Multi-energy system scheduling modeling method, device, equipment and medium in severe weather

Abstract

The invention provides a multi-energy system scheduling modeling method, device, equipment and medium under severe weather, and relates to the technical field of comprehensive energy system optimization scheduling, and the method comprises the steps: obtaining comprehensive energy system data, and building an electric-gas-thermal coupled comprehensive energy system scheduling and optimization model based on the comprehensive energy system data; performing scene division on the original wind and light output data to generate a plurality of typical day scene data; taking the plurality of typical day scene data as input data, solving the electric-gas-thermal coupled integrated energy system scheduling and optimization model by using a hierarchical robust optimization algorithm to obtain an optimal solution, and outputting a final scheduling strategy according to the optimal solution; according to the method, multi-energy complementation and collaborative energy optimization are realized, the energy utilization rate of the comprehensive energy system is improved, the influence of wind and light uncertainty on the stability of the system is effectively reduced, and the method can better adapt to different scenes.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system optimization and scheduling technology, and more specifically, to a method, apparatus, equipment, and medium for multi-energy system scheduling modeling under severe weather conditions. Background Technology

[0002] With the acceleration of the global energy transition, the penetration rate of renewable energy sources such as wind and solar power in the power system continues to rise. However, the uncertainty of these renewable energy sources brings many challenges to the planning and operation of the power system. Traditional power systems mainly rely on fossil fuels, and their power output is relatively stable. However, with the increase in the proportion of wind and solar power generation, the supply and demand balance of the power system becomes more complex, requiring better handling of the integration of new energy sources. Wind power generation is mainly concentrated in winter and early spring, while photovoltaic power generation is affected by sunshine conditions, exhibiting obvious diurnal and seasonal fluctuations. This mismatch in supply and demand time may not only lead to increased system operating costs but also affect the system's safety and reliability, reducing the absorption and penetration capacity of new energy sources. To address these challenges, Integrated Energy Systems (IES) have emerged as a new energy system management model. IES can integrate multiple energy forms such as electricity, heat, and hydrogen, achieving efficient utilization and optimized allocation of energy through energy conversion, storage, and distribution. In IES, energy storage systems are an important means to address the uncertainty of wind and solar power. Short-term energy storage (such as electrical energy storage and thermal energy storage) can smooth out intraday load fluctuations and improve the absorption rate of renewable energy; long-term energy storage (such as hydrogen energy storage and methane energy storage) can solve the problem of seasonal supply and demand imbalance and realize cross-seasonal energy transfer.

[0003] However, existing technologies still have many shortcomings in handling the uncertainties of wind and solar power generation. First, while traditional energy storage systems (such as battery storage) can cope with short-term fluctuations, their capacity is limited and they cannot solve long-term supply and demand imbalances. Second, existing integrated energy systems often only consider economic efficiency when optimizing scheduling, neglecting environmental benefits and system reliability. Furthermore, existing technologies for handling the uncertainties of wind and solar power output are relatively simple, typically using deterministic scenarios for analysis, which fails to fully reflect the volatility and intermittency of wind and solar power generation.

[0004] In recent years, solid oxide fuel cells (SOFCs) and solid oxide electrolyzers (SOECs), as efficient and flexible energy conversion devices, have gradually attracted attention from academia and industry. Theoretically, the synergistic effect of SOECs and SOFCs has significant advantages, but existing research has limited its application in optimizing integrated energy systems with uncertain wind and solar power. Most current studies focus on single-mode operation of SOECs or SOFCs (e.g., considering only hydrogen production through electrolysis or only power generation), failing to fully leverage the potential of their synergistic operation. Furthermore, existing research often employs simple scenario partitioning methods when dealing with wind and solar uncertainties, failing to fully consider the complexity and diversity of wind and solar power output. Moreover, current research lacks refined modeling of the coupling relationships between electro-gas-heat multi-energy flows, focusing primarily on single energy forms.

[0005] Furthermore, traditional robust optimization methods target the worst-case scenario, ensuring system feasibility under extreme conditions, but often leading to overly conservative resource allocation (e.g., energy storage capacity redundancy exceeding 30%), significantly increasing investment costs. Even with a two-stage robust optimization framework to address wind power uncertainties, the cost is still 25% higher than stochastic optimization methods. On the other hand, stochastic optimization relies on accurate wind and solar power output probability distributions, but these distribution parameters are difficult to obtain accurately in practice, especially in data-scarce regions where errors are significant. Interval optimization reduces reliance on probability distributions, but its results only guarantee feasibility, not optimal economics. Moreover, existing methods often ignore the correlation between wind and solar power output and load demand, but traditional models do not fully utilize this coupling characteristic, resulting in low energy storage dispatch efficiency. Summary of the Invention

[0006] The purpose of this invention is to provide a method, device, equipment and medium for scheduling and modeling of multi-energy systems under severe weather conditions. It can realize multi-energy complementarity and collaborative energy optimization, improve the energy utilization rate of the entire system, effectively reduce the impact of wind and solar uncertainties on the stability of the system, and better adapt to different scenarios.

[0007] The technical solution of this invention is as follows:

[0008] Firstly, this application provides a method for scheduling modeling of multi-energy systems under severe weather conditions, which includes the following steps:

[0009] Acquire integrated energy system data and establish an integrated energy system scheduling and optimization model based on the integrated energy system data that couples electricity, gas, and heat.

[0010] The raw solar power output data is divided into scenarios to generate multiple typical daily scenario data.

[0011] Using data from multiple typical daily scenarios as input, a hierarchical robust optimization algorithm is used to solve the integrated energy system scheduling and optimization model of the electricity-gas-heat coupling to obtain the optimal solution, and the final scheduling strategy is output based on the optimal solution.

[0012] Furthermore, the calculation formulas for the above-mentioned integrated energy system model scheduling and optimization model include:

[0013]

[0014]

[0015] In the formula, C buy For electrical costs, and The prices of the upstream electricity and gas grids at time t are respectively; P t e,buy and These represent the electricity and gas purchase volumes at time t, respectively, where T is time; C om For the total equipment operation and maintenance cost, C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOEC and C om,SOFC The respective operation and maintenance costs of SOEC and SOFC are C. om,other c is the total maintenance cost of the remaining equipment; EB c SOEC c SOFC , c GT c hss c gss The respective operating and maintenance costs per unit power for EB, SOEC, SOFC, wind power, wind curtailment, photovoltaic, solar curtailment, GT, thermal storage tanks, and gas storage tanks; P t EB 、P GT,t P represents the electrical power generated or consumed by EB and GT at time t, respectively. t wind,use 、P t wind,abn 、P t PV,use 、P t PV,abn These are wind power used by wind turbines, wind power curtailed by wind turbines, solar power used by photovoltaic systems, and solar power curtailed by photovoltaic systems. and P represents the energy storage and release power of the thermal storage tank and the gas storage tank at time t, respectively. t SOFC and P t SOEC P represents the power generation of SOFC and the electrolysis power of SOEC at time t, respectively; t loadThe load at time t; Let t be the heat load at time t; P represents the gas load at time t. t GT P is the power generation capacity of GT. t SOFC Let P be the power generation of the SOFC at time t. t load Let P be the electrical load at time t. t SOEC Let H be the power consumption of SOEC at time t. t EB Let H be the heating power of EB at time t. t GT Let H be the heating power of GT at time t. t SOFC Let E be the heating power of the SOFC at time t. in,t GT V is the input fuel energy. t SOFC Let t be the hydrogen production rate of SOFC.

[0016] Furthermore, the above-mentioned scene segmentation of the original wind and solar power output data uses the k-means clustering algorithm, and its calculation formula includes:

[0017]

[0018] In the formula, N is the class center of the cluster. i For cluster C i The number of samples in C i Let be the i-th cluster, γ be the sample set, MSE be the minimum squared error, and m be the number of centroids.

[0019] Furthermore, the process of solving the integrated energy system scheduling and optimization model with electro-gas-thermal coupling using the hierarchical robust optimization algorithm includes:

[0020] A two-stage, multi-objective DRO model is constructed based on a comprehensive energy system scheduling and optimization model;

[0021] The two-stage multi-objective DRO model is decomposed into main and sub-problems using a column and constraint generation algorithm for iterative solution until the difference between the optimized values ​​of the main and sub-problems is less than the set convergence accuracy, at which point the optimal solutions of the main and sub-problems are obtained.

[0022] Based on the optimal solutions to the main and subproblems, lower and upper bound update values ​​are provided for the scheduling and optimization model of the integrated energy system with electric-gas-thermal coupling, and the final scheduling strategy is obtained.

[0023] Furthermore, the calculation formulas for the above two-stage multi-objective DRO model include:

[0024]

[0025] st h(x)≤0

[0026] g(x k ,y k ,z k )≤0

[0027]

[0028] In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k Let y be the probability of scene k occurring, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring. k z k Let f1 and f2 be the continuous and integer variables for the second stage in scenario k, respectively. f1 and f2 are both operating costs, h(x) represents the constraints for the first stage, and g(x) represents the constraints for the second stage. C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOFC For SOFC operation and maintenance costs, C buy For the cost of purchasing electricity and gas, C om,other For the total maintenance cost of the remaining equipment, C om,SOEC This refers to the operation and maintenance costs of SOEC.

[0029] Furthermore, the calculation formula for the above columns and constraint generation algorithm is as follows:

[0030]

[0031] st h(x)≤0

[0032]

[0033] g(x k ,y k ,z k )≤0

[0034]

[0035] In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k (l) Let y be the probability of scene k occurring in the l-th iteration, l be the iteration number, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring in the l-th iteration. k z ky represents the continuous and integer variables in the second stage of scenario k, respectively. k (l) z k (l) All are auxiliary variables in the l-th iteration, f1 and f2 are both operating costs, h(x) is the constraint related to the first stage, g(x) is the constraint related to the second stage, η is the relaxation amount of the subproblem, K is the total number of scenarios, and x k * This is the solution obtained in the l-th step of the main problem.

[0036] Secondly, this application provides a multi-energy system scheduling modeling device under severe weather conditions, comprising:

[0037] The model building module is used to acquire integrated energy system data and build an integrated energy system scheduling and optimization model based on the integrated energy system data that couples electricity, gas and heat.

[0038] The scene segmentation module is used to segment the raw wind and solar power output data into scenes to generate multiple typical daily scene data.

[0039] The optimization scheduling module is used to take data from multiple typical daily scenarios as input data, and use a hierarchical robust optimization algorithm to solve the integrated energy system scheduling and optimization model of electric-gas-heat coupling to obtain the optimal solution, and output the final scheduling strategy based on the optimal solution.

[0040] Furthermore, the calculation formulas for the above-mentioned integrated energy system model scheduling and optimization model include:

[0041]

[0042] In the formula, C buy For electrical costs, and The prices of the upstream electricity and gas grids at time t are respectively; P t e,buy and These represent the electricity and gas purchase volumes at time t, respectively, where T is time; C om For the total equipment operation and maintenance cost, C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOEC and C om,SOFC The respective operation and maintenance costs of SOEC and SOFC are C. om,other c is the total maintenance cost of the remaining equipment; EB c SOEC c SOFC , c GT c hss c gssThe respective operating and maintenance costs per unit power for EB, SOEC, SOFC, wind power, wind curtailment, photovoltaic, solar curtailment, GT, thermal storage tanks, and gas storage tanks; P t EB 、P GT,t P represents the electrical power generated or consumed by EB and GT at time t, respectively. t wind,use 、P t wind,abn 、P t PV,use 、P t PV,abn These are wind power used by wind turbines, wind power curtailed by wind turbines, solar power used by photovoltaic systems, and solar power curtailed by photovoltaic systems. and P represents the energy storage and release power of the thermal storage tank and the gas storage tank at time t, respectively. t SOFC and P t SOEC P represents the power generation of SOFC and the electrolysis power of SOEC at time t, respectively; t load The load at time t; Let t be the heat load at time t; P represents the gas load at time t. t GT P is the power generation capacity of GT. t SOFC Let P be the power generation of the SOFC at time t. t load Let P be the electrical load at time t. t SOEC Let H be the power consumption of SOEC at time t. t EB Let H be the heating power of EB at time t. t GT Let H be the heating power of GT at time t. t SOFC Let E be the heating power of the SOFC at time t. in,t GT V is the input fuel energy. t SOFC Let t be the hydrogen production rate of SOFC.

[0043] Furthermore, the above-mentioned scene segmentation of the original wind and solar power output data uses the k-means clustering algorithm, and its calculation formula includes:

[0044]

[0045] In the formula, N is the class center of the cluster. i For cluster Ci The number of samples in C i Let be the i-th cluster, γ be the sample set, MSE be the minimum squared error, and m be the number of centroids.

[0046] Furthermore, the process of solving the integrated energy system scheduling and optimization model with electro-gas-thermal coupling using the hierarchical robust optimization algorithm includes:

[0047] A two-stage, multi-objective DRO model is constructed based on a comprehensive energy system scheduling and optimization model;

[0048] The two-stage multi-objective DRO model is decomposed into main and sub-problems using a column and constraint generation algorithm for iterative solution until the difference between the optimized values ​​of the main and sub-problems is less than the set convergence accuracy, at which point the optimal solutions of the main and sub-problems are obtained.

[0049] Based on the optimal solutions to the main and subproblems, lower and upper bound update values ​​are provided for the scheduling and optimization model of the integrated energy system with electric-gas-thermal coupling, and the final scheduling strategy is obtained.

[0050] Furthermore, the calculation formulas for the above two-stage multi-objective DRO model include:

[0051]

[0052] st h(x)≤0

[0053] g(x k ,y k ,z k )≤0

[0054]

[0055] In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k Let y be the probability of scene k occurring, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring. k z k Let f1 and f2 be the continuous and integer variables for the second stage in scenario k, respectively. f1 and f2 are both operating costs, h(x) represents the constraints for the first stage, and g(x) represents the constraints for the second stage. C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOFC For SOFC operation and maintenance costs, C buy For the cost of purchasing electricity and gas, C om,other For the total maintenance cost of the remaining equipment, C om,SOEC This refers to the operation and maintenance costs of SOEC.

[0056] Furthermore, the calculation formula for the above columns and constraint generation algorithm is as follows:

[0057]

[0058] st h(x)≤0

[0059]

[0060] g(x k ,y k ,z k )≤0

[0061]

[0062] In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k (l) Let y be the probability of scene k occurring in the l-th iteration, l be the iteration number, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring in the l-th iteration. k z k y represents the continuous and integer variables in the second stage of scenario k, respectively. k (l) z k (l) All are auxiliary variables in the l-th iteration, f1 and f2 are both operating costs, h(x) is the constraint related to the first stage, g(x) is the constraint related to the second stage, η is the relaxation amount of the subproblem, K is the total number of scenarios, and x k * This is the solution obtained in the l-th step of the main problem.

[0063] Thirdly, this application provides an electronic device, comprising:

[0064] Memory, used to store one or more programs;

[0065] processor;

[0066] When one or more of the above programs are executed by the above processor, the method for scheduling and modeling of multi-energy systems under severe weather conditions, as described in any of the first aspects above, is implemented.

[0067] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-energy system scheduling modeling method under severe weather conditions as described in any of the first aspects above.

[0068] Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

[0069] (1) The present invention provides a multi-energy system scheduling modeling method, device, equipment and medium under severe weather conditions. Based on the synergy of SOEC, SOFC and gas storage and thermal storage systems, and considering the uncertainty of wind and solar integrated energy systems, the invention generates typical scenarios to characterize the uncertainty of renewable energy, and uses two-stage sub-Bruker optimization, combined with column and constraint generation algorithm, to obtain the optimal scheduling strategy, thereby achieving the minimum cost and maximum profit of the system.

[0070] (2) Through the coupling of the electric, thermal, and gas networks, this invention achieves multi-energy complementarity and synergistic energy optimization, thereby improving the overall energy utilization rate of the system. Under optimized scheduling, through the hybrid energy storage system and energy conversion system, the entire energy system reduces the occurrence of wind and solar curtailment, effectively reducing the impact of wind and solar uncertainties on the stability of the system;

[0071] (3) Under the overall optimized scheduling, the renewable energy capacity, absorption capacity and penetration rate of the system are greatly improved. Combining the two-equipment collaborative operation mode of SOEC and SOFC, which can both electrolyze and generate electricity, the power system incentivizes the renewable energy system to absorb and penetrate energy by switching the working state of the two devices, expands the storage capacity of renewable energy, reduces the additional costs caused by wind and solar curtailment, and converts renewable energy into gas energy for storage, which is convenient for subsequent energy collaborative use;

[0072] (4) This invention, through a small number of typical days after k-means clustering, can better reveal the characteristics of wind and solar uncertainty. Dimensionality reduction not only improves computational efficiency but also preserves the main features of the data, making subsequent optimization and scheduling more efficient. At the same time, this invention combines distributed optimization, addressing the energy scheduling problem of the entire system in a distributed manner by focusing on different stages, ensuring that the system can still operate under the most unfavorable conditions. It effectively handles the uncertainty of wind and solar power, increases the stability of the system, better adapts to different scenarios, and improves the overall system's energy utilization efficiency and wind and solar power absorption capacity. Attached Figure Description

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0074] Figure 1 This is a flowchart illustrating the steps of a multi-energy system scheduling modeling method under severe weather conditions according to the present invention.

[0075] Figure 2 This is a schematic diagram of an integrated energy system;

[0076] Figure 3 This is a distribution chart of data for four typical days;

[0077] Figure 4 This is a schematic diagram of the power load dispatch results for four typical days;

[0078] Figure 5 A schematic diagram showing the heat load scheduling results for four typical days;

[0079] Figure 6 A schematic diagram showing the results of pneumatic load scheduling for four typical days;

[0080] Figure 7 This is a schematic structural block diagram of an electronic device according to an embodiment of the present invention.

[0081] Icons: 101, memory; 102, processor; 103, communication interface. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0083] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0084] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0085] It should be noted that, in this document, the term "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0086] Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0087] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the various embodiments and features described below can be combined with each other.

[0088] Example 1

[0089] Please see Figure 1 , Figure 1 The diagram shows the steps of a multi-energy system scheduling modeling method under severe weather conditions provided in an embodiment of this application.

[0090] Firstly, this application provides a method for scheduling modeling of multi-energy systems under severe weather conditions, which includes the following steps:

[0091] Acquire integrated energy system data and establish an integrated energy system scheduling and optimization model based on the integrated energy system data that couples electricity, gas, and heat.

[0092] The raw solar power output data is divided into scenarios to generate multiple typical daily scenario data.

[0093] Using data from multiple typical daily scenarios as input, a hierarchical robust optimization algorithm is used to solve the integrated energy system scheduling and optimization model of the electricity-gas-heat coupling to obtain the optimal solution, and the final scheduling strategy is output based on the optimal solution.

[0094] As a preferred implementation method, the calculation formula for the integrated energy system model scheduling and optimization model includes:

[0095]

[0096]

[0097] In the formula, C buy For electrical costs, and The prices of the upstream electricity and gas grids at time t are respectively; P t e,buy and These represent the electricity and gas purchase volumes at time t, respectively, where T is time; C om For the total equipment operation and maintenance cost, C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOEC and C om,SOFC The respective operation and maintenance costs of SOEC and SOFC are C. om,other c is the total maintenance cost of the remaining equipment; EB c SOEC c SOFC , cGT c hss c gss The respective operating and maintenance costs per unit power for EB, SOEC, SOFC, wind power, wind curtailment, photovoltaic, solar curtailment, GT, thermal storage tanks, and gas storage tanks; P t EB 、P GT,t P represents the electrical power generated or consumed by EB and GT at time t, respectively. t wind,use 、P t wind,abn 、P t PV,use 、P t PV,abn These are wind power used by wind turbines, wind power curtailed by wind turbines, solar power used by photovoltaic systems, and solar power curtailed by photovoltaic systems. and P represents the energy storage and release power of the thermal storage tank and the gas storage tank at time t, respectively. t SOFC and P t SOEC P represents the power generation of SOFC and the electrolysis power of SOEC at time t, respectively; t load The load at time t; Let t be the heat load at time t; P represents the gas load at time t. t GT P is the power generation capacity of GT. t SOFC Let P be the power generation of the SOFC at time t. t load Let P be the electrical load at time t. t SOEC Let H be the power consumption of SOEC at time t. t EB Let H be the heating power of EB at time t. t GT Let H be the heating power of GT at time t. t SOFC Let E be the heating power of the SOFC at time t. in,t GT V is the input fuel energy. t SOFC Let t be the hydrogen production rate of SOFC.

[0098] As a preferred implementation method, the raw wind and solar power output data is divided into scenes using the k-means clustering algorithm, and its calculation formula includes:

[0099]

[0100] In the formula, N is the class center of the cluster. i For cluster C i The number of samples in C i Let be the i-th cluster, γ be the sample set, MSE be the minimum squared error, and m be the number of centroids.

[0101] As a preferred implementation method, the process of solving the scheduling and optimization model of the integrated energy system with electro-gas-thermal coupling using a hierarchical robust optimization algorithm includes:

[0102] A two-stage, multi-objective DRO model is constructed based on a comprehensive energy system scheduling and optimization model;

[0103] The two-stage multi-objective DRO model is decomposed into main and sub-problems using a column and constraint generation algorithm for iterative solution until the difference between the optimized values ​​of the main and sub-problems is less than the set convergence accuracy, at which point the optimal solutions of the main and sub-problems are obtained.

[0104] Based on the optimal solutions to the main and subproblems, lower and upper bound update values ​​are provided for the scheduling and optimization model of the integrated energy system with electric-gas-thermal coupling, and the final scheduling strategy is obtained.

[0105] As a preferred implementation method, the calculation formula of the two-stage multi-objective DRO model includes:

[0106]

[0107] st h(x)≤0

[0108] g(x k ,y k ,z k )≤0

[0109]

[0110] In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k Let y be the probability of scene k occurring, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring. k z k Let f1 and f2 be the continuous and integer variables for the second stage in scenario k, respectively. f1 and f2 are both operating costs, h(x) represents the constraints for the first stage, and g(x) represents the constraints for the second stage. C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOFC For SOFC operation and maintenance costs, C buy For the cost of purchasing electricity and gas, C om,other For the total maintenance cost of the remaining equipment, C om,SOEC This refers to the operation and maintenance costs of SOEC.

[0111] As a preferred implementation, the calculation formula for the column and constraint generation algorithm is as follows:

[0112]

[0113] st h(x)≤0

[0114]

[0115] g(x k ,y k ,z k )≤0

[0116]

[0117] In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k (l) Let y be the probability of scene k occurring in the l-th iteration, l be the iteration number, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring in the l-th iteration. k z k y represents the continuous and integer variables in the second stage of scenario k, respectively. k (l) z k (l) All are auxiliary variables in the l-th iteration, f1 and f2 are both operating costs, h(x) is the constraint related to the first stage, g(x) is the constraint related to the second stage, η is the relaxation amount of the subproblem, K is the total number of scenarios, and x k * This is the solution obtained in the l-th step of the main problem.

[0118] Secondly, this application provides a multi-energy system scheduling modeling device under severe weather conditions, comprising:

[0119] The model building module is used to acquire integrated energy system data and build an integrated energy system scheduling and optimization model based on the integrated energy system data that couples electricity, gas and heat.

[0120] The scene segmentation module is used to segment the raw wind and solar power output data into scenes to generate multiple typical daily scene data.

[0121] The optimization scheduling module is used to take data from multiple typical daily scenarios as input data, and use a hierarchical robust optimization algorithm to solve the integrated energy system scheduling and optimization model of electric-gas-heat coupling to obtain the optimal solution, and output the final scheduling strategy based on the optimal solution.

[0122] As a preferred implementation method, the calculation formula for the integrated energy system model scheduling and optimization model includes:

[0123]

[0124] In the formula, C buy For electrical costs, and The prices of the upstream electricity and gas grids at time t are respectively; P t e,buy and These represent the electricity and gas purchase volumes at time t, respectively, where T is time; C om For the total equipment operation and maintenance cost, C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOEC and C om,SOFC The respective operation and maintenance costs of SOEC and SOFC are C. om,other c is the total maintenance cost of the remaining equipment; EB c SOEC c SOFC , c GT c hss c gss The respective operating and maintenance costs per unit power for EB, SOEC, SOFC, wind power, wind curtailment, photovoltaic, solar curtailment, GT, thermal storage tanks, and gas storage tanks; P t EB 、P GT,t P represents the electrical power generated or consumed by EB and GT at time t, respectively. t wind,use 、P t wind,abn 、P t PV,use 、P t PV,abn These are wind power used by wind turbines, wind power curtailed by wind turbines, solar power used by photovoltaic systems, and solar power curtailed by photovoltaic systems. and P represents the energy storage and release power of the thermal storage tank and the gas storage tank at time t, respectively. t SOFC and P t SOEC P represents the power generation of SOFC and the electrolysis power of SOEC at time t, respectively; t load The load at time t; Let t be the heat load at time t; P represents the gas load at time t. t GT P is the power generation capacity of GT. t SOFC Let P be the power generation of the SOFC at time t.t load Let P be the electrical load at time t. t SOEC Let H be the power consumption of SOEC at time t. t EB Let H be the heating power of EB at time t. t GT Let H be the heating power of GT at time t. t SOFC Let E be the heating power of the SOFC at time t. in,t GT V is the input fuel energy. t SOFC Let t be the hydrogen production rate of SOFC.

[0125] As a preferred implementation method, the raw wind and solar power output data is divided into scenes using the k-means clustering algorithm, and its calculation formula includes:

[0126]

[0127]

[0128] In the formula, N is the class center of the cluster. i For cluster C i The number of samples in C i Let be the i-th cluster, γ be the sample set, MSE be the minimum squared error, and m be the number of centroids.

[0129] As a preferred implementation method, the process of solving the scheduling and optimization model of the integrated energy system with electro-gas-thermal coupling using a hierarchical robust optimization algorithm includes:

[0130] A two-stage, multi-objective DRO model is constructed based on a comprehensive energy system scheduling and optimization model;

[0131] The two-stage multi-objective DRO model is decomposed into main and sub-problems using a column and constraint generation algorithm for iterative solution until the difference between the optimized values ​​of the main and sub-problems is less than the set convergence accuracy, at which point the optimal solutions of the main and sub-problems are obtained.

[0132] Based on the optimal solutions to the main and subproblems, lower and upper bound update values ​​are provided for the scheduling and optimization model of the integrated energy system with electric-gas-thermal coupling, and the final scheduling strategy is obtained.

[0133] As a preferred implementation method, the calculation formula of the two-stage multi-objective DRO model includes:

[0134]

[0135] st h(x)≤0

[0136] g(x k ,y k ,z k )≤0

[0137]

[0138] In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k Let y be the probability of scene k occurring, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring. k z k Let f1 and f2 be the continuous and integer variables for the second stage in scenario k, respectively. f1 and f2 are both operating costs, h(x) represents the constraints for the first stage, and g(x) represents the constraints for the second stage. C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOFC For SOFC operation and maintenance costs, C buy For the cost of purchasing electricity and gas, C om,other For the total maintenance cost of the remaining equipment, C om,SOEC This refers to the operation and maintenance costs of SOEC.

[0139] As a preferred implementation, the calculation formula for the column and constraint generation algorithm is as follows:

[0140]

[0141] st h(x)≤0

[0142]

[0143] g(x k ,y k ,z k )≤0

[0144]

[0145] In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k (l) Let y be the probability of scene k occurring in the l-th iteration, l be the iteration number, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring in the l-th iteration. k z k y represents the continuous and integer variables in the second stage of scenario k, respectively. k (l) z k (l)All are auxiliary variables in the l-th iteration, f1 and f2 are both operating costs, h(x) is the constraint related to the first stage, g(x) is the constraint related to the second stage, η is the relaxation amount of the subproblem, K is the total number of scenarios, and x k * This is the solution obtained in the l-th step of the main problem.

[0146] Example 2

[0147] In Embodiment 2 of this invention, an integrated energy system (IES) for wind and solar uncertainty based on two-stage partial Bruker optimization is used as an example to examine the effectiveness of the proposed model. A schematic diagram of the integrated energy system is shown below. Figure 2 As shown in Table 1, the device parameters in IES are as follows:

[0148]

[0149] Table 1 shows the device parameters in IES, and the typical daily data generated is as follows: Figure 3 As shown, the integrated electricity and heat energy dispatch diagrams for four typical days are as follows: Figure 4-6 As shown. The model was built using the YALMIP toolkit in Matlab R2024b and solved using the GUROBI simulator.

[0150] To explore the feasibility of this system under different renewable energy penetration rates and to provide support for the stability of the system under adverse weather conditions and uneven wind and solar power output, we set up five groups of different wind power penetration rates of 100%, 75%, 50%, 25% and 0% to conduct comparative experiments, comparing the costs, hourly electricity purchase, hourly gas purchase, hourly wind curtailment and hourly solar curtailment under different conditions.

[0151] To explore the roles and advantages of SOEC and SOFC in the system, we compared systems without SOEC and SOFC with those with these devices. We selected wind power penetration rates of 100%, 75%, 50%, 25%, and 0% to differentiate the typical characteristics of the provided data under different conditions, better identifying the impact of wind and solar uncertainty. We employed the k-means clustering algorithm and used the elbow method to determine the optimal number of clusters. The elbow method determines the optimal number of clusters by observing the curve of the sum of squared clusters (SSE) of the dataset as a function of the K value. In this method, as the K value increases, the SSE gradually decreases, but the rate of decrease slows down. The "elbow" (inflection point) of the elbow plot is the point where the rate of decrease in SSE begins to slow down. This point is generally considered the optimal K value. The calculation results are plotted as an elbow diagram, as shown below. Figure 2 As shown, the optimal number of clusters is 4.

[0152] like Figure 3As shown, photovoltaic (PV) power generation and wind power generation exhibit four distinct sets of characteristics. Typical days 1 and 4 show high PV power output and relatively low wind power output, characterized by long hours of sunshine, high light intensity, and low wind speed. Typical day 2 shows both PV and wind power generation at low levels, characterized by short sunshine hours, low light intensity, and slow wind speed. Typical day 3 shows relatively low PV power output and relatively high wind power output, characterized by short sunshine hours, low light intensity, and high wind speed. This demonstrates that the k-means clustering results are reasonable and effectively distinguish four groups of significantly different typical days, with each group representing extreme weather conditions under different circumstances.

[0153] like Figure 4 As shown, the optimal power dispatch results indicate that photovoltaic power accounts for a relatively large proportion on the first and fourth days, while wind power accounts for a relatively large proportion on the third day. On a typical day, day 2, with both wind and solar power output low, more electricity is purchased. Furthermore, due to the internal SOEC, SOFC, and hydrogen storage tanks, the curtailment of wind and solar power within the renewable energy system is extremely low, even reaching zero. Effective and rational use and storage of renewable energy, or energy conversion for storage, is of great significance for peak shaving and valley filling in the system. Specifically, when renewable energy power generation is high, the system avoids using gas turbines for power generation as much as possible. When renewable energy supply is insufficient, SOFCs and gas turbines operate simultaneously, along with purchased electricity, all working together to meet the system's load demand.

[0154] In the gas dispatch diagram 5, due to the large gas load of the system, it is necessary to purchase gas to meet the system's demand. The gas in the hydrogen storage tank is easily depleted. This is because, on the one hand, the gas turbine needs gas to generate electricity, and on the other hand, SOFC also needs to use gas for the gas-to-electricity conversion process. Therefore, when the supply of new energy sources is insufficient, gas plays the role of energy storage.

[0155] In thermal regulation Figure 6 In China, the heat load varies under different weather conditions, resulting in differences in the energy capacity of thermal storage tanks. Furthermore, electric boilers also perform an electro-thermal conversion process, thus affecting the demand for electricity and indirectly influencing the demand for gas energy.

[0156] To verify the system's stability under adverse weather conditions and uneven wind and solar power output, we evaluated the optimal scheduling under different wind and solar power penetration rates, as shown in Table 2:

[0157]

[0158] The parameters for the five scenarios in this comparative scheme are exactly the same as those in the previous section. These data intuitively reflect the system's performance under different conditions. As the penetration rate of new energy sources increases, the system's operating costs gradually decrease, indicating that the utilization of new energy sources can effectively reduce dependence on traditional energy sources and lower operating costs. Simultaneously, the average hourly electricity and gas purchases also decrease with increasing penetration, demonstrating that the system can more effectively utilize internal new energy sources and reduce the purchase of external energy sources at high penetration rates.

[0159] Furthermore, the hourly average curtailment of wind and solar power significantly decreased under high penetration rates, indicating that the system can better absorb these energy sources when renewable energy supply is sufficient, thus improving energy utilization efficiency. These results validate the effectiveness and adaptability of the proposed scheduling strategy under different renewable energy penetration rates, demonstrating the system's flexibility and reliability in coping with renewable energy fluctuations and uncertainties, and indicating the reliability of energy systems based on SOFC and SOEC. By comparing the scheduling results under different penetration rates, valuable references can be provided for the planning and operation of practical integrated energy systems.

[0160] To explore the roles and advantages of SOEC and SOFC in the system, we compared the overall energy system dispatch costs with and without SOEC and SOFC equipment, as shown in Table 3:

[0161]

[0162] It can be seen that, across all wind power penetration rates, the system cost containing both SOEC and SOFC is lower than that without these devices. This indicates that the synergistic effect of SOEC and SOFC can effectively reduce system operating costs, especially at high wind power penetration rates, where the cost advantage is more pronounced. Therefore, the introduction of SOEC and SOFC significantly improves system flexibility. At high wind power penetration rates, SOEC and SOFC can better absorb wind power, reduce wind curtailment, and thus improve energy utilization efficiency. Through flexible scheduling of SOEC and SOFC, the system exhibits stronger adaptability and stability.

[0163] Example 3

[0164] Please see Figure 7 , Figure 7 This is a schematic structural block diagram of an electronic device provided in an embodiment of this application.

[0165] An electronic device includes a memory 101, a processor 102, and a communication interface 103. The memory 101, processor 102, and communication interface 103 are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The memory 101 can be used to store software programs and modules. The processor 102 executes the software programs and modules stored in the memory 101 to perform various functional applications and data processing. The communication interface 103 can be used for signaling or data communication with other node devices.

[0166] The memory 101 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0167] The processor 102 can be an integrated circuit chip with signal processing capabilities. The processor 102 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0168] It is understood that the structure shown in the figure is for illustrative purposes only. The multi-energy system scheduling modeling method under severe weather conditions may include more or fewer components than shown in the figure, or have a different configuration. The components shown in the figure can be implemented using hardware, software, or a combination thereof.

[0169] In the embodiments provided in this application, it should be understood that the disclosed methods can also be implemented in other ways. The embodiments described above are merely illustrative. For example, the flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0170] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0171] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0173] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for scheduling and modeling multi-energy systems under severe weather conditions, characterized in that, Includes the following steps: Acquire integrated energy system data and establish an integrated energy system scheduling and optimization model based on the integrated energy system data that couples electricity, gas, and heat. The raw solar power output data is divided into scenarios to generate multiple typical daily scenario data. Using data from multiple typical daily scenarios as input, a hierarchical robust optimization algorithm is used to solve the integrated energy system scheduling and optimization model of the electricity-gas-heat coupling to obtain the optimal solution, and the final scheduling strategy is output based on the optimal solution.

2. The multi-energy system scheduling modeling method under severe weather conditions as described in claim 1, characterized in that, The calculation formulas for the integrated energy system model scheduling and optimization model include: In the formula, C buy For electrical costs, and These are the prices of the upstream electricity and gas networks at time t, respectively. and These represent the electricity and gas purchase volumes at time t, respectively, where T is time; C om For the total equipment operation and maintenance cost, C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOEC and C om,SOFC The respective operation and maintenance costs of SOEC and SOFC are C. om,other c is the total operation and maintenance cost of the remaining equipment; EB c SOEC c SOFC , c GT c hss c gss The respective operating and maintenance costs per unit power for EB, SOEC, SOFC, wind power, wind curtailment, photovoltaic, solar curtailment, GT, thermal storage tanks, and gas storage tanks; P t EB P GT,t P represents the electrical power generated or consumed by EB and GT at time t, respectively. t wind,use P t wind,abn , These are wind power used by wind turbines, wind power curtailed by wind turbines, solar power used by photovoltaic systems, and solar power curtailed by photovoltaic systems. and P represents the energy storage and release power of the thermal storage tank and the gas storage tank at time t, respectively. t SOFC and P t SOEC P represents the power generation of SOFC and the electrolysis power of SOEC at time t, respectively; t load The load at time t; Let t be the heat load at time t; P represents the gas load at time t. t GT For the power generation capacity of GT, Let P be the power generation of the SOFC at time t. t load Let be the electrical load at time t. Let t be the power consumption of SOEC. Let H be the heating power of EB at time t. t GT Let GT be the heating power at time t. Let E be the heating power of the SOFC at time t. in,t GT For the input fuel energy, Let t be the hydrogen production rate of SOFC.

3. The multi-energy system scheduling modeling method under severe weather conditions as described in claim 1, characterized in that, The scene segmentation of the raw wind and solar power output data adopts the k-means clustering algorithm, and its calculation formula includes: In the formula, N is the class center of the cluster. i For cluster C i The number of samples in C i Let be the i-th cluster, γ be the sample set, MSE be the minimum squared error, and m be the number of centroids.

4. The multi-energy system scheduling modeling method under severe weather conditions as described in claim 1, characterized in that, The process of solving the integrated energy system scheduling and optimization model with electro-gas-thermal coupling using the hierarchical robust optimization algorithm includes: A two-stage, multi-objective DRO model is constructed based on a comprehensive energy system scheduling and optimization model; The two-stage multi-objective DRO model is decomposed into main and sub-problems using a column and constraint generation algorithm for iterative solution until the difference between the optimized values ​​of the main and sub-problems is less than the set convergence accuracy, at which point the optimal solutions of the main and sub-problems are obtained. Based on the optimal solutions to the main and subproblems, lower and upper bound update values ​​are provided for the scheduling and optimization model of the integrated energy system with electric-gas-thermal coupling, and the final scheduling strategy is obtained.

5. The multi-energy system scheduling modeling method under severe weather conditions as described in claim 4, characterized in that, The calculation formulas for the two-stage multi-objective DRO model include: In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k Let y be the probability of scene k occurring, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring. k z k Let f1 and f2 be the continuous and integer variables for the second stage in scenario k, respectively. f1 and f2 are both operating costs, h(x) represents the constraints for the first stage, and g(x) represents the constraints for the second stage. C om,EB For the operation and maintenance costs of electric boiler EB, C om,SOFC For SOFC operation and maintenance costs, C buy For the cost of purchasing electricity and gas, C om,other For the total maintenance cost of the remaining equipment, C om,SOEC This refers to the operation and maintenance costs of SOEC.

6. The multi-energy system scheduling modeling method under severe weather conditions as described in claim 4, characterized in that, The calculation formula for the column and constraint generation algorithm is as follows: In the formula, x is the decision variable for the first stage, and y and z are the decision variables for the second stage. k Let p be the decision variable for stage 1 in scenario k. k (l) Let y be the probability of scene k occurring in the l-th iteration, l be the iteration number, ψ be the comprehensive norm fuzzy set, and y be the probability of scene k occurring in the l-th iteration. k z k y represents the continuous and integer variables in the second stage of scenario k, respectively. k (l) z k (l) All are auxiliary variables in the l-th iteration, f1 and f2 are both operating costs, h(x) is the constraint related to the first stage, g(x) is the constraint related to the second stage, η is the relaxation amount of the subproblem, K is the total number of scenarios, and x k * This is the solution obtained in the l-th step of the main problem.

7. A multi-energy system scheduling modeling device under severe weather conditions, characterized in that, include: The model building module is used to acquire integrated energy system data and build an integrated energy system scheduling and optimization model based on the integrated energy system data that couples electricity, gas and heat. The scene segmentation module is used to segment the raw wind and solar power output data into scenes to generate multiple typical daily scene data. The optimization scheduling module is used to take data from multiple typical daily scenarios as input data, and use a hierarchical robust optimization algorithm to solve the integrated energy system scheduling and optimization model of electric-gas-heat coupling to obtain the optimal solution, and output the final scheduling strategy based on the optimal solution.

8. An electronic device, characterized in that, include: Memory, used to store one or more programs; processor; When the processor executes the one or more programs, it implements the multi-energy system scheduling modeling method under severe weather conditions as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the multi-energy system scheduling modeling method under severe weather conditions as described in any one of claims 1-6.

Images