Cost optimization method for integrated energy system

Abstract

The invention discloses a cost optimization method for an integrated energy system. The method comprises the following steps: S1, constructing a distributed robust multi-target optimization scheduling model of the integrated energy system according to the requirements of minimum economic operation cost, minimum carbon emission and highest efficiency of # imgabs0 #; s2, expanding a traditional univariate LAP adjustment method, and proposing a multivariate LAP adjustment model; and S3, expanding the node marginal electricity price and a convex hull pricing method into the integrated energy system, and carrying out convexity on the original system model. And S4, performing example simulation in the integrated energy system, solving the constructed multi-objective optimization scheduling model of the integrated energy system, and further solving the node energy price in the integrated energy system based on a convex hull pricing method. According to the method, the problems that the economic operation cost minimization, the carbon emission minimization and the # imgabs1 # efficiency maximization cannot be realized in the optimal scheduling process of the existing comprehensive energy system are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy system cost optimization, and in particular, relates to a cost optimization method for an integrated energy system. Background Art

[0002] In recent years, the optimization scheduling and pricing of the Integrated Energy System (IES) have attracted increasing attention as an important means to achieve efficient energy utilization and energy conservation and emission reduction. The traditional scheduling method with a single economic objective can no longer meet the development needs of the modern energy system. It is urgent to build a multi-objective optimization scheduling model that comprehensively considers economy, environmental protection and energy efficiency, and combines it with an effective pricing mechanism to guide the optimal allocation of internal resources in the system.

[0003] The construction of the existing integrated energy pricing model mainly adopts two ideas. The first is to concentrate the sales rights of multiple energy sources to the same entity and unify the sales distribution of different types of energy. The advantage of this model is that it can better realize the complementary utilization of multiple energy sources. However, this decision-making on the production and consumption of multiple energy sources from the perspective of decision-makers who control the overall situation is feasible in the local integrated energy market. However, the unified pricing model of global centralized optimization is too ideal and has the following problems: the market centralized clearing optimization model is too complex, the market operation cost is high, and the transparency is low; the technical difficulty is high and it is difficult to supervise. Since centralized optimization requires market members to provide detailed information, it not only increases transaction costs, but also has the risk of information asymmetry and reduced market efficiency. The second is that each energy operator is independent of each other and is indirectly managed in the form of an energy market. Under this energy sharing model among multiple integrated energy systems, multi-energy transactions present multi-subject characteristics, and the original centralized energy management system is no longer applicable. Many scholars have tried to use distributed energy management methods based on decomposition theory to solve the energy pricing of multi-energy and multi-subject integrated energy systems. That is, the centralized optimization problem is decomposed into a series of sub-problems belonging to controllable resources, and the main problem is introduced to guide the sub-problems to obtain the optimal solution through multiple iterations. However, this type of algorithm has problems such as poor robustness under unstable conditions such as communication delays. At the same time, it also takes less consideration into the possibility that each subject will pursue the maximization of their own interests and reach an inefficient equilibrium state of disorderly competition, which will limit the potential of the system.

[0004] The defect of the existing technology is that the existing integrated energy system lacks the ability to minimize economic operation costs, minimize carbon emissions and Efficiency maximization scheduling method. Summary of the invention

[0005] The existing integrated energy system cannot achieve the minimization of economic operation costs, carbon emissions and To solve the problem of efficiency maximization, the present invention provides a cost optimization method for an integrated energy system.

[0006] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0007] A cost optimization method for an integrated energy system, comprising the steps of:

[0008] S1, based on the minimum economic operation cost, minimum carbon emission and The most efficient demand constructs a distributed robust multi-objective optimization scheduling model for the integrated energy system;

[0009] S2, expand the traditional univariate LAP adjustment method and propose a multivariate LAP adjustment model;

[0010] S3. Extend the node marginal electricity price and convex hull pricing method to the integrated energy system and convexify the original system model.

[0011] S4. Carry out case simulation in the integrated energy system, solve the multi-objective optimization scheduling model of the integrated energy system, and further solve the node energy price in the integrated energy system based on the convex hull pricing method.

[0012] Furthermore, the detailed steps of step S1 include:

[0013] S101. Construct the economic operation cost objective function of the integrated energy system, further consider the adjustment cost for the uncertainty of renewable energy and cooling, heating and electricity load demand, and set the objective function to minimize the adjustment cost expectation under the worst case distribution in the fuzzy set;

[0014] S102. Calculate the carbon emissions generated within the optimized operation system of the integrated energy system;

[0015] S103, Construction The efficiency maximization objective function is transformed into a linear programming problem.

[0016] Furthermore, the detailed steps of step S2 include:

[0017] S201. Preset electric energy storage, thermal energy storage, micro gas turbine, waste heat boiler, gas boiler, absorption chiller, etc. to meet normal operating conditions and perform the first stage operation constraints;

[0018] S202, expanding the traditional univariate LAP adjustment method and proposing a multivariate LAP adjustment model;

[0019] S203. Adjust and constrain the interaction between gas turbines, waste heat boilers and gas boilers, electric refrigerators and absorption refrigerators, multi-energy storage, and upper-level power grids.

[0020] Furthermore, the detailed steps of step S3 include:

[0021] S301, the non-convex feasible domain of the start and stop state of the unit (∪ n∈ψ Ω) is transformed into its convex hull conv(∪ n∈ψ Ω), convexify the original system model;

[0022] S302. All 0-1 variables representing the operating status of the unit are relaxed to within the interval [0,1] to make them continuous variables.

[0023] Furthermore, the detailed steps of step S4 include:

[0024] S401. Obtain network parameters in the natural gas system, parameters in the thermal network, and relevant parameters of gas turbines, gas boilers, electric boilers, and energy storage equipment in the integrated energy system. The example simulation is programmed using YALMIP on the MATLAB platform, and the Gurobi solver is called for solution.

[0025] S402. Use the AE-GAN model to further generate prediction error data for each non-deterministic variable in the integrated energy system. Calculate the correlation between the real prediction error scenario and the generated scenario. Then use the t-distributed stochastic neighbor embedding (t-SNE) method and the principal component analysis (PCA) method to perform dimensionality reduction processing to obtain the correlation between the prediction error data of the real non-deterministic variables and the generated data.

[0026] S403. Based on the system parameters set above, the distributed robust opportunity optimization model of the integrated energy system based on AE-GAN constructed in this paper is solved.

[0027] S404. Based on the convex hull pricing method, further solve the node energy prices in the integrated energy system, namely, the node electricity price, node gas price, node heating price and node cooling price.

[0028] Furthermore, in step S101, the economic operation cost objective function of the integrated energy system is as follows:

[0029]

[0030] In the formula, F EC represents the economic operating cost of the integrated energy system, F 1 It represents the cost of purchasing electricity from the external power grid for the integrated energy system. 2 It represents the cost of purchasing gas from the natural gas market for the integrated energy system. It represents the unit cost of buying electricity from the external power grid. It represents the amount of electricity purchased from the external power grid. It represents the unit cost of selling electricity to the external power grid (i.e. the on-grid electricity price). It represents the amount of electricity sold to the external grid.

[0031] The operating cost of the integrated energy system should be:

[0032]

[0033] In the second stage (real-time stage), the uncertainty of renewable energy output and electrical load, heating load, and cooling load is fully considered. In order to avoid the corresponding prediction errors (e.g. ), the power / gas of flexible resources in the integrated energy system is further adjusted (operation re-dispatch), thus incurring adjustment costs

[0034] Further, in step S102, the Efficiency is used as an indicator to evaluate the resource utilization of energy systems.

[0035] The efficiency maximization objective function is as follows:

[0036]

[0037] In the formula, ψ e , q and ψ c They represent the energy quality coefficients of electrical energy, thermal energy and cold energy respectively.

[0038] Through Charnes-Cooper transformation, it is transformed into a linear programming problem. Considering the operating status of each component in the integrated energy system, the flow of various energies such as cooling, heating and electricity can be optimized, and the energy efficiency level of the integrated energy system can be improved.

[0039] Further, in step S202, given the corresponding random variable and The corresponding prediction error and It can be expressed as:

[0040]

[0041] In the formula, and They respectively represent the real data of wind power output, photovoltaic output and electrical load, thermal load and cooling load.

[0042] The traditional univariate LAP adjustment method is expanded, and the multivariate LAP adjustment model is proposed as follows:

[0043]

[0044]

[0045] Therefore, AWE t , AWH t and AWC t They represent all adjustment rates (variables) corresponding to different prediction errors. In addition, They represent the decision variables The adjustment results are obtained by combining conventional constraints and multi-energy flexibility constraints to expand it, and the distributed robust optimization model of the integrated energy system is completed.

[0046] Furthermore, in step S302, an approximate description method for the convex hull, namely the integer relaxation pricing method, is introduced. This method is to relax all 0-1 variables representing the operating status of the unit to within the interval [0,1] to make them continuous variables.

[0047] Further, in step S401, the distribution network in the integrated energy system is an improved IEEE-9 node power system, the thermal network / cooling network is based on the improved Bali 6-node thermal system, and the natural gas network adopts a 7-node system. The above three energy systems are organically coupled to form the integrated energy node system in this article. According to the annual average load demand forecast data and related parameters in the integrated energy system, according to the transaction electricity price, YALMIP is used for programming on the MATLAB platform, and the Gurobi solver is called for solution.

[0048] Furthermore, in step S403, the ocean hunting algorithm is used to process the multi-objective optimization problem. The operation cost obtained by the scheduling method based on the multi-objective genetic optimization algorithm is 7.7% higher than that of the method in this paper, and the carbon emissions are increased by 8.0%. The efficiency was reduced by 9.7%. The operation cost obtained by the scheduling method based on the multi-objective particle swarm optimization algorithm increased by 3.5% compared with the method proposed in this paper, and the carbon emissions were reduced by 2.5%. The efficiency was reduced by 9.3%. The operation cost obtained by the scheduling method based on the decomposed multi-objective evolutionary algorithm was 6.4% higher than that of the method in this paper, and the carbon emissions increased by 3.0%. The efficiency is reduced by 8.3%. Therefore, the optimal compromise solution based on the multi-objective marine predator algorithm proposed in this paper is in terms of operating cost, carbon emissions and It has greater advantages in three aspects such as efficiency.

[0049] In view of the non-deterministic factors in the integrated energy system, a comparative analysis will be conducted with the robust optimization and stochastic optimization models.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] Through the construction of the model, analogous to the relevant definition of the node marginal electricity price package pricing method, it is extended to the integrated energy system, and then through example simulation, the effectiveness of the model in reducing economic operating costs is verified and confirmed.

[0052] The model constructs a distributed robust multi-objective optimization scheduling model and takes into account economic operation costs, carbon emissions and Compared with the single-objective optimization model, the three objectives of efficiency can more comprehensively balance the various economic and environmental indicators of system operation. At the same time, by extending the traditional single-variable LAP adjustment method, a multivariate LAP adjustment model is proposed, which can more flexibly respond to the uncertainty of renewable energy and load demand and reduce the adjustment cost caused by prediction errors.

[0053] When calculating the carbon emissions generated by the optimized operation of the integrated energy system, the model takes it as an optimization target and minimizes carbon emissions by adjusting the operating status and energy distribution of each device within the system. The model can flexibly respond to the uncertainty of renewable energy and load demand, reduce additional carbon emissions caused by prediction errors by adjusting strategies, consider the flow and conversion of multiple energies such as electricity, heat, and cold energy in the integrated energy system, achieve multi-energy complementarity through optimized scheduling, and improve the overall energy efficiency level. Compared with traditional energy systems, this model has significant advantages in reducing carbon emissions and helps promote the green transformation of energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is an overall flow chart of a method for optimizing the price of an integrated energy system in an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of a node of an integrated energy system in an embodiment of the present invention;

[0056] Figure 3 4 is a comparison diagram of the cumulative probability distribution of real data and generated data in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments and drawings. The contents mentioned in the implementation modes are not intended to limit the present invention.

[0058] like Figure 1 As shown, this embodiment provides a cost optimization method for an integrated energy system.

[0059] S1, based on the minimum economic operation cost, minimum carbon emission and The most efficient demand constructs a distributed robust multi-objective optimization scheduling model for the integrated energy system;

[0060] S2, expand the traditional univariate LAP adjustment method and propose a multivariate LAP adjustment model;

[0061] S3. Extend the node marginal electricity price and convex hull pricing method to the integrated energy system and convexify the original system model.

[0062] S4. Carry out case simulation in the integrated energy system, solve the multi-objective optimization scheduling model of the integrated energy system, and further solve the node energy price in the integrated energy system based on the convex hull pricing method.

[0063] The detailed steps of step S1 include:

[0064] S101. Construct the economic operation cost objective function of the integrated energy system, further consider the adjustment cost for the uncertainty of renewable energy and cooling, heating and electricity load demand, and set the objective function to minimize the adjustment cost expectation under the worst case distribution in the fuzzy set;

[0065] S102. Calculate the carbon emissions generated within the optimized operation system of the integrated energy system;

[0066] S103, Construction The efficiency maximization objective function is transformed into a linear programming problem.

[0067] The detailed steps of step S2 include:

[0068] S201. Preset electric energy storage, thermal energy storage, micro gas turbine, waste heat boiler, gas boiler, absorption chiller, etc. to meet normal operating conditions and perform the first stage operation constraints;

[0069] S202, expanding the traditional univariate LAP adjustment method and proposing a multivariate LAP adjustment model;

[0070] S203. Adjust and constrain the interaction between gas turbines, waste heat boilers and gas boilers, electric refrigerators and absorption refrigerators, multi-energy storage, and upper-level power grids.

[0071] The detailed steps of step S3 include:

[0072] S301, the non-convex feasible domain of the start and stop state of the unit (∪ n∈ψ Ω) is transformed into its convex hull conv(∪ n∈ψ Ω), convexify the original system model;

[0073] S302. All 0-1 variables representing the operating status of the unit are relaxed to within the interval [0,1] to make them continuous variables.

[0074] The detailed steps of step S4 include:

[0075] S401. Obtain network parameters in the natural gas system, parameters in the thermal network, and relevant parameters of gas turbines, gas boilers, electric boilers, and energy storage equipment in the integrated energy system. The example simulation is programmed using YALMIP on the MATLAB platform, and the Gurobi solver is called for solution.

[0076] S402. Use the AE-GAN model to further generate prediction error data for each non-deterministic variable in the integrated energy system. Calculate the correlation between the real prediction error scenario and the generated scenario. Then use the t-distributed stochastic neighbor embedding (t-SNE) method and the principal component analysis (PCA) method to perform dimensionality reduction processing to obtain the correlation between the prediction error data of the real non-deterministic variables and the generated data.

[0077] S403. Based on the system parameters set above, the distributed robust opportunity optimization model of the integrated energy system based on AE-GAN constructed in this paper is solved.

[0078] S404. Based on the convex hull pricing method, further solve the node energy prices in the integrated energy system, namely, the node electricity price, node gas price, node heating price and node cooling price.

[0079] In step S101, the economic operation cost objective function of the integrated energy system is as follows:

[0080]

[0081] In the formula, F EC represents the economic operating cost of the integrated energy system, F 1 It represents the cost of purchasing electricity from the external power grid for the integrated energy system. 2 It represents the cost of purchasing gas from the natural gas market for the integrated energy system. It represents the unit cost of buying electricity from the external power grid. It represents the amount of electricity purchased from the external power grid. It represents the unit cost of selling electricity to the external power grid (i.e. the on-grid electricity price). It represents the amount of electricity sold to the external grid.

[0082] The operating cost of the integrated energy system should be:

[0083]

[0084] In the second stage (real-time stage), the uncertainty of renewable energy output and electrical load, heating load, and cooling load is fully considered. In order to avoid the corresponding prediction errors (e.g. ), the power / gas of flexible resources in the integrated energy system is further adjusted (operation re-dispatch), thus incurring adjustment costs

[0085] In step S102, introduce Efficiency is used as an indicator to evaluate the resource utilization of energy systems.

[0086] The efficiency maximization objective function is as follows:

[0087]

[0088] In the formula, ψ e , q and ψ c They represent the energy quality coefficients of electrical energy, thermal energy and cold energy respectively.

[0089] Through Charnes-Cooper transformation, it is transformed into a linear programming problem. Considering the operating status of each component in the integrated energy system, the flow of various energies such as cooling, heating and electricity can be optimized, and the energy efficiency level of the integrated energy system can be improved.

[0090] In step S202, given the corresponding random variable and The corresponding prediction error and It can be expressed as:

[0091]

[0092] In the formula, and They respectively represent the real data of wind power output, photovoltaic output and electrical load, thermal load and cooling load.

[0093] The traditional univariate LAP adjustment method is expanded, and the multivariate LAP adjustment model is proposed as follows:

[0094]

[0095] Therefore, AWE t , AWH t and AWC t They represent all adjustment rates (variables) corresponding to different prediction errors. In addition, They represent the decision variables The adjustment results are obtained by combining conventional constraints and multi-energy flexibility constraints to expand it, and the distributed robust optimization model of the integrated energy system is completed.

[0096] In step S302, an approximate description method for the convex hull is introduced, namely, the integer relaxation pricing method, which relaxes all 0-1 variables representing the operating status of the unit to within the interval [0,1] to make them continuous variables.

[0097] In step S401, the distribution network in the integrated energy system is an improved IEEE-9 node power system, the thermal network / cooling network is based on the improved Bali 6-node thermal system, and the natural gas network adopts a 7-node system. The above three energy systems are organically coupled to form the integrated energy node system in this article. According to the annual average load demand forecast data and related parameters in the integrated energy system, according to the transaction electricity price, YALMIP is used for programming on the MATLAB platform, and the Gurobi solver is called for solution.

[0098] In step S403, the ocean hunting algorithm is used to process the multi-objective optimization problem. The operation cost obtained by the scheduling method based on the multi-objective genetic optimization algorithm is 7.7% higher than that of the method in this paper, and the carbon emissions are increased by 8.0%. The efficiency was reduced by 9.7%. The operation cost obtained by the scheduling method based on the multi-objective particle swarm optimization algorithm increased by 3.5% compared with the method proposed in this paper, and the carbon emissions were reduced by 2.5%. The efficiency was reduced by 9.3%. The operation cost obtained by the scheduling method based on the decomposed multi-objective evolutionary algorithm was 6.4% higher than that of the method in this paper, and the carbon emissions increased by 3.0%. The efficiency is reduced by 8.3%. Therefore, the optimal compromise solution based on the multi-objective marine predator algorithm proposed in this paper is in terms of operating cost, carbon emissions and It has greater advantages in three aspects such as efficiency.

[0099] In view of the non-deterministic factors in the integrated energy system, a comparative analysis will be conducted with the robust optimization and stochastic optimization models.

[0100] Compared with the prior art, the present invention has the following beneficial effects:

[0101] Through the construction of the model, analogous to the relevant definition of the node marginal electricity price package pricing method, it is extended to the integrated energy system, and then through example simulation, the effectiveness of the model in reducing economic operating costs is verified and confirmed.

[0102] The model constructs a distributed robust multi-objective optimization scheduling model and takes into account economic operation costs, carbon emissions and Compared with the single-objective optimization model, the three objectives of efficiency can more comprehensively balance the various economic and environmental indicators of system operation. At the same time, by extending the traditional single-variable LAP adjustment method, a multivariate LAP adjustment model is proposed, which can more flexibly respond to the uncertainty of renewable energy and load demand and reduce the adjustment cost caused by prediction errors.

[0103] When calculating the carbon emissions generated by the optimized operation of the integrated energy system, the model takes it as an optimization target and minimizes carbon emissions by adjusting the operating status and energy distribution of each device within the system. The model can flexibly respond to the uncertainty of renewable energy and load demand, reduce additional carbon emissions caused by prediction errors by adjusting strategies, consider the flow and conversion of multiple energies such as electricity, heat, and cold energy in the integrated energy system, achieve multi-energy complementarity through optimized scheduling, and improve the overall energy efficiency level. Compared with traditional energy systems, this model has significant advantages in reducing carbon emissions and helps promote the green transformation of energy systems.

[0104] The above is a detailed introduction to a cost optimization method for an integrated energy system provided by the present application. The description of the specific embodiments is only used to help understand the method and its core idea of ​​the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A cost optimization method for an integrated energy system, characterized in that: Includes steps: S1, based on the minimum economic operation cost, minimum carbon emission and The most efficient demand constructs a distributed robust multi-objective optimization scheduling model for the integrated energy system; S2, expand the traditional univariate LAP adjustment method and propose a multivariate LAP adjustment model; S3. Extend the node marginal electricity price and convex hull pricing method to the integrated energy system and convexify the original system model. S4. Carry out case simulation in the integrated energy system, solve the multi-objective optimization scheduling model of the integrated energy system, and further solve the node energy price in the integrated energy system based on the convex hull pricing method.

2. A cost optimization method for an integrated energy system according to claim 1, characterized in that: The detailed steps of step S1 include: S101. Construct the economic operation cost objective function of the integrated energy system, and further set the objective function to minimize the adjustment cost expectation under the worst case distribution in the fuzzy set for the uncertain adjustment cost of renewable energy and cooling, heating and electricity load demand; S102. Calculate the carbon emissions generated within the optimized operation system of the integrated energy system; S103, Construction The efficiency maximization objective function is transformed into a linear programming problem.

3. A cost optimization method for an integrated energy system according to claim 2, characterized in that: The detailed steps of step S2 include: S201, presetting the electric energy storage, thermal energy storage, micro gas turbine, waste heat boiler, gas boiler and absorption chiller to meet the normal operating conditions and perform the first stage operation constraints; S202, expanding the traditional univariate LAP adjustment method and proposing a multivariate LAP adjustment model; S203. Adjust and constrain the interaction between gas turbines, waste heat boilers, gas boilers, electric refrigerators, absorption refrigerators, multi-energy storage and upper-level power grids.

4. A cost optimization method for an integrated energy system according to claim 3, characterized in that: The detailed steps of step S3 include: S301, the non-convex feasible domain of the start and stop state of the unit (∪ n∈ψ Ω) is transformed into its convex hull conv(∪ n∈ψ Ω), convexify the original system model; S302. All 0-1 variables representing the operating status of the unit are relaxed to within the interval [0,1] to make them continuous variables.

5. A cost optimization method for an integrated energy system according to claim 4, characterized in that: The detailed steps of step S4 include: S401, obtaining network parameters in the natural gas system, parameters in the thermal network, and relevant parameters of gas turbines, gas boilers, electric boilers, and energy storage equipment in the integrated energy system, and performing example simulation programming using YALMIP on the MATLAB platform, and calling the Gurobi solver for solving; S402, using the AE-GAN model to further generate prediction error data of various non-deterministic variables in the integrated energy system, calculating the correlation between the real prediction error scenario and the generated scenario, and then using the t-distributed stochastic neighbor embedding (t-SNE) method and the principal component analysis (PCA) method to perform dimensionality reduction processing on them, and obtaining the correlation between the prediction error data of the real non-deterministic variables and the generated data; S403, based on the parameters in the natural gas system set in step S401, solving the distributed robust opportunity optimization model of the integrated energy system based on AE-GAN constructed in this paper; S404. Further solve the node energy price in the integrated energy system based on the convex hull pricing method.

6. A cost optimization method for an integrated energy system according to claim 5, characterized in that: In step S101, the economic operation cost objective function of the integrated energy system is as follows: In the formula, F EC It represents the economic operation cost of the integrated energy system, F1 represents the cost of electricity purchased by the integrated energy system from the external power grid, and F2 represents the cost of gas purchased by the integrated energy system from the natural gas market; It represents the unit cost of buying electricity from the external power grid. It represents the amount of electricity purchased from the external power grid. It represents the unit cost of selling electricity to the external grid. It represents the amount of electricity sold to the external grid; The operating cost of the integrated energy system should be: In the second stage, the uncertainty of renewable energy output and electrical load, thermal load, and cooling load is fully considered. Represents adjustment cost.

7. A cost optimization method for an integrated energy system according to claim 6, characterized in that: In step S102, introduce efficiency as a metric for assessing resource utilization in energy systems; The efficiency maximization objective function is as follows: In the formula, ψ e , q and ψ c They represent the energy quality coefficients of electrical energy, thermal energy and cold energy respectively; Through Charnes-Cooper transformation, it is transformed into a linear programming problem.

8. A cost optimization method for an integrated energy system according to claim 7, characterized in that: In step S202, given the corresponding random variable and The corresponding prediction error and It can be expressed as: In the formula, and They represent the real data of wind power output, photovoltaic output and electrical load, heating load and cooling load respectively; The traditional univariate LAP adjustment method is expanded, and the multivariate LAP adjustment model is proposed as follows: Therefore, AWE t , AWH t and They represent all adjustment rates (variables) corresponding to different forecast errors. In addition, They represent the decision variables The adjustment result.

9. A cost optimization method for an integrated energy system according to claim 8, characterized in that: In step S302, an approximate description method for the convex hull is introduced, which is to relax all 0-1 variables representing the operating status of the unit to within the interval [0,1] to make them continuous variables.

10. A cost optimization method for an integrated energy system according to claim 9, characterized in that: In step S401, in the integrated energy system, the distribution network is an improved IEEE-9 node power system, the thermal network / cooling network is based on the improved Bali 6-node thermal system, and the natural gas network adopts a 7-node system; the above three energy systems are organically coupled to form the integrated energy node system in this article. According to the annual average load demand forecast data and related parameters in the integrated energy system and the transaction electricity price, YALMIP is used for programming on the MATLAB platform, and the Gurobi solver is called for solving.

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