Flexible pricing method for power grid connection energy hub and related device

Abstract

The invention discloses a flexibility pricing method for a power grid connection energy hub and a related device, and the method comprises the steps: determining an objective function with the maximum and optimal profit based on the flexibility source of electricity and the flexibility source of a heat energy department, and setting a corresponding constraint condition, thereby constructing an upper-level model; taking the cost minimization of purchasing energy from an upstream network as an objective function and setting corresponding constraint conditions so as to construct a lower-level model; constructing a flexible energy hub model, and combining the upper-level model, the lower-level model and the flexible energy hub model to obtain a double-layer optimization model; and solving the double-layer optimization model to obtain a flexible service price and an optimal operation strategy. Therefore, a more comprehensive solution is provided for dealing with load fluctuation and instability of renewable energy sources.

Description

Technical Field

[0001] The present invention relates to the technical field of power system energy dispatching, and in particular to a flexibility pricing method and related devices for a power grid connected to an energy hub. Background Art

[0002] As a system that integrates multiple energy forms (such as electricity, heat, gas, etc.) and provides centralized management, energy hubs can improve overall energy efficiency and enhance system flexibility by coordinating different energy resources, thereby coping with the imbalance between supply and demand caused by the uncertainty of renewable energy. In particular, when the results of renewable energy generation deviate from intraday scheduling and real-time scheduling, the system's balancing ability and flexibility are particularly important.

[0003] To address these challenges, recent technologies have proposed strategies to apply a variety of flexibility resources within energy hubs, such as controllable distributed power sources, energy storage systems, and demand response programs. These resources can not only smooth out power generation fluctuations, but also provide flexibility services in the market and create additional economic value. However, how to formulate a reasonable pricing mechanism for these flexibility services remains a technical hotspot.

[0004] There are currently two main solutions: one is the application of energy storage systems (such as batteries and electric vehicles): energy storage and release are achieved through energy storage systems to cope with fluctuations in electricity demand and unstable output of renewable energy. Batteries and electric vehicles are often used to balance loads and increase the flexibility of the power grid. However, although energy storage systems can alleviate short-term fluctuations, they are costly and have prominent battery life issues in long-term large-scale applications. In addition, the charging and discharging efficiency and capacity of energy storage systems are limited, making it difficult to fully meet the flexibility requirements in a high-penetration renewable energy environment. The second is a demand response plan: by adjusting the user's electricity consumption behavior to balance electricity supply and demand, users adjust their electricity consumption under price incentives, thereby reducing the burden on the power grid and improving the system's regulation capabilities. However, the demand response plan requires a high degree of user participation and flexibility in its implementation, and the actual response may not be as effective as expected. For large-scale power grids, this approach may not be able to respond to changes quickly and accurately during high-load periods, and there is a lag. Summary of the invention

[0005] The present invention provides a flexibility pricing method and related device for a grid-connected energy hub, which are used to provide a more comprehensive solution when dealing with load fluctuations and renewable energy instability.

[0006] In view of this, the first aspect of the present application provides a flexibility pricing method for a grid-connected energy hub, the method comprising:

[0007] Based on the flexibility sources of the electrical and thermal sectors, the objective function of maximizing profits is determined and the corresponding constraints are set, thereby constructing the upper-level model;

[0008] The lower-level model is constructed by taking the minimization of the cost of purchasing energy from the upstream network as the objective function and setting the corresponding constraints;

[0009] Constructing a flexible energy hub model, and combining the upper-level model, the lower-level model and the flexible energy hub model to obtain a two-layer optimization model;

[0010] The two-layer optimization model is solved to obtain the flexibility service price and the optimal operation strategy.

[0011] Optionally, the objective function of the upper-level model is expressed as:

[0012] ;

[0013] In the formula, adjusting electricity prices for services up and down for power flexibility; Up-regulating service power for power flexibility; down-regulating service power for power flexibility; adjusting the heat price of services up and down for thermal flexibility; up-regulating service power for thermal flexibility; down-regulating service power for thermal flexibility; To optimize the goal of maximizing profits; is the scene probability; i and t are the device number and time step respectively.

[0014] Optionally, the constraints corresponding to the upper-level model include:

[0015] Total flexibility power gained by the energy hub for both up and down operation modes;

[0016] flexibility in the thermal sector;

[0017] Electricity demand response, electrical energy storage systems (EES), combined heat and power, responsive loads and flexibility of thermal energy storage systems in both upward and downward operation modes.

[0018] Optionally, the objective function of the lower-level model is expressed as:

[0019] ;

[0020] In the formula, the cost of purchasing energy from upstream networks; is the occurrence probability of scenario s; is the cost coefficient per unit of electrical energy; For electrical energy output or consumption; is the cost coefficient per unit of thermal energy; For heat output or consumption; is the unit cost coefficient of other resources; The usage of this resource.

[0021] Optionally, the constraints corresponding to the lower-level model include:

[0022] Active-reactive power balance of different buses in the power system;

[0023] Heat / gas power balance in heat / gas network nodes;

[0024] active and reactive power passing through electrical distribution lines, thermal pipelines, and natural gas pipelines;

[0025] voltage, temperature, and pressure limitations;

[0026] Apparent power limitations through electric power lines, heat pipelines, and natural gas pipelines.

[0027] Optionally, the constructing of the flexible energy hub model includes:

[0028] Designing operating constraints that constrain the operation mode of internal resources to obtain a flexible energy hub model;

[0029] The operation constraints include:

[0030] Active, reactive, thermal and natural gas power balance constraints for energy hubs;

[0031] The output capacity constraints of electricity and heat;

[0032] Electricity demand response constraints;

[0033] Thermal demand response constraints;

[0034] Constraints on charging rate, discharging rate and stored energy of the energy storage system;

[0035] The charging rate, discharging rate and storage energy constraints of thermal energy storage systems;

[0036] Flexibility constraints of energy hubs in the electricity and heat sectors.

[0037] Optionally, it also includes:

[0038] In the process of constructing the two-level optimization model, the uncertainty parameters are modeled in the form of stochastic bipolar least squares programming.

[0039] A second aspect of the present application provides a flexibility pricing system for a grid-connected energy hub, the system comprising:

[0040] A first construction unit is used to determine the profit maximization optimization objective function based on the flexibility sources of the electrical sector and the flexibility sources of the thermal energy sector and set corresponding constraints, thereby constructing an upper-level model;

[0041] The second construction unit is used to construct a lower-level model by taking minimization of the cost of purchasing energy from the upstream network as the objective function and setting corresponding constraints;

[0042] A third construction unit is used to construct a flexible energy hub model, and combine the upper-level model, the lower-level model and the flexible energy hub model to obtain a two-layer optimization model;

[0043] The solving unit is used to solve the two-layer optimization model to obtain the flexibility service price and the optimal operation strategy.

[0044] A third aspect of the present invention provides a flexibility pricing device for a grid-connected energy hub, the device comprising a processor and a memory:

[0045] The memory is used to store program code and transmit the program code to the processor;

[0046] The processor is used to execute the steps of the flexibility pricing method for the grid-connected energy hub as described in the first aspect according to the instructions in the program code.

[0047] A fourth aspect of the present invention provides a computer-readable storage medium for storing program code, wherein the program code is used to execute the flexibility pricing method for a grid-connected energy hub as described in the first aspect above.

[0048] It can be seen from the above technical solutions that the present invention has the following advantages:

[0049] The flexibility pricing method for the grid-connected energy hub provided by the present invention comprehensively coordinates and optimizes various flexibility resources through the energy hub (EH). Not only energy storage systems and demand response are utilized, but also controllable distributed power sources are combined to form a multi-energy collaborative management framework, thereby providing a more comprehensive solution when dealing with load fluctuations and renewable energy instability. Furthermore, the present invention proposes a flexibility pricing mechanism for determining the economic benefits of energy hubs when providing up and down regulation services. This mechanism makes the market participation of flexibility resources more attractive, incentivizing them to provide services at the right time, thereby optimizing the operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 A schematic flow chart of a method for pricing flexibility of a power grid-connected energy hub provided in an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of the structure of a flexibility pricing system for a power grid-connected energy hub provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] It should be noted that when constructing a two-layer optimization model, the present invention needs to obtain the following data:

[0055] 1. Network topology data.

[0056] Electrical network: including the number of nodes, line parameters (resistance, reactance), transformer parameters, etc.

[0057] Natural gas network: including number of nodes, pipeline parameters (length, diameter, roughness), compressor station parameters, etc.

[0058] Thermal network: including the number of nodes, pipeline parameters (length, diameter, insulation material), heat exchanger parameters, etc.

[0059] 2. Load data.

[0060] Power load: includes hourly load data of each node, which can usually be obtained through historical data or prediction models.

[0061] Heat load: includes hourly heat load data of each node, which can also be obtained through historical data or prediction models.

[0062] Natural gas load: includes hourly natural gas consumption data for each node, which can also be obtained through historical data or forecasting models.

[0063] 3. Renewable energy data.

[0064] Wind turbine (WT): includes the rated capacity, location, and hourly power generation data of each wind turbine (which can be calculated from meteorological data and wind turbine characteristics).

[0065] Photovoltaic panels (PV): including the rated capacity, location, and hourly power generation data of each PV panel (which can be calculated using meteorological data and PV panel characteristics).

[0066] 4. Energy storage equipment data.

[0067] Battery Energy Storage System (BESS): includes battery capacity, charging and discharging efficiency, charging and discharging rate limit, etc.

[0068] Electric vehicles (EVs): including the number of EVs, battery capacity, charging and discharging efficiency, charging and discharging rate limits, and user behavior patterns (such as charging time and location).

[0069] 5. Flexibility resource data.

[0070] Controllable distributed generation (DGs): including the rated capacity, fuel cost, emission factor, etc. of each distributed generation unit.

[0071] Responsive loads (DRPs): Includes the adjustment range, response speed, user engagement, etc. of various responsive loads.

[0072] Thermal Energy Storage System (TES): includes the capacity, charging and discharging efficiency, charging and discharging rate limit, etc. of the thermal energy storage system.

[0073] It should be noted that the present invention is a flexibility pricing method for grid-connected energy hubs under uncertain energy resource conditions, and explains the flexibility pricing problem of energy hubs (EHs) connected to electricity, heat and gas networks. The proposed design is expressed as a two-level optimization. In the upper-level problem, the profit maximization of EH-based flexibility resources (FSs) is modeled as a process that takes into account its flexibility constraints. In addition, the optimal operation of these networks and EHs is considered to minimize the cost of purchased energy in the lower-level problem. This problem is limited by the network operation model and the operation-flexibility representation of the EH. Therefore, the flexibility pricing method of the present invention can be described as follows.

[0074] See also Figure 1 , a flexibility pricing method for a grid-connected energy hub provided in an embodiment of the present invention includes:

[0075] Step 101: Determine the profit maximization optimization objective function based on the flexibility sources of the electricity sector and the thermal energy sector and set corresponding constraints, thereby constructing an upper-level model.

[0076] It should be noted that this step attempts to maximize the expected profit of flexibility resources (FS) by obtaining flexibility services in EH. Renewable energy sources (RESs) may lead to inconsistent results between day-ahead (DA) and real-time (RT) operations, resulting in supply and demand imbalance in the RT operating system, which is called low flexibility. Among them, "day-ahead" usually refers to the day before the actual electricity consumption day, when the operator optimizes the scheduling and resource allocation of the energy system based on the predicted energy demand, renewable energy output, uncertainty factors, and quotations submitted by market participants.

[0077] This problem can be solved by flexible resources FS, including electric energy storage systems (EES), demand response (DRP), and combined heat and power (CHP), to cope with power fluctuations due to RESs in RT dispatch compared with DA operation. The sources of flexibility in the electrical and thermal sectors are divided according to the different roles of flexibility resources (FS): The first income is the income obtained by providing flexibility services by electrical flexibility resources (FS) within the energy hub EH. Since the thermal power of combined heat and power (CHP) depends on its active power (i.e., CHP cannot control its thermal power independently), the flexibility of flexibility resources in thermal energy is low. The second income comes from the income obtained from providing flexibility services in the thermal sector.

[0078] The optimization objective is as follows:

[0079] ;

[0080] In the formula, adjusting electricity prices for services up and down for power flexibility; Up-regulating service power for power flexibility; down-regulating service power for power flexibility; adjusting the heat price of services up and down for thermal flexibility; Up-regulation of service power for thermal flexibility; down-regulating service power for thermal flexibility; To optimize the goal of maximizing profits; is the scene probability; i, t are the device number and time step respectively.

[0081] Constraint one is the total flexibility power obtained by the EH for both upward and downward operation modes.

[0082] ;

[0083] In the formula, The amount of additional energy that the energy hub can provide to the system to balance the rise / fall in demand; The additional energy that can be provided by adjusting users’ demand-side management strategies; is the energy released by the discharge operation; The ability to provide more energy by adjusting its operating state; Energy saved by reducing the energy consumption demand of users; The ability to absorb excess energy for charging operations; To reduce its output to match the ability of falling demand.

[0084] The second constraint is the flexibility of the thermal energy sector.

[0085] ;

[0086] In the formula, The amount of additional energy that the source hub can provide to the system to balance the increase / decrease in demand; Additional energy available to increase / decrease demand-side management strategies for users; The energy released / absorbed by the discharge operation.

[0087] Constraint three is the flexibility of the power demand response DRP, the electric energy storage system EES, the combined heat and power CHP, the thermal DRP and the TES in both the upward and downward operation modes. The purpose of improving the system flexibility is to make the results of the real-time RT and day-ahead DA operations equal. Due to the uncertainty of RESs generation, these results may not be the same. Therefore, in order to improve flexibility, from a modeling point of view, it is necessary to minimize the deviation of the active (or thermal) power in the scenario, predicting the uncertainty value compared with the scenario corresponding to the deterministic model (the first scenario). Therefore, in the following constraint, the flexibility power of FS is equal to the difference of the active power of IS (Integrated System) in scenario s compared with the first scenario. If this power deviation is positive, its value is considered as flexibility in the upward mode. Otherwise, it is in the downward mode. Among them, the upward flexibility mode refers to when the actual generation of renewable energy in the real-time (RT) dispatch is lower than the predicted value in the day-ahead (DA) dispatch, the system needs additional generation capacity to make up for this gap. The downward flexibility mode refers to when the actual generation of renewable energy in the RT dispatch is higher than the predicted value in the DA dispatch, the system needs to reduce the generation to avoid excess.

[0088] ;

[0089] In the formula, is demand response power; is the discharge power of the energy storage device; is the electrical power of cogeneration; Release heat for thermal energy storage; Supply heat for combined heat and power.

[0090] Step 102: Taking minimizing the cost of purchasing energy from the upstream network as the objective function and setting corresponding constraints, a lower-level model is constructed.

[0091] It should be noted that this step models the operation of the electricity, heat and gas network with the energy hub (EH). The purpose is to determine the flexibility price, active power and thermal power of the FS under constraints. The constraints include the constraints of the network operation model and the operational flexibility model of the energy hub EH.

[0092] Objective function. The objective function is intended to minimize the cost of purchasing energy from upstream networks (Upstream networks refer to those networks that are located in the higher-level part of the energy transmission and distribution chain. These networks usually have a large coverage area and a high transmission capacity and are responsible for transporting energy from the production site to the final user or intermediate nodes such as EHs.) The cost of purchasing energy is equal to the product of the energy price and the power received from the upstream network. If this power is positive, then this formula calculates the cost of this network. But if the power received from the upstream network is negative, then this formula calculates the income obtained through the sale of electricity.

[0093] Objective function: ;

[0094] In the formula, is the occurrence probability of scenario s; is the cost coefficient per unit of electrical energy; For electrical energy output or consumption; is the cost coefficient per unit of thermal energy; For heat output or consumption; Unit cost factors for other resources (such as natural gas or other fuels); The usage of this resource.

[0095] The first constraint is the active-reactive power balance of different buses in the power system.

[0096] ;

[0097] In the formula, It is the active / reactive power supply; Active / reactive power provided to nodes for combined heat and power or energy conversion equipment; is the power transmission coefficient between node ne and other nodes / devices; is the active / reactive power flowing from node ne to node l; is the power demand coefficient; It is the active / reactive power demand.

[0098] The second constraint is the heat / gas power balance in the heat / gas network nodes.

[0099] ;

[0100] In the formula, for heat / gas supply; is heat transfer efficiency / gas transfer efficiency; / heat / gas supplied to nodes for cogeneration or energy conversion equipment; / is the heat / gas loss coefficient; / is the heat / gas flowing from node nh to node l; / for heat demand / gas demand.

[0101] Constraint three is the active and reactive power passing through electric power distribution lines, thermal pipelines, and natural gas pipelines.

[0102] ;

[0103] In the formula, / / / Active power / reactive power / heat / gas flow; / is the node conductance / susceptance; , is the voltage amplitude; , is the voltage phase angle; is the heat transfer coefficient; , is the node temperature; is the gas transmission coefficient; , is the gas pressure.

[0104] Constraint four is voltage, temperature and pressure limitations.

[0105] ;

[0106] In the formula, / is the minimum / maximum allowed voltage value of the node; / is the minimum / maximum allowable temperature value; / The minimum / maximum values ​​allowed for gas pressure.

[0107] Constraint five is the apparent power limit through power lines, thermal pipelines, and natural gas pipelines.

[0108] ;

[0109] In the formula, is the maximum permissible apparent power capacity; is the maximum heat flow allowed; is the maximum gas flow allowed.

[0110] Step 103: construct a flexible energy hub model, and combine the upper-level model, the lower-level model and the flexible energy hub model to obtain a two-layer optimization model.

[0111] In one embodiment, constructing a flexible energy hub model includes: designing an operation constraint condition constraining an operation mode of internal resources to obtain a flexible energy hub model;

[0112] It should be noted that the flexible energy hub model of the present invention mainly constrains the operation mode of internal resources. It can be understood that after the present invention proposes the upper model and the lower model, it proposes a separate operation model of a flexible energy hub (EH), mainly to ensure that the optimization of the entire system not only takes into account the economy (through the upper model), but also takes into account the technical feasibility and operational constraints (through the lower model). The operation model of the flexible energy hub further refines how these resources can realize their flexibility services in actual operation, and together with the upper and lower models, it forms a two-layer optimization model, and ensures that these services can be effectively integrated into the power grid. This model further explores how various resources within the EH (including energy storage devices, responsive loads, and controllable distributed generation) operate, and how they provide flexibility services. It defines the specific behavior patterns of these resources, such as the charging and discharging strategies of energy storage devices, the adjustment methods of responsive loads, etc.

[0113] The operating constraints include:

[0114] Constraint 1: Active, reactive, thermal and natural gas power balance constraints of the energy hub;

[0115] It should be noted that constraint 1 is the active, reactive, thermal and natural gas power balance constraint of EH. The active power of EH is equal to the difference between the total power consumed by CHP, RES, EES charging mode and electric DRP and EH passive load and EES charging mode.

[0116] ;

[0117] In the formula, / / / Active / reactive / thermal energy / gas output by the energy hub.

[0118] Constraint 2: Output capacity limit constraints for electricity and heat;

[0119] It should be noted that the thermal power and natural gas power of CHP in constraint 2 are calculated based on active power, and the output capacity limitations of electricity and heat are taken into account.

[0120] ;

[0121] In the formula, / is the electrical / thermal efficiency of the cogeneration equipment.

[0122] Constraint 3: Power demand response constraint;

[0123] It should be noted that constraint three is the power DRP constraint, and consumers participating in DRP control their active power consumption according to energy prices. When energy prices are low, consumers' active power consumption will increase, while when energy prices are high, it will decrease.

[0124] ;

[0125] In the formula, is the demand response flexibility coefficient, which indicates the proportion of power that can be adjusted;

[0126] Constraint 4: Thermal demand response constraint;

[0127] It should be noted that constraint four is a thermal DRP constraint, which is similar to constraint three.

[0128] ;

[0129] Constraint 5: Constraints on the charging rate, discharging rate and stored energy of the energy storage system;

[0130] It should be noted that constraint five is the constraint on the EES charging rate, discharging rate and stored energy.

[0131] ;

[0132] In the formula, / is the maximum charging / discharging power of the energy storage system; / It is the charging / discharging efficiency of the energy storage system.

[0133] Constraint 6: Constraints on the charging rate, discharging rate and stored energy of the thermal energy storage system;

[0134] It should be noted that constraint six is ​​the constraint on the charging rate, discharging rate and storage energy of TES.

[0135] ;

[0136] In the formula, is the maximum charging power of the heat storage system; is the maximum heat release power of the heat storage system; / is the charging / discharging efficiency of the heat storage system.

[0137] Constraint 7: Flexibility constraints on the electricity and heat sectors of the energy hub.

[0138] It should be noted that constraint seven is the flexibility constraint of EH in the electricity and heat parts.

[0139] ;

[0140] In the formula, / is the permissible range of power / heat variation; Labels for indicating upper and lower bounds of power and thermal flexibility constraints, used for bivariate relationships in optimization problems

[0141] Step 104: Solve the two-layer optimization model to obtain the flexibility service price and the optimal operation strategy.

[0142] It should be noted that the upper model and the lower model are converted into a single-layer model through the Karush-Kuhn-Tucker (KKT) condition. The present invention uses the commercial solver Gurobi to solve the double-layer optimization model, and finally obtains the flexibility service price and the optimal operation strategy. The flexibility service price includes energy price, energy storage service price, and demand response price; the optimal operation strategy includes power generation scheduling of power generation units, energy storage scheduling, and demand response scheduling strategy.

[0143] Furthermore, in one embodiment, it also includes:

[0144] In the process of constructing the two-level optimization model, the uncertain parameters are modeled in the form of stochastic bipolar least squares programming.

[0145] It should be noted that in the process of constructing the two-level optimization model, since parameters such as load, energy price, and renewable energy power are all uncertain parameters, in order to obtain a reliable optimal solution, these parameters of the present invention are modeled in the form of stochastic bipolar least squares programming (SBSP). Specifically: First, the Monte Carlo method (MCS) generates many scenarios. In each scenario, the parameters are estimated according to their mean and standard deviation. Then the normal probability distribution function (PDF) is used to find the probability of the selected load level and energy price in the scenario. It also calculates the photovoltaic (PV) / wind turbine (WT) RES renewable energy power according to the Beta / Weibull PDF. The probability value of the electric vehicle parameters is also proportional to the Rayleigh PDF. Next, the probability of a given scenario is found by multiplying the probabilities of the uncertainty parameters in the scenario. The Kantorovich method selects a certain number of generated scenarios. In the Kantorovich method, scenarios that are close to each other are selected. The probability of each new scenario is equal to the ratio of the probability of the scenario to the total of the selected scenarios.

[0146] In summary, unlike relying solely on energy storage systems or a single demand response plan, the flexibility pricing method for the grid-connected energy hub of the present invention comprehensively coordinates and optimizes multiple flexibility resources through the energy hub (EH). Not only energy storage systems and demand response are utilized, but also controllable distributed power sources are combined to form a multi-energy collaborative management framework, thereby providing a more comprehensive solution when dealing with load fluctuations and renewable energy instability. Furthermore, the present invention proposes a flexibility pricing mechanism for determining the economic benefits of energy hubs when providing up and down regulation services. This mechanism makes the market participation of flexibility resources more attractive, incentivizing them to provide services at the right time, thereby optimizing the operation of the power system.

[0147] The above is a flexibility pricing method for a power grid connected to an energy hub provided in an embodiment of the present invention, and the following is a flexibility pricing system for a power grid connected to an energy hub provided in an embodiment of the present invention.

[0148] See also Figure 2 , a flexibility pricing system for a power grid-connected energy hub provided in an embodiment of the present invention includes:

[0149] The first construction unit 201 is used to determine the profit maximization optimization objective function based on the flexibility sources of the electricity sector and the flexibility sources of the thermal energy sector and set corresponding constraints, so as to construct an upper-level model.

[0150] The second construction unit 202 is used to construct a lower-level model by taking minimizing the cost of purchasing energy from the upstream network as the objective function and setting corresponding constraints.

[0151] The third construction unit 203 is used to construct a flexible energy hub model, and combine the upper-level model, the lower-level model and the flexible energy hub model to obtain a two-layer optimization model.

[0152] The solving unit 204 is used to solve the double-layer optimization model to obtain the flexibility service price and the optimal operation strategy.

[0153] Furthermore, an embodiment of the present invention also provides a flexibility pricing device for a grid-connected energy hub, the device comprising a processor and a memory:

[0154] The memory is used to store program code and transmit the program code to the processor;

[0155] The processor is used to execute the steps of the flexibility pricing method for the grid-connected energy hub as described in the above method embodiment according to the instructions in the program code.

[0156] Furthermore, a computer-readable storage medium is provided in an embodiment of the present invention, wherein the computer-readable storage medium is used to store program code, and the program code is used to execute the flexibility pricing method for the grid-connected energy hub described in the above method embodiment.

[0157] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0158] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0159] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0162] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flexibility pricing method for a grid-connected energy hub, characterized in that: include: Based on the flexibility sources of the electrical and thermal sectors, the objective function of maximizing profits is determined and the corresponding constraints are set, thereby constructing the upper-level model; The lower-level model is constructed by taking the minimization of the cost of purchasing energy from the upstream network as the objective function and setting the corresponding constraints; Constructing a flexible energy hub model, and combining the upper-level model, the lower-level model and the flexible energy hub model to obtain a two-layer optimization model; The two-layer optimization model is solved to obtain the flexibility service price and the optimal operation strategy.

2. The flexibility pricing method for a grid-connected energy hub according to claim 1, characterized in that: The objective function of the upper-level model is expressed as: ； In the formula, adjusting electricity prices for services up and down for power flexibility; Up-regulating service power for power flexibility; down-regulating service power for power flexibility; adjusting the heat price of services up and down for thermal flexibility; up-regulating service power for thermal flexibility; down-regulating service power for thermal flexibility; To optimize the goal of maximizing profits; is the scene probability; i and t are the device number and time step respectively.

3. The flexibility pricing method for a grid-connected energy hub according to claim 1, characterized in that: The constraints corresponding to the upper-level model include: Total flexibility power gained by the energy hub for both up and down operation modes; flexibility in the thermal energy sector; Electricity demand response, electrical energy storage systems (EES), combined heat and power, responsive loads and flexibility of thermal energy storage systems in both upward and downward operation modes.

4. The flexibility pricing method for a grid-connected energy hub according to claim 1, characterized in that: The objective function of the lower-level model is expressed as: ； In the formula, the cost of purchasing energy from upstream networks; is the occurrence probability of scenario s; is the cost coefficient per unit of electrical energy; For electrical energy output or consumption; is the cost coefficient per unit of thermal energy; For heat output or consumption; is the unit cost coefficient of other resources; The usage of this resource.

5. The flexibility pricing method for a grid-connected energy hub according to claim 1, characterized in that: The constraints corresponding to the lower-level model include: Active-reactive power balance of different buses in the power system; Heat / gas power balance in heat / gas network nodes; active and reactive power passing through electrical distribution lines, thermal pipelines, and natural gas pipelines; voltage, temperature, and pressure limitations; Apparent power limitations through electric power lines, heat pipelines, and natural gas pipelines.

6. The flexibility pricing method for a grid-connected energy hub according to claim 1, characterized in that: The construction of the flexible energy hub model includes: Designing operating constraints that constrain the operation mode of internal resources to obtain a flexible energy hub model; The operation constraints include: Active, reactive, thermal and natural gas power balance constraints for energy hubs; The output capacity constraints of electricity and heat; Electricity demand response constraints; Thermal demand response constraints; Constraints on charging rate, discharging rate and stored energy of the energy storage system; The charging rate, discharging rate and storage energy constraints of thermal energy storage systems; Flexibility constraints of energy hubs in the electricity and heat sectors.

7. The flexibility pricing method for a grid-connected energy hub according to claim 1, characterized in that: Also includes: In the process of constructing the two-level optimization model, the uncertainty parameters are modeled in the form of stochastic bipolar least squares programming.

8. A flexibility pricing system for a grid-connected energy hub, characterized in that: include: A first construction unit is used to determine the profit maximization optimization objective function based on the flexibility sources of the electrical sector and the flexibility sources of the thermal energy sector and set corresponding constraints, thereby constructing an upper-level model; The second construction unit is used to construct a lower-level model by taking minimization of the cost of purchasing energy from the upstream network as the objective function and setting corresponding constraints; A third construction unit is used to construct a flexible energy hub model, and combine the upper-level model, the lower-level model and the flexible energy hub model to obtain a two-layer optimization model; The solving unit is used to solve the two-layer optimization model to obtain the flexibility service price and the optimal operation strategy.

9. A flexibility pricing device for a grid-connected energy hub, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the flexibility pricing method for a grid-connected energy hub as described in any one of claims 1-7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the flexibility pricing method for a grid-connected energy hub as described in any one of claims 1-7.

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