Decentralized scheduling method and system for electric heating system based on Nash bargaining

Abstract

The present invention discloses a method and system for decentralized scheduling of electric and thermal systems, particularly in the field of optimized scheduling of integrated energy systems, and in particular, in the field of decentralized scheduling of electric and thermal systems based on Nash bargaining. The method comprises the following steps: quantifying the regulation capacity between the thermal system and the electric power system; constructing an electric and thermal system cooperative game optimization model based on Nash bargaining according to the uneven distribution of benefits between the thermal system and the electric power system in the electric and thermal system regulation capacity trading mechanism under incomplete information; solving the electric and thermal system cooperative game optimization model and distributing the cooperative surplus of the electric and thermal system regulation capacity trading mechanism by considering the multi-time-scale collaborative relationship between the thermal system and the electric power system. The present invention designs an electric and thermal system scheduling strategy based on multi-time-scale collaboration and an electric and thermal system regulation capacity trading mechanism under incomplete information, distributes the cooperative surplus through Nash bargaining, and promotes cooperation between the electric power system and the thermal system.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system optimization and scheduling, and in particular to a Nash bargaining-based electric heating system decentralized scheduling method and system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Traditional research generally treats the power and thermal systems as a single stakeholder. Centralized optimal scheduling requires the collection of data from both systems. However, in reality, the power and thermal systems typically belong to different stakeholders, and private data within these stakeholders, such as system parameters and user loads, is generally not disclosed. Consequently, centralized optimal scheduling faces the challenges of privacy breaches and compromised operational independence among these stakeholders.

[0004] Unlike centralized optimization and scheduling, decentralized optimization and scheduling only requires the exchange of a small amount of boundary information between stakeholders, and their internal private information does not need to be disclosed, effectively protecting the privacy of stakeholders. Furthermore, decentralized optimization and scheduling decomposes the overall optimization problem into sub-optimization problems that each stakeholder solves independently, maintaining operational independence. Therefore, research on decentralized optimization and scheduling for integrated electric and thermal energy systems is highly necessary.

[0005] The coordinated optimal scheduling of integrated electric and thermal energy systems is typically based on collective rationality, namely, minimizing the total operating costs of both the power and thermal systems. Compared to the separate optimal scheduling of the power and thermal systems, coordinated optimal scheduling of electric and thermal systems reduces the total cost of the integrated electric and thermal energy system, but at the same time harms the individual interests of the thermal system. Specifically, coordinated optimal scheduling of electric and thermal systems requires the thermal system to fully utilize the virtual energy storage characteristics of the heating network to provide regulation capabilities for the electric system, thus deviating from the optimal strategy for independent thermal system scheduling. This requires the heating network to increase its temperature, resulting in greater heat losses and, in turn, an increase in the total operating costs of the thermal system. Based on the assumption of individual rationality, the thermal system has no incentive to cooperate. Therefore, coordinated optimal scheduling of electric and thermal systems based on collective rationality is not incentive-compatible.

[0006] Currently, existing technologies utilize a cooperative game model based on Nash bargaining to treat the coordinated optimization of electricity and heat as a market game. This model alternates between optimizing the power system and optimizing the heat system, updating price signals until a balance is found, achieving incentive compatibility. However, existing cooperative game models based on Nash bargaining fail to consider the power and heat systems as independent stakeholders, fail to fully exploit the regulation capacity of thermal energy and achieve direct and dynamic sharing of regulation capacity between electricity and heat, and fail to consider the response differences between thermal and electricity across multiple time scales, resulting in unsatisfactory scheduling results. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a decentralized scheduling method and system for electric heating systems based on Nash bargaining, taking into account the actual operating status of the heating pipeline network, quantifying the regulation capacity of the heating pipeline network, designing an electric heating system scheduling strategy based on multi-time scale collaboration and an electric heating system regulation capacity trading mechanism under incomplete information, allocating cooperative surplus through Nash bargaining, minimizing the overall benefit cost of the electric heating integrated energy system, while reducing the total operating costs of the power system and the thermal system, thereby promoting cooperation between the power system and the thermal system.

[0008] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0009] A first aspect of the present invention provides a decentralized scheduling method for an electric heating system based on Nash bargaining, comprising the following steps:

[0010] Obtain the parameters of the integrated electric and thermal energy system to be dispatched and quantify the regulation capacity between the thermal system and the power system. The CHP unit is used as the regulation capacity transfer device from the thermal system to the power system. By changing the power and regulation capacity of the CHP unit, regulation capacity is provided to the power system to meet the regulation capacity requirements of the power system.

[0011] Establish a trading mechanism for the regulation capacity of electric and thermal systems under incomplete information to achieve power coordination and regulation capacity sharing between thermal and electric systems;

[0012] According to the uneven distribution of benefits between the thermal system and the power system in the regulation capacity trading mechanism of the electric heating system under incomplete information, a cooperative game optimization model of the electric heating system based on Nash bargaining is constructed.

[0013] Considering the multi-time-scale synergistic relationship between the thermal system and the power system, a decentralized solution algorithm is used to solve the cooperative game optimization model of the electric heating system, distribute the cooperative surplus of the electric heating system regulation capacity trading mechanism, and complete the decentralized scheduling of the electric heating system.

[0014] Furthermore, the specific steps to quantify the regulation capability between the thermal system and the power system are as follows:

[0015] Taking the CHP unit as the heat source, the regulation capacity of the thermal system is quantified through the synergy between the CHP unit and the dynamic characteristics of the heating network;

[0016] Based on incomplete information conditions, the regulation capability provided by the thermal system to the power system is quantified using the feasible domain of the regulation capability of the electric-thermal system.

[0017] Furthermore, through the synergistic effect of the dynamic characteristics of the CHP unit and the heating network, in the process of quantifying the regulating capacity of the thermal system, the changes in the heat source power and regulating capacity are compensated by controlling the overall temperature regulation of the heating network.

[0018] Furthermore, the feasible domain of the regulation capacity of the electric heating system is regarded as a polyhedron composed of the feasible domains of multiple CHP units, and obtaining the range of the feasible domain is equivalent to obtaining the volume of the polyhedron.

[0019] Furthermore, in the electric and thermal system regulation capacity trading mechanism under incomplete information, the power system and the thermal system simultaneously carry out power coordination and regulation capacity sharing through CHP units, allowing the thermal system to provide more regulation capacity for the power system.

[0020] Furthermore, the specific steps of constructing a cooperative game optimization model for the electric heating system based on Nash bargaining include:

[0021] According to the power system sub-problem and the thermal system sub-problem, the power system objective function and the thermal system objective function are constructed respectively;

[0022] Based on Nash bargaining theory, a cooperative game optimization model between the electric and thermal systems is established according to the objective functions of the power system and the thermal system.

[0023] According to the cooperative game optimization model between the electric and thermal systems, the cooperative benefit maximization sub-problem and the transaction payment sub-problem are set;

[0024] Set corresponding wind power acceptance risk metrics and system constraints.

[0025] Furthermore, the specific steps of using a distributed solution algorithm to solve the cooperative game optimization model of the electric heating system are as follows:

[0026] Construct the electric-thermal system coupling constraints according to the corresponding system constraints;

[0027] Relaxing the coupling constraints of the electric and thermal systems decouples the cooperative game optimization model between the electric and thermal systems into the power system subproblem and the thermal system subproblem;

[0028] Based on the cooperative benefit maximization sub-problem, the power system sub-problem and the thermal system sub-problem are solved respectively;

[0029] Based on the transaction payment subproblem, the power system subproblem and the thermal system subproblem are solved separately.

[0030] A second aspect of the present invention provides a distributed scheduling system for electric heating systems based on Nash bargaining, comprising:

[0031] The regulation capacity quantification module is configured to obtain parameters of the electric and thermal integrated energy system to be dispatched and quantify the regulation capacity between the thermal system and the power system. The CHP unit is used as the regulation capacity transmission device from the thermal system to the power system. The module provides regulation capacity to the power system by changing the power and regulation capacity of the CHP unit to meet the regulation capacity requirements of the power system.

[0032] The regulation capacity sharing module is configured to establish a regulation capacity trading mechanism for the electric and thermal systems under incomplete information, so as to achieve power coordination and regulation capacity sharing between the thermal and electric systems;

[0033] The cooperative game optimization module is configured to construct a Nash bargaining-based cooperative game optimization model for the electric heating system based on the uneven distribution of benefits between the thermal system and the power system in the electric heating system regulation capacity trading mechanism under incomplete information;

[0034] The optimization scheduling module is configured to consider the multi-time-scale synergy between the thermal system and the power system, adopt a distributed solution algorithm to solve the cooperative game optimization model of the electric heating system, distribute the cooperative surplus of the electric heating system regulation capacity trading mechanism, and complete the decentralized scheduling of the electric heating system.

[0035] A third aspect of the present invention provides a medium having a program stored thereon, which, when executed by a processor, implements the steps of the Nash bargaining-based decentralized scheduling method for an electric heating system as described in the first aspect of the present invention.

[0036] The fourth aspect of the present invention provides a device comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the distributed scheduling method for electric heating systems based on Nash bargaining as described in the first aspect of the present invention are implemented.

[0037] One or more of the above technical solutions have the following beneficial effects:

[0038] The present invention discloses a decentralized scheduling method and system for electric heating systems based on Nash bargaining. On the basis of the traditional coordinated scheduling strategy for electric heating systems, the method further quantifies the regulation capability that can be provided by the dynamic characteristics of the heating pipeline network, designs a coordinated scheduling strategy for electric heating systems based on multi-time scale collaboration, defines the feasible domain of regulation capability, and realizes direct dynamic sharing of regulation capability between electric heating systems under the background of decentralized operation of electric heating entities, making full use of the regulation capability of the thermal system, thereby helping the power system to improve its new energy absorption capacity.

[0039] The present invention proposes a trading mechanism for the regulation capacity of electric and thermal systems under incomplete information, so that the regulation capacity provided by the thermal system can be directly used as a scheduling resource to participate in the coordinated scheduling of the electric and thermal integrated energy system. A cooperative game optimization model based on Nash bargaining is introduced. On the basis of the regulation capacity trading between electric and thermal systems, the cooperative surplus of the electric and thermal systems is allocated, so that the overall benefit cost of the electric and thermal systems is minimized while the total operating costs of the power system and the thermal system are reduced, thereby promoting cooperation between the power system and the thermal system, and realizing that the power system obtains rich regulation capacity resources while the interests of the thermal system are protected.

[0040] The coordinated optimization and scheduling of integrated electric and thermal energy systems involves the coupling of multiple time scales between the electric and thermal systems. This invention uses CHP units to couple electric and thermal energy during production and use. Given the different time characteristics of electric and thermal energy, namely rapid electric energy transmission and timely response, and thermal energy transmission with thermal delays and slower response, this invention adopts a coordinated scheduling mechanism for electric and thermal systems. This discretizes the thermal scheduling cycle within a region, aligning the thermal scheduling time scale with the electric power scheduling time scale. The integrated electric and thermal energy system simultaneously schedules both the electric and thermal systems within a single time period, achieving coordinated electric and thermal system decision-making on the same time scale.

[0041] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0043] Figure 1 Schematic diagram of the electric heating system regulation capacity trading mechanism under incomplete information in Example 1 of the present invention;

[0044] Figure 2 This is a schematic diagram of electrothermal multi-time scale coordination in Example 1 of the present invention;

[0045] Figure 3 Schematic diagram of the decentralized solution process of the cooperative game optimization model based on ADMM in Example 1 of the present invention. DETAILED DESCRIPTION

[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0047] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations;

[0048] Example 1:

[0049] The first embodiment of the present invention provides a distributed scheduling method for an electric heating system based on Nash bargaining, such as Figure 1 As shown in the figure, the power system and the thermal system are coupled through CHP (Combined Heat and Power) units. By considering the dynamic characteristics of the heating network, the unit output and regulation capacity are optimized, thereby improving the regulation capacity of the power system and realizing coordinated optimization scheduling of the electric and thermal systems.

[0050] The specific steps include:

[0051] Step 1: Quantify the regulation capacity between the thermal system and the power system under the background of incomplete information.

[0052] Because heating pipelines have a certain heat storage capacity, they can be considered virtual energy storage devices, acting as energy buffers and delayed response. Therefore, in traditional electric-heat coordinated scheduling strategies, by considering the dynamic characteristics of the heating pipeline network and optimizing its operating state, the power regulation space of the CHP units in the electric-heat coupled equipment is expanded, breaking the "heat-to-power" limitation and improving the regulation capacity of the power system. However, under this strategy, the sharing of regulation capacity between electric-heat systems is more of an indirect sharing, with the power system still utilizing its own regulation capacity, ignoring the direct utilization and modeling of the regulation capacity provided by the thermal system. This embodiment, building on the above-mentioned traditional electric-heat coordinated scheduling strategy, further quantifies the regulation capacity provided by the dynamic characteristics of the heating pipeline network, allowing the regulation capacity provided by the thermal system to be directly used as a scheduling resource in the coordinated scheduling of the electric-heat integrated energy system. Because in the context of incomplete information, only a small amount of boundary information is exchanged between electric-heat entities, the regulation capacity provided by the thermal system needs to be aggregated for privacy protection. This embodiment constructs a feasible domain of the thermal system's regulation capacity to enable direct sharing of regulation capacity between the thermal and power systems.

[0053] When conducting coordinated optimization and scheduling of electric and thermal integrated energy systems under incomplete information, the problem of multi-time-scale coordination between the electric and thermal systems is involved. Through CHP units, electric energy and thermal energy are coupled together during the production and use process. However, the time characteristics of electric energy and thermal energy are different. Electric energy is transmitted quickly and responds promptly, while thermal energy transmission has thermal delay characteristics and responds slowly, such as Figure 2As shown. For example, based on the day-ahead scheduling, the intraday electricity scheduling time scale is optimized at 15 minutes, while the scheduling time scale of thermal energy is still 1 hour due to its large inertia. With the help of the dynamic characteristic model of the heating network based on the quality weight method, the real-time temperature operation status of the heating network can be obtained. Therefore, under the electric heating system collaborative scheduling mechanism proposed in this embodiment, the thermal scheduling cycle is discretized in the region, the thermal scheduling time scale is consistent with the electric power scheduling time scale, and the electric heating integrated energy system simultaneously schedules the electric power system and the thermal system in a single time period, realizing the coordinated decision-making of the electric heating system on the same time scale.

[0054] Step 1.1: Using the CHP unit as the heat source, quantify the regulation capacity of the thermal system through the synergistic effect of the CHP unit and the dynamic characteristics of the heating network.

[0055] In one specific embodiment, the dynamic characteristics of the heating network provide it with virtual energy storage capabilities, capable of storing or releasing heat, thus possessing a certain degree of regulation capability. By quantifying the regulation capability provided by the dynamic characteristics of the heating network, the regulation capability provided by the heating network can be directly utilized as a scheduling resource in the coordinated scheduling of the electric heating system. This embodiment uses CHP units as the device for transferring regulation capability from the thermal system to the electric power system. By varying the power and regulation capability of the CHP units, regulation capability is provided to the electric power system to meet the regulation capability requirements of the electric power system.

[0056] The regulation capability of a CHP unit is expressed as the margin of power generation that can be adjusted in a given period of time compared to the power generation under the desired operating conditions. The electrical or thermal regulation capability provided by a CHP unit can be expressed as:

[0057] (1).

[0058] Where, 、 are the power up-regulation and down-regulation capabilities provided by CHP unit k at time t, respectively; 、 are the thermal power increase and decrease capabilities of CHP unit k at time t, respectively; is the thermoelectric ratio.

[0059] The dynamic characteristics of the heating network result in a time-series delayed matching of the thermal power and heat load of the CHP unit, making it functionally equivalent to a virtual energy storage device, with both "heat storage" and "heat release" processes. Accordingly, the regulation capacity provided by the thermal system through the heat source node is expressed as:

[0060] (2).

[0061] Where, represents the maximum upward regulation capability provided by the thermal system through CHP unit k at time t, represents the maximum downward regulation capability provided by the thermal system through CHP unit k at time t, is the specific heat capacity of hot water, is the working fluid flow rate of the pipeline where CHP unit k is located at time t, In order to take into account the heat transfer delay of the dynamic characteristics of the heating network, the outlet water temperature of the water supply pipe of CHP unit k at time t is: is the maximum water supply temperature of CHP unit k, is the minimum water supply temperature of CHP unit k.

[0062] Since rapid changes in the working fluid temperature in the heating network may aggravate the damage it causes to the heating network, in order to reduce the pipeline failure rate, slow down pipeline aging, and improve heating safety, a temperature gradient constraint is set to limit the adjustment speed of the working fluid temperature in the pipeline network:

[0063] (3),

[0064] (4).

[0065] Where, and are the supply water temperature and return water temperature of pipe j at time t, and are the supply water temperature and return water temperature of pipe j at time t+1, and are the increases in the supply and return water temperatures of pipe j at time t, and are the increases in the supply and return water temperatures of pipe j at time t+1, and are the reductions in the supply and return water temperatures of pipe j at time t, and are the decreases in the supply and return water temperatures of pipe j at time t+1, respectively; and are the rate limits for increasing and decreasing the supply water temperature, respectively; and are the rate limits for return water temperature increase and decrease, respectively.

[0066] This embodiment considers using the CHP unit as a heat source in conjunction with the dynamic characteristics of the heating network, using the heating network as an "energy buffer" between the heat source and the heat load in the electric heating system. This helps unleash the flexible and controllable potential of the heat source and smoothes energy fluctuations on the source side of the thermal system. The thermal system itself has certain virtual energy storage properties. Based on the dynamic characteristics of the heating network, changes in heat source power and regulation capacity can be compensated by controlling the overall temperature of the heating network.

[0067] The pipe node temperatures of the heating network include two categories: the water supply pipe node temperatures connected to the heat source nodes and the temperatures of other water supply and return water nodes. Among them, the water supply pipe node temperatures connected to the heat source nodes are affected by the heat source and are controllable, while the temperatures of other water supply and return water nodes are affected by the heat source but are difficult to control and vary within a certain temperature range.

[0068] The nodes in the heating network can be divided into three categories: (1) heat source nodes connected to the heat source (i.e., the nodes where the CHP units are located); (2) heat load nodes connected to the heat exchange station; and (3) heat intersection nodes connecting multiple pipelines. By eliminating the heat intersection nodes and the upstream pipelines connected to them in the network and transferring the parameters of the upstream pipelines to the downstream pipelines connected to the heat intersection nodes, the heating network can be simplified into a star network centered on the heat source node, thereby facilitating calculations. The simplified star network only contains the heat source node, the heat load node, and the pipelines connecting the heat source node and the heat load node. The parameters of all pipelines on the path from the heat source node to the heat load node in the original network are transferred to the new pipelines connecting the heat source node and the heat load node in the star network.

[0069] For the star-shaped pipe network after equalization, its parameters are:

[0070] (5).

[0071] Where, is the thermal insulation coefficient after equalization, is the thermal insulation coefficient of pipe j on the path from the heat source node to the heat load node, is the delay after equalization, is the time delay of pipeline j on the path from the heat source node to the heat load node, is the heat load node set, It is the set of pipes j that pass through the path from the heat source node to the heat load node o in the heating network.

[0072] For the star-shaped pipe network after equalization, the supply water temperature at the load node and the return water temperature at the heat source node are:

[0073] (6).

[0074] Where, and are the supply water temperature and return water temperature of the heat load node o at time t, and are the supply water temperature and return water temperature of the node where CHP unit k is located at time t, is the working fluid flow rate of pipe j connected to heat load node o, is the working fluid flow rate of pipeline j connected to the node where CHP unit k is located. It is the set of nodes where the CHP units are located.

[0075] For heat source nodes and heat load nodes in a thermal system where heat exchange occurs, the thermal output of the node depends on the hot water parameters and the temperature difference between the supply and return water temperatures at the node. Furthermore, the supply and return water temperatures must meet the upper and lower limits of the pipeline design temperature. In this embodiment, the heat source node is the node where the CHP unit is located, and the heat load node is the node where the heat exchange station is located.

[0076] (7),

[0077] (8).

[0078] (9).

[0079] Where, is the power of CHP unit k at time t, is the power of heat load node o at time t. is the upper limit of the water supply temperature in the thermal system pipes, is the lower limit of the water supply temperature in the thermal system pipes, is the upper limit of the return water temperature of the pipe in the thermal system, It is the lower limit of the return water temperature of the pipe in the thermal system.

[0080] When the CHP unit provides regulation capability, the actual CHP unit thermal power is .

[0081] (10).

[0082] Where, is the water supply temperature value of the node where CHP unit k is located at time t, which is affected by the regulation capacity of the CHP unit.

[0083] However, changes in thermal power will lead to thermal imbalance in the thermal system. This imbalance will be borne by the temperature change of the entire heating network. The temperature change reflected at each heat source node can be expressed as:

[0084] (11).

[0085] Where, is the overall temperature change of the node where CHP unit k is located; is the actual water supply temperature of the node where CHP unit k is located; is the actual return water temperature of the node where CHP unit k is located.

[0086] The actual heat balance equation is:

[0087] (12).

[0088] (13),

[0089] (14),

[0090] (15),

[0091] (16),

[0092] (17).

[0093] Where: and are the actual supply water temperature and return water temperature of the heat load exchange node respectively.

[0094] Combined with the dynamic model of the heating network, the actual temperature and temperature changes of any node in the heating network at any time can be obtained through the thermal system regulation capability model based on source-network coordination.

[0095] pass , the virtual energy storage capacity of the heating network that supports the regulation capability of the CHP unit can be quantified, thereby measuring the regulation capability of the CHP unit. It can ensure that the regulation capacity of the CHP units and the virtual energy storage capacity of the heating pipeline network will not be over-utilized, thereby ensuring the safe operation of the power system and the thermal system.

[0096] Step 1.2: Based on incomplete information conditions, the feasible domain of the electric-thermal system regulation capacity is used to quantify the regulation capacity provided by the thermal system to the power system.

[0097] In a specific embodiment, the CHP unit is used as a transmission device for regulating capacity between electric and thermal systems. The thermal system can adjust the thermal power of the CHP unit to meet the regulating capacity requirements of the power system. Under incomplete information, only the power and regulating capacity information of the CHP unit are shared between electric and thermal entities to achieve privacy protection of electric and thermal entities. Therefore, the feasible domain of the regulating capacity of the electric and thermal system is defined to quantify the regulating capacity provided by the thermal system to the power system. Among them, the feasible domain of the regulating capacity of the thermal system is , the feasible region of the regulation capability of the power system is The feasible domain of the electric heating system regulation capacity is regarded as a polyhedron composed of the feasible domains of multiple CHP units, and obtaining the feasible domain range is equivalent to obtaining the volume of the polyhedron. Therefore, the feasible domain of the regulation capacity can be expressed as

[0098] (18),

[0099] (19).

[0100] Where, and are the upper and lower bounds of the feasible region of the power system’s regulation capability at time t, and are the upper and lower bounds of the feasible region of the regulation capacity of the thermal system at time t, and are the upward and downward electrical regulation capabilities of CHP unit k at time t, and is the upward and downward thermal regulation capability of CHP unit k at time t, is the heat-to-electricity ratio, and C is the collection of CHP units.

[0101] In practice, expressions containing product terms (or logarithmic terms) are not easy to solve. To solve this problem, a linear objective function can be used to approximate the above equation, that is, the volume of the polyhedron is replaced by the sum of the lengths of each dimension of the polyhedron. The specific expression is as follows:

[0102] (20),

[0103] (twenty one).

[0104] In addition to the power system being able to utilize the regulation capacity of thermal power units to respond to disturbances, the heating network can also utilize dynamic characteristics to store or release heat, adjust the power of CHP units, and provide regulation capacity for the power system. Specifically, based on the affine strategy, each regulation capacity resource is adjusted according to the disturbance power allocation coefficient. The regulation capacity should match the degree of wind power fluctuation. The relevant constraints are expressed as:

[0105] (twenty two),

[0106] (twenty three),

[0107] (twenty four).

[0108] Where, represents the random adjustment power of thermal power unit i at time t; Indicates the regulating capacity provided by the thermal system; and are the affine strategy functions of thermal power units and thermal systems respectively; represents the random wind power disturbance of wind farm w at time t, represents the predicted power generation of wind farm w at time t, and They represent the upper and lower bounds of the feasible region of the power system’s regulation capability, Represents the demand of electric load e at time t.

[0109] Step 2: Construct a trading mechanism for the regulation capacity of the electric heating system under incomplete information to achieve power coordination and regulation capacity sharing between the thermal system and the power system.

[0110] like Figure 1 As shown in the figure, in the electric-thermal system regulation capacity trading mechanism under incomplete information, the power system and the thermal system simultaneously carry out power coordination and regulation capacity sharing through CHP units, so that the thermal system can provide more regulation capacity for the power system.

[0111] Traditional research on coordinated scheduling strategies for electric and thermal systems only considers power coordination between electric and thermal systems. When a power disturbance occurs in the power system, the power system can only leverage its own regulation capacity to mitigate the disturbance. This can achieve a certain degree of regulation capacity sharing between electric and thermal systems. For example, when the power system's regulation capacity is insufficient, changing the power generation of CHP units can alleviate the constraints on reserve capacity imposed by thermal power units in the power system, increase the reserve headroom of the generator units, and thus improve the power system's regulation capacity. However, this type of regulation capacity sharing between electric and thermal systems is still indirect; in essence, the power system still utilizes its own regulation capacity, not that of the thermal system. Furthermore, if the power system itself lacks regulation capacity, the thermal system cannot share its surplus regulation capacity with the power system. If the thermal power units in the power system only increase reserve headroom without improving the reserve response rate, the system's regulation capacity may not be improved. Therefore, this embodiment proposes a regulation capacity trading mechanism for electric and thermal systems under incomplete information. This mechanism enables simultaneous power coordination and regulation capacity sharing between electric and thermal systems, allowing the thermal system to provide more regulation capacity to the power system. Incomplete information refers to a small amount of boundary information, which in this example refers to the power and regulation capacity of the CHP units. Under the decentralized optimal scheduling mechanism with incomplete information, each stakeholder performs scheduling independently, maintaining operational independence. Only a small amount of boundary information needs to be exchanged between stakeholders, and their internal private information does not need to be disclosed, effectively protecting the privacy of the stakeholders.

[0112] Game theory can effectively address the issues arising from multi-agent transactions, and therefore has been introduced into the optimization and scheduling of integrated energy systems. However, most current research focuses solely on horizontal cooperation between peer entities (multiple parks, multiple energy hubs), lacking in-depth study of vertical cooperation between non-peer entities (heterogeneous energy sources such as electricity, heat, gas, and cooling). This example applies cooperative game theory between non-peer power and thermal systems, considering both the interaction of electric and thermal power and the direct sharing of regulation capacity between them.

[0113] When the thermal system provides regulation to the power system, its operating state adjustments deviate from the optimal strategy for independent thermal system scheduling, resulting in a loss of benefits for the thermal system. Therefore, a cooperative game optimization model based on Nash bargaining is introduced. By allocating cooperative surpluses through Nash bargaining, the overall benefit cost of the electric and thermal systems is minimized, while the total operating costs of all entities in the power and thermal systems are reduced.

[0114] Step 3: According to the uneven distribution of benefits between the thermal system and the power system in the electric heating system regulation capacity trading mechanism under incomplete information, a cooperative game optimization model of the electric heating system based on Nash bargaining is constructed.

[0115] In the proposed electric and thermal system regulation capacity trading mechanism, the cooperating entities are the power system and the thermal system. To avoid conflicts of interest among the cooperating entities, Nash bargaining is introduced to allocate the cooperative surplus of the cooperative alliance. The implementation of the electric and thermal system regulation capacity trading mechanism is mainly divided into two stages:

[0116] Phase One: Determine the volume of regulatory capacity transactions between electric and thermal entities. All collaborating entities need to coordinate and optimize with the ultimate goal of maximizing social welfare. During this phase, each entity simply communicates its desired transaction times and volumes to the others. Through iteration, the optimal regulatory capacity trading strategy for the coordinated scheduling of the electric and thermal systems can be determined, maximizing the overall benefits of the electric and thermal systems. By adjusting its operating status, the thermal system provides regulatory capacity to the power system, promoting the integration of new energy sources and further reducing the overall energy costs of the electric and thermal systems.

[0117] Phase Two: Determining the transaction price for regulating capacity among the heating and electric power entities. Following the conclusion of Phase One cooperative negotiation, the regulatory capacity trading strategy among the heating and electric power entities has been determined. Therefore, the primary objective of this phase is to maximize payment benefits, namely, to determine the optimal regulatory capacity transaction price among each entity. During the bargaining process, price decisions are still made through decentralized optimization methods. Each cooperating entity simply communicates its desired regulatory capacity transaction price to the other entities, thereby ensuring that its own benefits remain at a sufficient level and that all entities are willing to participate in the bargaining alliance.

[0118] The electric heating system regulation capacity trading mechanism described in this embodiment involves two entities: the power system and the thermal system. It is a cooperative game problem, essentially a distribution of optimal individual benefits based on maximizing the benefits of the cooperative alliance. The optimal regulation capacity trading volume and price among the cooperating entities are determined through the two aforementioned stages. Furthermore, to fully protect the information privacy of each entity, the ADMM distributed algorithm is employed in both stages to solve the problem. This effectively protects the privacy of each entity while fully reflecting the balanced interaction between the different entities involved.

[0119] Step 3.1: Construct the power system objective function and the thermal system objective function according to the power system subproblem and the thermal system subproblem respectively.

[0120] In a specific embodiment, the optimization objective of the power system sub-problem is To minimize the total cost of the power system. The total cost of the power system is composed of the power generation cost and backup configuration cost of thermal power units, the power generation cost of CHP units, the risk cost of wind power acceptance, and the cost of purchasing backup from the thermal system. The specific objective function is as follows:

[0121] (25).

[0122] Where, is the operating cost of thermal power units, is the risk cost of wind power, including the cost of wind curtailment and the cost of power shortage. is the operating cost of CHP unit power generation, is the cost of the power system purchasing backup capacity from the thermal system. G is the set of thermal power units, C is the set of CHP units, W is the set of wind farms, and N is the set of cost segments. and are the power generation power of the nth section of thermal power unit i at time t and the power generation cost coefficient of the nth section of thermal power unit i, and are the upward and downward reserve capacities configured for thermal power unit i at time t, and are the cost coefficients of reserve capacity adjustment and reduction for thermal power unit i, respectively; and are the power generation capacity and power generation cost coefficient of the CHP unit in the nth section, and are the upward and downward reserve capacities of the CHP unit at time t due to the regulation of the heating network, and are the upward and downward reserve capacity cost coefficients for CHP units, respectively; is the wind curtailment cost coefficient, is the power shortage cost coefficient, The risk of wind curtailment, There is a risk of power shortage.

[0123] Optimization objective of the thermal system subproblem To minimize the total cost of the thermal system.

[0124] (26).

[0125] Where, is the heat production operating cost of the CHP unit, and are the thermal power of the nth section of CHP unit k at time t and the heat production cost coefficient of the nth section of CHP unit, and are the upward and downward reserve capacities of CHP unit k at time t due to the regulation of the heating network, and are the reserve capacity cost coefficients for upward and downward regulation of CHP unit k, respectively.

[0126] Step 3.2: Based on Nash bargaining theory, a cooperative game optimization model between the electric and thermal systems is established according to the objective functions of the power system and the thermal system.

[0127] This example establishes a cooperative game optimization model between electric heating systems based on Nash bargaining theory. This model determines the transaction price and volume of regulatory capacity between electric heating systems from both the perspectives of overall rationality and individual rationality. Nash bargaining theory, based on the concept of cooperative games, can simultaneously take into account both individual and collective interests. It is often used in multi-agent interest distribution problems and can meet incentive compatibility conditions. Its standard model can be expressed as:

[0128] (27).

[0129] Where M is the total number of subjects; is the breaking point of Nash bargaining, that is, the operating cost of subject m when not participating in Nash bargaining; is the operating cost of subject m after participating in Nash bargaining. When , it means that subject m cannot benefit from participating in the Nash bargaining transaction, and subject m no longer participates in the transaction.

[0130] The Nash bargaining model is used to describe the collaborative bargaining process among electric heating systems. The model can be expressed as:

[0131] (28).

[0132] Where, is the operating cost of entity m after participating in Nash bargaining when transaction costs are not included; It is the transaction price of regulation capacity between electric heating entities; It is the transaction volume of regulatory capacity between electric heating entities.

[0133] Since Equation (28) involves the product of the transaction price and the transaction volume of the regulatory capacity, it is a non-convex nonlinear model that is difficult to solve directly. Therefore, the original model is equivalently converted into two easily solvable subproblems: the cooperative benefit maximization subproblem and the energy payment subproblem. Solving these two subproblems in sequence can obtain the optimal solution to the original problem.

[0134] Step 3.3: Set the cooperative benefit maximization subproblem and transaction payment subproblem based on the cooperative game optimization model between the electric heating systems.

[0135] Step 3.3.1: Sub-problem of maximizing cooperation benefits.

[0136] Formula (29) is the sub-problem of maximizing the cooperative benefit after conversion, that is, the sum of the total operating costs of all participating cooperative game entities is minimized:

[0137] (29).

[0138] Where, is the operating cost of entity m after participating in Nash bargaining when transaction costs are not included;

[0139] Among them, for the power system, when transaction costs are not included, its operating cost before participating in the cooperative game is:

[0140] (30).

[0141] For the thermal system, the operating cost of the thermal system before participating in the cooperative game is:

[0142] (31).

[0143] Step 3.3.2: Transaction payment sub-problem.

[0144] Formula (32) is the transaction payment sub-problem obtained after conversion.

[0145] (32).

[0146] Where, and Solve the sub-problem of maximizing the cooperative benefit for subject m and obtain the optimal cost and regulatory capacity transaction volume.

[0147] Step 3.4: Set the corresponding wind power acceptance risk metric and system constraints.

[0148] Step 3.4.1: Wind power acceptance risk measurement.

[0149] When the probability distribution is known, the expected benefit loss of the power system caused by wind power disturbance exceeding the effective safety domain of wind power acceptance is defined as the wind power acceptance risk measure. Specifically, the wind power acceptance risk can be expressed as follows:

[0150] (33).

[0151] Where, express expected value; Represents wind power The probability density function of and represent the penalty factors for wind curtailment and power shortage respectively; and They are respectively the lower and upper limits of the effective safety domain for wind power absorption.

[0152] Using the K-block based piecewise linear method, (33) can be approximated by the following linear expression with auxiliary variables and constraints:

[0153] (34).

[0154] Where, The risk of wind curtailment, There is a risk of power shortage. 、 、 、 is a constant coefficient; N is the number of segments.

[0155] It's important to note that the piecewise linearization method employed here differs from traditional piecewise linearization methods in that it does not introduce integer variables during the linearization process. This improves the computational efficiency of the optimization model and enhances the convergence of the decentralized optimization algorithm. Furthermore, in the piecewise linearization method described above, the more segments selected, the more accurate the solution. Of course, increasing the number of segments also increases the computational burden. In actual operation, the method selects an appropriate number of segments to balance model solution efficiency and accuracy.

[0156] Step 3.4.1: System constraints.

[0157] Step 3.4.1.1: Power system constraints.

[0158] 1) Constraints on unit power generation capacity

[0159] (35),

[0160] (36).

[0161] Where, and are the maximum and minimum power generation of thermal power unit i; and are the maximum and minimum generating capacities of CHP unit k.

[0162] 2) Unit climbing constraints

[0163] To ensure the standby response rate, the ramp rate of the unit should be fully considered during the optimization process:

[0164] (37),

[0165] (38).

[0166] Where, and is the upward and downward climbing capability of thermal power unit i; and is the upward and downward ramping capability of CHP unit k.

[0167] 3) Branch flow constraints

[0168] (39).

[0169] Where, 、 、 and are the power generation distribution transfer factors of thermal power unit i, CHP unit k, wind farm w and electric load e corresponding to line l. is the power flow of line l under disturbance, is the maximum transmission capacity of line l.

[0170] Step 3.4.1.2: Thermal system constraints.

[0171] 1) Dynamic characteristics of heating network:

[0172] The dynamic characteristics of a heating network are primarily reflected in transmission delay and temperature loss. Because hot water flows slowly, there's a temperature difference between the pipe inlet and outlet, causing some heat energy to be stored within the network. However, due to the temperature difference between the hot water and the surrounding environment, some heat is lost during the flow, causing the temperature to drop.

[0173] ① Transmission delay.

[0174] Due to the heat transfer delay in the temperature transmission of the heating pipe , the outlet water temperature of pipe j at time t can be calculated by Estimation of the pipe inlet water temperature at time:

[0175] (40),

[0176] (41).

[0177] Where j is the number of the thermal pipe, is the time delay of thermal pipe j, is the density of water, is the radius of the thermal pipe.

[0178] From formula (41), we can see that the transmission delay is a continuous variable. For the convenience of calculation and explanation, the transmission delay To convert units, take , The scheduling period interval. It is generally not an integer variable. Therefore, this embodiment adopts the quality weight method to process the transmission delay. The quality weight method can overcome the impact of the heating network transmission delay not being an integer multiple of the scheduling time on the coordinated scheduling of electricity and heat, and discretize the scheduling cycle of the thermal system, thereby helping the electric heating system to achieve multi-time scale scheduling of electricity and heat.

[0179] The outlet water temperature at time t can be expressed as:

[0180] (42),

[0181] (43).

[0182] Where, is the ceiling function, is not less than The smallest integer, is not less than The smallest integer, is the outlet water temperature of pipe j at time t, is the water inlet temperature of pipe j at time t, and a and b are the mass weight coefficients. This gives an expression for the water outlet temperature of the pipe that only considers the heat transfer delay.

[0183] ②Temperature loss.

[0184] Considering only the temperature loss of hot water and ignoring the transmission delay, the outlet water temperature is:

[0185] (44),

[0186] (45).

[0187] Where, is the ambient temperature, is the pipe temperature loss coefficient, and c is the insulation coefficient.

[0188] When considering transmission delay and temperature loss at the same time, the outlet water temperature of the pipeline can be expressed as

[0189] (46).

[0190] 2) Heating network constraints:

[0191] For any type of heating network, the node flow continuity equation must be satisfied. That is, for any node in the heating network, the flow rate flowing into the node is equal to the flow rate flowing out of the node. The node flow continuity equation in the heating network is similar to Kirchhoff's current law in the power network and can be expressed as:

[0192] (47).

[0193] Where, and are the sets of pipelines connected to, ending at, and starting from node n, respectively; and are the working fluid flow rates of hot water in pipes j and l at time t respectively.

[0194] The heat supply network must also satisfy the conservation of thermal energy. Hot water from different pipes flows into the same node and is mixed in temperature. After mixing, the hot water flowing out of the node has the same temperature, as shown in the following formula:

[0195] (48).

[0196] Where, is the outlet water temperature of pipe j at time t; is the water inlet temperature of pipe l at time t.

[0197] Step 4: Use a decentralized solution algorithm to solve the cooperative game optimization model of the electric heating system, distribute the cooperative surplus of the electric heating system regulation capacity trading mechanism, and complete the decentralized scheduling of the electric heating system.

[0198] Step 4.1: Construct the electrothermal system coupling constraints based on the corresponding system constraints.

[0199] The electric heating system consists of two main bodies, the power system and the thermal system. The scheduling information between these two main bodies has a certain degree of privacy. This embodiment uses the ADMM method to decompose the electric heating system optimization problem into power system sub-problems and thermal system sub-problems for decentralized solution. While ensuring the privacy of information of different subjects, the optimal scheduling of the electric heating system is achieved by exchanging a small amount of boundary information. Based on the ADMM decoupling mechanism, this embodiment uses the power and regulation capacity of the CHP units on the connecting line between the electric and thermal main bodies as coupling variables. At the connecting line, the electric heating system is decoupled into the power and thermal main bodies for optimization, namely:

[0200] (49).

[0201] Where, 、 denote the coupling variables of the electric power and thermal systems at the tie line q respectively; 、 、 They are the electric power, upward regulation capability and downward regulation capability of the CHP unit on the thermal system side, which are solved by the thermal system; 、 、 Respectively 、 、 Correspondingly, the electric power injected into the power system by the CHP unit, the upward regulation capability, and the downward regulation capability are solved by the power system.

[0202] There are two types of coupling constraints in the proposed scheduling method: the electric heating system transmission power coupling constraint and the electric heating system reserve capacity coupling constraint. The electric heating system transmission power coupling constraint is closely related to the active power basis point of the exchange. The electric heating system reserve capacity coupling constraint is determined based on the exchanged reserve capacity. The two constraints can be expressed as:

[0203] (50),

[0204] (51).

[0205] You can use global variables in ADMM To ensure the consistency of boundary information, the specific representation is as follows:

[0206] (52).

[0207] Step 4.2: Relax the coupling constraints of the electric-thermal system and decouple the cooperative game optimization model of the electric-thermal system into the power system subproblem and the thermal system subproblem.

[0208] In a specific implementation, in order to protect the privacy of electricity and heat entities and reduce the difficulty of solution and communication burden, a decentralized solution framework based on the ADMM algorithm is designed. The augmented Lagrangian function is used to relax the coupling constraints, namely, equations (53)-(54), so that the original electric and thermal integrated energy system optimization problem is decomposed into power system subproblems and thermal system subproblems, and can be solved in each single system area. After solving in each system area, the coupling variable values ​​are exchanged with the adjacent system, the problem parameters in the area are updated, and the problem is solved iteratively continuously until the residual of the coupling variables exchanged between the adjacent systems meets the stopping condition and the solution process is completed to obtain the optimal solution.

[0209] Specifically, according to the ADMM algorithm, by relaxing the coupling constraints, the optimization problem of the electric-thermal integrated energy system is decoupled into the power system subproblem and the thermal system subproblem, so the original objective functions (25)-(26) are reformulated as:

[0210] (53),

[0211] (54).

[0212] Where, 、 is the Lagrange multiplier, is the penalty factor. The above multiplier will be updated in the iterative process until the final decision is obtained.

[0213] The Lagrange multiplier update formula is as follows:

[0214] (55),

[0215] (56).

[0216] By controlling 、 and The consistency of the coupling information between the power system and the thermal system can be achieved, and the purpose of exchanging a small amount of boundary information to achieve the optimal scheduling of the electric and thermal systems can be achieved.

[0217] The basis for judging whether the iteration converges is as follows:

[0218] (57).

[0219] Where, 、 Indicates the The original residual and dual residual of the ADMM algorithm after iterations; 、 Indicates the maximum original error and dual error allowed in engineering applications. and At the same time less than and When , the iteration stops and the optimal result is output; otherwise, it needs to continue iteration until it converges.

[0220] In order to improve the convergence speed of the algorithm, this embodiment proposes an improved ADMM algorithm to solve the electrothermal system. Compared with the traditional ADMM, the adaptive ADMM can select different , thereby improving the efficiency of the algorithm:

[0221] (58).

[0222] Where, is the proportionality coefficient; and is the scaling factor. When the original residual and the dual residual are less than the given convergence accuracy, the iteration of the adaptive ADMM ends and the optimal result is output.

[0223] Step 4.3: Solve the power system subproblem and the thermal system subproblem based on the cooperative benefit maximization subproblem. Solve the power system subproblem and the thermal system subproblem based on the transaction payment subproblem.

[0224] In a specific embodiment, Figure 3 As shown, the solution process is as follows.

[0225] Step 1: Initialization. Set initial values ​​and input line parameters, unit parameters and other data.

[0226] Step 2: Solve the cooperative benefit maximization subproblem. Set the initial Lagrange multiplier 、 , and the number of iterations , and solve the electric heating subsystem in turn.

[0227] Step 3: Determine the convergence condition of the cooperative benefit maximization subproblem. If all entities can simultaneously meet the convergence condition (57), then terminate the iteration process, output the optimal cost and optimal transaction volume in the decision result, and use this as the initial value of the transaction payment subproblem. Otherwise, update the multiplier according to (59) and set the number of iterations. , and return to step 2.

[0228] Step 4: Solve the transaction payment subproblem. Set the initial Lagrange multiplier 、 , and the number of iterations , calculate the transaction price corresponding to the transaction volume in the electric heating system in turn.

[0229] Step 5: Determine the convergence conditions of the transaction payment subproblem. If the convergence conditions (57) are met at the same time, terminate the iteration process and output the transaction price in the decision result. Otherwise, update the multiplier according to (56) and set the number of iterations. , and return to step 4.

[0230] Example 2:

[0231] A second embodiment of the present invention provides a distributed scheduling system for an electric heating system based on Nash bargaining, including:

[0232] The regulation capacity quantification module is configured to obtain parameters of the electric and thermal integrated energy system to be dispatched and quantify the regulation capacity between the thermal system and the power system. The CHP unit is used as the regulation capacity transmission device from the thermal system to the power system. The module provides regulation capacity to the power system by changing the power and regulation capacity of the CHP unit to meet the regulation capacity requirements of the power system.

[0233] The regulation capacity sharing module is configured to establish a regulation capacity trading mechanism for the electric and thermal systems under incomplete information, so as to achieve power coordination and regulation capacity sharing between the thermal and electric systems;

[0234] The cooperative game optimization module is configured to construct a Nash bargaining-based cooperative game optimization model for the electric heating system based on the uneven distribution of benefits between the thermal system and the power system in the electric heating system regulation capacity trading mechanism under incomplete information;

[0235] The optimization scheduling module is configured to consider the multi-time-scale synergy between the thermal system and the power system, adopt a distributed solution algorithm to solve the cooperative game optimization model of the electric heating system, distribute the cooperative surplus of the electric heating system regulation capacity trading mechanism, and complete the decentralized scheduling of the electric heating system.

[0236] Example 3:

[0237] A third embodiment of the present invention provides a medium having a program stored thereon. When the program is executed by a processor, the steps of the Nash bargaining-based decentralized scheduling method for an electric heating system as described in the first embodiment of the present invention are implemented.

[0238] Example 4:

[0239] Embodiment 4 of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the distributed scheduling method of the electric heating system based on Nash bargaining as described in Embodiment 1 of the present invention are implemented.

[0240] The steps involved in the above embodiments 2, 3 and 4 correspond to those in the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of the embodiment 1.

[0241] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0242] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A decentralized scheduling method for electric heating systems based on Nash bargaining, characterized in that: The following steps are involved: Obtain the parameters of the electric and thermal integrated energy system to be dispatched and quantify the regulation capacity between the thermal system and the power system. The specific steps are as follows: Using the CHP unit as the heat source, the regulation capacity of the thermal system is quantified through the synergy between the CHP unit and the dynamic characteristics of the heating network. The changes in the heat source power and regulation capacity are compensated by controlling the overall temperature regulation of the heating network. Based on incomplete information, the feasible domain of the electric-thermal system's regulation capacity is used to quantify the regulation capacity provided by the thermal system to the power system. The feasible domain of the electric-thermal system's regulation capacity is regarded as a polyhedron composed of the feasible domains of multiple CHP units, and obtaining the range of the feasible domain is equivalent to obtaining the volume of the polyhedron. The CHP unit is used as the regulation capacity transmission device from the thermal system to the power system. By changing the power and regulation capacity of the CHP unit, the regulation capacity provided to the power system is met. Establish a trading mechanism for the regulation capacity of electric and thermal systems under incomplete information to achieve power coordination and regulation capacity sharing between thermal and electric systems; According to the uneven distribution of benefits between the thermal system and the power system in the regulation capacity trading mechanism of the electric heating system under incomplete information, a cooperative game optimization model of the electric heating system based on Nash bargaining is constructed. Considering the multi-time-scale synergistic relationship between the thermal system and the power system, a decentralized solution algorithm is used to solve the cooperative game optimization model of the electric heating system, distribute the cooperative surplus of the electric heating system regulation capacity trading mechanism, and complete the decentralized scheduling of the electric heating system.

2. The Nash bargaining-based decentralized scheduling method for electric heating systems according to claim 1, characterized in that: In the regulation capacity trading mechanism of electric and thermal systems under incomplete information, the power system and the thermal system simultaneously carry out power coordination and regulation capacity sharing through CHP units, so that the thermal system can provide more regulation capacity for the power system.

3. The Nash bargaining-based decentralized scheduling method for electric heating systems according to claim 1, characterized in that: The specific steps of constructing a cooperative game optimization model for an electric heating system based on Nash bargaining include: According to the power system sub-problem and the thermal system sub-problem, the power system objective function and the thermal system objective function are constructed respectively; Based on Nash bargaining theory, a cooperative game optimization model between the electric and thermal systems is established according to the objective functions of the power system and the thermal system. According to the cooperative game optimization model between the electric and thermal systems, the cooperative benefit maximization sub-problem and the transaction payment sub-problem are set; Set corresponding wind power acceptance risk metrics and system constraints.

4. The Nash bargaining-based decentralized scheduling method for electric heating systems according to claim 1, characterized in that: The specific steps of using the distributed solution algorithm to solve the cooperative game optimization model of the electric heating system are as follows: Construct the electric-thermal system coupling constraints according to the corresponding system constraints; Relaxing the coupling constraints of the electric and thermal systems decouples the cooperative game optimization model between the electric and thermal systems into the power system subproblem and the thermal system subproblem; Based on the cooperative benefit maximization sub-problem, the power system sub-problem and the thermal system sub-problem are solved respectively; Based on the transaction payment subproblem, the power system subproblem and the thermal system subproblem are solved separately.

5. A distributed dispatching system for electric heating systems based on Nash bargaining, characterized in that: include: The regulation capacity quantification module is configured to obtain the parameters of the electric and thermal integrated energy system to be dispatched and quantify the regulation capacity between the thermal system and the power system. The specific steps are as follows: Taking the CHP unit as the heat source, the regulation capacity of the thermal system is quantified through the synergistic effect of the CHP unit and the dynamic characteristics of the heating network. The changes in the heat source power and regulation capacity are compensated by controlling the overall temperature regulation of the heating network. Based on the condition of incomplete information, the regulation capacity provided by the thermal system to the power system is quantified using the feasible domain of the electric heating system regulation capacity. The feasible domain of the electric heating system regulation capacity is regarded as a polyhedron composed of the feasible domains of multiple CHP units, and the calculation of the feasible domain range is equivalent to the calculation of the volume of the polyhedron. Among them, the CHP unit is used as the regulation capacity transmission device from the thermal system to the power system. By changing the power and regulation capacity of the CHP unit, the regulation capacity is provided to the power system to meet the regulation capacity requirements of the power system. The regulation capacity sharing module is configured to establish a regulation capacity trading mechanism for the electric and thermal systems under incomplete information, so as to achieve power coordination and regulation capacity sharing between the thermal and electric systems; The cooperative game optimization module is configured to construct a Nash bargaining-based cooperative game optimization model for the electric heating system based on the uneven distribution of benefits between the thermal system and the power system in the electric heating system regulation capacity trading mechanism under incomplete information; The optimization scheduling module is configured to consider the multi-time-scale synergy between the thermal system and the power system, adopt a distributed solution algorithm to solve the cooperative game optimization model of the electric heating system, distribute the cooperative surplus of the electric heating system regulation capacity trading mechanism, and complete the decentralized scheduling of the electric heating system.

6. A computer-readable storage medium, characterized in that A plurality of instructions are stored therein, and the instructions are suitable for being loaded by a processor of a terminal device and executing the Nash bargaining-based decentralized scheduling method for an electric heating system according to any one of claims 1 to 4.

7. A terminal device, characterized in that: The invention comprises a processor and a computer-readable storage medium, wherein the processor is used to implement various instructions; the computer-readable storage medium is used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executed by the Nash bargaining-based decentralized scheduling method of the electric heating system according to any one of claims 1 to 4.

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