Multi-park operation optimization method and system for carbon transaction and point-to-point transaction

Abstract

The invention discloses a multi-park operation optimization method and system for carbon transaction and point-to-point transaction in the field of power consumption optimization scheduling, and the method comprises the steps: obtaining the load capacity and new energy output of each park, the node carbon potential and unit carbon price of a power grid and a gas network, and the parameters of all equipment in the park; inputting the data into a pre-constructed Nash game model to obtain an operation optimization result, a point-to-point transaction volume and a point-to-point transaction price of each park; wherein the Nash game model takes minimization of operation cost as a target, the Nash game model is converted into a cooperative alliance total cost minimization sub-problem and an energy transaction price determination sub-problem, the two sub-problems are solved by adopting an improved acceleration alternating direction multiplier method, and a global optimal operation scheme of each park is output. According to the invention, energy sharing and benefit maximization among the parks can be realized, carbon emission is effectively reduced, and low-carbon and efficient operation of the parks is promoted.

Description

Technical Field

[0001] The present invention relates to a multi-park operation optimization method and system for carbon trading and point-to-point trading, belonging to the technical field of power optimization scheduling. Background Art

[0002] The carbon trading mechanism came into being and became an important market-based means for the world to respond to climate change and reduce greenhouse gas emissions.

[0003] Carbon trading is a market-based emissions reduction mechanism that, by pricing carbon emissions, guides businesses to optimize their production and energy mix, thereby reducing greenhouse gas emissions. Specifically, carbon trading consists of two main forms: quota trading and project-based emissions reduction trading. Quota trading involves the government allocating carbon emission quotas to businesses based on specific standards. Businesses can sell their remaining allowances when their emissions fall below their quotas, and otherwise purchase them from the market. Project-based emissions reduction trading, on the other hand, allows businesses to invest in clean energy projects to generate emission reductions and then sell them.

[0004] At the same time, the power industry, as a major source of carbon emissions, is also a crucial area for achieving emission reduction targets. Traditional power dispatching primarily focuses on reducing operating costs and ensuring power supply security. However, with the development of a low-carbon economy, relying solely on traditional optimized dispatching is no longer sufficient to meet the needs of modern power systems. Therefore, integrating carbon trading mechanisms into power system optimized dispatching can not only help power companies reduce carbon emission costs, but also promote the use of clean energy, improving the economic and environmental performance of the entire system.

[0005] Peer-to-peer trading is gaining popularity. Peer-to-peer trading is a distributed electricity trading model that allows energy producers and consumers to transact directly, rather than relying on traditional power companies as intermediaries. This trading model can effectively improve energy efficiency, reduce transaction costs, and encourage users to actively participate in energy management and green energy consumption.

[0006] Introducing a peer-to-peer trading model within a multi-park power supply system can complement power surpluses and shortages across parks, further improving energy efficiency. For example, surplus power generated by photovoltaic power generation within one park can be sold to other parks through peer-to-peer trading, reducing power waste. Furthermore, this trading model supports the integration and dispatch of emerging loads such as electric vehicles and energy storage equipment, providing strong support for the development of smart energy systems.

[0007] Despite the numerous advantages of carbon trading and peer-to-peer transactions, they still face numerous challenges in practical application. For example, the uncertainty of carbon market prices can affect companies' emission reduction strategies and economic benefits, while the complexity of peer-to-peer transactions increases the difficulty of power system scheduling.

[0008] Furthermore, differences among parks in infrastructure, technology, and energy demand also present obstacles to comprehensive optimization. To fully leverage the advantages of carbon trading and peer-to-peer transactions, a new approach to optimizing multi-park operations is urgently needed. Summary of the Invention

[0009] The purpose of the present invention is to provide a multi-park operation optimization method and system for carbon trading and point-to-point trading, which can solve the problem that the optimized operation of multi-park power supply systems lacks comprehensive consideration of carbon trading and point-to-point trading.

[0010] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0011] In one aspect, the present invention provides a multi-park operation optimization method for carbon trading and peer-to-peer trading, comprising: Obtain the load of each park, renewable energy output, node carbon potential of the power grid and gas network, unit carbon price, and parameters of all equipment in the park; The load of each park, renewable energy output, node carbon potential of the power grid and gas network, unit carbon price, and parameters of all equipment in the park are input into a pre-built Nash game model to obtain the operation optimization results of each park as well as the peer-to-peer transaction volume and peer-to-peer transaction price; Among them, the Nash game model aims to minimize operating costs. The Nash game model is converted into the sub-problem of minimizing the total cost of the cooperative alliance and the sub-problem of determining the energy transaction price. The improved accelerated alternating direction multiplier method is used to solve the two sub-problems and output the global optimal operation plan for each park.

[0012] In combination with the first aspect, further, all equipment within each park includes wind turbines WT, photovoltaic generators PV, cogeneration units CHP, electric boilers EB, gas boilers GB, conversion efficiency of energy conversion equipment, and interactive equipment connected to the upstream distribution network and gas network.

[0013] In combination with the first aspect, further, the process of constructing the Nash game model includes: Calculate the node carbon potential of each park based on its renewable energy output, power purchased from the grid, gas network, and other parks; Establishing a carbon trading cost model based on the node carbon potential and unit carbon price, inputting the unit carbon price, parameters of all equipment in the park, and the node carbon potential of each park into the carbon trading model to obtain the carbon trading cost of each park; Establish a cooperative alliance framework among multiple parks, and build a Nash game model for the park based on the cooperative alliance framework and the carbon trading model.

[0014] Combined with the first aspect, further, the node carbon potential of each park is calculated based on the renewable energy output of each park, the power purchased from the power grid, gas network and other parks, including Based on the real-time carbon emission intensity of the regional power grid and the carbon emission factor of the gas network, the node carbon potential of the power grid and gas network is obtained; The node carbon potential of the power grid and gas network, the output of new energy, the purchased energy power, and the parameters of all equipment in the park are input into the pre-built node carbon potential model to obtain the node carbon potential of each park. The node carbon potential of each park is divided into the node carbon potential of the power, gas, and thermal sub-networks, and their expressions are: a. Node carbon potential of each park's power subgrid: ; ; in, represents the carbon potential of the power subgrid node of park i at time t; represents the carbon flow rate of the cogeneration unit i in the park at time t; represents the carbon flow rate of electricity purchased by park i from the upstream distribution network; represents the carbon flow rate of electricity delivered from park j to park i; represents the carbon flow rate of the electric energy storage in park i releasing energy at time t; represents the total energy production power of park i at time t; represents the energy discharging power of the electric energy storage in park i at time t; represents the electric power output of the cogeneration unit in park i at time t; N represents the total number of parks participating in the cooperative alliance; j represents the index value of the park; represents the electric power output transmitted from park j to park i at time t; represents the node carbon potential of the gas subnetwork in park i; It represents the electrical efficiency of the combined heat and power unit; represents the carbon potential of the upstream distribution network to the node of park i at time t; represents the power purchased by park i from the upstream distribution network at time t; represents the node carbon potential of the park j power subgrid at time t; Indicates the efficiency of line transmission; represents the wind power generated in park i at time t; represents the photovoltaic power generation power of park i at time t; b. Node carbon potential of each park gas subnetwork: ; ; in, represents the node carbon potential of the gas subnetwork in the park j; represents the node carbon potential of the upstream gas subnetwork; represents the carbon flow rate of the upstream gas subgrid; represents the carbon flow rate delivered from park j to the gas sub-grid of park i; Indicates the gas power released by gas energy storage; represents the gas power delivered by the upstream gas subnet to park i; represents the gas power delivered from park j to park i; Indicates the efficiency of the line in transmitting gas power; c. Node carbon potential of each park's thermal subnetwork: ; ; in, represents the carbon potential of the node in the thermal subnetwork of park i; represents the node carbon potential of the thermal subnetwork of park j; represents the carbon flow rate of the CHP unit; Indicates the carbon flow rate of the gas boiler; represents the carbon flow rate of the electric boiler; represents the carbon flow rate of thermal energy storage release; represents the carbon flow rate from park j to the thermal subnetwork of park i; Indicates the thermal power of the cogeneration unit; Indicates the thermal power released by thermal energy storage; Indicates the thermal power of the gas boiler; Indicates the thermal power of the electric boiler; represents the heat power delivered from park j to park i; Indicates the efficiency of the electric boiler; Indicates the efficiency of the gas boiler; Indicates the efficiency of the combined heat and power unit; Indicates the efficiency of the circuit in transferring thermal power.

[0015] In combination with the first aspect, further, the expression for obtaining the carbon trading cost of each park is: ; in, represents the carbon trading cost of park i; represents the unit carbon price; represents the carbon quota of park i; represents the actual carbon emissions of park i.

[0016] In conjunction with the first aspect, further, the establishment of a multi-park cooperative alliance framework includes: Identify multiple parks to participate in the cooperation, with each park acting as an independent stakeholder; Determine the methods of energy exchange between parks, including direct energy transmission between parks, and energy purchase from external electricity, heat, and gas networks or reverse sales of electricity to external power grids.

[0017] In combination with the first aspect, further, outputting the global optimal operation plan of each park includes: Based on the input data of the Nash game model, construct the objective function of the cooperative alliance total cost minimization sub-problem; The improved accelerated alternating direction multiplier algorithm is used to iteratively optimize the objective function of the cooperative alliance total cost minimization sub-problem, and the operation optimization results of each park and the point-to-point transaction volume are calculated; Based on the optimization results, the objective function of the energy trading price subproblem is constructed using the point-to-point transaction volume and the parameters of all equipment in the park as input. The objective function of the energy trading price subproblem is optimized twice using an improved accelerated alternating direction multiplier algorithm to obtain the point-to-point transaction price. The iterative process of the improved accelerated alternating direction multiplier algorithm includes: Step S701: Initialize parameters, including primary variables, dual variables, acceleration factors, and penalty factors. The initial primary variables include peer-to-peer transaction volume and peer-to-peer transaction price. Step S702: updating the peer-to-peer transaction volume by solving the minimum value of the objective function of the cooperative alliance total cost minimization sub-problem; Step S703: Based on the updated point-to-point transaction volume, the minimum value of the objective function of the energy transaction price sub-problem is solved, and the point-to-point transaction price is updated; Step S704: Based on the updated primary variable and penalty factor, update the dual variable according to the rules of the improved accelerated alternating direction multiplier algorithm, and calculate the combined residual; Among them, if the combined residual meets the monotonically decreasing condition, the acceleration factor and the dual variable are updated; if the monotonically decreasing condition is not met, the improved accelerated alternating direction multiplier algorithm is restarted; if the combined residual is less than the preset threshold, the iteration is terminated and the global optimal operation plan is output, otherwise return to step S701.

[0018] In combination with the first aspect, further, the objective function of minimizing the total cost of the cooperative alliance and the objective function expression of the sub-problem of determining the energy transaction price are respectively: ; Where N represents the total number of parks participating in the cooperative alliance; i represents the index value of the participating cooperative park; represents the total operating cost of park i; represents the interaction cost between park i and the upper-level energy network; represents the energy storage maintenance cost of park i; represents the maintenance cost of new energy equipment in park i; represents the maintenance cost of energy conversion equipment in park i; represents the carbon trading cost of park i; ; in, represents the negotiation breakdown cost of park i; represents the total cost of park i after the optimization of the cooperative alliance total cost minimization sub-problem; It represents the income obtained by park i through peer-to-peer transactions.

[0019] In combination with the first aspect, further, the iterative process of the improved accelerated alternating direction multiplier algorithm is expressed as follows: ; in, represents the optimization result of the main variable x in the kth iteration; x represents the main variable; represents the intermediate variable introduced by the acceleration step in the kth iteration; represents the dual variable introduced by the acceleration step in the kth iteration; represents the penalty factor for the kth iteration; represents the optimization result of the auxiliary variable y in the kth iteration; y represents the auxiliary variable; represents the dual variable after the kth iteration; represents the combined residual; represents the penalty factor after adjustment for the k+1th iteration; Indicates the penalty factor adjustment step size parameter; represents the original residual; represents the dual residual.

[0020] Secondly, a multi-park operation optimization system for carbon trading and peer-to-peer trading, including: The data acquisition module is used to obtain the load of each park, the output of renewable energy, the node carbon potential of the power grid and gas network, the unit carbon price, and the parameters of all equipment in the park; An optimization processing module is used to input the load of each park, the output of renewable energy, the node carbon potential of the power grid and gas network, the unit carbon price, and the parameters of all equipment in the park into a pre-built Nash game model to obtain the operation optimization results of each park as well as the peer-to-peer transaction volume and peer-to-peer transaction price; Among them, the Nash game model aims to minimize operating costs. The Nash game model is converted into a sub-problem of minimizing the total cost of the cooperative alliance and a sub-problem of determining the energy transaction price. The improved accelerated alternating direction multiplier method is used to solve the two sub-problems and output the global optimal operation plan for each park.

[0021] Compared with the prior art, the present invention has the following beneficial effects: This method obtains each park's load, renewable energy output, node carbon potential of the power grid and gas network, unit carbon price, and parameters of all equipment within the park. These parameters are then input into a pre-built Nash game model to generate optimized operational results for each park, as well as peer-to-peer transaction volumes and prices. In this process, the Nash game model, with the goal of minimizing operating costs, is transformed into a subproblem of minimizing the total cost of the cooperative alliance and a subproblem of determining the energy transaction price. These two subproblems are solved using an improved accelerated alternating direction multiplier method, ultimately outputting the globally optimal operational plan for each park.

[0022] This method fully considers the impact of carbon trading and peer-to-peer transactions, enabling the park to effectively reduce carbon emissions during optimized operation, achieve optimization of economic and environmental benefits, and at the same time improve computing efficiency and solution speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 FIG2 is a flow chart of a multi-park operation optimization method for carbon trading and point-to-point trading provided by an embodiment of the present invention; Figure 2 The figure shows a schematic diagram of a multi-park cooperative alliance architecture provided by an embodiment of the present invention; Figure 3 FIG2 is a flow chart of solving the Nash game model provided by an embodiment of the present invention; Figure 4 FIG2 is a flow chart of an improved accelerated ADMM algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0025] The term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " generally indicates an "or" relationship between the related objects. Example 1

[0026] See also Figure 1 This embodiment introduces a multi-park operation optimization method for carbon trading and point-to-point trading, including: Step S1: Obtain the load of each park, renewable energy output, node carbon potential of the power grid and gas network, unit carbon price, and parameters of all equipment in the park; Step S2: Input each park's load, renewable energy output, node carbon potential of the power grid and gas network, unit carbon price, and parameters of all equipment within the park into a pre-built Nash game model to obtain the operational optimization results of each park (output of each device, purchased energy power), as well as point-to-point transaction volume and point-to-point transaction price; Among them, the Nash game model aims to minimize operating costs. The Nash game model is transformed into the sub-problem of minimizing the total cost of the cooperative alliance and the sub-problem of determining the energy trading price. The improved accelerated alternating direction multiplier method (ADMM algorithm) is used to solve the two sub-problems and output the global optimal operation plan for each park.

[0027] In summary, this method not only enables energy sharing and maximizes benefits among parks, but also effectively reduces carbon emissions, promoting low-carbon, efficient park operations. By constructing a node carbon potential model and carbon trading model for the park energy subnetwork, the carbon trading costs of each park can be accurately calculated and incorporated into operating costs, allowing the park to fully consider the impact of carbon emissions during optimized operation. Furthermore, utilizing an improved accelerated ADMM algorithm can effectively increase computational speed, reduce reliance on initial value selection, and ensure rapid identification of optimal solutions in complex multi-park systems, optimizing the system's economic and environmental benefits. Example 2

[0028] Based on Example 1, this example further details the specific implementation steps of the multi-park operation optimization method for carbon trading and point-to-point trading.

[0029] In step S1, detailed information about each park is first obtained, including its load, renewable energy output, node carbon potential of the grid and gas networks, unit carbon price, and parameters of all equipment within the park. This includes wind turbines (WT), photovoltaic generators (PV), combined heat and power (CHP), electric boilers (EB), gas boilers (GB), conversion efficiency of energy conversion equipment, and interconnection equipment with upstream distribution and gas networks.

[0030] For equipment within the park, it is necessary to collect their technical parameters, such as the output power of wind turbines WT and photovoltaic generators PV, the electric power output and thermal power of combined heat and power units CHP, the energy consumption rate of electric boilers EB and gas boilers GB, the conversion efficiency of energy conversion equipment, the purchased energy power of interactive equipment connected to the upstream distribution network and gas network, and the charging and discharging power of electric, thermal, and gas energy storage devices.

[0031] Next, in step S2, we input the collected data into the pre-built Nash game model. The model construction process is as follows: Step S31: Calculate the node carbon potential of each park based on its renewable energy output, power purchased from the power grid, gas network, and other parks; Specifically, based on the real-time carbon emission intensity of the regional power grid and the carbon emission factor of the gas network, the node carbon potential of the power grid and gas network is obtained, and then input into the pre-built node carbon potential model together with the new energy output, purchased energy power and parameters of all equipment in the park to calculate the node carbon potential of each park.

[0032] Among them, the node carbon potential of each park is divided into the node carbon potential of electricity, gas and heat sub-grids.

[0033] Taking the campus energy subnet as an energy node and ignoring its internal carbon flow rate, the node carbon potential expressions of the campus are: a. Node carbon potential of each park's power subgrid: ; ; in, represents the carbon potential of the power subgrid node of park i at time t; represents the carbon flow rate of the cogeneration unit i in the park at time t; represents the carbon flow rate of electricity purchased by park i from the upstream distribution network; represents the carbon flow rate of electricity delivered from park j to park i; represents the carbon flow rate of the electric energy storage in park i releasing energy at time t; represents the total energy production power of park i at time t; represents the energy discharging power of the electric energy storage in park i at time t; represents the electric power output of the cogeneration unit in park i at time t; N represents the total number of parks participating in the cooperative alliance; j represents the index value of the park; represents the electric power output transmitted from park j to park i at time t; represents the node carbon potential of the gas subnetwork in park i; It represents the electrical efficiency of the combined heat and power unit; represents the carbon potential of the upstream distribution network to the node of park i at time t; represents the power purchased by park i from the upstream distribution network at time t; represents the node carbon potential of the park j power subgrid at time t; Indicates the efficiency of line transmission; represents the wind power generated in park i at time t; represents the photovoltaic power generation power of park i at time t; b. Node carbon potential of each park gas subnetwork: ; ; in, represents the node carbon potential of the gas subnetwork in the park j; represents the node carbon potential of the upstream gas subnetwork; represents the carbon flow rate of the upstream gas subgrid; represents the carbon flow rate delivered from park j to the gas sub-grid of park i; Indicates the gas power released by gas energy storage; represents the gas power delivered by the upstream gas subnet to park i; represents the gas power delivered from park j to park i; Indicates the efficiency of the line in transmitting gas power; c. Node carbon potential of each park's thermal subnetwork: ; ; in, represents the carbon potential of the node in the thermal subnetwork of park i; represents the node carbon potential of the thermal subnetwork of park j; represents the carbon flow rate of the CHP unit; Indicates the carbon flow rate of the gas boiler; represents the carbon flow rate of the electric boiler; represents the carbon flow rate of thermal energy storage release; represents the carbon flow rate from park j to the thermal subnetwork of park i; Indicates the thermal power of the cogeneration unit; Indicates the thermal power released by thermal energy storage; Indicates the thermal power of the gas boiler; Indicates the thermal power of the electric boiler; represents the heat power delivered from park j to park i; Indicates the efficiency of the electric boiler; Indicates the efficiency of the gas boiler; Indicates the efficiency of the combined heat and power unit; Indicates the efficiency of the circuit in transferring thermal power.

[0034] It should be noted that the carbon flow rate of the energy storage device is calculated during its charging and discharging process to accurately assess its impact on the park's carbon emissions. During the charging process, the energy storage device acts as a load, and its carbon flow rate is equal to the node carbon potential of the energy storage multiplied by the power charged by the energy storage. During the discharge process, the energy storage device acts as a production device, and its carbon flow rate is equal to the node carbon potential of the energy storage multiplied by the energy storage discharge power divided by the energy storage discharge efficiency.

[0035] Therefore, the carbon flow rate expression of the energy storage device is: ;

[0036] in, represents the nodal carbon potential of the e-th type of energy storage in park i at time t; represents the historical node carbon potential of the e-th energy storage in park i at time t-1; represents the state of charge of the e-th type of energy storage in park i at time t-1; represents the maximum capacity of the e-th type of energy storage in park i; represents the charging carbon flow rate of the e-th type of energy storage in park i at time t; represents the carbon flow rate of energy release of type e energy storage in park i at time t; Represents the time interval, that is, the step size of the optimization scheduling; represents the nodal carbon potential of the e-th type of energy storage in park i at time t; represents the charging power of the e-th type of energy storage in park i at time t; represents the energy release power of the e-th type of energy storage in park i at time t; Indicates the energy release efficiency of the e-type energy storage; Represents the e-th type of energy; R represents the set of energy types, that is, R ={ E (electricity), T (hot),G (gas)}.

[0037] Step S32: Based on the calculated node carbon potential and the given unit carbon price, a carbon trading cost model is established. The unit carbon price, the parameters of all equipment in the park, and the node carbon potential of each park are input into the carbon trading model to obtain the carbon trading cost of each park. Specifically, the expression of the carbon trading cost is: ; ; in, represents the actual carbon emissions of park i; T represents the total number of time periods in the scheduling cycle; t represents the index of the current time period; represents the carbon flow rate of gas consumption of the gas boiler in park i at time t; represents the carbon flow rate of electricity consumption of the electric boiler in park i at time t; represents the power of the e-th type of energy transmitted from park i to park j at time t; represents the carbon quota of park i; represents the carbon emission factor of the combined heat and power unit; represents the gas power consumed by the cogeneration unit in park i at time t; represents the equivalent carbon emission factor of the electric boiler; represents the power consumed by the electric boiler in park i at time t; represents the carbon emission factor of the gas boiler; represents the gas power consumed by the gas boiler in park i at time t; represents the carbon trading cost of park i. If the actual carbon emissions Below carbon quota , then profit can be made by selling the remaining quota (cost is negative), otherwise quota needs to be purchased; Represents the unit carbon price, which is determined by the carbon trading market.

[0038] Step S33: Establish a cooperative alliance framework among multiple parks. According to the cooperative alliance framework, further construct the Nash game model of the park on the basis of the carbon trading model.

[0039] For details, see Figure 2 The cooperative alliance structure is composed of participants from different parks. Members cooperate through energy mutual assistance. Since the alliance participates in carbon trading, the process of energy transmission is accompanied by the transmission of carbon flow and the change of node carbon potential. The exchange of parks is mainly carried out in two ways: 1) Energy transmission between parks to achieve energy sharing; 2) Determine the method of energy exchange between parks, including determining the multiple parks participating in the cooperation, directly transmitting energy between parks to achieve sharing, and each park purchasing energy from external electricity, heat, and gas networks or selling electricity to external power grids. As an independent interest entity, each park has information barriers between each other to protect its own privacy data.

[0040] Furthermore, the goal of the constructed Nash game model is to maximize the product of the difference between the operating cost of each park and the negotiation breakdown cost. The constraint condition is that the operating cost of each park is less than or equal to the negotiation breakdown cost. The expression of its objective function is: ; in, represents the negotiation breakdown cost of park i, that is, the cost of the worst operation plan without cooperation. In this embodiment of the present invention, the operation result without any cooperation among the entities is selected as the negotiation breakdown point; represents the operating cost of park i, which includes the operation and maintenance costs of each device, carbon trading costs, interaction costs with the upper-level energy network, and peer-to-peer transaction benefits. Its expression is as follows: ; ; in, represents the interaction cost between park i and the upper-level energy network (grid, gas network); represents the energy storage maintenance cost of park i; represents the maintenance cost of new energy equipment in park i; represents the maintenance cost of energy conversion equipment in park i; represents the net income of peer-to-peer transactions in park i; represents the transaction price of the e-th type of energy sold by park i to park j; represents the transaction price of the e-th type of energy sold by park j to park i; represents the transaction price of the e-th type of energy sold by park j to park i; represents the gas purchase price at time t; represents the gas power purchased by park i from the gas network at time t; represents the electricity purchase price at time t; represents the electricity price at time t; represents the power sold by park i to the grid at time t; represents the unit maintenance cost of the e-th type of energy storage; represents the charging power of the e-th type of energy storage at park i at time t; represents the energy release power of zone i to the e-th type of energy storage at time t; Represents the unit operation and maintenance cost of photovoltaic equipment; represents the photovoltaic output power of park i at time t; Represents the unit operation and maintenance cost of wind power equipment; represents the wind power output of park i at time t; represents the unit operation and maintenance cost of the combined heat and power (CHP) unit; represents the gas consumption power of the combined heat and power (CHP) unit in park i at time t; represents the unit operation and maintenance cost of electric boiler (EB); represents the gas consumption power of the electric boiler (EB) in park i at time t; represents the unit operation and maintenance cost of gas boiler (GB); represents the gas consumption power of the gas boiler (GB) in park i at time t.

[0041] Furthermore, the Nash game model constructed above is a non-convex nonlinear model. It is difficult to find the global optimal solution by directly solving it. Therefore, the Nash game model is transformed into two sub-problems: minimizing the total cost of the cooperative alliance and determining the energy transaction price. These two sub-problems are solved in sequence to obtain the optimal solution. Figure 3 The specific process is as follows: Sub-problem 1: Minimizing the total cost of the cooperative alliance Step S71: Based on the input data of the Nash game model, construct the objective function of the cooperative alliance total cost minimization sub-problem; The expression of the objective function of minimizing the total cost of the cooperative alliance is: ; Where N represents the total number of parks participating in the cooperative alliance; i represents the index value of the participating cooperative park; represents the total operating cost of park i; represents the interaction cost between park i and the upper-level energy network; represents the energy storage maintenance cost of park i; represents the maintenance cost of new energy equipment in park i; represents the maintenance cost of energy conversion equipment in park i; represents the carbon trading cost of park i.

[0042] Step S72: Using an improved accelerated alternating direction multiplier algorithm, iteratively optimize the objective function of the cooperative alliance total cost minimization sub-problem to calculate the operation optimization results and point-to-point transaction volume of each park; Sub-problem 2: Determining energy transaction prices Step S73: Based on the optimization results, the energy transaction price sub-problem objective function is constructed with the point-to-point transaction volume and the parameters of all equipment in the park as input. The energy transaction price sub-problem objective function is optimized twice using the improved accelerated alternating direction multiplier algorithm to obtain the point-to-point transaction price; wherein the total cost of the energy transaction price is constrained not to exceed the negotiation breakdown cost. ; The expression of the objective function of the energy transaction price sub-problem is: ; in, represents the negotiation breakdown cost of park i; represents the total cost of park i after the optimization of the cooperative alliance total cost minimization sub-problem; It represents the income obtained by park i through peer-to-peer transactions.

[0043] Furthermore, in order to solve the coupling problem of energy trading variables among multiple parks in the constraints, the following main variables are introduced for decoupling: ; in, represents the type e of energy that park j expects to trade with park i; A shared variable representing the price of the e-th type of energy traded between park i and park j.

[0044] For subproblems 1 and 2, where the objective function takes a negative value, it can be transformed into a minimum value problem, and the augmented Lagrangian function is as follows: ; ; in, The augmented Lagrangian function that minimizes the total cost of the cooperative alliance; represents the total operating cost of park i; The dual variable representing the constraint association of the e-th type of energy transaction volume between parks; represents the expected energy trading volume of park j to park i; The penalty factor representing the total cost of the cooperative alliance; The augmented Lagrangian function representing the energy transaction price; represents the fixed energy trading volume determined by energy trading price optimization; The dual variable representing the price constraint association of the e-th type of energy transaction between parks; Represents the penalty factor of energy transaction price.

[0045] Further, see Figure 4 , the improved iterative process of the accelerated alternating direction multiplier algorithm includes: Step S701, initialization parameters, including main variables as well as , dual variables , acceleration factor , penalty factor , and set the threshold ; Among them, the initial main variables include peer-to-peer transaction volume and peer-to-peer transaction price; Step S702: updating the peer-to-peer transaction volume by solving the minimum value of the objective function of the cooperative alliance total cost minimization sub-problem; Step S703: Based on the updated point-to-point transaction volume, the minimum value of the objective function of the energy transaction price sub-problem is solved, and the point-to-point transaction price is updated; Step S704: Based on the updated primary variable and penalty factor, update the dual variable according to the rules of the improved accelerated alternating direction multiplier algorithm, and calculate the combined residual; Specifically, the improved accelerated alternating direction multiplier algorithm (ADMM) is used to iteratively optimize the two subproblems. The calculation process is as follows: Use algorithm rules to update the main variables alternately and and the dual variable , and according to the original residual and the dual residual Dynamically adjust penalty factors ; ; in, represents the optimization result of the first main variable x in the kth iteration; x represents the first main variable, corresponding to subproblem 1; represents the intermediate variable introduced by the acceleration step in the kth iteration; represents the dual variable introduced by the acceleration step in the kth iteration; represents the penalty factor for the kth iteration; represents the optimization result of the second main variable y in the k-th iteration; y represents the second main variable, corresponding to sub-problem 2; represents the dual variable after the kth iteration; represents the combined residual; represents the penalty factor after adjustment for the k+1th iteration; Indicates the penalty factor adjustment step size parameter; represents the original residual; represents the dual residual.

[0046] If the combined residual calculated above meets the monotonically decreasing condition, the acceleration factor and the dual variable are updated; if the monotonically decreasing condition is not met, the improved accelerated alternating direction multiplier algorithm is restarted; Specifically, if , then update according to the following formula, the expression is as follows: ; in, Indicates the acceleration factor of the k+1th iteration, which is used to adjust the iteration step size and accelerate the convergence of the algorithm; represents the acceleration factor of the kth iteration; represents the updated value of the intermediate variable y at the k+1th iteration, used for the acceleration step; represents the optimization result of the auxiliary variable y at the kth iteration; represents the optimization result of the auxiliary variable y at the k-1th iteration; represents the updated value of the intermediate variable at the k+1th iteration, used for the acceleration step; represents the dual variable after the kth iteration; represents the dual variable after the k-1th iteration.

[0047] If the monotonic decreasing condition is not met, restart according to the following formula to ensure the monotonicity of the algorithm. The expression is as follows: ; in, Represents the preset constant threshold, which is used to determine whether the algorithm needs to be restarted. It usually takes a value close to 1; represents the combined residual at the k-1th iteration; represents the combined residual at the kth iteration.

[0048] If the combined residual Less than the preset threshold , then after iterative convergence, the energy scheduling plan, transaction price and carbon trading strategy of each park are input to form a global optimal operation plan, otherwise return to step S701. Example 3

[0049] A multi-park operation optimization system for carbon trading and peer-to-peer transactions, including: The data acquisition module is used to obtain the load of each park, the output of renewable energy, the node carbon potential of the power grid and gas network, the unit carbon price, and the parameters of all equipment in the park; An optimization processing module is used to input the load of each park, the output of renewable energy, the node carbon potential of the power grid and gas network, the unit carbon price, and the parameters of all equipment in the park into a pre-built Nash game model to obtain the operation optimization results of each park as well as the peer-to-peer transaction volume and peer-to-peer transaction price; Among them, the Nash game model aims to minimize operating costs. The Nash game model is converted into a sub-problem of minimizing the total cost of the cooperative alliance and a sub-problem of determining the energy transaction price. The improved accelerated alternating direction multiplier method is used to solve the two sub-problems and output the global optimal operation plan for each park.

[0050] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A multi-park operation optimization method for carbon trading and peer-to-peer trading, characterized in that: include: Obtain the load of each park, renewable energy output, node carbon potential of the power grid and gas network, unit carbon price, and parameters of all equipment in the park; The load of each park, renewable energy output, node carbon potential of the power grid and gas network, unit carbon price, and parameters of all equipment in the park are input into a pre-built Nash game model to obtain the operation optimization results of each park as well as the peer-to-peer transaction volume and peer-to-peer transaction price; Among them, the Nash game model aims to minimize operating costs. The Nash game model is converted into the sub-problem of minimizing the total cost of the cooperative alliance and the sub-problem of determining the energy transaction price. The improved accelerated alternating direction multiplier method is used to solve the two sub-problems and output the global optimal operation plan for each park.

2. The multi-park operation optimization method for carbon trading and point-to-point trading according to claim 1 is characterized in that: All equipment within each park includes wind turbines WT, photovoltaic generators PV, combined heat and power units CHP, electric boilers EB, gas boilers GB, conversion efficiency of energy conversion equipment, and interactive equipment connected to the upstream distribution network and gas network.

3. The multi-park operation optimization method for carbon trading and peer-to-peer trading according to claim 1, characterized in that: The construction process of the Nash game model includes: Calculate the node carbon potential of each park based on its renewable energy output, power purchased from the grid, gas network, and other parks; Establishing a carbon trading cost model based on the node carbon potential and unit carbon price, inputting the unit carbon price, parameters of all equipment in the park, and the node carbon potential of each park into the carbon trading model to obtain the carbon trading cost of each park; Establish a cooperative alliance framework among multiple parks, and build a Nash game model for the park based on the cooperative alliance framework and the carbon trading model.

4. The multi-park operation optimization method for carbon trading and point-to-point trading according to claim 3 is characterized in that: The node carbon potential of each park is calculated based on the renewable energy output of each park, the power purchased from the power grid, gas network and other parks, including Based on the real-time carbon emission intensity of the regional power grid and the carbon emission factor of the gas network, the node carbon potential of the power grid and gas network is obtained; The node carbon potential of the power grid and gas network, the output of new energy, the purchased energy power, and the parameters of all equipment in the park are input into the pre-built node carbon potential model to obtain the node carbon potential of each park. The node carbon potential of each park is divided into the node carbon potential of the power, gas, and thermal sub-networks, and their expressions are: a. Node carbon potential of each park's power subgrid: ； ； in, represents the carbon potential of the power subgrid node of park i at time t; represents the carbon flow rate of the cogeneration unit i in the park at time t; represents the carbon flow rate of electricity purchased by park i from the upstream distribution network; represents the carbon flow rate of electricity delivered from park j to park i; represents the carbon flow rate of the electric energy storage in park i releasing energy at time t; represents the total energy production power of park i at time t; represents the energy discharging power of the electric energy storage in park i at time t; represents the electric power output of the cogeneration unit in park i at time t; N represents the total number of parks participating in the cooperative alliance; j represents the index value of the park; represents the electric power output transmitted from park j to park i at time t; represents the node carbon potential of the gas subnetwork in park i; It represents the electrical efficiency of the combined heat and power unit; represents the carbon potential of the upstream distribution network to the node of park i at time t; represents the power purchased by park i from the upstream distribution network at time t; represents the node carbon potential of the park j power subgrid at time t; Indicates the efficiency of line transmission; represents the wind power generated in park i at time t; represents the photovoltaic power generation power of park i at time t; b. Node carbon potential of each park gas subnetwork: ； ； in, represents the node carbon potential of the gas subnetwork in the park j; represents the node carbon potential of the upstream gas subnetwork; represents the carbon flow rate of the upstream gas subgrid; represents the carbon flow rate delivered from park j to the gas sub-grid of park i; Indicates the gas power released by gas energy storage; represents the gas power delivered by the upstream gas subnet to park i; represents the gas power delivered from park j to park i; Indicates the efficiency of the line in transmitting gas power; c. Node carbon potential of each park's thermal subnetwork: ； ； in, represents the carbon potential of the node in the thermal subnetwork of park i; represents the node carbon potential of the thermal subnetwork of park j; represents the carbon flow rate of the CHP unit; Indicates the carbon flow rate of the gas boiler; represents the carbon flow rate of the electric boiler; represents the carbon flow rate of thermal energy storage release; represents the carbon flow rate from park j to the thermal subnetwork of park i; Indicates the thermal power of the cogeneration unit; Indicates the thermal power released by thermal energy storage; Indicates the thermal power of the gas boiler; Indicates the thermal power of the electric boiler; represents the heat power delivered from park j to park i; Indicates the efficiency of the electric boiler; Indicates the efficiency of the gas boiler; Indicates the efficiency of the combined heat and power unit; Indicates the efficiency of the circuit in transferring thermal power.

5. The multi-park operation optimization method for carbon trading and point-to-point trading according to claim 3 is characterized in that: The expression for obtaining the carbon trading cost of each park is: ； in, represents the carbon trading cost of park i; represents the unit carbon price; represents the carbon quota of park i; represents the actual carbon emissions of park i.

6. The multi-park operation optimization method for carbon trading and point-to-point trading according to claim 3 is characterized in that: The establishment of a multi-park cooperative alliance framework includes: Identify multiple parks to participate in the cooperation, with each park acting as an independent stakeholder; Determine the methods of energy exchange between parks, including direct energy transmission between parks, and energy purchase from external electricity, heat, and gas networks or reverse sales of electricity to external power grids.

7. The multi-park optimization operation method for carbon trading and point-to-point trading according to claim 1 is characterized in that: The output of the global optimal operation plan for each park includes: Based on the input data of the Nash game model, construct the objective function of the cooperative alliance total cost minimization sub-problem; The improved accelerated alternating direction multiplier algorithm is used to iteratively optimize the objective function of the cooperative alliance total cost minimization sub-problem, and the operation optimization results of each park and the point-to-point transaction volume are calculated; Based on the optimization results, the objective function of the energy trading price subproblem is constructed using the point-to-point transaction volume and the parameters of all equipment in the park as input. The objective function of the energy trading price subproblem is optimized twice using an improved accelerated alternating direction multiplier algorithm to obtain the point-to-point transaction price. The iterative process of the improved accelerated alternating direction multiplier algorithm includes: Step S701: Initialize parameters, including primary variables, dual variables, acceleration factors, and penalty factors. The initial primary variables include peer-to-peer transaction volume and peer-to-peer transaction price. Step S702: updating the peer-to-peer transaction volume by solving the minimum value of the objective function of the cooperative alliance total cost minimization sub-problem; Step S703: Based on the updated point-to-point transaction volume, the minimum value of the objective function of the energy transaction price sub-problem is solved, and the point-to-point transaction price is updated; Step S704: Based on the updated primary variable and penalty factor, update the dual variable according to the rules of the improved accelerated alternating direction multiplier algorithm, and calculate the combined residual; Among them, if the combined residual meets the monotonically decreasing condition, the acceleration factor and the dual variable are updated; if the monotonically decreasing condition is not met, the improved accelerated alternating direction multiplier algorithm is restarted; if the combined residual is less than the preset threshold, the iteration is terminated and the global optimal operation plan is output, otherwise return to step S701.

8. The multi-park optimization operation method for carbon trading and point-to-point trading according to claim 7 is characterized in that: The objective function expressions of the cooperative alliance total cost minimization and the energy transaction price determination sub-problem are: ； Where N represents the total number of parks participating in the cooperative alliance; i represents the index value of the participating cooperative park; represents the total operating cost of park i; represents the interaction cost between park i and the upper-level energy network; represents the energy storage maintenance cost of park i; represents the maintenance cost of new energy equipment in park i; represents the maintenance cost of energy conversion equipment in park i; represents the carbon trading cost of park i; ； in, represents the negotiation breakdown cost of park i; represents the total cost of park i after the optimization of the cooperative alliance total cost minimization sub-problem; It represents the income obtained by park i through peer-to-peer transactions.

9. The multi-park optimization operation method for carbon trading and point-to-point trading according to claim 7 is characterized in that: The expression of the iterative process of the improved accelerated alternating direction multiplier algorithm is: ； in, represents the optimization result of the main variable x in the kth iteration; x represents the main variable; represents the intermediate variable introduced by the acceleration step in the kth iteration; represents the dual variable introduced by the acceleration step in the kth iteration; represents the penalty factor for the kth iteration; represents the optimization result of the auxiliary variable y in the kth iteration; y represents the auxiliary variable; represents the dual variable after the kth iteration; represents the combined residual; represents the penalty factor after adjustment for the k+1th iteration; Indicates the penalty factor adjustment step size parameter; represents the original residual; represents the dual residual.

10. A multi-park operation optimization system for carbon trading and point-to-point trading, characterized by: include: The data acquisition module is used to obtain the load of each park, the output of renewable energy, the node carbon potential of the power grid and gas network, the unit carbon price, and the parameters of all equipment in the park; An optimization processing module is used to input the load of each park, the output of renewable energy, the node carbon potential of the power grid and gas network, the unit carbon price, and the parameters of all equipment in the park into a pre-built Nash game model to obtain the operation optimization results of each park as well as the peer-to-peer transaction volume and peer-to-peer transaction price; Among them, the Nash game model aims to minimize operating costs. The Nash game model is converted into a sub-problem of minimizing the total cost of the cooperative alliance and a sub-problem of determining the energy transaction price. The improved accelerated alternating direction multiplier method is used to solve the two sub-problems and output the global optimal operation plan for each park.

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