Distributed energy hub scheduling method, device and equipment and storage medium

Abstract

The invention relates to the technical field of electric power energy, in particular to a distributed energy hub scheduling method, device and equipment and a storage medium, and the method comprises the following steps: building an energy hub model of a distributed energy aggregation point; determining an interactive operation mode of the distributed energy hub, and performing interaction between the distributed energy aggregation point and a superior power grid, a heat supply network and a gas network and interaction between the distributed energy aggregation point and a lower-level user; constructing a distributed energy hub cooperative operation optimization model; based on the Nash bargaining theory, cooperative gaming is carried out between the distributed transducer benefit model and the load aggregator benefit model; and introducing an auxiliary variable and a shared variable to carry out distributed solution by applying an alternating multiplier method (ADMM) algorithm. According to the method, through coupling of various energy conversion devices and energy storage devices, the operation efficiency of the system is optimized, benefit maximization of each participant can be ensured by adopting a game theory and an optimization algorithm, and the problem of cooperative scheduling of the multi-energy system can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power energy, and in particular to a distributed energy hub scheduling method, device, equipment and storage medium. Background Art

[0002] The global energy crisis and environmental pollution problems have posed new challenges to energy management and scheduling. Especially in the global context of addressing climate change and promoting sustainable development, how to achieve efficient energy utilization and low carbon emissions has become the focus of global attention. Traditional centralized heating systems mainly rely on a single energy supply, suffering from problems such as low energy efficiency and large carbon emissions, and cannot meet the current social demand for energy environmental friendliness and sustainability. Therefore, with the increasing demand for green energy, new power systems and distributed energy systems have emerged as the key to solving energy crisis and environmental pollution problems. By introducing electric heating equipment, combined heat and power (CHP) units, gas turbines, gas boilers, as well as electricity storage and heat storage equipment, these systems can achieve efficient conversion and supply of multiple energies such as electricity, heat, and gas, and have strong flexibility and adaptability.

[0003] However, although these distributed energy systems have significant advantages in theory, in practical applications, how to effectively schedule and manage these systems to ensure efficient energy utilization and low carbon emissions still faces major challenges. Current research mostly focuses on the optimal scheduling of single energy systems, and there is a lack of comprehensive scheduling and coordination research on multi-energy systems. Especially in distributed energy hubs, how to optimize the energy interaction between different energy conversion stations and how to respond to diverse electric and heat load demands on the user side are still difficult points. In order to achieve low-carbon optimal scheduling of the system, it is necessary to consider the coordination and balance between different energies in the multi-energy system.

[0004] In addition, the operation mode of distributed energy hubs involves not only technical issues but also complex issues in aspects such as policies, markets, and interest distribution. Most of the existing scheduling methods and models cannot meet the actual needs and lack dynamic scheduling and optimization management in complex environments. Therefore, there is an urgent need for a new distributed energy hub scheduling method that can solve the collaborative scheduling problem of multi-energy systems and promote the construction of a low-carbon and efficient energy utilization system. Summary of the Invention

[0005] In view of at least one of the above technical problems, the present invention provides a distributed energy hub scheduling method, device, equipment and storage medium, and uses method improvements to solve the collaborative scheduling problem of multi-energy systems.

[0006] According to a first aspect of the present invention, there is provided a distributed energy hub scheduling method, including the following steps:

[0007] Establish an energy hub model for distributed energy aggregation points, introduce various energy conversion devices and electricity and heat storage devices to form an electric heat compensation station, and determine the relationships of energy input, output, and energy coupling devices;

[0008] Determine the interactive operation mode of the distributed energy hub, and conduct interactions between the distributed energy aggregation point and the superior power grid, heat grid, and gas grid, as well as interactions with lower-level users;

[0009] Construct an optimization model for the cooperative operation of the distributed energy hub, including an optimization model for the benefits of distributed energy traders considering the carbon trading mechanism and a benefit model for load aggregators considering the integrated electricity and heat demand response;

[0010] Based on the Nash bargaining theory, conduct a cooperative game between the benefit models of distributed energy traders and load aggregators;

[0011] Use the alternating direction method of multipliers (ADMM) algorithm, introduce auxiliary variables and shared variables for distributed solution.

[0012] In some embodiments of the present invention, the mathematical models of the input side, coupling side, and output side of the energy hub are represented by the following formula: In the formula, and are the electrical, thermal, and gas powers input to the energy hub of the energy conversion station respectively; and are the electrical and thermal powers output from the energy hub of the energy conversion station respectively; P REN is the output electrical power of distributed photovoltaics or wind power; P DHE is the electrical power consumed by the electric heating equipment; S CHP , S GT and S GB are the input powers of the CHP unit, gas turbine, and gas boiler respectively; and are the power generation efficiency and heat production efficiency of the CHP equipment respectively; η GT is the power generation efficiency of the gas turbine; η GB is the heat production efficiency of the gas boiler; η DHE is the electro-thermal conversion efficiency of the electric heating equipment; and are the charging and discharging powers of the battery respectively; and are the heat storage and heat release powers of the hot water storage tank respectively; and are the power changes caused by the electrical and thermal load demand responses respectively.

[0013] In some embodiments of the present invention, the interactive operation mode includes energy purchase from the superior network, energy purchase and sale among peer energy traders, and energy sale to lower-level users.

[0014] In some embodiments of the present invention, the cooperative operation optimization model includes energy sales revenue, energy interaction revenue, energy purchase cost, network transmission cost, wind and photovoltaic curtailment costs, and carbon trading costs. The objective function of the distributed energy conversion merchant can be expressed as:

[0015]

[0016] In the formula, U i is the benefit function of the distributed energy conversion merchant i; is the energy sales revenue of the distributed energy conversion merchant i to its own users; is the energy interaction revenue with other energy conversion merchants; is the energy purchase cost of the distributed energy conversion merchant i from the superior network, including electricity purchase cost, heat purchase cost, and gas purchase cost; is the network transmission cost generated during the energy interaction with other energy conversion merchants, and this cost should be shared equally by the energy purchaser and the energy seller; is the wind and photovoltaic curtailment cost of the energy conversion merchant i; is the carbon trading cost of the distributed energy conversion merchant i.

[0017] In some embodiments of the present invention, the integrated demand response model in the load aggregator benefit model includes a load curtailment model and a load transfer model:

[0018] The expression of the load curtailment model is:

[0019]

[0020] In the formula, and respectively represent the electricity load and heat load that can be curtailed by the electric and heat user i at time t; and represent the load curtailment coefficients of electricity and heat for the electric and heat user i; and represent the electricity and heat load demands of the electric and heat user i at time t without considering the integrated demand response;

[0021] The expression of the load transfer model is:

[0022]

[0023] In the formula, and respectively represent the electricity load and heat load that can be transferred by the electric and heat user i at time t; and represent the load transfer coefficients of electricity and heat for the electric and heat user i;

[0024] The expression of the load aggregator benefit objective function is:

[0025]

[0026] wherein, U LA is the benefit function of the load aggregator; is the energy utilization utility of the i-th electric-thermal energy user; is the cost of loss of energy utilization satisfaction of the i-th electric-thermal energy user; is the energy utilization cost paid by the i-th electric-thermal energy user to the distributed energy converter i.

[0027] In some embodiments of the present invention, according to the basic principle of Nash bargaining, the cooperative operation model for the distributed energy hub can be expressed as:

[0028]

[0029] wherein, and are the breakdown points of negotiation for the load aggregator and the distributed energy converter i respectively. The breakdown point of negotiation refers to the worst-case scenario, that is, the negotiation between the load aggregator and the distributed energy converter, as well as between individual energy converters fails, and there is no cooperation between them. The load aggregator does not purchase energy from the energy conversion station but directly purchases energy from the superior network. and respectively represent the benefits improved by the load aggregator and the distributed energy converter i through cooperative operation;

[0030] The solution of this model is achieved by solving two sub-problems of maximizing social benefits and maximizing payment benefits. The specific models of the two sub-problems are as follows:

[0031] Sub-problem 1: Maximizing social benefits:

[0032]

[0033] Sub-problem 2: Maximizing payment benefits

[0034]

[0035] wherein, in sub-problem 1, since the energy utilization cost of the load aggregator is offset by the energy sales revenue of all distributed energy converters, and the energy interaction revenue between each distributed energy converter is offset, the energy trading price variable in sub-problem 1 is eliminated, and only the energy trading power variable remains. In sub-problem 2, the variable with the superscript "*" is the result optimized by sub-problem 1.

[0036] In some embodiments of the present invention, when the ADMM algorithm is used to solve the problem of maximizing social benefits, auxiliary variables are introduced to decouple the power of energy purchase and sale. The distributed optimization objective functions of the load aggregator and the energy converter are respectively:

[0037]

[0038] In the formula, and are the augmented Lagrangian functions of the load aggregator and the distributed energy converter i with respect to sub-problem 1, respectively; and are the optimization variables of the load aggregator, representing the electric power and heat power purchased by the electric-thermal energy user from the distributed energy converter i at time t, respectively; and are the optimization variables of the distributed energy converter i, representing the electric power and heat power sold by the energy converter i to the electric-thermal energy user at time t, respectively; and are the electric power and heat power supplied by the distributed energy converter j to the energy converter i at time t, respectively; and are the dual variables of the energy trading power between the energy converter i and the load aggregator at time t, respectively; and are the dual variables of the energy interaction power between the distributed energy converters at time t, respectively; ρ 1 is the penalty factor of sub-problem 1; where The subscript 2 indicates taking the square root of the vector, representing the Euclidean norm, and the superscript 2 indicates the square of the Euclidean norm;

[0039] The update formula for the dual variables in sub-problem 1 is:

[0040]

[0041] The convergence condition of sub-problem 1 is that the dual residual is less than the convergence value, and the convergence formula can be expressed as:

[0042]

[0043] In the formula, the superscript k represents the current iteration number; ε 1 represents the convergence threshold of sub-problem 1.

[0044] According to the second aspect of the present invention, a distributed energy hub scheduling device is further provided, including:

[0045] An energy hub modeling module, configured to establish an energy hub model of a distributed energy aggregation point, introduce various energy conversion devices and electricity storage and heat storage devices, form an electric heat compensation station, and determine the energy input, output and the relationship of energy coupling devices;

[0046] An interactive operation mode determination module, configured to determine the interactive operation mode of the distributed energy hub, and perform interactions between the distributed energy aggregation point and the upper-level power grid, heat grid and gas grid, as well as interactions with the lower-level users;

[0047] A cooperative operation optimization model construction module for constructing a distributed energy hub cooperative operation optimization model, including a distributed energy converter benefit optimization model considering a carbon trading mechanism and a load aggregator benefit model considering integrated electric and thermal demand response;

[0048] A cooperative game module for conducting a cooperative game between the distributed energy converter benefit model and the load aggregator benefit model based on the Nash bargaining theory;

[0049] A distributed solution module that uses the alternating direction method of multipliers (ADMM) algorithm, introduces auxiliary variables and shared variables for distributed solution. According to the third aspect of the present invention, there is also provided a distributed energy hub scheduling device, including a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method is implemented.

[0050] According to the fourth aspect of the present invention, there is also provided a distributed energy hub scheduling storage medium, including a storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method is implemented.

[0051] The beneficial effects of the present invention are as follows: Through the coupling of various energy conversion devices and energy storage devices, the present invention coordinates the interaction relationships with the superior power grid, heat grid, gas grid, and lower-level users, optimizes the operation efficiency of the system, and uses game theory and optimization algorithms to ensure that each participating party can maximize its interests; compared with the prior art, it can solve the coordinated scheduling problem of multi-energy systems and promote the construction of a low-carbon and efficient energy utilization system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0053] Figure 1 It is a flowchart of the steps of the distributed energy hub scheduling method in the embodiment of the present invention;

[0054] Figure 2 It is a working operation schematic diagram of the distributed energy hub scheduling method in the embodiment of the present invention;

[0055] Figure 3 It is a schematic diagram of the interaction framework of the distributed energy hub in the embodiment of the present invention;

[0056] Figure 4The output prediction curve of renewable energy for each distributed energy transducer in the embodiment of the present invention;

[0057] Figure 5 The electric load prediction curve of the electro-thermal energy user in the embodiment of the present invention;

[0058] Figure 6 The schematic diagram of the specific operation process of step S30 in the embodiment of the present invention;

[0059] Figure 7 The schematic diagram of the specific operation process of step S40 in the embodiment of the present invention;

[0060] Figure 8 The schematic diagram of the specific operation process of step S50 in the embodiment of the present invention;

[0061] Figure 9 The energy trading price chart among distributed energy transducers in the embodiment of the present invention;

[0062] Figure 10 The energy purchase price chart of the load aggregator in the embodiment of the present invention;

[0063] Figure 11 The comparison chart of the system carbon emissions of Scheme 1 and Scheme 2 in the embodiment of the present invention;

[0064] Figure 12 The comparison chart of the new energy consumption of Scheme 1 and Scheme 2 in the embodiment of the present invention. Detailed implementation manners

[0065] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0067] As Figures 1 to 12 shown in the distributed energy hub scheduling method, the method includes the following steps:

[0068] S10: Establish an energy hub model for the distributed energy aggregation point, introduce various energy conversion devices and electricity storage and heat storage devices to form an electric heat compensation station, and determine the energy input, output and the relationship of energy coupling devices;

[0069] S20: Determine the interactive operation mode of the distributed energy hub, and conduct interactions between the distributed energy aggregation point and the superior power grid, heat network, and gas network, as well as with the lower-level users;

[0070] S30: Construct an optimization model for the cooperative operation of the distributed energy hub, including an optimization model for the benefits of the distributed energy converter considering the carbon trading mechanism and a benefit model for the load aggregator considering the integrated electric and heat demand response;

[0071] S40: Based on the Nash bargaining theory, conduct a cooperative game between the benefit models of the distributed energy converter and the load aggregator;

[0072] S50: Use the alternating direction method of multipliers (ADMM) algorithm, introduce auxiliary variables and shared variables for distributed solution.

[0073] As Figure 1 shown, in step S10, as Figure 2 and Figure 3 shown, according to the above technical solution for constructing the energy hub model of the distributed energy aggregation point, introduce various energy conversion devices and electricity storage and heat storage devices, determine the parameters of each device, and construct a distributed energy aggregation point energy hub model that can achieve efficient conversion and supply of multiple energies such as electricity, heat, and gas based on the mathematical models of the input side, coupling side, and output side of the energy hub and the energy transfer relationship formula between the input power of the superior gas network and each energy coupling device.

[0074] Among them, the parameters of the energy supply devices at each distributed energy aggregation point are shown in Table 1:

[0075] Table 1 Parameters of the functional devices at the distributed energy aggregation point

[0076]

[0077]

[0078] In step S20, as Figure 4 and Figure 5 shown, according to the above technical solution for realizing the interactive operation mode, establish the connection and communication mechanisms between the distributed energy aggregation point and the superior power grid, heat network, gas network, and lower-level users, clarify the specific rules and operation procedures of each energy interaction method, and ensure that the distributed energy aggregation point can effectively purchase, sell, and transmit energy according to the actual energy demand and supply situation. For example, when the grid load is too high, electrical energy can be dispatched from the energy storage device; when the gas network supply is insufficient, it may be necessary to adjust the energy input of the heat network, etc., to achieve interactions with each entity.

[0079] In step S30, two main aspects are considered: the distributed energy converter benefit optimization model and the load aggregator benefit model. In the distributed energy converter benefit optimization model, this part of the model takes into account the carbon trading mechanism, that is, how to create benefits for distributed energy providers through carbon emission trading and optimize their operating efficiency. In the load aggregator benefit model, integrated electric and thermal demand response is concerned, that is, how to find a balance between electricity and heat demands, meet the demands while minimizing energy waste.

[0080] In step S40, considering the interest balance of all parties, so that each party can obtain better benefits through cooperation, the purpose of applying the Nash bargaining theory here is to predict through a mathematical model how all parties can achieve optimal transactions and resource scheduling under different market conditions.

[0081] In step S50, the ADMM algorithm is an algorithm for distributed optimization. It decomposes large-scale optimization problems into multiple sub-problems for parallel computing by introducing auxiliary variables and shared variables, and finally reaches the global optimum. This method is particularly suitable for large-scale distributed systems because it can efficiently perform calculations in multiple places and maintain global coordination, and is suitable for real-time operation of distributed energy scheduling systems.

[0082] In the above embodiments, the present invention coordinates the interaction relationships with the upper-level power grid, heat grid, gas grid and lower-level users through the coupling of various energy conversion devices and energy storage devices, optimizes the operating efficiency of the system, and uses game theory and optimization algorithms to ensure that all participating parties can maximize their interests; compared with the prior art, it can solve the coordinated scheduling problem of multi-energy systems and promote the construction of a low-carbon and efficient energy utilization system.

[0083] In the embodiments of the present invention, the mathematical models of the input side, coupling side and output side of the energy hub are represented by the following formula: In the formula, and are the electric, thermal and gas powers input to the energy hub of the energy conversion station respectively; and are the electric and thermal powers output from the energy hub of the energy conversion station respectively; P REN is the output electric power of distributed photovoltaic or wind power; P DHE is the electric power consumed by the electric heating equipment; S CHP , S GT and S GB are the input powers of the CHP unit, gas turbine and gas boiler respectively; and are the power generation efficiency and heat production efficiency of the CHP equipment respectively; η GT is the power generation efficiency of the gas turbine; η GBis the heat production efficiency of the gas boiler; η DHE is the electro-thermal conversion efficiency of the electric heating equipment; and are the charging and discharging powers of the storage battery, respectively; and are the heat storage and heat release powers of the hot water storage tank, respectively; and are the power changes caused by the electrical and thermal load demand responses, respectively. In this embodiment, by modeling the power input and output, efficiency parameters of different devices, and the charging and discharging characteristics of energy storage devices, a comprehensive description of the energy conversion, storage, and supply process is achieved. Through these variables, the model can support the coordinated scheduling of energy under the constraints of different load demands, energy supplies, and system efficiencies, thereby improving the overall efficiency and low-carbon operation ability of the system.

[0084] In an embodiment of the present invention, the interactive operation mode includes energy purchase from the superior network, energy purchase and sale with peer energy converters, and energy sale to lower-level users. It should be understood here that the superior network refers to large-scale energy supply networks such as power grids, heat grids, and gas grids, which may be regional or national public energy systems. In this mode, the interaction between the distributed energy hub and the superior network is mainly reflected in energy purchase. When the energy production capacity of the distributed energy hub itself, such as photovoltaic and wind power, is insufficient to meet the demand, it needs to purchase energy from the superior network to ensure the stability and reliability of the supply. In addition, when there is an excess of energy, the distributed energy hub may also sell the surplus energy, such as excess electricity, to the superior network to balance the energy supply and demand. Such an interactive mechanism not only ensures an adequate energy supply within the system but also provides a certain income for the hub system. Peer energy converters refer to other energy exchange devices or energy suppliers at the same level as the distributed energy hub, and the interaction between them is mainly based on energy trading. In this mode, the distributed energy hub can purchase energy from other energy converters, especially when the energy supply within the system is insufficient or some energy conversion devices, such as electric heating equipment and gas boilers, malfunction. The hub can make up for the energy gap through transactions with other energy converters. The distributed energy hub not only conducts transactions with the superior network and peer energy converters but also needs to sell energy to lower-level users, such as households, commercial buildings, and industrial users. This is one of the core objectives of the energy hub system, that is, to supply the generated or stored energy, such as electricity, heat, and gas, to the end users. Therefore, the distributed energy hub needs to perform precise scheduling according to these demands, and the system will adjust the energy supply according to the user's demand response. Through energy purchase from the superior power grid, energy purchase and sale between peer energy converters, and energy sale to lower-level users, the distributed energy hub can flexibly adjust according to real-time energy demands, market changes, and supply and demand conditions to ensure the stability and efficiency of the system operation. Such an interactive mode can not only improve the flexibility and reliability of energy use but also promote low-carbon and efficient energy utilization, providing support for sustainable development.

[0085] In an embodiment of the present invention, the cooperative operation optimization model includes energy sale revenue, energy interaction revenue, energy purchase cost, grid connection cost, curtailment cost of wind and light, and carbon trading cost. The objective function of the distributed energy converter can be expressed as:

[0086]

[0087] In the formula, U i is the benefit function of the distributed energy converter i; is the energy sale revenue of the distributed energy converter i to its own users; is the energy interaction revenue with other energy converters; is the energy purchase cost of distributed energy transducer i from the upper-level network, including electricity purchase cost, heat purchase cost, and gas purchase cost; is the network passing cost generated during energy interaction with other energy transducers, and this cost should be evenly shared by both the energy purchaser and the energy seller; is the cost of abandoned wind and light of energy transducer i; is the carbon trading cost of distributed energy transducer i. Through the combination of the above revenues and costs, the objective function of the distributed energy transducer can be used to quantify its overall benefits. The goal of the energy transducer is to maximize the value of this objective function, which means finding a balance among the energy selling revenue, energy interaction revenue, and energy purchase cost, while minimizing the network passing cost, abandoned wind and light cost, and carbon trading cost. In this embodiment, the cooperative operation optimization model formulates the optimal operation strategy for the distributed energy transducer. By maximizing its objective function, it can achieve efficient energy scheduling and low-carbon optimization while ensuring the interests of all participating parties.

[0088] In the embodiment of the present invention, the integrated demand response model in the load aggregator benefit model includes a curtailable load model and a shiftable load model:

[0089] The expression of the curtailable load model is:

[0090]

[0091] In the formula, and respectively represent the curtailable electric load and heat load of electric-heat user i at time t; and represent the curtailable electric and heat load coefficients of electric-heat user i; and represent the electric and heat load demands of electric-heat user i at time t without considering the integrated demand response;

[0092] The expression of the shiftable load model is:

[0093]

[0094] In the formula, and respectively represent the shiftable electric load and heat load of electric-heat user i at time t; and represent the shiftable electric and heat load coefficients of electric-heat user i;

[0095] The expression of the load aggregator benefit objective function is:

[0096]

[0097] In the formula, U LA is the benefit function of the load aggregator; is the energy consumption utility of the electric - heat energy user i; is the cost of loss of energy consumption satisfaction of the electric - heat energy user i; is the energy consumption cost paid by the electric - heat energy user i to the distributed energy converter i. In this embodiment, demand response can be realized. That is, through the load curtailment and load shifting models, the load aggregator can flexibly respond to the fluctuations of the grid load, optimize the load distribution of the grid, avoid power shortages during peak hours, and balance the power supply and demand. In addition, by reasonably adjusting the load response of users, both the stable operation of the grid can be achieved and the comprehensive benefits of users and the system can be maximized. The establishment of the above - mentioned model and objective function can help the load aggregator optimize the operation of the power system while meeting the user's needs, improve energy efficiency, reduce the peak - valley difference, and ensure the stability and sustainability of energy supply.

[0098] In the embodiment of the present invention, according to the basic principle of Nash bargaining, the cooperative operation model of the distributed energy hub can be expressed as:

[0099]

[0100] In the formula, and are the breakdown points of negotiation of the load aggregator and the distributed energy converter i respectively. The breakdown point of negotiation refers to the worst - case scenario, that is, the negotiation between the load aggregator and the distributed energy converter, as well as between each energy converter fails, and there is no cooperation between them. The load aggregator does not purchase energy from the energy conversion station but directly purchases energy from the superior network. and respectively represent the benefits improved by the load aggregator and the distributed energy converter i through cooperative operation;

[0101] This model is solved by solving two sub - problems of maximizing social benefits and maximizing payment benefits. The specific models of the two sub - problems are as follows:

[0102] Sub - problem 1: Maximizing social benefits:

[0103]

[0104] Sub - problem 2: Maximizing payment benefits

[0105]

[0106] In sub-problem 1, since the energy consumption cost of the load aggregator is offset by the energy sales revenue of all distributed energy converters, and the energy interaction revenues among the distributed energy converters are offset, the energy trading price variable in sub-problem 1 is eliminated, and only the energy trading power variable remains. In sub-problem 2, the variables with superscript "*" are the optimization results of sub-problem 1. In this embodiment, the negotiation breakdown point refers to the situation in the cooperation negotiation where, if no agreement can be reached between the load aggregator and the distributed energy converters, each party will ultimately choose its own optimal solution. In this sub-problem, the energy trading price variable is eliminated, and the model only includes the energy trading power variable. The maximization of social benefits focuses on the overall operation efficiency of the system, rather than simply the payment issues of a single participant. Therefore, the energy trading price is no longer a key factor in the optimization process of sub-problem 1. The key lies in optimizing the quantity of energy trading, that is, the power flow, to maximize the system benefits. At this time, the energy consumption cost of the load aggregator is offset by the energy sales revenue of all distributed energy converters, that is, the fees paid by the load aggregator to each energy converter and the revenues obtained by the energy converters from the load aggregator will ultimately offset each other, which directly affects the energy flow and the overall benefits.

[0107] In sub-problem 2, the maximization of payment benefits focuses on the payment arrangements among all parties during the cooperative operation, that is, how to distribute the additional benefits brought by the cooperative operation among all participating parties. This sub-problem mainly focuses on how to maximize the payment schemes and collaborative revenues of all parties, so that the cooperation is beneficial to all parties. The variables with superscript "*" in the model represent the results optimized in sub-problem 1. This means that when solving the maximization of payment benefits, the energy trading power and the overall benefits optimized in the stage of maximizing social benefits are known. With this information, the payment arrangements between the load aggregator and all distributed energy converters can be further optimized to ensure that both or all parties in the cooperation can obtain reasonable payment shares.

[0108] By combining these two sub-problems, first determine the optimal energy trading power distribution through the maximization of social benefits, and then ensure that all participating parties can obtain a fair revenue distribution through the maximization of payment benefits. This step-by-step solution method can not only ensure the maximization of the overall system benefits, but also reasonably distribute the revenues, avoiding the interruption of cooperation due to unreasonable payments. Through the framework of the Nash bargaining theory, the interest balance among all parties in the cooperation is ensured, achieving an ideal game result. Through this step-by-step optimization method, the model can maximize the system benefits while ensuring the balance of the interests of all parties and the smooth progress of the cooperation.

[0109] Based on the above embodiment, when the ADMM algorithm is used to solve the problem of maximizing social benefits, auxiliary variables are introduced to decouple the power of energy purchase and sale. The distributed optimization objective functions of the load aggregator and the energy converters are respectively:

[0110]

[0111] wherein and are the augmented Lagrangian functions of the load aggregator and the distributed energy converter \(i\) with respect to sub - problem 1, respectively; and are the optimization variables of the load aggregator, representing the electrical power and thermal power purchased by the electrical - thermal energy user from the distributed energy converter \(i\) at time \(t\), respectively; and are the optimization variables of the distributed energy converter \(i\), representing the electrical power and thermal power sold by the energy converter \(i\) to the electrical - thermal energy user at time \(t\), respectively; and are the electrical power and thermal power supplied by the distributed energy converter \(j\) to the energy converter \(i\) at time \(t\), respectively; and are the dual variables of the energy trading power between the energy converter \(i\) and the load aggregator at time \(t\), respectively; and are the dual variables of the energy interaction power between the distributed energy converters at time \(t\), respectively; \(\rho\) 1 is the penalty factor of sub - problem 1; where The subscript 2 of the vector represents taking the square root of the vector, representing the Euclidean norm, and the superscript 2 represents the square of the Euclidean norm;

[0112] The update formula for the dual variables in sub - problem 1 is:

[0113]

[0114] The convergence condition of sub - problem 1 is that the dual residual is less than the convergence value, and the convergence formula can be expressed as:

[0115]

[0116] wherein, the superscript \(k\) represents the current iteration number; \(\varepsilon\) 1 represents the convergence threshold of sub - problem 1.

[0117] In this embodiment, in this model, the ADMM algorithm is used to solve the problem of maximizing the social benefits of a distributed energy hub. By performing distributed solution on the optimization objective functions of the load aggregator and the distributed energy converter, the maximization of the global benefits is achieved. To effectively solve the problem, the ADMM algorithm introduces auxiliary variables to decouple the relationship between the energy purchase power and the energy selling power, enabling different participants, namely the load aggregator and the distributed energy converter, to independently optimize their decision variables in their respective objective functions, thus simplifying the problem. The augmented Lagrangian function incorporates the constraints in the problem into the objective function and adjusts the update of the decision variables through a penalty factor, thereby better coordinating the energy transactions between the load aggregator and the distributed energy converter. The dual variables will be updated as the optimization process progresses to ensure that all constraints are satisfied during the solution process, ultimately achieving the goal of maximizing social benefits. In this embodiment, by introducing auxiliary variables and the augmented Lagrangian function, the ADMM algorithm can decouple the energy purchase and selling powers in the distributed energy hub, enabling the load aggregator and the distributed energy converter to optimize independently. By continuously iteratively updating the dual variables and adjusting the system parameters, the ADMM algorithm can effectively solve the problem of maximizing social benefits and ultimately achieve the maximization of global benefits.

[0118] In addition, in the embodiments of the present invention, as Figure 6 shown, the specific operations of step S30 include the following steps:

[0119] S31: For the benefit optimization model of the distributed energy converter, determine the electricity purchase, heat purchase, gas purchase prices, the power generation and heat production efficiencies of each device, the electro-thermal conversion efficiency, the charge and discharge power, the heat storage and release power, as well as the renewable energy power generation prediction and actual output and other data according to the actual system parameters, and substitute them into the corresponding formulas to calculate the energy selling revenue, the energy interaction revenue, the energy purchase cost, the grid connection cost, the wind and light abandonment cost, and the carbon trading cost, thereby constructing an accurate benefit objective function for the distributed energy converter;

[0120] S32: For the benefit optimization model of the load aggregator, collect information such as the energy consumption utility function and the energy consumption satisfaction loss cost function of the electro-thermal energy users, and construct a benefit objective function for the load aggregator in combination with the trading relationship with the distributed energy converter to accurately reflect its benefit situation in the system.

[0121] As Figure 7 shown, step S40 specifically includes the following steps:

[0122] S41: Determine the negotiation breakdown points of the load aggregator and the distributed energy converter, the states when both parties do not cooperate;

[0123] S42: Calculate the benefits improved by the two parties through cooperative operation, and substitute them into the formula of the cooperative operation model;

[0124] S43: By solving two sub-problems of maximizing social benefits and maximizing payment benefits, find the equilibrium solution to achieve Pareto optimality for each entity.

[0125] As Figure 8 shown, step S50 specifically includes the following steps:

[0126] S51: Initialize the system state parameters and input parameters;

[0127] S52: The load aggregator obtains the shared variable of the energy selling price, substitutes it into its benefit optimization formula for optimization, and updates the energy purchasing price variable. The distributed energy converter obtains the shared variables of the energy purchasing price and the interaction price, substitutes them into its benefit optimization formula for optimization, and updates the energy selling price and the interaction price variables. At the same time, both parties respectively obtain the corresponding power variables, namely the energy purchasing power, the energy selling power, the interaction power, etc., and perform optimization and update;

[0128] S53: In each iteration process, update the shared variables and the dual variables according to the formula;

[0129] S54: Check the convergence condition. If it is not satisfied, continue the iteration. If it is satisfied, output the energy trading price and power among each entity to complete the low-carbon optimal scheduling of the distributed energy hub.

[0130] The parameter settings of the ADMM algorithm are as follows: The maximum number of iterations is 500; the convergence thresholds of sub-problems 1 and 2 are 10-2 and 10-5 respectively, and the penalty factors are both 10-2. To verify the effectiveness of the proposed scheme in this embodiment, the following two schemes are respectively set for comparison:

[0131] Scheme 1: The cooperative operation optimization scheme proposed in this embodiment;

[0132] Scheme 2: There is no energy interaction among the distributed energy converters, and each energy converter only supplies the users in the responsible area. To ensure the profitability of the distributed energy converters, the energy price supplied to the users is processed at 1.2 times the price of the superior network. The electro-thermal energy users perform comprehensive demand response according to the price published by the energy converter.

[0133] In the economic analysis of this embodiment, after optimization, the comparison of the revenue-cost values of the distributed energy converters and the load aggregator under the two schemes is shown in Tables 2 and 3 below:

[0134] Table 2 Comparison of revenue-cost of distributed energy converters under each scheme

[0135]

[0136] Table 3 Comparison of revenue-cost of load aggregators under each scheme

[0137]

[0138] As can be seen from the above table, Solution 1 takes into account the cooperative game among various entities. By continuously optimizing the energy price and balancing the interest relationship among various entities, the energy trading price between the distributed energy converter and the load aggregator is lower than the energy purchase price of the distributed energy converter from the superior network, and the energy sales revenue is also lower than the energy sales revenue value of Solution 2 without considering cooperative operation. In Solution 1, Converter A shows a state of selling energy to other converters at the end of a scheduling period, and its interaction revenue is positive, which is equal to the opposite of the sum of the interaction revenues of Converters B and C. In Solution 2, each converter operates independently and there is no energy connection between them, so the interaction revenue and the network passing cost are both 0.

[0139] Since the natural gas price of the superior network is lower than the electricity price and the gas price, in Solution 1, each energy converter station preferentially uses the renewable energy power supply and gas energy supply within the station and between stations to reduce the proportion of heat and electricity purchased from the superior network, and its energy purchase cost and the cost of wind and light abandonment are also lower than the corresponding cost values of Solution 2. Also, since the capacity of the gas turbines in each energy converter station is small, the carbon emissions from purchasing energy from the superior network mainly come from coal-fired units. The greater the difference in energy purchase costs, the greater the carbon trading cost, so that the carbon trading cost value of each converter in Solution 2 is higher than that in Solution 1.

[0140] Considering all cost and revenue modules, the benefit values of Converters A, B, and C in Solution 1 are all higher than those in Solution 2. The total benefit value of the converter alliance in Solution 1 is 73,660 yuan, which is about 19.72% higher than the total benefit value of 61,527 yuan in Solution 2, indicating that the cooperative game operation optimization model proposed in this embodiment is beneficial to improving the interests of the distributed energy converter alliance. The benefit values of electric and thermal energy users 1, 2, and 3 in Solution 1 are all higher than those in Solution 2. The total benefit value of the load aggregator in Solution 1 is 384,583 yuan, which is 13,654 yuan higher than the total benefit value of 370,929 yuan in Solution 2, indicating that the cooperative game operation optimization model of this method is beneficial to reducing the energy consumption cost of the load aggregator and thus improving its profit value. Through the multi-entity cooperative game, the total benefit value of each energy converter and load aggregator in the distributed energy conversion system can be effectively improved, fully reflecting the effectiveness and economy of this method.

[0141] In the energy trading price analysis of this embodiment, as Figure 9As shown in the figure, regarding the optimization of the trading electricity price, most of its prices are distributed around 0.3 yuan / kWh. When the electricity price of the superior power grid is in the peak and normal periods, the trading electricity price among distributed energy converters can be appropriately increased, so as to increase the electricity energy interaction income of each energy converter and reduce the power purchase cost from the superior power grid. Regarding the optimization of the trading heat price, similar to the trading electricity price, the heat price is mostly distributed around 0.25 yuan / kWh. When the trading heat quantity among energy converters increases, the trading heat price can be appropriately increased, so as to increase the heat energy interaction income of the corresponding energy converter. Generally speaking, the optimized energy trading price is less than the energy purchase price from the superior network. Therefore, when the internal energy supply of the energy conversion station is insufficient, priority should be given to achieving the optimal operation economy of energy converters through energy interaction. Such as Figure 10 As shown in the figure, the energy purchase price of the load aggregator is less than the external price at any time period, which can effectively reduce the energy purchase cost of electro-thermal energy users. To sum up, through the distributed energy converter system Nash bargaining model proposed by this device, the economic benefits of distributed energy converters and load aggregators can be improved.

[0142] In the carbon emission analysis of this embodiment, such as Figure 11 As shown in the figure, the carbon dioxide emissions of the distributed energy converter system of Scheme 1 are only higher than those of Scheme 2 during the time period from 07:00 to 09:00, and are lower than those of Scheme 2 in the remaining time periods. After a scheduling cycle, the total carbon emissions of the system in Scheme 1 are reduced by about 38.97% compared with Scheme 2, thus verifying the low-carbon nature of the cooperative operation model proposed in this embodiment. Such as Figure 12 As shown in the figure, in Scheme 2, each energy converter operates independently, and in some time periods, new energy cannot be consumed, resulting in the phenomenon of abandoned wind and light; after considering the cooperative operation of energy converters in Scheme 1, renewable energy can be consumed in a timely manner, and the phenomenon of abandoned wind and light is reduced. After a scheduling cycle, the renewable energy consumption rate of Scheme 2 is only 81.35%, and the renewable energy consumption rate of Scheme 1 can reach 100%, which shows that after cooperative games among distributed energy converters, the consumption rate of renewable energy can be effectively improved.

[0143] Those skilled in the art should know that the embodiments of the present application can be provided as methods, devices, electronic devices or computer storage medium products. Therefore, the embodiments of the present application can completely adopt hardware embodiments, hardware and software combined embodiments or pure software embodiments. The test detection process data processing device in the embodiments of the present application will be introduced below. The device embodiments in the following text correspond to the method embodiments in the above text. Those skilled in the art can understand the following implementation process based on the above description, and no detailed description will be given here.

[0144] In an embodiment of the present invention, a distributed energy hub scheduling device is further proposed, including:

[0145] An energy hub modeling module, which is used to establish an energy hub model for a distributed energy aggregation point, introduce various energy conversion devices and electricity storage and heat storage devices to form an electric heat compensation station, and determine the relationships of energy input, output and energy coupling devices;

[0146] An interactive operation mode determination module, which is used to determine the interactive operation mode of the distributed energy hub, and conduct interactions between the distributed energy aggregation point and the superior power grid, heat network and gas network, as well as interactions with the lower-level users;

[0147] A cooperative operation optimization model construction module, which is used to construct a cooperative operation optimization model for the distributed energy hub, including a distributed energy trader benefit optimization model considering the carbon trading mechanism and a load aggregator benefit model considering the integrated electric and heat demand response;

[0148] A cooperative game module, which conducts a cooperative game between the distributed energy trader benefit model and the load aggregator benefit model based on the Nash bargaining theory;

[0149] A distributed solution module, which uses the alternating direction method of multipliers (ADMM) algorithm, introduces auxiliary variables and shared variables for distributed solution.

[0150] In the following parts of the embodiments of the present invention, the embodiments of the electronic device and the computer storage medium in the embodiments of the present invention are introduced. The embodiments of the electronic device and the computer storage medium in the following text correspond to the method embodiments in the above text. Those skilled in the art can understand the implementation process in the following text based on the above description, and details will not be described here again.

[0151] In an embodiment of the present invention, a computer device is also proposed, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method is implemented.

[0152] The embodiments of the present application also provide a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned method is implemented.

[0153] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0154] The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be implemented directly in hardware, in software modules executed by a processor, or in a combination thereof. The software modules may be disposed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0155] Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A distributed energy hub scheduling method, characterized in that: The following steps are involved: Establish an energy hub model of distributed energy aggregation points, introduce a variety of energy conversion equipment and electricity and heat storage equipment to form an electric heating station, and determine the relationship between energy input, output and energy coupling equipment; Determine the interactive operation mode of distributed energy hubs, and conduct the interaction between distributed energy aggregation points and upper-level power grids, heat networks and gas networks, as well as interaction with lower-level users; Construct a distributed energy hub cooperative operation optimization model, including a distributed energy converter benefit optimization model considering the carbon trading mechanism and a load aggregator benefit model considering the comprehensive demand response of electricity and heat; Based on Nash bargaining theory, a cooperative game is conducted between the distributed energy converters and load aggregators in their benefit models; The alternating direction multiplier method ADMM algorithm is used to introduce auxiliary variables and shared variables for distributed solution.

2. The distributed energy hub scheduling method according to claim 1, characterized in that: The mathematical model of the input side, coupling side and output side of the energy hub is expressed as follows: In the formula, and They are the electricity, heat and gas power input into the energy hub of the energy conversion station respectively; and are the electrical and thermal power output of the energy hub of the energy conversion station respectively; P REN is the output power of distributed photovoltaic or wind power; P DHE The electric power consumed by the electric heating equipment; S CHP , S GT and S GB are the input powers of CHP unit, gas turbine and gas boiler respectively; and are the power generation efficiency and heat generation efficiency of CHP equipment respectively; η GT is the power generation efficiency of the gas turbine; η GB is the heat generation efficiency of the gas boiler; η DHE The electric-to-heat conversion efficiency of electric heating equipment; and are the charging and discharging power of the battery respectively; and They are the heat storage and heat release power of the hot water tank respectively; and They are the power changes caused by the electricity and heat load demand responses, respectively.

3. The distributed energy hub scheduling method according to claim 1, characterized in that: The interactive operation mode includes energy purchase from the upper-level network, energy purchase and sale from the same-level energy converters, and energy sale with lower-level users.

4. The distributed energy hub scheduling method according to claim 1, characterized in that: The cooperative operation optimization model includes energy sales revenue, energy interaction revenue, energy purchase cost, grid access cost, wind and solar curtailment cost, and carbon trading cost. The objective function of the distributed energy converter can be expressed as: Where U i is the benefit function of distributed energy conversion quotient i; The revenue from energy sales by distributed energy converter i to its own users; To benefit from energy interaction with other energy converters; The cost of energy purchased by distributed energy converter i from the upper network, including the cost of electricity, heat and gas; The grid connection cost incurred when interacting with other energy converters should be shared equally by the energy purchaser and the energy seller; is the wind and solar curtailment cost of energy converter i; is the carbon trading cost of distributed energy converter i.

5. The distributed energy hub scheduling method according to claim 4, characterized in that: The comprehensive demand response model in the load aggregator benefit model includes a curtailable load model and a transferable load model: The curtailable load model expression is: In the formula, and They represent the electric load and heat load that can be reduced by electric heating user i at time t respectively; and It represents the coefficient of the electricity and heat load that can be reduced for electric heating user i; and represents the electricity and heat load demand of electric and heat user i at time t without considering comprehensive demand response; The transferable load model expression is: In the formula, and They represent the electric load and heat load that can be transferred by electric and heat user i at time t respectively; and represents the transferable electricity and heat load coefficient of electric heating user i; The load aggregator benefit objective function expression is: Where U LA is the benefit function of the load aggregator; The energy usage of electric and thermal energy user i; The energy satisfaction loss cost of electric heat energy user i; The energy cost paid by electric thermal energy user i to distributed energy converter i.

6. The distributed energy hub scheduling method according to claim 5, characterized in that: According to the basic principle of Nash bargaining, the cooperative operation model of distributed energy hubs can be expressed as: In the formula, and are the negotiation breakdown points of the load aggregator and the distributed energy converter i, respectively. The negotiation breakdown point refers to the worst scenario, that is, the negotiation between the load aggregator and the distributed energy converter, and between the energy converters fails, and there is no cooperation between them. The load aggregator does not purchase energy from the energy converter station, but directly purchases energy from the upper network. and They represent the benefits improved by the load aggregator and distributed energy converter i through cooperative operation respectively; The model is solved by solving two sub-problems: maximizing social benefits and maximizing payment benefits. The specific models of the two sub-problems are as follows: Sub-problem 1: Maximizing social benefits: Sub-problem 2: Maximizing payment benefits In the formula, in sub-problem 1, since the energy consumption cost of the load aggregator offsets the energy sales income of all distributed energy converters, the energy interaction income between the distributed energy converters offsets each other, so the energy trading price variable in sub-problem 1 is eliminated, and only the energy trading power variable is contained. In sub-problem 2, the variables with a superscript "*" are the optimization results of sub-problem 1.

7. The distributed energy hub scheduling method according to claim 6, characterized in that: When the ADMM algorithm solves the problem of maximizing social benefits, auxiliary variables are introduced to decouple the power of energy purchase and sale. The distributed optimization objective functions of load aggregators and energy converters are: In the formula, and are the augmented Lagrangian functions of the load aggregator and distributed energy converter i with respect to subproblem 1, respectively; and are the optimization variables of the load aggregator, representing the electric power and thermal power purchased by the electric and thermal energy users from the distributed energy converter i at time t; and are the optimization variables of distributed energy converter i, representing the electric power and thermal power sold by energy converter i to the electric and thermal energy users at time t; and are the electrical power and thermal power supplied by distributed energy converter j to energy converter i at time t, respectively; and are the dual variables of the energy transaction power between energy converter i and load aggregator at time t; and are the dual variables of the energy interaction power between distributed energy converters at time t; ρ1 is the penalty factor of sub-problem 1; The subscript 2 represents the square root of the vector, which represents the Euclidean norm, and the superscript 2 represents the square of the Euclidean norm; The update formula for the dual variable in subproblem 1 is: The convergence condition of subproblem 1 is that the dual residual is less than the convergence value, and the convergence formula can be expressed as: Where, the superscript k represents the current iteration number; ε1 represents the convergence threshold of subproblem 1.

8. A distributed energy hub dispatching device, characterized in that: include: Energy hub modeling module, used to establish the energy hub model of distributed energy aggregation points, introduce a variety of energy conversion equipment and electricity and heat storage equipment, form an electric heating station, and determine the relationship between energy input, output and energy coupling equipment; The interactive operation mode determination module is used to determine the interactive operation mode of the distributed energy hub, and to interact with the upper-level power grid, heat network and gas network as well as with the lower-level users; The cooperative operation optimization model building module is used to build a distributed energy hub cooperative operation optimization model, including a distributed energy converter benefit optimization model considering the carbon trading mechanism and a load aggregator benefit model considering the comprehensive demand response of electricity and heat; The cooperative game module conducts cooperative games between distributed energy converters and load aggregators based on Nash bargaining theory; The distributed solution module uses the alternating direction multiplier method ADMM algorithm and introduces auxiliary variables and shared variables for distributed solution.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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