. The system cannot influence when these are used and switched on.

[0007] For the purposes of the invention, costs are a monetary equivalent of the energy used. The costs preferably include the amount payable upon purchase of energy, such as the price of electricity, gas, or other energy sources.

[0008] Using the boundary conditions, disturbance variables, and costs predicted by the prediction model, the system model determines the control variables of the electrical and thermal components of the energy system. Preferably, the determination is carried out in such a way that the total energy costs are reduced.

[0009] Preferred components are an electric vehicle, an electrochemical storage device, and / or a combined heat and power engine. Control variables preferably include parameters for setting the state of charge of the electric vehicle, setting the state of charge of the stationary storage device, and / or the operating mode of the combined heat and power engine.

[0010] In this context, parameters are understood as quantities that are unchanging or only change over a long period of time.

[0011] The method according to the invention with thermal system, electrical system and system model thus enables the optimization of the energy management system with a view to reducing the overall energy costs.

[0012] The thermal system and the electrical system are coupled via a combined heat and power engine. This allows energy from the electrical system to be transferred to the thermal system and / or energy from the thermal system to the electrical system through conversion. This enables optimized storage and / or utilization of, for example, excess energy. The thermal model and the electrical model are preferably coupled via a combined heat and power engine model. The combined heat and power engine model simulates the operation of a combined heat and power engine.

[0013] The thermal model is preferably described as a system of differential equations consisting of heat storage and heat transfers. A hot water storage tank, an air volume, a building structure, and / or a water volume can be considered as a heat storage. Heat transfers can be considered as thermal radiation, a transfer of heat from a building structure to its surroundings, a heat engine to a water volume, and / or air to a building structure. The inclusion of heat storage and heat transfers in the thermal model enables a precise representation of the energy distribution in the thermal system.

[0014] The thermal model comprises various parameters and states. The thermal model preferably includes values ​​of fixed parameters, particularly for the geometry and / or volume of the heat storage system. The values ​​of variable or non-a-priori determinable parameters are preferably estimated using a system identification method and incorporated into the thermal model. Measurable state values, such as flow temperatures, return temperatures, and / or room temperatures, are preferably recorded directly and incorporated into the thermal model. Non-measurable state values ​​are preferably estimated using the model using a state estimation method and incorporated into the thermal model.

[0015] In this context, a system identification procedure is understood as a procedure that identifies parameters and parameter values ​​by collecting data and analyzing this data.

[0016] In this context, states are understood as quantities that change instantaneously over time.

[0017] The electrical model is preferably described as a system of differential equations consisting of resistances, inductances, and capacitances. The electrical model comprises various parameters and states. Parameter values ​​are preferably stored in the electrical model in a parameterizable manner. Values ​​of long-term variable parameters, such as internal resistances, are preferably estimated using a system identification method and incorporated into the electrical model. Externally recorded state values, such as voltages and / or current flows, are preferably directly considered, and non-measurable state values ​​are estimated using a state estimation method and incorporated into the electrical model.

[0018] The prediction model preferably includes a production model, a consumption model, and / or an availability model. The various models model the components of the electrical and / or thermal system. The use of prediction models with a production model, a consumption model, and / or an availability model enables an accurate prediction of boundary conditions, disturbances, and / or costs.

[0019] Production models model energy management components that produce electrical and / or thermal energy, such as a photovoltaic system or a solar thermal system. The production model of a photovoltaic system is preferably data-driven using static parameter values, such as maximum electrical output, geographical location, and / or orientation of the photovoltaic modules. The production model is preferably data-driven using weather forecast data. The data-driven use of static parameters and weather forecast data makes it possible, for example, to calculate a theoretical maximum production of electrical output for the day to be predicted with a time resolution. The production model is preferably data-driven initially and is continuously updated during use to enable better forecasts for the building.By comparing the model with current data from the thermal and electrical system during use, the model is updated and can thus be adapted to the current, actual production.

[0020] The consumption model models the energy consumption of the components of the thermal and electrical system. To configure the consumption model, a consumption database preferably stores gross electricity consumption, the status of a hot water tank, data from active devices in the local device network, a day of the week, a time of day, and / or other data from a building automation system such as data from lights and / or roller shutters. Based on the consumption database, a consumption model is preferably configured, which predicts gross electricity consumption and hot water consumption based on the current data from the active devices. The consumption model is preferably configured initially and is continuously updated during use. By programming the model with current data from the thermal and electrical system during use, the model is continuously updated.

[0021] The availability model models the availability of energy storage devices in the electrical and thermal system. To configure the availability model, the current charge level of an electric vehicle and the connection status are stored in an availability database, along with data from the building automation system and / or a user's manual charging plan. Based on the availability database, an availability model is preferably configured that predicts the storage device's availability based on the current data.

[0022] Determining the control variables of the system model is preferably implemented as model-based optimization. A mathematical system is preferably used to calculate the optimization. In this context, optimization means calculating the optimal decision considering all input data. By using optimization methods for the system model in conjunction with electricity price data, a predictive strategy can be implemented to reduce the operating costs of the energy management system. The boundary conditions for the system model preferably include at least one temperature limit and / or at least one state of charge limit and / or power limit.

[0023] The energy management system according to the invention comprises a controller, a system model with an electrical model and a thermal model, a combined heat and power engine model and a prediction model, and is configured to carry out the method according to the invention.

[0024] A computer program product according to the invention comprises a program which, when executed by a controller of an energy management system, causes the controller to carry out the method according to the invention.

[0025] Preferred embodiments are explained in more detail with reference to the following figures. Fig. 1 an embodiment of the energy management system according to the invention, Fig. 2 an embodiment of the method according to the invention, Fig. 3 a prediction of the boundary conditions carried out within the framework of the embodiment of the method, Fig. 4 a production model of the embodiment, Fig. 5 a consumption model of the embodiment and Fig. 6 an availability model of the embodiment.

[0026] Fig. 1 shows an embodiment of an energy management system 1 according to the invention. The energy management system 1 is set up to Fig. 2 to carry out the procedures shown for optimising an energy management system for buildings.

[0027] The energy management system 1 comprises a thermal system 2 and an electrical system 3, a system model 4, and a prediction model 4. The thermal system 2 comprises heat storage and heat transfers. In this exemplary embodiment, the heat storage is the air volume, and the building structure is the heat storage. Heat transfers here are thermal radiation, the transfer of heat from the building structure to the environment, the heat transfer of the combined heat and power engine 101 to the water volume, the heat transfer of the air volume to the building structure, and the heat transfer of solar radiation 209 to the building structure. The electrical system 3 here comprises as components a photovoltaic system 204, a refrigerator 208, a stationary battery storage system 202, and an electric vehicle 201. The refrigerator 208 and the solar system 204 are non-controllable electrical consumers. The stationary battery storage system 202 and the electric vehicle 201 are controllable electrical consumers.

[0028] The system model 4 consists of a thermal model 5 and an electrical model 6, which model the electrical and thermal components of the energy management system. The thermal model 5 and the electrical model 6 are coupled by a combined heat and power engine model 11. The electrical system and the thermal system are coupled by a combined heat and power engine 101.

[0029] The method consists of the following three steps: First, the prediction of boundary conditions 7, disturbance variables 8, and costs 9 for the system model 4. Second, the determination of the optimized manipulated variables 10 of the system model 4 using the predicted boundary conditions 7, disturbance variables 8, and costs 9 with a view to reducing overall energy costs. Third, the operation of the thermal system 2 and the electrical system 3 using the determined manipulated variables 10 of the system model 4.

[0030] The boundary conditions 7 here include a time-based limit of the target state of charge 37 for the electric vehicle storage unit 201, limits of the availability data of an electric vehicle storage unit 201, time-based limits of the state of charge of the stationary storage units 202, time-based limits of the interior temperature 203, and time-based limits of a hot water temperature. The target state of charge here is at least 80% between 9:00 a.m. and 8:00 p.m. The availability of the electric vehicle storage unit is from 12:00 a.m. to 3:00 p.m. and from 5:00 p.m. to midnight. The target state of charge of the electric vehicle storage unit at 3:00 p.m. is 60%. The interior temperature here is at least 22°C and a maximum of 25°C. The hot water temperature limit here includes a temperature between 40 and 60°C from 6:00 a.m. to 8:00 a.m., otherwise 40°C.

[0031] The disturbance variables 8 include non-controllable electrical consumers, here a refrigerator, data of the actual production of the photovoltaics 204, heat losses to the environment 205 and heat radiation from an environment (not shown here) and a hot water supply.

[0032] Costs 9 include the time-resolved electricity price for both the purchase and the feed-in.

[0033] The control variables 10 here include the setting of the charging and discharging current 206 of the electric vehicle, the setting of the charging and discharging current 207 of the stationary storage 202 and the operating mode of a combined heat and power engine 101 and the thermal power not shown here.

[0034] Thermal model 5 is described as a system of differential equations consisting of heat transfers 12 and heat storages 13. Heat storages here are the air volume (not shown), the building structure, and the water volume. Heat transfers 12 here are thermal radiation from the sun 209, the transfer of heat 205 from a building structure to its surroundings, from a combined heat and power engine 101 to a water volume, and / or from air to a building structure. Thermal model 5 is configured with various parameter values ​​and state values. Values ​​of invariant parameters are statically stored in thermal model 5. In this case, invariant parameters are the dimensions and orientation of the photovoltaic system and the volume of the hot water storage tank. For the thermal model, variable and non-a-priori determinable parameter values ​​are estimated using a system identification procedure and incorporated into thermal model 5.One parameter that cannot be determined a priori is the coefficient of performance of the combined heat and power plant. Measurable state values ​​are recorded directly and incorporated into thermal model 5. Measurable states are the flow temperatures, the return temperatures, and the room temperatures. Non-measurable state values ​​are estimated using a state estimation method and incorporated into thermal model 5. Non-measurable state values ​​are heat losses to the surroundings and heat losses in pipes.

[0035] Electrical model 6 is described as a system of differential equations consisting of resistances, inductances, and capacitances. Electrical model 6 is parameterized with various parameter values ​​and state values. Fixed parameter values ​​are stored statically in the electrical model. A fixed parameter is the nominal capacity of the stationary storage device. Variable parameter values ​​are estimated using a system identification method and incorporated into electrical model 6. A variable parameter is the internal resistance of the storage device. Externally acquired state values ​​are directly considered. Externally acquired state values ​​are the nominal capacities of the stationary storage device. Non-measurable state values ​​are estimated using a state estimation method and incorporated into electrical model 6.

[0036] Fig. 3 shows an embodiment of the prediction model 17 for predicting boundary conditions 7. The prediction model 17 comprises a production model 18, a consumption model 19 and an availability model 20.

[0037] Fig. 4 shows an embodiment of the production model 18. The production model 18 models components of the energy system that produce electrical and / or thermal energy, here the photovoltaics 204, and the electric vehicle 201. A production database 27 is created for the data input of the production model 18. The theoretical maximum production 24, actual production 25, and the a posteriori weather forecast for the time of the observed production 26 are stored in the production database 27. The production model 18 predicts the expected production in a time-resolved manner based on the weather forecast 26 and the calculated theoretical maximum production 24. The production model 18 is data input based on the production database 27. The production model 18 is initially data input and is continuously updated during use.

[0038] Fig. Figure 5 shows an exemplary embodiment of consumption model 19. Consumption model 19 models the energy consumption of the thermal 2 and electrical 3 systems. A consumption database 31 is created to configure consumption model 19. Consumption database 31 stores the gross electricity consumption 29, the status of the hot water tank 30, and data from active devices in the home network. Data 31 includes the day of the week, the time, and building automation data. The building automation data includes information about the status of the lights and blinds. Based on consumption database 32, a consumption model 19 is configured, which determines the gross electricity consumption and the hot water consumption 33 in a time-resolved manner based on the current data of the active devices, the time of day, and the day of the week. Consumption model 19 is initially configured and is continuously updated during use.

[0039] Fig.6 shows an exemplary embodiment of the structure of the availability model 20. The availability model 20 models the availability of the electrical storage in the electrical system 3. To configure the availability model 20, various data are stored in an availability database 36. The stored data here are: "the current state of charge of the electric vehicle 34, the connection state, the building automation data, and a manual charging plan 35 of the user." Based on the availability database 36, an availability model 20 is configured. The availability model 20 predicts the time-based target state of charge 37 of the electric vehicle based on the current building automation data, state of charge, connection state, and charging plan.The time-based target state of charge 37 includes, first, when the electric vehicle 201 will be available, second, the expected state of charge upon arrival, and third, the minimum required state of charge upon departure. Finally, the minimum and maximum permissible state of charge of the vehicle are determined over time based on the availability model and any available charging plans. This enables optimal use of the vehicle's storage system within the overall energy system.

[0040] Optimizing system model 4 is implemented as model-based optimization. A mathematical system is used to calculate the optimization.

[0041] For the combined heat and power model 22, a model is assumed that models the coefficient of performance depending on the operating mode, the flow and return flow, and the ambient temperature. The combined heat and power model 22 is continuously updated based on the operating data in order to obtain more precise values ​​for the specific installation situation and system characteristics.

[0042] Furthermore, the energy management system 1 includes a control unit. The computer program with the system model and prediction model is implemented on the control unit.

[0043] The method according to the invention is carried out using the computer program product 23 according to the invention. The computer program product 23 consists of a program 24 which, when executed by a controller 21, causes the controller 21 to carry out the method according to the invention. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] DE 102123213465

[0002]

Claims

[1] Method for operating an energy management system for buildings, wherein the energy management system (1) comprises a thermal system (2), an electrical system (3) and a system model (4) with a thermal model (5) and an electrical model (6), comprising the steps - Predicting boundary conditions (7), disturbances (8) and costs (9) for the system model (4), - Determining the optimized control variables (10) of the system model (4) by means of the predicted boundary conditions (7), disturbance variables (8) and costs (9) with regard to total energy cost reduction, - Operating the thermal system (2) and the electrical system (3) by means of the determined control variables (10) of the system model (4). [2] Method according to claim 1, wherein the thermal model (5) and the electrical model (6) are coupled by means of a combined heat and power engine model (11). [3] Method according to one of the preceding claims, wherein the thermal model (5) is described as a differential equation system of heat transfers (12) and heat storages (13). [4] Method according to one of the preceding claims, wherein the electrical model (6) is described as a differential equation system of resistors (14), inductances (15) and capacitances (16). [5] Method according to one of the preceding claims, wherein prediction models (17) are used to predict boundary conditions (7), wherein the prediction models (17) comprise a production model (18), a consumption model (19) and / or an availability model (20). [6] Method according to one of the preceding claims, wherein the determination of the manipulated variables (10) of the system model (4) is implemented as model-based optimization. [7] Energy management system (1) configured to carry out the method according to one of the preceding claims, comprising a controller (21), a combined heat and power engine model (11), a system model (4) with an electrical model (6), a thermal model (5), a combined heat and power engine model (22), a production model (18), a consumption model (19) and an availability model (20). [8] Computer program product (23) comprising a program (24) which, when executed by a controller (21), causes the controller (21) to carry out a method according to the invention according to one of claims 1 to 6.

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