Method and device for optimizing regional power transformation path considering uncertain factors

Abstract

The application provides a regional power transformation path optimization method and device considering uncertain factors. The method comprises the following steps: obtaining a historical curve of regional new energy power generation output, and subtracting an output curve corresponding to a new energy power generation supporting transmission channel from the historical curve to obtain an output result; generating a new energy power generation output typical scene through a clustering algorithm according to the output result; determining a capacity confidence of new energy power generation participating in power balance according to the power generation output typical scene; inputting a preset carbon emission budget and the capacity confidence of new energy power generation participating in power balance into a pre-established regional power transformation path optimization model; and analyzing the regional power transformation path optimization model to obtain a final regional power transformation optimization path.

Description

Technical Field

[0001] This application belongs to the field of power technology. Specifically, it relates to a method and device for optimizing the regional power transformation path considering uncertain factors. Background Art

[0002] Currently, China's energy structure is mainly dominated by fossil energy. Energy combustion is the main source of carbon dioxide emissions in China, accounting for more than 87% of all carbon dioxide emissions. And the emissions of the power system account for 41% of the carbon dioxide emissions from energy combustion, which is the main area for future carbon emission reduction.

[0003] Factors such as the carbon emission budget of the power system and the potential of new energy resources have a significant impact on the realization of the regional "dual-carbon" goal. In particular, the carbon emission budget directly affects the regional power transformation path and process. Under the constraint of the "dual-carbon" goal, it is necessary to overall consider the difficulty of carbon emission reduction, carbon emission transfer between industries and regions, and evaluate the future carbon emission budget of the regional power system.

[0004] Most existing studies on power transformation paths use scenario analysis methods. That is, considering the differences in different carbon emission budgets, new energy installed capacity, and other major influencing factors borne by the power system, different development scenarios are designed to deduce the power transformation paths under different scenarios. However, there are large uncertainties in the factors affecting power transformation. For example, due to the uncertainty of the reduction potential of non-carbon dioxide greenhouse gases, there are large differences in the estimated values of the remaining carbon emission budgets in different regions in the future. The output of new energy power generation "depends on the weather", and its ability to participate in power balance is greatly affected by weather factors. Existing studies make decisions using the determined values of major influencing factors in specific scenario analysis, without comprehensively considering the impact of uncertainty on power transformation, making the decision results tend to be conservative or aggressive. Summary of the Invention

[0005] This application provides a method and device for optimizing the regional power transformation path considering uncertain factors, so as to at least solve the problem that the existing technology does not comprehensively consider the impact of uncertainty on power transformation, making the decision results tend to be conservative or aggressive.

[0006] According to the first aspect of this application, a method for optimizing the regional power transformation path considering uncertain factors is provided, including:

[0007] Obtain the historical curve of the regional new energy power generation output, and subtract the output curve corresponding to the new energy power generation supporting the external transmission power channel from it to obtain the output result;

[0008] Generate typical scenarios of new energy power generation output through a clustering algorithm according to the output result;

[0009] Determine the capacity confidence of new energy power generation participating in power balance according to the typical scenarios of power generation output;

[0010] The preset carbon emission budget and the capacity confidence of new energy power generation participating in the power balance are input into the pre-established regional power transition path optimization model;

[0011] The final optimized path for regional power transition is obtained by analyzing the regional power transition path optimization model.

[0012] In one embodiment, the final optimized regional power transition path is obtained by analyzing the regional power transition path optimization model, including:

[0013] Analyze the regional power transition path optimization model. If the model has a feasible solution, use the power supply consumption corresponding to the optimization result as the benchmark value of the objective function.

[0014] Determine the weighting coefficients for carbon emission budgets and the capacity confidence deviation coefficients for renewable energy generation participating in the power balance;

[0015] Set the expected cost deviation based on the baseline value of the objective function;

[0016] The final regional power transition optimization path is determined based on the objective function benchmark value, expected cost deviation, and weighting coefficients.

[0017] In one embodiment, the regional power transition path optimization method considering uncertainties further includes:

[0018] Increase the carbon emissions budget when the model has no feasible solution.

[0019] In one embodiment, the process of establishing a regional power transition path optimization model includes:

[0020] Construct the power system investment consumption function and the power system operation consumption function;

[0021] Based on the power system investment consumption function and the power system operation consumption function, an optimization model for the power transition path is established with the goal of minimizing power supply consumption.

[0022] In one embodiment, the power transition path optimization model is expressed as:

[0023]

[0024] Where t represents the horizontal year, T represents the length of the research period, r represents the discount rate, and C INV,t C represents the annual investment cost of a power system. OP,t Let t represent the annual operating cost of the power system, where both investment cost and operating cost are consumption costs, and the cost function is the consumption function.

[0025] According to another aspect of this application, a regional power transition path optimization device considering uncertainties is also provided, comprising:

[0026] The acquisition unit is used to acquire the historical curve of regional new energy power generation output and subtract the output curve of the new energy power generation corresponding to the external transmission channel from it to obtain the output result.

[0027] The power output scenario acquisition unit is used to generate typical new energy power generation scenarios based on the power output results using a clustering algorithm.

[0028] The capacity confidence unit is used to determine the capacity confidence of new energy power generation participating in the power balance based on typical power generation output scenarios.

[0029] The data input unit is used to input the preset carbon emission budget and the capacity confidence of new energy power generation participating in the power balance into the pre-established regional power transition path optimization model;

[0030] The parsing unit is used to parse the regional power transition path optimization model to obtain the final regional power transition optimization path.

[0031] In one embodiment, the parsing unit includes:

[0032] The parsing module is used to parse the regional power transition path optimization model. If the model has a feasible solution, the power supply consumption corresponding to the optimization result is used as the benchmark value of the objective function.

[0033] The factor determination module is used to determine the weighting coefficients of the carbon emission budget and the capacity confidence deviation coefficient for the participation of new energy power generation in the power balance.

[0034] The expected cost deviation setting module is used to set the expected cost deviation based on the objective function benchmark value;

[0035] The optimization path determination module is used to determine the final regional power transition optimization path based on the objective function baseline value, expected cost deviation, and weighting coefficients.

[0036] In one embodiment, the regional power transition path optimization device that considers uncertainties further includes:

[0037] A carbon emission budget module is added to increase the carbon emission budget when the model has no feasible solution.

[0038] In one embodiment, the process of establishing a regional power transition path optimization model includes:

[0039] Construct the power system investment consumption function and the power system operation consumption function;

[0040] Based on the power system investment consumption function and the power system operation consumption function, an optimization model for the power transition path is established with the goal of minimizing power supply consumption.

[0041] In one embodiment, the power transition path optimization model is expressed as:

[0042]

[0043] Where t represents the horizontal year, T represents the length of the research period, r represents the discount rate, and C INV,t C represents the annual power system investment cost per ton (t). OP,t Let t represent the annual operating cost of the power system, where both investment cost and operating cost are consumption costs, and the cost function is the consumption function.

[0044] This application constructs an optimization model for the power transition path by considering constraints such as regional power system carbon emission budget, carbon neutrality, and power balance, with the objective of minimizing regional power supply consumption. Furthermore, it characterizes the uncertainties in the carbon emission budget and the confidence level of new energy generation capacity, establishing a robust optimization model to maximize the range of uncertainty variations. This achieves a robust optimization solution for the power transition path, thereby more rationally determining the path for regional power transition. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This application provides a method for optimizing regional power transition paths that takes into account uncertainties.

[0047] Figure 2 This is a specific implementation of the regional power transition path optimization method that considers uncertainties in the embodiments of this application.

[0048] Figure 3 A structural block diagram of the regional power transition path optimization device considering uncertainties provided in this application.

[0049] Figure 4 This is a specific implementation of an electronic device in the embodiments of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] To address the problems existing in the background technology, this application provides a regional power transition path optimization method that considers uncertainties. First, considering constraints such as regional power system carbon emission budget, carbon neutrality, and power balance, a power transition path optimization model is constructed with the objective of minimizing regional power supply consumption. Then, the uncertainties of the carbon emission budget and the confidence level of new energy generation capacity are characterized, and a robust optimization model is established to maximize the range of variation of uncertainties. Finally, the solution process for the robust optimization of the power transition path is given.

[0052] like Figure 1 The diagram shows a flowchart of a regional power transition path optimization method that considers uncertainties, including:

[0053] S101: Obtain the historical curve of regional renewable energy power generation output, and subtract the output curve of renewable energy power generation corresponding to the external transmission channel from it to obtain the output result.

[0054] New energy sources include wind power and solar energy.

[0055] S102: Generate typical scenarios of new energy power generation output based on the power output results using a clustering algorithm.

[0056] Among them, typical scenarios represent typical scenarios of power generation from new energy sources such as wind power and solar power.

[0057] S103: Determine the capacity confidence level of new energy power generation participating in power balance based on typical power generation output scenarios.

[0058] The system includes a pre-defined mapping relationship between typical scenarios and confidence levels. Once a typical scenario is determined, the confidence level of new energy (wind power, solar power) power generation participating in the power balance corresponding to the typical scenario is determined from the mapping relationship.

[0059] S104: Input the preset carbon emission budget and the capacity confidence of new energy power generation participating in the power balance into the pre-established regional power transition path optimization model;

[0060] S105: Analyze the regional power transition path optimization model to obtain the final regional power transition optimization path.

[0061] The analysis process includes first determining the historical renewable energy power generation curve of the region and generating typical renewable energy power generation scenarios based on the curve; secondly, based on pre-set data such as capacity confidence and carbon emission budget, the above-mentioned pre-set data is input into the regional power transition path optimization model to obtain the solution of the regional power transition path optimization model, thereby obtaining the result of regional power transition path optimization.

[0062] Figure 1 The method shown can be implemented by PCs, servers, etc. This method characterizes the uncertainty of carbon emission budget and new energy power generation capacity confidence, establishes a robust optimization model to maximize the range of variation of uncertainties, realizes the solution of robust optimization of power transition path, and thus more reasonably determines the path of regional power transition.

[0063] Specifically, the process of establishing a regional power transition path optimization model first includes:

[0064] Construct the power system investment consumption function and the power system operation consumption function;

[0065] Based on the power system investment consumption function and the power system operation consumption function, an optimization model for the power transition path is established with the goal of minimizing power supply consumption.

[0066] In one specific embodiment, a regional power transition path optimization model is first constructed:

[0067] objective function

[0068] With the goal of minimizing electricity supply consumption during the research period, an optimization model for the power transition path is established, expressed as follows:

[0069]

[0070] In the formula: t represents the horizontal year, T represents the length of the research period (2020–2060), r represents the discount rate, and C... INV,t C represents the annual investment cost of a power system. OP,t Let t represent the annual operating cost of the power system, where both investment cost and operating cost are consumption costs, and the cost function is the consumption function.

[0071] (1) Power system investment costs

[0072] Investment cost is expressed as

[0073]

[0074] In the formula: p represents the power supply type, and Ω represents the set of power supply types. This represents the unit capacity investment cost of power type p in a given year. This represents the newly installed capacity of power type p in a given year.

[0075] (2) Power system operating costs

[0076] The operating costs of a power system include fuel costs, operation and maintenance costs, and carbon capture costs, expressed as...

[0077] C OP,t =CF,t +C M,t +C C,t (3)

[0078] In the formula: C F,t C M,t C C,t These represent the annual fuel cost, operation and maintenance cost, and carbon capture cost of the power system, respectively.

[0079] Fuel cost C F,t Represented as

[0080]

[0081]

[0082]

[0083] In the formula: λ t,p c represents the annual fuel demand per unit of electricity generated by a power source (t). t,p XP represents the unit cost of fuel per unit of t per year. t,p D represents the annual power generation of a horizontal power source p. t Let π represent the number of days in a horizontal year t, S represent the set of typical scenarios for renewable energy power generation output, and π represent the number of days in a horizontal year t. s XP represents the probability of typical scenario s occurring. t,p,s,n This represents the output of a power supply with a horizontal annual power supply p in a typical scenario during the time period n.

[0084] Operation and maintenance cost C M,t Represented as

[0085]

[0086]

[0087] In the formula: X represents the fixed annual maintenance cost per unit capacity of a power supply (tp) and the variable annual maintenance cost per kilowatt-hour, respectively. t,p X t-1,p These represent the cumulative installed capacity of power source p in horizontal years t and t-1, respectively. Φ represents the annual decommissioning capacity of power source t, where Φ represents the set of transmission channels. L represents the fixed operation and maintenance cost per unit capacity of a horizontal transmission channel with annual capacity of t and the variable operation and maintenance cost per kilowatt-hour, respectively. t,j LP represents the capacity of the horizontal transmission channel j in year t. t,j This represents the amount of electricity transmitted by the horizontal transmission channel j in a given year.

[0088] Carbon capture cost C C,t Represented as

[0089] C C,t = c t,cap Q t (9)

[0090] In the formula: c t,cap represents the unit carbon capture cost in the horizontal year t, and Q t represents the carbon capture volume in the horizontal year t.

[0091] Constraint conditions

[0092] The main constraint conditions include: regional power balance constraint, energy storage operation constraint, system reserve constraint, carbon emission budget constraint, carbon neutrality constraint, carbon capture constraint, etc.

[0093] (1) Regional power balance constraint

[0094] The supporting power sources of the outgoing transmission channels should be subtracted in the regional power balance, so the regional power balance constraint is expressed as

[0095]

[0096]

[0097] In the formula: respectively represent the charging and discharging power of the energy storage at time period n in scenario s of the horizontal year t, EP t,p,s,n represents the output of the power source p supporting the transmission channel at time period n in scenario s of the horizontal year t, LP t,j,s,n represents the power of the transmission channel j at time period n in scenario s of the horizontal year t, χ j represents the 0-1 state variable of the transmission channel j, taking 1 for the incoming channel and 0 for the outgoing channel, LD t,s,n represents the power load at time period n in scenario s of the horizontal year t.

[0098] (2) Energy storage operation constraint

[0099]

[0100] SE t,s,24 = SE t,s,0 (13)

[0101] SE t,min ≤ SE t,s,n ≤ SE t,max (14)

[0102] In the formula, SE t,s,n and SE t,s,n-1 respectively represent the remaining energy levels of the energy storage at time period n and time period n-1 in scenario s of the horizontal year t, are the charging and discharging efficiencies of the energy storage respectively, SE t,max and SEt,min They are the upper and lower limits of the energy stored in the energy storage for the horizontal year t, SE t,s,0 It represents the initial energy of the energy storage in scenario s of horizontal year t, SE t,s,24 It represents the energy stored in the energy storage at time period 24 in scenario s of horizontal year t.

[0103] (3) System reserve constraint

[0104]

[0105]

[0106] In the formula, β t It represents the reserve rate of the system in horizontal year t, γ t,p It represents the capacity confidence level of power source p in horizontal year t, γ t,j It represents the capacity confidence level of transmission channel j in horizontal year t, E t,p It represents the scale of power source p supporting the external transmission channel in horizontal year t; α W and α L They respectively represent the capacity confidence levels of wind power and solar power generation, They respectively represent the maximum power loads at noon peak and evening peak in horizontal year t.

[0107] (4) Generator output constraint

[0108]

[0109] In the formula, They respectively represent the upper and lower limits of the power generation output of power source p in horizontal year t.

[0110] (5) Wind power / solar power generation installed capacity constraint

[0111]

[0112] In the formula, They respectively represent the upper and lower limits of the installed capacity of power source p in horizontal year t.

[0113] (6) Carbon emission budget constraint

[0114]

[0115] In the formula, υ p It represents the carbon emission coefficient per degree of power source p, α B It represents the carbon emission budget of the power system during the research period, H t It represents the scale of carbon sinks such as forests and oceans in horizontal year t.

[0116] (7) Carbon neutrality constraint

[0117] In 2060, the total carbon emissions from the power system will be less than the sum of carbon capture and carbon sink capacity, i.e.

[0118]

[0119] (8) Carbon capture constraints

[0120]

[0121] In the formula, This represents the upper limit of carbon capture in the power system per year.

[0122] Robust Optimization Model for Power Transition Path

[0123] The uncertainty set model for the carbon emission budget of the power system, the confidence level of wind power and solar power generation capacity is shown in the following equation.

[0124]

[0125] In the formula, ξ represents the estimated value of the carbon emissions budget. B The deviation coefficient representing the carbon emissions budget; ξ represents the estimated confidence levels of wind power capacity and solar power capacity. W ξ L The deviation coefficient represents the uncertainty of wind power and solar power generation.

[0126] ψ=τ B ξ B +τ W ξ W +τ L ξ L (twenty three)

[0127] In the formula, τ B τ W τ L The weights are respectively the carbon emission budget, the confidence level of wind power capacity, and the deviation coefficient of the confidence level of solar power capacity. This invention calculates these weights based on the decision-maker's understanding of the uncertainty of each variable.

[0128] make

[0129]

[0130] Among them, C ij Indicator x i With x j The uncertainty scale between them satisfies C ij =1 / C ji Let x1 represent α B x2 represents α Wx3 represents α L .

[0131] make

[0132]

[0133] By solving for the eigenvectors of matrix Γ and normalizing them, we can obtain the weights τ of the carbon emission budget, wind power capacity confidence level, and solar power capacity confidence level deviation coefficient. B τ W τ L .

[0134] This invention establishes a robust optimization model for power transition paths based on information decision theory. This model maximizes the variation range of carbon emission budget, wind power capacity confidence, and solar power capacity confidence within the uncertainty set, while ensuring that power supply consumption is within an acceptable range.

[0135]

[0136] In the formula: κ represents the deviation in electricity supply consumption; Indicates the upper and lower limits of the carbon emission budget deviation coefficient; This indicates the upper and lower limits of the confidence deviation coefficient for wind power capacity; represents the upper and lower limits of the confidence deviation coefficient for solar power generation capacity, and f0 represents the baseline value of the objective function. This represents the maximum value of minf as each uncertain quantity changes.

[0137] In another embodiment, the regional power transition path optimization method considering uncertainties provided in this application includes:

[0138] Obtain the historical curves of regional wind and solar power output, and subtract the corresponding output curves of wind and solar power generation associated with the external transmission channels to obtain the output results;

[0139] Based on the output results, typical scenarios for wind power and solar power output are generated using a clustering algorithm.

[0140] The capacity confidence level for wind power and solar power to participate in power balance is determined based on the typical power generation scenarios described above.

[0141] The preset carbon emission budget and the capacity confidence of wind power and solar power in the power balance are input into the pre-established regional power transition path optimization model;

[0142] The optimization model for the regional power transition path is analyzed. If the model has a feasible solution, the power supply consumption corresponding to the optimization result is used as the benchmark value of the objective function.

[0143] Determine the weighting coefficients for carbon emission budgets, wind power capacity confidence levels, and solar power capacity confidence level deviation coefficients;

[0144] Set the expected cost deviation based on the baseline value of the objective function;

[0145] The final regional power transition optimization path is determined based on the objective function benchmark value, the expected cost deviation, and the weighting coefficients.

[0146] In one embodiment, the final optimized regional power transition path is obtained by analyzing the regional power transition path optimization model, including:

[0147] Analyze the regional power transition path optimization model. If the model has a feasible solution, use the power supply consumption corresponding to the optimization result as the benchmark value of the objective function.

[0148] Determine the weighting coefficients for carbon emission budgets, wind power capacity confidence levels, and solar power capacity confidence level deviation coefficients;

[0149] Set the expected cost deviation based on the baseline value of the objective function;

[0150] The final regional power transition optimization path is determined based on the objective function benchmark value, expected cost deviation, and weighting coefficients.

[0151] In one embodiment, the regional power transition path optimization method considering uncertainties further includes:

[0152] Increase the carbon emissions budget when the model has no feasible solution.

[0153] In one specific embodiment, such as Figure 2 As shown, the specific steps for implementing the regional power transition path optimization method considering uncertainties provided in this application are as follows:

[0154] (1) Obtain the historical power output curves of regional wind power and solar power generation, and subtract the power output curves corresponding to wind power and solar power generation for external transmission channels;

[0155] (2) Typical scenarios of wind power and solar power generation output are generated through clustering algorithms;

[0156] (3) Determine the capacity confidence level for wind power and solar power to participate in the power balance;

[0157] (4) Set a carbon emission budget for the regional power system during the research period;

[0158] (5) Input the confidence values ​​of carbon emission budget, wind power, and solar power generation capacity into the regional power transition path optimization model;

[0159] (6) If the model has a feasible solution, the power supply consumption corresponding to the optimization result is used as the benchmark value f0 of the objective function. If the model has no feasible solution, the carbon emission budget of the regional power system is increased and the process returns to step (4).

[0160] (7) Determine the weighting coefficient τ for the carbon emission budget, wind power capacity confidence level, and solar power capacity confidence level deviation coefficient. B τ W τ L ;

[0161] (8) Set the decision-maker's expected cost deviation κ;

[0162] (9) Solve the robust optimization model based on the baseline value f0, and use the optimization result as the final power planning scheme.

[0163] Based on the same inventive concept, this application also provides a regional power transition path optimization device considering uncertainties, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of this regional power transition path optimization device considering uncertainties is similar to that of the regional power transition path optimization method considering uncertainties, the implementation of the regional power transition path optimization device considering uncertainties can refer to the implementation of the regional power transition path optimization method considering uncertainties, and will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0164] According to another aspect of this application, a regional power transition path optimization device that considers uncertainties is also provided, such as... Figure 3 As shown, it includes:

[0165] The acquisition unit 201 is used to acquire the historical curves of regional wind power and solar power output, and subtract the output curves of wind power and solar power generation associated with the external transmission channel to obtain the output result.

[0166] The power output scenario acquisition unit 202 is used to generate typical power output scenarios of wind power and solar power generation based on the power output results through a clustering algorithm.

[0167] The capacity confidence unit 203 is used to determine the capacity confidence of wind power and solar power in the power balance based on typical power generation scenarios;

[0168] The data input unit 204 is used to input the preset carbon emission budget, the capacity confidence of wind power and solar power in the power balance into the pre-established regional power transition path optimization model;

[0169] The analysis unit 205 is used to analyze the regional power transition path optimization model to obtain the final regional power transition optimization path.

[0170] In one embodiment, the parsing unit includes:

[0171] The parsing module is used to parse the regional power transition path optimization model. If the model has a feasible solution, the power supply consumption corresponding to the optimization result is used as the benchmark value of the objective function.

[0172] The factor determination module is used to determine the weighting coefficients for carbon emission budget, wind power capacity confidence level, and solar power capacity confidence level deviation coefficient;

[0173] The expected cost deviation setting module is used to set the expected cost deviation based on the objective function benchmark value;

[0174] The optimization path determination module is used to determine the final regional power transition optimization path based on the objective function baseline value, expected cost deviation, and weighting coefficients.

[0175] In one embodiment, the regional power transition path optimization device that considers uncertainties further includes:

[0176] A carbon emission budget module is added to increase the carbon emission budget when the model has no feasible solution.

[0177] In one embodiment, the process of establishing a regional power transition path optimization model includes:

[0178] Construct the power system investment consumption function and the power system operation consumption function;

[0179] Based on the power system investment consumption function and the power system operation consumption function, an optimization model for the power transition path is established with the goal of minimizing power supply consumption.

[0180] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0181] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0182] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0183] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0184] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0185] This application also provides a specific implementation of an electronic device capable of implementing all the steps in the methods described above. See [link to implementation details]. Figure 4 The electronic device specifically includes the following:

[0186] Processor 301, memory 302, communications interface 303, bus 304, and non-volatile memory 305;

[0187] The processor 301, memory 302, and communication interface 303 communicate with each other through the bus 304.

[0188] The processor 301 is used to invoke the computer program in the memory 302 and the non-volatile memory 305. When the processor executes the computer program, it implements all the steps in the method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0189] Obtain the historical curves of regional wind and solar power output, and subtract the output curves of wind and solar power generation associated with the external transmission channels to obtain the output results;

[0190] Based on the output results, typical scenarios for wind power and solar power output are generated using a clustering algorithm;

[0191] Determine the capacity confidence level for wind and solar power to participate in power balance based on typical power generation scenarios;

[0192] Input the preset carbon emission budget, the capacity confidence of wind power and solar power in the power balance into the pre-established regional power transition path optimization model;

[0193] The final optimized path for regional power transition is obtained by analyzing the regional power transition path optimization model.

[0194] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the methods in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the methods in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0195] Obtain the historical curves of regional wind and solar power output, and subtract the output curves of wind and solar power generation associated with the external transmission channels to obtain the output results;

[0196] Based on the output results, typical scenarios for wind power and solar power output are generated using a clustering algorithm;

[0197] Determine the capacity confidence level for wind and solar power to participate in power balance based on typical power generation scenarios;

[0198] Input the preset carbon emission budget, the capacity confidence of wind power and solar power in the power balance into the pre-established regional power transition path optimization model;

[0199] The final optimized path for regional power transition is obtained by analyzing the regional power transition path optimization model.

[0200] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, for hardware + program embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. Although the embodiments in this specification provide the method operation steps as shown in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many steps and does not represent the only execution order. In actual device or terminal product execution, the methods can be executed in the order shown in the embodiments or drawings or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in the process, method, product, or apparatus that includes said elements is not excluded. For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this specification, the functions of each module can be implemented in one or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0201] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The various embodiments in this specification are described in a progressive manner, and similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. In the description of this specification, the reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the embodiments of this specification.

[0202] In this specification, the illustrative expressions of the terms used do not necessarily refer to the same embodiments or examples. Furthermore, those skilled in the art can combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, without contradiction. The above descriptions are merely embodiments of this specification and are not intended to limit the embodiments of this specification. Various modifications and variations can be made to the embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the embodiments of this specification should be included within the scope of the claims of the embodiments of this specification.

Claims

1. A method for optimizing regional power transition paths considering uncertainties, characterized in that, including: Obtain the historical curve of new energy power generation output in the region, subtract the output curve corresponding to the new energy power generation supporting the external power transmission channel from it, and obtain the output result; Generate typical scenarios of new energy power generation output through a clustering algorithm based on the output result; Determine the capacity confidence of new energy power generation participating in power balance according to the typical scenarios of power generation output; Input the preset carbon emission budget and the capacity confidence of new energy power generation participating in power balance into the pre-established regional power transformation path optimization model; Analyze the regional power transformation path optimization model to obtain the final regional power transformation optimization path, specifically including: Analyze the regional power transformation path optimization model. If the regional power transformation path optimization model has a feasible solution, use the power supply consumption corresponding to the optimization result as the target function reference value; Determine the weight coefficients of the carbon emission budget and the deviation coefficient of the capacity confidence of new energy power generation participating in power balance; Set the expected cost deviation according to the target function reference value; Determine the final regional power transformation optimization path according to the target function reference value, the expected cost deviation and the weight coefficients.

2. The method for optimizing regional power transition paths considering uncertainties according to claim 1, characterized in that, It also includes: When the regional power transformation path optimization model has no feasible solution, increase the carbon emission budget and re-analyze the regional power transformation path optimization model to obtain the final regional power transformation optimization path.

3. The method for optimizing regional power transition paths considering uncertainties according to claim 1, characterized in that, The establishment process of the regional power transformation path optimization model includes: Construct a power system investment consumption function and a power system operation consumption function; Based on the power system investment consumption function and the power system operation consumption function, establish a power transformation path optimization model with the goal of minimizing power supply consumption.

4. The regional power transition path optimization method considering uncertainties according to claim 3, characterized in that, The power transformation path optimization model is expressed as: in, t Indicates horizontal year, T Indicates the length of the research period. r Indicates the discount rate. Indicates horizontal year t Power system investment costs Indicates horizontal year t The operating cost of a power system is defined by both investment cost and operating cost, which are both consumption costs. The cost function is the consumption function.

5. A regional power transition path optimization device considering uncertainties, characterized in that, including: An acquisition unit for obtaining the historical curve of new energy power generation output in the region, subtracting the output curve corresponding to the new energy power generation supporting the external power transmission channel from it, and obtaining the output result; An output scenario acquisition unit for generating typical scenarios of new energy power generation output through a clustering algorithm based on the output result; A capacity confidence unit for determining the capacity confidence of new energy power generation participating in power balance according to the typical scenarios of power generation output; A data input unit for inputting the preset carbon emission budget and the capacity confidence of new energy power generation participating in power balance into the pre-established regional power transformation path optimization model; An analysis unit for analyzing the regional power transformation path optimization model to obtain the final regional power transformation optimization path, specifically including: An analysis module for analyzing the regional power transformation path optimization model. If the regional power transformation path optimization model has a feasible solution, use the power supply consumption corresponding to the optimization result as the target function reference value; A factor determination module for determining the weight coefficients of the carbon emission budget and the deviation coefficient of the capacity confidence of new energy power generation participating in power balance; An expected cost deviation setting module for setting the expected cost deviation according to the target function reference value; An optimization path determination module for determining the final regional power transformation optimization path according to the target function reference value, the expected cost deviation and the weight coefficients.

6. The regional power transition path optimization device considering uncertainties according to claim 5, characterized in that, It also includes: The carbon emission budget addition module is used to add the carbon emission budget when the regional power transition path optimization model has no feasible solution, and to re-analyze the regional power transition path optimization model to obtain the final regional power transition optimization path.

7. The regional power transition path optimization device considering uncertainties according to claim 5, characterized in that, The process of establishing the regional power transition path optimization model includes: Construct the power system investment consumption function and the power system operation consumption function; Based on the power system investment consumption function and the power system operation consumption function, an optimization model for the power transition path is established with the goal of minimizing power supply consumption.

8. The regional power transition path optimization device considering uncertainties according to claim 7, characterized in that, The power transition path optimization model is expressed as follows: in, t Indicates horizontal year, T Indicates the length of the research period. r Indicates the discount rate. Indicates horizontal year t Power system investment costs Indicates horizontal year t The operating cost of a power system is defined by both investment cost and operating cost, which are both consumption costs. The cost function is the consumption function.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the regional power transition path optimization method considering uncertainties as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the regional power transition path optimization method considering uncertainties as described in any one of claims 1 to 4.

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