Multi-integrated energy system scheduling method, device, equipment and storage medium

Abstract

The invention relates to a multi-integrated energy system scheduling method and device, equipment and a storage medium. The method comprises the following steps: obtaining a target cooperative game model, wherein the target cooperative game model takes the minimization of the total operation cost of a plurality of integrated energy systems as a target function; performing equivalent conversion on the target cooperative game model to obtain a benefit maximization sub-problem and an energy payment sub-problem; solving the benefit maximization sub-problem and the energy payment sub-problem, and determining a transaction strategy and a transaction price strategy of each integrated energy system; and determining a scheduling strategy according to the transaction strategy and the transaction price strategy of each integrated energy system. By adopting the method, the benefit of each comprehensive energy system can be balanced when the scheduling strategy is determined.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, device, equipment and storage medium for scheduling a multi-integrated energy system. Background Art

[0002] A regional integrated energy system is a system that organically integrates multiple energy forms (such as electricity, heat, gas, etc.) and realizes comprehensive utilization within a certain area. In addition, the regional integrated energy system includes multiple integrated energy systems. The integrated energy systems achieve a balance of interests through energy complementarity, flexible transactions and coordination. The coordinated optimization operation of multiple integrated energy systems has become a research hotspot.

[0003] In the prior art, a day-ahead dispatching model for an electricity-gas integrated energy system that considers energy coordination management is proposed from the perspective of energy interaction. However, the existing dispatching model mainly focuses on the optimization of the system as a whole, ignoring the coordination of interests between different integrated energy systems. Summary of the invention

[0004] Based on this, it is necessary to provide a multi-integrated energy system scheduling method, device, equipment and storage medium that can balance the interests of each integrated energy system when determining the scheduling strategy to address the above technical problems.

[0005] In a first aspect, the present application provides a multi-integrated energy system scheduling method, comprising:

[0006] Obtaining a target cooperative game model, wherein the target cooperative game model takes minimizing the total operating cost of multiple integrated energy systems as an objective function;

[0007] By equivalently transforming the target cooperative game model, we can obtain the benefit maximization subproblem and the energy payment subproblem.

[0008] Solve the benefit maximization sub-problem and energy payment sub-problem, and determine the transaction strategy and transaction price strategy of each comprehensive energy system;

[0009] The dispatching strategy is determined based on the trading strategies and trading price strategies of each integrated energy system.

[0010] In one embodiment, before obtaining the target cooperative game model, the method further includes:

[0011] The calculation model of the total operating cost of each integrated energy system is obtained. The calculation model is used to determine the total operating cost based on the energy purchase cost, carbon trading cost, solar power abandonment cost, wind power abandonment cost, compensation cost and interaction cost between the integrated energy system and other integrated energy systems. Among them, the carbon trading cost is determined according to the step-by-step carbon trading cost function.

[0012] In one embodiment, solving the benefit maximization subproblem and the energy payment subproblem and determining the transaction strategy and transaction price strategy of each integrated energy system include:

[0013] Obtain the objective function of the benefit maximization subproblem, perform iterative solution based on the distributed optimization algorithm, and determine the trading strategy of each integrated energy system; obtain the objective function of the energy payment subproblem, bring the optimal solution of the benefit maximization subproblem into the energy payment subproblem, perform iterative solution based on the distributed optimization algorithm, and determine the trading price strategy of each integrated energy system.

[0014] In one embodiment, the distributed optimization algorithm performs iterative solving to determine the trading strategy of each integrated energy system, including:

[0015] Obtain the augmented Lagrangian function corresponding to the benefit maximization subproblem; determine the update model based on the augmented Lagrangian function; perform multiple update iterations based on the update model until the convergence condition is met or the number of iterations reaches the preset number of iterations, and obtain the trading strategy of each integrated energy system.

[0016] In one embodiment, there is randomness in the output of renewable energy in the integrated energy system, and a total operating cost calculation model of each integrated energy system is obtained, including:

[0017] Obtain historical output data of renewable energy; determine multiple target typical output scenarios based on the historical output data; and determine the total operating cost calculation model of each integrated energy system based on each typical output scenario.

[0018] In one embodiment, renewable energy includes photovoltaic and wind energy, and multiple target typical output scenarios are determined based on historical output data, including:

[0019] Based on the kernel density estimation method and historical output data, the photovoltaic output probability density function and wind power output probability density function for multiple time periods are determined; based on the photovoltaic output probability density function and the wind power output probability density function, the wind and solar power output joint probability distribution function for each time period is determined; based on the wind and solar power output joint probability distribution function for each time period, multiple target typical output scenarios are determined.

[0020] In a second aspect, the present application also provides a multi-integrated energy system scheduling device, comprising:

[0021] An acquisition module is used to acquire a target cooperative game model, wherein the target cooperative game model takes minimization of the total operating cost of multiple integrated energy systems as an objective function;

[0022] The conversion module is used to perform equivalent conversion on the target cooperative game model to obtain the benefit maximization subproblem and the energy payment subproblem;

[0023] The solution module is used to solve the benefit maximization sub-problem and the energy payment sub-problem and determine the transaction strategy and transaction price strategy of each comprehensive energy system;

[0024] The determination module is used to determine the dispatching strategy according to the transaction strategy and transaction price strategy of each integrated energy system.

[0025] In one of the embodiments, before obtaining the target cooperative game model, the acquisition module is also used to obtain a total operating cost calculation model for each integrated energy system, and the calculation model is used to determine the total operating cost based on the energy purchase cost, carbon trading cost, solar power abandonment cost, wind power abandonment cost, compensation cost and interaction cost between the integrated energy system and other integrated energy systems, wherein the carbon trading cost is determined based on a step-by-step carbon trading cost function.

[0026] In one of the embodiments, the solution module is specifically used to obtain the objective function of the benefit maximization sub-problem, perform iterative solution according to the distributed optimization algorithm, and determine the trading strategy of each integrated energy system; obtain the objective function of the energy payment sub-problem, and bring the optimal solution of the benefit maximization sub-problem into the energy payment sub-problem, perform iterative solution according to the distributed optimization algorithm, and determine the trading price strategy of each integrated energy system.

[0027] In one of the embodiments, the solution module is specifically used to obtain the augmented Lagrangian function corresponding to the benefit maximization sub-problem; determine the update model based on the augmented Lagrangian function; according to the update model, perform multiple update iterations until the convergence condition is met or the number of iterations reaches a preset number of iterations, and obtain the trading strategy of each integrated energy system.

[0028] In one of the embodiments, there is randomness in the output of renewable energy in the integrated energy system, and an acquisition module is specifically used to obtain historical output data of renewable energy; determine multiple target typical output scenarios based on the historical output data; and determine a total operating cost calculation model for each integrated energy system based on each typical output scenario.

[0029] In one of the embodiments, renewable energy includes photovoltaic and wind energy, and an acquisition module is specifically used to determine the photovoltaic output probability density function and the wind energy output probability density function for multiple time periods based on the kernel density estimation method and historical output data; determine the joint probability distribution function of wind and solar output for each time period according to the photovoltaic output probability density function and the wind energy output probability density function; and determine multiple target typical output scenarios according to the joint probability distribution function of wind and solar output for each time period.

[0030] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any of the methods described in the first aspect is implemented.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any of the methods described in the first aspect above.

[0032] In a fifth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements any of the methods described in the first aspect above.

[0033] The above-mentioned multi-integrated energy system scheduling method, device, equipment and storage medium obtain the target cooperative game model, which takes the minimization of the total operating cost of multiple integrated energy systems as the objective function; then, the target cooperative game model is equivalently transformed to obtain the benefit maximization sub-problem and the energy payment sub-problem; then, the benefit maximization sub-problem and the energy payment sub-problem are solved to determine the trading strategy and trading price strategy of each integrated energy system; finally, the scheduling strategy is determined according to the trading strategy and trading price strategy of each integrated energy system. By considering the interests and interactive behaviors of different integrated energy systems, the target cooperative game model can accurately characterize the game strategy of each integrated energy system in the regional energy integrated system, convert the optimization problem of minimizing the total operating cost into two sub-problems, and obtain the trading strategy and trading price strategy of each integrated energy system by solving the two sub-problems. The scheduling strategy obtained in this way can not only meet the goal of cost minimization, but also balance the interests of each integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 A schematic diagram of a flow chart of a multi-integrated energy system scheduling method in one embodiment;

[0036] Figure 2 A flowchart of the steps of determining the transaction strategy and transaction price strategy of each comprehensive energy system in one embodiment;

[0037] Figure 3 A schematic diagram of a flow chart of steps for determining a trading strategy for each integrated energy system in one embodiment;

[0038] Figure 4 A flowchart of steps for obtaining a calculation model for the total operating cost of each integrated energy system in an embodiment;

[0039] Figure 5 A schematic diagram of a flow chart of steps for determining multiple target typical output scenarios according to historical output data in one embodiment;

[0040] Figure 6 A schematic diagram of steps for determining typical output scenarios of multiple targets in one embodiment;

[0041] Figure 7 A schematic diagram of a flow chart of a multi-integrated energy system scheduling method in another embodiment;

[0042] Figure 8 A schematic diagram of a scheduling result of a first comprehensive energy system in an embodiment;

[0043] Fig. 9 A schematic diagram of a scheduling result of a second comprehensive energy system in an embodiment;

[0044] Fig.10 A schematic diagram of a scheduling result of a third comprehensive energy system in an embodiment;

[0045] Fig.11 It is a structural block diagram of a multi-integrated energy system scheduling device in one embodiment;

[0046] Fig.12 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] A regional integrated energy system is a system that organically integrates multiple energy forms (such as electricity, heat, gas, etc.) and realizes comprehensive utilization within a certain area. In addition, the regional integrated energy system includes multiple integrated energy systems. The integrated energy systems achieve a balance of interests through energy complementarity, flexible transactions and coordination. The coordinated optimization operation of multiple integrated energy systems has become a research hotspot.

[0049] In the prior art, a day-ahead dispatching model for an electricity-gas integrated energy system that considers energy coordination management is proposed from the perspective of energy interaction. However, the existing dispatching model mainly focuses on the optimization of the system as a whole, and ignores the coordination of interests between different integrated energy systems.

[0050] In view of this, the present application provides a multi-integrated energy system scheduling method that balances the interests of each integrated energy system. The multi-integrated energy system scheduling method provided in the embodiment of the present application, its execution subject can be a multi-integrated energy system scheduling device, and the multi-integrated energy system scheduling device can be implemented by software, hardware, or a combination of software and hardware. It can be embedded in or independent of the processor in the computer device in the form of hardware, or it can be stored in the memory of the computer device in the form of software. In the following method embodiments, the execution subject is a computer device as an example for explanation, wherein the computer device can be a server, and the embodiment of the present application does not limit the specific type of computer device.

[0051] In an exemplary embodiment, Figure 1 As shown, a multi-integrated energy system scheduling method is provided, including the following steps 101 to 104. Among them:

[0052] S101, obtaining a target cooperative game model.

[0053] Among them, the target cooperation game model takes minimizing the total operating cost of multiple integrated energy systems as its objective function.

[0054] Optionally, in the target cooperative game model, cooperative game may mean that participants can obtain more benefits through cooperation than acting alone. In the field of integrated energy systems, each integrated energy system can optimize resource allocation through cooperation.

[0055] Optionally, the target cooperative game model can be determined based on the Nash negotiation theory. As a cooperative negotiation theory, the core ideas of the Nash negotiation theory include individual rationality, Pareto optimality, symmetry, invariance and independence. It emphasizes individual rationality and fairness in resource allocation, and provides a favorable analytical tool for effective cooperation. In the negotiation process, participants can use the principles of the Nash negotiation theory to pursue the optimal solution and maximize cooperation, thereby achieving a win-win situation. In the target cooperative game model, each integrated energy system needs to formulate its own trading strategy and trading price strategy to minimize its own costs. Through consultation with other integrated energy systems, a reasonable trading plan is jointly formulated to achieve the overall cost minimization goal.

[0056] Optionally, the target cooperative game model can be expressed by the following formula:

[0057]

[0058] in, is the number of integrated energy systems, is the operating cost of the ith integrated energy system, Represents the transaction cost before participating in the cooperative game. is the basic cost of the ith integrated energy system that does not participate in the sharing of electricity and heat energy (the cost of the ith integrated energy system before participating in the P2P electricity and heat energy sharing); is the cost of the i-th integrated energy system participating in the electricity and heat energy sharing.

[0059] Optionally, the total operating cost may be determined based on a total operating cost calculation model.

[0060] S102, perform equivalent transformation on the target cooperative game model to obtain the benefit maximization subproblem and the energy payment subproblem.

[0061] Optionally, the optimization variables in the target cooperative game model are the energy transaction volume and transaction price of each integrated energy system. It is a non-convex and nonlinear multivariable coupling optimization problem, which is relatively difficult to solve directly. Therefore, the solution of the model can be converted into two sub-problems of sequentially solving the optimization.

[0062] S103, solving the benefit maximization sub-problem and the energy payment sub-problem, and determining the transaction strategy and transaction price strategy of each comprehensive energy system.

[0063] Optionally, the transaction strategy of each integrated energy system can be determined by solving the benefit maximization sub-problem; the transaction price strategy of each integrated energy system can be determined by solving the energy payment sub-problem.

[0064] Optionally, the benefit maximization subproblem and the energy payment subproblem can be regarded as optimization problems. When solving the benefit maximization subproblem and the energy payment subproblem, they can be solved by optimization methods to determine the trading strategies and trading price strategies of each integrated energy system. The embodiments of the present application do not limit the optimization methods. For example, the benefit maximization subproblem and the energy payment subproblem can be solved separately by a particle swarm optimization algorithm.

[0065] S104, determining a dispatching strategy based on the transaction strategies and transaction price strategies of each integrated energy system.

[0066] Optionally, after determining the trading strategies and corresponding trading price strategies of each integrated energy system, a corresponding dispatching strategy can be obtained, which has the minimization of total operating costs as a dispatching goal.

[0067] Optionally, the operating parameters of each integrated energy system can be monitored in real time, and the market transaction information can be monitored. Then, the dispatch strategy can be fed back and adjusted in real time based on the monitored information. For example, if it is found that the real-time transaction price deviates greatly from the predicted price, or if the energy equipment fails, the dispatch strategy can be modified in time. For example, if a power generation equipment suddenly fails, the dispatch strategy should be adjusted quickly to reduce dependence on the equipment, increase the power generation of other normal equipment, or purchase more electricity from outside to ensure the balance of energy supply and demand.

[0068] The above-mentioned multi-integrated energy system scheduling method obtains the target cooperative game model, which takes the minimization of the total operating cost of multiple integrated energy systems as the objective function; then, the target cooperative game model is equivalently transformed to obtain the benefit maximization subproblem and the energy payment subproblem; then, the benefit maximization subproblem and the energy payment subproblem are solved to determine the trading strategy and trading price strategy of each integrated energy system; finally, the scheduling strategy is determined according to the trading strategy and trading price strategy of each integrated energy system. By considering the interests and interactive behaviors of different integrated energy systems, the target cooperative game model can accurately characterize the game strategy of each integrated energy system in the regional energy integrated system, and convert the optimization problem of minimizing the total operating cost into two subproblems. By solving the two subproblems, the trading strategy and trading price strategy of each integrated energy system are obtained. The scheduling strategy obtained in this way can not only meet the goal of cost minimization, but also balance the interests of each integrated energy system.

[0069] In an exemplary embodiment, optionally, before obtaining the target cooperative game model, the method further includes:

[0070] Obtain the total operating cost calculation model for each comprehensive energy system.

[0071] Among them, the calculation model is used to determine the total operating cost based on the energy purchase cost, carbon trading cost, solar power abandonment cost, wind power abandonment cost, compensation cost and the interaction cost between the integrated energy system and other integrated energy systems. Among them, the carbon trading cost is determined according to the step-by-step carbon trading cost function.

[0072] Alternatively, the total cost calculation model for the operation of the integrated energy system can be expressed by the following formula:

[0073]

[0074] Where F represents the total operating cost, is the energy purchase cost, represents the carbon trading cost, represents the cost of abandoned light, is the cost of wind curtailment, To compensate for the cost, It is the interaction cost between the integrated energy system and other integrated energy systems.

[0075] Optionally, the purchased energy cost can be calculated by the following function:

[0076]

[0077] in, is the electricity price in period t, is the amount of electricity purchased by the i-th integrated energy system in period t, is the gas price in period t, The amount of natural gas purchased by the i-th integrated energy system in period t.

[0078] Optionally, the prior art does not consider the carbon trading mechanism when calculating the carbon trading cost, i.e. ,in, The carbon trading mechanism unit price does not take into account the ladder carbon trading mechanism. The carbon emission model established in this way is too simple and not accurate enough. The carbon trading cost in the embodiment of the present application can be calculated by the following function:

[0079]

[0080] in, is the carbon trading cost of the i-th integrated energy system. When it is positive, it indicates that additional carbon emissions need to be purchased. When it is negative, it indicates that there is a partial profit that can be obtained by selling surplus carbon emissions through carbon trading. It is the base price for carbon trading; is the length of the carbon emission interval; c is the price growth rate; is the carbon emission quota of the i-th integrated energy system, is the actual carbon emissions.

[0081] Optional, carbon emission quota It can be calculated by the following formula:

[0082]

[0083] in, is the number of carbon emission quotas for electricity in the i-th integrated energy system, is the carbon emission factor of electricity; is the amount of electricity purchased by the i-th integrated energy system from the external power grid in period t; is the number of carbon emission quotas of the CHP unit in the i-th integrated energy system; is the carbon emission factor of natural gas; is the amount of electricity generated by the cogeneration unit in the i-th integrated energy system in time period t; is the heat generated by the i-th integrated energy system for the combined heat and power unit in time period t; is the number of carbon emission quotas of the gas turbine in the i-th integrated energy system; is the heat generated by the gas turbine of the i-th integrated energy system in period t.

[0084] Optional, actual carbon emissions It can be calculated by the following formula:

[0085]

[0086] in, To convert CO emissions per unit of electricity 2 , To convert the CO emissions per unit of natural gas 2 , Carbon emission quotas for power-to-gas units that convert clean energy into natural gas.

[0087] Optional, wind curtailment costs It can be calculated by the following formula:

[0088]

[0089] The cost of wind curtailment refers to the economic losses and potential costs caused by the failure to fully utilize the electricity generated by the wind power generation system due to various reasons. represents the penalty cost per unit of wind power abandonment, It represents the wind curtailment of the i-th integrated energy system at time t.

[0090] Optional, abandoned light cost It can be calculated by the following formula:

[0091]

[0092] The cost of abandoned light refers to the economic losses and potential costs caused by the failure to fully utilize the electricity generated by the photovoltaic power generation system due to various reasons. It represents the penalty cost per unit abandoned optical power. It represents the amount of abandoned light of the i-th integrated energy system at time t.

[0093] Optional, compensation costs It can be calculated by the following formula:

[0094]

[0095] in, , They represent the unit compensation coefficients of transferable load and alternative load participating in demand response (DR); represents the transferable load of the i-th integrated energy system in period t; It represents the alternative load of the i-th integrated energy system in period t.

[0096] Optionally, the interaction costs between the integrated energy system and other integrated energy systems It can be expressed by the following formula:

[0097]

[0098] in, is the traded electricity between the i-th integrated energy system and other integrated energy systems at time t; if >0, it means that the i-th integrated energy system sells electricity to other integrated energy systems; >0, indicating that the i-th integrated energy system gains benefits from contributing shared electric energy; conversely, it indicates the cost that the i-th CIES needs to pay for obtaining the shared electric energy of other CIES; Indicates the electricity selling price; Indicates the electricity purchase price.

[0099] The constraints for constructing P2P power trading are as follows:

[0100] The power transmitted or received between the integrated energy systems at each moment t should be within the transmission power limit of the connecting line, and satisfy ,in, Represents the maximum transmission power of connecting lines i and j during time period t.

[0101] Optionally, the integrated energy system participates in power trading to satisfy energy sharing balance constraints and transaction payment balance constraints:

[0102]

[0103] in, represents the number of integrated energy systems participating in the electricity trading, Represents the interactive shared energy balance among all integrated energy systems; Represents the transaction payment balance between all integrated energy systems.

[0104] In an exemplary embodiment, Figure 2 As shown, optionally, solving the benefit maximization sub-problem and the energy payment sub-problem and determining the transaction strategy and transaction price strategy of each integrated energy system includes the following steps 201 to 202. Among them:

[0105] S201, obtain the objective function of the benefit maximization sub-problem, perform iterative solution based on the distributed optimization algorithm, and determine the trading strategy of each integrated energy system.

[0106] Optionally, the objective function of the benefit maximization sub-problem can be expressed by the following formula:

[0107]

[0108] in, ) Substitute the total operating cost of the integrated energy system into the total operating cost calculation model of the i-th integrated energy system to obtain the total operating cost of the integrated energy system.

[0109] Optionally, depending on the transaction payment balance constraint, it can be ignored when solving the benefit maximization subproblem ,Right now =0. and is the multiple coupling between all integrated energy systems, therefore, an auxiliary variable vector is introduced and , transforming the benefit maximization subproblem into a dual-coupling model with equivalent multiple coupling constraints and ,in, .

[0110] Optionally, the distributed optimization algorithm may be an algorithm for solving optimization problems in a distributed system. In a distributed system, computing resources (such as processors, storage devices) and data are distributed across multiple interconnected integrated energy systems. The purpose of the distributed optimization algorithm is to find a global optimal (or approximately optimal) solution through collaboration and information exchange between these integrated energy systems, subject to certain constraints, so that the objective function of the benefit maximization subproblem is optimized.

[0111] In one possible implementation, the distributed optimization algorithm may include a gradient descent method, a stochastic gradient descent method, and the like.

[0112] In one possible implementation, Figure 3 As shown, optionally, the distributed optimization algorithm performs iterative solution to determine the trading strategy of each integrated energy system, including the following steps 301 to 303. Among them:

[0113] S301, obtaining the augmented Lagrangian function corresponding to the benefit maximization subproblem.

[0114] Optionally, the augmented Lagrangian function corresponding to the benefit maximization subproblem can be expressed by the following formula:

[0115]

[0116] in, The Lagrange multiplier representing the amount of electricity traded between integrated energy systems, represents the penalty parameter, which is the initial value. .

[0117] S302: Determine an update model according to the augmented Lagrangian function.

[0118] Optionally, the update model can be expressed by the following formula:

[0119]

[0120]

[0121] Among them, k represents the current iteration number, and the Lagrange multiplier The update of can be determined by the Lagrangian multiplier update model, which can be expressed by the following formula:

[0122]

[0123] S303, according to the updated model, multiple update iterations are performed until the convergence condition is met or the number of iterations reaches a preset number of iterations, and the trading strategy of each integrated energy system is obtained.

[0124] Optionally, each integrated energy system updates its own trading strategy locally according to the updated model to obtain an updated trading strategy , and then the trading strategies between the various integrated energy systems alternate with each other to determine ,During an update and iteration process, the trading strategies of each integrated energy system are repeatedly updated.

[0125] Optionally, during an update iteration, the Lagrangian multiplier may be updated according to the Lagrangian multiplier update model, and after the update is completed, the number of iterations is increased by 1.

[0126] Optionally, whether the convergence condition is met may be determined by a convergence condition judgment model, wherein the convergence condition judgment model may be expressed by the following formula:

[0127]

[0128] in, Represents the set dual residual convergence value. If the convergence condition is met, the iteration is terminated, otherwise it enters the next round of iteration.

[0129] In the above method, the augmented Lagrangian function corresponding to the benefit maximization subproblem is obtained, and the update model is determined according to the augmented Lagrangian function. According to the update model, multiple update iterations are performed until the convergence condition is met or the number of iterations reaches the preset number of iterations, so as to obtain the trading strategy of each integrated energy system, allowing different integrated energy systems to deal with their own subproblems respectively, and coordinating the global optimization process only by transmitting a small amount of information (such as Lagrangian multipliers and updated values ​​of some variables).

[0130] S202, obtaining the objective function of the energy payment sub-problem, and bringing the optimal solution of the benefit maximization sub-problem into the energy payment sub-problem, performing iterative solution according to the distributed optimization algorithm, and determining the transaction price strategy of each integrated energy system.

[0131] Optionally, the optimal interactive electricity and heat energy obtained in sub-problem 1 are substituted, and according to the formula of the Nash negotiation model, the Nash negotiation benefit distribution model of multiple integrated energy systems is obtained as shown in the following formula:

[0132]

[0133] Optionally, the above formula aims to maximize the benefits that can be increased by participating in P2P transactions compared to not participating in P2P transactions in an alliance composed of multiple integrated energy systems. The inequality in the above formula is to ensure that each integrated energy system that contributes to energy sharing can obtain benefits. Since the natural logarithm is a strictly monotonically increasing convex function, taking the logarithm of the above formula can transform the maximum value problem into a minimum value problem that is easy to solve. After conversion, the objective function can be expressed by the following formula:

[0134]

[0135] Just as the energy sharing balance constraint is equivalent to the double coupling constraint in the Nash negotiation profit allocation model, the transaction payment balance constraint is decoupled and converted into a dual-coupled constraint as shown below:

[0136]

[0137] in, represents the optimal transaction power solved in the benefit maximization sub-problem of the i-th integrated energy system Expected transaction electricity price; It represents the expected transaction electricity price of the jth integrated energy subsystem; when = When , it indicates that the i-th integrated energy system and the j-th integrated energy system have reached a consensus on the transaction electricity price.

[0138] Optionally, the objective function of the energy payment sub-problem is iteratively solved according to a distributed optimization algorithm, and the solution method is the same as that in the above step 201.

[0139] Optionally, get the augmented Lagrangian for the energy payment subproblem:

[0140]

[0141] in, The Lagrange multiplier representing the amount of electricity traded, represents the penalty parameter, which is the initial value. .

[0142] Get the corresponding update model, expressed by the following formula:

[0143]

[0144]

[0145] Among them, k represents the number of current iterations, and the Lagrange multiplier The update of can be determined by the following update model:

[0146]

[0147] Optionally, each integrated energy system calculates its transaction price strategy locally according to the updated model. The latest price information is exchanged between each integrated energy system. The transaction electricity price can be updated by executing the following steps. The i-th integrated energy system updates its decision by updating the model. , other integrated energy systems receive updated decision information , and update its own decision information ,In an update and iteration process, the transaction price strategies of each ,integrated energy system are repeatedly updated.

[0148] Optionally, during an update iteration, the Lagrange multiplier can be updated based on the Lagrange multiplier model. After the update is completed, the number of iterations increases by 1.

[0149] Optionally, whether the convergence condition is met may be determined by a convergence condition judgment model, wherein the convergence condition judgment model may be expressed by the following formula:

[0150]

[0151] in, Represents the set dual residual convergence value. If the convergence condition is met, the iteration is terminated, otherwise it enters the next round of iteration.

[0152] The above-mentioned target cooperative game model is converted into two benefit maximization sub-problems and energy payment sub-problems, and the transaction strategy of the integrated energy system is first determined to achieve the maximization of the benefits of the regional integrated energy system. After achieving the maximization of the benefits of the entire regional integrated energy system, the energy payment sub-problem is how to achieve fair and reasonable distribution of benefits. In this way, by solving the two sub-problems, the obtained scheduling strategy can not only meet the goal of cost minimization, but also balance the interests of each integrated energy system.

[0153] In an exemplary embodiment, Figure 4 As shown, optionally, there is randomness in the output of renewable energy in the integrated energy system, and the total operation cost calculation model of each integrated energy system is obtained, including the following steps 401 to 403. Among them:

[0154] S401, obtaining historical output data of renewable energy.

[0155] Optionally, the historical output data of the renewable energy source may be determined through historical output records, and the method for obtaining the historical output data is not limited in the embodiment of the present application.

[0156] Optionally, historical output data of renewable energy sources of multiple integrated energy systems in the regional integrated energy system may be obtained.

[0157] S402: Determine multiple target typical output scenarios based on historical output data.

[0158] Optionally, the typical output scenario can be the typical output power situation of the integrated energy system under different external conditions. Through the analysis of the typical output scenario, we can better understand the operating laws of the integrated energy system, and provide important reference for the planning, scheduling and stable operation of the integrated energy system.

[0159] Optionally, cluster analysis may be performed on the historical output data to determine multiple target typical output scenarios. In the embodiment of the present application, the specific method of cluster analysis is not limited, and may be a K-means clustering method or a hierarchical clustering method.

[0160] S403: Determine a total operating cost calculation model for each integrated energy system according to each typical output scenario.

[0161] Alternatively, the use of renewable energy for power generation has less impact on the ecological environment, but the natural properties of renewable energy determine the uncertainty of its power generation output and its correlation with regional renewable energy output. In order to ensure the safe and reliable operation of the regional integrated energy system, it is necessary to consider the randomness and correlation of renewable energy output during the planning and operation stages.

[0162] Optionally, a typical total operating cost calculation model is determined based on typical output scenarios. The total operating cost calculation model obtained in this way has strong adaptability and can accurately determine the total operating cost of each integrated energy system for various output scenarios of the integrated energy system.

[0163] In an exemplary embodiment, Figure 5 As shown, optionally, renewable energy includes photovoltaic and wind energy, and multiple target typical output scenarios are determined according to historical output data, including the following steps 501 to 503. Among them:

[0164] S501, determining a photovoltaic power output probability density function and a wind power output probability density function in multiple time periods based on a kernel density estimation method and historical power output data.

[0165] Optional,

[0166] S502, determining a wind-solar output joint probability distribution function for each time period according to the photovoltaic output probability density function and the wind power output probability density function.

[0167] S503, determining multiple target typical output scenarios according to the wind and solar power output joint probability distribution function in each time period.

[0168] Optionally, multiple target typical output scenarios can be determined by sampling the joint probability distribution function of wind and solar output in each time period, and obtaining the sampled wind turbine and photovoltaic output in each time period based on the sampling results and the inverse transformation of the joint probability distribution function of wind and solar output.

[0169] In one possible implementation, a typical daily curve may be generated based on the sampled wind turbine and photovoltaic outputs in each time period, and a plurality of target typical output scenarios may be determined based on each typical daily curve.

[0170] In another possible implementation method, a cluster analysis method can be used to cluster multiple groups of sampling results to generate a preset number of target typical output scenarios, where the preset number can be determined based on the seasonal characteristics of wind and solar output in the area where the regional integrated energy system is located, and the probability of occurrence of each scenario is calculated.

[0171] Optional, such as Figure 6 As shown in the figure, it is a flow chart of the method for generating wind and solar output scenarios. Based on the historical wind and solar output data (one point per hour) (x and y in the figure represent the unit wind turbine and photovoltaic output respectively), the commonly used Gaussian kernel function is first selected based on the kernel density estimation method to generate the probability density function of wind and solar output in each period within 24 hours. and Then, considering the correlation between wind and solar, the joint probability distribution function of wind and solar output in each period is established based on Copula theory. Regarding the selection of the Coupla function, since among the binary Archimedean Coupla functions, the Gumbel and Clayton Coupla functions can only describe the non-negative relationship between variables, the Frank Coupla function can take into account both the non-negative and negative correlation of variables, and wind and solar power often have a negatively correlated complementary relationship, in the embodiment of the present application, the Frank Coupla function can be selected to describe the wind-solar correlation. Finally, the wind-solar output joint probability distribution function of each time period is sampled, and the sampled wind turbine and photovoltaic output x and y of each time period are obtained based on the sampling results and the wind-solar output joint probability distribution function of the wind-solar power. This will eventually generate a typical daily curve that takes into account the wind-solar correlation and randomness, and determine multiple typical output scenarios based on each typical daily curve.

[0172] As an optional implementation, Figure 7 As shown, the multi-integrated energy system scheduling method provided in the embodiment of the present application may include the following specific steps:

[0173] S701, obtaining historical output data of renewable energy.

[0174] Among them, renewable energy includes photovoltaic and wind power.

[0175] S702, determining the photovoltaic power output probability density function and the wind power output probability density function in multiple time periods based on the kernel density estimation method and the historical power output data.

[0176] S703, determining a wind-solar output joint probability distribution function for each time period according to the photovoltaic output probability density function and the wind power output probability density function.

[0177] S704, determining multiple target typical output scenarios according to the wind and solar power output joint probability distribution function in each time period.

[0178] S705: Determine a total operating cost calculation model for each integrated energy system according to each typical output scenario.

[0179] Among them, the calculation model is used to determine the total operating cost based on the energy purchase cost, carbon trading cost, solar power abandonment cost, wind power abandonment cost, compensation cost and the interaction cost between the integrated energy system and other integrated energy systems. Among them, the carbon trading cost is determined according to the step-by-step carbon trading cost function.

[0180] S706, obtaining a target cooperative game model.

[0181] Among them, the target cooperation game model takes minimizing the total operating cost of multiple integrated energy systems as its objective function.

[0182] S707, perform equivalent transformation on the target cooperative game model to obtain the benefit maximization subproblem and the energy payment subproblem.

[0183] S708, obtaining the objective function and augmented Lagrangian function corresponding to the benefit maximization sub-problem.

[0184] S709, determining an updated model according to the augmented Lagrangian function.

[0185] S710, performing multiple update iterations according to the updated model until a convergence condition is met or the number of iterations reaches a preset number of iterations, thereby obtaining a trading strategy for each integrated energy system.

[0186] S711, obtain the objective function of the energy payment sub-problem, and bring the optimal solution of the benefit maximization sub-problem into the energy payment sub-problem.

[0187] S712, taking the augmented Lagrangian function corresponding to the energy payment sub-problem, and determining the transaction price strategy of each integrated energy system according to the same method as step S709 and step S710.

[0188] S713, determining a dispatching strategy according to the transaction strategies and transaction price strategies of each integrated energy system.

[0189] For example, take the regional integrated energy system including three integrated energy systems as an example. Figures 8 to 10 Schematic diagram of the scheduling results of each comprehensive energy system determined according to the method provided in the embodiment of the present application, Figure 8 This is a schematic diagram of the dispatch results of the first comprehensive energy system. Fig. 9 This is a schematic diagram of the dispatch results of the first comprehensive energy system. Fig.10 This is a schematic diagram of the scheduling results of the first integrated energy system. A scheduling result schematic diagram includes an electric power balance diagram, a thermal power balance diagram, a gas power balance diagram and a cold power balance diagram. In addition, the costs of each integrated energy system are shown in Table 1. It can be seen that compared with the previous benefit analysis, the total cost of each integrated energy system has decreased after participating in P2P transactions.

[0190]

[0191] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0192] Based on the same inventive concept, the embodiment of the present application also provides a multi-comprehensive energy system scheduling device for implementing the multi-comprehensive energy system scheduling method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more multi-comprehensive energy system scheduling device embodiments provided below can refer to the limitations of the multi-comprehensive energy system scheduling method above, and will not be repeated here.

[0193] In an exemplary embodiment, Fig.11 As shown, a multi-integrated energy system scheduling device is provided, including: an acquisition module 1101, a conversion module 1102, a solution module 1103 and a determination module 1104, wherein:

[0194] An acquisition module 1101 is used to acquire a target cooperative game model, wherein the target cooperative game model takes minimization of the total operating cost of multiple integrated energy systems as an objective function;

[0195] A conversion module 1102 is used to perform equivalent conversion on the target cooperative game model to obtain a benefit maximization sub-problem and an energy payment sub-problem;

[0196] A solution module 1103 is used to solve the benefit maximization sub-problem and the energy payment sub-problem, and determine the transaction strategy and transaction price strategy of each comprehensive energy system;

[0197] The determination module 1104 is used to determine the dispatching strategy according to the transaction strategy and transaction price strategy of each integrated energy system.

[0198] In an exemplary embodiment, before obtaining the target cooperative game model, the acquisition module 1101 is also used to obtain a total operating cost calculation model for each integrated energy system, and the calculation model is used to determine the total operating cost based on the energy purchase cost, carbon trading cost, solar power abandonment cost, wind power abandonment cost, compensation cost and interaction cost between the integrated energy system and other integrated energy systems, wherein the carbon trading cost is determined based on a step-by-step carbon trading cost function.

[0199] In an exemplary embodiment, the solution module 1103 is specifically used to obtain the objective function of the benefit maximization sub-problem, perform iterative solution according to the distributed optimization algorithm, and determine the trading strategy of each integrated energy system; obtain the objective function of the energy payment sub-problem, and bring the optimal solution of the benefit maximization sub-problem into the energy payment sub-problem, perform iterative solution according to the distributed optimization algorithm, and determine the trading price strategy of each integrated energy system.

[0200] In an exemplary embodiment, the solution module 1103 is specifically used to obtain the augmented Lagrangian function corresponding to the benefit maximization sub-problem; determine the update model according to the augmented Lagrangian function; according to the update model, perform multiple update iterations until the convergence condition is met or the number of iterations reaches a preset number of iterations, and obtain the trading strategy of each integrated energy system.

[0201] In an exemplary embodiment, there is randomness in the output of renewable energy in the integrated energy system, and the acquisition module 1101 is specifically used to obtain historical output data of renewable energy; determine multiple target typical output scenarios based on the historical output data; and determine the total operating cost calculation model of each integrated energy system based on each typical output scenario.

[0202] In an exemplary embodiment, renewable energy includes photovoltaic and wind energy, and acquisition module 1101 is specifically used to determine the photovoltaic output probability density function and wind energy output probability density function for multiple time periods based on the kernel density estimation method and historical output data; determine the joint probability distribution function of wind and solar output for each time period according to the photovoltaic output probability density function and the wind energy output probability density function; determine multiple target typical output scenarios according to the joint probability distribution function of wind and solar output for each time period.

[0203] Each module in the above-mentioned multi-integrated energy system dispatching device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0204] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.12 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-integrated energy system scheduling method is implemented.

[0205] Those skilled in the art will understand that Fig.12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0206] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps described in any of the above method embodiments when executing the computer program.

[0207] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps described in any of the above method embodiments are implemented.

[0208] In one embodiment, a computer program product is provided, including a computer program, which implements the steps described in any of the above method embodiments when executed by a processor.

[0209] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0210] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0211] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A multi-integrated energy system scheduling method, characterized in that: The method comprises: Obtaining a target cooperative game model, wherein the target cooperative game model takes minimizing the total operating cost of multiple integrated energy systems as an objective function; Performing equivalent transformation on the target cooperative game model, obtaining the benefit maximization subproblem and the energy payment subproblem; Solving the benefit maximization sub-problem and the energy payment sub-problem, and determining the transaction strategy and transaction price strategy of each of the comprehensive energy systems; The scheduling strategy is determined according to the transaction strategy and transaction price strategy of each of the comprehensive energy systems.

2. The method according to claim 1, characterized in that: Before obtaining the target cooperative game model, the method further includes: A calculation model for the total operating cost of each of the integrated energy systems is obtained, wherein the calculation model is used to determine the total operating cost based on the energy purchase cost, carbon trading cost, solar power abandonment cost, wind power abandonment cost, compensation cost and interaction cost between the integrated energy system and other integrated energy systems of the integrated energy system, wherein the carbon trading cost is determined based on a step-by-step carbon trading cost function.

3. The method according to claim 1, characterized in that: The solving of the benefit maximization sub-problem and the energy payment sub-problem and determining the transaction strategy and transaction price strategy of each of the comprehensive energy systems include: Obtaining the objective function of the benefit maximization sub-problem, performing iterative solution according to a distributed optimization algorithm, and determining the trading strategy of each of the integrated energy systems; The objective function of the energy payment subproblem is obtained, and the optimal solution of the benefit maximization subproblem is brought into the energy payment subproblem, and an iterative solution is performed according to a distributed optimization algorithm to determine the transaction price strategy of each of the integrated energy systems.

4. The method according to claim 3, characterized in that The distributed optimization algorithm performs iterative solving to determine the trading strategy of each of the integrated energy systems, including: Obtaining an augmented Lagrangian function corresponding to the benefit maximization subproblem; Determine an update model according to the augmented Lagrangian function; According to the update model, multiple update iterations are performed until the convergence condition is met or the number of iterations reaches a preset number of iterations, thereby obtaining the trading strategy of each of the integrated energy systems.

5. The method according to claim 2, characterized in that: There is randomness in the output of renewable energy in the integrated energy system, and the total cost calculation model for obtaining each integrated energy system includes: Obtaining historical output data of the renewable energy; Determine multiple target typical output scenarios according to the historical output data; A total operating cost calculation model for each of the integrated energy systems is determined based on each of the typical output scenarios.

6. The method according to claim 5, characterized in that The renewable energy includes photovoltaic and wind energy, and the determining of a plurality of target typical output scenarios according to the historical output data includes: Determine the photovoltaic power output probability density function and the wind power output probability density function for multiple time periods based on the kernel density estimation method and the historical power output data; Determine the wind-solar output joint probability distribution function for each of the time periods according to the photovoltaic output probability density function and the wind power output probability density function; According to the joint probability distribution function of wind and solar power output in each of the time periods, multiple target typical output scenarios are determined.

7. A multi-integrated energy system dispatching device, characterized in that: The device comprises: An acquisition module is used to acquire a target cooperative game model, wherein the target cooperative game model takes minimization of the total operating cost of multiple integrated energy systems as an objective function; A conversion module, used for performing equivalent conversion on the target cooperative game model to obtain a benefit maximization subproblem and an energy payment subproblem; A solution module, used to solve the benefit maximization sub-problem and the energy payment sub-problem, and determine the transaction strategy and transaction price strategy of each of the comprehensive energy systems; A determination module is used to determine the scheduling strategy according to the transaction strategy and transaction price strategy of each of the integrated energy systems.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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