New energy consumption evaluation method and system based on credibility evaluation

Abstract

The invention belongs to the technical field of new energy power systems, and discloses a new energy consumption evaluation method and system based on credibility evaluation, and the method comprises the steps: carrying out the fuzzy modeling of uncertain factors of a power grid through employing double triangular fuzzy variables, and obtaining the fuzzy variables of the uncertain factors; establishing a unit commitment model based on the fuzzy variables, and constructing an objective function and constraint conditions; solving the unit combination model to obtain output and start-stop decisions of the thermal power generating unit; and according to the fuzzy variables of the uncertain factors and the output and start-stop decision of the thermal power generating unit, evaluating the new energy consumption capability of the power grid based on credibility measurement. According to the method, on the basis of fuzzy theory credibility measurement and analysis of wind power output prediction errors, double triangular fuzzy variables are adopted for new energy output, then a unit combination model under the large-scale wind power access condition is established, output of a thermal power generating unit is solved through an improved genetic swarm algorithm, and the power output prediction accuracy is improved. And the accuracy of new energy consumption capability evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power systems, and in particular to a new energy consumption assessment method and system based on credibility evaluation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the progress of society, new energy power generation such as wind power generation and solar power generation will usher in large-scale development. my country expects the total installed capacity of new energy power generation to reach 1.2 billion kilowatts in 2030. Similarly, many countries also use new energy power generation as the main power source for new power systems. By the end of 2021, the total installed capacity of wind power and photovoltaic power generation in the world has reached 1,674GW, and some European countries have achieved large-scale new energy grid connection.

[0004] However, due to the unpredictability of the power generation of new energy units, low inertia in grid-related performance, and low short-circuit capacity, the power system faces severe challenges in absorbing large-scale new energy. The problem of new energy absorption is becoming increasingly serious, with a large number of wind and solar abandonment problems, which has affected the utilization efficiency of new energy. In 2021, the new energy abandonment rate in the northwest region was 5.6%, of which the new energy abandonment rate in Qinghai reached 10.8%. Therefore, it is of great significance to fully consider the volatility of new energy output and the carrying capacity of the power system, and accurately evaluate the new energy absorption capacity to promote the absorption of new energy. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a new energy consumption assessment method and system based on credibility evaluation. Based on the fuzzy theory credibility measurement, on the basis of analyzing the wind power output prediction error, the new energy output is modeled by a fuzzy modeling method of double triangle fuzzy variables, and a unit combination model under large-scale wind power access conditions is established. The improved genetic group algorithm is used to solve the output of the thermal power unit, thereby improving the accuracy of the new energy consumption capacity assessment.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a new energy consumption assessment method based on credibility evaluation, comprising the following steps:

[0008] The double-triangular fuzzy variables are used to carry out fuzzy modeling of the uncertain factors of the power grid and obtain the fuzzy variables of the uncertain factors.

[0009] Establish a unit commitment model based on fuzzy variables of uncertain factors, and construct the objective function and constraint conditions of the unit commitment model;

[0010] The improved genetic swarm algorithm is used to solve the unit combination model to obtain the output and start-stop decision of the thermal power unit;

[0011] According to the fuzzy variables of uncertain factors, the output and start-stop decisions of thermal power units, the new energy absorption capacity of the power grid is evaluated based on credibility measurement.

[0012] As an optional implementation, the grid uncertainties mainly include wind power output and load output, the fuzzy variables of the uncertainties are equal to the sum of the predicted values ​​of the uncertainties and the prediction errors, and the prediction errors are obtained through the membership function of the prediction errors.

[0013] As an optional implementation, a double trigonometric function is used to fit the membership function of the prediction error.

[0014] As an optional implementation method, based on the power generation cost of thermal power units, the start-up and shutdown cost of thermal power units, and the operation and maintenance cost of wind power, the following objective function is constructed:

[0015]

[0016] Among them, P Git is the planned output of unit i in the tth scheduling period, I it is the start / stop status of unit i in the tth scheduling cycle, C i is the fuel cost function of unit i, S it is the startup cost of unit i in the tth scheduling period, P Wt The predicted power emitted by wind power in the tth scheduling period.

[0017] As an optional implementation method, when solving the unit combination model using the improved genetic swarm algorithm, only the start and stop of the unit are encoded, and the generator output is optimized using the effective set method of quadratic programming.

[0018] As an optional implementation, the constraints of the unit combination model include upper and lower limit constraints on unit output, upper and lower limit constraints on DC transmission power, spare capacity constraints, unit ramp rate constraints, unit minimum start-up and shutdown time constraints, system power balance constraints, positive spare opportunity constraints, and negative spare opportunity constraints.

[0019] In a second aspect, the present invention provides a new energy consumption assessment system based on credibility evaluation, comprising:

[0020] The fuzzy modeling module is configured to: use double-triangular fuzzy variables to perform fuzzy modeling on uncertain factors of the power grid to obtain fuzzy variables of the uncertain factors;

[0021] The objective function and constraint condition building module is configured to: establish a unit commitment model based on fuzzy variables of uncertain factors, and build an objective function and constraint conditions of the unit commitment model;

[0022] The model solving module is configured to: solve the unit combination model using an improved genetic swarm algorithm to obtain the output and start-stop decision of the thermal power unit;

[0023] The absorption assessment module is configured to evaluate the new energy absorption capacity of the power grid based on the credibility measurement according to the fuzzy variables of uncertain factors, the output and start-stop decisions of thermal power units.

[0024] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0025] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0026] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention discloses a method and system for evaluating the consumption of new energy based on credibility evaluation. On the basis of analyzing the prediction error of wind power output, double triangle fuzzy variables are used to perform fuzzy modeling on the output of new energy, so as to solve the problem of uncertainty modeling of power grid. On the basis of the fuzzy variables of the output of new energy, a unit combination model under the condition of large-scale wind power access is established, and the unit combination model is solved by using the improved genetic group algorithm. Only the start and stop of the unit is encoded, and the effective set method of quadratic programming is used to optimize the output of the generator, which improves the global search optimization performance, thereby obtaining the start and stop and output of the thermal power unit. According to the start and stop and output of the thermal power unit and fuzzy variables, a credibility-based new energy consumption capacity evaluation method is proposed by using the fuzzy theory credibility measurement, which can effectively evaluate the system's new energy consumption capacity evaluation, which is of great significance to further improve the consumption of new energy.

[0029] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0031] Figure 1 A data processing flow chart of a new energy consumption assessment method based on credibility evaluation provided in Example 1 of the present invention;

[0032] Figure 2 It is a fuzzy characteristic fitting diagram of wind power prediction error of the present invention;

[0033] Figure 3 It is a flow chart of the unit combination optimization method of the present invention;

[0034] Figure 4 This is a schematic diagram of the improved 10-machine 39-node test system of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0037] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0039] Example 1

[0040] like Figure 1 As shown, this embodiment provides a new energy consumption assessment method based on credibility evaluation, including the following steps:

[0041] S1 uses double triangle fuzzy variables to perform fuzzy modeling on the uncertain factors of the power grid and obtain the fuzzy variables of the uncertain factors;

[0042] S2 establishes the unit commitment model based on the fuzzy variables of uncertain factors, and constructs the objective function and constraint conditions of the unit commitment model;

[0043] S3 uses the improved genetic swarm algorithm to solve the unit combination model and obtain the output and start-stop decision of the thermal power unit;

[0044] S4 evaluates the grid’s renewable energy absorption capacity based on credibility measurement according to the fuzzy variables of uncertain factors, the output of thermal power units, and the start-stop decisions.

[0045] Among them, the uncertainties of the power grid mainly include wind power output and load output.

[0046] There are two types of fuzzy modeling of renewable energy power generation output: one is to directly model renewable energy power generation output using trapezoidal and triangular fuzzy parameters; the other is to use Cauchy distribution to perform fuzzy modeling on output prediction errors, and combine the predicted values ​​to obtain a fuzzy model of output. The former modeling method is simple and easy to calculate, but it has a heavy subjective characteristic and is not accurate enough. The latter modeling method uses prediction errors to increase the accuracy of the model, but the use of Cauchy distribution complicates the modeling and is not conducive to the analysis of the impact on the power grid.

[0047] The key to fuzzy modeling is to determine the membership function. In some cases, the membership function can be determined by fuzzy statistical experiments. The present disclosure combines the advantages of the above two modeling methods and fuzzy statistical experiments to propose a fuzzy modeling method based on double triangular fuzzy variables.

[0048] The fuzzy modeling method based on double triangle fuzzy variables is used to carry out fuzzy modeling of wind power output:

[0049] First, define the absolute prediction error of wind power output as:

[0050] ξ W1 =P ξW1 -P W1 (1)

[0051] Where P ξW1 , P W1 are the actual output and predicted output of the wind farm in the first cycle, P ξW1 is the fuzzy parameter, ξ W1 is the prediction error, and is the fuzzy parameter. The prediction error can be determined by fuzzy statistics.

[0052] The purpose of the fuzzy statistical experiment is to study the uncertainty of membership degrees using statistical certainty methods. Its characteristic is that in each experiment, the element ξ is fixed, while the set A` is variable, being an ordinary variable set in the universe of discourse. A` is associated with the fuzzy set A according to certain conditions. The basic requirement of the fuzzy statistical experiment is to make a definite judgment on whether the fixed element ξ in the universe of discourse belongs to the set A` in each experiment. A more in-depth fuzzy statistical experiment requires making a definite judgment at a deeper level, which means that in each experiment, A` must be a determined ordinary set. Conduct n experiments and calculate the membership frequency of ξ to the fuzzy set A:

[0053] The membership frequency of ξ to A = the number of times of "ξ ∈ A`" / n (2)

[0054] Many experiments show that as n increases, the membership frequency also shows stability, which is called the stability of the membership frequency. The value where the frequency stabilizes is called the membership degree of the element ξ to the fuzzy set A.

[0055] Through the analysis of the predicted values and actual values of the wind farm output in the past, a large number of prediction errors can be statistically counted, as well as the number of times each prediction error appears. The membership degree with the most occurrences of the prediction error is 1. Each prediction error is equivalent to one experiment. Through the above fuzzy simulation, the membership degree of the prediction error can be obtained, and by fitting together the membership degrees of all prediction errors, the membership degree function of the prediction error can be obtained.

[0056] This disclosure selects the measured data of a wind farm in the Shandong Peninsula for one month as the research object. It can be seen from the prediction error distribution map that the prediction errors mostly concentrate in the range of [-34, 45]. This disclosure uses a double trigonometric function to fit the membership degree function as Figure 2 shown

[0057] The double triangular fuzzy variable is composed of a five-tuple (r1, r2, r3, r4, r5) of crisp numbers, where r1 < r2 < r3 < r4 < r5. According to formula (1), the modeled variable of the wind farm output can be expressed as the sum of the predicted value and the prediction error, as shown in the following formula:

[0058] P ξW1 = ξ W1 + P W1 (3)

[0059] In many applications, due to the limitation of the prediction samples, an approximate membership degree function can only be established at the beginning, and then it can be gradually modified by increasing the samples to make it perfect. To judge whether the membership function conforms to the actual situation, mainly depends on whether it correctly reflects the overall characteristics of the change process from the element belonging to the set to not belonging to the set, rather than the membership degree value of a single element.

[0060] The fuzzy modeling method based on double triangle fuzzy variables is used to carry out fuzzy modeling of load output:

[0061] According to the same method, the load output can also be modeled using fuzzy modeling. The load model variable is equal to the sum of the predicted value and the predicted error, as shown in the following formula:

[0062] P ξL1 =ξ L1 +P L1 (4)

[0063] Where: P ξL1 , P L1 are the actual load output and predicted load output of the first cycle, P ξL1 is the fuzzy parameter, ξ L1 is the prediction error, and is the fuzzy parameter. The prediction error can be determined by fuzzy statistics.

[0064] According to the fuzzy variables of uncertain factors, based on the power generation cost of thermal power units, the start-up and shutdown cost of thermal power units, and the operation and maintenance cost of wind power, the following objective function is constructed:

[0065]

[0066] Among them, P Git is the planned output of unit i in the tth scheduling period, I it is the start / stop status of unit i in the tth scheduling cycle, C i is the fuel cost function of unit i, S it is the startup cost of unit i in the tth scheduling period, P Wt is the predicted power of wind power in the tth dispatch period, D is the fuel cost function of the wind farm, N t is the unit combination model operation cycle (usually 24 hours), N G is the number of thermal power generating units in the power grid.

[0067] Among them, the unit fuel cost function C i Expressed as output P Git The quadratic function of :

[0068]

[0069] In the formula, c i 、b i and a i is the fuel cost coefficient of unit i.

[0070] Unit startup cost S it It is related to the boiler temperature. Long-term shutdown of the boiler for cooling will increase the startup cost of the unit. it The function expression is:

[0071]

[0072] In the formula, H Ci and C Ci Hot start cost, cold start cost, M Di is the minimum downtime, C STi is the cold start time, O Tit For downtime.

[0073] Wind farms do not use raw materials for power generation. This model only considers the operation and maintenance costs of wind farms. To simplify the model, this disclosure uniformly adopts a linear model, such as formula (8):

[0074] D(P Wt )=b W P Wt (8)

[0075] Where b W It is the operating cost of a wind farm when it generates unit power.

[0076] Constraints for constructing unit commitment models:

[0077] 1) Upper and lower limits of the unit output:

[0078] P Gimin ≤P Git ≤P Gimax ,ifI it =1(9)

[0079] Where P Gimin ,P Gimax are the minimum output and maximum output of unit i respectively.

[0080] 2) Upper and lower limits of DC transmission power:

[0081] P Dmin ≤P Dt ≤P Dmax (10)

[0082] Where P Dmin ,P Dmax are the minimum and maximum values ​​of DC transmission power respectively.

[0083] 3) Reserve capacity constraints:

[0084] U it =min(P Gimax -P Git ,T 10 r ui )(11)

[0085] D it =min(PGit -P Gimin ,T 10 r di )(12)

[0086] In the formula, r ui and r di are the ramp-up rate and ramp-down rate of the dispatching period of unit i, respectively, T 10 is the spinning standby response time (10min).

[0087] 4) Unit climbing rate constraints:

[0088] P Git ≤min(P Gimax ,P Gi(t-1) +T 10 r ui ),ifI it I i(t-1) =1(13)

[0089] P Git ≥max(P Gimin ,P Gi(t-1) -T 10 r di ),ifI it I i(t-1) =1(14)

[0090] P Git ≤P Gimin +T 10 r ui ,ifI i(t-1) =0 and I it =1(15)

[0091] P Gi(t-1) ≤P Gimin +T 10 r di ,ifI i(t-1) =1 and I it =0(16)

[0092] Where max and min are the maximum and minimum functions respectively.

[0093] 5) Minimum start-up and shutdown time constraints of the unit:

[0094]

[0095] Where, T on,i(t-1) and T off,i(t-1) T is the continuous start-up time and shutdown time of unit i to t-1 scheduling period, on,i and T off,i They are the minimum start-up time and minimum shutdown time of the unit respectively.

[0096] 6) System power balance constraints:

[0097] In each unit combination cycle, the output of conventional units maintains power balance with the predicted wind power output and the predicted load output.

[0098]

[0099] Where P Lt is the load forecast within period t, N Gt It is expressed as the number of thermal power units operating in period t.

[0100] 7) Positive reserve opportunity constraints:

[0101] The positive standby of the units can cope with uncertainties such as negative errors in wind power forecasts and positive errors in load forecasts at a certain confidence level.

[0102]

[0103] In the formula, Cr is the credibility measure of the event, β1 is the fuzzy confidence level, P ξLt , P ξwt is the fuzzy variable of wind power output and load.

[0104] 8) Negative spare opportunity constraint:

[0105] When the generator set is forced to shut down, the system lacks power, the significance of negative reserve is not obvious, and its opportunity constraint does not need to be considered. The negative reserve of the unit can cope with uncertainties such as positive errors in wind power forecasts and negative errors in load forecasts at a certain confidence level.

[0106]

[0107] In the formula, β2 is the fuzzy confidence level.

[0108] The improved genetic swarm algorithm is used to solve the unit combination model to obtain the output and start-stop decision of the thermal power unit. When the improved genetic swarm algorithm is used to solve the unit combination model, only the start-stop of the unit is encoded, and the effective set method of quadratic programming is used to optimize the generator output.

[0109] The unit combination model used in the present disclosure belongs to a non-linear, multi-constrained, non-convex optimization problem involving both discrete and continuous decision variables. The unit combination decision is divided into two aspects: start and stop and generator output optimization. The solution methods include priority sorting, integer programming, dynamic programming, mixed integer programming, branch and bound, Lagrangian relaxation, as well as intelligent algorithms, simulated annealing, genetic algorithms and particle swarm algorithms. Compared with other algorithms, genetic algorithms can globally optimize, set fewer parameters, and converge quickly. The present disclosure uses improved genetics (GA) to make unit start and stop decisions, and then uses the effective set method of quadratic programming to optimize the generator output.

[0110] The specific implementation steps of the algorithm are as follows:

[0111] 1) Input the original data of the receiving power grid, including the parameters of the generators in the receiving power grid, the predicted parameters of wind power output and load, and the predicted errors.

[0112] 2) Input the control parameters of the GA algorithm, and set the maximum number of iterations and population size. Randomly generate the start and stop states of the units in each period, and the start and stop states of the units form binary decision variables (0 and 1 represent the shutdown and start states of the units, respectively) as population individuals.

[0113] 3) Preprocessing to form a feasible solution that satisfies the spinning reserve constraint and the start-up and shutdown time constraint. Both of these constraints are closely related to the start-up and shutdown status of the unit. Preprocessing will reduce the GA operation time and improve the quality of the optimal solution.

[0114] 4) Under the preprocessed feasible solution, dynamic economic scheduling is performed according to the effective set method of quadratic programming, and the opportunity constraints are processed using clear equivalence classes, and the objective function is calculated at the same time, as shown in formula (5).

[0115] 5) Generate new genomes using crossover and mutation.

[0116] 6) For the generated individuals, dynamic economic scheduling is performed according to the effective set method of quadratic programming, and clear equivalence classes are used to handle opportunity constraints, while calculating the objective function.

[0117] 7) The new and old populations are merged and sorted, and the best individuals are selected to enter the next iterative optimization.

[0118] 8) Loop to step 5) until the number of iterations or the predetermined convergence accuracy is reached. The present disclosure adopts a larger number of iterations. The specific process is as follows Figure 3 shown.

[0119] According to the fuzzy variables of uncertain factors, the output and start-stop decisions of thermal power units, the new energy absorption capacity of the power grid is evaluated based on credibility measurement.

[0120] There are three measures in fuzzy mathematics: possibility measure, necessity measure and credibility measure. Credibility measure is an important concept in fuzzy mathematics. It plays an important role in the definition of expected value and can judge the inevitability of the occurrence of events. It can make up for the shortcomings of the fuzzy characteristics of possibility measure analysis and has been widely used in power systems, such as fuzzy power flow, maintenance plan, risk assessment, transmission network planning, dynamic economic dispatch, unit combination, reactive power optimization, etc.

[0121] Assuming that ξ is a fuzzy variable on the possibility space (Θ, P(Θ), Pos), its possibility measure Pos can be derived from the membership function, that is,

[0122] Pos{θ∈Θ|ξ(θ)=x}=μ(x),x∈R (21)

[0123] Where Pos{A} is the possibility measure of event A, and μ(x) is the membership function of the fuzzy variable ξ.

[0124] The necessity measure of a set A is defined as the set of opposites A c The impossibility of

[0125] Nec{A}=1-Pos{A c} (twenty two)

[0126] Where Nes{A} is the necessity measure of event A.

[0127] The credibility measure of an event is defined as the average of possibility and necessity, that is,

[0128]

[0129] Where Cr{A} is the credibility measure of event A.

[0130] In fuzzy mathematics, if the possibility measure of a fuzzy event is 1, the event may not be true. Similarly, if the necessity measure of the event is 0, the event may be true. However, if the credibility of the event is 1, it must be true. On the contrary, if the credibility measure is 0, the event is more likely to be false.

[0131] The credibility distribution Φ(x) indicates that the fuzzy variable ξ takes a value less than or equal to x. Φ(x) is:

[0132] Φ(x)=Cr{θ∈Θ|ξ(θ)≤x} (24)

[0133] Before large-scale renewable energy generation was connected to the power system, the uncertainty of the power grid was concentrated on the random shutdown of conventional thermal power units and load fluctuations. Load fluctuations are highly periodic, so it is easy for conventional units to adjust their output to maintain power balance in the power grid. However, after large-scale renewable energy generation was connected to the power system, the uncertainty of the power system increased the intermittent, fuzzy and non-storable nature of the output of renewable energy such as wind power and solar power, and the existing prediction technology has a large prediction error, resulting in the need for strong output adjustment of conventional units to smooth the load and forecast the output of renewable energy.

[0134] If the load and renewable energy output fluctuations and forecast errors exceed the regulation capacity of conventional thermal power, it is necessary to reduce the renewable energy output, resulting in wind and solar power abandonment, which means that the power system cannot fully absorb the renewable energy output. Since the conventional thermal power units are shut down, the system will need more active power, and the absorption capacity of renewable energy generation will be greatly improved. Therefore, this disclosure does not consider this uncertainty.

[0135] The regulation capacity of conventional thermal power units is determined by the spinning reserve capacity, and the negative spinning reserve capacity ensures the consumption of new energy. The negative spinning reserve capacity of the system at time t satisfies formula (25):

[0136]

[0137] Where D t is the negative spinning reserve capacity of the entire power grid for period t.

[0138] The unbalanced amount of the power system in the system period t is:

[0139]

[0140] According to the definition of credibility, the new energy consumption capacity of the power grid in period t can be expressed as:

[0141] Φ(t)=Cr{P Δt ≤D t}(27)

[0142] Simulation verification and analysis:

[0143] In order to verify the effectiveness of the unit combination method proposed in this disclosure, this disclosure uses an improved 10-machine 39-node test system to perform a case test on the combination method. Figure 4 The confidence level of wind power prediction error and load prediction is 95%. The present disclosure adopts MATLAB programming to solve, with a maximum number of iterations of 200 and a population of 50.

[0144] In order to analyze different solution methods of the unit commitment model, the following two solution methods are compared.

[0145] Method 1: Add the effective set method of quadratic programming to the traditional genetic algorithm.

[0146] Method 2: Using traditional genetic algorithm.

[0147] The above two methods are used to solve the model respectively, and the start-stop status and power generation cost of the unit combination in each time period are obtained. The results show that when the effective set method of quadratic programming is added, the cost of method one is slightly lower than that of method two, indicating that the genetic algorithm adopted in the present invention is more capable of finding the minimum value.

[0148] The two methods are used to analyze the new energy absorption capacity of the system in each period. The results show that the new energy absorption capacity evaluation proposed in this disclosure can correctly reflect the regulation capacity of the system and effectively analyze the absorption capacity of the system. From the absorption capacity results, as the prediction time increases, the credibility of new energy absorption decreases. This is because the prediction error increases, and the operating cost of the system and the new energy absorption constitute a certain contradiction.

[0149] Example 2

[0150] This embodiment provides a new energy consumption assessment system based on credibility evaluation, including:

[0151] The fuzzy modeling module is configured to: use double-triangular fuzzy variables to perform fuzzy modeling on uncertain factors of the power grid to obtain fuzzy variables of the uncertain factors;

[0152] The objective function and constraint condition building module is configured to: establish a unit commitment model based on fuzzy variables of uncertain factors, and build an objective function and constraint conditions of the unit commitment model;

[0153] The model solving module is configured to: solve the unit combination model using an improved genetic swarm algorithm to obtain the output and start-stop decision of the thermal power unit;

[0154] The absorption assessment module is configured to evaluate the new energy absorption capacity of the power grid based on the credibility measurement according to the fuzzy variables of uncertain factors, the output and start-stop decisions of thermal power units.

[0155] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0156] In further embodiments, there is also provided:

[0157] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, it will not be described in detail here.

[0158] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0159] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0160] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is completed.

[0161] The method in Example 1 can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0162] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in embodiment 1 is implemented.

[0163] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0164] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0165] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, and the like. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, and the like.

[0166] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0167] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A new energy consumption assessment method based on credibility evaluation, characterized in that: The following steps are involved: The double-triangular fuzzy variables are used to carry out fuzzy modeling of the uncertain factors of the power grid and obtain the fuzzy variables of the uncertain factors. Establish a unit commitment model based on fuzzy variables of uncertain factors, and construct the objective function and constraint conditions of the unit commitment model; The improved genetic swarm algorithm is used to solve the unit combination model to obtain the output and start-stop decision of the thermal power unit; According to the fuzzy variables of uncertain factors, the output and start-stop decisions of thermal power units, the new energy absorption capacity of the power grid is evaluated based on credibility measurement.

2. A new energy consumption assessment method based on credibility evaluation as claimed in claim 1, characterized in that: The grid uncertainties mainly include wind power output and load output. The fuzzy variables of the uncertainties are equal to the sum of the predicted values ​​of the uncertainties and the prediction errors. The prediction errors are obtained through the membership function of the prediction errors.

3. A new energy consumption assessment method based on credibility evaluation as claimed in claim 2, characterized in that: The membership function of the prediction error is fitted using a double trigonometric function.

4. A new energy consumption assessment method based on credibility evaluation as claimed in claim 1, characterized in that: Based on the power generation cost of thermal power units, the start-up and shutdown cost of thermal power units, and the operation and maintenance cost of wind power, the following objective function is constructed: Among them, P Git is the planned output of unit i in the tth scheduling period, I it is the start / stop status of unit i in the tth scheduling cycle, C i is the fuel cost function of unit i, S it is the startup cost of unit i in the tth scheduling period, P Wt The predicted power emitted by wind power in the tth scheduling period.

5. A new energy consumption assessment method based on credibility evaluation as claimed in claim 1, characterized in that: When solving the unit commitment model using the improved genetic swarm algorithm, only the start and stop of the units are coded, and the generator output is optimized using the effective set method of quadratic programming.

6. A new energy consumption assessment method based on credibility evaluation as claimed in claim 1, characterized in that: The constraints of the unit combination model include upper and lower limit constraints of unit output, upper and lower limit constraints of DC transmission power, spare capacity constraints, unit ramp rate constraints, minimum unit start-up and shutdown time constraints, system power balance constraints, positive spare opportunity constraints and negative spare opportunity constraints.

7. A new energy consumption assessment system based on credibility evaluation, characterized in that: include: The fuzzy modeling module is configured to: use double-triangular fuzzy variables to perform fuzzy modeling on uncertain factors of the power grid to obtain fuzzy variables of the uncertain factors; The objective function and constraint condition building module is configured to: establish a unit commitment model based on fuzzy variables of uncertain factors, and build an objective function and constraint conditions of the unit commitment model; The model solving module is configured to: solve the unit combination model using an improved genetic swarm algorithm to obtain the output and start-stop decision of the thermal power unit; The absorption assessment module is configured to evaluate the new energy absorption capacity of the power grid based on the credibility measurement according to the fuzzy variables of uncertain factors, the output and start-stop decisions of thermal power units.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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