Power system installed capacity domain generation method, equipment, medium and product

Abstract

The invention discloses a power system installed capacity domain generation method and device, a medium and a product, and relates to the field of power system installed capacity domain generation, and the method comprises the steps: obtaining a one-time investment coefficient investment upper limit expected value and a new energy consumption rate lower limit expected value of a target power system, and generating a new energy consumption rate lower limit expected value through a power system installed capacity domain generation model, determining an installed capacity domain of the target power system; the installed capacity domain is composed of an upper bound of the installed capacity domain and a lower bound of the installed capacity domain; wherein the power system installed capacity domain generation model is obtained by training a CNN-BiLSTM-BiGRU neural network model by using a training data set; the training data set is established by using a multi-target coronavirus disease optimization algorithm and a wave search algorithm; the CNN-BiLSTM-BiGRU neural network model comprises a convolutional neural network, a bidirectional long-short-term memory neural network and a bidirectional gating circulation unit which are connected in sequence. The accuracy of generating the installed capacity domain of the power system is improved.

Description

Technical Field

[0001] The present application relates to the field of power system installed capacity domain generation, and in particular to a power system installed capacity domain generation method, equipment, medium and product. Background Art

[0002] With the deepening of global energy transformation, the proportion of renewable energy electricity has increased year by year. Due to the randomness and volatility of renewable energy such as wind power and photovoltaics, the current power system is facing the problem of high new energy absorption rate and safe and stable operation of the power system. The new energy absorption rate has also become an important assessment indicator of the current power system. Reasonable power structure arrangement on the source side is a key factor affecting the new energy absorption rate. Excessive construction of new energy unit capacity or too little energy storage configuration will lead to a large amount of new energy abandonment. Even if appropriate operation and scheduling strategies can promote the absorption of some new energy, the increase in new energy absorption rate is limited due to the safety requirements of system operation. At the same time, the existing capacity design method is difficult to fully describe the multi-dimensional attributes of power system capacity, and lacks intuitive decision support tools when dealing with the development trend of power supply capacity. The installed capacity domain of the power system constructed based on domain theory can clearly show the capacity boundary of the power system and provide capacity boundary support for the power supply development of the power system from a long-term perspective. Summary of the invention

[0003] The purpose of this application is to provide a method, device, medium and product for generating an installed capacity domain of an electric power system, so as to improve the accuracy of generating the installed capacity domain of the electric power system.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for generating an installed capacity domain of a power system, comprising:

[0006] Obtain the expected upper limit value of the one-time investment coefficient of the target power system and the expected lower limit value of the new energy consumption rate;

[0007] According to the expected upper limit of the one-time investment coefficient and the expected lower limit of the new energy consumption rate, the installed capacity domain of the target power system is determined by using the power system installed capacity domain generation model; the installed capacity domain is composed of the upper limit of the installed capacity domain and the lower limit of the installed capacity domain; wherein, the power system installed capacity domain generation model is obtained by training the CNN-BiLSTM-BiGRU neural network model using a training data set; the training data set is established using a multi-objective coronavirus disease optimization algorithm and a wave search algorithm; the training data set includes the upper limit of the one-time investment coefficient of each stage of the training power system, the lower limit of the new energy consumption rate and the corresponding installed capacity domain labels; the CNN-BiLSTM-BiGRU neural network model includes a convolutional neural network, a bidirectional long short-term memory neural network and a bidirectional gated recurrent unit connected in sequence.

[0008] Optionally, the CNN-BiLSTM-BiGRU neural network model is trained using the training data set, specifically including:

[0009] Build a training dataset;

[0010] Inputting the upper limit of the one-time investment coefficient of each stage of the training power system and the lower limit of the new energy consumption rate into the current CNN-BiLSTM-BiGRU neural network model to obtain the upper limit prediction value of the installed capacity domain and the lower limit prediction value of the installed capacity domain;

[0011] Determining a loss function value according to the installed capacity domain upper bound prediction value, the installed capacity domain lower bound prediction value and the corresponding installed capacity domain label;

[0012] Determine whether the training end condition is met; the training end condition is that the maximum number of iterations is reached or the loss function value is less than a preset value;

[0013] If yes, the training is terminated and the current CNN-BiLSTM-BiGRU neural network model is used as the power system installed capacity domain generation model;

[0014] If not, adjust the model parameters of the current CNN-BiLSTM-BiGRU neural network model according to the loss function value, and return "input the upper limit of the one-time investment coefficient of each stage of the training power system and the lower limit of the new energy consumption rate into the current CNN-BiLSTM-BiGRU neural network model to obtain the upper limit prediction value of the installed capacity domain and the lower limit prediction value of the installed capacity domain".

[0015] Optionally, construct a training data set, specifically including:

[0016] Generate the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system by using a random function;

[0017] Generate an initial installed capacity domain of the training power system using a multi-objective coronavirus disease optimization algorithm;

[0018] Based on the initial installed capacity domain, the wave search algorithm is used to determine the installed capacity domain labels corresponding to the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system to obtain a training data set.

[0019] Optionally, a multi-objective coronavirus disease optimization algorithm is used to generate an initial installed capacity domain of the training power system, specifically including:

[0020] Establish a multi-objective optimization model; the multi-objective optimization model takes the minimum total coefficient and the minimum new energy consumption rate as the objective function, and takes the unit capacity constraint, unit combination constraint and clean low-carbon drive constraint as the constraint conditions;

[0021] Solving the multi-objective optimization model using a multi-objective coronavirus disease optimization algorithm to determine an optimal solution set in the installed capacity domain;

[0022] According to the optimal solution set of the installed capacity domain, the initial installed capacity domain of the training power system is determined; the upper limit of the initial installed capacity domain is the maximum value of the power capacity in the optimal solution set of the installed capacity domain; the lower limit of the initial installed capacity domain is the minimum value of the power capacity in the optimal solution set of the installed capacity domain.

[0023] Optionally, the objective function is:

[0024]

[0025] Among them, F a is the total cost; T a is the number of days in the current phase; is the unit capacity investment cost of the power supply equipment at stage n; The installed capacity of power equipment for the nth phase; The installed capacity of power equipment for the n+1th phase; is the maintenance cost per unit capacity of the power supply equipment at stage n; τ s is the weight coefficient of the sth scenario; is the unit power operating cost of the electrochemical energy storage device at stage n; is the charging power of the electrochemical energy storage device in the nth stage, the sth scene, the tth period; is the discharge power of the electrochemical energy storage device in the nth stage, the sth scene, and the tth period; Δt is the period interval; is the unit electricity operation cost of the nth stage of thermal power; is the actual output of thermal power in the nth stage, the sth scenario and the tth period; The penalty cost per unit of electricity for load shedding, is the actual load shedding in the tth period of the sth scenario in the nth stage; G a is the new energy consumption rate, N r the number of stages for power capacity decisions; is the actual output of wind power in the nth stage, the sth scenario and the tth period; is the actual output of the photovoltaic power generation in the nth stage, the sth scene and the tth period; is the installed capacity of wind power in the nth stage; is the installed capacity of the nth stage of PV; is the predicted capacity factor of wind power in the nth stage, the sth scenario and the tth period; is the predicted capacity factor of the PV system in the nth stage, the sth scenario and the tth period.

[0026] Optionally, the unit capacity constraint is:

[0027]

[0028] in, The installed capacity of power equipment for the nth phase; The installed capacity of power equipment for the n+1th phase; is the lower limit of installed capacity in stage n; is the upper limit of installed capacity in the nth stage; is the unit capacity investment cost of the nth stage of power supply equipment, The installed capacity of power equipment for stage n-1; Invest an upper limit on the expected one-time investment cost for stage n;

[0029] The unit combination constraints include wind and solar operation constraints, thermal power operation constraints, electrochemical energy storage equipment operation constraints and spinning reserve capacity constraints;

[0030] The wind and solar operation constraints are:

[0031]

[0032] in, is the actual output of wind power in the nth stage, the sth scenario and the tth period; is the actual output of the photovoltaic power generation in the nth stage, the sth scene and the tth period; is the installed capacity of wind power in the nth stage; is the installed capacity of the nth stage of PV; is the predicted capacity factor of wind power in the nth stage, the sth scenario and the tth period; is the predicted capacity factor of the PV at the nth stage, the sth scenario, and the tth period;

[0033] The thermal power operation constraints are:

[0034]

[0035] in, is the actual output of thermal power in the nth stage, the sth scenario and the tth period; is the actual output of thermal power in the nth stage, the sth scenario and the t-1th period; is the ramp power coefficient of thermal power; is the grid-connected capacity of thermal power in the nth stage, the sth scenario and the tth period, is the ramp-down power coefficient of thermal power; is the minimum technical output coefficient of thermal power; is the maximum technical output coefficient of thermal power; is the total installed capacity of thermal power, is the installed capacity of the jth unit of the nth stage of thermal power; The start and stop status of the jth unit in the nth stage, sth scene, and tth period of thermal power;

[0036] The operating constraints of electrochemical energy storage devices are:

[0037]

[0038] in, The equivalent actual output of the electrochemical energy storage device in the nth stage, the sth scenario and the tth period; is the discharge power of the electrochemical energy storage device in the nth stage, the sth scene, the tth period; is the charging power of the electrochemical energy storage device in the nth stage, the sth scene, the tth period; is the installed capacity of the nth stage of the electrochemical energy storage device; is the state of charge of the electrochemical energy storage device at the nth stage, the sth scene, and the tth period; is the state of charge of the electrochemical energy storage device at the t-1 period of the nth stage, the sth scene; is the charging efficiency of the electrochemical energy storage device at the nth stage; is the discharge efficiency of the electrochemical energy storage device at the nth stage; is the rated energy of the electrochemical energy storage device at the nth stage; Δt is the time interval; The lowest state of charge; The highest state of charge; is the initial charge state of the sth scene in the nth stage; The state of charge at the end of the sth scene in the nth stage;

[0039] The spinning reserve capacity constraint is:

[0040]

[0041] in, is the output power of the electrochemical energy storage device in the nth stage, the sth scene, and the tth period; is the positive reserve factor of wind power; is the positive reserve factor of photovoltaic; is the positive reserve factor of the electric load; is the power of the electric load in the nth stage, the sth scene, the tth period; is the negative reserve coefficient of wind power; is the negative reserve factor of photovoltaic; is the negative reserve factor of the electric load;

[0042] The clean and low-carbon driving constraints include carbon emission limit constraints, new energy power ratio constraints and minimum new energy consumption rate constraints;

[0043]

[0044] Among them, τ s is the weight coefficient of the sth scenario; is the carbon emission factor of the nth stage of thermal power generation; is the carbon emission limit for the nth stage, The requirement for the proportion of new energy electricity in the nth stage; is the expected new energy consumption rate in the nth stage.

[0045] Optionally, based on the initial installed capacity domain, a wave search algorithm is used to determine the installed capacity domain labels corresponding to the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system, specifically including:

[0046] Normalizing the maximum total cost and the maximum new energy consumption rate corresponding to the optimal solution set in the installed capacity domain to obtain a total cost benchmark value and a new energy consumption rate benchmark value;

[0047] According to the total cost benchmark value and the new energy consumption rate benchmark value, the reduction process objective function is solved to obtain the reduction process benchmark target value; the reduction process objective function is Loss = δ f f a +δ g g a ; Where Loss is the benchmark target value of the reduction process, f a is the total cost benchmark value; g a is the benchmark value of new energy consumption rate; f is the weight of economic index; δg is the weight of the consumption rate indicator;

[0048] According to the total cost benchmark value, the new energy consumption rate benchmark value and the reduction process benchmark target value, the wave search algorithm is used to determine the installed capacity domain labels corresponding to the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system.

[0049] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for generating an installed capacity domain of a power system.

[0050] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for generating an installed capacity domain of a power system.

[0051] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for generating installed capacity domains of a power system.

[0052] According to the specific embodiments provided in this application, this application has the following technical effects:

[0053] The present application provides a method, device, medium and product for generating an installed capacity domain of a power system, obtains the expected upper limit of the one-time investment coefficient and the expected lower limit of the new energy consumption rate of the target power system, and uses the installed capacity domain generation model of the power system to determine the installed capacity domain of the target power system; the installed capacity domain is composed of the upper limit of the installed capacity domain and the lower limit of the installed capacity domain; wherein, the installed capacity domain generation model of the power system is obtained by training the CNN-BiLSTM-BiGRU neural network model using a training data set; the training data set is established using a multi-objective coronavirus disease optimization algorithm and a wave search algorithm; the CNN-BiLSTM-BiGRU neural network model includes a convolutional neural network, a bidirectional long short-term memory neural network and a bidirectional gated recurrent unit connected in sequence. The present application improves the accuracy of generating the installed capacity domain of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A schematic diagram of a flow chart of a method for generating an installed capacity domain of a power system provided in one embodiment of the present application;

[0056] Figure 2 This is a diagram of the architecture of the method for generating the installed capacity domain of the power system of this application;

[0057] Figure 3 This is the overall flow chart of the method for generating the installed capacity domain of the power system of this application;

[0058] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions 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.

[0060] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0061] In an exemplary embodiment, Figure 1 and Figure 2 As shown, a method for generating an installed capacity domain of a power system is provided, comprising the following steps:

[0062] S1: Obtain the expected upper limit of the one-time investment coefficient of the target power system and the expected lower limit of the new energy consumption rate.

[0063] S2: According to the expected upper limit of the one-time investment coefficient and the expected lower limit of the new energy consumption rate, the installed capacity domain of the target power system is determined by using the power system installed capacity domain generation model; the installed capacity domain is composed of the upper limit of the installed capacity domain and the lower limit of the installed capacity domain; wherein, the power system installed capacity domain generation model is obtained by training the CNN-BiLSTM-BiGRU neural network model using a training data set; the training data set is established using a multi-objective coronavirus disease optimization algorithm and a wave search algorithm; the training data set includes the upper limit of the one-time investment coefficient of each stage of the training power system, the lower limit of the new energy consumption rate and the corresponding installed capacity domain labels; the CNN-BiLSTM-BiGRU neural network model includes a convolutional neural network, a bidirectional long short-term memory neural network and a bidirectional gated recurrent unit connected in sequence.

[0064] The first layer of the CNN-BiLSTM-BiGRU neural network model uses convolutional neural networks (CNN) and performs maximum pooling transformation, in which each updated feature vector is considered to be a new input feature. The convolution layer and the maximum pooling layer realize the aggregation of feature vectors and output the maximum value, effectively reducing the data dimension; the second layer uses BiLSTM (Bidirectional Long Short-Term Memory) to capture the long-term bidirectional interaction between the time steps of sequence data, and a Dropout layer is added after the BiLSTM layer to reduce overfitting; the third layer uses BiGRU (Bidirectional Gated Recurrent Unit), which has two GRU sequence processing models and can process data forward and backward.

[0065] As an optional implementation method, the CNN-BiLSTM-BiGRU neural network model is trained using a training data set, specifically including:

[0066] S21: Build training dataset.

[0067] This application also proposes a power system installed capacity domain based on domain theory.

[0068] First, domain theory uses projection observation technology to present the implicit mathematical analytical expression of the system operation model in spatial geometry based on different analytical perspectives. It is an important method to support the construction of the power system operation space. Using domain theory, from the perspective of the installed capacity of wind, solar, thermal and storage (wind power, photovoltaic, thermal power, and electrochemical energy storage) of the power system, the installed capacity domain of the power system is established. It is defined as: taking into account the growth demand of the power load and the characteristics of the power structure of the power system, considering the system economy and the improvement of the new energy absorption capacity, and satisfying the power balance, reliable operation, and clean and low-carbon driving constraints in the long-term scale. The decision points are the annual installed capacity of various types of power equipment. The expression of the installed capacity domain model of the power system is:

[0069]

[0070] Among them: Ω is the installed capacity domain of the power system; x is the key decision vector corresponding to the decision point of the system in the long-term scale, including the installed capacity of each type of power supply equipment in each stage; z represents the discrete variables of the system, including the start and stop status of thermal power units in each period, the charging and discharging status of electrochemical energy storage equipment, etc.; y represents the vector composed of the remaining continuous variables of the system, including the specific output status of each unit and other auxiliary variables. f(x)≤0 is the unit capacity constraint; g(x,y,z)≤0 represents the unit combination constraint; h(y,z)=0 is the power balance constraint; k(y,z)≤0 is the clean and low-carbon driving constraint.

[0071] Secondly, for the above types of constraints, the specific constraint expressions of the power system installed capacity domain model are as follows.

[0072] 1) Constraint type 1: Unit capacity constraints mainly include upper and lower limit constraints of unit capacity, unit capacity connection constraints and upper limit constraints of one-time investment cost.

[0073]

[0074] in: The installed capacity of power equipment for the nth phase; The installed capacity of power equipment for the n+1th phase; is the lower limit of installed capacity in stage n; is the upper limit of installed capacity in the nth stage; is the unit capacity investment cost of the nth stage of power supply equipment, The installed capacity of power equipment for stage n-1; Put an upper limit on the expected one-time investment cost for stage n.

[0075] 2) Constraint type 2: Unit combination constraints mainly include wind, solar, thermal and storage equipment operation constraints and spinning reserve capacity constraints.

[0076] Wind and solar operation constraints:

[0077]

[0078] in: is the actual output of wind power in the nth stage, the sth scenario and the tth period; is the actual output of the photovoltaic power generation in the nth stage, the sth scene and the tth period; is the installed capacity of wind power in the nth stage; is the installed capacity of the nth stage of PV; is the predicted capacity factor of wind power in the nth stage, the sth scenario and the tth period; is the predicted capacity factor of the PV system in the nth stage, the sth scenario and the tth period.

[0079] Thermal power operation constraints:

[0080]

[0081] in: is the actual output of thermal power in the nth stage, the sth scenario and the tth period; is the actual output of thermal power in the nth stage, the sth scenario and the t-1th period; is the ramp power coefficient of thermal power; is the grid-connected capacity of thermal power in the nth stage, the sth scenario and the tth period, is the ramp-down power coefficient of thermal power; is the minimum technical output coefficient of thermal power; is the maximum technical output coefficient of thermal power; is the total installed capacity of thermal power, is the installed capacity of the jth unit of the nth stage of thermal power; It is the start and stop status of the jth unit in the tth period of the nth stage of the thermal power plant.

[0082] Electrochemical energy storage equipment operation constraints:

[0083]

[0084] Further linearization using the Big M method, introducing binary auxiliary variables

[0085]

[0086] in: The equivalent actual output of the electrochemical energy storage device in the nth stage, the sth scenario and the tth period; is the discharge power of the electrochemical energy storage device in the nth stage, the sth scene, the tth period; is the charging power of the electrochemical energy storage device in the nth stage, the sth scene, the tth period; is the installed capacity of the nth stage of the electrochemical energy storage device; is the state of charge of the electrochemical energy storage device at the nth stage, the sth scene, and the tth period; is the state of charge of the electrochemical energy storage device at the t-1 period of the nth stage, the sth scene; is the charging efficiency of the electrochemical energy storage device at the nth stage; is the discharge efficiency of the electrochemical energy storage device at the nth stage; is the rated energy of the electrochemical energy storage device at the nth stage; Δt is the time interval; The lowest state of charge; The highest state of charge; is the initial charge state of the sth scene in the nth stage; The state of charge at the end of the sth scene in the nth stage; is the charge and discharge status of the electrochemical energy storage device in the nth stage, the sth scene and the tth period; M is a constant coefficient.

[0087] Spinning reserve capacity constraints:

[0088]

[0089] in: is the output power of the electrochemical energy storage device in the nth stage, the sth scene, and the tth period; is the positive reserve factor of wind power; is the positive reserve factor of photovoltaic; is the positive reserve factor of the electric load; is the power of the electric load in the nth stage, the sth scene, the tth period; is the negative reserve coefficient of wind power; is the negative reserve factor of photovoltaic; is the negative reserve factor of the electric load.

[0090] 3) Constraint type 3: Power balance constraint

[0091]

[0092] in: is the actual electrical load.

[0093] 4) Constraint type 4: Clean and low-carbon driving constraints mainly include carbon emission limit constraints, new energy electricity share constraints and minimum new energy consumption rate constraints.

[0094]

[0095] Where: τ s is the weight coefficient of the sth scenario; is the carbon emission factor of the nth stage of thermal power generation; is the carbon emission limit for the nth stage, The requirement for the proportion of new energy electricity in the nth stage; is the expected new energy consumption rate in the nth stage.

[0096] In practical applications, the specific process of establishing the installed capacity domain data set (training data set) is as follows: generate the upper limit of the one-time investment cost and the lower limit of the new energy consumption rate in each stage through a random function, and further adopt the installed capacity domain generation method based on the multi-objective coronavirus disease optimization algorithm and the installed capacity domain boundary improvement method based on the wave search algorithm to obtain the corresponding upper and lower bounds of the installed capacity domain. The installed capacity domain data set is constructed by the generated massive input data and output data. A set of data in the installed capacity domain data set includes the upper limit of the one-time investment cost in each stage, the lower limit of the new energy consumption rate, the upper bound of the installed capacity domain, and the lower bound of the installed capacity domain.

[0097] As an optional implementation, constructing a training data set specifically includes:

[0098] S211: Generate the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of the training power system at each stage by using a random function.

[0099] S212: Generate an initial installed capacity domain of the training power system using a multi-objective coronavirus disease optimization algorithm, specifically including:

[0100] Step 1: Establish a multi-objective optimization model; the multi-objective optimization model takes the minimum total coefficient and the minimum new energy consumption rate as the objective function, and takes the unit capacity constraint, unit combination constraint and clean and low-carbon drive constraint as the constraint conditions.

[0101] The new energy consumption rate is an important indicator for the current power system assessment, and the reasonable power structure arrangement on the source side is a key factor affecting the new energy consumption target. In order to enhance the potential of the installed capacity domain in the new energy consumption level, a multi-objective optimization model considering the new energy consumption rate is established, and the Pareto solution set of the multi-objective problem is used to generate the installed capacity domain.

[0102] The multi-objective optimization model considering the new energy consumption rate is as follows:

[0103]

[0104] Among them, F a is the total cost; T a is the number of days in the current phase; is the maintenance cost per unit capacity of the power supply equipment at stage n; is the unit power operating cost of the electrochemical energy storage device at stage n; is the unit electricity operation cost of the nth stage of thermal power; The penalty cost per unit of electricity for load shedding, is the actual load shedding in the tth period of the sth scenario in the nth stage; G a is the new energy consumption rate, N rThe number of stages for power capacity decisions.

[0105] Step 2: Using a multi-objective coronavirus disease optimization algorithm, the multi-objective optimization model is solved to determine the optimal solution set in the installed capacity domain.

[0106] Secondly, for the above multi-objective optimization problem, a multi-objective coronavirus disease optimization algorithm is used for rapid solution. This algorithm is based on the coronavirus disease optimization algorithm. In the optimization process, archives are used to store non-dominated Pareto optimal solutions. By simulating the coronavirus replication process, a roulette wheel selection strategy is used to select effective archive solutions. It has excellent results in solving the global optimization problem of two objective functions.

[0107] The coronavirus disease optimization algorithm is a heuristic optimization algorithm inspired by the replication mechanism of coronavirus hijacking human cells. It is mainly divided into four stages: virus entry and peeling, virus replication, virus mutation, and new virus release. The search mechanism of the multi-objective coronavirus disease optimization algorithm is consistent with the coronavirus disease optimization algorithm. In particular, the multi-objective coronavirus disease optimization algorithm uses a dominance operator to compare solutions that consider multiple objective functions. All Pareto optimal solutions obtained during the optimization process are stored in an archive, and an archive controller is used to decide whether to retain or delete solutions in the archive.

[0108] The archive controller works as follows:

[0109] 1) If the archive is empty, the current solution should be accepted.

[0110] 2) If another solution dominates the archive, the corresponding solution should be deleted from the archive.

[0111] 3) If another solution is not dominant, the corresponding solution should be stored in the archive.

[0112] In order to improve the coverage of Pareto optimal solutions, solutions must be selected from the least crowded areas in the archive to promote improvements in other areas, and there are many neighboring solutions that should be deleted from the archive. MOVOIDOA uses a roulette wheel selection strategy to select a solution from the area with the least number of solutions in the archive; when the number of solutions in the archive reaches the upper limit, solutions with many neighboring solutions should be deleted. The probabilities of the two choices are as follows:

[0113]

[0114] Where: P i O is the probability of choosing a new solution, P i D is the probability of deleting the solution; c MO is a constant, N iMO Represents the number of neighboring solutions of the ith solution.

[0115] Finally, the multi-objective coronavirus disease optimization algorithm solves the multi-objective optimization model considering the new energy consumption rate, and obtains the Pareto optimal solution set of the multi-objective optimization problem considering the new energy consumption rate. Among all the solutions, the maximum power capacity is selected as the upper bound and the minimum power capacity is selected as the lower bound to obtain the initial installed capacity domain of each type of power source that expands with the increase of stages.

[0116] Step 3: According to the optimal solution set of the installed capacity domain, determine the initial installed capacity domain of the training power system; the upper limit of the initial installed capacity domain is the maximum value of the power capacity in the optimal solution set of the installed capacity domain; the lower limit of the initial installed capacity domain is the minimum value of the power capacity in the optimal solution set of the installed capacity domain.

[0117] S213: Based on the initial installed capacity domain, using a wave search algorithm, determine the installed capacity domain labels corresponding to the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system to obtain a training data set.

[0118] In practical applications, a method for improving the installed capacity domain boundary based on the wave search algorithm is proposed. By using the virtual boundary and iterative optimization method, the economic performance and the new energy consumption rate are combined to gradually reduce the installed capacity domain and effectively improve the effect of the installed capacity domain.

[0119] As an optional implementation mode, based on the initial installed capacity domain, a wave search algorithm is used to determine the installed capacity domain labels corresponding to the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system, specifically including:

[0120] (1) The maximum total cost and the maximum new energy consumption rate corresponding to the optimal solution set in the installed capacity domain are normalized to obtain a total cost benchmark value and a new energy consumption rate benchmark value.

[0121] First, the maximum total cost and the maximum new energy absorption rate in the Pareto optimal solution set (optimal solution set in the installed capacity domain) in step 2 are extracted, and the economic index (corresponding to the total cost) and the absorption rate index (corresponding to the new energy absorption rate) of the capacity design scheme can be normalized.

[0122] Furthermore, the fairness principle is used to extract a set of capacity design schemes from the Pareto optimal solution set, and the total cost and new energy consumption rate under the scheme are regarded as benchmark values. After normalization, the benchmark economic index (total cost benchmark value) and the benchmark consumption rate index (new energy consumption rate benchmark value) are obtained.

[0123]

[0124] Where: ΔF a is the total cost increment; is the minimum total cost; is the maximum value of total cost, ΔG a The increase in the consumption rate of new energy; It is the minimum increment of new energy consumption rate; The maximum value of the increase in the new energy consumption rate; is the normalized total cost benchmark value; It is the normalized benchmark value of new energy consumption rate.

[0125] (2) According to the total cost benchmark value and the new energy consumption rate benchmark value, the reduction process objective function is solved to obtain the reduction process benchmark target value; the reduction process objective function is Loss = δ f f a +δ g g a ; Where Loss is the benchmark target value of the reduction process, f a is the total cost benchmark value; g a is the benchmark value of new energy consumption rate; f is the weight of economic index; δ g is the weight of the consumption rate indicator.

[0126] Secondly, a process of installed capacity domain reduction is proposed by using virtual boundary and iterative optimization methods.

[0127] The objective function of the installed capacity domain reduction process is as follows, and the baseline target value of the reduction process can be obtained based on the baseline economic index and the baseline absorption rate index.

[0128] Loss = δ f f a +δ g g a (14)

[0129] Where: f a is the total cost benchmark value; g a is the benchmark value of new energy consumption rate; f is the weight of economic index; δ g is the weight of the consumption rate indicator.

[0130] (3) Based on the total cost benchmark value, the new energy consumption rate benchmark value and the reduction process benchmark target value, a wave search algorithm is used to determine the installed capacity domain labels corresponding to the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system.

[0131] 1) The capacity of each type of power equipment will be initialized in the initial installed capacity domain using a random function, and the reduction process target value will be calculated under the reduction process target function and compared with the reduction process benchmark target value.

[0132] 2) Use virtual boundaries to describe the upper and lower boundaries of the power installed capacity domain.

[0133] If the reduction process target value is worse than the reduction process benchmark target value, the virtual boundary sub-element is corrected according to the distance from the upper and lower bounds of the initial installed capacity domain. The virtual boundary sub-element is averaged with the adjacent initial installed capacity domain boundary elements to generate new virtual upper and lower bounds.

[0134] If the reduction process target value is better than the reduction process benchmark target value, the virtual boundary sub-element starts from the current capacity, and the virtual boundary repels to both sides to generate new virtual upper and lower bounds, and determines the exclusion range coefficient according to the excellence of the current target value.

[0135]

[0136] Where: R ex is the exclusion range coefficient, α ex is a constant coefficient, Loss bo To reduce the process baseline target value.

[0137] 3) The wave search algorithm is used to continuously update the capacity of the power supply equipment and generate a new virtual boundary until the virtual boundary fluctuation range is smaller than the set threshold, and the iteration ends.

[0138]

[0139] Where: N r is the number of stages for power capacity decision, γ k is the set threshold coefficient.

[0140] Finally, the wave search algorithm is used to determine the iteration direction and accelerate convergence.

[0141] WSA (Wave search algorithm) utilizes the unique algorithm design ideas of radar technology, adopts various improved greedy mechanisms, and utilizes the gradient information of the problem to be optimized, so that the algorithm has the characteristics of high precision, high efficiency, and strong adaptability. This application uses the wave search algorithm to determine the iteration direction, which is specifically divided into three stages: transmitting electromagnetic waves, reflecting electromagnetic waves, and receiving electromagnetic waves. It further introduces the fitting gradient descent method based on the central difference method to improve the search efficiency and accuracy. The specific process is to update the position of the three stages of transmitting electromagnetic waves, reflecting electromagnetic waves, and receiving electromagnetic waves in turn according to the initialization of the electromagnetic wave particle position, and further improve the position information through the fitting gradient descent method based on the central difference method to complete this iteration; the position is continuously updated until the upper limit of the number of iterations is reached.

[0142] Strategy for the electromagnetic wave emission phase:

[0143]

[0144] Where: w is the waveform control coefficient, is the i-th element of a column vector that follows a normal distribution and is arranged in order, X best is the current optimal position, X i is the current location, In accordance with X best The position matrix after rearrangement of proximity, is the newly generated position, L max is the maximum target value within the group.

[0145] The function of formula (17) is to simulate the outward diffusion of electromagnetic waves, reduce the possibility of falling into the local optimum, and improve the search efficiency. Formula (18) is an improved greedy mechanism, which makes the group position no worse than the current group position when the group position fluctuates outward.

[0146] Reflection electromagnetic wave stage strategy:

[0147]

[0148] Where: β w is the reflection intensity coefficient, r1 w is a random value between 0 and 1, is the number of particles that simulate the reflected electromagnetic waves. It is a position matrix that is rearranged in order from small to large target values.

[0149] Strategy for the electromagnetic wave receiving stage: The electromagnetic wave particle update judgment strategy for the electromagnetic wave receiving stage is consistent with the formula (20) for the electromagnetic wave reflecting stage.

[0150]

[0151] Where: w is the receiving coefficient, η w is a random number that follows a normal distribution, is the number of particles that simulate receiving electromagnetic waves. is the uniform optimal position, λ w is the correction factor, is a random value between 0 and 1.

[0152] A deterministic optimization technique is further introduced, namely the fitted gradient descent method based on the central difference method. This method uses the central difference method to fit the analytical information of the problem to be optimized and is used to search for the optimal solution to improve the search efficiency and accuracy. Its mathematical expression is:

[0153]

[0154] in: ε=10 -6 , is the i-th element of the gradient, α w is the step size coefficient.

[0155] S22: Input the upper limit of the one-time investment coefficient of each stage of the training power system and the lower limit of the new energy consumption rate into the current CNN-BiLSTM-BiGRU neural network model to obtain the upper limit prediction value of the installed capacity domain and the lower limit prediction value of the installed capacity domain.

[0156] S23: Determine a loss function value according to the installed capacity domain upper bound prediction value, the installed capacity domain lower bound prediction value and the corresponding installed capacity domain label.

[0157] S24: Determine whether a training end condition is met; the training end condition is that a maximum number of iterations is reached or a loss function value is less than a preset value.

[0158] S25: If yes, the training is terminated, and the current CNN-BiLSTM-BiGRU neural network model is used as a power system installed capacity domain generation model.

[0159] S26: If not, adjust the model parameters of the current CNN-BiLSTM-BiGRU neural network model according to the loss function value, and return "input the upper limit of the one-time investment coefficient of each stage of the training power system and the lower limit of the new energy consumption rate into the current CNN-BiLSTM-BiGRU neural network model to obtain the upper limit prediction value of the installed capacity domain and the lower limit prediction value of the installed capacity domain".

[0160] In practical applications, the installed capacity domain dataset is input into the CNN-BiLSTM-BiGRU neural network model for continuous training. After the training is completed, the finished neural network model is obtained. The upper limit of the expected one-time investment cost and the lower limit of the new energy consumption rate are input into the CNN-BiLSTM-BiGRU neural network model, and the corresponding upper and lower limits of the installed capacity domain can be quickly obtained.

[0161] This application proposes a method for generating a power system installed capacity domain, such as Figure 3 As shown in the figure, firstly, the installed capacity domain of the power system is proposed based on the domain theory; secondly, a multi-objective optimization model considering the new energy absorption capacity is established, and the multi-objective coronavirus disease optimization algorithm is used to solve it and generate the initial installed capacity domain; thirdly, the installed capacity domain reduction process is proposed using the virtual boundary and iterative optimization method, and the wave search algorithm is used to determine the iteration direction to generate the reduced installed capacity domain. Finally, based on the installed capacity domain dataset, a CNN-BiLSTM-BiGRU neural network model is constructed to realize the rapid generation of the installed capacity domain.

[0162] This application proposes a method for generating a power system installed capacity domain, which has the following advantages:

[0163] (1) The power system installed capacity domain based on domain theory proposed in this application can comprehensively describe the multi-dimensional attributes and constraints of the power system capacity, clearly display the boundaries and potential bottlenecks of the power system capacity, and provide decision makers with an intuitive and powerful decision support tool.

[0164] (2) This application uses a multi-objective coronavirus disease optimization algorithm to achieve rapid solution of multi-objective optimization problems considering the new energy absorption capacity. By utilizing the algorithm's excellent global search and local optimization capabilities, rapid optimization can be achieved under complex multi-objective problems, thereby improving the efficiency and effectiveness of optimization.

[0165] (3) This application proposes a capacity domain reduction process that uses virtual boundaries and iterative optimization, and uses a wave search algorithm to determine the iteration direction. It uses the dynamic characteristics of the wave to search and optimize, which can quickly cover the search space, accelerate the convergence process, and improve computing efficiency.

[0166] (4) This application adopts the CNN-BiLSTM-BiGRU neural network model, which combines the advantages of CNN, BiLSTM and BiGRU, has powerful time series feature extraction and processing capabilities, and can fully capture the complex relationships in the data. The neural network finished model can quickly generate the installed capacity domain boundary under the input data, and significantly improves the accuracy of the generated installed capacity domain, which is expected to provide strong decision-making support for the efficient management and optimization of the power structure of the power system.

[0167] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for generating an installed capacity domain of a power system is implemented.

[0168] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0169] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above-mentioned method for generating an installed capacity domain of a power system when executing the computer program.

[0170] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above-mentioned method for generating an installed capacity domain of a power system when executed by a processor.

[0171] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above-mentioned method for generating an installed capacity domain of a power system when executed by a processor.

[0172] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0173] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0174] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0175] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0176] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for generating a power system installed capacity domain, characterized in that: include: Obtain the expected upper limit value of the one-time investment coefficient of the target power system and the expected lower limit value of the new energy consumption rate; According to the expected upper limit of the one-time investment coefficient and the expected lower limit of the new energy consumption rate, the installed capacity domain of the target power system is determined by using the power system installed capacity domain generation model; the installed capacity domain is composed of the upper limit of the installed capacity domain and the lower limit of the installed capacity domain; wherein, the power system installed capacity domain generation model is obtained by training the CNN-BiLSTM-BiGRU neural network model using a training data set; the training data set is established using a multi-objective coronavirus disease optimization algorithm and a wave search algorithm; the training data set includes the upper limit of the one-time investment coefficient of each stage of the training power system, the lower limit of the new energy consumption rate and the corresponding installed capacity domain labels; the CNN-BiLSTM-BiGRU neural network model includes a convolutional neural network, a bidirectional long short-term memory neural network and a bidirectional gated recurrent unit connected in sequence.

2. The method for generating a power system installed capacity domain according to claim 1, characterized in that: The CNN-BiLSTM-BiGRU neural network model is trained using the training data set, including: Build a training dataset; Inputting the upper limit of the one-time investment coefficient of each stage of the training power system and the lower limit of the new energy consumption rate into the current CNN-BiLSTM-BiGRU neural network model to obtain the upper limit prediction value of the installed capacity domain and the lower limit prediction value of the installed capacity domain; Determining a loss function value according to the installed capacity domain upper bound prediction value, the installed capacity domain lower bound prediction value and the corresponding installed capacity domain label; Determine whether the training end condition is met; the training end condition is that the maximum number of iterations is reached or the loss function value is less than a preset value; If yes, the training is terminated and the current CNN-BiLSTM-BiGRU neural network model is used as the power system installed capacity domain generation model; If not, adjust the model parameters of the current CNN-BiLSTM-BiGRU neural network model according to the loss function value, and return "input the upper limit of the one-time investment coefficient of each stage of the training power system and the lower limit of the new energy consumption rate into the current CNN-BiLSTM-BiGRU neural network model to obtain the upper limit prediction value of the installed capacity domain and the lower limit prediction value of the installed capacity domain".

3. The method for generating a power system installed capacity domain according to claim 1, characterized in that: Construct a training data set, including: Generate the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system by using a random function; Generate an initial installed capacity domain of the training power system using a multi-objective coronavirus disease optimization algorithm; Based on the initial installed capacity domain, the wave search algorithm is used to determine the installed capacity domain labels corresponding to the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system to obtain a training data set.

4. The method for generating a power system installed capacity domain according to claim 3, characterized in that: The initial installed capacity domain of the training power system is generated by using a multi-objective COVID-19 optimization algorithm, specifically including: Establish a multi-objective optimization model; the multi-objective optimization model takes the minimum total coefficient and the minimum new energy consumption rate as the objective function, and takes the unit capacity constraint, unit combination constraint and clean low-carbon drive constraint as the constraint conditions; Solving the multi-objective optimization model using a multi-objective coronavirus disease optimization algorithm to determine an optimal solution set in the installed capacity domain; According to the optimal solution set of the installed capacity domain, the initial installed capacity domain of the training power system is determined; the upper limit of the initial installed capacity domain is the maximum value of the power capacity in the optimal solution set of the installed capacity domain; the lower limit of the initial installed capacity domain is the minimum value of the power capacity in the optimal solution set of the installed capacity domain.

5. The method for generating a power system installed capacity domain according to claim 4, characterized in that: The objective function is: Among them, F a is the total cost; T a is the number of days in the current phase; is the unit capacity investment cost of the power supply equipment at stage n; The installed capacity of power equipment for the nth phase; The installed capacity of power equipment for the n+1th phase; is the maintenance cost per unit capacity of the power supply equipment at stage n; τ s is the weight coefficient of the sth scenario; is the unit power operating cost of the electrochemical energy storage device at stage n; is the charging power of the electrochemical energy storage device in the nth stage, the sth scene, the tth period; is the discharge power of the electrochemical energy storage device in the nth stage, the sth scene, and the tth period; Δt is the period interval; is the unit electricity operation cost of the nth stage of thermal power; is the actual output of thermal power in the nth stage, the sth scenario and the tth period; The penalty cost per unit of electricity for load shedding, is the actual load shedding in the tth period of the sth scenario in the nth stage; G a is the new energy consumption rate, N r the number of stages for power capacity decisions; is the actual output of wind power in the nth stage, the sth scenario and the tth period; is the actual output of the photovoltaic power generation in the nth stage, the sth scene and the tth period; is the installed capacity of wind power in the nth stage; is the installed capacity of the nth stage of photovoltaic power generation; is the predicted capacity factor of wind power in the nth stage, the sth scenario and the tth period; is the predicted capacity factor of the PV system in the nth stage, the sth scenario and the tth period.

6. The method for generating a power system installed capacity domain according to claim 4, characterized in that: The unit capacity constraint is: in, The installed capacity of power equipment for the nth phase; The installed capacity of power equipment for the n+1th phase; is the lower limit of installed capacity in stage n; is the upper limit of installed capacity in the nth stage; is the unit capacity investment cost of the nth stage of power supply equipment, The installed capacity of power equipment for stage n-1; Invest an upper limit on the expected one-time investment cost for stage n; The unit combination constraints include wind and solar operation constraints, thermal power operation constraints, electrochemical energy storage equipment operation constraints and spinning reserve capacity constraints; The wind and solar operation constraints are: in, is the actual output of wind power in the nth stage, the sth scenario and the tth period; is the actual output of the photovoltaic power generation in the nth stage, the sth scene and the tth period; is the installed capacity of wind power in the nth stage; is the installed capacity of the nth stage of photovoltaic power generation; is the predicted capacity factor of wind power in the nth stage, the sth scenario and the tth period; is the predicted capacity factor of the PV at the nth stage, the sth scenario, and the tth period; The thermal power operation constraints are: in, is the actual output of thermal power in the nth stage, the sth scenario and the tth period; is the actual output of thermal power in the nth stage, the sth scenario and the t-1th period; is the ramp power coefficient of thermal power; is the grid-connected capacity of thermal power in the nth stage, the sth scenario and the tth period, is the ramp-down power coefficient of thermal power; is the minimum technical output coefficient of thermal power; is the maximum technical output coefficient of thermal power; is the total installed capacity of thermal power, is the installed capacity of the jth unit of the nth stage of thermal power; The start and stop status of the jth unit in the nth stage, sth scene, and tth period of thermal power; The operating constraints of electrochemical energy storage devices are: in, The equivalent actual output of the electrochemical energy storage device in the nth stage, the sth scenario and the tth period; is the discharge power of the electrochemical energy storage device in the nth stage, the sth scene, the tth period; is the charging power of the electrochemical energy storage device in the nth stage, the sth scene, the tth period; is the installed capacity of the nth stage of the electrochemical energy storage device; is the state of charge of the electrochemical energy storage device at the nth stage, the sth scene, and the tth period; is the state of charge of the electrochemical energy storage device at the t-1 period of the nth stage, the sth scene; is the charging efficiency of the electrochemical energy storage device at the nth stage; is the discharge efficiency of the electrochemical energy storage device at the nth stage; is the rated energy of the electrochemical energy storage device at the nth stage; Δt is the time interval; The lowest state of charge; The highest state of charge; is the initial state of charge of the sth scene in the nth stage; The state of charge at the end of the sth scene in the nth stage; The spinning reserve capacity constraint is: in, is the output power of the electrochemical energy storage device in the nth stage, the sth scene, and the tth period; is the positive reserve factor of wind power; is the positive reserve coefficient of photovoltaic; is the positive reserve factor of the electric load; is the power of the electric load in the nth stage, the sth scene, the tth period; is the negative reserve coefficient of wind power; is the negative reserve factor of photovoltaic; is the negative reserve factor of the electric load; The clean and low-carbon driving constraints include carbon emission limit constraints, new energy power ratio constraints and minimum new energy consumption rate constraints; Among them, τ s is the weight coefficient of the sth scenario; is the carbon emission factor of the nth stage of thermal power generation; is the carbon emission limit for the nth stage, The requirement for the proportion of new energy electricity in the nth stage; is the expected new energy consumption rate in the nth stage.

7. The method for generating a power system installed capacity domain according to claim 4, characterized in that: Based on the initial installed capacity domain, the installed capacity domain labels corresponding to the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system are determined by using a wave search algorithm, specifically including: Normalizing the maximum total cost and the maximum new energy consumption rate corresponding to the optimal solution set in the installed capacity domain to obtain a total cost benchmark value and a new energy consumption rate benchmark value; According to the total cost benchmark value and the new energy consumption rate benchmark value, the reduction process objective function is solved to obtain the reduction process benchmark target value; the reduction process objective function is Loss = δ f f a +δ g g a ; Where Loss is the benchmark target value of the reduction process, f a is the total cost benchmark value; g a is the benchmark value of new energy consumption rate; f is the weight of economic index; δ g is the weight of the consumption rate indicator; According to the total cost benchmark value, the new energy consumption rate benchmark value and the reduction process benchmark target value, the wave search algorithm is used to determine the installed capacity domain labels corresponding to the upper limit of the one-time investment coefficient and the lower limit of the new energy consumption rate of each stage of the training power system.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for generating an installed capacity domain of a power system according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for generating an installed capacity domain of a power system according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for generating an installed capacity domain of a power system according to any one of claims 1 to 7 is implemented.

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