A Flexible Load Credible Capacity Calculation Method for Power Grid Planning

Abstract

The present invention provides a method for calculating the credible capacity of flexible loads for power grid planning. According to the historical power consumption load data of the regional distribution network obtained, the typical daily loads in different seasons are determined; according to the uncertain factors affecting the flexible load response, the probability distribution model corresponding to the uncertain factors is selected, and within the corresponding confidence interval, the uncertain factors are converted into deterministic quantitative correction parameters to realize the correction of the flexible load response quantity; within the dispatching period, with the goal of minimizing the operation cost of the regional distribution network, an optimization model of the regional distribution network considering flexible loads is established, the constraint conditions of the model are configured, and iterative solution is carried out to obtain the typical daily demand response value; according to the calculated typical daily demand response value, combined with the flexible load response quantity, the response quantity and certainty of the user under the corresponding incentive level are calculated; the present invention can determine the credible capacity of system operation and provide a basis for load assessment during power grid planning.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid planning, and particularly relates to a method for calculating the credible capacity of flexible loads for power grid planning. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] Power balance is a key link in power grid planning. By combining the load forecasting results and the existing substation capacity, and according to the capacity-to-load ratio guidelines, the capacity of new substations is determined. Due to the large-scale growth of flexible loads and their dual uncertainties of source and load, some loads will have the ability to actively participate in system regulation, increasing the uncertainty of load characteristics and thus enhancing the uncertainty of the planning boundary. Therefore, it is necessary to reasonably evaluate the equivalent output and credible capacity of flexible loads to delimit the boundary for load forecasting in the power grid planning process.

[0004] Under certain incentives, the reduction / transfer amount of flexible loads has a certain randomness, which brings a certain degree of uncertainty. In existing research, statistical distribution laws or linear models are mostly used to describe the uncertain behavior of user responses at a certain incentive level to delimit the response boundary of flexible loads. However, the adjustable potential of flexible loads is affected by various factors and has non-linear characteristics, and there are certain limitations in using linear models to estimate the magnitude of flexible loads. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a method for calculating the credible capacity of flexible loads for power grid planning, which can determine the credible capacity of system operation and provide a basis for load assessment in power grid planning.

[0006] According to some embodiments, the present invention adopts the following technical solutions:

[0007] A method for calculating the credible capacity of flexible loads for power grid planning includes the following steps:

[0008] According to the historical power consumption load data of the regional distribution network obtained, use the clustering method to extract features and determine the typical daily loads in different seasons;

[0009] According to the uncertain factors affecting the response of flexible loads, select the probability distribution model corresponding to the uncertain factors, and within the corresponding confidence interval, convert the uncertain factors into deterministic quantitative correction parameters to realize the correction of the response amount of flexible loads;

[0010] Within the dispatching period, with the goal of minimizing the operation cost of the regional distribution network, establish an optimization model of the regional distribution network considering flexible loads and configure the constraint conditions of the model;

[0011] Using the typical daily loads in different seasons, the optimization model of the regional distribution network is iteratively solved under constraint conditions to obtain the typical daily demand response values;

[0012] According to the calculated typical daily demand response values and combined with the flexible load response amounts, calculate the response amounts and certainty degrees of users at the corresponding incentive levels.

[0013] As an alternative implementation, the clustering method is the k-means clustering algorithm.

[0014] As an alternative implementation, the extracted features include the maximum value, minimum value, average value, and standard deviation of the hourly electricity consumption load data of the regional distribution network throughout the year.

[0015] As an alternative implementation, the uncertain factors include meteorology, day type, industry type, and price incentives.

[0016] As an alternative implementation, the specific process of converting uncertain factors into deterministic quantitative correction parameters includes: correcting the flexible load response amounts at each moment through comprehensive weighting, and obtaining the response certainty degree and the user response amount probability curve through Monte Carlo simulation under the sampling of multiple response results.

[0017] As an alternative implementation, the operating cost of the regional distribution network includes the cost of purchasing electricity from the main grid by the distribution network and the fees paid by the system to users when the distributed energy storage is under dispatch.

[0018] As an alternative implementation, the constraint conditions include power balance constraints, peak-valley difference rate constraints, and physical constraints of distributed energy storage, where:

[0019] The power balance constraint is that the credibility of the equality holds is greater than the set confidence level. The equality is the difference between the baseline load of the distribution network and the power interaction between the distribution network and the main grid, which is equal to the sum of the demand response amounts of other types of users plus the charging power of the distributed energy storage battery minus the discharging power of the distributed energy storage battery.

[0020] A flexible load credible capacity calculation system for power grid planning, comprising:

[0021] A feature extraction module, configured to extract features using a clustering method according to the acquired historical electricity consumption load data of the regional distribution network to determine the typical daily loads in different seasons;

[0022] A flexible load response module, configured to select a probability distribution model corresponding to the uncertain factors according to the uncertain factors affecting the flexible load response, and convert the uncertain factors into deterministic quantitative correction parameters within the corresponding confidence interval to achieve the correction of the flexible load response amounts;

[0023] The optimization model construction module is configured to establish an optimization model of the regional distribution network considering flexible loads with the goal of minimizing the operating cost of the regional distribution network within a scheduling period, and configure the constraint conditions of the model.

[0024] The solution module is configured to use the typical daily loads in different seasons to iteratively solve the optimization model of the regional distribution network under the constraint conditions to obtain the typical daily demand response value.

[0025] The calculation module is configured to calculate the response amount and certainty of users at the corresponding incentive level according to the calculated typical daily demand response value in combination with the flexible load response amount.

[0026] A computer-readable storage medium stores multiple instructions, and the instructions are adapted to be loaded and executed by a processor of a terminal device to perform the steps of the above method.

[0027] A terminal device includes a processor and a computer-readable storage medium. The processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are adapted to be loaded and executed by the processor to perform the steps of the above method.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] By considering the uncertainty of the flexible load response behavior, the present invention aims at minimizing the system operation cost, takes into account constraints such as the credibility opportunity of the flexible load, calculates its credible capacity, and obtains the response potential of the flexible load and its corresponding certainty level under a certain confidence level, thereby quantitatively analyzing the adjustable ability of the flexible load to participate in the power supply balance of the power system under different regulation strategies, and providing a basis for load assessment in grid planning.

[0030] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The specification drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0032] Figure 1 It is a schematic flowchart of at least one embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The present invention will be further described below in conjunction with the drawings and embodiments.

[0034] It should be noted that the following detailed description is illustrative and aims to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention pertains.

[0035] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0036] As Figure 1 shown, a method for calculating the credible capacity of flexible loads for power grid planning is characterized in that: within the research period T, an uncertainty model of the flexible load response behavior is constructed, a flexible load uncertainty response model is established, and the credible capacity is calculated by taking the minimum system operation cost as the goal and considering constraints such as credibility opportunities, providing a basis for load assessment during power grid planning. The specific steps include:

[0037] Step 1: Adopt the k-means clustering algorithm. According to the hourly electricity load data of the regional distribution network obtained, using the maximum value, minimum value, average value, and standard deviation as extraction features, obtain the typical daily load in spring, summer, autumn, and winter.

[0038] Step 2: Establish a flexible load response uncertainty model, analyze the uncertain factors affecting the flexible load response, select the probability distribution model corresponding to the uncertain factors, and select the confidence level α corresponding to the correction of the uncertain factors. Within the confidence interval [0, α], convert the uncertain factors into deterministic quantification correction parameters, and correct the flexible load response amount at time t through comprehensive weighting.

[0039] Through Monte Carlo simulation, under the sampling of n response results, obtain the response certainty and the probability curve of the user response amount.

[0040] The uncertain factors include meteorology, day type, industry type, and price incentive.

[0041] Step 3: Within the scheduling period T, with the goal of minimizing the operation cost of the regional distribution network, establish an optimization model of the regional distribution network considering flexible loads. The objective function is:

[0042]

[0043] In the formula, C is the total system operation cost; C 1 represents the power purchase cost of the distribution network from the main grid; C2 represents the cost paid by the system to users when the distributed energy storage accepts scheduling. T is the number of time periods in a research cycle, and Δt is the time interval for each time period; P lt represents the baseline load of the distribution network in the t-th time period; P IL,t represents the demand response volume of other types of users; P ch,t 、P dis,t represent the charging power and discharging power of the distributed energy storage battery in the t-th time period respectively, pr t is the time-of-use electricity price of the power grid; pr IL,t is the compensation electricity price for demand response.

[0044] Step 4: Establish the constraints of the objective function, including power balance constraints, peak-valley difference rate constraints, and physical constraints of distributed energy storage.

[0045] (1) Among them, the traditional power balance constraint is represented by using the credibility opportunity constraint of flexible load response:

[0046] Pr{P lt -P CC =P IL,t +P ch,t -P dis,t}≥γ (2)

[0047] Among them, P CC represents the interactive power between the distribution network and the main network, and γ is the confidence level.

[0048] (2) Peak-valley difference rate constraint:

[0049]

[0050] Among them, N represents dividing the research cycle T into N equal time periods, P av,t is the average equivalent load in the N-th time period, and β represents the peak-valley difference rate that can be set. Among them:

[0051] (3) Distributed energy storage constraint conditions:

[0052] The capacity at the previous moment + the charge and discharge power in the current time period * time = the capacity at the current moment.

[0053] E t =E t-1 +η ch ·P ch,t ·Δt-Δt·P dis,t / η d (4)

[0054] In the formula, E t and E t-1 represent the remaining battery capacity in the t-th time period and the (t - 1)-th time period respectively, η chand η d They are the charging efficiency and discharging efficiency of the energy storage battery respectively.

[0055] The inequality constraint for the remaining battery power in the t-th period is:

[0056] E min ≤E t ≤E BESS (5)

[0057] In the formula, E BESS represents the rated capacity of the energy storage configuration, and E min is the lower limit of the energy storage capacity.

[0058] The charging and discharging power of the energy storage does not exceed the limit value.

[0059] 0 ≤ P ch,t ≤ P max

[0060] 0 ≤ P dis,t ≤ P max (6)

[0061] In the formula, P max is the maximum charging and discharging power limit of the energy storage system.

[0062] Step 5: Based on the base load data of four typical days, combined with the objective function and constraint conditions obtained in Steps 3 and 4, establish a dynamic programming model, and solve the above model through the particle swarm optimization algorithm to obtain the demand response values of typical days, including other demand response resource quantities and distributed energy storage operation strategies.

[0063] Step 6: According to the calculated demand response values of typical days, combined with the flexible load response model in Step 2, simulate and calculate the response quantity and certainty of users under specific incentive levels.

[0064] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0065] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more flows and / or one or more blocks. Figure 1 one or more flows and / or blocks Figure 1 for implementing the functions specified in one or more blocks.

[0066] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means for implementing the functions specified in one or more flows and / or one or more blocks. Figure 1 one or more flows and / or blocks Figure 1 for implementing the functions specified in one or more blocks.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or one or more blocks. Figure 1 one or more flows and / or blocks Figure 1 for implementing the functions specified in one or more blocks.

[0068] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for calculating the credible capacity of flexible loads for power grid planning, characterized in that: It includes the following steps: According to the historical electricity load data of the regional distribution network obtained, use the clustering method to extract features and determine the typical daily loads in different seasons; According to the uncertain factors affecting the flexible load response, select the probability distribution model corresponding to the uncertain factors, and within the corresponding confidence interval, convert the uncertain factors into deterministic quantitative correction parameters to realize the correction of the flexible load response amount; The specific process of converting the uncertain factors into deterministic quantitative correction parameters includes: realizing the correction of the flexible load response amount at each moment through comprehensive weighting, and through Monte Carlo simulation, under the sampling of multiple response results, obtaining the response certainty and the probability curve of the user response amount; During the scheduling period, with the goal of minimizing the operating cost of the regional distribution network, establish an optimization model of the regional distribution network considering flexible loads and configure the constraint conditions of the model; the operating cost of the regional distribution network includes the cost of purchasing electricity from the main grid by the distribution network and the fees paid by the system to users when the distributed energy storage is dispatched; the constraint conditions include power balance constraints, peak-valley difference rate constraints, and physical constraints of distributed energy storage, where: The power balance constraint is that the credibility of the equality holding is greater than the set confidence level, and the equality is the difference between the baseline load of the distribution network and the interactive power between the distribution network and the main grid, which is equal to the sum of the demand response amounts of other types of users plus the charging power of the distributed energy storage battery minus the discharging power of the distributed energy storage battery; Using the typical daily loads in different seasons, under the constraint conditions, iteratively solve the optimization model of the regional distribution network to obtain the typical daily demand response value; According to the calculated typical daily demand response value, combined with the flexible load response amount, calculate the response amount and certainty of the user at the corresponding incentive level.

2. A method for calculating the credible capacity of flexible loads for power grid planning according to claim 1, characterized in that: The clustering method is the k-means clustering algorithm.

3. A method for calculating the credible capacity of flexible loads for power grid planning according to claim 1, characterized in that: The feature extraction includes the maximum value, minimum value, average value, and standard deviation of the hourly electricity load data of the regional distribution network throughout the year.

4. A method for calculating the credible capacity of flexible loads for power grid planning according to claim 1, characterized in that: The uncertain factors include meteorology, day type, industry type, and price incentives.

5. A system for calculating the credible capacity of flexible loads for power grid planning, characterized in that: It includes: A feature extraction module configured to extract features according to the historical electricity load data of the regional distribution network obtained and use the clustering method to determine the typical daily loads in different seasons; A flexible load response module configured to select the probability distribution model corresponding to the uncertain factors affecting the flexible load response and convert the uncertain factors into deterministic quantitative correction parameters within the corresponding confidence interval to realize the correction of the flexible load response amount; The specific process of converting uncertain factors into deterministic quantitative correction parameters includes: correcting the flexible load response amount at each moment through comprehensive weighting, and obtaining the response certainty and the user response amount probability curve through Monte Carlo simulation and sampling of multiple response results; The optimization model construction module is configured to establish an optimization model of the regional distribution network considering flexible loads with the goal of minimizing the operating cost of the regional distribution network within the scheduling period, and configure the constraint conditions of the model; the operating cost of the regional distribution network includes the power purchase cost of the distribution network from the main grid and the fees paid by the system to users when the distributed energy storage is dispatched; the constraint conditions include power balance constraints, peak-valley difference rate constraints, and physical constraints of distributed energy storage, where: The power balance constraint is that the credibility of the equality holds is greater than the set confidence level, and the equality is the difference between the baseline load of the distribution network and the interactive power between the distribution network and the main grid, which is equal to the sum of the demand response amounts of other types of users plus the charging power of the distributed energy storage battery minus the discharging power of the distributed energy storage battery; The solution module is configured to use the typical daily load in different seasons to iteratively solve the regional distribution network optimization model under the constraint conditions to obtain the typical daily demand response value; The calculation module is configured to calculate the response amount and certainty of the user under the corresponding incentive level according to the calculated typical daily demand response value and in combination with the flexible load response amount.

6. A computer-readable storage medium, characterized in that: it stores multiple instructions, and the instructions are suitable for being loaded and executed by the processor of the terminal device to perform the steps of the method described in any one of claims 1-4.

7. A terminal device, characterized in that: it includes a processor and a computer-readable storage medium, the processor is used to implement each instruction; the computer-readable storage medium is used to store multiple instructions, and the instructions are suitable for being loaded and executed by the processor to perform the steps of the method described in any one of claims 1-4.

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