Electric heating integrated system planning method considering multi-dimensional operation risk and investment-benefit ratio

Abstract

The invention discloses an electric heating integrated system planning method considering a multi-dimensional operation risk and an investment-benefit ratio. Comprising the following steps: constructing a regional heat supply network segmented virtual heat storage model considering adjustment capability quantification, wherein the regional heat supply network segmented virtual heat storage model specifically comprises a regional heat supply network segmented virtual heat storage model and a regional heat supply network adjustment capability quantification model considering double restrictions of equipment and pipelines; constructing an electric heating integrated system collaborative planning model considering the multi-dimensional risk and the investment-benefit ratio; and solving the electric heating comprehensive system collaborative planning model by a collaborative planning model solving method based on Meccke envelope relaxation and dynamic step size sequence boundary contraction, and generating an optimal planning scheme of the electric heating comprehensive system.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a planning method for an integrated electric and thermal system that considers multidimensional operational risks and investment-benefit ratio. Background Technology

[0002] The power supply side of the power system exhibits strong randomness and volatility, highlighting the increasing operational risks. Besides traditional flexible resources such as pumped storage and electrochemical energy storage, combined heat and power (CHP) is also a key means to mitigate operational risks. Benefiting from its thermal storage characteristics, regional heating networks can provide real-time flexibility to the power system without additional investment or impacting the thermal comfort of end-users. Therefore, CHP units have been widely deployed. It is projected that by the end of 2025, the installed capacity of CHP units will reach 434.4 million kilowatts, meeting over 63% of heating demand. Rational planning is the primary step in maximizing the regulatory capacity of CHP and mitigating power system operational risks; therefore, researching power-heat coordinated planning methods for improving flexibility and risk mitigation is of great significance.

[0003] While numerous patents and publications have explored the collaborative planning of electrothermal systems, the research primarily focuses on integrated electrothermal energy systems in industrial parks, with limited research on large-scale combined heat and power (CHP) systems. Furthermore, existing patents and literature concentrate on improving system operational efficiency, enhancing system resilience, and reducing carbon emissions, with little research considering collaborative planning to mitigate system operational risks. Electrothermal system operational risks encompass multiple dimensions, including adequacy, flexibility, and safety. Flexibility risk is related to the supply-demand balance of the system's regulation capacity, necessitating accurate quantification of the electrothermal system's regulation capacity to characterize flexibility risk. In electrothermal systems, besides traditional flexible resources such as pumped storage, electrochemical energy storage, and thermal power units, the thermal storage characteristics of regional heating networks can also provide additional regulation capacity to the power system. Therefore, accurately quantifying the regulation capacity of regional heating networks, taking into account their thermal storage characteristics, is a crucial issue that needs to be addressed.

[0004] Besides constraint modeling, the design of optimization objectives for electrothermal synergistic planning that considers system operational risk improvement also needs further research. To comprehensively consider investment economics and system operational risk, existing literature typically measures different types of risk using costs, and then uses the minimum sum of investment, operating, and risk costs as the planning objective. However, the monetization of risk costs is often uncertain; underestimating or overestimating risk costs will affect investment options. Furthermore, this objective tends to select projects with large investment scales but lower total costs, potentially ignoring schemes with higher unit investment benefits. Existing patents and literature have designed optimization objectives based on the benefit / cost ratio, ensuring that each unit of cost input brings maximum benefit. However, these studies only consider the benefit of investment in reducing operating costs, without taking into account the benefit of reducing operational risk. Therefore, further research is needed on investment benefit ratio indicators that can consider multiple dimensions of operational risk, such as adequacy, flexibility, and safety.

[0005] In conclusion, the planning method for integrated electrothermal systems that considers multidimensional operational risks and investment-benefit ratios needs further research. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a planning method for an integrated electrothermal system that considers multidimensional operational risks and investment-benefit ratio.

[0007] According to one aspect of the present invention, a planning method for an integrated electrothermal system considering multidimensional operational risks and investment-benefit ratio is provided, comprising:

[0008] The construction of a segmented virtual thermal storage model for a regional heating network that considers the quantification of regulation capacity specifically includes a segmented virtual thermal storage model for the regional heating network and a model for quantifying the regulation capacity of the regional heating network that considers both equipment and pipeline constraints.

[0009] Based on the segmented virtual thermal storage model of the regional heating network and the quantitative model of the regional heating network regulation capacity, a collaborative planning model for the integrated electric and thermal system considering multidimensional risks and investment benefit ratio is constructed.

[0010] The collaborative planning model of the electrothermal integrated system is solved using a collaborative planning model solution method based on McCormick envelope relaxation and dynamic step size sequential boundary contraction, generating the optimal planning scheme for the electrothermal integrated system.

[0011] According to another aspect of the present invention, an electrothermal integrated system planning device considering multidimensional operational risks and investment benefit ratio is provided, comprising:

[0012] The first construction module is used to build a segmented virtual thermal storage model of the regional heating network that considers the quantification of regulation capacity. Specifically, it includes a segmented virtual thermal storage model of the regional heating network and a quantification model of the regional heating network's regulation capacity that considers both equipment and pipeline constraints.

[0013] The second construction module is used to construct a collaborative planning model for an integrated electric and thermal system that considers multidimensional risks and investment benefit ratios, based on the segmented virtual thermal storage model of the regional heating network and the quantitative model of the regional heating network's regulation capacity.

[0014] The generation module is used to solve the collaborative planning model of the electrothermal integrated system based on the collaborative planning model solution method of McCormick envelope relaxation and dynamic step size sequential boundary contraction, and generate the optimal planning scheme of the electrothermal integrated system.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the methods described in any of the above aspects of the present invention.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0017] Therefore, the segmented virtual thermal storage model for regional heating networks constructed in this invention, which considers the quantification of regulation capacity, can accurately quantify the power regulation capacity of regional heating networks while taking into account thermal storage characteristics. The collaborative planning model for integrated electro-thermal systems constructed in this invention, which considers multidimensional risks and investment-benefit ratios, can generate the planning scheme with the highest unit investment benefit while simultaneously optimizing the collaborative optimization of substation and line expansion, as well as the configuration of electrochemical energy storage, cogeneration units, and electric boilers. The collaborative planning model solution method constructed in this invention, based on McCormick envelope relaxation and dynamic step-size sequential boundary contraction, achieves efficient solution of the fractional optimization model by relaxing the fractional objective using the McCormick envelope method and iteratively tightening the boundaries of the relaxed variables using the dynamic step-size sequential boundary contraction method. Attached Figure Description

[0018] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0019] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present invention for a planning method of an integrated electrothermal system that considers multidimensional operational risks and investment benefit ratios;

[0020] Figure 2 This is a schematic diagram of an integrated electrothermal system consisting of a 500kV power grid and two regional heating networks, provided in an exemplary embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of electrical load curves under five scenarios provided by an exemplary embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of heat load curves under five scenarios provided by an exemplary embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of photovoltaic output curves under five scenarios provided by an exemplary embodiment of the present invention;

[0024] Figure 6 This is a schematic diagram of wind power output curves under five scenarios provided by an exemplary embodiment of the present invention;

[0025] Figure 7 This is a schematic diagram of the structure of an electrothermal integrated system planning device that considers multidimensional operational risks and investment benefit ratio, provided by an exemplary embodiment of the present invention;

[0026] Figure 8 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0027] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0028] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0029] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0030] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0031] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0032] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0033] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0034] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0035] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0036] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0037] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0038] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0039] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0040] Exemplary methods

[0041] Figure 1This is a flowchart illustrating an exemplary embodiment of the present invention regarding a comprehensive electrothermal system planning method that considers multidimensional operational risks and investment-benefit ratios. This embodiment can be applied to electronic devices, such as... Figure 1 As shown, the planning method 100 for an integrated electrothermal system considering multidimensional operational risks and investment-benefit ratio includes the following steps:

[0042] Step 101, constructing a segmented virtual thermal storage model for the regional heating network considering the quantification of regulation capacity, specifically includes a segmented virtual thermal storage model for the regional heating network and a quantification model for the regional heating network's regulation capacity considering both equipment and pipeline constraints:

[0043] Step 102: Based on the segmented virtual thermal storage model of the regional heating network and the quantitative model of the regional heating network regulation capacity, construct a collaborative planning model for the integrated electric and thermal system that considers multidimensional risks and investment benefit ratio.

[0044] Step 103: Solve the collaborative planning model of the electrothermal integrated system based on the McCormick envelope relaxation and dynamic step size sequential boundary contraction method to generate the optimal planning scheme of the electrothermal integrated system.

[0045] Specifically, the implementation steps of the electrothermal integrated system planning method considering multidimensional operational risks and investment benefit ratio provided by this invention are as follows:

[0046] S1. Construct a segmented virtual thermal storage model for the regional heating network that considers the quantification of regulation capacity. This model can accurately quantify the power regulation capacity of the regional heating network while taking into account thermal storage characteristics.

[0047] S2. To achieve coordinated optimization of substation and line expansion, as well as the configuration of electrochemical energy storage, cogeneration units and electric boilers.

[0048] S3. A solution method for the collaborative programming model based on McCormick envelope relaxation and dynamic step-size sequential boundary contraction is proposed. The McCormick envelope method is used to relax the fractional optimization objective of the collaborative programming model, and the boundary of the relaxed variables is iteratively tightened by the dynamic step-size sequential boundary contraction method, thereby generating an accurate planning scheme for the electrothermal integrated system.

[0049] Furthermore, the regional heating network segmented virtual thermal storage model considering regulation capacity quantification described in S1 specifically includes a regional heating network segmented virtual thermal storage model and a regional heating network regulation capacity quantification model considering both equipment and pipeline constraints:

[0050] The segmented virtual thermal storage model for the regional heating network is as follows:

[0051]

[0052] Where: N DHS For the set of nodes in the regional heating network; L DHSFor the collection of regional heating network pipelines; and For the spanning tree variable; and These represent the heat charging and heat dissipation states of the beginning of pipe mn at time t under scenario s, respectively. and These represent the heat charging and heat dissipation states at the end of pipe mn at time t under scenario s, respectively. and These represent the heat charging and heat dissipation power at the beginning of pipe mn at time t under scenario s, respectively. and These represent the heat charging and heat dissipation power at the beginning of pipe mn at time t under scenario s, respectively. This represents the upper limit of the charging and discharging power of pipe mn; Let mn be the heat storage in pipe mn at time t under scenario s; and These are the pipeline heat charging and heat dissipation efficiencies, respectively. and These represent the lower and upper limits of the heat storage capacity of pipe mn, respectively; c w ρ is the specific heat capacity of water. w The density of water; Let mn be the upper limit of the flow rate in pipe mn; This is the upper limit of the water supply pipe temperature; The lower limit of the temperature of the return water pipeline; L represents the cross-sectional area of ​​pipe mn; mn Let m be the length of the pipe; Let m be the thermal power of node m at time t in scenario s. and These represent the thermal power of the combined heat and power (CHP) and EB at node m at time t in scenario s, respectively. Let be the power of the heat load at node m at time t under scenario s; Let m be the upper limit of hot water flow rate at node m.

[0053] The quantitative model for the regional heating network regulation capacity considering both equipment and pipeline constraints is as follows:

[0054]

[0055]

[0056] In the formula: A set of heating station nodes in a regional heating network; and These represent the power up-regulation capability and down-regulation capability of the regional heating network at time t under scenario s, respectively. and These represent the power up-adjustment and down-adjustment capabilities of the combined heat and power generation at node m at time t under scenario s; and These represent the power up-adjustment and down-adjustment capabilities of the electric boiler at node m at time t in scenario s. and These represent the changes in heat power at node m in scenario s at time t, caused by the upward and downward adjustment of electrical power. and These are the electro-thermal conversion coefficients for combined heat and power units and electric boilers, respectively.

[0057] Furthermore, the electrothermal integrated system collaborative optimization planning model considering multidimensional operational risks and investment benefit ratios, as described in S2, is specifically as follows:

[0058] The optimization objective of the model is to maximize the investment benefit ratio, in order to avoid f ope and f risk The impact of different orders of magnitude on optimization is addressed by normalizing the two types of investment benefit ratio indicators and setting weights to freely adjust investment bias, as shown in the following formula:

[0059]

[0060]

[0061] In the formula: λ1 and λ2 are the weights; f ope To reflect the return on investment for reduced operating costs; f risk ΔC is an indicator reflecting the return on investment in reducing multidimensional risks. ope and ΔW risk These represent the reductions in operating costs and multidimensional risks, respectively; C inv For investment costs; N TPS L is the set of power transmission network nodes; TPS A collection of power transmission lines; These are the investment cost coefficients for substations, power lines, combined heat and power units, electric boilers, and electrochemical energy storage, respectively. These are the corresponding investment recovery coefficients, which are used to evenly distribute the equipment investment cost during the planning period to each year of the lifespan. Expand the capacity of substation i at power grid node; The expansion factor for power line ij; and These refer to the newly built capacity of the cogeneration unit and EB at node m of the regional heating network, respectively. New capacity for electrochemical energy storage at grid node i; The number of days in scenario s; The electrical power of the combined heat and power unit at node m of the regional heat network during time period t under scenario s; The operating cost coefficient for the cogeneration unit at node m; Let k be the power of the non-cogeneration thermal power unit k during time period t under scenario s; For non-cogeneration thermal power units k, the operating cost coefficient is... This is a reference value for annual operating costs, i.e., the operating costs in the year prior to planning; and These represent the predicted power and actual power of photovoltaic k at time t under scenario s, respectively. and These represent the predicted power and actual power of wind power k at time t under scenario s, respectively. The over-limit power of the substation on node i of the transmission network during time period t under scenario s; The over-limit power of transmission line ij in time period t under scenario s; and These represent the insufficient capacity of the power grid for both upward and downward adjustments at time t under scenario s. This serves as a reference value for risk indicators, i.e., the total risk before planning.

[0062] The model's constraints include planning constraints and operational constraints. Planning constraints include substation expansion constraints, power line expansion constraints, electrochemical energy storage construction constraints, and combined heat and power (CHP) unit and electric boiler construction constraints.

[0063]

[0064] In the formula: and These represent the planned capacity, initial capacity, and maximum capacity of substation i at node i of the power transmission network. This represents the maximum expansion factor of transmission line ij; and ΔS line These represent the planned capacity, initial capacity, and expansion step size of transmission line ij, respectively. and These represent the planned capacity, initial capacity, and maximum capacity of electrochemical energy storage at transmission network node i, respectively. and These are the planned capacity, initial capacity, and maximum capacity of the cogeneration unit at node m of the regional heating network, respectively. and These represent the planned capacity, initial capacity, and maximum capacity of the electric boiler at node m of the regional heating network.

[0065] Operational constraints include transmission network operation constraints, regional heating network operation constraints, thermal power, photovoltaic power, wind power, pumped storage, electrochemical energy storage operation constraints, and electrothermal system regulation capacity constraints:

[0066]

[0067]

[0068] θ min ≤θ i,t,s ≤θ max ,i∈N TPS (38)

[0069]

[0070]

[0071]

[0072] In the formula: and These are the sets of thermal power, photovoltaic power, wind power, and pumped storage power connected to node i of the power transmission network; Let i be the power of the transmission network node i during time period t in scenario s; Let k be the power of the pumped storage system during time period t under scenario s; Let be the power of the electrochemical energy storage at node i of the transmission network during time period t under scenario s; Let be the power of the load on node i of the transmission network during time period t under scenario s; Let θ represent the power of transmission line ij in scenario s during time period t; i,t,s θ represents the voltage phase angle of node i in the transmission network during time period t under scenario s; max and θ min These are the upper and lower limits of the voltage phase angle, respectively; B ij The susceptance of the transmission line ij; k sub and k line These are the upper limit coefficients for substation and transmission line power, respectively. and These are the start-up and shutdown state variables of thermal power unit k during time period t under scenario s; and These are the upper limits of the gradient descent rate and the gradient climb rate for thermal power unit k, respectively. and Let k be the minimum start-up and shutdown time of the thermal power unit. and These are the power generation and pumping state variables of pumped storage k in scenario s during time period t, respectively. and These represent the power generation, pumping power, and total power of the pumped storage k during time period t in scenario s, respectively. and These represent the power generation capacity and the upper limit of the pumping power of the pumped storage system k, respectively. and These are the upper and lower reservoir capacities of pumped storage k during time period t in scenario s, respectively. and These are the minimum and maximum reservoir capacities of the upper reservoir with pumped storage capacity k, respectively. and These are the minimum and maximum reservoir capacities of the pumped storage reservoir with capacity k, respectively. The average water-to-electricity conversion factor for power generation; The average water volume to electricity conversion factor during pumping; and These are the charging and discharging state variables of electrochemical energy storage at node i during time period t in scenario s; and These represent the charging power, discharging power, and total power of electrochemical energy storage at node i during time period t in scenario s. and These are the upper limits of the charging power and the discharging power of the electrochemical energy storage at node i, respectively; The amount of electrochemical energy stored at node i during time period t in scenario s; and These represent the maximum and minimum electrochemical energy storage capacities at node i, respectively; η EES,c and η EES,f These are the charging efficiency and discharging efficiency of electrochemical energy storage, respectively. and These represent the system's up-adjustment capability and down-adjustment capability at time t under scenario s, respectively. and These represent the system's up and down adjustment requirements at time t under scenario s; and These represent upward and downward adjustments to the demand coefficients for photovoltaic power, respectively. and These are the upward and downward adjustments to the demand coefficient for wind power, respectively. and These are the demand coefficients for load increases and decreases, respectively.

[0073] Furthermore, the collaborative programming model solution method based on McCormick envelope relaxation and dynamic step-size sequential boundary contraction described in S3 includes model relaxation based on McCormick envelope and relaxation tightening based on dynamic step-size sequential boundary contraction:

[0074] (1) The model relaxation based on the McCormick envelope is specifically as follows:

[0075] First, perform variable substitution on the score target to make Z CPR =1 / C inv This transforms the objective (21) of the collaborative programming model into a fractional form (80) and introduces new constraints (81). In (80)-(81), there are three types of product terms between continuous variables, namely bilinear terms, namely Z... CPR ΔC, Z CPRΔW risk and Z CPR C inv This makes the planning model difficult to solve. To address this problem, the McCormick envelope relaxation method is used to relax the three types of bilinear terms according to (82).

[0076]

[0077] Z CPR C inv =1 (81)

[0078]

[0079] In the formula: ZC is a bilinear term; and Z These are the upper and lower limits of Z; and C These are the upper and lower limits of C.

[0080] (2) The relaxation tightening based on dynamic step size sequential boundary contraction is specifically as follows:

[0081] The dynamic step-size sequential boundary contraction method, based on the idea of ​​stochastic gradient descent, dynamically adjusts the contraction step size of the slack variables during iteration, avoiding the influence of manually set parameters on the iteration process and improving solution accuracy. According to the gradient descent algorithm, in the (r+1)th iteration, the boundary contraction step size ε of the slack variables... r+1 As shown in equation (83), where α r Let be the learning rate for iteration. It is worth noting that for the maximization problem studied in this paper, the solution of the relaxation model provides an upper bound (UB) for the original model. Substituting the solution obtained from the relaxation model back into the original problem yields a lower bound (LB) for the original problem. Therefore, in order to dynamically adjust the boundary contraction step size of the relaxation variables according to the degree of model relaxation, the relaxation gap is used to characterize the learning rate of iteration, that is, the relative error representation of the upper and lower bounds. Thus, the learning rate can be expressed by equation (84).

[0082]

[0083] Where: ε r Let α be the boundary shrinkage step size of the slack variables in the r-th iteration; r Let be the learning rate for the r-th iteration; UB represents the partial derivative of the objective function. r and LB r Let be the upper and lower bounds of the original problem in the r-th iteration, respectively.

[0084] The dynamic step-size sequential boundary contraction method takes as input a convergence threshold, initial values ​​for the contraction step size of the relaxation variables, and initial values ​​for the upper and lower bounds of the relaxation variables. After initializing the loop indicator, the loop begins. The relaxation model is solved based on the latest boundary conditions to obtain the upper bound of the original model. Then, the relaxation variables C are fixed. inv ΔC ope ΔW risk and Z CPR Based on this, solve the relaxation model to obtain the lower bound of the original model. Calculate the relaxation gap of the original problem according to equation (84), and calculate the relaxation error of the bilinear term according to equations (85)-(87). Determine whether the relaxation gap and the relaxation error of the bilinear term both satisfy the convergence threshold. If they do, output the solution result; otherwise, update the variable shrinkage step size according to equation (83). Update the upper and lower limits of the variables according to equations (88)-(91). Repeat this process until the relaxation gap and the relaxation error of the bilinear term both satisfy the convergence threshold.

[0085]

[0086] In the formula: and ZC inv ZC ope and ZW risk relaxation error; and The variables C in the r-th iteration are respectively inv ΔC ope ΔW risk and Z CPR The boundary contraction step size; and C represents the process of the r-th iteration. inv The upper and lower limits; and ΔC represents the value of ΔC during the r-th iteration. ope The upper and lower limits; and ΔW in the r-th iteration is respectively risk The upper and lower limits; and Z represents the process in the r-th iteration. CPR The upper and lower limits.

[0087] In an exemplary embodiment of the present invention, in order to verify the effectiveness and efficiency of the planning method proposed in the present invention, the following is employed: Figure 2 The test was conducted on an integrated electrothermal system consisting of a 500kV power grid and two regional heating networks. The transmission network comprises 12 nodes, and the connection status of thermal power, photovoltaic power, wind power, pumped storage, and electrochemical energy storage at each node is as follows. Figure 2As shown, detailed power supply and load data are presented in Table 1. Both regional heating networks contain 10 nodes, coupled to the transmission network through cogeneration units and electric boilers configured in heating stations H1 and H11. Heat load data are shown in Table 2. Electricity load, heat load, photovoltaic, and wind power curves under five typical scenarios are shown in Table 2. Figures 3-6 As shown.

[0088] Table 1. Power Supply and Load Parameters of Transmission Network (MW)

[0089]

[0090] Table 2 Regional heating network load parameters (MW)

[0091]

[0092] (1) Analysis of the planning results of the method of the present invention

[0093] Table 3 shows the investment benefit ratio analysis results of the planning scheme. Table 4 shows the maximum utilization rate of the newly built equipment. Table 5 shows the comparison of operating costs and risk indicators before and after the planning. In terms of power transmission network planning, power lines P11-P12 are expanded by 1000MVA, and substation P12 is expanded by 700MVA. In addition, substations P11 and P12 are equipped with 100MW / 100MWh and 300MW / 600MWh electrochemical energy storage, respectively. In terms of regional heating network planning, heating stations H1 and H11 add electric boilers of 300MW and 350MW, respectively. As shown in Table 4, the maximum utilization rate of the newly built electrochemical energy storage and electric boilers is 100%. Therefore, the method of this invention can reasonably configure the capacity of electrochemical energy storage and electric boilers. As shown in Table 5, compared with before the planning, the operating cost of the system after the planning is reduced by 12.13%, the adequacy risk is reduced by 68.96%, the flexibility risk is reduced by 57.87%, and the safety risk is reduced by 67.79%. It is evident that planning effectively improved the system's operational economic efficiency and significantly reduced its operational risks.

[0094] In summary, the method of the present invention can generate reasonable substation and line expansion schemes, as well as configuration schemes for electrochemical energy storage, cogeneration units and electric boilers. While reducing operating costs by 12.13%, it also reduces adequacy, flexibility and safety risks by 68.96%, 57.87% and 67.79%, respectively.

[0095] Table 3. Investment Benefit Ratio Analysis of the Planning Scheme

[0096]

[0097] Table 4 Maximum Utilization Rate of Newly Built Equipment

[0098]

[0099] Table 5 Comparison of operating costs and risk indicators before and after the planning

[0100]

[0101] (2) Efficiency analysis of the optimization planning model considering the investment benefit ratio of the method of the present invention

[0102] To analyze the efficiency of the optimization planning model considering the cost-benefit ratio of the method of this invention, the following two cases are set up for comparison with the method of this invention. Table 6 shows the cost and benefit comparison between the method of this invention and MSCM planning.

[0103] MSCM-1: Based on the method of this invention, the optimization objective is replaced by minimizing the sum of planning cost, operating cost and risk cost, with a risk penalty cost of 500 yuan / MWh.

[0104] MSCM-2: Based on the method of this invention, the optimization objective is replaced by minimizing the sum of planning cost, operating cost and risk cost, with a risk penalty cost of 1000 yuan / MWh.

[0105] As shown in Table 4, the investment costs of MSCM-1 and MSCM-2 are 49.65% and 74.68% higher than the method of this invention, respectively. However, this results in a 4.71% and 6.28% reduction in operating costs for MSCM-1 and MSCM-2, respectively, and a 46.15% and 56.56% reduction in total risk compared to the method of this invention. However, the investment benefit of the method of this invention is higher than f ope and f risk The improvements compared to MSCM-1 were 12.10% and 19.91% respectively, demonstrating that the investment efficiency of the method of this invention is higher than that of f. ope and f risk These represent increases of 20.54% and 34.26% respectively compared to MSCM-2. Furthermore, when the risk penalty cost is set at 500 yuan / MWh, the total investment, operating, and risk costs of the method of this invention are 10.23 × 10⁻⁶. 9 Yuan, compared to MSCM-1's 9.41×10 9 The cost per unit area is 8.71%. When the risk penalty cost is set at 1000 yuan / MWh, the total cost of investment, operation, and risk for the method of this invention is 11.34 × 10⁻⁶. 9 Yuan, compared to MSCM-1's 9.69×10 9 The cost per unit is 17.03%. This shows that MSCM tends to choose projects with large investment scale but lower total cost, while the method of this invention can achieve a planning scheme with a greater investment benefit ratio than MSCM.

[0106] On the other hand, a comparison of MSCM-1 and MSCM-2 shows that the planning schemes of MSCM differ significantly depending on the choice of risk penalty cost. The investment cost of MSCM-2 is 16.99% higher than that of MSCM-1, but the cost-effectiveness is higher. ope and f risk These figures represent reductions of 7.00% and 10.69% respectively compared to MSCM-1. This demonstrates that when using MSCM, the monetization of risk costs is often uncertain; underestimating or overestimating risk costs can negatively impact investment decisions.

[0107] In summary, compared with existing planning models that aim to minimize total cost, the collaborative optimization planning model constructed in this paper, which considers multidimensional operational risks and investment benefit ratios, solves the problem of the planning scheme being affected by risk monetization valuation. Furthermore, the investment-operational investment benefit ratio and investment risk-benefit ratio are increased by more than 12.10% and 19.91% respectively compared with existing models.

[0108] Table 6. Cost and benefit comparison of the method of this invention and MSCM planning.

[0109]

[0110] (3) Efficiency analysis of the segmented virtual thermal storage model of the regional heating network considering the quantification of regulation capacity in the method of the present invention

[0111] To analyze the efficiency of the segmented virtual thermal storage model for regional heating networks considering the quantification of regulation capacity in the method of this invention, the following two cases are set up for comparison with the method of this invention. Table 7 shows the comparison of planning costs and benefits of CF-VTM and EFM. Table 8 shows the comparison of wind and solar energy absorption, insufficient regulation capacity, and tidal current exceedance situations of the method of this invention, CF-VTM, and EFM.

[0112] EFM: Based on the method of this invention, the heat storage characteristics and electrical power regulation capability of the regional heating network are not considered.

[0113] CF-VTM: Based on the method of this invention, the power regulation capability of the regional heating network is not considered.

[0114] As shown in Table 7, compared with the method of the present invention, the investment cost of CF-VTM increased by 23.44%, the operating cost decreased by 2.35%, and the total risk increased by 12.22%. Therefore, compared with CF-VTM, the method of the present invention has a lower risk. ope and f riskThese figures represent increases of 5.79% and 32.41%, respectively. As shown in Table 8, compared to CF-VTM, the annual insufficient capacity for upward and downward regulation of the proposed method is reduced by 18.87% and 11.90%, respectively. Therefore, compared to CF-VTM, the flexibility risk of the proposed method is reduced by 16.97%, and the improvement in flexibility risk is increased by 17.11%. Consequently, as shown in Table 7, compared to CF-VTM, the annual photovoltaic and wind power absorption capacity of the proposed method is increased by 0.33% and 0.34%, respectively, which results in a 6.75% increase in the improvement in sufficiency risk compared to CF-VTM.

[0115] As shown in Table 7, compared with the method of the present invention, the investment cost of EFM increased by 26.32%, the operating cost increased by 0.34%, and the total risk increased by 31.22%. Therefore, compared with EFM, the method of the present invention has lower risk. ope and f risk These figures represent increases of 30.30% and 52.66%, respectively. As shown in Table 8, compared to EFM, since the regional heating network cannot provide power regulation capability to the transmission network under EFM, the annual insufficient upward and downward regulation capacity of the method of this invention is reduced by 20.37% and 33.07%, respectively. Therefore, compared to EFM, the flexibility risk improvement of the method of this invention is increased by 31.02%. Therefore, as shown in Table 8, compared to EFM, the annual photovoltaic power absorption and annual wind power absorption of the method of this invention are increased by 1.38% and 0.17%, respectively, which makes the sufficiency risk improvement of the method of this invention 13.04% higher than that of EFM.

[0116] In summary, by considering the heat storage characteristics and power regulation capabilities of the regional heating network in the planning of the integrated electrothermal system, the method of this invention improves the investment benefit ratio in terms of reducing operating costs and multidimensional risks by 30.30% and 52.66%, respectively.

[0117] Table 7. Comparison of the costs and benefits of the present invention method, CF-VTM, and EFM planning.

[0118]

[0119]

[0120] Table 8 Comparison of the present invention method, CF-VTM and EFM wind and solar energy absorption, insufficient regulation capacity and tidal current exceedance.

[0121]

[0122] Based on the above analysis, the effectiveness and efficiency of the proposed integrated electrothermal system planning method, which considers multidimensional operational risks and investment benefit ratios, have been verified.

[0123] Therefore, the segmented virtual thermal storage model for regional heating networks constructed in this invention, which considers the quantification of regulation capacity, can accurately quantify the power regulation capacity of regional heating networks while taking into account thermal storage characteristics. The collaborative planning model for integrated electro-thermal systems constructed in this invention, which considers multidimensional risks and investment-benefit ratios, can generate the planning scheme with the highest unit investment benefit while simultaneously optimizing the collaborative optimization of substation and line expansion, as well as the configuration of electrochemical energy storage, cogeneration units, and electric boilers. The collaborative planning model solution method constructed in this invention, based on McCormick envelope relaxation and dynamic step-size sequential boundary contraction, achieves efficient solution of the fractional optimization model by relaxing the fractional objective using the McCormick envelope method and iteratively tightening the boundaries of the relaxed variables using the dynamic step-size sequential boundary contraction method.

[0124] Exemplary device

[0125] Figure 7 This is a schematic diagram of the structure of an electrothermal integrated system planning device that considers multi-dimensional operational risks and investment benefit ratio, provided by an exemplary embodiment of the present invention. Figure 7 As shown, the device 700 includes:

[0126] The first construction module 710 is used to construct a segmented virtual thermal storage model of the regional heating network that considers the quantification of regulation capacity. Specifically, it includes a segmented virtual thermal storage model of the regional heating network and a quantification model of the regional heating network's regulation capacity that considers both equipment and pipeline constraints.

[0127] The second construction module 720 is used to construct a collaborative planning model for an integrated electric and thermal system that considers multidimensional risks and investment benefit ratios, based on the segmented virtual thermal storage model of the regional heating network and the quantitative model of the regional heating network's regulation capacity.

[0128] The generation module 730 is used to solve the collaborative planning model of the electrothermal integrated system based on the collaborative planning model solution method of McCormick envelope relaxation and dynamic step size sequential boundary contraction, and generate the optimal planning scheme of the electrothermal integrated system.

[0129] Exemplary electronic devices

[0130] Figure 8 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 8 As shown, the electronic device 80 includes one or more processors 81 and memory 82.

[0131] The processor 81 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0132] The memory 82 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 81 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may further include an input device 83 and an output device 84, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0133] In addition, the input device 83 may also include, for example, a keyboard, a mouse, etc.

[0134] The output device 84 can output various information to the outside. The output device 84 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0135] Of course, for the sake of simplicity, Figure 8 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0136] Exemplary computer program products and computer-readable storage media

[0137] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0138] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0139] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0140] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0141] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0142] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0143] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0144] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0145] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0146] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A planning method for an integrated electrothermal system considering multidimensional operational risks and investment-benefit ratio, characterized in that, include: The construction of a segmented virtual thermal storage model for a regional heating network that considers the quantification of regulation capacity specifically includes a segmented virtual thermal storage model for the regional heating network and a model for quantifying the regulation capacity of the regional heating network that considers both equipment and pipeline constraints. Based on the segmented virtual thermal storage model of the regional heating network and the quantitative model of the regional heating network regulation capacity, a collaborative planning model for the integrated electric and thermal system considering multidimensional risks and investment benefit ratio is constructed. The collaborative planning model of the electrothermal integrated system is solved using a collaborative planning model solution method based on McCormick envelope relaxation and dynamic step size sequential boundary contraction, generating the optimal planning scheme for the electrothermal integrated system.

2. The method according to claim 1, characterized in that, The expression for the segmented virtual thermal storage model of the regional heating network is: Where: N DHS For the set of nodes in the regional heating network; L DHS For the collection of regional heating network pipelines; and For the spanning tree variable; and These represent the heat charging and heat dissipation states of the beginning of pipe mn at time t under scenario s, respectively. and These represent the heat charging and heat dissipation states at the end of pipe mn at time t under scenario s, respectively. and These represent the heat charging and heat dissipation power at the beginning of pipe mn at time t under scenario s, respectively. and These represent the heat charging and heat dissipation power at the beginning of pipe mn at time t under scenario s, respectively. This represents the upper limit of the charging and discharging power of pipe mn; Let mn be the heat storage in pipe mn at time t under scenario s; and These are the pipeline heat charging efficiency and heat release efficiency, respectively. and These represent the lower and upper limits of the heat storage capacity of pipe mn, respectively; c w ρ is the specific heat capacity of water. w The density of water; Let mn be the upper limit of the flow rate in pipe mn; This is the upper limit of the water supply pipe temperature; The lower limit of the temperature of the return water pipeline; L is the cross-sectional area of ​​pipe mn; mn Let m be the length of the pipe. Let m be the thermal power of node m at time t in scenario s. and These represent the thermal power of the combined heat and power (CHP) and EB at node m at time t in scenario s, respectively. Let be the power of the heat load at node m at time t under scenario s; Let m be the upper limit of hot water flow rate at node m.

3. The method according to claim 1, characterized in that, The expression for the quantitative model of the regional heating network regulation capacity is: In the formula: A set of heating station nodes in a regional heating network; and These represent the power up-regulation capability and down-regulation capability of the regional heating network at time t under scenario s, respectively. and These represent the power up-adjustment and down-adjustment capabilities of the combined heat and power generation at node m at time t under scenario s; and These represent the power up-adjustment and down-adjustment capabilities of the electric boiler at node m at time t in scenario s. and These represent the changes in heat power at node m in scenario s at time t, caused by the upward and downward adjustment of electrical power. and These are the electro-thermal conversion coefficients for combined heat and power units and electric boilers, respectively.

4. The method according to claim 1, characterized in that, The optimization objective expression of the collaborative optimization planning model for the electrothermal integrated system is: in, In the formula: λ1 and λ2 are the weights; f ope To reflect the return on investment for reduced operating costs; f risk ΔC is an indicator reflecting the investment benefit ratio that reflects the reduction of multidimensional risks. ope and ΔW risk These represent the reductions in operating costs and multidimensional risks, respectively; C inv For investment costs; N TPS L is the set of nodes in the power transmission network. TPS A collection of power transmission lines; These are the investment cost coefficients for substations, power lines, combined heat and power units, electric boilers, and electrochemical energy storage, respectively. These are the corresponding investment recovery coefficients, which equally distribute the equipment investment cost during the planning period to each year of the lifespan. Expand the capacity of substation i at power grid node; The expansion factor for power line ij; and These refer to the newly built capacity of the cogeneration unit and EB at node m of the regional heating network, respectively. New capacity for electrochemical energy storage at grid node i; The number of days in scenario s; The electrical power of the combined heat and power unit at node m of the regional heat network during time period t under scenario s; The operating cost coefficient for the cogeneration unit at node m; Let k be the power of the non-cogeneration thermal power unit k during time period t under scenario s; For non-cogeneration thermal power units k, the operating cost coefficient is... This is a reference value for annual operating costs, i.e., the operating costs in the year prior to planning; and These represent the predicted power and actual power of photovoltaic k at time t under scenario s, respectively. and These represent the predicted power and actual power of wind power k at time t under scenario s, respectively. The over-limit power of the substation on node i of the transmission network during time period t under scenario s; The over-limit power of transmission line ij in time period t under scenario s; and These represent the insufficient capacity of the power grid to adjust upward and downward at time t under scenario s; This serves as a reference value for risk indicators.

5. The method according to claim 4, characterized in that, The constraints of the collaborative optimization planning model for the electrothermal integrated system include planning constraints and operational constraints, wherein... The planning constraints include substation expansion constraints, power line expansion constraints, electrochemical energy storage construction constraints, and combined heat and power (CHP) unit and electric boiler construction constraints, the expressions of which are: In the formula: and These represent the planned capacity, initial capacity, and maximum capacity of substation i at node i of the power transmission network. This represents the maximum expansion factor of transmission line ij; and ΔS line These represent the planned capacity, initial capacity, and expansion step size of transmission line ij, respectively. and These represent the planned capacity, initial capacity, and maximum capacity of electrochemical energy storage at transmission network node i, respectively. and These are the planned capacity, initial capacity, and maximum capacity of the cogeneration unit at node m of the regional heating network, respectively. and These represent the planned capacity, initial capacity, and maximum capacity of the electric boiler at node m of the regional heating network. The operational constraints include transmission network operational constraints, regional heating network operational constraints, thermal power, photovoltaic, wind power, pumped storage, electrochemical energy storage operational constraints, and electrothermal system regulation capacity constraints, the expressions of which are: i min ≤θ i,t,s ≤θ max ,i∈N TPS (38) In the formula: and These are the sets of thermal power, photovoltaic power, wind power, and pumped storage power connected to node i of the power transmission network; Let i be the power of the transmission network node i during time period t in scenario s; Let k be the power of the pumped storage system during time period t under scenario s; Let be the power of the electrochemical energy storage at node i of the transmission network during time period t under scenario s; Let be the power of the load on node i of the transmission network during time period t under scenario s; Let θ represent the power of transmission line ij in scenario s during time period t; i,t,s θ represents the voltage phase angle of node i in the transmission network during time period t under scenario s; max and θ min These are the upper and lower limits of the voltage phase angle, respectively; B ij The susceptance of the transmission line ij; k sub and k line These are the upper limit coefficients for substation and transmission line power, respectively. and These are the start-up and shutdown state variables of thermal power unit k during time period t under scenario s, respectively; and These are the upper limits of the gradient descent rate and the gradient climb rate for thermal power unit k, respectively. and Let k be the minimum start-up and shutdown time of the thermal power unit. and These are the power generation and pumping state variables of pumped storage k in scenario s during time period t, respectively. and These represent the power generation, pumping power, and total power of the pumped storage k during time period t in scenario s, respectively. and These represent the power generation capacity and the upper limit of the pumping power of the pumped storage system k, respectively. and These represent the upper and lower reservoir capacities of the pumped storage capacity k during time period t in scenario s; and These are the minimum and maximum reservoir capacities of the upper reservoir with pumped storage capacity k, respectively. and These are the minimum and maximum reservoir capacities of the pumped storage reservoir with capacity k, respectively. The average water-to-electricity conversion factor for power generation; The average water volume to electricity conversion factor during pumping; and These are the charging and discharging state variables of electrochemical energy storage at node i during time period t in scenario s; and These represent the charging power, discharging power, and total power of electrochemical energy storage at node i during time period t in scenario s. and These are the upper limits of the charging power and the discharging power of the electrochemical energy storage at node i, respectively; The amount of electrochemical energy stored at node i during time period t in scenario s; and These represent the maximum and minimum electrochemical energy storage capacities at node i, respectively; η EES,c and η EES,f These are the charging efficiency and discharging efficiency of electrochemical energy storage, respectively. and These represent the system's up-adjustment capability and down-adjustment capability at time t under scenario s, respectively. and These represent the system's up and down adjustment requirements at time t under scenario s; and These represent upward and downward adjustments to the demand coefficients for photovoltaic power, respectively. and These are the upward and downward adjustments to the demand coefficient for wind power, respectively. and These are the demand coefficients for load increases and decreases, respectively.

6. The method according to claim 5, characterized in that, The collaborative programming model of the electrothermal integrated system is solved using a collaborative programming model solution method based on McCormick envelope relaxation and dynamic step-size sequential boundary contraction, generating the optimal planning scheme for the electrothermal integrated system, including: The fractional objective in the collaborative planning model of the electrothermal integrated system is replaced with a variable to obtain an optimization objective without fractional form; Preset constraints are introduced into the non-fractional optimization objective to obtain an updated optimization objective; The updated optimization objective is relaxed using the McCormick envelope relaxation method to obtain the relaxed optimization objective. The boundary of the relaxation variables in the relaxation optimization objective is iteratively tightened using a dynamic step-size sequential boundary contraction method to generate the optimal planning scheme for the electrothermal integrated system.

7. The method according to claim 6, characterized in that, The variable substitution expression is: Z CPR =1 / C inv In the formula, C i nv represents the fractional objective in the optimization objective, and Z represents the fractional objective. CPR The target after replacement; The expression for the update and optimization objective is: Z CPR C inv =1(81) The expression for the McCormick envelope relaxation method is as follows: In the formula: ZC is a bilinear term; and Z These are the upper and lower limits of Z; and C These are the upper and lower limits of C.

8. The method according to claim 6, characterized in that, The learning rate expression for the dynamic step-size sequential boundary shrinkage method is: In the formula, ε r Let α be the boundary shrinkage step size of the slack variables in the r-th iteration; r Let be the learning rate for the r-th iteration; ▽F is the partial derivative of the objective function; UB r and LB r These are the upper and lower bounds of the original problem in the r-th iteration, respectively; ε r+1 The boundary shrinkage step size for slack variables; The expression for calculating the relaxation error of the bilinear term in the dynamic step-size sequential boundary contraction method is as follows: The expression for updating the upper and lower bounds of a variable is: In the formula, and ZC inv ZC ope and ZW risk relaxation error; and The variables C in the r-th iteration are respectively inv ΔC ope ΔW risk and Z CPR The boundary contraction step size; and C represents the process of the r-th iteration. inv The upper and lower limits; and ΔC represents the value of ΔC during the r-th iteration. ope The upper and lower limits; and ΔW in the r-th iteration is respectively risk The upper and lower limits; and Z represents the process in the r-th iteration. CPR The upper and lower limits.

9. A planning device for an integrated electrothermal system considering multidimensional operational risks and investment-benefit ratio, characterized in that, include: The first construction module is used to build a segmented virtual thermal storage model of the regional heating network that considers the quantification of regulation capacity. Specifically, it includes a segmented virtual thermal storage model of the regional heating network and a quantification model of the regional heating network's regulation capacity that considers both equipment and pipeline constraints. The second construction module is used to construct a collaborative planning model for the integrated electric and thermal system that considers multidimensional risks and investment benefit ratios based on the segmented virtual thermal storage model of the regional heating network and the quantitative model of the regional heating network regulation capacity. The generation module is used to solve the collaborative planning model of the electrothermal integrated system based on the collaborative planning model solution method of McCormick envelope relaxation and dynamic step size sequential boundary contraction, and generate the optimal planning scheme of the electrothermal integrated system.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-8.

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