A multi-objective bi-level optimization method and device for multi-region integrated energy systems

Abstract

This disclosure relates to a multi-objective, two-layer optimization method and apparatus for a multi-regional integrated energy system. The method includes: establishing an upper-layer multi-objective programming model and a lower-layer optimization scheduling model; using the unit capacity of various types of energy supply units as initial values, generating velocity and position expressions for upper-layer particles based on a particle swarm optimization algorithm; solving the expression using a Gurobi solver to generate the daily power output state of various types of energy supply units in the multi-regional integrated energy system; updating the velocity and position expressions of the upper-layer particles based on the upper-layer multi-objective programming model and iteratively calculating; stopping the iteration when preset conditions are met, and calculating and generating a capacity configuration scheme for the multi-regional integrated energy system based on the velocity and position expressions of the upper-layer particles. This disclosure's two-layer optimization strategy incorporates the system's operational characteristics into the planning process, improving the feasibility of the planning scheme; regional energy mutual assistance can optimize the system's operation mode and improve overall efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of energy allocation and scheduling, and more specifically, to a multi-objective, two-layer optimization method and apparatus for a multi-regional integrated energy system. Background Technology

[0002] Integrated energy systems (IES) are of great significance to the construction of modern energy systems and are one of the important technologies for promoting high-quality coordinated development of the economy, energy, and environment. To improve the efficiency of IES, optimization research needs to be conducted at the planning and operation levels. Since different stakeholders have different concerns about the benefits of IES, the selection of objectives also varies in multi-objective optimization of IES. Currently, there are two main approaches to multi-objective optimization of IES. One is to transform the multi-objective optimization problem into a single-objective function using penalty coefficients or weight coefficients, and then use single-objective optimization methods to solve the multi-objective problem. The other is to use multi-objective intelligent optimization algorithms to obtain the Pareto optimal solution set of the configuration scheme, and then find a multi-objective optimal solution for the non-dominated solutions in the solution set according to the decision-maker's intention. In summary, how to balance the various dimensions of benefits brought by multi-objective optimization of IES and maximize the overall benefits under multi-objective optimization is a problem that needs to be solved in the operation and planning of IES.

[0003] Existing research on the optimization modeling of industrial parks' ecosystems (IES) often focuses on pursuing the park's own interests and optimizing a single park structure. There is currently no research on achieving multi-regional optimized operation while considering the interests of all parties involved.

[0004] Therefore, one or more methods are needed to solve the above problems.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this disclosure is to provide a multi-objective, two-layer optimization method and apparatus for multi-regional integrated energy systems, thereby overcoming, to at least some extent, one or more problems caused by the limitations and defects of related technologies.

[0007] According to one aspect of this disclosure, a multi-objective bi-level optimization method for a multi-regional integrated energy system is provided, comprising:

[0008] Step S110: Based on the unit capacity of various energy supply units in the multi-region integrated energy system, establish an upper-level multi-objective programming model and generate the constraints of the upper-level multi-objective programming model; based on the daily operating cost of various energy supply units in the multi-region integrated energy system, establish a lower-level optimization scheduling model and generate the constraints of the lower-level optimization scheduling model.

[0009] Step S120: In the upper-level multi-objective programming model, the unit capacity of various types of random power supply units is used as the initial value, and the velocity and position expressions of the upper-level particles are generated based on the particle swarm algorithm.

[0010] Step S130: Using the velocity and position expressions of the upper-layer particles as the capacity constraints of the lower-layer optimization scheduling model, the model is solved using the gurobi solver to generate the daily power state of various energy supply units in the multi-region integrated energy system.

[0011] Step S140: Using the daily power state of the lower-level optimization scheduling model as input, update the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model, and repeat step S130 for iterative calculation based on the updated velocity and position expressions of the upper-level particles.

[0012] Step S150: If the convergence of the upper-level multi-objective programming model is greater than the preset convergence or the number of iterations is greater than the preset number of iterations, then stop the iteration and calculate and generate a multi-region integrated energy system capacity configuration scheme based on the velocity and position expressions of the upper-level particles.

[0013] In one exemplary embodiment of this disclosure, the method further includes:

[0014] Based on the unit capacity of various energy supply units in a multi-regional integrated energy system, a mechanism is established to minimize the system's annual economic cost C. total Maximize the system Efficiency E S Minimize annual carbon emissions W CE A higher-level multi-objective programming model with the objective of: The objective function of the higher-level multi-objective programming model is:

[0015] minZ=η1C total / C total,R -η2E S / E S,R +η3W CE / W CE,R ,

[0016] Where η1, η2, and η3 are respectively C total E S W CE The weighting factors reflect the importance of each indicator; Ctotal,R E S,R W CE,R C total E S W CE Reference value;

[0017] The constraints for generating the upper-level multi-objective programming model based on the aforementioned upper-level multi-objective programming model are as follows:

[0018]

[0019] Among them, V i,j , These refer to the unit capacity and its configurable lower and upper limits.

[0020] In one exemplary embodiment of this disclosure, the method further includes:

[0021] Based on the daily operating costs of various energy supply units in a multi-regional integrated energy system, the objective function of the lower-level optimization scheduling model is established as follows:

[0022]

[0023] in, The typical daily operating cost within each park of a multi-regional integrated energy system. For unit operation and maintenance costs, For fuel costs, For environmental penalties, For the cost of inter-regional power exchange, For profit;

[0024] The constraints of the lower-level optimal scheduling model are generated based on the lower-level optimal scheduling model. The constraints of the lower-level optimal scheduling model include energy balance constraints, unit output constraints, energy storage equipment operation constraints, power interaction constraints between the multi-regional integrated energy system and the power grid / heat network, and power interaction constraints and state constraints between regions.

[0025] In one exemplary embodiment of this disclosure, the method further includes:

[0026] In the upper-level multi-objective programming model, the unit capacity of various types of energy supply units is used as the initial value. The inputs are the basic data of capacity configuration, the unit operation parameters of the scheduling layer, typical daily load data, energy price, pollutant emission penalty coefficient, and constraint parameters. The velocity and position expressions of the upper-level particles are generated based on the particle swarm algorithm, and the number of iterations is initialized.

[0027] In one exemplary embodiment of this disclosure, the method further includes:

[0028] In the aforementioned upper-level multi-objective programming model, using the unit capacity of various types of energy supply units as initial values, the velocity and position expressions of the upper-level particles generated based on the particle swarm optimization algorithm are as follows:

[0029]

[0030] Where, ω k,m (t) represents the weighting coefficients, with subscripts k and m indicating the particle's index and dimension, respectively; t represents the current iteration number; v k,m (t), x k,m (t) represents velocity and position, respectively, p k,m (t), p g,m (t) represents the optimal position of the m-generation particle itself and the global optimal position, respectively; c1 and c2 are the acceleration factors in the interval [0,2]; r1 and r2 are random real numbers in the interval [0,1]; ω max and ω min They are ω k,m (t) represents the upper and lower limits of the value.

[0031] In one exemplary embodiment of this disclosure, the method further includes:

[0032] Based on the daily power output status of the lower-level optimization scheduling model, the daily optimal operation scheduling result is generated based on the daily processing status.

[0033] Based on the daily optimal operation scheduling results, the annual operating cost and system cost are calculated and generated. Efficiency, annual carbon emissions;

[0034] Based on the aforementioned annual operating costs and system Efficiency and annual carbon emissions are used to update the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model.

[0035] In one exemplary embodiment of this disclosure, the method further includes:

[0036] If the convergence of the upper-level multi-objective programming model is greater than the preset convergence or the number of iterations is greater than the preset number of iterations, then the iteration is stopped.

[0037] Based on the velocity and position expressions of the upper-layer particles, a multi-region integrated energy system capacity configuration scheme is calculated and generated.

[0038] Based on the multi-region integrated energy system capacity configuration scheme, an operation and scheduling scheme corresponding to the multi-region integrated energy system capacity configuration scheme is generated.

[0039] In one aspect of this disclosure, a multi-objective, two-layer optimization device for a multi-region integrated energy system is provided, comprising:

[0040] The model building module is used to establish an upper-level multi-objective programming model and generate the constraints of the upper-level multi-objective programming model based on the unit capacity of various energy supply units in the multi-regional integrated energy system, and to establish a lower-level optimization scheduling model and generate the constraints of the lower-level optimization scheduling model based on the daily operating cost of various energy supply units in the multi-regional integrated energy system.

[0041] The particle generation module is used to generate the velocity and position expressions of upper-level particles in the upper-level multi-objective programming model, using the unit capacity of various types of energy supply units as the initial value and based on the particle swarm algorithm.

[0042] The gurobi solver module is used to use the velocity and position expressions of the upper-level particles as capacity constraints of the lower-level optimization scheduling model, and solves the problem based on the gurobi solver to generate the daily power state of various energy supply units in the multi-region integrated energy system.

[0043] The particle update module is used to update the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model, taking the daily power state of the lower-level optimization scheduling model as input, and repeating the gurobi solution step for iterative calculation based on the updated velocity and position expressions of the upper-level particles.

[0044] The configuration scheme generation module is used to stop iterating if the convergence of the upper-level multi-objective programming model is greater than a preset convergence or the number of iterations is greater than a preset number of iterations, and to calculate and generate a multi-region integrated energy system capacity configuration scheme based on the velocity and position expressions of the upper-level particles.

[0045] An exemplary embodiment of this disclosure provides a multi-objective, two-tiered optimization method for a multi-regional integrated energy system. The method includes: establishing an upper-level multi-objective programming model and a lower-level optimization scheduling model; using the unit capacity of various types of energy-supplying units as initial values, generating velocity and position expressions for upper-level particles based on a particle swarm optimization algorithm; solving the expression using a Gurobi solver to generate the daily power output state of various types of energy-supplying units in the multi-regional integrated energy system; updating the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model and iteratively calculating; stopping the iteration when a preset condition is met, and calculating and generating a capacity configuration scheme for the multi-regional integrated energy system based on the velocity and position expressions of the upper-level particles. This two-tiered optimization strategy incorporates the system's operational characteristics into the planning process, improving the feasibility of the planning scheme; regional energy mutual assistance can optimize the system's operation mode and improve overall efficiency.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0047] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0048] Figure 1 A flowchart is shown below illustrating a multi-objective bi-level optimization method for a multi-region integrated energy system according to an exemplary embodiment of the present disclosure;

[0049] Figure 2 A diagram of a campus integrated energy system architecture considering power interconnection is shown, according to an exemplary embodiment of the present disclosure, of a multi-objective two-layer optimization method for a multi-region integrated energy system.

[0050] Figure 3 A flowchart illustrating the solution process of a multi-objective bi-level optimization method for a multi-regional integrated energy system according to an exemplary embodiment of the present disclosure is shown.

[0051] Figure 4 A schematic block diagram of a multi-objective, two-layer optimization device for a multi-region integrated energy system according to an exemplary embodiment of the present disclosure is shown. Detailed Implementation

[0052] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0053] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0054] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0055] In this example embodiment, a multi-objective, two-layer optimization method for a multi-region integrated energy system is first provided; (Refer to...) Figure 1 As shown, the multi-objective bi-level optimization method for a multi-regional integrated energy system may include the following steps:

[0056] Step S110: Based on the unit capacity of various energy supply units in the multi-region integrated energy system, establish an upper-level multi-objective programming model and generate the constraints of the upper-level multi-objective programming model; based on the daily operating cost of various energy supply units in the multi-region integrated energy system, establish a lower-level optimization scheduling model and generate the constraints of the lower-level optimization scheduling model.

[0057] Step S120: In the upper-level multi-objective programming model, the unit capacity of various types of random power supply units is used as the initial value, and the velocity and position expressions of the upper-level particles are generated based on the particle swarm algorithm.

[0058] Step S130: Using the velocity and position expressions of the upper-layer particles as the capacity constraints of the lower-layer optimization scheduling model, the model is solved using the gurobi solver to generate the daily power state of various energy supply units in the multi-region integrated energy system.

[0059] Step S140: Using the daily power state of the lower-level optimization scheduling model as input, update the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model, and repeat step S130 for iterative calculation based on the updated velocity and position expressions of the upper-level particles.

[0060] Step S150: If the convergence of the upper-level multi-objective programming model is greater than the preset convergence or the number of iterations is greater than the preset number of iterations, then stop the iteration and calculate and generate a multi-region integrated energy system capacity configuration scheme based on the velocity and position expressions of the upper-level particles.

[0061] An exemplary embodiment of this disclosure discloses a multi-objective, two-tiered optimization method for a multi-regional integrated energy system. The method includes: establishing an upper-level multi-objective programming model and a lower-level optimization scheduling model; using the unit capacity of various types of energy-supplying units as initial values, generating velocity and position expressions for upper-level particles based on a particle swarm optimization algorithm; solving the expression using a Gurobi solver to generate the daily power output state of various types of energy-supplying units in the multi-regional integrated energy system; updating the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model and iteratively calculating; stopping the iteration when a preset condition is met, and calculating and generating a capacity configuration scheme for the multi-regional integrated energy system based on the velocity and position expressions of the upper-level particles. This two-tiered optimization strategy incorporates the system's operating characteristics into the planning process, improving the feasibility of the planning scheme; regional energy mutual assistance can optimize the system's operation mode and improve overall efficiency.

[0062] The following will further explain a multi-objective, two-layer optimization method for a multi-region integrated energy system in this example embodiment.

[0063] In step S110, an upper-level multi-objective programming model can be established based on the unit capacity of various energy supply units in the multi-region integrated energy system, and the constraints of the upper-level multi-objective programming model can be generated. A lower-level optimal scheduling model can be established based on the daily operating cost of various energy supply units in the multi-region integrated energy system, and the constraints of the lower-level optimal scheduling model can be generated.

[0064] In this example embodiment, a typical integrated energy system (IES) architecture is as follows: Figure 2 As shown, the area comprises industrial, commercial, and residential zones. The industrial zone's basic energy supply equipment includes photovoltaic (PV), wind turbines (WT), diesel engines (DE), gas turbines (GT), waste heat boilers (WHB), gas boilers (GB), absorption refrigerators (AR), electric refrigerators (ER), electric energy storage (EES), thermal energy storage (TES), and cold energy storage (CES). The commercial zone's basic energy supply equipment includes rooftop photovoltaic (RPV), DE, electric boilers (EB), ER, EES, TES, and CES. The residential zone's basic energy supply equipment includes RPV, EB, ER, ground source heat pumps (GSHP), EES, TES, and CES.

[0065] In this example embodiment, the method further includes:

[0066] Based on the unit capacity of various energy supply units in a multi-regional integrated energy system, a mechanism is established to minimize the system's annual economic cost C. total Maximize the system Efficiency E S Minimize annual carbon emissions W CE A higher-level multi-objective programming model with the objective of: The objective function of the higher-level multi-objective programming model is:

[0067] minZ=η1C total / C total,R -η2E S / E S,R +η3W CE / W CE,R ,

[0068] Where η1, η2, and η3 are respectively C total E S W CE The weighting factors reflect the importance of each indicator; C total,R E S,R W CE,R C total E S W CE Reference value;

[0069] The constraints for generating the upper-level multi-objective programming model based on the aforementioned upper-level multi-objective programming model are as follows:

[0070]

[0071] Among them, V i,j , These refer to the unit capacity and its configurable lower and upper limits.

[0072] In this example embodiment, the multi-objective, two-tiered planning strategy for multi-region IES integrates IES equipment capacity optimization and operation scheduling into a unified framework. The upper layer is the planning layer, which considers the system's annualized economic cost, system... Efficiency, system annual carbon emissions, and other factors are combined to form the upper-level optimization objective. The optimal unit capacity configuration is solved using the linear decreasing weight particle swarm optimization (LDWPSO) algorithm. The lower level is the operation layer, which takes into account system operation and maintenance costs, fuel costs, environmental penalty costs, and regional interaction costs for multi-dimensional day-ahead optimization scheduling. The optimal operation scheduling strategy for each region is obtained by using yalmip to call the gurobi solver.

[0073] The upper-level model is a multi-objective capacity optimization configuration model that considers weights, aiming to minimize the system's annual economic cost C. total Maximize the system Efficiency E S Minimize annual carbon emissions W CE The objective is to transform the multi-objective optimization problem into a single-objective optimization problem by multiplying each objective by its corresponding weighting factor and then summing the results. The combined objective function Z is shown in the following equation.

[0074] minZ=η1C total / C total,R -η2E S / E S,R +η3W CE / W CE,R

[0075] In the formula: η1, η2, and η3 are C total E S W CE The weighting factors reflect the importance of each indicator; C total,R E S,R W CE,R C total E S W CE The reference value is used as the benchmark for the corresponding sub-target.

[0076] In this example embodiment, the constraints of the upper-level multi-objective programming model, specifically the capacity range constraints of various power supply units, are as follows:

[0077]

[0078] In the formula: V i,j , These refer to the unit capacity and its configurable lower and upper limits.

[0079] In this example embodiment, the method further includes:

[0080] Based on the daily operating costs of various energy supply units in a multi-regional integrated energy system, the objective function of the lower-level optimization scheduling model is established as follows:

[0081]

[0082] in, The typical daily operating cost within each park of a multi-regional integrated energy system. For unit operation and maintenance costs, For fuel costs, For environmental penalties, For the cost of inter-regional power exchange, For profit;

[0083] The constraints of the lower-level optimal scheduling model are generated based on the lower-level optimal scheduling model. The constraints of the lower-level optimal scheduling model include energy balance constraints, unit output constraints, energy storage equipment operation constraints, power interaction constraints between the multi-regional integrated energy system and the power grid / heat network, and power interaction constraints and state constraints between regions.

[0084] In this example embodiment, the lower-level model supports day-ahead optimal scheduling of the IES, determines the optimal output of the units in each region of the IES, and forms the optimal day-ahead scheduling scheme.

[0085] The lower-level model uses typical daily operating costs within the park. The objective function is to minimize the unit's operating and maintenance costs. fuel costs Environmental penalty costs Inter-regional power exchange costs and benefits As shown in the following formula.

[0086]

[0087] In this example embodiment, the constraints in the lower-level optimization scheduling model include energy balance constraints, unit output constraints, energy storage device operation constraints, IES (Environmental Engineering Systems) and grid / heat network interaction power constraints, and inter-regional interaction power constraints. Among these:

[0088] The energy balance constraint is:

[0089]

[0090] The power balance relationship is

[0091] In the formula: These are the output power of the power supply unit and the input power of the power consumption unit, respectively. These represent the charging and discharging power of the energy storage device, respectively; J E i J EES i J cone i These represent the number of power supply units, energy storage devices, and power consumption units operating in region i.

[0092] The thermal power balance relationship is similar to the cold power balance relationship.

[0093] The unit output constraint is:

[0094]

[0095] In the formula: and These are the upper and lower limits of the unit's output, respectively. This is the state variable of the unit; when the unit is running and shut down, this variable takes the values ​​1 and 0, respectively.

[0096] The following interaction mechanism is set: No region of the IES can support other regions while purchasing electricity from the grid, nor can it sell electricity to the grid while receiving electricity from other regions.

[0097] In step S120, in the upper-level multi-objective programming model, the unit capacity of various types of energy supply units can be used as the initial value, and the velocity and position expressions of the upper-level particles can be generated based on the particle swarm algorithm.

[0098] In this example embodiment, the method further includes:

[0099] In the upper-level multi-objective programming model, the unit capacity of various types of energy supply units is used as the initial value. The inputs are the basic data of capacity configuration, the unit operation parameters of the scheduling layer, typical daily load data, energy price, pollutant emission penalty coefficient, and constraint parameters. The velocity and position expressions of the upper-level particles are generated based on the particle swarm algorithm, and the number of iterations is initialized.

[0100] In step S130, the velocity and position expressions of the upper-level particles can be used as capacity constraints for the lower-level optimized scheduling model. The model is solved using the gurobi solver to generate the daily power state of various energy supply units in the multi-region integrated energy system.

[0101] In this example embodiment, the method further includes: to prevent local optima in the early stages of optimization iteration and local oscillations in the later stages, the LDWPSO algorithm is used to improve the global optimization capability by introducing a weight coefficient ω. k,m (t), controlling the velocity and position updates of the particles. In the upper-level multi-objective programming model, using the unit capacity of various types of energy supply units as the initial value, the velocity and position expressions of the upper-level particles are generated based on the particle swarm algorithm as follows:

[0102]

[0103] Where, ω k,m (t) represents the weighting coefficients, with subscripts k and m indicating the particle's index and dimension, respectively; t represents the current iteration number; v k,m (t), x k,m (t) represents velocity and position, respectively, p k,m (t), p g,m (t) represents the optimal position of the m-generation particle itself and the global optimal position, respectively; c1 and c2 are the acceleration factors in the interval [0,2]; r1 and r2 are random real numbers in the interval [0,1]; ω max and ω min They are ω k,m (t) represents the upper and lower limits of the value.

[0104] In the embodiment of this example, for the lower-level scheduling problem of the above-mentioned multi-objective bi-level optimization model, this disclosure linearizes the nonlinear unit characteristics, etc., and solves it using a mixed-integer linear programming method.

[0105] In step S140, the daily power state of the lower-level optimization scheduling model can be used as input to update the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model, and step S130 can be repeated for iterative calculation based on the updated velocity and position expressions of the upper-level particles.

[0106] In this example embodiment, the method further includes:

[0107] Based on the daily power output status of the lower-level optimization scheduling model, the daily optimal operation scheduling result is generated based on the daily processing status.

[0108] Based on the daily optimal operation scheduling results, the annual operating cost and system cost are calculated and generated. Efficiency, annual carbon emissions;

[0109] Based on the aforementioned annual operating costs and system Efficiency and annual carbon emissions are used to update the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model.

[0110] In the embodiments of this example, as Figure 3 As shown, the data includes basic capacity configuration data for the planning layer, unit operating parameters for the scheduling layer, typical daily load data, energy prices, pollutant emission penalty coefficients, and constraint parameters. First, within the upper and lower limits of unit capacity in each region, the capacity of power supply units, energy storage devices, and other equipment is randomly configured to initialize the upper-level particles. Then, the above data is assigned to the optimization model of the scheduling layer in the form of upper limits for unit output and upper limits for energy storage charging and discharging. Subsequently, the gurobi solver is called to configure the optimal operation scheduling results for each typical day, and the annual operating cost and system load data calculated from the scheduling results are then used. Operational solutions such as efficiency and annual carbon emissions are fed back to the planning layer to update the velocity and position of each particle.

[0111] In step S150, if the convergence of the upper-level multi-objective programming model is greater than a preset convergence or the number of iterations is greater than a preset number of iterations, the iteration can be stopped, and a multi-region integrated energy system capacity configuration scheme can be generated based on the velocity and position expressions of the upper-level particles.

[0112] In this example embodiment, the method further includes:

[0113] If the convergence of the upper-level multi-objective programming model is greater than the preset convergence or the number of iterations is greater than the preset number of iterations, then the iteration is stopped.

[0114] Based on the velocity and position expressions of the upper-layer particles, a multi-region integrated energy system capacity configuration scheme is calculated and generated.

[0115] Based on the multi-region integrated energy system capacity configuration scheme, an operation and scheduling scheme corresponding to the multi-region integrated energy system capacity configuration scheme is generated.

[0116] In this example embodiment, steps S130-S140 are repeated iteratively until the maximum number of iterations is reached or the convergence requirement is met. The program then terminates, outputting the globally optimal capacity configuration scheme and the optimal runtime scheduling scheme under that capacity configuration.

[0117] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0118] Furthermore, in this example embodiment, a multi-objective, two-layer optimization device for a multi-region integrated energy system is also provided. (Refer to...) Figure 4 As shown, the multi-objective, two-layer optimization device 400 for a multi-region integrated energy system may include: a model building module 410, a particle generation module 420, a Gurobi solution module 430, a particle update module 440, and a configuration scheme generation module 450. Wherein:

[0119] The model building module 410 is used to establish an upper-level multi-objective programming model and generate the constraints of the upper-level multi-objective programming model based on the unit capacity of various energy supply units in the multi-regional integrated energy system, and to establish a lower-level optimization scheduling model and generate the constraints of the lower-level optimization scheduling model based on the daily operating cost of various energy supply units in the multi-regional integrated energy system.

[0120] The particle generation module 420 is used to generate the velocity and position expressions of upper-level particles based on the particle swarm algorithm, using the unit capacity of various types of energy supply units as the initial value in the upper-level multi-objective programming model.

[0121] The gurobi solver module 430 is used to use the velocity and position expressions of the upper-level particles as capacity constraints of the lower-level optimization scheduling model, and solves the problem based on the gurobi solver to generate the daily power state of various energy supply units in the multi-region integrated energy system.

[0122] The particle update module 440 is used to update the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model, taking the daily power state of the lower-level optimization scheduling model as input, and repeating the gurobi solution step for iterative calculation based on the updated velocity and position expressions of the upper-level particles.

[0123] The configuration scheme generation module 450 is used to stop iterating if the convergence of the upper-level multi-objective programming model is greater than a preset convergence or the number of iterations is greater than a preset number of iterations, and to calculate and generate a multi-region integrated energy system capacity configuration scheme based on the velocity and position expressions of the upper-level particles.

[0124] The specific details of each of the above-mentioned multi-region integrated energy system multi-objective two-layer optimization device modules have been described in detail in the corresponding multi-region integrated energy system multi-objective two-layer optimization method, so they will not be repeated here.

[0125] It should be noted that although several modules or units of a multi-region integrated energy system multi-objective dual-layer optimization device 400 have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0126] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0127] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0128] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A multi-objective, two-level optimization method for a multi-regional integrated energy system, characterized in that, The method includes: Step S110: Based on the unit capacity of various energy supply units in the multi-regional integrated energy system, establish an upper-level multi-objective programming model and generate the constraints of the upper-level multi-objective programming model; based on the daily operating cost of various energy supply units in the multi-regional integrated energy system, establish a lower-level optimal scheduling model and generate the constraints of the lower-level optimal scheduling model; based on the unit capacity of various energy supply units in the multi-regional integrated energy system, establish a system to minimize the annual economic cost of the system. Maximize system efficiency Minimize annual carbon emissions A higher-level multi-objective programming model with the objective of: The objective function of the higher-level multi-objective programming model is: ， in, , , They are respectively , , The weighting factors reflect the importance of each indicator; , , They are respectively , , Reference value; The constraints for generating the upper-level multi-objective programming model based on the aforementioned upper-level multi-objective programming model are as follows: ， in, , , These refer to the unit capacity and its configurable lower and upper limits; Based on the daily operating costs of various energy supply units in a multi-regional integrated energy system, the objective function of the lower-level optimization scheduling model is established as follows: in, The typical daily operating cost within each park of a multi-regional integrated energy system. For unit operation and maintenance costs, (t) represents fuel cost, For environmental penalties, For the cost of inter-regional power exchange, For profit; The constraints of the lower-level optimization scheduling model are generated based on the lower-level optimization scheduling model. The constraints of the lower-level optimization scheduling model include energy balance constraints, unit output constraints, energy storage equipment operation constraints, power interaction constraints between the multi-regional integrated energy system and the power grid / heat network, and power interaction constraints and state constraints between regions. Step S120: In the upper-level multi-objective programming model, the unit capacity of various types of random power supply units is used as the initial value, and the velocity and position expressions of the upper-level particles are generated based on the particle swarm algorithm. Step S130: Using the velocity and position expressions of the upper-layer particles as the capacity constraints of the lower-layer optimization scheduling model, the model is solved using the gurobi solver to generate the daily power state of various energy supply units in the multi-region integrated energy system. Step S140: Using the daily power state of the lower-level optimization scheduling model as input, update the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model, and repeat step S130 for iterative calculation based on the updated velocity and position expressions of the upper-level particles. Step S150: If the convergence of the upper-level multi-objective programming model is greater than the preset convergence or the number of iterations is greater than the preset number of iterations, then stop the iteration and calculate and generate a multi-region integrated energy system capacity configuration scheme based on the velocity and position expressions of the upper-level particles.

2. The method as described in claim 1, characterized in that, The method further includes: In the upper-level multi-objective programming model, the unit capacity of various types of energy supply units is used as the initial value. The inputs are the basic data of capacity configuration, the unit operation parameters of the scheduling layer, typical daily load data, energy price, pollutant emission penalty coefficient, and constraint parameters. The velocity and position expressions of the upper-level particles are generated based on the particle swarm algorithm, and the number of iterations is initialized.

3. The method as described in claim 2, characterized in that, The method further includes: In the aforementioned upper-level multi-objective programming model, using the unit capacity of various types of energy supply units as initial values, the velocity and position expressions of the upper-level particles generated based on the particle swarm optimization algorithm are as follows: ， in, For weighting coefficients, subscripts k , m These are the particle's index and dimension, respectively. t This represents the current iteration number; , They are velocity and position, respectively. , They are respectively m The particle searches for its own optimal position and the global optimal position; , The acceleration factor is the value in the interval [0,2]. , These are random real numbers within the interval [0,1]. ω max and ω min They are The upper and lower limits of the value.

4. The method as described in claim 1, characterized in that, The method further includes: Based on the daily power status of the lower-level optimization scheduling model, the daily optimal operation scheduling result is generated. Based on the daily optimal operation scheduling results, the annual operating cost, system efficiency, and annual carbon emissions are calculated and generated. Based on the annual operating cost, system efficiency, and annual carbon emissions, the velocity and position expressions of the upper-level particles are updated according to the upper-level multi-objective programming model.

5. The method as described in claim 1, characterized in that, The method further includes: If the convergence of the upper-level multi-objective programming model is greater than the preset convergence or the number of iterations is greater than the preset number of iterations, then the iteration is stopped. Based on the velocity and position expressions of the upper-layer particles, a multi-region integrated energy system capacity configuration scheme is calculated and generated. Based on the multi-region integrated energy system capacity configuration scheme, an operation and scheduling scheme corresponding to the multi-region integrated energy system capacity configuration scheme is generated.

6. A multi-objective, two-layer optimization device for a multi-region integrated energy system, characterized in that, The device is used in the method as described in claim 1, the device comprising: The model building module is used to establish an upper-level multi-objective programming model and generate the constraints of the upper-level multi-objective programming model based on the unit capacity of various energy supply units in the multi-regional integrated energy system, and to establish a lower-level optimization scheduling model and generate the constraints of the lower-level optimization scheduling model based on the daily operating cost of various energy supply units in the multi-regional integrated energy system. The particle generation module is used to generate the velocity and position expressions of upper-level particles in the upper-level multi-objective programming model, using the unit capacity of various types of energy supply units as the initial value and based on the particle swarm algorithm. The gurobi solver module is used to use the velocity and position expressions of the upper-level particles as capacity constraints of the lower-level optimization scheduling model, and solves the problem based on the gurobi solver to generate the daily power state of various energy supply units in the multi-region integrated energy system. The particle update module is used to update the velocity and position expressions of the upper-level particles based on the upper-level multi-objective programming model, taking the daily power state of the lower-level optimization scheduling model as input, and repeating the Gurobi solution steps for iterative calculation based on the updated velocity and position expressions of the upper-level particles. The configuration scheme generation module is used to stop iterating if the convergence of the upper-level multi-objective programming model is greater than a preset convergence or the number of iterations is greater than a preset number of iterations, and to calculate and generate a multi-region integrated energy system capacity configuration scheme based on the velocity and position expressions of the upper-level particles.

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