Demand response planning benefit measuring and calculating method based on energy storage investment planning model and related equipment

Abstract

The embodiment of the invention provides a demand response planning benefit measuring and calculating method based on an energy storage investment planning model and related equipment, and relates to the technical field of power systems, and the method comprises the steps: building an energy storage investment planning model which does not consider demand response; generating a load demand scene based on the deep convolutional generative adversarial network; demand response related constraints are determined based on the load demand scene; adjusting the energy storage investment planning model based on the demand response related constraints; and calculating the demand response benefit based on the actual data of the power grid and the adjusted energy storage investment planning model. According to the technical scheme of the invention, the method fully considers the synergistic relationship and substitution between the energy storage and the demand response, can be widely applied to different power grid systems, and has good expandability.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a demand response planning benefit calculation method based on an energy storage investment planning model and related equipment. Background Art

[0002] With the advancement of the country's "dual carbon goals", the uncertainty of power grid operation has increased sharply, and the pressure on power grid source-load balance caused by the sudden change in new energy output and load demand has increased dramatically. In recent years, with the rapid growth of summer peak load and the further widening of peak-to-valley difference, regional energy demand peaks have appeared, and the problem of power source-load balance has become prominent. In response to these challenges, demand response has gradually become an important means to improve the flexibility and stability of the power system.

[0003] Energy storage systems can store excess electricity when there is an oversupply of electricity and release it when electricity demand peaks, thereby shaving peaks and filling valleys and smoothing load fluctuations. The traditional view is that energy storage systems are key to regulating loads and responding to fluctuations in renewable energy, but their high investment and operating costs limit large-scale deployment. At the same time, demand response, as a relatively flexible and low-cost load regulation method, is showing potential alternative benefits to energy storage. The cost of demand response is usually much lower than the investment and operating costs of energy storage systems. Energy storage systems need to consider factors such as equipment purchase, installation, maintenance, and charging and discharging losses, while demand response encourages users to adjust their electricity consumption through market mechanisms without the need for large equipment investments.

[0004] In this context, studying the substitution benefits of demand response for energy storage can provide a more economical solution for power system planning. By combining demand response with energy storage systems and giving full play to the advantages of both, it can not only reduce the overall cost of the power system, but also improve the operational flexibility of the system and achieve a higher level of renewable energy consumption.

[0005] On the one hand, current research focuses on the optimal dispatch strategy after incorporating demand response, and lacks a method for calculating the benefits of demand response. To this end, a reasonable method for calculating the benefits of demand response should be established. Based on the energy storage investment planning model and combined with the substitution benefits of demand response for energy storage, the benefits of demand response should be analyzed to provide a basis for decision-making at all levels of government. On the other hand, existing planning schemes that include energy storage are often based on specific typical scenarios. In order to ensure the safety of the power system in all scenarios, it is necessary to iteratively add scenarios through safety verification to repeatedly solve the planning problem. The calculation is very time-consuming and unacceptable even at the planning level. Summary of the invention

[0006] The embodiments of the present application provide a demand response planning benefit calculation method and related equipment based on an energy storage investment planning model to solve the technical problems existing in the prior art.

[0007] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0008] According to a first aspect of an embodiment of the present application, a method for calculating the benefit of demand response planning based on an energy storage investment planning model is provided, comprising:

[0009] Establish an energy storage investment planning model that does not consider demand response;

[0010] Generate load demand scenarios based on deep convolutional generative adversarial networks;

[0011] Determining demand response related constraints based on the load demand scenario;

[0012] Adjusting the energy storage investment planning model based on the demand response related constraints;

[0013] The demand response benefits are calculated based on actual grid data and the adjusted energy storage investment planning model.

[0014] In some embodiments of the present application, based on the above solution, the step of establishing an energy storage investment planning model that does not consider demand response includes:

[0015] The objective function of constructing the energy storage investment planning model is:

[0016] Determine the constraints of the energy storage investment planning model, the constraints include: upper and lower limits of generator output, minimum start and stop time constraints, upper and lower limits of charge and discharge power, exclusive logic constraints of charge and discharge, upper and lower limits of energy storage charge state, and power balance constraints.

[0017] In some embodiments of the present application, based on the aforementioned solution, the method of generating a load demand scenario based on a deep convolutional generative adversarial network includes:

[0018] Taking all historical hourly information of load demands as input data, and preprocessing the input data;

[0019] The preprocessed input data is fed into a deep convolutional generative adversarial network to generate load demand scenarios.

[0020] In some embodiments of the present application, based on the above solution, after generating the load demand scenario, it also includes:

[0021] Binary cross entropy is used as the evaluation criterion to evaluate the effect of load demand scenario generation.

[0022] In some embodiments of the present application, based on the above solution, adjusting the energy storage investment planning model based on the demand response related constraints includes:

[0023] Determining a demand response cost associated with the demand response-related constraints, and adding the demand response cost to an objective function of the energy storage investment planning model;

[0024] The demand response constraint is used to adjust the power balance constraint of the energy storage investment planning model.

[0025] In some embodiments of the present application, based on the above-mentioned solution, the calculation of demand response benefits based on actual grid data and the adjusted energy storage investment planning model includes:

[0026] Based on actual grid data, the adjusted energy storage investment planning model is used to first calculate the total cost of grid planning including energy storage, and then calculate the total cost of grid planning taking demand response and energy storage into account;

[0027] The demand response benefit is determined based on the total cost of the grid planning including energy storage and the total cost of the grid planning considering demand response and energy storage.

[0028] According to a second aspect of an embodiment of the present application, a demand response planning benefit calculation device based on an energy storage investment planning model is provided, comprising:

[0029] Establishing a unit for establishing an energy storage investment planning model without considering demand response;

[0030] A generation unit, used for generating load demand scenarios based on a deep convolutional generative adversarial network;

[0031] A determination unit, configured to determine demand response related constraints based on the load demand scenario;

[0032] An adjustment unit, configured to adjust the energy storage investment planning model based on the demand response related constraints;

[0033] The calculation unit is used to calculate the demand response benefit based on the actual data of the power grid and the adjusted energy storage investment planning model.

[0034] According to a third aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes the method according to the first aspect.

[0035] According to a fourth aspect of the embodiments of the present application, there is provided an electronic device, including: a memory and a processor;

[0036] The memory is used to store computer instructions;

[0037] The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in the first aspect.

[0038] The technical solution of the present application fully considers the synergistic relationship and substitution effect between energy storage and demand response, can be widely applied to different power grid systems, and has good scalability.

[0039] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0041] Figure 1 A flow chart of a method for calculating the benefit of demand response planning based on an energy storage investment planning model according to an embodiment of the present application is shown;

[0042] Figure 2 A block diagram of a demand response planning benefit calculation device based on an energy storage investment planning model according to an embodiment of the present application is shown;

[0043] Figure 3 A block diagram of an electronic device according to an embodiment of the present application is shown;

[0044] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0045] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0046] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0047] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0048] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Some embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0051] See also Figure 1 , shows a flow chart of a method for calculating the benefit of demand response planning based on an energy storage investment planning model according to an embodiment of the present application.

[0052] like Figure 1 As shown, a method for calculating the benefit of demand response planning based on an energy storage investment planning model is presented, including steps S100 to S500.

[0053] refer to Figure 1 , step S100, establishing an energy storage investment planning model without considering demand response.

[0054] It should be noted that the energy storage investment planning model takes the minimization of the total system cost including investment and operation as the objective function, and takes into account the constraints on the traditional thermal power unit side, the energy storage side and the system side.

[0055] In some feasible embodiments, based on the above solution, the step of establishing an energy storage investment planning model that does not consider demand response includes:

[0056] The objective function of constructing the energy storage investment planning model is:

[0057] Determine the constraints of the energy storage investment planning model, the constraints include: upper and lower limits of generator output, minimum start and stop time constraints, upper and lower limits of charge and discharge power, exclusive logic constraints of charge and discharge, upper and lower limits of energy storage charge state, and power balance constraints.

[0058] Exemplarily, the specific process of step S100 is as follows:

[0059] (1) Construct the objective function:

[0060] min K sys =K inv +K oper ;

[0061]

[0062] Where: K sys is the total system cost; K inv is the system investment cost; K oper is the system operation cost; P s and E s are the power and capacity of energy storage planning respectively. This paper mainly studies the capacity planning of energy storage, and its installation nodes are known boundary conditions; 1 and 2 are the unit power cost coefficient and unit capacity cost coefficient of energy storage respectively; H s For the continuous charge and discharge time of energy storage, this paper selects the most common electrochemical energy storage at present for research, H s Select 2 hours; t r is the inflation rate; d r is the discount rate; Y is the investment period of energy storage. The first item in the formula for calculating the system operation cost is the start-up and shutdown cost of the traditional unit, and the second item is the coal consumption cost of the traditional unit; S is the total number of scenarios; T is the total number of time periods contained in the key scenario set; G is the total number of traditional units; is the start-up and shutdown cost of the g-th traditional unit in the t-th period under the s-th scenario; P g,t,s is the output of the gth traditional unit in the tth period under the sth scenario.

[0063] (2) Determine the constraints:

[0064] For traditional generator sets, it is necessary to consider the upper and lower limits of generator output, generator climbing constraints, and minimum start and stop time constraints; at the energy storage constraint level, it is necessary to consider the upper and lower limits of charging and discharging power, the mutually exclusive logic constraints of charging and discharging, the upper and lower limits of energy storage charge state, and the power balance constraints.

[0065] In the energy storage model, the mutually exclusive logic constraints of charging and discharging are as follows:

[0066] P c,t,s Pdc,t,s =0;

[0067] Where: P c,t,s and P dc,t,s are respectively the charging and discharging power of the energy storage in the tth period under the sth scenario.

[0068] The power balance constraints are as follows:

[0069]

[0070] Where: P s Power planned for energy storage; D n,t,s is the load demand of the nth node in the tth period under the sth scenario; N is the total number of nodes; F l,t,s is the transmission power of the lth line in the tth time period under the sth scenario; Represents the set of lines with the first and last endpoints being n; Represents a set of lines with n end points.

[0071] refer to Figure 1 , step S200, generating a load demand scenario based on a deep convolutional generative adversarial network.

[0072] It should be noted that by sampling random variables or assuming that they obey certain specific distributions to generate a large number of scenarios to simulate the uncertainty of load demand, the power system planning problem considering the uncertainty of load demand is transformed into a deterministic planning optimization problem under multiple scenarios, which has the advantages of intuitive model and clear physical meaning.

[0073] Based on historical load demand data, a load demand scenario adversarial generation method based on deep convolutional generative adversarial network (DCGAN) is proposed. Through self-game training of the generative adversarial network, diverse load demand scenarios consistent with the distribution of historical data are generated without the need for a specific model of random variables. The time series coupling characteristics of load demand can be considered, and the correlation of load demands at different locations and of different types can be comprehensively considered.

[0074] In some feasible embodiments, based on the above solution, the method of generating a load demand scenario based on a deep convolutional generative adversarial network includes:

[0075] Taking all historical hourly information of load demands as input data, and preprocessing the input data;

[0076] The preprocessed input data is fed into a deep convolutional generative adversarial network to generate load demand scenarios.

[0077] It should be noted that the deep convolutional generative adversarial network includes a generator and a discriminator. In this embodiment, it can be seen from the data input dimension of the deep convolutional generative adversarial network that one sample corresponds to multiple historical output scenarios of different types of load demands. For this reason, a two-dimensional convolutional neural network is selected to construct the generator and discriminator. The two-dimensional convolutional neural network can not only extract multi-hidden layer features, but also share convolution kernels, which has significant advantages in processing high-dimensional data.

[0078] For the generator network: the input is a random noise vector, and the dimension can be adjusted according to the actual situation. The output is the generated load demand scene image, which has the same size as the input image. The structure includes input layer, intermediate layer, output layer, activation function, upsampling and output layer activation function. In order to improve the integrity of the convolution kernel sliding adoption, the random noise is converted into an n×n dimensional matrix and input into the generator. In terms of activation function: the tanh activation function is selected in the output layer, and the ReLU activation function is selected in the remaining layers. Finally, the generator outputs the generated scene.

[0079] For the discriminator network: convert the real samples and generated samples into a matrix as the input of the discriminator. In the first few layers of the network, use the LeakyReLU function as the activation function, and finally use the fully connected layer and sigmoid activation function to map the output results to the interval [0,1] to reflect the judgment results.

[0080] It should be noted that before using the deep convolutional generative adversarial network, the deep convolutional generative adversarial network needs to be trained.

[0081] For the training of deep convolutional generative adversarial networks, the generator and discriminator are usually trained alternately. When training the generator G, its weight is updated from two aspects: the error between the generated sample G(z) and the real data x and the discriminator's judgment result. In the process of training the discriminator D, the real data and the samples generated by the generator are input into the discriminator. The discriminator outputs the probability that the received sample belongs to the real data, and updates the network parameters through the discriminator's judgment error. The Adam optimization algorithm is used, the learning rate is set to 0.0002, and the loss function uses the binary cross entropy loss function.

[0082] In some feasible embodiments, based on the above solution, after generating the load demand scenario, the following is further included:

[0083] Binary cross entropy is used as the evaluation criterion to evaluate the effect of load demand scenario generation.

[0084] For example, the binary cross entropy is as follows:

[0085]

[0086] Where: x is the real sample.

[0087] refer to Figure 1 , step S300, determining demand response related constraints based on the load demand scenario.

[0088] Exemplarily, the demand response related constraints are as follows:

[0089] 0≤DR n,t,s ≤DR n,s,max ;

[0090] Where: DR n,s,max is the maximum value of the demand response load of node n in the sth scenario, and D represents the load demand.

[0091] refer to Figure 1 , step S400, adjusting the energy storage investment planning model based on the demand response related constraints.

[0092] In this embodiment, in addition to considering the constraints on the traditional thermal power unit side, the energy storage side, and the system side, demand response related constraints are added to obtain an energy storage expansion planning model that considers demand response.

[0093] In some feasible embodiments, based on the above solution, adjusting the energy storage investment planning model based on the demand response related constraints includes:

[0094] Determining a demand response cost associated with the demand response-related constraints, and adding the demand response cost to an objective function of the energy storage investment planning model;

[0095] The demand response constraint is used to adjust the power balance constraint of the energy storage investment planning model.

[0096] Exemplarily, based on the above example, step S400 is specifically as follows:

[0097] The objective function changes as follows:

[0098] min K sysDR =K inv +K oper +K dr ;

[0099]

[0100] Where: K sysDR is the total system cost after considering demand response; K dr is the demand response cost; α DR is the cost per unit load adjustment; |DR n,t,s | represents the absolute value of the demand response quantity of the nth node at time t in the sth scenario.

[0101] In addition to the constraints on the traditional thermal power unit side, the energy storage side, and the system side, demand response related constraints are added to the constraints.

[0102] The power balance constraints will be adjusted as follows:

[0103]

[0104] Where: DR n,t,s is the demand response load of the nth node in the tth period under the sth scenario.

[0105] refer to Figure 1 , step S500, calculating the demand response benefit based on the actual data of the power grid and the adjusted energy storage investment planning model.

[0106] In some feasible embodiments, based on the above solution, the calculation of demand response benefits based on actual grid data and the adjusted energy storage investment planning model includes:

[0107] Based on actual grid data, the adjusted energy storage investment planning model is used to first calculate the total cost of grid planning including energy storage, and then calculate the total cost of grid planning taking demand response and energy storage into account;

[0108] The demand response benefit is determined based on the total cost of the grid planning including energy storage and the total cost of the grid planning considering demand response and energy storage.

[0109] In summary, the technical solution of the present application generates load demand scenarios based on deep convolutional generative adversarial networks. The load demand scenarios can take into account different types of renewable energy, different sites of the same type of renewable energy, and the coupling relationship between renewable energy and loads. In addition, the technical solution of the present application establishes a reasonable demand response benefit calculation method. Based on the energy storage investment planning model, combined with the substitution benefit of demand response to energy storage, the demand response benefit is analyzed, and the economic benefits of introducing demand response to replace energy storage are studied. The technical solution fully considers the synergistic relationship and substitution effect between energy storage and demand response, can be widely used in different power grid systems, and has good scalability. The example analysis shows that the demand response benefit calculation method based on the energy storage planning model of the technical solution of the present application can be fully based on the past energy storage investment planning model, quantify the energy storage investment planning cost saved after the introduction of demand response, and provide a reasonable basis for relevant management departments to make decisions.

[0110] The following describes an embodiment of the device of the present application, which can be used to execute a demand response planning benefit calculation method based on an energy storage investment planning model in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the above method of the present application.

[0111] Reference Figure 2 As shown, according to an embodiment of the present application, a demand response planning benefit calculation device 200 based on an energy storage investment planning model includes:

[0112] Establishing unit 201, used to establish an energy storage investment planning model without considering demand response;

[0113] A generating unit 202, configured to generate a load demand scenario based on a deep convolutional generative adversarial network;

[0114] A determination unit 203, configured to determine demand response related constraints based on the load demand scenario;

[0115] An adjustment unit 204, configured to adjust the energy storage investment planning model based on the demand response related constraints;

[0116] The calculation unit 205 is used to calculate the demand response benefit based on the actual data of the power grid and the adjusted energy storage investment planning model.

[0117] like Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, the steps of the above-mentioned demand response planning benefit calculation method based on the energy storage investment planning model are implemented.

[0118] Since the electronic device introduced in this embodiment is a device used to implement a demand response planning benefit calculation device based on an energy storage investment planning model in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0119] During the specific implementation process, when the computer program 311 is executed by a processor, any implementation method in the embodiments corresponding to the first aspect can be implemented.

[0120] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0121] It should be noted that Figure 4 The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0122] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402 and RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0123] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0124] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0125] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0126] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0127] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0128] As another aspect, the present application further provides a computer program product or a computer program, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes a method for calculating the benefit of demand response planning based on an energy storage investment planning model described in the above embodiment.

[0129] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements a demand response planning benefit calculation method based on an energy storage investment planning model described in the above embodiment.

[0130] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0131] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0132] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. It should be understood that the present application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for calculating the benefit of demand response planning based on an energy storage investment planning model, characterized in that: include: Establish an energy storage investment planning model that does not consider demand response; Generate load demand scenarios based on deep convolutional generative adversarial networks; Determining demand response related constraints based on the load demand scenario; Adjusting the energy storage investment planning model based on the demand response related constraints; The demand response benefits are calculated based on actual grid data and the adjusted energy storage investment planning model.

2. The method according to claim 1, characterized in that The energy storage investment planning model established without considering demand response includes: The objective function of constructing the energy storage investment planning model is: Determine the constraints of the energy storage investment planning model, the constraints include: upper and lower limits of generator output, minimum start and stop time constraints, upper and lower limits of charge and discharge power, exclusive logic constraints of charge and discharge, upper and lower limits of energy storage charge state, and power balance constraints.

3. The method according to claim 1, characterized in that The load demand scenario generated based on the deep convolutional generative adversarial network includes: Taking all historical hourly information of load demands as input data, and preprocessing the input data; The preprocessed input data is fed into a deep convolutional generative adversarial network to generate load demand scenarios.

4. The method according to claim 3, characterized in that After generating the load demand scenario, it also includes: Binary cross entropy is used as the evaluation criterion to evaluate the effect of load demand scenario generation.

5. The method according to claim 2, characterized in that: The adjusting the energy storage investment planning model based on the demand response related constraints includes: Determining a demand response cost associated with the demand response-related constraints, and adding the demand response cost to an objective function of the energy storage investment planning model; The demand response constraint is used to adjust the power balance constraint of the energy storage investment planning model.

6. The method according to claim 1, characterized in that The calculation of demand response benefits based on actual grid data and the adjusted energy storage investment planning model includes: Based on actual grid data, the adjusted energy storage investment planning model is used to first calculate the total cost of grid planning including energy storage, and then calculate the total cost of grid planning taking demand response and energy storage into account; The demand response benefit is determined based on the total cost of the grid planning including energy storage and the total cost of the grid planning considering demand response and energy storage.

7. A demand response planning benefit calculation device based on an energy storage investment planning model, characterized in that: include: Establishing a unit for establishing an energy storage investment planning model without considering demand response; A generation unit, used for generating load demand scenarios based on a deep convolutional generative adversarial network; A determination unit, configured to determine demand response related constraints based on the load demand scenario; An adjustment unit, configured to adjust the energy storage investment planning model based on the demand response related constraints; The calculation unit is used to calculate the demand response benefit based on the actual data of the power grid and the adjusted energy storage investment planning model.

8. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer is enabled to execute the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer instructions; The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-6.

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