Power grid dispatching method and device considering electric alkene hydrogen electromagnetic energy supply load characteristics and wind power consumption and medium

Abstract

The invention discloses a power grid dispatching method and device considering electric alkene hydrogen electromagnetic energy supply load characteristics and wind power consumption and a medium. The method comprises the steps that S100, a load aggregator schedules a heavy-load electric olefin hydrogen electromagnetic energy supply load and a light-load electric olefin hydrogen electromagnetic energy supply load in a centralized mode; s200, based on a Latin hypercube sampling method, multiple scenes of wind power generation power changes are obtained, and the probability of each scene is calculated; s300, constructing a power grid dispatching model under a plurality of constraint conditions based on the plurality of objective functions; wherein the plurality of objective functions consider the heavy load type electro-olefinic hydrogen electromagnetic energy supply load and the light load type electro-olefinic hydrogen electromagnetic energy supply load, and the probability of the scene of wind power generation power change; and S400, performing power grid dispatching by using the improved whale optimization algorithm. The objective function comprises the minimum wind curtailment amount, the minimum compensation amount, the minimum increased electricity consumption and the minimum standby amount; the constraint conditions comprise supply and demand balance constraint, power constraint of the electric alkene hydrogen electromagnetic energy supply unit, climbing constraint of the electric alkene hydrogen electromagnetic energy supply unit, wind power output constraint and heat storage tank heat storage constraint. The method has the beneficial technical effects that the wind power absorption capability is improved; the wind power output can be accurately predicted; the power grid operation cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy technology, and in particular relates to a power grid dispatching method, device and medium that take into account the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption. Background Art

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

[0003] In recent years, my country has vigorously promoted the use of electricity to replace coal. The traditional method of using coal heating or catalysts to crack and crack heavy chemical raw materials to produce small molecules such as olefins and methane is not conducive to environmental protection. Among the measures to replace coal with electricity, the promotion and application of electric olefin hydrogen electromagnetic energy supply can effectively reduce the use of coal resources, help adjust the terminal energy consumption structure, increase the proportion of electricity in terminal energy consumption, promote the active consumption of renewable energy such as wind and solar, promote energy cleanliness, and reduce air pollution. Electric olefin hydrogen electromagnetic energy supply refers to the use of electricity to crack and crack heavy chemical raw materials to produce small molecules such as olefins and methane and then produce hydrogen. During the low electricity consumption period, hydrogen can be produced by using the surplus wind power during the low electricity consumption period, and the hydrogen energy can be stored or used by downstream industries; during the peak electricity consumption period, the stored hydrogen energy can be used to generate electricity using fuel cells and incorporated into the public power grid. The power demand required for electric olefin hydrogen electromagnetic energy supply is the electric olefin hydrogen electromagnetic energy supply load.

[0004] However, there are also a series of problems in promoting the development of electric olefin hydrogen electromagnetic energy supply on a large scale. First, electric olefin hydrogen electromagnetic energy supply increases the load on the power grid, and wind power output has large volatility and uncertainty, which affects the safe and stable operation of the power grid. Wind power is intermittent and volatile, and requires high peak-shaving capabilities of the power grid. At present, thermal power dominates peak-shaving, and flexibility resources (such as energy storage, grid interaction, and demand response) are insufficient, making it difficult to quickly adapt to wind power fluctuations. Secondly, the clean heating cost of electric olefin hydrogen electromagnetic energy supply is high, and ordinary heavy chemical users are less enthusiastic about using electric olefin hydrogen electromagnetic energy supply.

[0005] Existing grid dispatching methods often only dispatch a single type of load or energy, and lack a dispatching strategy that comprehensively considers the electric hydrogen electromagnetic energy supply load and wind power consumption. This leads to low dispatching efficiency, failure to fully utilize renewable energy, and may increase grid operation costs.

[0006] When existing power grid dispatching methods generate wind power change scenarios, they often use simple random sampling or Monte Carlo methods. These methods may not accurately reflect the actual changes in wind power, thus affecting the accuracy and reliability of the dispatching model.

[0007] When setting the objective function, existing grid dispatching methods often only consider a single objective (such as minimizing cost) and ignore multi-objective optimization. At the same time, the constraints may not be comprehensive enough and fail to fully consider the actual situation and limitations of grid operation.

[0008] Existing grid dispatching methods may have limitations in searching for global optimal solutions when solving complex grid dispatching problems, especially when facing complex constraints. This results in the dispatching scheme being unable to obtain the optimal solution in a short time and may not be able to adapt to the uncertainty of grid operation. Summary of the invention

[0009] In view of the above-mentioned deficiencies of the prior art, the present invention aims to provide a grid dispatching method taking into account the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption.

[0010] One purpose of the method is to comprehensively consider the differences between heavy-loaded hydrogen-electric electromagnetic energy supply loads and light-loaded hydrogen-electric electromagnetic energy supply loads, and accurately generate wind power change scenarios. Through the flexible scheduling of hydrogen-electric electromagnetic energy supply loads, the wind power absorption capacity is improved to adapt to the volatility and uncertainty of wind power. Through the centralized scheduling of different types of hydrogen-electric electromagnetic energy supply loads by load aggregators, more refined load management can be achieved; the load balance of the power grid can be optimized, and the economy and reliability of power grid operation can be improved. The hydrogen-electric electromagnetic energy supply load has certain flexible characteristics, including the flexible characteristics of the heat source and the flexibility of heat use by heavy chemical users. Therefore, the inventors considered further absorbing wind power through load characteristic analysis to increase the benefits of the participants.

[0011] One purpose of the method is to generate multiple wind power generation change scenarios through the Latin hypercube sampling method, calculate the probability of each scenario, enhance the power grid dispatching ability to handle random events, and improve the stability and prediction accuracy of the power grid.

[0012] One purpose of the method is to set a complete objective function and constraint conditions, and use the improved whale optimization algorithm to dispatch the power grid, reduce wind abandonment, and improve the adaptability and absorption capacity of the power grid to wind power.

[0013] One purpose of the method is to use the improved whale optimization algorithm to improve the efficiency and accuracy of solving the power grid dispatching model and improve the quality and implementation effect of the dispatching plan.

[0014] In order to achieve the above object, the present invention provides a grid dispatching method considering the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption, comprising the following steps:

[0015] S100: The heavy-load electric hydrogen electromagnetic energy supply load and the light-load electric hydrogen electromagnetic energy supply load are centrally dispatched by the load aggregator;

[0016] S200: Based on the Latin hypercube sampling method, multiple scenarios of wind power generation change are obtained, and the probability of each scenario is calculated;

[0017] S300: Based on multiple objective functions and under multiple constraints, construct a power grid dispatching model; wherein the multiple objective functions take into account the heavy-load electric hydrogen electromagnetic energy supply load and the light-load electric hydrogen electromagnetic energy supply load, as well as the probability of the scenario of wind power generation power change;

[0018] S400: Based on the power grid dispatching model, the improved whale optimization algorithm is used to perform power grid dispatching.

[0019] In order to achieve the above-mentioned object, the present invention also provides a power grid dispatching device considering the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption, including a memory, a processor and a computer program, wherein the memory is connected to the processor:

[0020] a memory configured to store the computer program;

[0021] a processor configured to run the computer program;

[0022] Among them, when the computer program is run by the processor, it executes the power grid dispatching method that takes into account the load characteristics of electric olefins and hydrogen electromagnetic energy supply and wind power consumption.

[0023] In order to achieve the above-mentioned objectives, the present invention also provides a non-transitory computer-readable storage medium, which is configured to store computer-readable instructions, wherein when the computer-readable instructions are executed by a processor, the power grid dispatching method considering the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption is executed.

[0024] Compared with the prior art, the present invention has the following beneficial technical effects:

[0025] Improve dispatching efficiency and energy utilization: By comprehensively considering the differences between heavy-duty electric hydrogen electromagnetic energy supply loads and light-duty electric hydrogen electromagnetic energy supply loads, the wind power consumption capacity can be improved through flexible dispatching of electric hydrogen electromagnetic energy supply loads to adapt to the uncertainty of wind power. Different types of electric hydrogen electromagnetic energy supply loads can be centrally dispatched through load aggregators.

[0026] Accurately predict wind power output: Generate multiple wind power generation power change scenarios through the Latin hypercube sampling method, calculate the probability of each scenario, enhance the grid dispatching ability to handle random events, and improve the stability and prediction accuracy of the grid. The Latin hypercube sampling method can more accurately reflect the actual changes in wind power. By accurately generating wind power change scenarios, wind power output can be predicted more accurately, thereby optimizing the dispatch of wind power resources.

[0027] Optimize grid dispatching, reduce grid operation costs, and reduce wind curtailment: The present invention sets multiple objective functions (including minimizing wind curtailment, minimizing compensation amount, minimizing increased power consumption, and minimizing reserve capacity) and multiple constraints (including supply and demand balance constraints, power constraints of electric hydrogen electromagnetic power supply units, climbing constraints, wind power output constraints, and heat storage tank heat storage constraints), which can comprehensively consider the economy and environmental protection of grid operation.

[0028] Optimizing the performance of the power grid dispatching algorithm: The present invention adopts the improved whale optimization algorithm for power grid dispatching, which has the advantages of fast convergence speed and strong global search ability. Through strategies such as wandering foraging, encirclement and contraction, and spiral predation, the global optimal solution can be found more efficiently, improving the efficiency and accuracy of solving the power grid dispatching model. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0030] Figure 1A A flowchart of a power grid dispatching method considering load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption provided for a specific embodiment of the present invention;

[0031] Figure 1B A schematic diagram of a power grid dispatching device taking into account the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption provided for a specific embodiment of the present invention;

[0032] Figure 2 A timing diagram of power, heating power and heat storage capacity of an electric olefin hydrogen boiler provided in a specific embodiment of the present invention;

[0033] Figure 3 A schematic diagram of a load dispatching management mode of an electric hydrogen electromagnetic energy supply is provided for a specific embodiment of the present invention;

[0034] Figure 4 A flow chart of an improved whale optimization algorithm provided for a specific embodiment of the present invention;

[0035] Figure 5 A curve diagram showing the relationship between an electric hydrogen electromagnetic energy supply load and other loads provided in a specific embodiment of the present invention;

[0036] Figure 6 A wind power generation power change scenario set provided for a specific embodiment of the present invention;

[0037] Figure 7The thermal power and wind power output results in scenario 1 provided for a specific embodiment of the present invention;

[0038] Figure 8 A specific embodiment of the present invention provides a relationship between the unit standby cost and the wind abandonment amount and the expected value of the standby amount;

[0039] Fig. 9 A comparison of wind power and thermal power output results under scenario 1 and scenario 2 provided for a specific embodiment of the present invention;

[0040] Fig.10 A comparison of the electromagnetic energy supply load of hydrogen in scenario 1 and scenario 2 provided for a specific embodiment of the present invention;

[0041] Fig.11 A comparison of the electromagnetic energy supply load of hydrogen in scenario 1 and scenario 3 provided for a specific embodiment of the present invention;

[0042] Fig.12 A comparison of wind power and thermal power planned output under scenario 1 and scenario 4 provided for a specific embodiment of the present invention;

[0043] Fig.13 A comparison of the electromagnetic energy supply load of hydrogen peroxide under scenario 1 and scenario 4 is provided for a specific embodiment of the present invention.

[0044] Fig.14 A specific embodiment of the present invention provides a power grid dispatching device that takes into account the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption. DETAILED DESCRIPTION

[0045] The present invention is further described in detail below through the accompanying drawings and specific embodiments.

[0046] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in this embodiment have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0047] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0048] Figure 1AA flowchart of a grid dispatching method considering load characteristics of electric olefin hydrogen electromagnetic energy supply and wind power consumption is provided for a specific embodiment of the present invention. As shown in FIG1 , in a specific embodiment of the present invention, the present invention provides a grid dispatching method considering load characteristics of electric olefin hydrogen electromagnetic energy supply and wind power consumption, including the following steps:

[0049] S100: The heavy-load electric hydrogen electromagnetic energy supply load and the light-load electric hydrogen electromagnetic energy supply load are centrally dispatched by the load aggregator;

[0050] S200: Based on the Latin hypercube sampling method, multiple scenarios of wind power generation change are obtained, and the probability of each scenario is calculated;

[0051] S300: Based on multiple objective functions and under multiple constraints, construct a power grid dispatching model; wherein the multiple objective functions take into account the heavy-load electric hydrogen electromagnetic energy supply load and the light-load electric hydrogen electromagnetic energy supply load, as well as the probability of the scenario of wind power generation power change;

[0052] S400: Based on the power grid dispatching model, the improved whale optimization algorithm is used to perform power grid dispatching.

[0053] Figure 1B The present invention provides a schematic diagram of a power grid dispatching device that takes into account the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption, which is provided for a specific embodiment of the present invention.

[0054] like Figure 1B As shown in the figure, in the load characteristic dispatching architecture of the electric hydrogen electromagnetic energy supply, sensors and other measuring units measure the user's physical state quantity (including the current load output state of responsive small and medium loads, as well as load forecasts and new energy output forecasts and other information), and convert the physical quantity into digital information. The above digital information of the user is transmitted to the control center managed by the load aggregator (integrated energy dispatching operator) through the communication network and the terminal device. The control units such as the driver convert the control information into a physical drive signal, which acts on the responsive small and medium loads and energy storage equipment in the electric hydrogen electromagnetic energy supply system to realize the control of the electric hydrogen electromagnetic energy supply system.

[0055] In a specific embodiment of the present invention, the step S100 (centrally dispatching the heavy-load electric hydrogen electromagnetic energy supply load and the light-load electric hydrogen electromagnetic energy supply load by the load aggregator) includes the following steps:

[0056] The load aggregator collects the operating data of heavy-load electric hydrogen electromagnetic energy supply load and light-load electric hydrogen electromagnetic energy supply load respectively, and formulates dispatching strategies respectively to optimize the load balance of the power grid;

[0057] The load aggregator decomposes the overall dispatch instructions received from the ancillary service market, issues heavy-load dispatch instructions for heavy-load electric, hydrogen and electromagnetic energy supply loads, and issues light-load dispatch instructions for light-load electric, hydrogen and electromagnetic energy supply loads.

[0058] Figure 2 A timing diagram of the power, heating power and heat storage capacity of an electric hydrogen boiler is provided for a specific embodiment of the present invention. Figure 2 As shown, this is a power timing diagram of a 3.2MW electric hydrogen electromagnetic boiler, where the horizontal axis is time, the left vertical axis is the output power, the right vertical axis is the heat storage capacity, the purple curve is the heat storage capacity, the brown curve is the heating power, and the blue curve is the electric boiler power.

[0059] Heavy-duty electric olefin hydrogen electromagnetic energy supply load refers to the energy supply method provided by electric olefin hydrogen (referring to the use of electricity to crack heavy chemical raw oil to prepare olefins and methane and other small molecules to produce hydrogen) technology when the electromagnetic load (i.e. the general term for electric load and magnetic load) is high in the power system. This type of load usually refers to the load that consumes a lot of electricity in the power grid and has a greater impact on the stability of the power grid. For example, large industrial users and data centers require a lot of electricity during operation, and their power supply stability and power quality need to be given priority in power grid dispatching. In heavy-load mode, the electric olefin hydrogen electromagnetic energy supply load needs to be able to provide high-power energy output; in order to improve energy utilization efficiency, efficient power conversion technology may be used; it needs to have a more stable energy output capability.

[0060] Light-load electro-hydrogen electromagnetic energy supply load refers to the energy supply method provided by electro-hydrogen technology in the power system when the electromagnetic load is low. This type of load refers to the load that consumes less electricity in the power grid and has less impact on the stability of the power grid. For example, small commercial users and residential users in residential areas have relatively low electricity consumption in the power grid. In light-load mode, the system has a lower demand for power output; it is more flexible and can adjust the energy output according to the actual needs of the system; it is more energy-saving and environmentally friendly.

[0061] The present invention distinguishes between heavy-load electric hydrogen electromagnetic energy supply loads and light-load electric hydrogen electromagnetic energy supply loads, and can reasonably allocate power resources according to load characteristics to improve the operating efficiency of the power system; better match the actual needs of the system with the energy supply, thereby improving energy utilization efficiency; through flexible scheduling of light and heavy loads, it can better absorb renewable energy such as wind power, reduce wind abandonment, and promote the absorption of new energy; by giving priority to ensuring the power supply of heavy-load electric hydrogen electromagnetic energy supply loads, reduce the instability risk caused by excessive load on the power grid, and improve the stability of the power grid; through scheduling of light-load electric hydrogen electromagnetic energy supply loads, it can enhance the flexibility of the power grid and increase the adaptability of the power grid to fluctuations in renewable energy, such as the intermittent and uncertain nature of wind power and photovoltaics.

[0062] In the prior art, the heat dynamic equation of the electrohydrogen electromagnetic energy load is shown in the following formula (1):

[0063]

[0064] In formula (1): R absorb is the heat absorbed by the electromagnetic energy supply load of the electric hydrogen, J / ㎡; t is the unit time, s; W dispersed The power of heat generated by the electromagnetic energy load for the hydrogen-electrolyte, W; A dispersed The heating area of ​​the hydrogen electromagnetism energy load is m2.

[0065] In the prior art, the heat dissipation equation of the electrohydrogen electromagnetic energy supply load is shown in the following formula (2):

[0066]

[0067] In formula (2): R dissipate Heat dissipated by the electromagnetic energy supply load of hydrogen, J / ㎡; T indoor T is the temperature inside the boiler of the hydrogen-electromagnetic energy supply load, ℃; outdoor is the temperature of the outside of the electric hydrogen electromagnetic energy load boiler, ℃; α is the heat conduction coefficient of the electric hydrogen electromagnetic energy load boiler material, W / (℃㎡); β is the surface area of ​​the electric hydrogen electromagnetic energy load boiler in contact with the outside, ㎡.

[0068] The capacity of a single electric hydrogen electromagnetic energy supply load station or user is too small to participate in electricity market transactions. Figure 3 A schematic diagram of a load dispatching management mode of electrohydrogen electromagnetic energy supply is provided for a specific embodiment of the present invention. Figure 3 As shown, multiple electric hydrogen electromagnetic energy supply loads can be centrally dispatched through load aggregators to form a certain scale of shiftable load or interruptible load, and load aggregators can act as agents for multiple electric hydrogen electromagnetic energy supply loads to centrally participate in the power ancillary service market.

[0069] In a specific embodiment of the present invention, an optimization mechanism for the characteristics of the electric hydrogen electromagnetic energy supply load based on the wind and solar power output is provided, and a characteristic model of the electric hydrogen electromagnetic energy supply load is established. By considering the operation mode of the electric hydrogen electromagnetic boiler in different time periods (such as valley electricity + flat electricity), a heat dynamic model and a heat loss model of the electric hydrogen electromagnetic energy supply load are established. Two operation modes, namely supply without storage and supply and storage at the same time, are adopted, so that the electric hydrogen electromagnetic load operates and stores heat during the valley period, and releases heat during the peak period, thereby achieving peak shaving and valley filling. The present invention can effectively balance the load of the power system, reduce wind power waste, and improve the efficiency of electric energy utilization.

[0070] For the electric hydrogen electromagnetic energy supply load station, it is more economical to use electricity during the valley period. Therefore, under normal circumstances, the electric hydrogen electromagnetic energy supply load is concentrated at night. The simple operation mode can adopt the valley electricity + flat electricity operation mode, which can be divided into two forms.

[0071] Supply-only, no-storage mode: The electric hydrogen electromagnetic boiler operates at "flat power" and only supplies heat to the heavy chemical industry system, and the heat storage tank does not store heat, so it is referred to as supply-only, no-storage mode;

[0072] Supply-and-storage method: During operation, it not only supplies heat to the chemical system but also stores heat in the heat storage tank, so it is referred to as supply-and-storage method.

[0073] The heat storage type electric hydrogen electromagnetic load has both the storage function of the heat storage tank device for thermal energy and the dual function of wind power consumption. There are many ways to cooperate between the heat sources. When the electricity load is low, the low-peak electricity is used for heating and stored in the heat storage device, and the heat is released from the heat storage device during the peak electricity consumption, realizing "peak shaving and valley filling".

[0074] By considering the operation mode of the electric hydrogen electromagnetic boiler in different time periods (such as valley power + flat power), the heat dynamic model and heat loss model of the electric hydrogen electromagnetic energy supply load are established. The two operation modes of supply without storage and supply and storage are adopted, so that the electric hydrogen electromagnetic load operates and stores heat during the valley period, and releases heat during the peak period, thereby realizing peak shaving and valley filling. Therefore, the present invention can effectively balance the load of the power system, reduce wind power waste, and improve the efficiency of electric energy utilization.

[0075] The wind power output forecast error can be roughly considered to obey the normal distribution, the expectation of which is 0 and the variance is The calculation formula of wind power output standard deviation is shown in formula (3):

[0076]

[0077] In formula (3): C wind is the installed capacity of wind power, MW.

[0078] The Latin hypercube sampling method is a type of stratified sampling. This method is to first cut the cumulative distribution curve into multiple equidistant intervals, and then randomly sample each interval. The steps are as follows:

[0079] (1) Sampling

[0080] Assume that the required data dimension of each wind power output scenario is K, X k (k=1,2,...,K) is the data of any wind power output scenario, and the corresponding cumulative probability distribution function is Y k =F k (X k). The number of wind power output scenarios is N, and the cumulative probability distribution function Y k The graph is cut into N intervals, and the length of all intervals is equal to 1 / N. The midpoint value of each interval after cutting is regarded as the corresponding interval Y k The value of X is inverted using the inverse function k The value of X k The sampling value of can be expressed as formula (4).

[0081]

[0082] After sampling of each wind power output scenario is completed, all random wind power output values ​​are randomly arranged into a row in the matrix, and finally a K×N order sampling matrix or experimental matrix is ​​formed, as shown in formula (5).

[0083]

[0084] (2) Arrangement

[0085] For a K×N-order matrix V, a correlation coefficient matrix ρ with a dimension of K×K can be generated, as shown in formula (6):

[0086]

[0087] In formula (6): ij Represents the correlation coefficient between the i-th row and the j-th row.

[0088] As shown in formula (7), the square root of the correlation coefficient matrix is ​​used to measure the correlation between different sampling matrices:

[0089]

[0090] Permutation matrix L KN Each row of data is located in the same place as the sampling matrix X KN The main steps are as follows:

[0091] (1) For the permutation matrix L KN Perform initial arrangement, that is, randomly arrange the elements in each row.

[0092] (2) The permutation matrix L KN The row-to-row data correlation matrix ρ L To express, where ρ L It is a positive definite symmetric matrix. Then, we use the Cholesky decomposition method to obtain a non-singular lower triangular matrix D, as shown in the following formula (8):

[0093] ρ L ×{ρ Liji×1,2,…,K; j=1,2,…,K}=DD T (8)

[0094] Since the matrix D is a non-singular matrix, it has an inverse matrix, so a matrix with a smaller correlation can be obtained, as shown in the following formula (9):

[0095] G KN =D -1 L KN (9)

[0096] With the help of Iman and Conover, it can be proved that the matrix G KN With the matrix L KN Compared with , the correlation has been reduced. However, the matrix G KN The data contained in may not be integers, so the order of the data cannot be obtained. The root mean square of the correlation coefficient of the sampling matrix is ​​ρ Xrms It represents and can be calculated by formula (10).

[0097]

[0098] In formula (10): s is the probability of scenario s; T is the number of dispatching periods in a day; S is the number of wind power output scenarios; is the wind power output in the tth dispatch period under scenario s; f t is the probability density function of wind power output in the tth scheduling period.

[0099] If the day-ahead wind power output, dispatchable electric hydrogen electromagnetic energy load and base load are comprehensively considered and optimized, the efficiency of the entire system can be effectively improved.

[0100] The present invention uses wind power forecast output and forecast deviation to represent actual wind power output, adopts Latin hypercube sampling method to generate multiple wind power output scenarios, and through sampling and correlation coefficient matrix calculation, the present invention reduces the impact of wind power output forecast error on system scheduling, improves the accuracy of wind power consumption and the stability of system scheduling, and thus better adapts to the uncertainty of wind power output.

[0101] In a specific embodiment of the present invention, the step S200 (obtaining multiple scenarios of wind power generation power change based on the Latin hypercube sampling method, and calculating the probability of each scenario) includes the following steps:

[0102] S210: Determine the cumulative probability distribution function of wind power generation;

[0103] S220: dividing the cumulative probability distribution function into N equal probability intervals;

[0104] S230: Perform random sampling in each interval to generate sampling values;

[0105] S240: Arranging the sampled values ​​using a correlation coefficient matrix to form a plurality of wind power generation scenarios;

[0106] S250: Calculate the probability of each wind power generation scenario.

[0107] When considering the use of thermal storage type electric hydrogen electromagnetic energy load to absorb wind power, it is necessary to take into account the strong volatility of wind power. In the existing technology, the general short-term prediction error is about 15%. The actual output of wind power can be expressed by the predicted output of wind power and the prediction deviation, as shown in formula (11):

[0108]

[0109] In formula (11): is the actual wind power output at time t, MW; is the predicted wind power output at time t, MW; is the wind power forecast deviation at time t, MW.

[0110] In a specific embodiment of the present invention, the Latin hypercube sampling method and the correlation coefficient matrix method are used to simulate wind power prediction errors, optimize scheduling strategies, reduce the uncertainty caused by prediction errors, improve the robustness and stability of system scheduling, and reduce the negative impact of wind power prediction errors on system operations.

[0111] The Latin hypercube sampling method is a type of stratified sampling. This method is to first cut the cumulative distribution curve into multiple equidistant intervals, and then randomly sample each interval. The steps are as follows:

[0112] Sampling: Sampling the random variables within each stratified interval to ensure that all intervals have corresponding random sampling points.

[0113] Arrangement: Adjust the order of random sampling points to minimize the correlation between random sampling points.

[0114] By simulating and generating multiple possible wind power output scenarios based on the Latin hypercube sampling method, the present invention can effectively analyze and optimize the response characteristics of the electric hydrogen electromagnetic energy supply load, thereby improving the wind power absorption capacity and reducing the wind power abandonment phenomenon.

[0115] In step S300, the objective function and constraints of the load scheduling of the hydrogen-electricity electromagnetic energy supply are set.

[0116] In a specific embodiment of the present invention, the objective function includes: minimizing the amount of wind abandonment, minimizing the compensation amount, minimizing the increased power consumption and minimizing the reserve amount. By setting the objective function of the electric hydrogen electromagnetic energy load scheduling, the present invention can optimize the scheduling, ensure the efficient operation of the system, reduce the system cost and environmental impact, improve the wind power consumption, reduce the wind abandonment rate and reserve cost, and optimize energy utilization.

[0117] The constraints include: supply and demand balance constraints, power constraints of the electric hydrogen electromagnetic energy supply unit, climbing constraints of the electric hydrogen electromagnetic energy supply unit, wind power output constraints and heat storage constraints of the heat storage tank. By setting the constraints of the electric hydrogen electromagnetic energy supply load scheduling, the present invention can ensure the stability and reliability of the system during the scheduling process, ensure that the scheduling strategy meets the physical and economic constraints of the system operation, and improve the reliability of the system operation.

[0118] In the optimization management, the present invention considers minimizing the amount of wind abandoned, minimizing the amount of compensation, minimizing the increased power consumption and minimizing the amount of reserve as the objective function. By setting the objective functions of minimizing the amount of wind abandoned, minimizing the amount of compensation and the increased power consumption in the multi-objective optimization model, the present invention can realize the flexible scheduling of the electromagnetic energy supply load of the electric olefin hydrogen, optimize the allocation of power grid resources, and enhance the stability and security of the power grid.

[0119] Objective function (1): Minimize the amount of wind curtailment

[0120] The objective function (1) is to minimize the amount of abandoned wind, as shown in formula (12):

[0121]

[0122] In formula (12): S is the wind power output scenario set; — is the wind curtailment switch variable under the t-th scheduling period scenario s; T is the time period of a day; ρ s is the probability of scene s appearing; is the wind power output in scenario s during the t-th scheduling period, MW. is the wind power consumption in the tth dispatch period, MW.

[0123] In a specific embodiment of the present invention, the step of obtaining the minimized wind abandonment comprises the following steps:

[0124] In each wind power generation scenario, the first difference between wind power output and wind power consumption is calculated;

[0125] When the first difference is a positive number, the first difference is multiplied by the probability of the wind power generation scenario and the wind abandonment switch variable to obtain the scenario wind abandonment amount;

[0126] The wind abandonment volume of all wind power generation scenarios is summed up to obtain the total wind abandonment volume;

[0127] The predicted wind power output is optimized, and wind power is absorbed by dynamically scheduling the electric hydrogen electromagnetic energy load, thereby minimizing the overall wind abandonment.

[0128] By accurately predicting wind power output and dynamically adjusting the operating state of the electromagnetic load, the present invention can maximize the absorption of wind power, reduce the wind abandonment rate, improve the utilization efficiency of wind power, and reduce energy waste.

[0129] Objective function (2): Minimize the compensation amount

[0130] When scheduling, it is necessary to consider the minimum compensation for heavy chemical users. Since the electromagnetic energy supply load of hydrogen and electricity will be concentrated in the off-peak period when not participating in the scheduling, it will enjoy the off-peak transmission and distribution price. If it is to be deployed to the peak period, it is necessary to compensate the heat source station for the electricity fee. Specifically, as shown in formula (13):

[0131]

[0132] In formula (13): is the heavy-duty electric hydrogen electromagnetic energy supply load in the t-th dispatching period, MW; is the light-load electric hydrogen electromagnetic energy supply load in the t-th dispatching period, MW; is the heavy-duty electric hydrogen electromagnetic energy supply load in the tth scheduling period without optimal scheduling, MW; is the light-load electric hydrogen electromagnetic energy supply load in the tth scheduling period without optimal scheduling, MW; is the compensation for the heavy-duty electric hydrogen electromagnetic energy load participating in the dispatch in the tth dispatch period, RMB / MWh; C user is the compensation for the light-load electric hydrogen electromagnetic energy supply load participating in the dispatch, RMB / MWh; t0 is the duration of each dispatch period, h.

[0133] In a specific embodiment of the present invention, obtaining the minimized compensation amount includes the following steps:

[0134] In each dispatching period, the second difference between the heavy-duty electric hydrogen electromagnetic energy supply load after optimized dispatching and the heavy-duty electric hydrogen electromagnetic energy supply load without optimized dispatching is multiplied by the compensation rate for the heavy-duty electric hydrogen electromagnetic energy supply load participating in dispatching, to obtain the heavy-duty compensation fee for the heavy-duty electric hydrogen electromagnetic energy supply load participating in dispatching;

[0135] In each scheduling period, the third difference between the light-load electric hydrogen electromagnetic energy supply load after optimized scheduling and the light-load electric hydrogen electromagnetic energy supply load without optimized scheduling is multiplied by the compensation rate for the heavy-load electric hydrogen electromagnetic energy supply load participating in the scheduling, so as to obtain the light-load compensation fee for the light-load electric hydrogen electromagnetic energy supply load participating in the scheduling;

[0136] The compensation amount is obtained by summing up the heavy load compensation fee and light load compensation fee of all dispatch periods and multiplying it by the duration of each dispatch period.

[0137] Minimize the compensation amount.

[0138] A specific embodiment of the present invention, through a scheduling strategy, schedules the heavy-duty electric hydrogen electromagnetic energy supply load to the peak period, which can minimize the compensation fee, reduce the additional burden on users, optimize the cost structure, and improve the economy of the system.

[0139] Objective function (3): Minimize the increased power consumption

[0140] When scheduling the light-load electric hydrogen electromagnetic energy supply load, the user's electricity consumption will increase. Although this part of the electricity consumption can help absorb wind power, it still causes unnecessary waste. Therefore, the increased electricity consumption of users should be minimized, as shown in formula (14):

[0141]

[0142] In a specific embodiment of the present invention, the step of obtaining the minimized increased power consumption comprises the following steps:

[0143] In each scheduling period, the third difference between the light-load electric hydrogen electromagnetic energy supply load after optimized scheduling and the light-load electric hydrogen electromagnetic energy supply load without optimized scheduling is multiplied by the duration of each scheduling period to obtain the increased power consumption after the light-load electric hydrogen electromagnetic energy supply load participates in the scheduling;

[0144] Minimize the increased power usage.

[0145] When the present invention schedules the light-load electric hydrogen electromagnetic load, it can avoid unnecessary waste of electric energy, minimize the increased power consumption, reduce the consumption of invalid electric energy, reduce the electricity cost, and improve the economy of system operation by accurately controlling the load scheduling period.

[0146] Objective function (4): Minimize the amount of spare parts

[0147] In order to make up for the deviation between the planned output and the actual output of the wind farm, the power dispatcher must reserve additional reserve capacity. When arranging wind power output a day ahead, if the planned wind power output is higher, the reserve capacity required during the day will be higher, as shown in formula (15):

[0148]

[0149] In formula (15): It is a spare switch variable.

[0150] In a specific embodiment of the present invention, the step of obtaining the minimized reserve amount includes the following steps:

[0151] In each wind power generation scenario, the fourth difference between wind power consumption and wind power output is calculated;

[0152] When the difference is a positive number, multiplying the fourth difference by the probability of the wind power generation scenario, and then multiplying by the reserve switch variable to obtain a scenario reserve amount;

[0153] The scenario reserve of all wind power generation scenarios is summed up to obtain the overall reserve;

[0154] The wind power output forecast is optimized, and wind power is absorbed by dynamically scheduling the electric hydrogen electromagnetic energy load, thereby minimizing the overall reserve capacity.

[0155] By optimizing wind power output prediction and electric hydrogen electromagnetic load scheduling, the present invention can reduce the demand for backup power, reduce the demand for backup capacity, minimize the backup amount, reduce the waste of backup power, reduce the system backup cost, and improve the efficiency of wind power consumption.

[0156] The following constraints need to be considered in the optimization management of electromagnetic energy supply load scheduling:

[0157] Constraint (1): Supply and demand balance constraint

[0158] At any time, the system supply and demand are equal, as shown in formula (16):

[0159]

[0160] In formula (16): is other fixed loads in the tth dispatch period, MW; is the power generation load of the jth pure condensing thermal power unit in the tth dispatching period, MW; is the power generation load of the jth cogeneration unit in the tth dispatch period, MW; is the power generation load of the j-th wind turbine in the t-th scheduling period, MW.

[0161] In a specific embodiment of the present invention, the step of obtaining the supply-demand balance constraint comprises the following steps:

[0162] Ensure that the power supply of the power grid matches the power demand of the power grid in any dispatch period;

[0163] Among them, the power supply of the power grid is the sum of the power generation load of pure condensing thermal power units, the power generation load of cogeneration units and the power generation load of wind turbine units;

[0164] The grid demand is the sum of heavy-load electric hydrogen electromagnetic energy supply load, light-load electric hydrogen electromagnetic energy supply load and other fixed loads.

[0165] During the dispatching process, the present invention ensures that all loads in the system (including fixed loads, wind power and thermal power) remain balanced at any time, can ensure the balance of supply and demand in the system, maintain stable operation of the system, and avoid power supply interruptions caused by load imbalance.

[0166] Constraint (2): Power constraint of the hydrogen-electromagnetic power supply unit

[0167] According to the operating characteristics of the thermal power unit, the coupling relationship exists between the electrical output and thermal output of the cogeneration unit. Usually, the operating mode of the thermal power unit is "determining electricity by heat". Equation (17) describes the electrical power constraint of the thermal power unit.

[0168]

[0169] In formula (17): is the minimum electric power of the jth thermal power unit at time t, MW; is the maximum electric power of the jth thermal power unit in the tth scheduling period, MW; is the electric power of the jth thermal power unit in the tth scheduling period.

[0170] In a specific embodiment of the present invention, the power constraint of the hydrogen-electro-olefin electromagnetic energy supply unit is:

[0171] Ensure that within any scheduling period, for each thermal power unit, the electric power is not less than the minimum electric power and not greater than the maximum electric power.

[0172] The present invention sets minimum and maximum electric power limits for the cogeneration unit and flexibly schedules it according to system requirements, thereby ensuring that the power output of the electric hydrogen electromagnetic energy supply unit is within a reasonable range and avoiding system overload or inefficient operation.

[0173] Constraint (3): Climbing constraints of the electric hydrogen electromagnetic energy supply unit

[0174] The power change of the thermal power unit needs to be within the ramp constraint range, as shown in formula (18):

[0175]

[0176] In formula (18): P chp,j,down is the maximum downward ramp rate of the jth thermal power unit, MW / min; P chp,j,up is the maximum upward climbing rate of the jth thermal power unit, MW / min.

[0177] In a specific embodiment of the present invention, the climbing constraint of the electric hydrogen electromagnetic energy supply unit is:

[0178] For each thermal power unit, ensure that the difference in power generation load between the previous and next scheduling periods is not less than the inverse of the maximum downward climbing rate and not greater than the maximum upward climbing rate.

[0179] By setting the maximum ramp rate of the thermal power unit, the present invention can ensure adaptation to load changes in a short time, avoid overly fast or overly slow responses, improve system scheduling flexibility, control the ramp rate of the electric hydrogen electromagnetic power supply unit, and prevent the negative impact of load fluctuations on the operation of the unit.

[0180] Constraint (4): Wind power output constraint

[0181] Formula (19) represents the wind power output constraint.

[0182]

[0183] In formula (19): is the power of the j-th wind turbine in the t-th scheduling period, MW; The predicted output of the j-th wind turbine in the t-th scheduling period, MW.

[0184] In a specific embodiment of the present invention, the wind power output constraint is:

[0185] Ensure that within any scheduling period, for each wind turbine, the electric power is not less than 0 and not greater than the predicted output of the wind turbine.

[0186] The present invention can ensure that the output of wind power meets the actual load demand by setting the maximum and minimum predicted output limits of the wind turbine group, and reasonably arrange the utilization of wind power generation, optimize wind power output, reduce wind power waste, and improve the absorption capacity.

[0187] Constraint (5): Heat storage tank heat storage constraint

[0188] There is an upper limit to the amount of heat stored in the heat storage tank, as shown in formula (20):

[0189]

[0190] In formula (20): Q is the heat storage capacity of the jth heat storage tank in the tth dispatch period, MWh; s,j,max is the maximum heat storage capacity of the jth heat storage tank, MWh.

[0191] In a specific embodiment of the present invention, the heat storage constraint of the heat storage tank is:

[0192] Ensure that in any scheduling period, for each heat storage tank, the heat storage capacity of the heat storage tank is not greater than the maximum heat storage capacity.

[0193] The present invention can ensure that the heat storage capacity of the heat storage tank does not exceed a safe range by setting a maximum heat storage capacity limit of the heat storage tank, while avoiding energy waste, improving the efficiency of the heat storage system, ensuring a balance between heat storage and release, and optimizing power consumption.

[0194] Optimization method for load dispatch management of hydrogen-electromagnetic energy supply;

[0195] Figure 4 A flowchart of an improved whale optimization algorithm (WOA) provided for a specific embodiment of the present invention combines three behaviors of wandering foraging, encirclement and contraction, and spiral predation to search for the optimal scheduling solution. By combining global optimization with local optimization, the algorithm convergence speed and accuracy are improved, and the local optimal solution is effectively avoided.

[0196] The present invention adopts the method of elite retention, compares the parent generation with the offspring generation, and retains individuals with higher fitness. In the early stage of the whale algorithm, the global optimization capability is adopted, and the elite retention mechanism is introduced in the later stage to retain individuals with higher fitness, and mix the parent generation and the offspring generation to avoid premature convergence. How to avoid local optimal solutions through the whale optimization algorithm, accelerate convergence and maintain global optimization capabilities, and improve the quality of the optimal solution.

[0197] like Figure 4 As shown in the figure, after the whale algorithm is improved, while retaining the global optimization ability of the algorithm in the early stage, it also successfully avoids the situation of obtaining the local optimal solution and improves the convergence speed of the algorithm in the later stage.

[0198] The present invention adopts the method of elite retention, compares the parent generation with the offspring generation, and retains individuals with higher fitness. In order to avoid the algorithm from fast convergence and falling into the local optimum, when the number of iterations does not exceed the first preset threshold (in a specific embodiment, the preset threshold is 70% of the maximum number of iterations), only the offspring generation is retained; otherwise, the parent generation individuals are mixed with the offspring individuals, and the individuals whose fitness ranking is in front of the second preset threshold (in a specific embodiment, the second preset threshold is the first 50%) are selected as new offspring individuals.

[0199] Figure 4 It shows that after the present invention improves the whale algorithm, it retains the global optimization ability of the algorithm in the early stage, successfully avoids obtaining the local optimal solution, and further improves the convergence speed of the algorithm in the later stage. Figure 4The present invention uses the improved whale optimization algorithm (WOA) to solve and search for the optimal power grid dispatching scheme. The WOA algorithm includes three position update behaviors: wandering foraging, encirclement and contraction, and spiral predation. By combining global optimization with local optimization, the convergence speed and accuracy of the algorithm are improved, and the local optimal solution is effectively avoided.

[0200] In a specific embodiment of the present invention, the step S400 (based on the power grid dispatching model, using the improved whale optimization algorithm to perform power grid dispatching) includes the following steps:

[0201] S410: Initialize the position and speed of the whale group;

[0202] S420: Update the whale's position by swimming foraging, encircling and spiraling;

[0203] S430: Evaluate the fitness of each offspring individual in the whale group;

[0204] S440: The offspring individuals with fitness not less than 70% are retained as elites;

[0205] S450: repeating steps S410 to S440 until a global optimal solution for the location of the prey, i.e., an optimal grid dispatching solution, is found;

[0206] Wherein, in step S450, it is determined whether the late iteration period has been reached;

[0207] If the late iteration period has not been reached, only the offspring individuals with a fitness of not less than 70% will be retained as elites;

[0208] If the late iteration has been reached, the parent individuals are mixed with the offspring individuals, and the individuals ranked in the top 50% of fitness are selected as new offspring individuals.

[0209] The present invention adopts global optimization capability in the early stage of the whale algorithm, introduces an elite retention mechanism in the later stage of the whale algorithm, retains individuals with higher fitness, and mixes parent and offspring to avoid premature convergence. It can accelerate convergence and maintain global optimization capability, improve the quality of the optimal solution, and avoid local optimal solutions.

[0210] (1) Wandering and foraging

[0211] The artificial whale uses the random position of an individual in the school of fish to guide the search for prey. The position update method is shown in the following equations (21)-(22):

[0212]

[0213] In formulas (21)-(22): t represents the current iteration number; and Represent the coefficient vectors respectively; represents the current position vector in the randomly selected artificial whale population; Represents the position vector.

[0214] vector and It can be obtained by the following formulas (23)-(24):

[0215]

[0216] In formulas (23)-(24), is a random vector.

[0217] In formulas (23)-(24), when or When the prey is located at the current random artificial whale individual Position, pick a random number As an influence on random individuals Current individual The factor of distance.

[0218] In formula (23)-(24), when When , the impact is weakened.

[0219] In a specific embodiment of the present invention, the wandering foraging comprises the following steps:

[0220] Randomly initialize the positions of the whale group in the search space;

[0221] Simulate the foraging behavior of whales and update the whale's position according to the preset random step size and direction through a random walk strategy;

[0222] Repeat the above steps continuously.

[0223] (2) Surrounding contraction

[0224] When the prey is discovered by the artificial whale, the whale's spatial position will be updated as follows:

[0225]

[0226] In formula (25)-(26), It is the best position for artificial whales. when When the artificial whale individual moves away from Surround the prey, move closer to the best position of the prey (best X), and gradually shrink the encirclement.

[0227] In a specific embodiment of the present invention, the surrounding shrinkage comprises the following steps:

[0228] Determine the position of the local optimal solution, that is, the position where the prey is captured;

[0229] Guiding the whale group to move toward the location of the prey to form an encirclement;

[0230] By gradually reducing the preset random step size, the scope of the encirclement is gradually reduced, thereby improving the search accuracy;

[0231] As the encirclement shrinks, the whale's position is continuously updated to approach the optimal solution;

[0232] Repeat the above steps until the predetermined search accuracy or number of iterations is reached.

[0233] (3) Spiral predation

[0234] In the best hunting position As it approaches its prey, its spatial position is updated as follows:

[0235]

[0236] In formula (27)-(28), represents the distance between the ith artificial whale and its prey; b is a constant used to determine the shape of the logarithmic spiral; l is a random number and l∈[-1,1].

[0237] In the process of the artificial whale hunting around the prey, not only the shrinking and surrounding mechanism is used, but also the spiral path method. Assuming that the probability of selecting the spiral path and the shrinking and surrounding mechanism is 50% each during optimization, this simultaneous behavior is modeled. The mathematical equation can be established according to the following formula:

[0238]

[0239] In formula (29), p is a random number whose value range is [0,1].

[0240] In a specific embodiment of the present invention, the spiral predation comprises the following steps:

[0241] Simulate the spiral feeding behavior of whales around their prey and calculate the distance between the whale and the local optimal solution;

[0242] According to the distance between the whale and the local optimal solution, the position of the whale is updated by adjusting the spiral radius and the rotation angle of the spiral line;

[0243] Repeat the above steps until the predetermined search accuracy or number of iterations is reached.

[0244] In order to reflect the effectiveness of load scheduling of hydrogen-electric electromagnetic energy supply in absorbing wind power, the present invention sets four simulation scenarios and calculates the corresponding load scheduling results, wind abandonment rate, cost and other contents under each scenario.

[0245] Through scenario analysis, the dispatch results can be optimized. By setting multiple dispatch scenarios (for example, scenarios 1 to 4), different load dispatch strategies are simulated respectively, and indicators such as wind abandonment rate, reserve capacity, and total cost are compared to select the optimal dispatch solution. By comparing different scenarios, the most appropriate dispatch strategy is selected to reduce system costs and improve wind power consumption efficiency.

[0246] By comparing the wind curtailment rate, reserve capacity, compensation amount and total cost under each dispatching scenario, the load dispatching results can be evaluated and the optimal dispatching strategy can be selected. The present invention can provide comparative analysis of multiple dispatching schemes to help decision makers select the most economical and efficient scheme.

[0247] Scenario 1: Only the electric energy on the supply side is dispatched, without considering the dispatch management of the electromagnetic energy supply load on the demand side, as shown in formula (30):

[0248]

[0249] In formula (30), C1 is the unit wind curtailment cost, RMB / kWh; C2 is the unit standby cost, RMB / kWh.

[0250] Scenario 2: In addition to dispatching the electric energy on the supply side, the heavy-load electromagnetic energy supply load on the demand side is also taken as the dispatch management object, but the dispatch management of the light-load electromagnetic energy supply load is not considered, as shown in formula (31):

[0251]

[0252] Scenario 3: In addition to dispatching the power on the supply side, the light-load electromagnetic load on the demand side is also taken as the dispatch management object, but the heavy-load electromagnetic energy supply load is not considered for dispatch management, as shown in (24):

[0253]

[0254] In formula (32), C3 is the unit cost of increasing electricity consumption, RMB / kWh.

[0255] Scenario 4: In addition to dispatching the power on the supply side, the heavy-load electromagnetic energy supply load and light-load electromagnetic energy supply load on the demand side are also taken as dispatch management objects. The objective function is shown in ((33)):

[0256]

[0257] After the scenario modeling is completed, the model parameters are set, such as the maximum storage capacity of the heat storage tank is 540,000 kWh, and the maximum power of the electric hydrogen electromagnetic energy supply load is 10MW. The relationship diagram of the electric hydrogen electromagnetic energy supply load and other loads is as follows: Figure 5 . Figure 5 A curve diagram showing the relationship between an electric hydrogen electromagnetic energy supply load and other loads provided for a specific embodiment of the present invention. Figure 5 The figure shows the relationship between the electric hydrogen electromagnetic energy supply load and other loads after the example scenario modeling is completed. Figure 5 The horizontal axis is time and the vertical axis is power.

[0258] The Latin hypercube sampling method is used to generate 10 scenarios of wind power generation changes and their corresponding probabilities, such as Figure 6 And as shown in Table 1. Figure 6 A wind power generation power change scenario set is provided for a specific embodiment of the present invention. Figure 6 Ten scenarios of wind power generation changes and their corresponding probabilities generated using the Latin hypercube sampling method are shown. Figure 6 The horizontal axis is time, and the vertical axis is the output power of different wind power.

[0259] Table 1 Probability of scenario set for wind power generation change

[0260] Scenario 1 2 3 4 5 6 7 8 9 10 Probability / % 0.29 2.78 1.03 0.57 17.27 22.45 6.86 21.36 20.02 7.37

[0261] Assuming that the thermal power unit is 1×300MW, excluding the thermal power plant, the detailed data of the unit is shown in Table 2.

[0262] Table 2 Thermal power unit parameters

[0263] Parameter Type Unit 1 <![CDATA[Minimum output P of thermal power unit cond,j,min (MW)]]> 100 <![CDATA[The maximum output P of a thermal power unit cond,j,max (MW)]]> 300 <![CDATA[The maximum rate of downward ramp of a thermal power unit P cond,j,down (MW / min)]]> 5 <![CDATA[The maximum upward ramp rate P of a thermal power unit cond,j,up (MW / min)]]> 6

[0264] The remaining parameters include the unit wind curtailment cost C1 of 0.5 yuan / kWh, the unit standby cost C2 of 0.1 yuan / kWh, the unit cost of increased electricity consumption C3 of 0.2 yuan / kWh, and the compensation of electromagnetic energy load. and C user The value is 0.15 yuan / kWh; if the electric hydrogen electromagnetic energy supply load is adjusted from valley to peak, an additional peak electricity price must be paid, and the compensation value is 0.262 yuan / kWh.

[0265] Scenario 1 only dispatches thermal power units and wind power. The planned output results of thermal power units and wind power in scenario 1 are as follows: Figure 7 shown. Figure 7 The thermal power and wind power output results in scenario 1 are provided for a specific embodiment of the present invention. Figure 7 The planned output results of thermal power units and wind power under scenario 1 are shown. Figure 7 The horizontal axis is time, and the vertical axis is the output power of wind power and thermal power.

[0266] After comprehensively considering various wind power scenarios and their probability of occurrence, the mathematical expectation value of wind power abandonment is 334.86MWh, and the mathematical expectation value of standby is 87.09MWh. At this time, the wind abandonment rate is 11.34%, and the wind abandonment volume is taken as one of the costs, and the total cost is 176,100 yuan. In the case of only dispatching power on the supply side, the wind abandonment rate is high, and the total cost is also at a high level.

[0267] Since the backup auxiliary service is purchased from the auxiliary service market, the price has certain volatility. Here we analyze the relationship between different unit backup costs (backup penalty values) and the abandoned wind volume and the expected value of the backup volume. The results are as follows: Figure 8 shown.

[0268] Figure 8 The relationship between the unit standby cost, the abandoned wind volume and the expected value of the standby volume is provided for a specific embodiment of the present invention. The standby auxiliary service is purchased from the auxiliary service market, and the price fluctuates to a certain extent. Figure 8 It is the relationship between different unit reserve costs (reserve penalties) and the wind curtailment volume and expected reserve volume. Figure 8 The horizontal axis is the reserve penalty value, the left vertical axis is the abandoned wind volume, and the right vertical axis is the reserve volume.

[0269] Scenario 2 dispatches thermal power, wind power and heavy-duty electromagnetic energy supply loads.

[0270] The planned output results of thermal power units and wind power in scenario 2 are as follows: Fig. 9 shown.

[0271] Fig. 9 A comparison of wind power and thermal power output results under scenario 1 and scenario 2 is provided for a specific embodiment of the present invention. Fig. 9 The planned output results of thermal power units and wind power under scenario 1 are shown, and the planned output results of thermal power units and wind power under scenario 2 are also shown. Fig. 9 The horizontal axis is time, and the vertical axis is wind power and thermal power output under different scenarios.

[0272] In scenario 2, the heavy-duty electromagnetic energy supply load is as follows Fig.10 shown.

[0273] Fig.10 A comparison of the electromagnetic energy supply load of hydrogen peroxide under scenario 1 and scenario 2 is provided for a specific embodiment of the present invention. Fig.10 The diagram shows the heavy-duty electromagnetic energy supply load under scenario 1, and also shows the load change diagram of the heavy-duty electromagnetic energy supply load under scenario 2. Fig.10 The horizontal axis is time, and the vertical axis is the heavy-duty electric hydrogen load under different scenarios.

[0274] After comprehensively considering various wind power scenarios and probabilities, the mathematical expectation of wind abandonment is 190.03MWh, the mathematical expectation of reserve is 75.14MWh, and the wind abandonment rate is 6.44%. At this time, the total cost is 146,400 yuan, of which the compensation cost for the electromagnetic energy load participating in load dispatch is 55,300 yuan. Compared with scenario 1, the wind abandonment volume, reserve volume expectation and total cost of scenario 2 are significantly reduced because load dispatch turns heavy-duty electromagnetic energy load into part of dispatchable resources, providing richer and more flexible dispatching means.

[0275] Scenario 3 dispatches wind power, thermal power and light-load electromagnetic energy supply loads.

[0276] Fig.11 This is the load change diagram of the light-load electromagnetic energy supply load after participating in load scheduling. Fig.11 A comparison of the electromagnetic energy supply load of hydrogen in scenario 1 and scenario 3 provided for a specific embodiment of the present invention; Fig.11 The diagram shows the light-load electromagnetic energy supply load after scenario 1 participates in load scheduling, and the load change diagram of the light-load electromagnetic energy supply load after scenario 3 participates in load scheduling. Fig.11 The horizontal axis is time, and the vertical axis is the light-load battery load in different scenarios.

[0277] After comprehensively considering various wind power scenarios and their probability of occurrence, the mathematical expectation value of wind abandonment is 325.23MWh, the mathematical expectation value of standby is 93.82MWh, and the wind abandonment rate is 11.02%. At this time, the total cost is 173,800 yuan, of which the compensation cost for the electromagnetic energy supply load participating in load dispatch is 16,000 yuan. It can be seen that whether the heavy-load electromagnetic energy supply load or the light-load electromagnetic energy supply load is dispatched and managed, the wind abandonment, standby amount and total cost can be effectively reduced. The total amount of light-load electromagnetic energy supply load is low, and the impact on the absorption of wind power is small, so the heavy-load electromagnetic energy supply load should be called first.

[0278] In scenario 3, priority is given to dispatching heavy-duty electric hydrogen electromagnetic energy supply loads to ensure that they play a greater role in absorbing more wind power, while light-duty loads are flexibly dispatched according to demand. In scenario 3, load dispatch can be optimized, system flexibility can be improved, and wind power absorption can be maximized.

[0279] Scenario 4 dispatches wind power, thermal power, heavy-load electromagnetic energy supply load and light-load electromagnetic energy supply load.

[0280] The planned output results of thermal power units and wind power under scenario 4 are as follows: Fig.12 shown. Fig.12A comparison of wind power and thermal power planned output under scenario 1 and scenario 4 provided for a specific embodiment of the present invention; Fig.12 The planned output results of thermal power units and wind power under scenario 1 are shown. Fig.12 The planned output results of thermal power units and wind power under scenario 4 are shown. Fig.12 The horizontal axis is time, and the vertical axis is the output power of wind power or thermal power.

[0281] The heavy-duty electromagnetic energy supply load and the light-duty electromagnetic energy supply load in scenario 4 are as follows: Fig.13 shown. Fig.13 A comparison of the electromagnetic energy supply load of hydrogen peroxide under scenario 1 and scenario 4 is provided for a specific embodiment of the present invention. Fig.13 The heavy-load electromagnetic energy supply load and the light-load electromagnetic energy supply load in scenario 1 are shown, and the heavy-load electromagnetic energy supply load and the light-load electromagnetic energy supply load in scenario 4 are also shown. Fig.13 The horizontal axis is time, the left vertical axis is the heavy-load battery load power, and the right vertical axis is the light-load battery load power.

[0282] After comprehensively considering various wind power scenarios and their probability of occurrence, the mathematical expectation of wind abandonment is 173.84MWh, the mathematical expectation of standby is 71.98MWh, and the wind abandonment rate is 5.89%, with a total cost of 139,000 yuan. It can be seen that the lowest wind abandonment and the lowest total cost can be obtained by simultaneously dispatching and managing the two types of electromagnetic energy supply loads.

[0283] Table 3 Optimization scheduling results under four scenarios

[0284]

[0285] By comparing the results of the four scenarios in Table 3, we can see that:

[0286] Only dispatching and managing heavy-duty electromagnetic energy supply loads can reduce the expected wind curtailment by 144.83MWh and the total cost by 29,700 yuan.

[0287] Only dispatching and managing light-load electromagnetic energy supply loads can reduce the expected wind curtailment by 9.63MWh and the total cost by 3,200 yuan;

[0288] Simultaneous dispatch and management of the two types of electromagnetic energy supply loads can reduce the expected value of wind abandonment by 161.02MWh and the total cost by 3,200 yuan, which is greater than the sum of the effects of separately dispatching and managing the two types of electromagnetic energy supply loads.

[0289] This shows that the combined optimization management of light-load electromagnetic energy supply loads and heavy-load electromagnetic energy supply loads can further promote wind power consumption and reduce system costs.

[0290] In scenario 4, by comprehensively considering the dispatching conditions of wind power, thermal power and electric hydrogen electromagnetic loads, the optimal dispatching scheme is selected to reduce the wind abandonment rate, reserve capacity and related costs. The present invention can reduce the total system cost, optimize resource allocation, and improve the system economy and environmental benefits through dispatching management.

[0291] Please refer to Fig.14 The present invention also provides a power grid dispatching device that takes into account the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption, which is preferably a computer device, including a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, any of the above methods is implemented.

[0292] In a specific embodiment of the present invention, the present invention also provides a power grid dispatching device considering the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption, including a memory, a processor and a computer program, wherein the memory is connected to the processor:

[0293] a memory configured to store the computer program;

[0294] a processor configured to run the computer program;

[0295] Among them, when the computer program is run by the processor, it executes the power grid dispatching method that takes into account the load characteristics of electric olefins and hydrogen electromagnetic energy supply and wind power consumption.

[0296] In a specific embodiment of the present invention, the present invention also provides a non-transitory computer-readable storage medium, which is configured to store computer-readable instructions, wherein when the computer-readable instructions are executed by a processor, the grid dispatching method considering the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption is executed.

[0297] Compared with the prior art, the present invention has the following beneficial technical effects:

[0298] Improve dispatching efficiency and energy utilization: By comprehensively considering the differences between heavy-duty electric hydrogen electromagnetic energy supply loads and light-duty electric hydrogen electromagnetic energy supply loads, the wind power consumption capacity can be improved through flexible dispatching of electric hydrogen electromagnetic energy supply loads to adapt to the uncertainty of wind power. Different types of electric hydrogen electromagnetic energy supply loads can be centrally dispatched through load aggregators.

[0299] Accurately predict wind power output: Generate multiple wind power generation power change scenarios through the Latin hypercube sampling method, calculate the probability of each scenario, enhance the grid dispatching ability to handle random events, and improve the stability and prediction accuracy of the grid. The Latin hypercube sampling method can more accurately reflect the actual changes in wind power. By accurately generating wind power change scenarios, wind power output can be predicted more accurately, thereby optimizing the dispatch of wind power resources.

[0300] Optimize grid dispatching, reduce grid operation costs, and reduce wind curtailment: The present invention sets multiple objective functions (including minimizing wind curtailment, minimizing compensation amount, minimizing increased power consumption, and minimizing reserve capacity) and multiple constraints (including supply and demand balance constraints, power constraints of electric hydrogen electromagnetic power supply units, climbing constraints, wind power output constraints, and heat storage tank heat storage constraints), which can comprehensively consider the economy and environmental protection of grid operation.

[0301] Optimizing the performance of the power grid dispatching algorithm: The present invention adopts the improved whale optimization algorithm for power grid dispatching, which has the advantages of fast convergence speed and strong global search ability. Through strategies such as wandering foraging, encirclement and contraction, and spiral predation, the global optimal solution can be found more efficiently, improving the efficiency and accuracy of solving the power grid dispatching model.

[0302] The above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A grid dispatching method considering the load characteristics of hydrogen-electric electromagnetic energy supply and wind power consumption, characterized in that: The following steps are involved: S100: The heavy-load electric hydrogen electromagnetic energy supply load and the light-load electric hydrogen electromagnetic energy supply load are centrally dispatched by the load aggregator; S200: Based on the Latin hypercube sampling method, multiple scenarios of wind power generation change are obtained, and the probability of each scenario is calculated; S300: Based on multiple objective functions and under multiple constraints, construct a power grid dispatching model; wherein the multiple objective functions take into account the heavy-load electric hydrogen electromagnetic energy supply load and the light-load electric hydrogen electromagnetic energy supply load, as well as the probability of the scenario of wind power generation power change; S400: Based on the power grid dispatching model, the improved whale optimization algorithm is used to perform power grid dispatching.

2. The power grid dispatching method according to claim 1, characterized in that: The step S100 includes the following steps: The load aggregator collects the operating data of heavy-load electric hydrogen electromagnetic energy supply load and light-load electric hydrogen electromagnetic energy supply load respectively, and formulates dispatching strategies respectively to optimize the load balance of the power grid; The load aggregator decomposes the overall dispatch instructions received from the ancillary service market, issues heavy-load dispatch instructions for heavy-load electric, hydrogen and electromagnetic energy supply loads, and issues light-load dispatch instructions for light-load electric, hydrogen and electromagnetic energy supply loads.

3. The power grid dispatching method according to claim 1, characterized in that: The step S200 includes the following steps: S210: Determine the cumulative probability distribution function of wind power generation; S220: dividing the cumulative probability distribution function into a plurality of equal probability intervals; S230: Perform random sampling in each interval to generate sampling values; S240: Arranging the sampled values ​​using a correlation coefficient matrix to form a plurality of wind power generation scenarios; S250: Calculate the probability of each wind power generation scenario.

4. The power grid dispatching method according to claim 1, characterized in that: The objective function includes: minimizing the amount of wind abandoned, minimizing the compensation amount, minimizing the increased power consumption and minimizing the reserve capacity; The constraints include: supply and demand balance constraints, power constraints of the electric hydrogen electromagnetic energy supply unit, climbing constraints of the electric hydrogen electromagnetic energy supply unit, wind power output constraints and heat storage constraints of the heat storage tank.

5. The power grid dispatching method according to claim 4, characterized in that: The step of obtaining the minimized wind abandonment volume comprises the following steps: In each wind power generation scenario, the first difference between wind power output and wind power consumption is calculated; When the first difference is a positive number, the first difference is multiplied by the probability of the wind power generation scenario and the wind abandonment switch variable to obtain the scenario wind abandonment amount; The wind abandonment volume of all wind power generation scenarios is summed up to obtain the total wind abandonment volume; The predicted wind power output is optimized, and wind power is absorbed by dynamically scheduling the electric hydrogen electromagnetic energy load, thereby minimizing the overall wind abandonment.

6. The power grid dispatching method according to claim 4, characterized in that: Obtaining the minimized compensation amount comprises the following steps: In each dispatching period, the second difference between the heavy-duty electric hydrogen electromagnetic energy supply load after optimized dispatching and the heavy-duty electric hydrogen electromagnetic energy supply load without optimized dispatching is multiplied by the compensation rate for the heavy-duty electric hydrogen electromagnetic energy supply load participating in dispatching, to obtain the heavy-duty compensation fee for the heavy-duty electric hydrogen electromagnetic energy supply load participating in dispatching; In each scheduling period, the third difference between the light-load electric hydrogen electromagnetic energy supply load after optimized scheduling and the light-load electric hydrogen electromagnetic energy supply load without optimized scheduling is multiplied by the compensation rate for the heavy-load electric hydrogen electromagnetic energy supply load participating in the scheduling, so as to obtain the light-load compensation fee for the light-load electric hydrogen electromagnetic energy supply load participating in the scheduling; The compensation amount is obtained by summing up the heavy load compensation fee and light load compensation fee of all dispatch periods and multiplying it by the duration of each dispatch period. Minimize the compensation amount.

7. The power grid dispatching method according to claim 4, characterized in that: The step of obtaining the minimization of the increased power consumption comprises the following steps: In each scheduling period, the third difference between the light-load electric hydrogen electromagnetic energy supply load after optimized scheduling and the light-load electric hydrogen electromagnetic energy supply load without optimized scheduling is multiplied by the duration of each scheduling period to obtain the increased power consumption after the light-load electric hydrogen electromagnetic energy supply load participates in the scheduling; Minimize the increased power usage.

8. The power grid dispatching method according to claim 4, characterized in that: The step of obtaining the minimized reserve comprises the following steps: In each wind power generation scenario, the fourth difference between wind power consumption and wind power output is calculated; When the difference is a positive number, multiplying the fourth difference by the probability of the wind power generation scenario, and then multiplying by the reserve switch variable to obtain a scenario reserve amount; The scenario reserve of all wind power generation scenarios is summed up to obtain the overall reserve; The wind power output forecast is optimized, and wind power is absorbed by dynamically scheduling the electric hydrogen electromagnetic energy load, thereby minimizing the overall reserve capacity.

9. The power grid dispatching method according to claim 4, characterized in that: The step of obtaining the supply-demand balance constraint comprises the following steps: Ensure that the power supply of the power grid matches the power demand of the power grid in any dispatch period; Among them, the power supply of the power grid is the sum of the power generation load of pure condensing thermal power units, the power generation load of cogeneration units and the power generation load of wind turbine units; The grid demand is the sum of heavy-load electric hydrogen electromagnetic energy supply load, light-load electric hydrogen electromagnetic energy supply load and other fixed loads.

10. The power grid dispatching method according to claim 4, characterized in that: The power constraint of the hydrogen-electrometallurgical electromagnetic energy supply unit is: Ensure that within any scheduling period, for each thermal power unit, the electric power is not less than the minimum electric power and not greater than the maximum electric power.

11. The power grid dispatching method according to claim 4, characterized in that: The climbing constraints of the electric hydrogen electromagnetic energy supply unit are: For each thermal power unit, ensure that the difference in power generation load between the previous and next scheduling periods is not less than the inverse of the maximum downward climbing rate and not greater than the maximum upward climbing rate.

12. The power grid dispatching method according to claim 4, characterized in that: The wind power output constraint is: Ensure that within any scheduling period, for each wind turbine, the electric power is not less than 0 and not greater than the predicted output of the wind turbine.

13. The power grid dispatching method according to claim 4, characterized in that: The heat storage constraint of the heat storage tank is: Ensure that in any scheduling period, for each heat storage tank, the heat storage capacity of the heat storage tank is not greater than the maximum heat storage capacity.

14. The power grid dispatching method according to claim 1, characterized in that: The step S400 includes the following steps: S410: Initialize the position and speed of the whale group; S420: Update the whale's position by swimming foraging, encircling and spiraling; S430: Evaluate the fitness of each offspring individual in the whale group; S440: retaining offspring individuals whose fitness is not less than a first preset threshold as elites; S450: repeating steps S410 to S440 until a global optimal solution for the location of the prey, i.e., an optimal grid dispatching solution, is found; Wherein, in step S450, it is determined whether the late iteration period has been reached; If the late iteration period has not been reached, only the offspring individuals whose fitness is not less than the first preset threshold are retained as elites; If the late iteration period has been reached, the parent individuals are mixed with the offspring individuals, and the individuals with fitness ranking above the second preset threshold are selected as new offspring individuals.

15. The power grid dispatching method according to claim 14, characterized in that: The wandering foraging comprises the following steps: Randomly initialize the positions of the whale group in the search space; Simulate the foraging behavior of whales and update the whale's position according to the preset random step size and direction through a random walk strategy; Repeat the above steps continuously.

16. The power grid dispatching method according to claim 14, characterized in that: The enveloping shrinkage comprises the following steps: Determine the position of the local optimal solution, that is, the position where the prey is captured; Guiding the whale group to move toward the location of the prey to form an encirclement; By gradually reducing the preset random step size, the scope of the encirclement is gradually reduced, thereby improving the search accuracy; As the encirclement shrinks, the whale's position is continuously updated to approach the optimal solution; Repeat the above steps until the predetermined search accuracy or number of iterations is reached.

17. The power grid dispatching method according to claim 14, characterized in that: The spiral predation comprises the following steps: Simulate the spiral feeding behavior of whales around their prey and calculate the distance between the whale and the local optimal solution; According to the distance between the whale and the local optimal solution, the position of the whale is updated by adjusting the spiral radius and the rotation angle of the spiral line; Repeat the above steps until the predetermined search accuracy or number of iterations is reached.

18. A power grid dispatching device considering the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption, characterized in that: The invention comprises a memory, a processor and a computer program, wherein the memory is connected to the processor: a memory configured to store the computer program; a processor configured to run the computer program; Wherein, when the computer program is executed by the processor, a power grid dispatching method considering the load characteristics of electric olefin hydrogen electromagnetic energy supply and wind power consumption according to any one of claims 1-17 is executed.

19. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is configured to store computer-readable instructions, wherein, when the computer-readable instructions are executed by a processor, a power grid dispatching method considering the load characteristics of electric hydrogen electromagnetic energy supply and wind power consumption according to any one of claims 1-17 is executed.

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