Electric heating system decentralized scheduling method and system based on Nash bargaining

Abstract

The invention relates to the technical field of integrated energy system optimization scheduling, in particular to an electric heating system decentralized scheduling method and system based on Nash bargaining. The method comprises the following steps: quantifying the regulation capability between a thermodynamic system and a power system; an electric heating system cooperative game optimization model based on Nash bargaining is constructed according to non-uniform benefit distribution of a thermodynamic system and an electric power system in an electric heating system regulation capability transaction mechanism under incomplete information; and a multi-time-scale cooperative relationship between the thermodynamic system and the power system is considered, an electric heating system cooperative game optimization model is solved, and cooperative residues of an electric heating system adjustment capability transaction mechanism are distributed. According to the method, an electric heating system scheduling strategy based on multi-time scale cooperation and an electric heating system adjusting capacity transaction mechanism under incomplete information are designed, cooperation residues are distributed through Nash bargaining, and cooperation of an electric power system and a thermodynamic system is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system optimization 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 regards the power system and the thermal system as the same stakeholder. To perform centralized optimization and dispatch, it is necessary to collect data from the power system and the thermal system. However, in reality, the power system and the thermal system usually belong to different stakeholder groups, and the privacy data within the subject, such as system parameters and user loads, cannot be disclosed. Therefore, centralized optimization and dispatch faces the problem of privacy leakage of different stakeholders and loss of operational independence.

[0004] Unlike centralized optimization and scheduling, decentralized optimization and scheduling only requires exchanging a small amount of boundary information of stakeholders, and their internal privacy information does not need to be disclosed, which effectively protects the privacy information of stakeholders. In addition, decentralized optimization and scheduling decomposes the entire optimization problem into sub-optimization problems that each stakeholder solves separately, maintaining operational independence. Therefore, it is very necessary to study the decentralized optimization and scheduling of electric and thermal integrated energy systems.

[0005] The coordinated optimization dispatch of the electric-thermal integrated energy system is usually based on collective rationality, that is, minimizing the total operating cost of the power system and the thermal system. Compared with the separate optimization dispatch of the power system and the thermal system, the coordinated optimization dispatch of the electric-thermal integrated energy system will reduce the total cost of the electric-thermal integrated energy system, but at the same time it will damage the individual interests of the thermal system. Specifically, the coordinated optimization dispatch of the electric-thermal system requires the thermal system to make full use of the virtual energy storage characteristics of the heating network to provide regulation capabilities for the electric system, thereby deviating from the optimal strategy when the thermal system is independently dispatched. The heating network needs to increase the temperature, resulting in more heat losses, which in turn increases the total operating cost of the thermal system. Based on the assumption of individual rationality, the thermal system has no motivation to participate in cooperation. Therefore, the coordinated optimization dispatch of electric-thermal based on overall rationality is not incentive compatible.

[0006] At present, the existing technology introduces a cooperative game model based on Nash bargaining, treats the coordinated optimization dispatch of electricity and heat as a market game, alternately calculates the optimal dispatch of the power system and the optimal dispatch of the thermal system, and updates the price signal until the equilibrium point is found, that is, incentive compatibility is achieved. However, the existing cooperative game model based on Nash bargaining does not focus on the power system and the thermal system as independent stakeholders, fails to fully tap the thermal energy regulation capacity and realize the direct dynamic sharing of the regulation capacity between electric energy and thermal energy, and fails to consider the response differences between thermal energy and electric energy from multiple time scales, resulting in unsatisfactory dispatch results. Summary of the invention

[0007] In view of the shortcomings of the prior art, 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 network, quantifying the regulation capacity of the heating 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: 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: Obtain the parameters of the electric-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, and the regulation capacity is provided 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. Construct 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 unequal distribution of benefits between the thermal system and the power system in the electric-heat system regulation capacity trading mechanism under incomplete information, a cooperative game optimization model of the electric-heat system based on Nash bargaining is constructed. Considering the multi-time scale synergy 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, and the cooperative surplus of the electric heating system regulation capacity trading mechanism is allocated to complete the decentralized scheduling of the electric heating system.

[0009] Furthermore, the specific steps to quantify the regulation capacity between the thermal system and the power system are: Taking CHP unit as heat source, the regulation capacity of thermal system is quantified through the synergy between CHP unit and dynamic characteristics of heating network; Based on the condition of incomplete information, the feasible domain of the regulation capacity of the electric-thermal system is used to quantify the regulation capacity provided by the thermal system to the power system.

[0010] 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.

[0011] 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.

[0012] Furthermore, in the regulation capacity trading mechanism of electric thermal systems under incomplete information, the electric 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 electric power system.

[0013] Furthermore, the specific steps of constructing the cooperative game optimization model of the 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 electric heating 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.

[0014] Furthermore, 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 sub-problems of the electric power system and the thermal system; 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.

[0015] A second aspect of the present invention provides a distributed dispatching system for an electric heating system based on Nash bargaining, comprising: The regulating capacity quantification module is configured to obtain the parameters of the electric-thermal integrated energy system to be dispatched, and quantify the regulating capacity between the thermal system and the power system, wherein the CHP unit is used as the regulating capacity transmission device from the thermal system to the power system, and the regulating capacity is provided to the power system by changing the power and regulating capacity of the CHP unit to meet the regulating capacity requirements of the power system; The regulation capacity sharing module is configured to construct a regulation capacity trading mechanism for the electric heating system under incomplete information, so as to realize power coordination and regulation capacity sharing between the thermal system and the electric power system; The cooperative game optimization module is configured to construct a cooperative game optimization model of the electric heating system based on Nash bargaining 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; 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.

[0016] The 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 decentralized scheduling method for an electric heating system based on Nash bargaining as described in the first aspect of the present invention.

[0017] The fourth aspect of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the distributed scheduling method of the electric heating system based on Nash bargaining as described in the first aspect of the present invention are implemented.

[0018] One or more of the above technical solutions have the following beneficial effects: The present invention discloses a decentralized scheduling method and system for an electric heating system based on Nash bargaining. On the basis of the traditional coordinated scheduling strategy for the electric heating system, the method further quantifies the regulation capability that can be provided by the dynamic characteristics of the heating network, designs a coordinated scheduling strategy for the electric heating system 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 the electric heating entities, making full use of the regulation capability of the thermal system, thereby helping the power system to improve its ability to absorb new energy.

[0019] The present invention proposes a trading mechanism for the regulating capacity of electric and thermal systems under incomplete information, so that the regulating 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, and introduces a cooperative game optimization model based on Nash bargaining. On the basis of the trading of regulating capacity between electric and thermal systems, the cooperative surplus of the electric and thermal systems is allocated, so that the electric and thermal systems can achieve the minimization of the overall benefit cost while reducing the total operating costs of the power system and the thermal system, thereby promoting the cooperation between the power system and the thermal system, and achieving the power system to obtain abundant regulating capacity resources while the interests of the thermal system are protected.

[0020] When conducting coordinated optimization scheduling of electric and thermal integrated energy systems, the coupling problem of multiple time scales between electric and thermal systems is involved. The present invention uses CHP units to couple electric energy and thermal energy together during production and use. In view of the different time characteristics of electric energy and thermal energy, electric energy transmission is rapid and timely, while thermal energy transmission has thermal delay characteristics and slow response. The present invention adopts an electric and thermal system coordinated scheduling mechanism, discretizes the thermal scheduling cycle in the region, and the thermal scheduling time scale is consistent with the electric power scheduling time scale. The electric and thermal integrated energy system simultaneously schedules the electric system and the thermal system in a single time period, realizing the decision-making coordination of the electric and thermal systems on the same time scale.

[0021] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings in the specification, 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.

[0023] Figure 1 Schematic diagram of the electric heating system regulation capacity trading mechanism under incomplete information in the first embodiment of the present invention; Figure 2 This is a schematic diagram of electrothermal multi-time scale coordination in Example 1 of the present invention; Figure 3 The figure is a schematic diagram of the decentralized solution process of the cooperative game optimization model based on ADMM in the first embodiment of the present invention. DETAILED DESCRIPTION

[0024] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0025] 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 also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or their combinations; Embodiment 1: Embodiment 1 of the present invention provides a distributed scheduling method for an electric heating system based on Nash bargaining, such as Figure 1As 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.

[0026] The specific steps include: Step 1: Quantify the regulation capacity between the thermal system and the power system under the background of incomplete information.

[0027] Since the heating pipeline has a certain heat storage capacity, it can be regarded as a virtual energy storage device, which plays the role of buffering energy and delaying response. Therefore, in the traditional electric and thermal collaborative scheduling strategy, by considering the dynamic characteristics of the heating network and optimizing its operating state, the power adjustment space of the CHP unit of the electric and thermal coupling equipment is expanded, breaking the limitation of "determining electricity by heat", and improving the regulation capacity of the power system. However, under this strategy, the sharing of regulation capacity between electric and thermal systems is more of an indirect sharing. The power system still uses its own regulation capacity, ignoring the direct use and modeling of the regulation capacity supply of the thermal system. Based on the above-mentioned traditional electric and thermal system collaborative scheduling strategy, this embodiment further quantifies the regulation capacity that can be provided by the dynamic characteristics of the heating network, 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. Since only a small amount of boundary information is exchanged between electric and thermal entities in the context of incomplete information, in order to protect privacy, it is necessary to aggregate the regulation capacity provided by the thermal system. This embodiment constructs a feasible domain of thermal system regulation capacity to realize direct sharing of the regulation capacity between the thermal system and the power system.

[0028] When conducting coordinated optimization scheduling of electric and thermal integrated energy systems under incomplete information, it involves the coordination problem of multiple time scales between the electric and thermal systems. 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 2 As shown. For example, on the basis of the day-ahead dispatch, the intraday electric energy dispatch time scale is optimized at 15 minutes, while the dispatch 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 dispatch mechanism proposed in this embodiment, the thermal dispatch cycle is discretized in the region, the thermal dispatch time scale is consistent with the electric power dispatch time scale, and the electric heating integrated energy system simultaneously dispatches 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.

[0029] Step 1.1: Taking the CHP unit as the heat source, quantify the regulation capacity of the thermal system through the synergy of the dynamic characteristics of the CHP unit and the heating network.

[0030] In a specific implementation, the dynamic characteristics of the heating network give it a virtual energy storage characteristic, which can store or release heat, and thus has a certain regulation capability. By quantifying the regulation capability that can be provided by the dynamic characteristics of the heating network, the regulation capability provided by the heating network can be directly used as a scheduling resource to participate in the coordinated scheduling of the electric heating system. In this embodiment, the CHP unit is used as the regulating capability transmission device from the thermal system to the electric power system, and the regulating capability is provided to the electric power system by changing the power and regulating capability of the CHP unit to meet the regulating capability requirements of the electric power system.

[0031] The regulation capability of a CHP unit is expressed as the adjustable margin of power generation in a given period 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: (1).

[0032] In the formula, , are the upward and downward regulation capabilities of the electric power provided by CHP unit k at time t, respectively; , are the thermal power up-regulation and down-regulation capabilities of CHP unit k at time t, respectively; is the thermoelectric ratio.

[0033] The dynamic characteristics of the heating network make the thermal power of the CHP unit and the thermal load have a delayed matching of supply and demand in the time series, making it functionally equivalent to a virtual energy storage device, that is, there are processes of "storing heat" and "releasing heat". Accordingly, the regulation capacity provided by the thermal system through the heat source node is expressed as: (2).

[0034] In the formula, represents the maximum upward regulation capacity provided by the thermal system through CHP unit k at time t, represents the maximum downward regulation capacity 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.

[0035] Since the rapid change of the working fluid temperature in the heating network may aggravate the damage to the heating network, in order to reduce the pipeline failure rate, slow down the pipeline aging, and improve the heating safety, a temperature gradient constraint is set to limit the adjustment speed of the working fluid temperature in the pipeline network: (3) (4).

[0036] In the formula, 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 decreases in the supply and return water temperatures of pipe j at time t, and are the decreases in the supply water temperature and return water temperature of pipe j at time t+1 respectively; and are the rate limits of increase and decrease of supply water temperature, respectively; and are the rate limits of return water temperature increase and decrease respectively.

[0037] This embodiment considers using the CHP unit as a heat source and coordinating 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, assisting in releasing the flexible and adjustable potential of the heat source, and smoothing the energy fluctuations on the source side of the thermal system. The thermal system itself has certain virtual energy storage characteristics. Based on the dynamic characteristics of the heating network, the changes in the heat source power and regulation capacity can be compensated by controlling the overall temperature regulation of the heating network.

[0038] The pipe node temperatures of the heating network include 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 not easy to control and vary within a certain temperature range.

[0039] The nodes in the heating network can be divided into three categories: (1) heat source nodes connected to the heat source (i.e., 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 calculation. The simplified star network only contains heat source nodes, heat load nodes, and pipelines connecting the heat source nodes and heat load nodes. 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.

[0040] For the star-shaped pipe network after equalization, the parameters are: (5).

[0041] In the formula, 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 passing through the path from the heat source node to the heat load node o in the heating network.

[0042] 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: (6).

[0043] In the formula, 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.

[0044] For heat source nodes and heat load nodes where heat exchange exists in the thermal system, the heat power of the node depends on the hot water parameters and the temperature difference between the supply water temperature and the return water temperature at the node. At the same time, the supply water and return water temperatures should 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.

[0045] (7) (8).

[0046] (9).

[0047] In the formula, 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 of the pipes in the thermal system, is the lower limit of the water supply temperature in the pipes of the thermal system, 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.

[0048] When the CHP unit provides regulation capability, the actual CHP unit thermal power is .

[0049] (10).

[0050] In the formula, It 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.

[0051] However, the change of thermal power will lead to thermal imbalance in the thermal system. This part of the unbalanced heat 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: (11).

[0052] In the formula, 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.

[0053] The actual heat balance equation is: (12).

[0054] (13), (14), (15), (16), (17).

[0055] Where: and are the actual supply water temperature and return water temperature of the heat load exchange node respectively.

[0056] Combined with the dynamic model of the heating network, the actual temperature and temperature change of any node in the heating network at any time can be obtained through the thermal system regulation capacity model based on source-network coordination.

[0057] pass , the virtual energy storage capacity of the heating network that supports the regulation capacity of the CHP unit can be quantified, thereby measuring the regulation capacity 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.

[0058] Step 1.2: Based on the incomplete information condition, 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.

[0059] In a specific implementation, 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 domain 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 regulation capacity can be expressed as (18) (19).

[0060] In the formula, and are the upper and lower bounds of the feasible region of the regulation capacity of the power system at time t, and are the upper and lower bounds of the feasible domain 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.

[0061] In actual solution, it is not easy to solve the expression containing product terms (or logarithmic terms). For this, a linear objective function can be used to approximate the above formula, that is, the sum of the lengths of each dimension of the polyhedron is used to replace the volume of the polyhedron, which is specifically expressed as follows: (20) (twenty one).

[0062] In addition to the power system being able to use the regulation capacity of thermal power units to deal with disturbances, the heating network can also use 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, and the regulation capacity should match the degree of wind power fluctuation. The relevant constraints are expressed as: (twenty two), (twenty three), (twenty four).

[0063] In the formula, represents the random adjustment power of thermal power unit i at time t; It indicates the regulating capacity provided by the thermal system; and are the affine strategy functions of the thermal power unit and the thermal system 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 regulation capacity of the power system, Represents the demand of electric load e at time t.

[0064] 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 electric power system.

[0065] like Figure 1As shown in the figure, in the regulation capacity trading mechanism of the electric thermal system under incomplete information, the electric power system and the thermal system simultaneously carry out power coordination and regulation capacity sharing through the CHP units, so that the thermal system can provide more regulation capacity for the electric power system.

[0066] In traditional research, the coordinated dispatch strategy of electric and thermal systems only considers the power coordination between electric and thermal systems. When a power disturbance occurs in the power system, the power system can only use its own regulation capability to counter the disturbance. This can achieve a certain degree of regulation capability sharing between electric and thermal systems. For example, when the regulation capability of the power system is insufficient, by changing the power generation power of the CHP unit, the restriction of the power generation power constraint of the thermal power unit in the power system on the spare capacity can be alleviated, and the spare space of the generator unit can be increased, thereby improving the regulation capability of the power system. However, the regulation capability sharing between the above-mentioned types of electric and thermal systems is still an indirect sharing. In essence, the power system still uses its own regulation capability, not the regulation capability of the thermal system. In addition, when the power system itself does not have the regulation capability, the thermal system cannot share the sufficient regulation capability with the power system. If the thermal power unit in the power system only increases the spare space but does not increase the spare response rate, it may not be able to improve the regulation capability of the system. Therefore, this embodiment proposes a regulation capability trading mechanism for electric and thermal systems under incomplete information, and the electric and thermal systems simultaneously carry out power coordination and regulation capability sharing, so that the thermal system provides more regulation capability for the power system. Among them, incomplete information refers to a small amount of boundary information, which in this embodiment is the power and regulation capacity of the CHP unit. Under the decentralized optimization scheduling mechanism under incomplete information, each stakeholder performs scheduling separately, maintaining operational independence; each stakeholder only needs to exchange a small amount of boundary information, and its internal privacy information does not need to be disclosed, which effectively protects the privacy information of the stakeholder.

[0067] Game theory can effectively solve the problems caused by multi-agent transactions, so it is introduced into the optimization and scheduling of integrated energy systems. However, most current studies only focus on the cooperation between horizontal peers (multiple parks, multiple energy hubs), and do not conduct in-depth research on the cooperation between vertical non-peer entities (heterogeneous energy entities such as electricity, heat, gas, and cold). This embodiment applies cooperative game theory between non-peer power systems and thermal systems, and considers the direct sharing of regulation capabilities between electricity and heat while considering the interaction of electric and thermal power.

[0068] When the thermal system provides regulation capacity for the power system, the operating state adjustment deviates from the optimal strategy when the thermal system is independently dispatched, resulting in loss of benefits for the thermal system. Therefore, a cooperative game optimization model based on Nash bargaining is introduced to allocate cooperative surplus through Nash bargaining, so that the overall benefit cost of the electric thermal system is minimized, while the total operating cost of each subject of the power system and the thermal system is reduced.

[0069] 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.

[0070] In the electric heating system regulation capacity trading mechanism proposed in the embodiment, the cooperation entities are the power system and the thermal system. At the same time, in order to avoid conflicts of interest among the cooperation entities, Nash bargaining is introduced to allocate the cooperation surplus of the cooperation alliance. The implementation of the electric heating system regulation capacity trading mechanism is mainly divided into two stages: Phase 1: Determine the transaction volume of regulation capacity between electric and thermal entities. Each cooperating entity needs to coordinate and optimize with the ultimate goal of maximizing social welfare. In this phase, each entity only needs to convey its desired transaction time and volume to other entities. Through iteration, the optimal regulation capacity transaction strategy under the coordinated scheduling strategy of the electric and thermal system can be finally obtained to maximize the overall benefits of the electric and thermal system. The thermal system provides regulation capacity for the power system by adjusting its operating status, promotes the consumption of new energy, and further reduces the overall energy cost of the electric and thermal system.

[0071] The second stage: determine the transaction price of the regulation capacity between the electric heating entities. After the first stage of cooperation and negotiation, the regulation capacity transaction strategy between the electric heating entities has been determined. Therefore, the main purpose of this stage is to maximize the payment benefits, that is, to determine the optimal regulation capacity transaction price between the entities. In the bargaining process, the price is still determined by the decentralized optimization method. Each cooperative entity only needs to convey its expected regulation capacity transaction price to other entities, so as to ensure that its own income is maintained at a sufficient level to ensure the willingness of each entity to participate in the bargaining cooperation alliance.

[0072] The electric heating system regulation capacity trading mechanism described in this embodiment involves two main bodies, the power system and the thermal system, and is a cooperative game problem. Its essence is the optimal distribution of individual interests based on the maximization of the benefits of the cooperative alliance. The optimal regulation capacity trading volume and trading price among the cooperative entities are determined through the above two stages. At the same time, in order to fully protect the information privacy security of each entity, the ADMM distributed algorithm is used to solve the problem in both stages, which not only effectively protects the privacy security of each entity, but also fully reflects the balanced interaction process between different entities in the interaction.

[0073] 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.

[0074] In a specific implementation, the power system sub-problem optimization objective To minimize the total cost of the power system. The total cost of the power system is composed of the power generation cost and standby 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 standby from the thermal system. The specific objective function is as follows: (25).

[0075] In the formula, is the operating cost of thermal power units, is the risk cost of wind power, including the cost of wind abandonment and the cost of power shortage. is the operating cost of CHP unit power generation, is the cost of the power system purchasing backup 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 respectively the power generation capacity 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 the reserve capacity adjustment and reduction of 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 They 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.

[0076] Optimization objective of the thermal system subproblem To minimize the total cost of the thermal system.

[0077] (26).

[0078] In the formula, 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 upward and downward reserve capacity cost coefficients of CHP unit k, respectively.

[0079] 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.

[0080] This embodiment establishes a cooperative game optimization model between electric heating systems based on the Nash bargaining theory, and decides the transaction price and transaction volume of the adjustment capacity between electric heating systems from the perspective of overall rationality and individual rationality. The Nash bargaining theory is based on the idea of ​​cooperative game, which can take into account both individual and collective interests. It is often used in the problem of multi-subject interest distribution and can meet the incentive compatibility conditions. Its standard model can be expressed as: (27).

[0081] In the formula, 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.

[0082] The Nash bargaining model is used to describe the collaborative bargaining process among electric heating systems. The model can be expressed as: (28).

[0083] In the formula, The operating cost of subject 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 regulation capacity between electric heating entities.

[0084] Since there is a product of the transaction price and the transaction volume in equation (28), it is a non-convex nonlinear model and difficult to solve directly. Therefore, the original model is equivalently converted into two easy-to-solve sub-problems: the cooperative benefit maximization sub-problem and the energy payment sub-problem. Solving the two sub-problems in turn can obtain the optimal solution of the original problem.

[0085] Step 3.3: Set the cooperative benefit maximization sub-problem and transaction payment sub-problem according to the cooperative game optimization model between the electric heating systems.

[0086] Step 3.3.1: Sub-problem of maximizing cooperation benefits.

[0087] 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: (29).

[0088] In the formula, The operating cost of subject m after participating in Nash bargaining when transaction costs are not included; Among them, for the power system, when transaction costs are not included, its operating cost before participating in the cooperative game is: (30).

[0089] For the thermal system, the operating cost of the thermal system before participating in the cooperative game is: (31).

[0090] Step 3.3.2: Transaction payment sub-problem.

[0091] Formula (32) is the transaction payment sub-problem obtained after conversion.

[0092] (32).

[0093] In the formula, and Solve the sub-problem of maximizing the cooperative benefit for subject m and obtain the optimal cost and regulatory capacity transaction volume.

[0094] Step 3.4: Set the corresponding wind power acceptance risk metric and system constraints.

[0095] Step 3.4.1: Wind power acceptance risk measurement.

[0096] 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: (33).

[0097] In the formula, 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 consumption.

[0098] Using the K-block based piecewise linear method, (33) can be approximated by the following linear expression with auxiliary variables and constraints: (34).

[0099] In the formula, The risk of wind curtailment. There is a risk of power shortage. , , , is a constant coefficient; N is the number of segments.

[0100] It should be pointed out that the piecewise linearization method used here is different from the traditional piecewise linear method. This method does not introduce integer variables in the linearization process, which will help improve the computational efficiency of the optimization model and enhance the convergence of the decentralized optimization algorithm. At the same time, in the above piecewise linearization method, the more segments are selected, the more accurate the solution obtained. Of course, as the number of segments increases, the computational burden will also increase. In actual operation, the method will select an appropriate number of segments to balance the solution efficiency and solution accuracy of the model.

[0101] Step 3.4.1: System constraints.

[0102] Step 3.4.1.1: Power system constraints.

[0103] 1) Constraints on unit power generation capacity (35), (36).

[0104] In the formula, and is the maximum and minimum power generation of thermal power unit i; and are the maximum and minimum generating capacities of CHP unit k.

[0105] 2) Unit climbing constraints To ensure the standby response rate, the ramp rate of the unit should be fully considered during the optimization process: (37), (38).

[0106] In the formula, and is the upward and downward climbing capability of thermal power unit i; and is the upward and downward climbing capability of CHP unit k.

[0107] 3) Branch flow constraints (39).

[0108] In the formula, , , and They 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.

[0109] Step 3.4.1.2: Thermal system constraints. 1) Dynamic characteristics of heating network: The dynamic characteristics of the heating network are mainly reflected in transmission delay and temperature loss. Since hot water flows slowly, there is a temperature difference between the temperature at the inlet and outlet of the pipe, so part of the heat energy is stored in the heating network. Due to the temperature difference between the hot water and the environment, part of the heat will be lost during the flow, resulting in a drop in temperature.

[0110] ① Transmission delay.

[0111] Due to the heat transfer delay in the temperature transmission of the heating pipeline , the outlet water temperature of pipe j at time t can be obtained by Estimation of the pipe water inlet temperature at time: (40), (41).

[0112] Where j is the number of the thermal pipeline, is the time delay of thermal pipeline j, is the density of water, is the radius of the thermal pipe.

[0113] 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. Generally, it is not an integer variable. For this reason, 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 realize multi-time scale scheduling of electricity and heat.

[0114] The outlet water temperature at time t can be expressed as: (42), (43).

[0115] In the formula, is the ceiling function, is not less than The smallest integer of is not less than The smallest integer of 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 quality weight coefficients. Thus, the expression of the pipe outlet water temperature that only takes into account the heat transfer delay can be obtained.

[0116] ②Temperature loss.

[0117] Only the temperature loss of hot water is taken into account, and the transmission delay is ignored. The outlet water temperature of the pipeline is: (44), (45).

[0118] In the formula, is the ambient temperature, is the pipeline temperature loss coefficient, and c is the insulation coefficient.

[0119] When considering transmission delay and temperature loss at the same time, the outlet water temperature of the pipeline can be expressed as (46).

[0120] 2) Heating network constraints: For any form of heating network, the node flow continuity equation must be satisfied, that is, for any node in the heating network, the flow into the node is equal to the flow 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: (47).

[0121] In the formula, and are the sets of pipes connected to node n and ending 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.

[0122] The heat supply network should also satisfy the law of thermal energy conservation. After hot water from different pipes flows into the same node, the temperature is mixed. After mixing, the hot water flowing out of the node has the same temperature, as shown in the following formula: (48).

[0123] In the formula, is the outlet water temperature of pipe j at time t; is the water inlet temperature of pipe l at time t.

[0124] Step 4: Use a decentralized solution algorithm to solve the cooperative game optimization model of the electric heating system, allocate the cooperative surplus of the electric heating system regulation capacity trading mechanism, and complete the decentralized scheduling of the electric heating system.

[0125] Step 4.1: Construct the electric-thermal system coupling constraints according to the corresponding system constraints.

[0126] The electric heating system includes two main bodies, the power system and the thermal system. The scheduling information between the main bodies has a certain degree of privacy. This embodiment decomposes the optimization problem of the electric heating system into the power system sub-problem and the thermal system sub-problem based on the ADMM method for decentralized solution. Under the premise of 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 unit on the electric and thermal main connecting line as coupling variables, and decouples the electric heating system into the electric and thermal main bodies at the connecting line for optimization, namely: (49).

[0127] In the formula, , They represent the coupling variables of the electric power and thermal systems at the tie line q respectively; , , They are the electric power, upward regulation capacity, and downward regulation capacity 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.

[0128] There are two types of coupling constraints in the proposed scheduling method, namely, the coupling constraint of the electric-heat system transmission power and the coupling constraint of the electric-heat system reserve capacity. The coupling constraint of the electric-heat system transmission power is closely related to the active power basis point of the exchange. The coupling constraint of the electric-heat system reserve capacity is determined based on the exchanged reserve capacity. The two constraints can be expressed as: (50), (51).

[0129] You can use the global variables in ADMM Ensure the consistency of boundary information. The specific representation is as follows: (52).

[0130] Step 4.2: Relax the coupling constraints of the electric-thermal system. Decouple the cooperative game optimization model of the electric-thermal system into the power system subproblem and the thermal system subproblem.

[0131] 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 sub-problems and thermal system sub-problems, 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 coupling variable residuals exchanged between the adjacent systems meet the stopping conditions, and the solution process completes the optimal solution.

[0132] 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 restated as: (53), (54).

[0133] In the formula, , is the Lagrange multiplier, is the penalty factor. The above multiplier will be updated in the iterative process until the final decision is obtained.

[0134] The Lagrange multiplier update formula is as follows: (55), (56).

[0135] By controlling , and The consistency of the coupling information between the power system and the thermal system can be achieved, and the purpose of achieving optimal scheduling of the electric and thermal systems by exchanging a small amount of boundary information can be achieved.

[0136] The basis for judging whether the iteration converges is as follows: (57).

[0137] In the formula, , Indicates The primal residual and dual residual of the ADMM algorithm after iterations; , It represents 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 to iterate until the iteration converges.

[0138] In order to improve the convergence speed of the algorithm, this embodiment proposes an improved ADMM algorithm to solve the electric heating system. Compared with the traditional ADMM, the adaptive ADMM can select different , thereby improving the efficiency of the algorithm: (58).

[0139] In the formula, 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.

[0140] 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.

[0141] In a specific embodiment, Figure 3 As shown, the solution process is as follows.

[0142] Step 1: Initialization. Set initial values ​​and input line parameters, unit parameters and other data.

[0143] Step 2: Solve the subproblem of maximizing the cooperative benefit. Set the initial Lagrange multiplier , , and the number of iterations , and solve the electric heating subsystems in turn.

[0144] Step 3: Determine the convergence condition of the cooperative benefit maximization subproblem. If all entities can simultaneously meet the convergence condition (57), the iteration process is terminated, the optimal cost and optimal transaction volume in the decision result are output, and this is used as the initial value of the transaction payment subproblem. Otherwise, the multiplier is updated according to (59), and the number of iterations is set. , and return to step 2.

[0145] 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.

[0146] Step 5: Determine the convergence condition of the transaction payment subproblem. If the convergence condition (57) can be met at the same time, the iteration process is terminated and the transaction price in the decision result is output. Otherwise, the multiplier is updated according to (56) and the number of iterations is set. , and return to step 4.

[0147] Embodiment 2: Embodiment 2 of the present invention provides a distributed dispatching system for electric heating systems based on Nash bargaining, including: The regulating capacity quantification module is configured to obtain the parameters of the electric-thermal integrated energy system to be dispatched, and quantify the regulating capacity between the thermal system and the power system, wherein the CHP unit is used as the regulating capacity transmission device from the thermal system to the power system, and the regulating capacity is provided to the power system by changing the power and regulating capacity of the CHP unit to meet the regulating capacity requirements of the power system; The regulation capacity sharing module is configured to construct a regulation capacity trading mechanism for the electric heating system under incomplete information, so as to realize power coordination and regulation capacity sharing between the thermal system and the electric power system; The cooperative game optimization module is configured to construct a cooperative game optimization model of the electric heating system based on Nash bargaining 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; 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.

[0148] Embodiment three: Embodiment 3 of the present invention provides a medium on which a program is stored. When the program is executed by a processor, the steps of the decentralized scheduling method of an electric heating system based on Nash bargaining as described in Embodiment 1 of the present invention are implemented.

[0149] Embodiment 4: 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 in the distributed scheduling method of the electric heating system based on Nash bargaining as described in Embodiment 1 of the present invention are implemented.

[0150] The steps involved in the above embodiments 2, 3 and 4 correspond to the method embodiment 1. For the specific implementation methods, please refer to the relevant description part of embodiment 1.

[0151] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0152] Although the above describes the specific implementation mode 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 creative work are still within the scope of protection of the present invention.

Claims

1. A decentralized dispatching method for electric heating system based on Nash bargaining, characterized in that: The following steps are involved: Obtain the parameters of the electric-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, and the regulation capacity is provided 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. Construct 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 unequal distribution of benefits between the thermal system and the power system in the electric-heat system regulation capacity trading mechanism under incomplete information, a cooperative game optimization model of the electric-heat system based on Nash bargaining is constructed. Considering the multi-time scale synergy 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, and the cooperative surplus of the electric heating system regulation capacity trading mechanism is allocated to 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: The specific steps to quantify the regulation capacity between the thermal system and the power system are: Taking CHP unit as heat source, the regulation capacity of thermal system is quantified through the synergy between CHP unit and dynamic characteristics of heating network; Based on the condition of incomplete information, the feasible domain of the regulation capacity of the electric-thermal system is used to quantify the regulation capacity provided by the thermal system to the power system.

3. The Nash bargaining-based decentralized scheduling method for electric heating systems according to claim 2, characterized in that: Through the synergistic effect of the dynamic characteristics of the CHP unit and the heating network, the regulation capacity of the thermal system is quantified, and the changes in heat source power and regulation capacity are compensated by controlling the overall temperature regulation of the heating network.

4. The Nash bargaining-based decentralized scheduling method for electric heating systems according to claim 3, characterized in that: 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.

5. The Nash bargaining-based decentralized scheduling method for electric heating systems according to claim 3, characterized in that: In the regulation capacity trading mechanism of electric thermal systems under incomplete information, the electric 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 electric power system.

6. The Nash bargaining-based decentralized scheduling method for electric heating systems according to claim 1, characterized in that: The specific steps of constructing the cooperative game optimization model of the 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 electric heating 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.

7. 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 sub-problems of the electric power system and the thermal system; 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.

8. A distributed dispatching system for electric heating system based on Nash bargaining, characterized in that: include: The regulating capacity quantification module is configured to obtain the parameters of the electric-thermal integrated energy system to be dispatched, and quantify the regulating capacity between the thermal system and the power system, wherein the CHP unit is used as the regulating capacity transmission device from the thermal system to the power system, and the regulating capacity is provided to the power system by changing the power and regulating capacity of the CHP unit to meet the regulating capacity requirements of the power system; The regulation capacity sharing module is configured to construct a regulation capacity trading mechanism for the electric heating system under incomplete information, so as to realize power coordination and regulation capacity sharing between the thermal system and the electric power system; The cooperative game optimization module is configured to construct a cooperative game optimization model of the electric heating system based on Nash bargaining 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; 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.

9. 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 decentralized scheduling method of an electric heating system based on Nash bargaining as described in any one of claims 1-7.

10. A terminal device, characterized in that: It includes a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the decentralized scheduling method of the electric heating system based on Nash bargaining as described in any one of claims 1-7.

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