A modeling and optimization method and system for integrated energy systems

Abstract

This invention discloses a modeling and optimization method and system for integrated energy systems. The method comprises: establishing a standardized component model framework including component variable classes, component constraint classes, and component objective classes; using component variable classes to represent decision variables in each component of the system, using component constraint classes to describe the internal rules or operational logic of each component, and using component objective classes to describe the optimization objective functions related to each component; associating component variables with specific relationships through an energy bus to generate energy flow balance constraints between components; integrating component variables, component constraints, component objectives, and energy flow balance constraints to form an overall system optimization model and solving it to obtain the optimal equipment configuration scheme and operation strategy. This method, by establishing a standardized unified "variable-constraint-objective" modeling framework, significantly reduces the workload of independent modeling of individual equipment and lowers the system integration complexity and error rate.

Description

Technical Field

[0001] This application mainly relates to the field of integrated energy system planning technology, specifically to a modeling and optimization method and system for integrated energy systems. Background Technology

[0002] To improve energy efficiency and promote the consumption of renewable energy, integrated energy systems have become the core paradigm of modern energy planning and operation. A typical integrated energy system usually involves the conversion, storage, and consumption of multiple energy sources (such as electricity, heat, cooling, gas, and hydrogen), and includes various heterogeneous devices such as wind turbines, photovoltaic arrays, energy storage devices, and electrolyzers. Modeling and optimizing such complex systems is fundamental to ensuring their economical design and efficient operation. Existing technologies typically establish independent mathematical models for each specific device in the system based on its physical laws. However, this traditional approach has several shortcomings when dealing with increasingly complex system integration requirements, specifically: The lack of a unified, universal device model necessitates the development and maintenance of a separate set of model code, parameter sets, and data interfaces for each device. This results in a massive workload for initial modeling, severely hindering the efficiency of design iteration, solution comparison, and multi-scenario analysis. Furthermore, integrating the independent models of different devices into a single system-level optimization model requires complex and error-prone data interface conversion and coupling. This process not only increases technical complexity but may also lead to transmission errors or compatibility issues due to inconsistent interfaces, reducing system optimization efficiency.

[0003] Therefore, there is a need for a generalized component modeling, modeling, and optimization technique for integrated energy systems, which can reduce the complexity of building integrated energy system models and improve the standardization, scalability, and engineering efficiency of overall optimization solutions. Summary of the Invention

[0004] This application proposes a modeling and optimization method for integrated energy systems to solve the above-mentioned technical problems, including: A standardized component model framework is established to describe various energy equipment components and load components in an integrated energy system. The standardized component model framework includes component variable classes, component constraint classes, and component target classes. The component variable class is used to represent component variables in a parameterized form. These component variables are decision variables in the energy equipment component and the load component. The component constraint class is used to represent component constraints based on constraint equality or inequality. These component constraints describe the internal physical laws or operating logic of the energy equipment component and the load component. The component objective class is used to represent the component objective. These component objectives describe the optimization objective function related to the energy equipment component and the load component. Based on the energy flow or material flow relationship between components, the component variables with energy flow or material flow correlation are associated with the corresponding energy bus, generating an energy flow balance constraint between components that satisfies the sum of energy supply equals the sum of energy consumption. By integrating all the component variables, component constraints, component objectives, and energy flow balance constraints, a holistic optimization model of the integrated energy system is formed. Solving this holistic optimization model yields the optimal configuration scheme and operating strategy for each device in the system. Furthermore, the definition attributes of the component variable class include: the variable type, the upper and / or lower bounds of the variable's values, and the variable's label; wherein, the variable type represents the mathematical properties of the variable; the upper and / or lower bounds represent the range of values ​​the variable can take; and the variable label distinguishes variable names between different devices.

[0005] Furthermore, the definition attributes of the component variable class also include: energy type attribute, used to characterize the energy type associated with the variable related to energy flow or matter flow; interface type attribute, used to characterize the flow direction of the variable related to energy flow or matter flow, including inflow attribute and outflow attribute; and time type attribute, used to characterize the time scale of the variable related to time item, including hourly, daily, or yearly.

[0006] Furthermore, the component constraint class is defined based on constraint equations or inequalities. The definition attributes of the component constraint class include: constraint participating variables, non-zero coefficients of each variable in the constraint equations or inequalities, upper and / or lower limits of the constraint, and constraint labels. The constraint participating variables represent the component variables participating in the constraint relationship. The non-zero coefficients of each variable in the constraint equations or inequalities, combined with the linear combination of their corresponding constraint participating variables, constitute terms of the equations or inequalities. The upper and lower limits of the constraints represent the range of values ​​that the terms can take. The constraint labels distinguish constraint names between different devices.

[0007] Furthermore, the definition attributes of the component constraint class also include special variables, which are optional attributes used to establish the association between variables that belong to the same physical quantity but are in different states in the constraint.

[0008] Furthermore, the different states include the states of variables belonging to the same physical quantity at different times.

[0009] Furthermore, the definition attributes of the component target class include: target participating variables and non-zero coefficients of each variable in the optimization objective function; wherein, the target participating variables are used to represent the component variables involved in the optimization objective function; the non-zero coefficients of each variable in the optimization objective function and the linear combination of their corresponding target participating variables constitute a term of the optimization objective function.

[0010] Further, the step of associating the component variables with energy flow or material flow relationships to the corresponding energy bus to generate energy flow balance constraints among components that satisfy the sum of energy supply equals the sum of energy consumption includes: identifying the energy type attribute and interface type attribute of all component variables in the system that are related to energy flow or material flow; associating component variables with the same energy type attribute to the corresponding energy bus; and for each energy bus, determining the sign of the non-zero coefficient corresponding to the energy flow balance constraint based on the interface type attribute of all component variables associated with that bus, and generating the energy flow balance constraint on that bus.

[0011] Furthermore, when integrating the component constraints, if the special variable attribute is identified in the component constraints of a certain constraint, then variable association constraints across time or across states are established based on the attribute.

[0012] Furthermore, the overall optimization model takes minimizing the total cost of system investment and operation as the optimization objective, and is solved using a mixed-integer linear programming algorithm; wherein, the total cost of system investment and operation includes one or more of equipment investment cost, energy procurement cost, and equipment start-up and shutdown penalty cost.

[0013] The present invention also provides a modeling and optimization system for integrated energy systems, the system comprising: A standardized component model framework construction module is used to establish a standardized component model framework describing various energy equipment components and load components in an integrated energy system. The standardized component model framework includes component variable classes, component constraint classes, and component objective classes. The component variable classes represent component variables in a parametric form; these component variables are the decision variables in the energy equipment components and load components. The component constraint classes represent component constraints based on constraint equality or inequality, and these component constraints describe the internal physical laws or operational logic of the energy equipment components and load components. The component objective classes represent the component objectives, and these component objectives describe the optimization objective functions related to the energy equipment components and load components. The energy flow balance constraint generation module is used to associate component variables with energy flow or material flow relationships with the corresponding energy bus based on the energy flow or material flow relationships between components, and generate energy flow balance constraints between components that satisfy the sum of energy supply equals the sum of energy consumption. The optimization model integration and solution module is used to integrate all the component variables, component constraints, component objectives, and energy flow balance constraints to form an overall optimization model of the integrated energy system. The overall optimization model is solved to obtain the optimal configuration scheme and operation strategy of each device in the system.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements any of the methods described above.

[0015] The present invention also provides a computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform any of the methods described above.

[0016] The present invention also provides a computer program product, characterized in that, Includes computer program instructions that cause a computer to perform any of the methods described above.

[0017] The beneficial effects of this invention are: 1. The technical solution of this invention addresses the common elements of various energy devices by constructing a unified "variable-constraint-objective" architecture. Based on this architecture, a system optimization model is established, significantly improving modeling efficiency, effectively reducing the complexity of device model integration, and minimizing data interaction errors that may result from manual intervention. Simultaneously, the speed of constructing system optimization problems is greatly accelerated, supporting rapid simulation and multi-scenario analysis, thereby enhancing the engineering applicability of the method.

[0018] 2. The technical solution of the present invention has good scalability and can flexibly adapt to new equipment types, providing a standardized tool foundation for the optimized configuration of integrated energy systems. Attached Figure Description

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is an overall flowchart of a modeling and optimization method for an integrated energy system according to an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the structure of a modeling and optimization system for an integrated energy system according to an embodiment of this application. Detailed Implementation

[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein, and therefore this application is not limited to the specific embodiments disclosed below.

[0023] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" plus the plural, not referring to the singular, may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0024] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0025] Figure 1 This is an overall flowchart of a modeling and optimization method for integrated energy systems according to an embodiment of this application, with reference to... Figure 1 As shown, the modeling and optimization method for integrated energy systems in this embodiment includes the following steps: Step S100: Establish a standardized component model framework to describe various energy equipment components and load components in the integrated energy system. The standardized component model framework includes component variable classes, component constraint classes, and component objective classes. Using the component variable classes, component variables are represented in a parametric form. Component variables are the decision variables in energy equipment components and load components. Using the component constraint classes, component constraints are represented based on constraint equality or inequality. Component constraints describe the internal physical laws or operating logic of energy equipment components and load components. Using the component objective classes, component objectives are represented. Component objectives describe the optimization objective functions related to energy equipment components and load components. Step S200: Based on the energy flow or material flow relationship between components, associate the component variables with energy flow or material flow relationship to the corresponding energy bus, and generate the energy flow balance constraint between components that satisfies the sum of energy supply equals the sum of energy consumption. Step S300: Integrate all component variables, component constraints, component objectives, and energy flow balance constraints to form an overall optimization model of the integrated energy system. Solve the overall optimization model to obtain the optimal configuration scheme and operation strategy for each device in the system. Specifically, collect the component variable classes, component constraint classes, and component objective classes generated from the instantiation of all equipment components and load components, all energy flow balance constraints, and all known parameters in the energy equipment components. Integrate and assemble these into a standard mixed-integer linear programming model. Calculate the optimal equipment configuration scheme and operation strategy by calling the optimization solver through the application programming interface. The known parameters in the energy equipment components are the input parameters of various equipment components during instantiation, such as the area of ​​a single photovoltaic cell and the investment cost of a single electrolytic cell.

[0026] Understandably, in the modeling and optimization of integrated energy systems, a modeling approach using generalized components is adopted for different energy devices. Based on the common parts of energy devices in the integrated energy system, mathematical modeling is used to generalize and abstract them, forming a standardized component model framework applicable to different devices, thereby improving the efficiency of the pre-planning modeling stage.

[0027] In one embodiment of this application, for energy devices in the system, based on their physical operating characteristics, a standardized component model framework is used to instantiate their corresponding component models.

[0028] Taking wind turbines, chillers, and electrochemical energy storage as examples, which have significant differences, the following different operating characteristics can be obtained: ① Fan: The output power of a single fan is related to the wind speed and the rated power.

[0029] in This refers to the rated power generation capacity of a single wind turbine. , These are the wind speeds at the fan's entry and exit points, respectively. The wind speed at which the rated power point is reached. , These are the hourly output power and wind speed of the fan.

[0030] ② Chiller units: The energy conversion relationship of the chiller unit is affected by the system COP under different operating conditions:

[0031] in This refers to the hourly electrical power of the chiller unit. This refers to the hourly cooling capacity of the chiller unit. It is the COP value of the unit corresponding to the unit load rate at different times.

[0032] ③ Electrochemical energy storage: The operation model of electrochemical energy storage devices involves the operational characteristics of different stages: charging, storage, and discharging.

[0033] in This represents the energy storage capacity at time t+1 and time t; , The electrical power flowing into and out of the port of the hourly energy storage device; , , These correspond to the charging, discharging, and equipment's own loss efficiency, respectively.

[0034] Understandably, the ComponentVar class is defined to define decision variables in energy equipment or loads. Each decision variable represents a variable that needs to be optimized during system planning and needs to be parameterized by a set of standardized attributes during definition.

[0035] The definition attributes of a component variable class include: the variable's type, the upper and / or lower bounds of the variable's values, and the variable's label; where the variable's type is used to represent the mathematical properties of the variable; the upper and / or lower bounds of the variable's values ​​are used to represent the range of values ​​the variable can take; and the variable's label is used to distinguish variable names between different devices.

[0036] The definition attributes of the component variable class also include: energy type attribute, which is used to characterize the energy type associated with the variable related to energy flow or matter flow; interface type attribute, which is used to characterize the flow direction of the variable related to energy flow or matter flow, including inflow and outflow attributes; and time type attribute, which is used to characterize the time scale of the variable related to time item, including hourly, daily, or yearly.

[0037] For example, decision variables involved in wind turbines include: the number of wind turbines in operation, the hourly start-up and shutdown status of the wind turbines, the hourly power generation of the wind turbines, and the hourly power wastage; decision variables involved in chiller units include: the number of chiller units in operation, the hourly start-up and shutdown status of the chiller units, the hourly power consumption of the units, the hourly load rate of the units, and the hourly cooling capacity of the units; decision variables involved in electrochemical energy storage devices include: the number / capacity of energy storage devices in operation, the hourly charge and discharge status, the hourly charge and discharge amount, and the amount of electricity stored at each moment.

[0038] Based on the characteristics of the decision variables described above, a generalized variable form, ComponentVar, is used to represent all decision variables. In this embodiment, the component variable class can be defined as:

[0039] The ComponentVar class represents decision variables in a parameterized form. Its defined attributes include: variable type (e.g., integer variables like the number of devices, continuous variables with hourly input or output, Boolean values ​​representing device start / stop status); upper limit (ub) and / or lower limit (lb) of the variable's value; and variable tag. In one embodiment of this application, the ComponentVar class also includes three optional attributes: energy type, interface type, and time type. The interface type attribute (portType) is for variables representing energy or material flow, specifying the directional relationship between energy / material and energy equipment, such as energy inflow or material outflow. The time type attribute (timeType) is used to characterize time-related variables, including hourly, daily, and yearly. The energy type attribute (energyType) is for variables directly related to energy flow, defining the type of energy, such as cold, heat, electricity, gas, or hydrogen.

[0040] In one embodiment of this application, load components in the system are defined using a component variable class, and a component constraint class can be selectively added to constrain the total load at a certain time scale.

[0041] Specifically, different types of energy will ultimately be assigned to corresponding energy loads. For load components, their component variable class ComponentVar only needs to be based on different time scales (such as 24h, 12h, etc.). The load values ​​are recorded hourly (24h, 8760h). Furthermore, since the load is the energy value that the system ultimately needs to meet, in its component variable class ComponentVar, type is a continuous variable, portType is the inflow, energyType corresponds to the load type it represents, and timeType is related to actual needs.

[0042] In some special scenarios, the load does not need to meet hourly constraints, but rather needs to meet overall constraints throughout the year. In this case, a component constraint class can be added to the load. For example, when defined as follows:

[0043] It can achieve:

[0044] in, This represents the minimum annual electricity load that needs to be met. Therefore, the above constraint can replace hourly energy supply and demand matching and is applicable to scenarios that require meeting the total annual energy supply.

[0045] Understandably, a component constraint class (ComponentCons) is defined to uniformly represent the operational constraints of each component model. Specifically, different energy devices have different operating characteristics, and the models used to represent their operation also have various forms. In order to achieve a consistent expression of the energy device operation model, a general component constraint class ComponentCons is defined.

[0046] Component constraint classes are defined based on constraint equality or inequality. The definition attributes of a component constraint class include: constraint participant variables, the non-zero coefficients of each variable in the constraint equality or inequality, the upper and / or lower limits of the constraint, and the constraint label. Among them, constraint participant variables are used to represent component variables participating in the constraint relationship; the non-zero coefficients of each variable in the constraint equality or inequality and the linear combination of their corresponding constraint participant variables constitute the terms of the equality or inequality; the upper and lower limits of the constraint are used to indicate the range of values ​​that the term can take; the constraint label is used to distinguish the constraint names between different devices.

[0047] Component constraint classes can be defined as:

[0048] The component constraint class `ComponentCons` is used to encapsulate the physical laws or operational logic of a device in a unified mathematical form. Its defined attributes include: constraint participant variables `vars`; the non-zero coefficient `noZeroCoeff` corresponding to the variable in the constraint; the upper limit `ub` and / or lower limit `lb` of the constraint; when the constraint is an equality, the upper and lower limits are equal; otherwise, they are unequal; and the constraint tag `tag`. In one embodiment of this application, the defined attributes of the component constraint class `ComponentCons` also include a defaultable attribute: a special variable `specialVar`, used to establish the association between variables belonging to the same physical quantity but in different states within the constraint.

[0049] In one embodiment of this application, different states include the states of variables belonging to the same physical quantity at different times, so as to characterize their temporal correlation characteristics.

[0050] Taking the operating characteristics of a wind turbine as an example, the following formula must be satisfied when the wind turbine is operating normally:

[0051] In the formula, It is the sum of the actual power generation capacity of this type of wind turbine. It is the power curtailment of the wind turbine. This refers to the number of wind turbines currently in operation. This is the power generation of a single wind turbine calculated based on wind speed. Among them, , , There are three decision variables. These are parameters calculated based on hourly wind speed. Using the defined component constraint class ComponentCons, and based on its included defined properties, the formula is rearranged to complete the wind turbine definition:

[0052] Taking electrochemical energy storage as an example, the hourly charge-discharge formula is defined by using the defined component constraint class ComponentCons and its included defined attributes.

[0053] In the formula, since and Both represent the stored energy in electrochemical energy storage, the difference being that they represent the stored energy values ​​at different points in time. Therefore, the non-zero coefficient of Esto at the next time step is marked in specialVar. Also, since the charge-discharge formula is an equation, both the upper limit ub and the lower limit lb are 0.

[0054] Taking chiller units as an example, in order to establish a constraint relationship between the commissioning identifier (Boolean value) and the number of units in operation for this type of equipment, the following inequality applies: ① ② ③ in, This serves as the commissioning identifier for this type of equipment; it is a Boolean variable. The number of chiller units in operation at a given moment is a variable that is an integer. This represents the minimum number of chiller units that can be put into operation at any given time. This represents the maximum number of chiller units that can be put into operation at any given time.

[0055] It can also be done through Complete the definition, for equation ①:

[0056] For equation ②:

[0057] For formula ③:

[0058] Where inf is a defined constant representing infinity, which can be 1 × 10⁻⁶. 9 , is used to complete the definition of inequalities.

[0059] Understandably, a component objective class (ComponentObj) is defined to uniformly represent the cost objectives of each component model. Specifically, when planning an integrated energy system, the objective function of energy equipment mainly involves the one-time investment, periodic maintenance costs, and energy consumption during operation. To achieve optimal system planning, the value of the objective function should be as low as possible.

[0060] The definition attributes of the component objective class include: objective participating variables and the non-zero coefficients of each variable in the optimization objective function; among them, the objective participating variables are used to represent the component variables involved in the optimization objective function; the non-zero coefficients of each variable in the optimization objective function and the linear combination of their corresponding objective participating variables constitute the terms of the optimization objective function.

[0061] In this embodiment, the component target class ComponentObj is used to construct the economic objective function term related to the equipment. Its definition includes: target participation variable vars and the non-zero coefficient noZeroCoeff corresponding to the variable in the optimization objective function; wherein, the component variable class ComponentObj defines at least one of the equipment's investment cost, operation and maintenance cost and operating cost through a linear combination of the target participation variable vars and its corresponding non-zero coefficient noZeroCoeff.

[0062] For example, for electrolytic cell equipment, its component target class can be defined as:

[0063] Where Num represents the number of electrolytic cells. The investment cost per unit of equipment; The hourly hydrogen production rate, For hourly maintenance costs; This represents the number of electrolytic cells that change their start-up and shutdown status hourly. Additional operational costs for start / stop penalties.

[0064] For wind turbines, we have:

[0065] Where Crf is the coefficient for annualized cost, which is a known constant. , , , These are the fixed costs, unit price, annual operation and maintenance costs, and usage and maintenance costs for this type of equipment.

[0066] Therefore, adopt It can be represented as:

[0067] Similar to wind turbines, the objective for electrochemical energy storage changes slightly; the decision variable used to calculate annual operating and maintenance costs is the energy storage capacity.

[0068] use It can be represented as:

[0069] In one embodiment of this application, step S200, which involves associating component variables with energy flow or material flow relationships to corresponding energy buses to generate energy flow balance constraints among components that satisfy the condition that the sum of energy supply equals the sum of energy consumption, includes: identifying the energy type attributes and interface type attributes of all component variables in the system that are related to energy flow or material flow; associating component variables with the same energy type attributes to energy buses of the corresponding energy type; and for each energy bus, determining the sign of the non-zero coefficient corresponding to the energy flow balance constraint based on the interface type attributes of all component variables associated with that bus, and generating the energy flow balance constraint on that bus.

[0070] Specifically, in the system's operational logic, components can form substitution relationships for energy supply or upstream and downstream relationships between energy supply and consumption. In the energy system topology, this relationship manifests as an upward-oriented connection of energy flow, where the energy output port of one component is connected to the energy input or output port of another component. After completing the construction of a generalized component model, based on the connection relationships and energy flow direction of devices in the system topology, and according to the attributes of the relevant variable ComponentVar, the energy type (energyType) and interface type (portType) of the variable are determined and mapped to the corresponding virtual bus. For each energy bus, the sum of the energy supply among the multiple components associated with it equals the sum of the energy consumption. A system-level ComponentCons balance constraint is established to characterize the energy conservation relationship on this bus: the sum of all variables flowing into (portType is "input") this bus equals the sum of all variables flowing out (portType is "output") this bus. The interface type (portType) corresponds to the non-zero coefficient (noZeroCoeff) of these variables in the constraint ComponentCons being either positive or negative.

[0071] Once the constraint equations on all energy buses are completed, the model for the entire system planning can be built.

[0072] In one embodiment of this application, in step S300, when integrating component constraint classes, if a special variable attribute is identified in a component constraint class instance of a certain constraint, then a variable association constraint across time or across states is established based on the attribute.

[0073] The overall optimization model aims to minimize the total cost of system investment and operation. The total cost of system investment and operation includes one or more of the following: equipment investment cost, energy procurement cost, and equipment start-up and shutdown penalty cost.

[0074] On the other hand, this application provides a generalized modeling and optimization system for integrated energy systems, including: The standardized component model framework construction module is used to establish a standardized component model framework to describe various energy equipment components and load components in an integrated energy system. The standardized component model framework includes component variable classes, component constraint classes, and component objective classes. The component variable classes are used to represent component variables in a parametric form. Component variables are the decision variables in energy equipment components and load components. The component constraint classes are used to represent component constraints based on constraint equality or inequality. Component constraints describe the internal physical laws or operating logic of energy equipment components and load components. The component objective classes are used to represent component objectives. Component objectives describe the optimization objective functions related to energy equipment components and load components. The energy flow balance constraint generation module is used to associate component variables with energy flow or material flow relationships with the corresponding energy bus based on the energy flow or material flow relationships between components, and generate energy flow balance constraints between components that satisfy the sum of energy supply equals the sum of energy consumption. The optimization model integration and solution module is used to integrate all component variables, component constraints, component objectives, and energy flow balance constraints to form an overall optimization model of the integrated energy system. The overall optimization model is solved to obtain the optimal configuration scheme and operation strategy of each device in the system.

[0075] On the other hand, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described modeling and optimization method.

[0076] On the other hand, this application provides a computer-readable storage medium that stores computer instructions for causing a computer to perform the above-described modeling and optimization methods.

[0077] On the other hand, this application provides a computer program product including computer program instructions that cause a computer to perform the above-described modeling and optimization methods.

[0078] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0079] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0080] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

Claims

1. A modeling and optimization method for integrated energy systems, characterized in that, Includes the following steps: A standardized component model framework is established to describe various energy equipment components and load components in an integrated energy system. The standardized component model framework includes component variable classes, component constraint classes, and component target classes. The component variable class is used to represent component variables in a parameterized form. These component variables are decision variables in the energy equipment component and the load component. The component constraint class is used to represent component constraints based on constraint equality or inequality. These component constraints describe the internal physical laws or operating logic of the energy equipment component and the load component. The component objective class is used to represent the component objective. These component objectives describe the optimization objective function related to the energy equipment component and the load component. Based on the energy flow or material flow relationship between components, the component variables with energy flow or material flow correlation are associated with the corresponding energy bus, generating an energy flow balance constraint between components that satisfies the sum of energy supply equals the sum of energy consumption. By integrating all the component variables, component constraints, component objectives, and energy flow balance constraints, an overall optimization model of the integrated energy system is formed. The overall optimization model is then solved to obtain the optimal configuration scheme and operation strategy of each device in the system.

2. The modeling and optimization method as described in claim 1, characterized in that, The definition attributes of the component variable class include: the variable type, the upper and / or lower limits of the variable's value, and the variable's label; The type of the variable is used to represent its mathematical properties; The upper and / or lower limits of the variable are used to represent the range of values ​​that the variable can take; The labels on the variables are used to distinguish variable names between different devices.

3. The modeling and optimization method as described in claim 2, characterized in that, The definition attributes of the component variable class also include: Energy type attribute, used to characterize the type of energy associated with variables related to energy flow or matter flow; Interface type attributes are used to characterize the direction of variables related to energy flow or material flow, including inflow and outflow attributes; The time type attribute is used to characterize the time scale of variables associated with time items, including hourly, daily, or yearly.

4. The modeling and optimization method as described in claim 1, characterized in that, The component constraint class is defined based on constraint equality or inequality. The definition attributes of the component constraint class include: constraint participating variables, non-zero coefficients of each variable in the constraint equality or inequality, upper and / or lower limits of the constraint, and constraint label. Wherein, the constraint participation variable is used to represent the component variable participating in the constraint relationship; The non-zero coefficients of each variable in the constraint equation or inequality, and the linear combination of the corresponding constraint participating variables, constitute the terms of the equation or inequality. The upper and lower limits of the constraint are used to represent the range of values ​​that the item can take; The constraint labels are used to distinguish constraint names between different devices.

5. The modeling and optimization method as described in claim 4, characterized in that, The definition attributes of the component constraint class also include special variables, which are optional attributes used to establish the association between variables that belong to the same physical quantity but are in different states in the constraint.

6. The modeling and optimization method as described in claim 5, characterized in that, The different states refer to the states of variables belonging to the same physical quantity at different times.

7. The modeling and optimization method as described in claim 1, characterized in that, The definition attributes of the component target class include: target participating variables, and the non-zero coefficients of each variable in the optimization objective function; Wherein, the target participating variable is used to represent the component variables involved in the optimization objective function; The non-zero coefficients of each variable in the objective function and the linear combination of their corresponding objective participating variables constitute a term of the objective function.

8. The modeling and optimization method as described in claim 3, characterized in that: The step of associating the component variables with energy flow or material flow relationships to the corresponding energy bus to generate energy flow balance constraints among components that satisfy the sum of energy supply equals the sum of energy consumption includes: Identify the energy type attribute and interface type attribute of all component variables in the system that are related to energy flow or material flow; Associate the component variables with the same energy type attribute with the corresponding energy bus; For each energy bus, based on the interface type attribute of all the component variables associated with that bus, determine the sign of the non-zero coefficient corresponding to it in the energy flow balance constraint, and generate the energy flow balance constraint on that bus.

9. The modeling and optimization method as described in claim 5, characterized in that, When integrating the component constraints, if the special variable attribute is identified in the component constraints of a certain constraint, then variable association constraints across time or across states are established based on the attribute.

10. The modeling and optimization method as described in claim 1, characterized in that, The overall optimization model aims to minimize the total cost of system investment and operation, and is solved using a mixed-integer linear programming algorithm. The total cost of the system investment and operation phase includes one or more of the following: equipment investment cost, energy procurement cost, and equipment start-up and shutdown penalty cost.

11. A modeling and optimization system for integrated energy systems, characterized in that, include: A standardized component model framework construction module is used to establish a standardized component model framework describing various energy equipment components and load components in an integrated energy system. The standardized component model framework includes component variable classes, component constraint classes, and component objective classes. The component variable classes represent component variables in a parametric form; these component variables are the decision variables in the energy equipment components and load components. The component constraint classes represent component constraints based on constraint equality or inequality, and these component constraints describe the internal physical laws or operational logic of the energy equipment components and load components. The component objective classes represent the component objectives, and these component objectives describe the optimization objective functions related to the energy equipment components and load components. The energy flow balance constraint generation module is used to associate component variables with energy flow or material flow relationships with the corresponding energy bus based on the energy flow or material flow relationships between components, and generate energy flow balance constraints between components that satisfy the sum of energy supply equals the sum of energy consumption. The optimization model integration and solution module is used to integrate all the component variables, component constraints, component objectives, and energy flow balance constraints to form an overall optimization model of the integrated energy system. The overall optimization model is solved to obtain the optimal configuration scheme and operation strategy of each device in the system.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method as described in any one of claims 1-10.

14. A computer program product, characterized in that, Includes computer program instructions that cause a computer to perform the method as described in any one of claims 1-10.

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