Method, device and equipment for evaluating multi-capability complementary strategy and storage medium

Abstract

The present disclosure relates to a method and device for evaluating a multi-energy complementary strategy, equipment and a storage medium. The method comprises: obtaining a multi-energy complementary strategy for a target new energy and a hydroelectric generating set to be evaluated, and establishing a corresponding multi-energy complementary system scheduling model. The multi-energy complementary system scheduling model involves multiple energy types, including the target new energy, hydroelectric power generation and external energy. The external output level of the external energy is solved based on a preset output guarantee probability of the target new energy. The preset output guarantee probability is used to represent the probability that the output level reaches the target output level. The sensitivity of the external output level of the external energy to the output guarantee probability of the target new energy is calculated based on the model solution result. The evaluation result of the multi-energy complementary strategy is determined based on the sensitivity. By calculating the sensitivity of the external output level of the external energy to the output guarantee probability, the dependence of the multi-energy complementary strategy on the external energy can be evaluated, which facilitates subsequent strategy optimization.

Description

Technical Field

[0001] This disclosure relates to the field of new energy power generation technology, and in particular to a method, apparatus, equipment and storage medium for evaluating multi-energy complementary strategies. Background Technology

[0002] In recent years, with the escalating global energy crisis, the demand for efficient and sustainable energy utilization methods has been increasing. Multi-energy complementary systems, which integrate various clean energy sources such as wind, solar, and hydropower, have been widely promoted and applied due to their significant advantage of high energy utilization. To fully leverage the advantages of multi-energy complementary systems and ensure their stable operation, it is necessary to formulate operational strategies and conduct reasonable evaluations of these strategies. Existing evaluation methods for multi-energy complementary systems often focus on the system's economic, environmental, and technical aspects, without considering the system's dependence on external energy sources. Since the output of new energy sources is affected by various factors such as weather conditions, geographical location, and equipment status, it has considerable uncertainty. If the system's operational strategy is highly dependent on external energy sources, regular fluctuations in new energy output may cause significant adjustments in external energy supply. This not only increases the operational pressure on external energy sources but may also seriously affect the stable operation of the system. Therefore, how to evaluate the dependence of multi-energy complementary systems on external energy sources in operational strategies is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] To address the aforementioned technical problems, this disclosure provides a method, apparatus, device, and storage medium for evaluating multi-energy complementary strategies.

[0004] A first aspect of this disclosure provides a method for evaluating multi-energy complementarity strategies, the method comprising:

[0005] Obtain the multi-energy complementary strategy for the target new energy unit and hydropower unit to be evaluated, and establish the multi-energy complementary system scheduling model corresponding to the multi-energy complementary strategy. The multi-energy complementary system scheduling model includes the constraints corresponding to multiple energy types and the objective function corresponding to the multi-energy complementary strategy. The multiple energy types include the target new energy, hydropower and external energy.

[0006] The external output level of the external energy in the multi-energy complementary system scheduling model is solved based on the preset output guarantee probability of the target new energy and the unit change of the output guarantee probability. The preset output guarantee probability is used to characterize the probability that the output level will reach the target output level.

[0007] The sensitivity of the external power output level of the external energy source to the power output guarantee probability of the target new energy source is calculated based on the model solution results.

[0008] The evaluation result of the multi-energy complementary strategy is determined based on the sensitivity.

[0009] A second aspect of this disclosure provides a multi-energy complementarity strategy evaluation apparatus, the apparatus comprising:

[0010] The model building module is used to obtain the multi-energy complementary strategy for the target new energy unit and hydropower unit to be evaluated, and to establish the multi-energy complementary system scheduling model corresponding to the multi-energy complementary strategy. The multi-energy complementary system scheduling model includes the constraints corresponding to various energy types and the objective function corresponding to the multi-energy complementary strategy. The various energy types include the target new energy, hydropower, and external energy.

[0011] The model solving module is used to solve the external output level of the external energy in the multi-energy complementary system scheduling model based on the preset output guarantee probability of the target new energy and the unit change of the output guarantee probability. The preset output guarantee probability is used to characterize the probability that the output level will reach the target output level.

[0012] The calculation module is used to calculate the sensitivity of the external power output level of the external energy source to the power output guarantee probability of the target new energy source based on the model solution results.

[0013] An evaluation module is used to determine the evaluation result of the multi-energy complementary strategy based on the sensitivity.

[0014] A third aspect of this disclosure provides a computer device including a memory and a processor, and a computer program, wherein the memory stores the computer program, and when the computer program is executed by the processor, it implements the multi-energy complementary strategy evaluation method of the first aspect described above.

[0015] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the multi-energy complementarity strategy evaluation method as described in the first aspect above.

[0016] The technical solution provided in this disclosure has the following advantages compared with the prior art:

[0017] In the multi-energy complementary strategy evaluation method, apparatus, equipment, and storage medium provided in this disclosure embodiment, the multi-energy complementary strategy to be evaluated for the target new energy unit and hydropower unit is obtained, and a multi-energy complementary system scheduling model corresponding to the multi-energy complementary strategy is established. The multi-energy complementary system scheduling model includes constraints corresponding to multiple energy types and objective functions corresponding to the multi-energy complementary strategy. The multiple energy types include the target new energy, hydropower, and external energy. Based on the preset output guarantee probability of the target new energy and the unit change of the output guarantee probability, the external output level of the external energy in the multi-energy complementary system scheduling model is solved. The preset output guarantee probability is used to characterize the probability that the output level will reach the target output level. Based on the model solution results, the sensitivity of the external output level of the external energy to the output guarantee probability of the target new energy is calculated. Based on the sensitivity, the evaluation result of the multi-energy complementary strategy is determined. It is possible to determine the sensitivity of the external output level of the external energy to the output guarantee probability, and evaluate the dependence of the multi-energy complementary strategy on external energy based on the sensitivity calculation results. This facilitates the subsequent screening of multi-energy complementary strategies with lower dependence on external energy based on the evaluation results, thereby improving the stability of the multi-energy complementary system operation. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0019] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a multi-energy complementarity strategy evaluation method provided in an embodiment of this disclosure;

[0021] Figure 2 This is a flowchart of a method for solving a multi-energy complementary system scheduling model provided in an embodiment of this disclosure;

[0022] Figure 3 This is a flowchart of another method for solving the scheduling model of a multi-energy complementary system provided in this embodiment of the disclosure;

[0023] Figure 4 This is a schematic diagram of the structure of a multi-energy complementary strategy evaluation device provided in an embodiment of this disclosure;

[0024] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure. Detailed Implementation

[0025] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0026] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0027] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0028] Figure 1 This is a flowchart of a multi-energy complementarity strategy evaluation method provided in an embodiment of this disclosure. The method can be executed by a multi-energy complementarity strategy evaluation device, which can be implemented in software and / or hardware. This device can be configured in an electronic device, such as a server or terminal, where the terminal specifically includes a mobile phone, computer, or tablet computer. Figure 1 As shown, the multi-energy complementarity strategy evaluation method provided in this embodiment includes the following steps:

[0029] S101. Obtain the multi-energy complementary strategy for the target new energy unit and hydropower unit to be evaluated, and establish the multi-energy complementary system scheduling model corresponding to the multi-energy complementary strategy. The multi-energy complementary system scheduling model includes the constraints corresponding to various energy types and the objective function corresponding to the multi-energy complementary strategy. The various energy types include the target new energy, hydropower and external energy.

[0030] The multi-energy complementarity strategy in this embodiment can be understood as a pre-defined operation strategy for a wind-solar-hydro multi-energy complementary system. For example, the multi-energy complementarity strategy may include a hydropower level suppression strategy for new energy fluctuations, a wind-solar-hydropower output tracking load strategy, etc., which are not limited here.

[0031] The target new energy source in this disclosure embodiment can be understood as a new energy source whose output is easily affected by various factors such as weather conditions, geographical location, and equipment status, such as wind power generation and photovoltaic power generation. External energy can be understood as energy that can be quickly started and stopped or its output level can be precisely adjusted, such as thermal power generation energy and external power sources, etc., and is not limited here.

[0032] In this embodiment, the multi-energy complementary strategy evaluation device can acquire the multi-energy complementary strategy to be evaluated for the target new energy unit and hydropower unit, extract the energy types involved in the strategy from the multi-energy complementary strategy, determine the constraints corresponding to the energy types involved in the strategy and the external energy types, and the objective function corresponding to the multi-energy complementary strategy. The device combines the constraints corresponding to the energy types involved in the strategy and the external energy types, as well as the objective function corresponding to the multi-energy complementary strategy, to obtain the multi-energy complementary system scheduling model corresponding to the multi-energy complementary strategy. The energy types involved in the strategy include the target new energy and hydropower, and the multiple energy types for which constraints need to be established in the multi-energy complementary system scheduling model include the target new energy, hydropower, and external energy.

[0033] In one exemplary embodiment of this disclosure, the multi-energy complementary strategy evaluation device can, after determining the multi-energy complementary strategy to be evaluated, extract the energy types involved in the multi-energy complementary strategy and the objective function corresponding to the multi-energy complementary strategy from a preset database, then find the constraints corresponding to each energy type according to the energy types involved in the strategy, and integrate the constraints corresponding to the energy types involved in the strategy, the objective function corresponding to the multi-energy complementary strategy, and the constraints corresponding to the preset external energy types to obtain a multi-energy complementary system scheduling model.

[0034] Optionally, the target new energy source may include wind power generation and / or photovoltaic power generation. The constraints include at least one of the following: the first output constraint corresponding to the target new energy source, the second output constraint and the ramp constraint corresponding to the external energy source, and the third output constraint, flow constraint, reservoir capacity constraint, outflow constraint and water level constraint corresponding to hydropower generation. The multi-energy complementary system scheduling model also includes the dynamic balance relationship between the operating parameters of the hydropower station.

[0035] Specifically, the first output constraint condition corresponding to the target new energy source can be expressed as follows:

[0036]

[0037] Where i∈[1,N] W ],j∈[1,N S ], t∈[1,T], N W N S T represents the number of wind farms, the number of photovoltaic power stations, and the total number of time periods considered, respectively. Let be the power outputs of the i-th wind farm and the j-th photovoltaic power station at time t, respectively. and Let be the output coefficients of the i-th wind farm and the j-th photovoltaic power station at time t, respectively, when the output guarantee probability is C%. and These represent the installed capacity of the i-th wind farm and the j-th photovoltaic power station, respectively.

[0038] The second output constraint condition corresponding to the external energy source can be expressed as follows:

[0039]

[0040] The ramp-up constraint for external energy can be expressed as follows:

[0041]

[0042] Where, positive integer n∈[1,N] g ], N g Number of external power supplies. and These are the lower and upper limits of the output of the nth external power source, respectively. Let be the output of the nth external power source at time t. Let n be the output of the nth external power source at time t-1. and Δt represents the maximum downward ramp rate and the maximum upward ramp rate of the nth external power source, respectively, and Δt is the time step.

[0043] The third output constraint condition for hydropower generation can be expressed as follows:

[0044]

[0045] The flow constraint for hydropower generation can be expressed as follows:

[0046]

[0047] The reservoir capacity constraint for hydropower generation can be expressed as follows:

[0048]

[0049] The outflow constraint for hydropower generation can be expressed as follows:

[0050]

[0051] The water level constraint conditions for hydropower generation can be expressed as follows:

[0052]

[0053] Where, positive integer k∈[1,N] h ], N h This represents the total number of hydropower stations. The smaller the value of k, the higher the elevation of the river basin. k = 1 indicates the upstream hydropower station. These represent the power output, power generation flow, reservoir capacity, outflow, and upper water level of the k-th hydropower station at time t. These represent the minimum and maximum values ​​of the unit output, power generation flow, reservoir capacity, outflow, and upper water level of the k-th hydropower station.

[0054] The dynamic balance relationship between the operating parameters of a hydropower station can be expressed as follows:

[0055]

[0056] Where, η k It is the overall output coefficient of the k-th hydropower station. These represent the net head, tailwater level, discharge volume, and natural inflow volume of the k-th hydropower station at time t, respectively. The natural inflow volume is calculated by combining rainfall and the geographical features near the reservoir. σ is the head loss of the kth hydropower station. k,0 σ k,1 σ k,2 σ k,3 σ k,4 These are the coefficients of each order in the upper water level-reservoir capacity relationship of the k-th hydropower station, μ k,0 μ k,1 μ k,2 μ k,3 μ k,4 These are the coefficients of each order in the tailwater level-outflow relationship of the k-th hydropower station. Let be the initial reservoir capacity of the k-th hydropower station.

[0057] Optionally, for the strategy of mitigating new energy fluctuations by maintaining a level playing field, the corresponding objective function can be expressed as follows:

[0058]

[0059] For the wind, solar, and hydropower output tracking load strategy, the corresponding objective function can be expressed as follows:

[0060]

[0061] Where, N W N S N h T represents the number of wind farms, photovoltaic power stations, hydropower stations, and the total number of time periods considered, respectively. Let be the power output levels of the i-th wind farm, the j-th photovoltaic power station, and the k-th hydropower station at time t, respectively. Let be the load power at time t.

[0062] S102. Based on the preset output guarantee probability of the target new energy source and the unit change of the output guarantee probability, the external output level of the external energy source in the multi-energy complementary system scheduling model is solved. The preset output guarantee probability is used to characterize the probability that the output level will reach the target output level.

[0063] In this embodiment of the disclosure, the output guarantee probability can be understood as the probability that the output level of the target new energy source will reach a set output level at a certain moment. There is a correlation between the output guarantee probability and the target output level. Under the condition that other conditions do not change, the higher the output guarantee probability, the lower the set output level. The preset output guarantee probability is used to characterize the probability that the output level will reach the target output level. The preset output guarantee probability and the unit change of the output guarantee probability can be preset.

[0064] In this embodiment, the multi-energy complementary strategy evaluation device can, after the multi-energy complementary system scheduling model is constructed, solve for the external output level of the external energy in the multi-energy complementary system scheduling model based on the preset output guarantee probability of the target new energy and the unit change of the output guarantee probability. Specifically, the preset output guarantee probability and the unit change of the output guarantee probability can be substituted into the multi-energy complementary system scheduling model, and the external output level (i.e., the variable in S101) can be determined according to the objective function. Find the optimal solution.

[0065] S103. Calculate the sensitivity of the external power output level of the external energy source to the power output guarantee probability of the target new energy source based on the model solution results.

[0066] In this embodiment of the disclosure, the sensitivity of the external energy output level to the guaranteed output probability of the target new energy source can be understood as the degree to which the external energy output level changes when the guaranteed output probability of the target new energy source changes. This sensitivity is used to assess the impact of the uncertainty in the output of the target new energy source on the external energy demand. A high sensitivity indicates that a small change in the guaranteed output probability of the target new energy source will lead to a large change in the output of the external energy source, indicating a strong dependence of the multi-energy complementary system on external energy. Conversely, a low sensitivity indicates that the uncertainty in the output of the target new energy source has a small impact on the external energy demand, indicating a weaker dependence of the multi-energy complementary system on external power sources.

[0067] In this embodiment of the disclosure, the multi-energy complementary strategy evaluation device can calculate the sensitivity of the external power output level in the model solution to the power output guarantee probability of the target new energy source after obtaining the model solution result of the multi-energy complementary system scheduling model.

[0068] In one exemplary embodiment of this disclosure, the multi-energy complementary strategy evaluation device can calculate the partial derivative of the external output level in the model solution with respect to the output guarantee probability, which can be specifically expressed as follows:

[0069]

[0070] in, Represents the l-th complementary strategy F l The external output level of the nth external energy source is specifically the trajectory of the external output level corresponding to multiple sampling points at multiple sampling times, where 'a' is the time sampling point variable and ΔC% is the unit change in the output guarantee probability.

[0071] S104. Evaluation results of multi-energy complementary strategies based on sensitivity.

[0072] In this embodiment of the disclosure, the multi-energy complementary strategy evaluation device can determine the evaluation result of the corresponding multi-energy complementary strategy based on the sensitivity of the external energy output level to the output guarantee probability of the target new energy after determining the sensitivity. Specifically, the sensitivity can be converted into the corresponding strategy score according to the conversion relationship between sensitivity and strategy score to obtain the evaluation result of the multi-energy complementary strategy.

[0073] This embodiment of the disclosure obtains the multi-energy complementary strategies for target new energy units and hydropower units to be evaluated, and establishes a multi-energy complementary system scheduling model corresponding to the multi-energy complementary strategies. The multi-energy complementary system scheduling model includes constraints corresponding to various energy types and objective functions corresponding to the multi-energy complementary strategies. The various energy types include target new energy, hydropower, and external energy. Based on the preset output guarantee probability of the target new energy and the unit change of the output guarantee probability, the external output level of the external energy in the multi-energy complementary system scheduling model is solved. The preset output guarantee probability is used to characterize the probability that the output level will reach the target output level. Based on the model solution results, the sensitivity of the external output level of the external energy to the output guarantee probability of the target new energy is calculated. Based on the sensitivity, the evaluation result of the multi-energy complementary strategy is determined. It can determine the sensitivity of the external output level of the external energy to the output guarantee probability, and evaluate the dependence of the multi-energy complementary strategy on external energy based on the sensitivity calculation results. This facilitates the subsequent selection of multi-energy complementary strategies with lower dependence on external energy based on the evaluation results, thereby improving the stability of the multi-energy complementary system operation.

[0074] Figure 2 This is a flowchart of a method for solving a multi-energy complementary system scheduling model provided in this disclosure, as shown in the embodiment. Figure 2 As shown, based on the above embodiments, the scheduling model of the multi-energy complementary system can be solved by the following method, wherein the external output level includes a reference external output level, a first external output level, and a second external output level.

[0075] S201. Based on the preset output guarantee probability and the unit change of the output guarantee probability, determine the upper limit output guarantee probability and the lower limit output guarantee probability.

[0076] In this embodiment of the disclosure, the multi-energy complementary strategy evaluation device can perform summation and subtraction based on the preset output guarantee probability and the unit change of the output guarantee probability to obtain the upper limit output guarantee probability and the lower limit output guarantee probability.

[0077] S202. Solve the external output level of external energy in the multi-energy complementary system scheduling model based on the preset output guarantee probability, the upper limit output guarantee probability and the lower limit output guarantee probability, respectively, to obtain the benchmark external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability and the second external output level corresponding to the lower limit output guarantee probability.

[0078] In this embodiment of the disclosure, the multi-energy complementary strategy evaluation device can substitute the preset output guarantee probability, the upper limit output guarantee probability, and the lower limit output guarantee probability into the multi-energy complementary system scheduling model to solve for the external output level of the external energy in the model. The external output level obtained after substituting the preset output guarantee probability is determined as the benchmark external output level, the external output level obtained after substituting the upper limit output guarantee probability is determined as the first external output level, and the external output level obtained after substituting the lower limit output guarantee probability is determined as the second external output level.

[0079] This embodiment of the invention determines the upper limit output guarantee probability and the lower limit output guarantee probability based on the preset output guarantee probability and the unit change of the output guarantee probability. It then solves for the external output level of the external energy source in the multi-energy complementary system scheduling model based on the preset output guarantee probability, the upper limit output guarantee probability, and the lower limit output guarantee probability, respectively. This yields the baseline external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability. This allows for the construction of multiple output guarantee probabilities, facilitating subsequent solutions for multiple external output levels corresponding to multiple output guarantee probabilities, and ultimately obtaining the sensitivity of the external output level to the output guarantee probability.

[0080] Figure 3 This is a flowchart of another method for solving the scheduling model of a multi-energy complementary system provided in this disclosure, as shown in the embodiment. Figure 3 As shown, based on the above embodiments, the scheduling model of the multi-energy complementary system can be solved by the following method, wherein the target output level includes the baseline target output level, the first target output level, and the second target output level.

[0081] S301. Obtain historical power output data of the target new energy source.

[0082] The historical output data in this embodiment can be understood as the output level data of the target new energy source collected in advance over a period of time, specifically the power generation data.

[0083] In this embodiment of the disclosure, the multi-energy complementary strategy evaluation device can obtain historical output data of the target new energy source in the multi-energy complementary system from the database.

[0084] S302. Based on historical output data, determine the baseline target output level corresponding to the preset output guarantee probability, the first target output level corresponding to the upper limit output guarantee probability, and the second target output level corresponding to the lower limit output guarantee probability.

[0085] In this embodiment of the disclosure, the multi-energy complementary strategy evaluation device can analyze historical output data after determining the preset output guarantee probability, the upper limit output guarantee probability, and the lower limit output guarantee probability. It then determines the baseline target output level corresponding to the preset output guarantee probability, the first target output level corresponding to the upper limit output guarantee probability, and the second target output level corresponding to the lower limit output guarantee probability. Specifically, the multi-energy complementary strategy evaluation device can sort the historical output data in ascending order. Based on the number of historical output data, it determines the data quantile number corresponding to the preset output guarantee probability, the upper limit output guarantee probability, and the lower limit output guarantee probability, respectively. Based on the data quantile number, it locates a target output data point in the sorted historical output data and determines the baseline target output level, the first target output level, and the second target output level based on the target output data.

[0086] Optionally, for the historical output data of the target new energy source with a statistical period of d days, the multi-energy complementary strategy evaluation device can extract the output value at time t of each day from the historical output data and arrange it in ascending order to obtain the historical output data sequence at time t. in This is the m-th historical output data point. Preset output guarantee probability C% data quantile N C％ The calculation method can be expressed as follows:

[0087] N C％ =max{floor[d×(1-C%)],1}

[0088] Where, N C％ Let N be an integer, satisfying 1 ≤ N C％ ≤d, floor indicates rounding down, for example, floor(0.95) = 0, floor(1.95) = 1. Determine the data quantile N. C％ Then, from the historical output data sequence Find the data quantile number N C％ The corresponding output data is the baseline target output level corresponding to the preset output guarantee probability C% at time t. It is expressed as follows:

[0089]

[0090] The method for determining the first target output level corresponding to the upper limit output guarantee probability and the second target output level corresponding to the lower limit output guarantee probability in the multi-energy complementary strategy evaluation device is similar to the method for determining the benchmark target output level corresponding to the preset output guarantee probability, and will not be described in detail here.

[0091] S303. Based on the benchmark target output level, the first target output level, the second target output level, and the pre-acquired installed capacity of the target new energy, determine the benchmark output constraint parameters, the first output constraint parameters, and the second output constraint parameters corresponding to the target new energy.

[0092] The output constraint parameter in this embodiment can be understood as the output coefficient in S102.

[0093] In this embodiment of the disclosure, the multi-energy complementary strategy evaluation device can, after determining the benchmark target output level, the first target output level, and the second target output level, calculate the benchmark output constraint parameter, the first output constraint parameter, and the second output constraint parameter corresponding to the target new energy source in combination with the pre-acquired installed capacity of the target new energy source. Specifically, it can calculate the ratio of the benchmark target output level, the first target output level, the second target output level to the installed capacity of the target new energy source to obtain the benchmark output constraint parameter, the first output constraint parameter, and the second output constraint parameter.

[0094] S304. Substitute the benchmark output constraint parameters, the first output constraint parameters, and the second output constraint parameters into the constraint conditions corresponding to the target new energy source, and solve the external output level of the external energy source in the multi-energy complementary system scheduling model to obtain the benchmark external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability.

[0095] In this embodiment of the disclosure, the multi-energy complementary strategy evaluation device can, after calculating the benchmark output constraint parameters, the first output constraint parameters, and the second output constraint parameters, substitute the benchmark output constraint parameters into the constraint conditions corresponding to the target new energy source, and solve for the external output level of the external energy source in the multi-energy complementary system scheduling model to obtain the benchmark external output level corresponding to the preset output guarantee probability. Then, it can substitute the first output constraint parameters into the constraint conditions corresponding to the target new energy source and solve for the external output level to obtain the first external output level corresponding to the upper limit output guarantee probability. Finally, it can substitute the second output constraint parameters into the constraint conditions corresponding to the target new energy source and solve for the external output level to obtain the second external output level corresponding to the lower limit output guarantee probability.

[0096] Optionally, historical output data includes output data of the target renewable energy source at different times within the target time period, target output level is the sequence data of output level of the target renewable energy source at different times within the target time period, and external output level is the sequence data of output level of external energy source at different times within the target time period.

[0097] Specifically, the multi-energy complementary strategy evaluation device can determine the target output level at different times corresponding to the same output guarantee probability based on the output data of the target new energy at different times within the target time period. Then, based on the target output level at different times, it can determine the output constraint parameters at different times, substitute them into the multi-energy complementary system scheduling model to solve for the external output level, and obtain the sequence data of the output level of the external energy at different times.

[0098] This embodiment of the disclosure acquires historical power output data of the target renewable energy source, and based on the historical power output data, determines a benchmark target power output level corresponding to a preset power output guarantee probability, a first target power output level corresponding to an upper limit power output guarantee probability, and a second target power output level corresponding to a lower limit power output guarantee probability. Based on the benchmark target power output level, the first target power output level, the second target power output level, and the pre-acquired installed capacity of the target renewable energy source, it determines benchmark power output constraint parameters, first power output constraint parameters, and second power output constraint parameters corresponding to the target renewable energy source. The benchmark power output constraint parameters, first power output constraint parameters, and second power output constraint parameters are then substituted into the target renewable energy source... The constraints corresponding to the source are determined, and the external output level of the external energy source in the multi-energy complementary system scheduling model is solved to obtain the benchmark external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability. By capturing the intraday changes in the reliability of the target new energy output, the output guarantee probability of the new energy source can be extended to the intraday dynamic change scenario. The target output level and the external output level are also dynamically changing, which is closer to the actual operation process of the multi-energy complementary system, further improving the accuracy of sensitivity calculation, thereby improving the reliability of strategy evaluation results.

[0099] Optionally, when the multi-energy complementary strategy evaluation device calculates the sensitivity of the external energy output level to the output guarantee probability of the target new energy source based on the model solution results, it can calculate the sensitivity of the external energy output level to the output guarantee probability of the target new energy source based on the benchmark external energy output level, the first external energy output level, the second external energy output level, the preset output guarantee probability, and the unit change of the output guarantee probability.

[0100] Specifically, the multi-energy complementary strategy evaluation device can use the perturbation method to calculate the sensitivity of each external energy output level to the output guarantee probability when determining the sensitivity of the external output level to the output guarantee probability, and use the median to improve the calculation accuracy. The specific calculation process can be expressed as follows:

[0101]

[0102] Where C0% is the preset output guarantee probability, and ΔC% is the unit change in the output guarantee probability. It is the l-th complementary strategy F l The baseline external output level of the nth external energy source under the preset output guarantee probability. It is the l-th complementary strategy F l The first external output level of the nth external energy source under the probability of guaranteeing the upper limit output. It is the l-th complementary strategy F l The second external output level of the nth external energy source under the lower limit output guarantee probability, s n (C0%,a,F l ) is the l-th complementary strategy F l The sensitivity of the external power output level of the nth external energy source under the preset power output guarantee probability.

[0103] After determining the sensitivity of the external power output level of the nth external energy source to the power output guarantee probability under the preset power output guarantee probability, a complementary strategy F is given. l The sensitivity S(C0%,F) of the external energy output level to the output guarantee probability of the target new energy source under the condition of a preset output guarantee probability C0%. l The calculation method is as follows:

[0104]

[0105] Where, N a N represents the total number of time sampling points. g The amount of external energy.

[0106] Figure 4 This is a schematic diagram of the structure of a multi-energy complementary strategy evaluation device provided in an embodiment of this disclosure. Figure 4As shown, the multi-energy complementary strategy evaluation device 400 includes: a model building module 410, a model solving module 420, a calculation module 430, and an evaluation module 440. The model building module 410 is used to acquire the multi-energy complementary strategy to be evaluated for the target new energy unit and hydropower unit, and to establish a multi-energy complementary system scheduling model corresponding to the multi-energy complementary strategy. The multi-energy complementary system scheduling model includes constraints corresponding to multiple energy types and an objective function corresponding to the multi-energy complementary strategy. The multiple energy types include the target new energy, hydropower, and external energy. The model solving module 420 is used to solve for the external output level of the external energy in the multi-energy complementary system scheduling model based on the preset output guarantee probability of the target new energy and the unit change in the output guarantee probability. The preset output guarantee probability is used to characterize the probability that the output level reaches the target output level. The calculation module 430 is used to calculate the sensitivity of the external output level of the external energy to the output guarantee probability of the target new energy based on the model solving results. The evaluation module 440 is used to determine the evaluation result of the multi-energy complementary strategy based on the sensitivity.

[0107] Optionally, the external output level includes a benchmark external output level, a first external output level, and a second external output level. The model solving module 420 includes: a first determining unit, used to determine an upper limit output guarantee probability and a lower limit output guarantee probability based on the preset output guarantee probability and the unit change of the output guarantee probability; and a solving unit, used to solve for the external output level of the external energy in the multi-energy complementary system scheduling model based on the preset output guarantee probability, the upper limit output guarantee probability, and the lower limit output guarantee probability, respectively, to obtain the benchmark external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability.

[0108] Optionally, the target output level includes a benchmark target output level, a first target output level, and a second target output level. The model solving module 420 further includes: an acquisition unit for acquiring historical output data of the target renewable energy source; a second determination unit for determining, based on the historical output data, the benchmark target output level corresponding to the preset output guarantee probability, the first target output level corresponding to the upper limit output guarantee probability, and the second target output level corresponding to the lower limit output guarantee probability; and a third determination unit for determining, based on the benchmark target output level, the first target output level, the second target output level, and the pre-acquired installed capacity of the target renewable energy source, the benchmark output constraint parameter, the first output constraint parameter, and the second output constraint parameter corresponding to the target renewable energy source.

[0109] Optionally, the solving unit is specifically used to substitute the benchmark output constraint parameter, the first output constraint parameter, and the second output constraint parameter into the constraint conditions corresponding to the target new energy source, and solve for the external output level of the external energy source in the multi-energy complementary system scheduling model to obtain the benchmark external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability.

[0110] Optionally, the historical output data includes the output data of the target new energy source at different times within the target time period, the target output level is the sequence data of the output level of the target new energy source at different times within the target time period, and the external output level is the sequence data of the output level of the external energy source at different times within the target time period.

[0111] Optionally, the calculation module 430 is specifically used to calculate the sensitivity of the external energy source's external energy output level to the target new energy source's output guarantee probability based on the benchmark external energy output level, the first external energy output level, the second external energy output level, the preset output guarantee probability, and the unit change of the output guarantee probability.

[0112] Optionally, the target new energy source includes wind power generation and / or photovoltaic power generation, and the constraints include at least one of the following: the first output constraint condition corresponding to the target new energy source, the second output constraint condition and the ramp constraint condition corresponding to the external energy source, and the third output constraint condition, flow constraint condition, reservoir capacity constraint condition, outflow constraint condition and water level constraint condition corresponding to the hydropower generation. The multi-energy complementary system scheduling model also includes the dynamic balance relationship between the operating parameters of the hydropower station.

[0113] The multi-energy complementary strategy evaluation device provided in this embodiment can execute the method described in any of the above embodiments. Its execution method and beneficial effects are similar, and will not be repeated here.

[0114] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure.

[0115] like Figure 5 As shown, the computer device may include a processor 510 and a memory 520 storing computer program instructions.

[0116] Specifically, the processor 510 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0117] Memory 520 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 520 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 520 may include removable or non-removable (or fixed) media. Where appropriate, memory 520 may be internal or external to the integrated gateway device. In a particular embodiment, memory 520 is a non-volatile solid-state memory. In a particular embodiment, memory 520 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0118] The processor 510 reads and executes computer program instructions stored in the memory 520 to perform the steps of the multi-energy complementarity strategy evaluation method provided in the embodiments of this disclosure.

[0119] In one example, the computer device may also include a transceiver 530 and a bus 540. Wherein, as... Figure 5 As shown, the processor 510, memory 520 and transceiver 530 are connected via bus 540 and communicate with each other.

[0120] Bus 540 may include hardware, software, or both. For example, and not limited to, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 540 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0121] This disclosure also provides a computer-readable storage medium that can store a computer program. When the computer program is executed by a processor, the processor implements the multi-energy complementarity strategy evaluation method provided in this disclosure.

[0122] The aforementioned storage medium may, for example, include a memory 520 containing computer program instructions, which can be executed by the processor 510 of the multi-energy complementarity strategy evaluation device to complete the multi-energy complementarity strategy evaluation method provided in the embodiments of this disclosure. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device. The aforementioned computer program may be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. 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 programming languages. The program code may be executed entirely on the user's computing device, partially on the user's 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.

[0123] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0124] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating multi-energy complementary strategies, characterized in that, The method includes: Obtain the multi-energy complementary strategy for the target new energy unit and hydropower unit to be evaluated, and establish the multi-energy complementary system scheduling model corresponding to the multi-energy complementary strategy. The multi-energy complementary system scheduling model includes the constraints corresponding to multiple energy types and the objective function corresponding to the multi-energy complementary strategy. The multiple energy types include the target new energy, hydropower and external energy. The external output level of the external energy in the multi-energy complementary system scheduling model is solved based on the preset output guarantee probability of the target new energy and the unit change of the output guarantee probability. The preset output guarantee probability is used to characterize the probability that the output level will reach the target output level. The external power output level includes a baseline external power output level, a first external power output level, and a second external power output level. The process of solving for the external power output level of the external energy source in the multi-energy complementary system scheduling model based on the preset power output guarantee probability of the target new energy source and the unit change in the power output guarantee probability includes: Based on the preset output guarantee probability and the unit change of the output guarantee probability, the upper limit output guarantee probability and the lower limit output guarantee probability are determined. The external output level of the external energy source in the multi-energy complementary system scheduling model is solved based on the preset output guarantee probability, the upper limit output guarantee probability, and the lower limit output guarantee probability, respectively, to obtain the benchmark external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability. The sensitivity of the external power output level of the external energy source to the power output guarantee probability of the target new energy source is calculated based on the model solution results. The sensitivity of the external power output level of the external energy source to the power output guarantee probability of the target new energy source, calculated based on the model solution results, includes: The sensitivity of the external energy source's external energy output level to the target new energy source's output guarantee probability is calculated based on the benchmark external energy output level, the first external energy output level, the second external energy output level, the preset output guarantee probability, and the unit change of the output guarantee probability. The evaluation result of the multi-energy complementary strategy is determined based on the sensitivity.

2. The method according to claim 1, characterized in that, The target output level includes a baseline target output level, a first target output level, and a second target output level. Before solving for the external output level of the external energy source in the multi-energy complementary system scheduling model based on the preset output guarantee probability, the upper limit output guarantee probability, and the lower limit output guarantee probability to obtain the baseline external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability, the method further includes: Obtain the historical output data of the target new energy source; Based on the historical output data, the baseline target output level corresponding to the preset output guarantee probability, the first target output level corresponding to the upper limit output guarantee probability, and the second target output level corresponding to the lower limit output guarantee probability are determined respectively. Based on the benchmark target output level, the first target output level, the second target output level, and the pre-acquired installed capacity of the target new energy, the benchmark output constraint parameters, the first output constraint parameters, and the second output constraint parameters corresponding to the target new energy are determined.

3. The method according to claim 2, characterized in that, The step of solving for the external output level of external energy sources in the multi-energy complementary system scheduling model based on the preset output guarantee probability, the upper limit output guarantee probability, and the lower limit output guarantee probability, respectively, to obtain the baseline external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability, includes: Substitute the benchmark output constraint parameter, the first output constraint parameter, and the second output constraint parameter into the constraint conditions corresponding to the target new energy source, and solve the external output level of the external energy source in the multi-energy complementary system scheduling model to obtain the benchmark external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability.

4. The method according to claim 2, characterized in that, The historical output data includes the output data of the target new energy source at different times within the target period. The target output level is the sequence data of the output level of the target new energy source at different times within the target period. The external output level is the sequence data of the output level of the external energy source at different times within the target period.

5. The method according to any one of claims 1-4, characterized in that, The target new energy source includes wind power and / or photovoltaic power generation. The constraints include at least one of the following: the first output constraint corresponding to the target new energy source, the second output constraint and the ramp constraint corresponding to the external energy source, and the third output constraint, flow constraint, reservoir capacity constraint, outflow constraint and water level constraint corresponding to the hydropower generation. The multi-energy complementary system scheduling model also includes the dynamic balance relationship between the operating parameters of the hydropower station.

6. A multi-energy complementary strategy evaluation device, characterized in that, The device includes: The model building module is used to obtain the multi-energy complementary strategy for the target new energy unit and hydropower unit to be evaluated, and to establish the multi-energy complementary system scheduling model corresponding to the multi-energy complementary strategy. The multi-energy complementary system scheduling model includes the constraints corresponding to various energy types and the objective function corresponding to the multi-energy complementary strategy. The various energy types include the target new energy, hydropower, and external energy. The model solving module is used to solve the external output level of the external energy in the multi-energy complementary system scheduling model based on the preset output guarantee probability of the target new energy and the unit change of the output guarantee probability. The preset output guarantee probability is used to characterize the probability that the output level will reach the target output level. The external power output level includes a baseline external power output level, a first external power output level, and a second external power output level. The process of solving for the external power output level of the external energy source in the multi-energy complementary system scheduling model based on the preset power output guarantee probability of the target new energy source and the unit change in the power output guarantee probability includes: Based on the preset output guarantee probability and the unit change of the output guarantee probability, the upper limit output guarantee probability and the lower limit output guarantee probability are determined. The external output level of the external energy source in the multi-energy complementary system scheduling model is solved based on the preset output guarantee probability, the upper limit output guarantee probability, and the lower limit output guarantee probability, respectively, to obtain the benchmark external output level corresponding to the preset output guarantee probability, the first external output level corresponding to the upper limit output guarantee probability, and the second external output level corresponding to the lower limit output guarantee probability. The calculation module is used to calculate the sensitivity of the external power output level of the external energy source to the power output guarantee probability of the target new energy source based on the model solution results. The sensitivity of the external power output level of the external energy source to the power output guarantee probability of the target new energy source, calculated based on the model solution results, includes: The sensitivity of the external energy source's external energy output level to the target new energy source's output guarantee probability is calculated based on the benchmark external energy output level, the first external energy output level, the second external energy output level, the preset output guarantee probability, and the unit change of the output guarantee probability. An evaluation module is used to determine the evaluation result of the multi-energy complementary strategy based on the sensitivity.

7. A computer device, characterized in that, include: Memory; processor; And a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the multi-energy complementarity strategy evaluation method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the multi-energy complementarity strategy evaluation method as described in any one of claims 1-5.

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