A method for improving flexibility of a multi-energy system considering flexibility cost

Abstract

The application discloses a multi-energy system flexibility improvement method considering flexibility cost, and relates to the technical field of energy system optimization. The method comprises the following steps: inputting system parameters and operation parameters of a multi-energy system under a basic scheduling scenario into a maximum flexibility optimization model to output maximum flexibility parameters that can be provided by the multi-energy system under the basic scheduling scenario; wherein the maximum flexibility optimization model determines boundary parameters of the multi-energy system under a flexibility scenario based on the system parameters, the operation parameters, a subsystem flexibility transfer matrix and a flexibility multi-element improvement path, so as to determine the maximum flexibility parameters; and inputting the maximum flexibility parameters, cost parameters and the system parameters into a flexibility cost optimization model to output a cost-optimized operation scheme of the multi-energy system under the maximum flexibility, so that an operation scheduling scheme of the multi-energy system that can provide the maximum flexibility and has the lowest economic cost can be output.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy system optimization, in particular to a multi-energy system flexibility improvement method considering flexibility cost. BACKGROUND

[0002] With the exhaustion of traditional fossil energy, the proportion of grid-connected new energy represented by wind power and photovoltaic power is gradually increasing. New energy output and power demand have randomness and volatility, and large-scale grid-connected new energy will lead to intensified system net load fluctuation. The power system cannot fully accommodate the power generation of new energy, and can only handle wind curtailment and light curtailment, hindering the development and utilization of new energy. Therefore, it is necessary to vigorously develop flexible resources and increase the system flexible adjustment capacity to cope with power fluctuations and insufficient climbing caused by net load changes, and to ensure the safe and stable operation of the new power system dominated by new energy.

[0003] Flexibility in the power system generally refers to the ability of the system to balance power supply and demand and respond to emergencies, and flexible resources are the fundamental prerequisite for the adjustability of the power system. The International Energy Agency classifies flexible resources into four categories: dispatchable power plants, energy storage facilities, interconnected grids, and demand side management responses.

[0004] With the closer coupling of the power system with the natural gas system, the heat system and other energy systems, the planning, operation and market boundaries between different energy subsystems are gradually broken, and the concept of integrated energy system emerges as the times require. Integrated energy system refers to an energy production and supply integrated system formed by organically coordinating and optimizing various types of energy generation, transmission and distribution, conversion, storage, consumption and other links in the process of planning, construction and operation. On the one hand, with the gradual increase of new energy penetration rate and the increase of energy supply scale brought by the construction of city-level integrated energy system, the elements in the integrated energy system that cannot be fully predicted and controlled and their corresponding energy total are becoming higher and higher. New energy consumption demand and energy stability demand put forward higher requirements for the multi-energy flow flexible resources and flexible adjustment capacity of the integrated energy system. On the other hand, the flexible resources of natural gas and heat systems can also be shared with the power system. For example, the multi-energy coupling devices in the integrated energy system, the gas / heat pipe network inertia and the gas / heat load flexible resources make it have a wider power and energy adjustment range, which not only reduces the system's demand for single energy flow flexibility, but also provides more diverse improvement paths and richer technical means for flexibility improvement.

[0005] However, the current research on the relationship between flexibility and cost is mainly aimed at the system planning level, and does not involve the research on the correlation between flexibility and operation control cost, so the economy of flexibility is often ignored in the process of flexibility improvement. SUMMARY

[0006] In view of this, the main objective of this application is to provide a method for improving the flexibility of a multi-functional system that takes into account the cost of flexibility, with the aim of improving system flexibility while also taking into account the economy of flexibility.

[0007] The first aspect of this application provides a method for improving the flexibility of a multi-functional system considering flexibility costs, the method comprising:

[0008] The system parameters and operating parameters of the multi-energy system under the basic scheduling scenario are input into the maximum flexibility optimization model to output the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario. The maximum flexibility optimization model determines the boundary parameters of the multi-energy system under the flexibility scenario based on the system parameters, operating parameters, subsystem flexibility transfer matrix, and multi-dimensional flexibility enhancement path. The maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario is determined based on the boundary parameters. The multi-dimensional flexibility enhancement path is used to characterize the flexibility transfer relationship of each subsystem in the multi-energy system, and the subsystem flexibility transfer matrix is ​​used to characterize the flexibility transfer relationship of each node in each subsystem.

[0009] The maximum flexibility parameter, the cost parameter of the multi-energy system, and the system parameters are input into the flexibility cost optimization model to output the cost-optimized operation scheme of the multi-energy system while providing maximum flexibility.

[0010] In some implementations of the first aspect of this application, the system parameters include:

[0011] The maximum output limit, minimum output limit, upward ramp limit, and downward ramp limit of controllable equipment for each energy type in a multi-energy system;

[0012] Energy conversion efficiency, maximum output power limit, minimum output power limit, upward ramp limit of energy conversion power, downward ramp limit of energy conversion power, upper limit of electro-thermal ratio of cogeneration coupling system in multi-energy system, and lower limit of electro-thermal ratio of cogeneration coupling system.

[0013] In a multi-energy system, the upper and lower limits of power utilization for controllable equipment of each energy type at each energy demand node;

[0014] Power transfer factors and cross-sectional load limits of each subsystem in a multi-energy system;

[0015] The runtime data includes:

[0016] The energy demand of controllable devices of each energy type in a multi-energy system, the energy demand of each coupled system, the uplink flexibility demand of each energy demand node, and the downlink flexibility demand.

[0017] In some implementations of the first aspect of this application, the multiple flexibility enhancement paths include: a first flexibility enhancement path and a second flexibility enhancement path;

[0018] The first flexibility enhancement path includes: allocating a portion of the output power of the first coupling system to the second coupling system so that the difference between the actual output power of the second coupling system and the maximum output power limit of the second coupling system is greater than the uphill ramp limit of the second coupling system, and making the difference between the actual output power of the first coupling system and the maximum output power limit of the first coupling system greater than the uphill ramp limit of the second coupling system.

[0019] The second path to improve flexibility includes: lowering the upper limit of the electro-thermal ratio of the cogeneration coupling system, raising the upper limit of the electro-thermal ratio of the cogeneration coupling system, and lowering the cross-sectional load limit of each subsystem.

[0020] In some implementations of the first aspect of this application, in the maximum flexibility optimization model and the flexibility cost optimization model, the output power of controllable devices of each energy type, the power output by each coupled system to the subsystem, and the energy obtained by the coupled system from the subsystem satisfy the following constraints:

[0021] in, This indicates the minimum output limit of the controllable equipment of the aforementioned energy type. This indicates the maximum output limit of the controllable equipment of the energy type mentioned above. This indicates the output power of the controllable device of the energy type. This represents the node input power vector of the subsystem. This indicates the energy demand of the controllable equipment of the energy type mentioned above. This represents the device transfer matrix from the coupled system to the subsystem. This represents the power output by the coupling system to the subsystem. The term refers to the set of all the aforementioned subsystems, including the power subsystem, gas transmission subsystem, and heating subsystem. This represents the energy matrix obtained by the coupled system from the subsystem. This represents the minimum output power limit of the coupled system. This indicates the maximum output power limit of the coupling system. This represents the power output by the coupled system to the thermal subsystem. This indicates the lower limit of the electrothermal ratio of the combined heat and power system. This indicates the upper limit of the electro-thermal ratio of the combined heat and power system. This represents the diagonal matrix used to label the combined heat and power (CHP) system. This represents the power output from the coupled system to the power subsystem. express The i-th value, This represents the power transfer factor of the subsystem. This indicates the cross-sectional load limit of the subsystem. This represents matrix summation. This indicates the lower limit of power utilization of controllable devices of each energy type at the energy demand node. This indicates the upper limit of power utilization by the energy demand node for each of the energy types of controllable devices. This indicates the energy conversion efficiency of the coupled system for the subsystem. This indicates the efficiency with which the coupled system obtains power from the subsystem. This indicates the energy demand of the coupled system. This represents the minimum power that the coupled system can utilize from the subsystem. This indicates the maximum power that the coupled system can utilize from the subsystem.

[0022] In some implementations of the first aspect of this application, the subsystem flexibility transfer matrix includes: an uplink flexibility transfer matrix and a downlink flexibility transfer matrix.

[0023] In some implementations of the first aspect of this application, the maximum flexibility optimization model and the flexibility cost optimization model are constrained by a set of preset constraints.

[0024] In some implementations of the first aspect of this application, the maximum flexibility optimization model is optimized iteratively through the following first objective function:

[0025]

[0026] Where max represents the optimization objective of maximizing the subsequent function, and sum represents the summation of all elements in the matrix. This represents the uplink flexibility transfer matrix. Represents the downlink flexibility transfer matrix. This indicates the maximum uplink flexibility that a multi-functional system can provide under basic scheduling scenarios. This indicates the maximum downlink flexibility that a multi-functional system can provide under basic scheduling scenarios.

[0027] In some implementations of the first aspect of this application, the flexibility cost optimization model, in addition to satisfying the preset set of constraints, also satisfies the following constraints:

[0028]

[0029] in, This indicates the maximum uplink flexibility that the multi-energy system can provide under the target energy demand. This indicates the maximum downlink flexibility that the multi-energy system can provide under the target energy demand.

[0030] In some implementations of the first aspect of this application, the cost parameters include: operating cost and over-limit cost. The flexibility cost optimization model is optimized iteratively through the following second objective function:

[0031] ;

[0032] ;

[0033] .

[0034] in, This indicates the cost exceeding the limit. This represents the operating cost. This refers to a short-term overload-capable device in the multi-energy system. This refers to the power supply equipment in the multi-energy system. This represents the primary energy supply cost coefficient of the energy supply equipment. This represents the secondary energy supply cost coefficient of the energy supply equipment. This indicates the output power of the power supply equipment. This represents the value of the variable corresponding to the short-term overloadable device in the basic scheduling scenario. This represents the difference between the value of the variable corresponding to the short-term overloadable device in the basic scheduling scenario and its value in the flexibility scenario. This represents the maximum value of the variable corresponding to the short-term overload-capable device. This represents the overload cost coefficient for the short-term overload-capable device. The value represents the secondary overload cost coefficient of the short-term overload-capable device, where x is the independent variable.

[0035] A second aspect of this application provides a device for enhancing the flexibility of a multi-energy system considering flexibility costs, the device comprising:

[0036] The flexibility optimization module is used to input the system parameters and operating parameters of the multi-energy system under the basic scheduling scenario into the maximum flexibility optimization model, so as to output the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario. Among them, the maximum flexibility optimization model determines the boundary parameters of the multi-energy system under the flexibility scenario based on the system parameters, operating parameters, subsystem flexibility transfer matrix, and flexibility multivariate improvement path, so as to determine the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario based on the boundary parameters. The flexibility multivariate improvement path is used to characterize the flexibility transfer relationship of each subsystem in the multi-energy system, and the subsystem flexibility transfer matrix is ​​used to characterize the flexibility transfer relationship of each node in each subsystem.

[0037] The cost optimization module is used to input the maximum flexibility parameter, the cost parameters of the multi-energy system, and the system parameters into the flexibility cost optimization model, so as to output the cost-optimized operation scheme of the multi-energy system while providing maximum flexibility.

[0038] The technical solution provided in this application has the following beneficial effects:

[0039] The technical solution provided in this application employs a two-stage optimization approach to ensure that the final operational scheme achieves both maximum flexibility and optimal economy. Specifically: The system parameters and operational parameters of the multi-energy system under the basic scheduling scenario are input into the maximum flexibility optimization model, which is then used to solve the problem. During the solution process, the flexibility transfer matrix of each subsystem is combined to determine the flexibility provision capacity within each subsystem, and the energy conversion between different subsystems is optimized by combining multiple flexibility enhancement paths. This yields the flexibility boundary parameters that the multi-energy system can provide under the basic scheduling scenario, and finally, the maximum flexibility parameters are obtained based on these boundary parameters, thus clarifying the upper limit of the flexibility provision capacity and providing a basis for subsequent economic optimization. After the maximum flexibility optimization model outputs its results, given the maximum flexibility, cost parameters and system parameters are input into the flexibility cost optimization model, which outputs the most economical flexibility provision scheme—that is, the operational scheduling scheme that provides maximum flexibility at the lowest cost under the target energy demand. Therefore, this application, through a two-layer optimization process of maximum flexibility optimization and flexibility cost optimization, can output an operational scheduling scheme for a multi-energy system that provides both maximum flexibility and the lowest economic cost. Attached Figure Description

[0040] Figure 1 A flowchart illustrating a method for improving the flexibility of a multi-functional system, taking into account the cost of flexibility, provided in an embodiment of this application;

[0041] Figure 2 A schematic diagram illustrating the flexibility enhancement path provided in the embodiments of this application;

[0042] Figure 3 A schematic diagram illustrating the optimized operation scheme of the multi-energy system provided in the embodiments of this application;

[0043] Figure 4 This is a schematic diagram of a multi-functional system flexibility enhancement device that takes into account flexibility costs, provided as an embodiment of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] Definitions:

[0046] Multi-energy system: refers to a new energy system concept formed by the coupling of multiple energy systems such as cold, heat, electricity, gas, and water in the energy production, transmission, and consumption links.

[0047] Flexibility: refers to an entity's ability to cope with fluctuations and changes in the environment.

[0048] Uplink flexibility, also known as multi-energy system uplink flexibility, refers to the ability of a multi-energy system to cope with increased energy demand.

[0049] Downsizing flexibility of multi-energy systems: This refers to the ability of a multi-energy system to cope with reduced energy demand.

[0050] Instantaneous flexibility of multi-energy systems: minute-level response to peak-shaving demands, manifested in the ability of power to cope with fluctuations in the energy demand of multi-energy systems.

[0051] See Figure 1 As shown in the embodiment of this application, a method for improving the flexibility of a multi-energy system considering flexibility costs is provided. The method includes:

[0052] S101: Input the system parameters and operating parameters of the multi-energy system under the basic scheduling scenario into the maximum flexibility optimization model to output the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario; wherein, the maximum flexibility optimization model determines the boundary parameters of the multi-energy system under the flexibility scenario based on the system parameters, operating parameters, subsystem flexibility transfer matrix, and flexibility multivariate improvement path, so as to determine the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario based on the boundary parameters; the flexibility multivariate improvement path is used to characterize the flexibility transfer relationship of each subsystem in the multi-energy system, and the subsystem flexibility transfer matrix is ​​used to characterize the flexibility transfer relationship of each node in each subsystem.

[0053] In the embodiments of this application, the basic scheduling scenario, serving as a benchmark for flexibility assessment, can be understood as the normal operation plan of the multi-energy system, i.e., the plan for the multi-energy system to classify various energy resources according to predetermined rules under the current demand-compliant conditions. The flexibility scenario, on the other hand, refers to the scenario in which the multi-energy system faces demand fluctuations. Furthermore, system parameters refer to the basic equipment characteristics of the multi-energy system, which do not fluctuate with system operating conditions; while operating parameters refer to the real-time energy demand and supply situation, which fluctuate with system operating conditions.

[0054] To describe the location and role of instantaneous flexibility within the subsystems of a multi-energy system, embodiments of this application provide a flexibility transfer matrix. Each row and column of this matrix represents a node that accepts and provides flexibility, respectively, where nodes that accept and provide flexibility are also called operating points. It should be noted that the subsystem flexibility transfer matrix itself does not cause changes in the subsystem's energy balance. The diversified flexibility enhancement path refers to increasing system flexibility in a multi-energy system without adding additional equipment by optimizing resource coordination between different energy subsystems, thereby increasing the adaptability to power demand fluctuations. Specifically, this can be achieved by adjusting the output of different coupled systems to maintain a certain reserve capacity, improving the response to fluctuations. For example, in a power subsystem, some units can be allowed to maintain a certain ramp-up space instead of operating at full load. Alternatively, operating boundaries can be appropriately relaxed, allowing the system to operate beyond limits for short periods to cope with sudden power fluctuations. For example, allowing lines to carry more power than rated for a short period.

[0055] In the maximum flexibility optimization model, the physical limitations of the system are described by operating parameters and system parameters. Then, the flexibility transfer relationship between nodes within each subsystem is determined by combining the subsystem flexibility transfer matrix. By optimizing the flexibility between different subsystems through multiple flexibility enhancement paths, the adjustable range of flexibility of the multi-energy system under different demand fluctuation scenarios can be calculated. This adjustable range of flexibility is described by boundary parameters. Then, based on the above boundary parameters, the flexibility that the multi-energy system can provide can be determined, that is, how much additional energy the multi-energy system can provide or how much energy supply can be reduced at most when facing demand fluctuations.

[0056] The technical solution provided in this application enhances the flexibility of a multi-functional system by adjusting the operation control scheme, specifically by adjusting the operating point and the boundary. Correspondingly, the multiple paths for improving flexibility include a first flexibility improvement path and a second flexibility improvement path.

[0057] Regarding adjusting the run point, existing technologies increase the degree of restriction of various operational constraints during the determination of the scheduling scheme, thereby making the scheduling scheme farther from the system operating boundary, and thus allowing for greater adjustments to the run point during actual operation. However, the method of obtaining a run point farther from the original boundary by shrinking the feasible region has the following drawbacks: First, it cannot be guaranteed that a feasible solution still exists for the run point scheduling optimization problem after shrinking; second, if the feasible region is not sufficiently shrunk, the system flexibility cannot be maximized; finally, the impact of shrinking different types of constraints on flexibility changes varies, and an unreasonable constraint reduction allocation scheme may lead to room for improvement in flexibility even after the feasible region has been reduced to a hyperplane with a volume of 0, when using that point as the run point.

[0058] Regarding adjusting operational boundaries, existing technologies involve adjusting the operational boundaries of individual devices. For example, adjusting the operational limits of lines can increase the system's transmittable energy, and enhancing the adaptability of multi-energy systems to varying power demands can increase their ability to cope with changes in operating points. However, improving system flexibility through overcoming the limits of a single type of device has the following drawbacks: First, the increased flexibility resulting from exploring the overcoming capabilities of a single device is constrained by the operational limits of devices whose overcoming capabilities are not considered. The maximum increase in flexibility is less than the increase achieved when considering the overcoming capabilities of all devices within the system simultaneously. Furthermore, simply changing the operational boundaries cannot unlock the increased flexibility that multi-energy systems can achieve by adjusting their operating points.

[0059] To address the shortcomings mentioned above, this application improves flexibility in two ways: by adjusting the operating point to enhance the instantaneous flexibility of the multi-energy system, and by changing the operating boundary to enhance the instantaneous flexibility of the multi-energy system. The operating point refers to the operating state of controllable equipment such as gas sources, thermal power units, and gas turbine units in the multi-energy system. The specific solutions are as follows:

[0060] Regarding changing the operating point, this application provides a first flexibility enhancement path, including: allocating a portion of the output power of the first coupling system to the second coupling system such that the difference between the actual output of the second coupling system and the maximum output limit of the second coupling system is greater than the uphill ramp limit of the second coupling system, and making the difference between the actual output of the first coupling system and the maximum output limit of the first coupling system greater than the uphill ramp limit of the second coupling system.

[0061] It should be noted that the maximum flexibility of a multi-energy system is comprised of the combined flexibility of all controllable energy sources within it. Therefore, without considering energy losses, given a fixed total energy demand, the system's available instantaneous flexibility can be increased by rationally allocating energy supply demand across different energy supply devices. Figure 2Taking the two coupled unit systems shown as examples, the total amount of instantaneous uplink flexibility that controllable devices in a multi-energy system can provide can be increased by changing the operating state of the coupled units. Figure 2 middle, , as well as These are the uplink flexibility available for a single device, the uplink ramping limit power, and the uplink flexibility available for the entire system.

[0062] Here, assuming that coupling unit 1 has a lower energy supply cost, economic considerations often lead to coupling unit 1, with its lower energy supply cost, supplying more power, while coupling unit 2, with its higher energy supply cost, supplies less power. This results in the energy supply equipment in a multi-energy system often operating near its unit output limit, such as... Figure 2 As shown in the upper part, when the multi-energy system requires the coupled system to output more power, only coupled unit 2 can provide more power, while coupled unit 1, which is already at its output limit, cannot provide more power. On the other hand, the maximum power output capacity of coupled unit 2 is constrained by the unit's ramp-up capability. The limitations. In summary, the maximum upswing flexibility that these two coupled units can provide at this point is... Therefore, in the initial state, this embodiment allocates a portion of the output power of coupling unit 1 to coupling unit 2, such that the difference between the actual output power of coupling unit 2 and its output limit is greater than the upward ramping capability of coupling unit 2, and the difference between the actual output power of coupling unit 1 and its output limit is also greater than the upward ramping capability of coupling unit 1. In this case, the sum of the upward adjustment flexibility of the two units becomes the sum of the upward ramping capabilities of the two units, i.e. .

[0063] Regarding changes in operational boundaries, embodiments of this application provide a second path to enhance flexibility, including: reducing the upper limit of the electrothermal ratio of the cogeneration coupling system in the coupling system, increasing the upper limit of the electrothermal ratio of the cogeneration coupling system, and reducing the cross-sectional load limit of each subsystem.

[0064] Since instantaneous flexibility manifests as a system's ability to provide power in response to changes in power demand, limiting system parameters such as line load limits and the heat-to-power ratio of cogeneration systems restricts the operating boundaries of the operating point range. This limits both power transmission and the process by which flexibility affects changes in power demand. Therefore, by appropriately relaxing the restrictions on power production and transmission within a safe range, flexibility can be more fully transferred to nodes requiring flexibility where power demand changes, provided that the coupled units provide greater flexibility.

[0065] S102: Input the maximum flexibility parameter, the cost parameter of the multi-energy system, and the system parameters into the flexibility cost optimization model to output the cost-optimized operation scheme of the multi-energy system while providing maximum flexibility.

[0066] In the embodiments of this application, the cost parameter refers to the economic parameter related to the daily operation of the multi-energy system, which is used to measure the cost consumption of the multi-energy system. Since the maximum flexibility optimization model calculates the maximum flexibility of the multi-energy system under the basic scheduling scenario, in order to ensure that the lowest operating cost is achieved while satisfying the maximum flexibility, flexibility cost optimization based on the cost parameter is further performed through the flexibility cost optimization model.

[0067] exist Figure 1 The process described employs a two-stage optimization scheme to ensure that the final operational plan achieves both maximum flexibility and optimal economy. Specifically: The system parameters and operational parameters of the multi-energy system under the basic scheduling scenario are input into the maximum flexibility optimization model, which is then used to solve the problem. During the solution process, the flexibility transfer matrix of each subsystem is used to determine the flexibility provision capacity within each subsystem, and the energy conversion between different subsystems is optimized using multiple flexibility enhancement paths. This yields the flexibility boundary parameters that the multi-energy system can provide under the flexibility scenario, and finally, the maximum flexibility parameter is obtained based on the flexibility boundary parameters, thus clarifying the upper limit of the flexibility provision capacity and providing a basis for subsequent economic optimization. After the maximum flexibility optimization model outputs its results, given the maximum flexibility, cost parameters and system parameters are input into the flexibility cost optimization model, which outputs the most economical flexibility provision scheme—that is, the operational scheduling scheme that provides maximum flexibility at the lowest cost under the target energy demand. Therefore, this application, through a two-layer optimization process of maximum flexibility optimization and flexibility cost optimization, can output an operational scheduling scheme for a multi-energy system that provides maximum flexibility while achieving the lowest economic cost.

[0068] In some implementations of the embodiments of this application, the system parameters specifically include the following parameters:

[0069] Maximum output limit of controllable equipment of each energy type in a multi-energy system Minimum output limit Uphill climbing limit and downhill climbing limits Among them, the output limit refers to the maximum or minimum power that can be provided under ideal conditions, and the gradeability limit refers to the power that can be increased or decreased per unit time under ideal conditions;

[0070] Coupled systems in a multi-energy system Energy conversion efficiency Maximum output power limit Minimum output power limit Energy conversion power uphill limit Energy conversion power downhill ramp limit Upper limit of the electrothermal ratio of a combined heat and power (CHP) system in a coupled system and the lower limit of the electrothermal ratio of cogeneration coupling systems The coupling system may include at least one of the following: a gas generator coupling system, an electric hydrogen production coupling system, an electric boiler coupling system, and a combined heat and power coupling system.

[0071] In a multi-energy system, the upper limit of power utilization of controllable equipment of each energy type at each energy demand node. and power utilization lower limit ;

[0072] Power transfer factor of each subsystem in a multi-energy system and subsystems Sectional load limit The power transfer factor refers to the power load that each node in the thermal subsystem, power subsystem, and gas transmission subsystem brings to the multi-energy system by the unit power input to each section of the subsystem, such as lines and pipes. It should be noted that the transfer factors of subsystems other than the power subsystem are derived by imitating the power transfer factor of the power system after DC simplification of the unified energy flow model of the multi-energy system in the embodiments of this application.

[0073] The specific runtime data includes the following parameters:

[0074] Energy demand of controllable equipment of various energy types in a multi-energy system Energy requirements of each coupled system And the uplink flexibility requirements of each energy demand node and downlink flexibility requirements .

[0075] In the embodiments of this application, system parameters and operating parameters are input into a flexibility evaluation physical model, which is used to calculate the maximum flexibility of the multi-energy system. In this process, a unified energy flow model is adopted and DC simplification is performed to remove high-order constraints in the model, thereby reducing computational complexity and avoiding excessive computational requirements in the solution process of the corresponding model for city-level multi-energy systems.

[0076] In some implementations of the embodiments of this application, in the maximum flexibility optimization model and the flexibility cost optimization model, the output power of controllable devices of each energy type is... Power output from each coupled system to the subsystem and the energy that the coupled system obtains from the subsystem. The following constraints must be met:

[0077] in, This indicates the minimum output limit of the controllable equipment of the aforementioned energy type. This indicates the maximum output limit of the controllable equipment of the energy type mentioned above. This indicates the output power of the controllable device of the energy type. This represents the node input power vector of the subsystem. This indicates the energy demand of the controllable equipment of the energy type mentioned above. This represents the device transfer matrix from the coupled system to the subsystem. This represents the power output by the coupling system to the subsystem. The term refers to the set of all the aforementioned subsystems, including the power subsystem, gas transmission subsystem, and heating subsystem. This represents the energy matrix obtained by the coupled system from the subsystem. This represents the minimum output power limit of the coupled system. This indicates the maximum output power limit of the coupling system. This represents the power output by the coupled system to the thermal subsystem. This indicates the lower limit of the electrothermal ratio of the combined heat and power system. This indicates the upper limit of the electro-thermal ratio of the combined heat and power system. This represents the diagonal matrix used to label the combined heat and power (CHP) system. This represents the power output from the coupled system to the power subsystem. express The i-th value, This represents the power transfer factor of the subsystem. This indicates the cross-sectional load limit of the subsystem. This represents matrix summation. This indicates the lower limit of power utilization of controllable devices of each energy type at the energy demand node. This indicates the upper limit of power utilization by the energy demand node for each of the energy types of controllable devices. This indicates the energy conversion efficiency of the coupled system for the subsystem. This indicates the efficiency with which the coupled system obtains power from the subsystem. This indicates the energy demand of the coupled system. It can also be expressed as ; This represents the minimum power that the coupled system can utilize from the subsystem. This indicates the maximum power that the coupled system can utilize from the subsystem.

[0078] In the embodiments of this application, the scenario corresponds to the energy demand. When each energy-consuming device in the multi-energy system generates energy demand according to the plan, the energy supply scenario faced by the multi-energy system is the basic scenario. Under the constraints of the operating boundary, the scenarios with the largest and smallest total energy demand that the multi-energy system can supply are the uplink flexibility scenario and the downlink flexibility scenario, respectively. Therefore, the flexibility scenario has two main differences from the basic scheduling scenario: first, the operating boundary is expanded compared to the basic scenario; second, the flexibility of the power difference compared to the basic scheduling scenario will lead to a change in the flexibility operating point.

[0079] In the embodiments of this application, the difference between the basic scheduling scenario and the flexible scenario can be reflected in the model construction. The former requires adjusting the constant terms in the inequality constraints corresponding to the flexible scenario, that is, adjusting the constant terms in the aforementioned constraints... , and Become , and The latter requires that the impact of power changes be reflected in all constraints. Among these, This indicates the degree to which the electrothermal ratio of a combined heat and power (CHP) system exceeds the limit. These two values ​​indicate the degree of overload on the cross-section. They are limited by the maximum overload limit. , ,in and The specific conditions and corresponding thermal stability of the equipment are determined separately, and this application does not impose any specific limitations on them.

[0080] In some implementations of this application, the subsystem flexibility transfer matrix specifically includes: an uplink flexibility transfer matrix. and downlink flexibility transfer matrix It should be noted that the subsystem flexibility transfer matrix does not cause a change in the subsystem's energy balance. Therefore, it is only necessary to ensure that the multi-energy system after adding flexibility transfer satisfies the inequality constraints provided by the preset constraint set. In addition, the natural satisfaction of equality constraints by the flexibility transfer matrix reduces the number of equality constraints that the model needs to consider, effectively reducing the model's data size and computational requirements.

[0081] In terms of energy processing, the output of each energy source in a flexibility scenario is simultaneously constrained by both energy processing limits and energy ramp-up limits. Therefore, in some implementations of this application, in the maximum flexibility optimization model and the flexibility cost optimization model, the output power of controllable devices for each energy type... The following constraints must be met:

[0082] in, This indicates the minimum output limit of energy-controllable equipment. This indicates the maximum output limit of energy-controllable equipment. This represents a diagonal matrix used to mark the nodes where energy sources are located in the subsystem. Indicates the output power of energy-controllable devices. express A 1-dimensional matrix of all ones. This represents the uplink flexibility transfer matrix. Represents the downlink flexibility transfer matrix. This represents the device transfer matrix from the coupled system to the subsystem. This represents the downhill ramp limit of the energy conversion power of the coupled system. This represents the uphill ramp limit of the energy conversion power of the coupled system. This indicates the downhill ramp limit for energy-type controllable equipment. This indicates the upward ramp limit for energy-type controllable equipment.

[0083] Regarding energy transmission, the flexibility scenario, compared to the basic scenario, does not experience cross-sectional overload after changes in cross-sectional load. Therefore, in some implementations of this application, in the maximum flexibility optimization model and the flexibility cost optimization model, the energy demand difference between the flexibility scenario and the basic scheduling scenario is... The following constraints must be met:

[0084] in, This represents the difference in energy demand between uplink flexibility scenarios and basic scheduling scenarios. This represents the energy demand difference between downlink flexibility scenarios and basic scheduling scenarios. This represents the uplink flexibility transfer matrix. This represents the downlink flexibility transfer matrix. express A dimensional matrix of all 1s.

[0085] Regarding energy conversion, this is reflected in the fact that the power that the coupled system can convert in a flexibility scenario does not exceed the energy conversion capacity limit, and the power utilized by the load of the coupled system does not exceed the power limit of the coupled system. Therefore, in some implementations of this application, in the maximum flexibility optimization model and the flexibility cost optimization model, the power output by the coupled system to the subsystem and the energy obtained by the coupled system from the subsystem satisfy the following constraints:

[0086]

[0087] in, This represents the minimum output power limit of the coupled system. This represents the maximum output power limit of the coupled system. This represents the power output from the coupled system to the subsystem. This represents the device transfer matrix from the coupled system to the subsystem. This represents the uplink flexibility transfer matrix. For the transpose of the uplink flexibility transfer matrix, Represents the downlink flexibility transfer matrix. This is the transpose of the downlink flexibility transfer matrix. express A 1-dimensional matrix of all ones. This is the transpose of the device transfer matrix from the coupled system to the subsystem. This indicates the lower limit of power utilization for controllable equipment of each energy type at the energy demand node. This indicates the upper limit of power utilization for controllable equipment of each energy type at the energy demand node. This represents the energy that the coupled system obtains from the subsystem.

[0088] Regarding the proportion of different types of energy, this is reflected in the range of the heat-to-power ratio of the cogeneration coupling system. Therefore, in some implementations of this application, in the maximum flexibility optimization model and the flexibility cost optimization model, the power output from the coupling system to the thermal subsystem and the power output from the coupling system to the electrical subsystem satisfy the following constraints:

[0089] ;

[0090] ;

[0091] ;

[0092] .

[0093] in, This represents the device transfer matrix from the thermal subsystem to the coupled system. This represents the uplink flexibility transfer matrix of the thermal subsystem. Represents the downlink flexibility transfer matrix of the thermal subsystem. for A 1-dimensional matrix of all ones. This represents the device transfer matrix from the power subsystem to the coupled system. This represents the uplink flexibility transfer matrix of the power subsystem. This represents the downlink flexibility transfer matrix of the power subsystem. for A 1-dimensional matrix of all ones. This indicates the lower limit of the electrothermal ratio of a combined heat and power (CHP) unit. This indicates the upper limit of the electro-thermal ratio in a combined heat and power (CHP) system. This represents a diagonal matrix used to label combined heat and power (CHP) units.

[0094] Regarding the flexibility requirement limit, this is reflected in the fact that the available flexibility for the load of the subsystem and the load of the coupled system is no greater than the actual flexibility requirement. Therefore, in some implementations of this application, in the maximum flexibility optimization model and the flexibility cost optimization model, the flexibility requirement of the flexibility requirement node satisfies the following constraints:

[0095]

[0096] in, This indicates the maximum uplink flexibility that the multi-functional system can provide under the basic scheduling scenario. This indicates the maximum downlink flexibility that the multi-functional system can provide under the basic scheduling scenario. This represents the uplink flexibility transfer matrix. This represents the downlink flexibility transfer matrix. express A 1-dimensional matrix of all ones. for A diagonal matrix of dimensions used to label the energy demand nodes in the subsystem. This indicates the uplink flexibility requirement of the energy demand node. This indicates the downlink flexibility requirement of the energy demand node. These respectively represent the power subsystem, the gas transmission subsystem, the thermal subsystem, and the coupling system.

[0097] In some implementations of the embodiments of this application, the physical model for flexibility evaluation is to optimize and iterate the maximum uplink flexibility and maximum downlink flexibility that the multi-energy system can provide under the basic scheduling scenario, under the constraints provided in the above embodiments, using the following first objective function, wherein the first objective function for optimization iteration is:

[0098]

[0099] Where max represents the optimization objective of maximizing the subsequent function, and sum represents the summation of all elements in the matrix. This represents the uplink flexibility transfer matrix. Represents the downlink flexibility transfer matrix. This indicates the maximum uplink flexibility that a multi-functional system can provide under basic scheduling scenarios. This indicates the maximum downlink flexibility that a multi-functional system can provide under basic scheduling scenarios.

[0100] It is understandable that the objective function obtained through the above iteration is... and This refers to the maximum uplink and downlink flexibility that a multi-functional system can provide under basic scheduling scenarios.

[0101] In some implementations of this application, the cost parameters include operating costs and over-limit costs. After obtaining the maximum flexibility that the multi-energy system can provide under the basic scheduling scenario, the flexibility cost can be optimized using a flexibility cost optimization model based on the operating costs and the over-limit costs of the equipment. In addition to satisfying a preset set of constraints, this flexibility cost optimization model also satisfies the following constraints:

[0102]

[0103] in, This indicates the maximum uplink flexibility that the multi-energy system can provide under the target energy demand. This indicates the maximum downlink flexibility that the multi-energy system can provide under the target energy demand.

[0104] After flexibility is guaranteed by constraints, the flexibility cost optimization model uses a second objective function focused on cost to calculate the most economical cost-optimized operating scheme for the multi-energy system, providing maximum flexibility under the target energy demand. Specifically, the flexibility cost optimization model performs iterative optimization using the following second objective function:

[0105] ;

[0106] ;

[0107] .

[0108] in, This indicates the cost exceeding the limit. This represents the operating cost. This refers to a short-term overload-capable device in the multi-energy system. This refers to the power supply equipment in the multi-energy system. This represents the primary energy supply cost coefficient of the energy supply equipment. This represents the secondary energy supply cost coefficient of the energy supply equipment. This indicates the output power of the power supply equipment. This represents the value of the variable corresponding to the short-term overloadable device in the basic scheduling scenario. This represents the difference between the value of the variable corresponding to the short-term overloadable device in the basic scheduling scenario and its value in the flexibility scenario. This represents the maximum value of the variable corresponding to the short-term overload-capable device. This represents the overload cost coefficient for the short-term overload-capable device. The value represents the secondary overload cost coefficient of the short-term overload-capable device, where x is the independent variable.

[0109] See Figure 3 As shown, Figure 3 This paper demonstrates the optimization process for improving the flexibility of a multi-energy system by considering flexibility costs. The core idea is to use a two-stage optimization model to first determine the maximum flexibility the system can provide, and then optimize the operating costs under this flexibility constraint to obtain the most economical flexibility provision scheme. Specifically, in the first stage, the flexibility requirements, energy requirements, and system constraints of the multi-energy system under the flexibility scenario are input into the maximum flexibility optimization model, and the output is the maximum flexibility capability that the multi-energy system can provide under the current constraints and requirements. In the second stage, the maximum flexibility parameter obtained in the first stage, the energy supply cost (characterizing the operating cost of each energy device in the multi-energy system), and the over-limit cost (characterizing the additional costs incurred by allowing system components to exceed their rated operating state for a short period) are input into the flexibility cost optimization model. The final output is the most economical operating scheme that provides the maximum flexibility, that is, the optimal economic operating scheme obtained by comprehensively considering energy supply costs and over-limit costs while satisfying the maximum flexibility requirement.

[0110] See Figure 4 As shown in the figure, this application provides a device for quantitatively evaluating the flexibility of a multi-energy system. The device includes:

[0111] The flexibility optimization module 401 is used to input the system parameters and operating parameters of the multi-energy system under the basic scheduling scenario into the maximum flexibility optimization model, so as to output the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario. The maximum flexibility optimization model determines the boundary parameters of the multi-energy system under the flexibility scenario based on the system parameters, operating parameters, subsystem flexibility transfer matrix, and flexibility multivariate improvement path, so as to determine the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario based on the boundary parameters. The flexibility multivariate improvement path is used to characterize the flexibility transfer relationship of each subsystem in the multi-energy system, and the subsystem flexibility transfer matrix is ​​used to characterize the flexibility transfer relationship of each node in each subsystem.

[0112] The cost optimization module 402 is used to input the maximum flexibility parameter, the cost parameter of the multi-energy system, and the system parameter into the flexibility cost optimization model to output the cost-optimized operation scheme of the multi-energy system under the condition of providing maximum flexibility.

[0113] Finally, it should be noted that in the embodiments of this application, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0114] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for improving the flexibility of a multi-energy system considering flexibility costs, characterized in that, The method includes: The system parameters and operating parameters of the multi-energy system under the basic scheduling scenario are input into the maximum flexibility optimization model to output the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario. The maximum flexibility optimization model determines the boundary parameters of the multi-energy system under the flexibility scenario based on the system parameters, the operating parameters, the subsystem flexibility transfer matrix, and the multi-dimensional flexibility enhancement path. The multi-dimensional flexibility enhancement path is used to characterize the flexibility transfer relationship of each subsystem in the multi-energy system, and the subsystem flexibility transfer matrix is ​​used to characterize the flexibility transfer relationship of each node in each subsystem. The maximum flexibility parameter, the cost parameter of the multi-energy system, and the system parameter are input into the flexibility cost optimization model to output the cost-optimized operation scheme of the multi-energy system under the condition of providing maximum flexibility. The cost parameters include: operating costs and over-limit costs. The flexibility cost optimization model is optimized iteratively through the following second objective function: ； in, This indicates the cost exceeding the limit. This represents the operating cost. This refers to a short-term overload-capable device in the multi-energy system. This refers to the power supply equipment in the multi-energy system. This represents the primary energy supply cost coefficient of the energy supply equipment. This represents the secondary energy supply cost coefficient of the energy supply equipment. This indicates the output power of the power supply equipment. This represents the value of the variable corresponding to the short-term overloadable device in the basic scheduling scenario. This represents the difference between the value of the variable corresponding to the short-term overloadable device in the basic scheduling scenario and its value in the flexibility scenario. This represents the maximum value of the variable corresponding to the short-term overload-capable device. This represents the overload cost coefficient for the short-term overload-capable device. The value represents the secondary overload cost coefficient of the short-term overload-capable device, where x is the independent variable.

2. The method according to claim 1, characterized in that, The system parameters include: The maximum output limit, minimum output limit, upward ramp limit, and downward ramp limit of each energy type controllable device in the multi-energy system; The energy conversion efficiency, maximum output power limit, minimum output power limit, energy conversion power upward ramp limit, energy conversion power downward ramp limit, upper limit of the electro-thermal ratio of the cogeneration coupling system in the multi-energy system, and lower limit of the electro-thermal ratio of the cogeneration coupling system in the system. The upper and lower limits of power utilization for each energy demand node in the multi-energy system for each controllable device of the energy type; The power transfer factor and cross-sectional load limit of each subsystem in the multi-energy system; The runtime data includes: The energy requirements of each energy type controllable device in the multi-energy system, the energy requirements of each coupling system, the uplink flexibility requirements of each energy demand node, and the downlink flexibility requirements.

3. The method according to claim 2, characterized in that, The multiple flexibility enhancement paths include: a first flexibility enhancement path and a second flexibility enhancement path; The first flexibility enhancement path includes: allocating a portion of the output power of the first coupling system to the second coupling system, such that the difference between the actual output power of the second coupling system and the maximum output power limit of the second coupling system is greater than the uphill ramp limit of the second coupling system; and, such that the difference between the actual output power of the first coupling system and the maximum output power limit of the first coupling system is greater than the uphill ramp limit of the second coupling system. The second flexibility enhancement path includes: lowering the upper limit of the electrothermal ratio of the cogeneration coupling system, raising the upper limit of the electrothermal ratio of the cogeneration coupling system, and lowering the cross-sectional load limit of each subsystem.

4. The method according to claim 1, characterized in that, The subsystem flexibility transfer matrix includes: an uplink flexibility transfer matrix and a downlink flexibility transfer matrix.

5. The method according to claim 4, characterized in that, The maximum flexibility optimization model and the flexibility cost optimization model are constrained by a preset set of constraints.

6. The method according to claim 5, characterized in that, The maximum flexibility optimization model is optimized iteratively using the following first objective function: ； Where max represents the optimization objective of maximizing the subsequent function. This represents the summation of all elements in the matrix. This represents the uplink flexibility transfer matrix. This represents the downlink flexibility transfer matrix. This indicates the maximum uplink flexibility that the multi-functional system can provide under the basic scheduling scenario. This indicates the maximum downlink flexibility that the multi-functional system can provide under the basic scheduling scenario.

7. The method according to claim 6, characterized in that, In the flexibility cost optimization model, in addition to satisfying the preset constraint set, the flexibility cost optimization model also satisfies the following constraints: ； in, This indicates the maximum uplink flexibility that the multi-energy system can provide under the target energy demand. This indicates the maximum downlink flexibility that the multi-energy system can provide under the target energy demand.

8. A device for enhancing the flexibility of a multi-energy system considering flexibility costs, characterized in that, The device includes: A flexibility optimization module is used to input the system parameters and operating parameters of the multi-energy system under a basic scheduling scenario into a maximum flexibility optimization model, so as to output the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario. The maximum flexibility optimization model determines the boundary parameters of the multi-energy system under the flexibility scenario based on the system parameters, the operating parameters, the subsystem flexibility transfer matrix, and the multi-dimensional flexibility enhancement path, so as to determine the maximum flexibility parameter that the multi-energy system can provide under the basic scheduling scenario based on the boundary parameters. The multi-dimensional flexibility enhancement path is used to characterize the flexibility transfer relationship of each subsystem in the multi-energy system, and the subsystem flexibility transfer matrix is ​​used to characterize the flexibility transfer relationship of each node in each subsystem. The cost optimization module is used to input the maximum flexibility parameter, the cost parameter of the multi-energy system, and the system parameter into the flexibility cost optimization model, so as to output the cost-optimized operation scheme of the multi-energy system under the condition of providing maximum flexibility; The cost parameters include: operating costs and over-limit costs. The flexibility cost optimization model is optimized iteratively through the following second objective function: ； in, This indicates the cost exceeding the limit. This represents the operating cost. This refers to a short-term overload-capable device in the multi-energy system. This refers to the power supply equipment in the multi-energy system. This represents the primary energy supply cost coefficient of the energy supply equipment. This represents the secondary energy supply cost coefficient of the energy supply equipment. This indicates the output power of the power supply equipment. This represents the value of the variable corresponding to the short-term overloadable device in the basic scheduling scenario. This represents the difference between the value of the variable corresponding to the short-term overloadable device in the basic scheduling scenario and its value in the flexibility scenario. This represents the maximum value of the variable corresponding to the short-term overload-capable device. This represents the overload cost coefficient for the short-term overload-capable device. The value represents the secondary overload cost coefficient of the short-term overload-capable device, where x is the independent variable.

Images