Source-network-load-storage integrated construction project scheme optimization method and device

Abstract

The invention discloses a source-network-load-storage integrated construction project scheme optimization method and device. The method comprises the steps of obtaining a first scheme which is not optimized currently, and dividing the first scheme into a plurality of first sub-projects according to distribution information of power transmission and transformation facilities in the first scheme; according to a preset evaluation calculation model, repeatedly iteratively screening a first number of second schemes consisting of a plurality of first sub-projects from all the first sub-projects through a genetic algorithm; the evaluation calculation model is used for calculating an evaluation result of the second scheme; the evaluation result comprises a first score obtained according to the evaluation result and a second score obtained according to the second scheme; and when an iteration condition is satisfied, inputting a first number of second schemes output at the moment into an evaluation calculation model, and selecting a second scheme with an optimal evaluation result from the second schemes. According to the method and the device, the evaluation accuracy can be improved, so that the comprehensive performance of the finally obtained integrated construction project is improved.

Description

Technical Field

[0001] The present application relates to the field of power grid construction planning, and in particular to a method and device for optimizing a plan for a source-grid-load-storage integrated construction project. Background Art

[0002] With the rapid development of energy internet, traditional power systems and new energy forms such as modern renewable energy and energy storage equipment are gradually integrated to form a source-grid-load-storage integration (i.e., an organic combination of power source, network, load and energy storage system). The construction of this integrated system involves multi-faceted engineering design and equipment configuration, including power transmission and transformation facilities, energy storage equipment, renewable energy power generation facilities, etc. This requires that multiple factors be considered during the design process, such as system stability, economy, environmental impact and future scalability.

[0003] As the development goals and boundary conditions of integrated projects in various places gradually become clear, such as the power supply, grid connection lines, loads, and energy storage facilities are in principle constructed by one investment entity; the integrated project is connected to the public grid as a whole, forming a clear physical interface with the public grid; the integrated project strengthens autonomous peak regulation and self-consumption, and in principle does not send power back to the large grid, and the grid company does not settle the power that is connected to the grid by itself; the comprehensive utilization rate of new energy in the integrated project is not less than 90%, etc. The above boundary conditions mean that the evaluation of a single power supply or energy storage project is no longer of decision-making significance. It is necessary to start from the overall evaluation of the integrated project, reduce the overall investment cost of the project as much as possible, improve the stability and environmental protection of the project, and improve the economic benefits.

[0004] Therefore, how to improve the accuracy of the evaluation based on the overall evaluation of the integrated project, so as to ensure that the integrated construction project finally obtained achieves the optimal performance while meeting the constraints, has become a technical problem that needs to be solved at present. Summary of the invention

[0005] The present application provides a method and device for optimizing the plan of a source-grid-load-storage integrated construction project to solve the technical problem of how to improve the accuracy of the evaluation based on the overall evaluation of the integrated project, thereby improving the comprehensive performance of the integrated construction project finally obtained.

[0006] In order to solve the above technical problems, in a first aspect, an embodiment of the present application provides a method for optimizing a source-grid-load-storage integrated construction project plan, including:

[0007] Acquire a first plan that is not currently optimized, and divide the first plan into a plurality of first sub-projects according to distribution information of power transmission and transformation facilities in the first plan;

[0008] According to a preset evaluation calculation model, a first number of second solutions consisting of a plurality of first sub-projects are repeatedly iterated and screened from all the first sub-projects by a genetic algorithm; the evaluation calculation model is used to calculate an evaluation result of the second solution; the evaluation result includes a first score obtained according to the evaluation result and a second score obtained according to the second solution;

[0009] When the iteration condition is met, the first number of the second solutions output at this time are input into the evaluation calculation model, and the second solution with the best evaluation result is selected from the second solutions.

[0010] Compared with the prior art, the embodiments of the present application have the following beneficial effects: in the construction of source-grid-load-storage integration, it is not only about optimizing the configuration of the power source and energy storage scale, but also involves the single-unit scale of the power source and energy storage and the project site selection, the capacity of the transmission and transformation equipment and the length of the line, etc., which need to be combined with the specific scheme for engineering design, so it is impossible to directly use intelligent algorithms for global search and optimization. By decomposing the first scheme into multiple sub-projects, a global search for the best combination of sub-projects is performed, so that the genetic algorithm can be used to solve the optimization problem of source-grid-load-storage integration construction, reducing the complexity of problem solving, so that the best second scheme can be found. In addition, since the source-grid-load-storage integration project is a long-term project, for example, the environmental protection of a scheme is not just a short-term assessment of the environmental impact during construction. After the project is completed and put into operation, the cumulative environmental impact year by year will still affect the environmental protection assessment of the current construction scheme, and the environmental protection assessment of the current construction scheme will further affect the decision-making of the current construction, thereby affecting the cumulative environmental impact year by year in the subsequent operation process. Therefore, in order to further improve the accuracy of solution evaluation, the impact of the evaluation results themselves on their applications is embedded in the evaluation calculation model, thereby improving the accuracy of the overall evaluation results, so that the optimal second solution that meets user needs can be selected from a large number of second solutions.

[0011] In some embodiments of the first aspect of the present application, the evaluation calculation model is used to calculate the evaluation result of the second scheme, including:

[0012] The evaluation calculation model includes: a yearly data calculation module and a result calculation module;

[0013] Inputting the second scheme into the yearly data calculation module to obtain yearly data;

[0014] According to the yearly data, the result calculation module is initialized to obtain the evaluation result of the second solution.

[0015] Compared with the prior art, the above embodiment has the following beneficial effects: by calculating the yearly data after the completion of the construction of the second solution, the degree of data refinement is improved, thereby improving the accuracy of the evaluation results calculated by the subsequent result calculation module.

[0016] In some embodiments of the first aspect of the present application, initializing the result calculation module according to the yearly data to obtain the evaluation result of the second solution includes:

[0017] The result calculation module includes a first function term and a preset second function term; the first function term is a functional relationship between the evaluation result and the first score; the second function term is a functional relationship between the second score and the second solution;

[0018] Generate the first function term according to the yearly data;

[0019] Initializing the second function item according to the yearly data, and obtaining the second score in combination with the second solution;

[0020] The first function term is solved iteratively by using the binary search method until the evaluation result obtained by solving the solution corresponds to the sum of the first score and the second score being equal to the evaluation result, and the iteration is terminated.

[0021] Compared with the prior art, the above embodiment has the following beneficial effects: in order to accurately evaluate the comprehensive performance of the source-grid-load-storage integrated construction project plan, when designing the result settlement model, the evaluation process is modeled as a nested calculation problem, so that the evaluation results of the source-grid-load-storage integrated construction project plan can be accurately obtained from the perspective of integrity and using the feedback mechanism.

[0022] In some embodiments of the first aspect of the present application, the result calculation module specifically includes:

[0023] The first function item is specifically:

[0024] G1=f1(G(X))

[0025] The second function item is specifically:

[0026] G2=f2(X)

[0027] The calculation formula of the evaluation result is specifically:

[0028] G(X)=G1+G2

[0029] Where G(X) is the evaluation result; G1 is the first score; G2 is the second score; X is the second solution; f1(G(X)) is the function whose independent variable is the evaluation result; and f2(X) is the function whose independent variable is the second solution.

[0030] In some embodiments of the first aspect of the present application, the iterative selection of a first number of second solutions consisting of a plurality of first sub-projects from all the first sub-projects by a genetic algorithm according to a preset evaluation calculation model comprises:

[0031] Constructing binary variables of each of the first sub-items, and randomly initializing each of the binary variables to initialize a first chromosome population; the first chromosome population includes a second number of chromosomes; each of the chromosomes can be decoded into the corresponding second scheme;

[0032] Decoding each of the chromosomes in the first chromosome population obtained in the current iteration into the corresponding second scheme, and inputting the second scheme into the evaluation calculation model to obtain the evaluation result of each of the second schemes;

[0033] Determine whether the evaluation result of the second scheme corresponding to at least one chromosome in the first chromosome population obtained in the current iteration satisfies a first preset condition;

[0034] If so, the second scheme corresponding to the optimal evaluation result is stored in a preset set; otherwise, the first chromosome population is updated through a roulette wheel selection operator, a crossover operator, and a mutation operator;

[0035] When an iteration condition is met, the updating of the first chromosome population is stopped, and a first number of the second solutions are screened out from the preset set.

[0036] Compared with the prior art, the above embodiment has the following beneficial effects: since in the construction of integrated source, grid, load and storage, it is not only about optimizing the configuration of power supply and energy storage scale, but also involves the single-unit scale and project site selection of power supply and energy storage, the capacity of transmission and transformation equipment and the length of the line, if all the variables are optimized, the solution space will be too large, and the best solution cannot be obtained through the optimization algorithm. Therefore, after decomposing the first solution into multiple sub-projects, corresponding binary variables are constructed for each sub-project, so that the genetic algorithm can be used to solve the best variable result, and the corresponding source, grid, load and storage integrated construction plan is generated, which effectively reduces the complexity of problem solving.

[0037] In a second aspect, the embodiment of the present application further provides a source-grid-load-storage integrated construction project plan optimization device, including: a first sub-project division module, a second plan screening module and an optimal plan selection module;

[0038] The first sub-project division module is used to obtain the currently unoptimized first solution, and divide the first solution into a plurality of first sub-projects according to the distribution information of the power transmission and transformation facilities in the first solution;

[0039] The second scheme screening module is used to iteratively screen a first number of second schemes consisting of a plurality of first sub-projects from all the first sub-projects by a genetic algorithm according to a preset evaluation calculation model; the evaluation calculation model is used to calculate an evaluation result of the second scheme; the evaluation result includes a first score obtained according to the evaluation result and a second score obtained according to the second scheme;

[0040] The optimal solution selection module is used to input the first number of the second solutions output at this time into the evaluation calculation model when the iteration condition is met, and select the second solution with the best evaluation result from the second solutions.

[0041] In some embodiments of the second aspect of the present application, the evaluation calculation model is used to calculate the evaluation result of the second scheme, including:

[0042] The evaluation calculation model includes: a yearly data calculation module and a result calculation module;

[0043] Inputting the second scheme into the yearly data calculation module to obtain yearly data;

[0044] According to the yearly data, the result calculation module is initialized to obtain the evaluation result of the second solution.

[0045] In some embodiments of the second aspect of the present application, initializing the result calculation module according to the yearly data to obtain the evaluation result of the second solution includes:

[0046] The result calculation module includes a first function term and a preset second function term; the first function term is a functional relationship between the evaluation result and the first score; the second function term is a functional relationship between the second score and the second solution;

[0047] Generate the first function term according to the yearly data;

[0048] Initializing the second function item according to the yearly data, and obtaining the second score in combination with the second solution;

[0049] The first function term is solved iteratively by using the binary search method until the evaluation result obtained by solving the solution corresponds to the sum of the first score and the second score being equal to the evaluation result, and the iteration is terminated.

[0050] In some embodiments of the second aspect of the present application, the result calculation module specifically includes:

[0051] The first function item is specifically:

[0052] G1=f1(G(X))

[0053] The second function item is specifically:

[0054] G2=f2(X)

[0055] The calculation formula of the evaluation result is specifically:

[0056] G(X)=G1+G2

[0057] Where G(X) is the evaluation result; G1 is the first score; G2 is the second score; X is the second solution; f1(G(X)) is the function whose independent variable is the evaluation result; and f2(X) is the function whose independent variable is the second solution.

[0058] In some embodiments of the second aspect of the present application, the iterative selection of a first number of second solutions consisting of a plurality of first sub-projects from all the first sub-projects by a genetic algorithm according to a preset evaluation calculation model comprises:

[0059] Constructing binary variables of each of the first sub-items, and randomly initializing each of the binary variables to initialize a first chromosome population; the first chromosome population includes a second number of chromosomes; each of the chromosomes can be decoded into the corresponding second scheme;

[0060] Decoding each of the chromosomes in the first chromosome population obtained in the current iteration into the corresponding second scheme, and inputting the second scheme into the evaluation calculation model to obtain the evaluation result of each of the second schemes;

[0061] Determine whether the evaluation result of the second scheme corresponding to at least one chromosome in the first chromosome population obtained in the current iteration satisfies a first preset condition;

[0062] If so, the second scheme corresponding to the optimal evaluation result is stored in a preset set; otherwise, the first chromosome population is updated through a roulette wheel selection operator, a crossover operator, and a mutation operator;

[0063] When an iteration condition is met, the updating of the first chromosome population is stopped, and a first number of the second solutions are screened out from the preset set. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic flow chart of a method for optimizing a source-grid-load-storage integrated construction project plan provided in some embodiments of the present application;

[0065] Figure 2 A schematic structural diagram of a source-grid-load-storage integrated construction project plan optimization device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] As the development goals and boundary conditions of integrated projects in various places gradually become clear, such as the power supply, grid connection lines, loads, and energy storage facilities are in principle constructed by one investment entity; the integrated project is connected to the public grid as a whole, forming a clear physical interface with the public grid; the integrated project strengthens autonomous peak regulation and self-consumption, and in principle does not send power back to the large grid, and the grid company does not settle the power that is connected to the grid by itself; the comprehensive utilization rate of new energy in the integrated project is not less than 90%, etc. The above boundary conditions mean that the evaluation of a single power supply or energy storage project is no longer of decision-making significance. It is necessary to start from the overall evaluation of the integrated project, reduce the overall investment cost of the project as much as possible, improve the stability and environmental protection of the project, and improve the economic benefits.

[0067] In order to solve the above technical problems, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0068] Embodiment 1

[0069] Please refer to Figure 1 , a source-grid-load-storage integrated construction project plan optimization method provided in an embodiment of the present application, including S10 to S30, specifically:

[0070] S10: Obtain a first plan that is not currently optimized, and divide the first plan into a plurality of first sub-projects according to distribution information of power transmission and transformation facilities in the first plan.

[0071] Preferably, in some embodiments of the present application, in the engineering design stage, the source, grid, load and storage resources that can be included in the project usually have clear distribution and range boundaries, so it is only necessary to select power sources and energy storage resources of reasonable scale and location according to load demand, design corresponding grid access and transmission plans, and build an efficient and interconnected source, grid, load and storage integrated project. Therefore, in order to reduce the complexity of the subsequent algorithm optimization process, the power sources and energy storage resources that can be constructed within the project boundary are divided into multiple minimized single projects according to engineering design principles and resource distribution characteristics.

[0072] For example, a source-grid-load-storage integrated construction project plan is divided into the following first sub-projects:

[0073] Photovoltaic project modularization: PV = [pv1…pv i …pv n ], where pv i is the i-th minimized single photovoltaic project scale.

[0074] Wind power project modularization: WD = [wd1…wd i …wd n ], where wd i is the i-th minimized single wind power project scale.

[0075] Modularization of CSP projects: CSP = [csp1…csp i …csp n ], where csp i is the i-th minimized single CSP project scale.

[0076] Energy storage project scale: BS = [bs1…bs i …bs n ], where bs i Minimize the size of the i-th single energy storage project (the default is 0.5C energy storage system).

[0077] S20: According to a preset evaluation calculation model, a first number of second solutions consisting of a plurality of first sub-projects are repeatedly iterated and screened from all the first sub-projects through a genetic algorithm; the evaluation calculation model is used to calculate an evaluation result of the second solution; the evaluation result includes a first score obtained according to the evaluation result and a second score obtained according to the second solution.

[0078] Furthermore, in some embodiments of the present application, the evaluation calculation model is used to calculate the evaluation result of the second solution, including:

[0079] The evaluation calculation model includes: a yearly data calculation module and a result calculation module;

[0080] Inputting the second scheme into the yearly data calculation module to obtain yearly data;

[0081] The result calculation module is initialized according to the yearly data to obtain the evaluation result of the second solution.

[0082] By calculating the yearly data after the completion of the construction of the second plan, the degree of data refinement is improved, thereby improving the accuracy of the evaluation results calculated by the subsequent result calculation module.

[0083] Preferably, in some embodiments of the present application, the evaluation results may adopt evaluation indicators such as economic index evaluation, source-grid-load-storage system stability evaluation indicators, source-grid-load-storage system life cycle, interaction between renewable energy generation and grid dispatch in the source-grid-load-storage system, and energy efficiency utilization rate of the source-grid-load-storage system. The present application does not limit the specific calculation formula of the evaluation result, but only limits the calculation problem of the evaluation result to an embedded calculation problem.

[0084] Further, in some embodiments of the present application, initializing the result calculation module according to the yearly data to obtain the evaluation result of the second solution includes:

[0085] The result calculation module includes a first function term and a preset second function term; the first function term is a functional relationship between the evaluation result and the first score; the second function term is a functional relationship between the second score and the second solution;

[0086] Generate the first function term according to the yearly data;

[0087] Initializing the second function item according to the yearly data, and obtaining the second score in combination with the second solution;

[0088] The first function term is solved iteratively by using the binary search method until the evaluation result obtained by solving the solution corresponds to the sum of the first score and the second score being equal to the evaluation result, and the iteration is terminated.

[0089] Preferably, in some embodiments of the present application, in order to better understand the present solution, the economic indicator evaluation and the interaction between renewable energy generation and grid dispatch in the source-grid-load-storage system are used as examples to give the year-by-year data required for the corresponding evaluation result settlement method.

[0090] When the economic index is used as the evaluation result, first obtain the following data in the second scheme: unit cost of wind power, photovoltaic, solar thermal, energy storage and other projects, unit cost of power transmission and distribution and supporting facilities, capital ratio, loan term, loan interest rate, value-added tax, business tax and surcharge, income tax and other tax rates, operation and maintenance cost ratio of total investment, residual value rate, equipment replacement cost and other operating parameters, project power purchase and price from the power grid, grid connection point main transformer capacity and basic electricity price, project grid surplus power and grid power, other income and expenditure items; input LCOE auxiliary calculation parameters, including discount rate, acceptable error, etc. Input the basic information such as the scale of the minimization of the scale of the single project and the estimated investment of the construction projects such as power transmission and distribution of the potential exploitable resources of the integrated project (such as wind power, photovoltaic, solar thermal, energy storage, etc.). Next, obtain the following yearly data based on the above data: annual capital repayment, total cost of operation and maintenance, depreciation, project residual value, value-added tax, business tax and surcharge, income tax, project power-related income and expenditure and other key items.

[0091] When the interaction between renewable energy generation and grid dispatch in the source-grid-load-storage system is used as the evaluation result, the following data in the second scheme are first obtained: time series forecast data of renewable energy generation, capacity factor of renewable energy, historical load data of the grid, historical capacity and dispatch capability of the grid, capacity and efficiency of energy storage, and charging and discharging strategies adopted, weather conditions (such as wind speed, light) in the areas where each wind power, photovoltaic, and solar thermal power generation equipment is located, etc. Next, the following yearly data are obtained based on the above data: annual wind power, photovoltaic, and solar thermal power generation, health status of energy storage, grid dispatch data, etc.

[0092] Furthermore, in some embodiments of the present application, the result calculation module specifically includes:

[0093] The first function item is specifically:

[0094] G1=f1(G(X))

[0095] The second function item is specifically:

[0096] G2=f2(X)

[0097] The calculation formula of the evaluation result is specifically:

[0098] G(X)=G1+G2

[0099] Where G(X) is the evaluation result; G1 is the first score; G2 is the second score; X is the second solution; f1(G(X)) is the function whose independent variable is the evaluation result; and f2(X) is the function whose independent variable is the second solution.

[0100] Preferably, in some embodiments of the present application, the first function term is solved iteratively by a binary search method until the evaluation result obtained by solving the solution corresponds to the sum of the first score and the second score being equal to the evaluation result, and the process of terminating the iteration is specifically as follows:

[0101] Since G(X)=G1+G2=f1(G(X))+f2(X), it is equivalent to G(X)=g(G(X)), that is, the evaluation result G(X) is a function related to itself, and further obtains f(G(X))=g(G(X))-G(X). First, take any two values ​​of G(X) G1(X) and G2(X), so that f(G1(X))>0 and f(G2(X))<0, and make Next, if f(G3(X))>0, let G1(X)=G3(X), and let If f(G3(X))<0, let G2(X)=G3(X), and let Repeat the previous step until f(G3(X)) is less than or equal to the acceptable error, and obtain the final evaluation result G3(X).

[0102] Preferably, in some embodiments of the present application, in order to better understand the present solution, the advantages of embedded computing are explained below using economic indicator evaluation and the interaction between renewable energy generation and grid scheduling as examples.

[0103] When the economic index is used as the evaluation result, the corresponding calculation formula of G(X) is:

[0104]

[0105] Among them, P ini The initial investment for the entire integrated project construction period; P&I It is the sum of the principal and interest that needs to be repaid to the bank each year under the project capital model; R d is the discount rate; T l The term of the project loan; Tax total To represent the annual tax cost of the project, due to Tax total The value-added tax in the calculation is calculated by deducting the input tax from the annual output tax of the project and then deducting the value-added tax deduction amount during the construction period. The output tax includes the income from the integrated project selling electricity to users. When calculating the evaluation results of economic indicators, the evaluation results represent the levelized cost of energy (LCOE). The unit price of electricity sold by the integrated project to users is LCOE. Similarly, Tax total The business tax, surcharge and income tax calculations in the LOCE are directly related to LOCE, and the income tax has taken depreciation deduction into account. total It is actually a function related to LCOE, namely Tax total is a function related to G(X); P O&M is the comprehensive operation and maintenance cost required for the project each year; S res P is the residual value of the corresponding asset in the current year after the service life of the project equipment expires or the project life cycle ends; T is the length of the project life cycle; P grid The electricity charges and basic electricity charges incurred by purchasing electricity from the power grid due to insufficient local power supply during certain periods of time; S grid The revenue from electricity charges for the project due to insufficient local load absorption capacity during certain periods of time and the need to supply electricity to the grid; S ser The income obtained by providing auxiliary services to the power grid for integrated projects and load users; load The compensation paid by the integrated project to the load users for the load regulation services provided; S green Proceeds from selling green certificates for integrated projects; E totalIt is the total annual electricity consumption of users of integrated project loads.

[0106] The above formula with economic efficiency as the evaluation index can be understood as:

[0107]

[0108]

[0109] When the interaction between renewable energy generation and grid dispatch is used as the evaluation result, the corresponding calculation formula of G(X) can be:

[0110]

[0111] Among them, f η (P schdule ,P power ) represents the dispatching operation of the power grid on the energy storage system every year P schdule And the charge and discharge power P of each operation power The impact of operations such as the above on the capacity loss of the energy storage system; SOH (P storage ,P generation ) represents the annual energy storage system capacity P storage and renewable energy generation P generation The impact of the number of charge and discharge times on the capacity loss of the energy storage system. Represents the current available capacity status of energy storage.

[0112] In the above technical formula for evaluating the interaction between renewable energy generation and grid dispatch, it is equivalent to

[0113] It can be seen from the above embodiments that in order to accurately evaluate the comprehensive performance of the source-grid-load-storage integrated construction project plan, when designing the result settlement model, the evaluation process is modeled as a nested calculation problem, so that the evaluation results of the source-grid-load-storage integrated construction project plan can be accurately obtained from the perspective of integrity and using the feedback mechanism.

[0114] Further, in some embodiments of the present application, the method of repeatedly iteratively selecting a first number of second solutions consisting of a plurality of first sub-projects from all the first sub-projects by a genetic algorithm according to a preset evaluation calculation model includes:

[0115] Constructing binary variables of each of the first sub-items, and randomly initializing each of the binary variables to initialize a first chromosome population; the first chromosome population includes a second number of chromosomes; each of the chromosomes can be decoded into the corresponding second scheme;

[0116] Decoding each of the chromosomes in the first chromosome population obtained in the current iteration into the corresponding second scheme, and inputting the second scheme into the evaluation calculation model to obtain the evaluation result of each of the second schemes;

[0117] Determine whether the evaluation result of the second scheme corresponding to at least one chromosome in the first chromosome population obtained in the current iteration satisfies a first preset condition;

[0118] If so, the second scheme corresponding to the optimal evaluation result is stored in a preset set; otherwise, the first chromosome population is updated through a roulette wheel selection operator, a crossover operator, and a mutation operator;

[0119] When an iteration condition is met, the updating of the first chromosome population is stopped, and a first number of the second solutions are screened out from the preset set.

[0120] Preferably, in some embodiments of the present application, the overall solution process of the genetic algorithm is:

[0121] S21: For the above four first sub-items, corresponding binary variable spaces are established respectively. The specific examples are as follows: Assume that x pv1 If it is 1, it means the photovoltaic project pv i is included in the second solution, otherwise it is not selected. One X represents a chromosome and corresponds to a second solution, and 4n is the chromosome length.

[0122] S22: According to the chromosome example shown in S21, m chromosomes are randomly generated to complete the initialization of the first chromosome population.

[0123] S23: Input X in the first chromosome population into the evaluation calculation model, and calculate the evaluation result of X corresponding to the second solution. The corresponding fitness function is: Calculate the evaluation results of the second scheme for all chromosomes.

[0124] S24: Using the roulette wheel method as the selection operator, the first chromosome population is optimized. First, the cumulative probability of the fitness of the m chromosomes is calculated respectively, that is, Then, the roulette wheel method is used for selection, that is, m random numbers from 0 to 1 are randomly generated, and chromosomes with probability ranges corresponding to the m random numbers are selected in turn to form a new population. i and C i+1 If the value is between , the i+1th chromosome is selected to enter the new population, and the newly generated population is used as the new first chromosome population.

[0125] S25: Further update the first chromosome population newly generated in S24 through the crossover operator. By setting the crossover rate P c , for example, 0.25, and further generate m random numbers from 0 to 1. If the i-th random number is less than P c , it means that the i-th chromosome will perform crossover calculations with other chromosomes whose random numbers are less than Pc. Taking the i-th and j-th chromosomes participating in the crossover calculation as an example, an integer k from 1 to n is randomly generated, and the new chromosome after the crossover calculation can be expressed as:

[0126]

[0127] in, as well as are the newly generated chromosomes i and j respectively; X j (1:k) and X i (1:k) represents the first k genes of the jth and ith chromosomes respectively; X i (k+1:n) and X j (k+1:n) represents the k+1 to n genes on the i and j chromosomes respectively.

[0128] S26: Further update the first chromosome population updated in S25 through the mutation operator. Determine the mutation rate P m , then the total number of genes that vary in the population is N = m × n × P m , by randomly generating N integers from 1 to m*n, and determining the position of the mutant gene according to the order of the genes in the population, and further randomly generating N integers of 0 or 1 to replace the mutant gene, the mutation is completed. Assuming that the first randomly generated integer is 2m and the second randomly generated integer is 1, it means that the 2mth gene in the chromosome is mutated and the gene in that position is replaced by 1.

[0129] S27: Repeat S23 to S26 until the number of cycles reaches the set maximum genetic generation T, or a chromosome with F(X) less than the set error ε is generated, and the genetic process is terminated. At this point, the chromosome X that makes the objective function F(X) reach the minimum b That is the optimal solution, and the gene value corresponding to the chromosome represents the theoretically optimal construction plan for source-grid-load-storage integration.

[0130] It can be seen from the above embodiments that in the construction of integrated source-grid-load-storage, it is not only about optimizing the configuration of the power source and energy storage scale, but also involves the single-unit scale of the power source and energy storage and the project site selection, the capacity of the transmission and transformation equipment, and the length of the line. If all the variables are optimized, the solution space will be too large, and the best solution cannot be obtained through the optimization algorithm. Therefore, after the first solution is decomposed into multiple sub-projects, corresponding binary variables are constructed for each sub-project, so that the genetic algorithm can be used to solve the best variable result and generate the corresponding source-grid-load-storage integrated construction plan, which effectively reduces the complexity of problem solving.

[0131] S30: When an iteration condition is met, the first number of the second solutions output at this time are input into the evaluation calculation model, and a second solution with the best evaluation result is selected from the second solutions.

[0132] In summary, the scheme optimization method for the integrated source-grid-load-storage construction project provided by the embodiment of the present application has the following beneficial effects: in the integrated source-grid-load-storage construction, it is not only the optimal configuration of the power source and energy storage scale, but also involves the single-unit scale of the power source and energy storage and the project site selection, the capacity of the transmission and transformation equipment and the length of the line, etc., which need to be combined with the specific scheme for engineering design, so it is impossible to directly use the intelligent algorithm for global search and optimization. By decomposing the first scheme into multiple sub-projects, a global search for the best combination of sub-projects is performed, so that the genetic algorithm can be used to solve the optimization problem of the integrated source-grid-load-storage construction, reducing the complexity of problem solving, so that the optimal second scheme can be found. In addition, since the integrated source-grid-load-storage project is a long-term project, for example, the environmental protection of a scheme is not just a short-term assessment of the environmental impact during construction. After the project is completed and put into operation, the cumulative environmental impact year by year will still affect the environmental protection assessment of the current construction scheme, and the environmental protection assessment of the current construction scheme will further affect the decision-making of the current construction, thereby affecting the cumulative environmental impact year by year in the subsequent operation process. Therefore, in order to further improve the accuracy of solution evaluation, the impact of the evaluation results themselves on their applications is embedded in the evaluation calculation model, thereby improving the accuracy of the overall evaluation results, so that the optimal second solution that meets user needs can be selected from a large number of second solutions.

[0133] Embodiment 2

[0134] refer to Figure 2 , a source-grid-load-storage integrated construction project plan optimization device provided in an embodiment of the present application, includes: a first sub-project division module 11, a second plan screening module 12 and an optimal plan selection module 13.

[0135] Furthermore, in some embodiments of the present application, the first sub-project division module 11 is used to obtain the currently unoptimized first plan, and divide the first plan into a number of first sub-projects according to the distribution information of the power transmission and transformation facilities in the first plan; the second plan screening module 12 is used to iteratively screen a first number of second plans consisting of a number of first sub-projects from all the first sub-projects through a genetic algorithm according to a preset evaluation calculation model; the evaluation calculation model is used to calculate the evaluation result of the second plan; the evaluation result includes a first score obtained according to the evaluation result and a second score obtained according to the second plan; the optimal plan selection module 13 is used to input the first number of the second plans output at this time into the evaluation calculation model when the iteration condition is met, and select the second plan with the best evaluation result from the second plan.

[0136] Furthermore, in some embodiments of the present application, the evaluation calculation model is used to calculate the evaluation results of the second scheme, including: the evaluation calculation model includes: a yearly data calculation module and a result calculation module; the second scheme is input into the yearly data calculation module to obtain yearly data; according to the yearly data, the result calculation module is initialized to obtain the evaluation results of the second scheme.

[0137] Further, in some embodiments of the present application, the result calculation module is initialized according to the yearly data to obtain the evaluation result of the second scheme, including: the result calculation module includes a first function term and a preset second function term; the first function term is a functional relationship between the evaluation result and the first score; the second function term is a functional relationship between the second score and the second scheme; the first function term is generated according to the yearly data; the second function term is initialized according to the yearly data, and the second score is obtained in combination with the second scheme; the first function term is solved iteratively by binary search until the evaluation result obtained corresponds to the sum of the first score and the second score equal to the evaluation result, and the iteration is terminated.

[0138] Furthermore, in some embodiments of the present application, the result calculation module specifically includes:

[0139] The first function item is specifically:

[0140] G1=f(G(X))

[0141] The second function item is specifically:

[0142] G2=f(X)

[0143] The calculation formula of the evaluation result is specifically:

[0144] G(X)=G1+G2

[0145] Where G(X) is the evaluation result; G1 is the first score; G2 is the second score; X is the second solution; f(G(X)) is the function whose independent variable is the evaluation result; and f(X) is the function whose independent variable is the second solution.

[0146] Further, in some embodiments of the present application, the method of repeatedly iteratively screening a first number of second schemes consisting of several first sub-projects from all the first sub-projects through a genetic algorithm according to a preset evaluation calculation model includes: constructing binary variables of each of the first sub-projects, and randomly initializing each of the binary variables to initialize the first chromosome population; the first chromosome population includes a second number of chromosomes; each of the chromosomes can be decoded into a corresponding second scheme; decoding each of the chromosomes in the first chromosome population obtained in the current iteration into a corresponding second scheme, and inputting the second scheme into the evaluation calculation model to obtain the evaluation result of each of the second schemes; judging whether there is at least one chromosome in the first chromosome population obtained in the current iteration whose corresponding evaluation result of the second scheme meets a first preset condition; if so, storing the second scheme corresponding to the optimal evaluation result in a preset set; otherwise, updating the first chromosome population through a roulette selection operator, a crossover operator, and a mutation operator; when the iteration condition is met, stopping updating the first chromosome population, and screening out a first number of the second schemes from the preset set.

[0147] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention. The embodiment of the present invention provides a source-grid-load-storage integrated construction project plan optimization device, which can implement any method item embodiment of the present invention, that is, the source-grid-load-storage integrated construction project plan optimization method provided in Example 1.

[0148] In summary, the scheme optimization device for the integrated source, grid, load and storage construction project provided by the embodiment of the present application has the following beneficial effects: in the integrated source, grid, load and storage construction, it is not only the optimal configuration of the power supply and energy storage scale, but also involves the single-unit scale of the power supply and energy storage and the project site selection, the capacity of the transmission and transformation equipment and the length of the line, etc., which need to be combined with the specific scheme for engineering design, so it is impossible to directly use the intelligent algorithm for global search and optimization. By decomposing the first scheme into multiple sub-projects, the sub-projects are searched globally for the best combination, so that the genetic algorithm can be used to solve the optimization problem of the integrated source, grid, load and storage construction, reducing the complexity of problem solving, so that the optimal second scheme can be found. In addition, since the integrated source, grid, load and storage project is a long-term project, for example, the environmental protection of a scheme is not just a short-term assessment of the environmental impact during construction. After the project is completed and put into operation, the cumulative environmental impact year by year will still affect the environmental protection assessment of the current construction scheme, and the environmental protection assessment of the current construction scheme will further affect the decision-making of the current construction, thereby affecting the cumulative environmental impact year by year in the subsequent operation process. Therefore, in order to further improve the accuracy of solution evaluation, the impact of the evaluation results themselves on their applications is embedded in the evaluation calculation model, thereby improving the accuracy of the overall evaluation results, so that the optimal second solution that meets user needs can be selected from a large number of second solutions.

[0149] Embodiment 3

[0150] Based on the above-mentioned embodiment of the method for optimizing the plan of a source-grid-load-storage integrated construction project, another embodiment of the present application provides a terminal device for optimizing the plan of a source-grid-load-storage integrated construction project. The terminal device for optimizing the plan of a source-grid-load-storage integrated construction project comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for optimizing the plan of a source-grid-load-storage integrated construction project of any embodiment of the present application is implemented.

[0151] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the source-grid-load-storage integrated construction project solution optimization device.

[0152] The source-grid-load-storage integrated construction project solution optimization device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The source-grid-load-storage integrated construction project solution optimization terminal device may include, but is not limited to, a processor and a memory.

[0153] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the source-grid-load-storage integrated construction project solution optimization device, and uses various interfaces and lines to connect the various parts of the entire source-grid-load-storage integrated construction project solution optimization device. The memory may be used to store the computer program and / or module, and the processor implements various functions of the source-grid-load-storage integrated construction project solution optimization device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0154] Embodiment 4

[0155] Based on the above-mentioned embodiment of the method for optimizing the plan of an integrated source-grid-load-storage construction project, another embodiment of the present application provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for optimizing the plan of an integrated source-grid-load-storage construction project of any embodiment of the present application.

[0156] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0157] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for optimizing a source-grid-load-storage integrated construction project plan, characterized in that: include: Acquire a first plan that is not currently optimized, and divide the first plan into a plurality of first sub-projects according to distribution information of power transmission and transformation facilities in the first plan; According to a preset evaluation calculation model, a first number of second solutions consisting of a plurality of first sub-projects are repeatedly iterated and screened from all the first sub-projects by a genetic algorithm; the evaluation calculation model is used to calculate an evaluation result of the second solution; the evaluation result includes a first score obtained according to the evaluation result and a second score obtained according to the second solution; When the iteration condition is met, the first number of the second solutions output at this time is input into the evaluation calculation model, and the second solution with the best evaluation result is selected from the second solutions.

2. A method for optimizing a source-grid-load-storage integrated construction project plan as claimed in claim 1, characterized in that: The evaluation calculation model is used to calculate the evaluation result of the second solution, including: The evaluation calculation model includes: a yearly data calculation module and a result calculation module; Inputting the second scheme into the yearly data calculation module to obtain yearly data; The result calculation module is initialized according to the yearly data to obtain the evaluation result of the second solution.

3. A method for optimizing a source-grid-load-storage integrated construction project plan as claimed in claim 2, characterized in that: Initializing the result calculation module according to the yearly data to obtain the evaluation result of the second solution includes: The result calculation module includes a first function term and a preset second function term; the first function term is a functional relationship between the evaluation result and the first score; the second function term is a functional relationship between the second score and the second solution; Generate the first function term according to the yearly data; Initializing the second function item according to the yearly data, and obtaining the second score in combination with the second solution; The first function term is solved iteratively by using the binary search method until the evaluation result obtained by solving the solution corresponds to the sum of the first score and the second score being equal to the evaluation result, and the iteration is terminated.

4. A method for optimizing a source-grid-load-storage integrated construction project plan as claimed in claim 3, characterized in that: The result calculation module specifically includes: The first function item is specifically: G1=f1(G(X)) The second function item is specifically: G2=f2(X) The calculation formula of the evaluation result is specifically: G(X)=G1+G2 Where G(X) is the evaluation result; G1 is the first score; G2 is the second score; X is the second solution; f1(G(X)) is the function whose independent variable is the evaluation result; and f2(X) is the function whose independent variable is the second solution.

5. The method for optimizing the construction project plan of source-grid-load-storage integration according to claim 1, characterized in that: The method of repeatedly iteratively selecting a first number of second solutions consisting of a plurality of first sub-projects from all the first sub-projects by using a genetic algorithm according to a preset evaluation calculation model comprises: Constructing binary variables of each of the first sub-items, and randomly initializing each of the binary variables to initialize a first chromosome population; the first chromosome population includes a second number of chromosomes; each of the chromosomes can be decoded into the corresponding second scheme; Decoding each of the chromosomes in the first chromosome population obtained in the current iteration into the corresponding second scheme, and inputting the second scheme into the evaluation calculation model to obtain the evaluation result of each of the second schemes; Determine whether the evaluation result of the second scheme corresponding to at least one chromosome in the first chromosome population obtained in the current iteration satisfies a first preset condition; If so, the second scheme corresponding to the optimal evaluation result is stored in a preset set; otherwise, the first chromosome population is updated through a roulette wheel selection operator, a crossover operator, and a mutation operator; When an iteration condition is met, the updating of the first chromosome population is stopped, and a first number of the second solutions are screened out from the preset set.

6. A device for optimizing the construction project plan of source-grid-load-storage integration, characterized in that: include: The first sub-project division module, the second solution screening module and the optimal solution selection module; The first sub-project division module is used to obtain the currently unoptimized first solution, and divide the first solution into a plurality of first sub-projects according to the distribution information of the power transmission and transformation facilities in the first solution; The second scheme screening module is used to iteratively screen a first number of second schemes consisting of a plurality of first sub-projects from all the first sub-projects by a genetic algorithm according to a preset evaluation calculation model; the evaluation calculation model is used to calculate an evaluation result of the second scheme; the evaluation result includes a first score obtained according to the evaluation result and a second score obtained according to the second scheme; The optimal solution selection module is used to input the first number of the second solutions output at this time into the evaluation calculation model when the iteration condition is met, and select the second solution with the best evaluation result from the second solutions.

7. A source-grid-load-storage integrated construction project plan optimization device as claimed in claim 6, characterized in that: The evaluation calculation model is used to calculate the evaluation result of the second solution, including: The evaluation calculation model includes: a yearly data calculation module and a result calculation module; Inputting the second scheme into the yearly data calculation module to obtain yearly data; The result calculation module is initialized according to the yearly data to obtain the evaluation result of the second solution.

8. The device for optimizing the construction project plan of source-grid-load-storage integration according to claim 7, characterized in that: Initializing the result calculation module according to the yearly data to obtain the evaluation result of the second solution includes: The result calculation module includes a first function term and a preset second function term; the first function term is a functional relationship between the evaluation result and the first score; the second function term is a functional relationship between the second score and the second solution; Generate the first function term according to the yearly data; Initializing the second function item according to the yearly data, and obtaining the second score in combination with the second solution; The first function term is solved iteratively by using the binary search method until the evaluation result obtained by solving the solution corresponds to the sum of the first score and the second score being equal to the evaluation result, and the iteration is terminated.

9. A source-grid-load-storage integrated construction project plan optimization device as claimed in claim 8, characterized in that: The result calculation module specifically includes: The first function item is specifically: G1=f1(G(X)) The second function item is specifically: G2=f2(X) The calculation formula of the evaluation result is specifically: G(X)=G1+G2 Where G(X) is the evaluation result; G1 is the first score; G2 is the second score; X is the second solution; f1(G(X)) is the function whose independent variable is the evaluation result; and f2(X) is the function whose independent variable is the second solution.

10. The device for optimizing the construction project plan of source-grid-load-storage integration according to claim 6, characterized in that: The method of repeatedly iteratively selecting a first number of second solutions consisting of a plurality of first sub-projects from all the first sub-projects by using a genetic algorithm according to a preset evaluation calculation model comprises: Constructing binary variables of each of the first sub-items, and randomly initializing each of the binary variables to initialize a first chromosome population; the first chromosome population includes a second number of chromosomes; each of the chromosomes can be decoded into the corresponding second scheme; Decoding each of the chromosomes in the first chromosome population obtained in the current iteration into the corresponding second scheme, and inputting the second scheme into the evaluation calculation model to obtain the evaluation result of each of the second schemes; Determine whether the evaluation result of the second scheme corresponding to at least one chromosome in the first chromosome population obtained in the current iteration satisfies a first preset condition; If so, the second scheme corresponding to the optimal evaluation result is stored in a preset set; otherwise, the first chromosome population is updated through a roulette wheel selection operator, a crossover operator, and a mutation operator; When an iteration condition is met, the updating of the first chromosome population is stopped, and a first number of the second solutions are screened out from the preset set.

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