Uncertain distributed power supply control method, system, equipment and medium

Abstract

The invention discloses an uncertain distributed power supply control method, system, equipment and medium, and the method relates to distributed power supply modeling, renewable energy source permeability, load flow calculation and transformer tap selection, and solves the negative influence of the randomness and volatility of renewable energy sources on the safe and stable operation of a distributed energy system. And the operation mode of each device in the distributed energy system is single, and the operation scheme is too conservative. The method comprises the following steps: 1, establishing a distributed power supply model; 2, establishing a renewable energy output prediction model, a load prediction model and an uncertainty model thereof according to the renewable energy historical data and the load historical data; and 3, based on the system power flow under multiple uncertainties, selecting a transformer tap, optimizing an energy storage operation strategy, and realizing effective control of the distributed power supply. The basic idea of the invention is to provide support for the operation control of the distributed power supply system from the aspects of safety, reliability, node voltage and the like.

Description

Technical Field

[0001] This invention relates to the field of distributed power supply and optimized control technology, and in particular to a method, system, device and medium for controlling distributed power supply with uncertainty. Background Technology

[0002] Energy is the cornerstone and crucial support of a nation's economy and security. Currently, the proportion of renewable energy in the energy structure remains relatively low, while fossil fuels account for a significant portion. However, the environmental problems caused by fossil fuels and the resulting climate change are frequent, necessitating a substantial increase in the proportion of renewable energy in the energy structure. Renewable energy power generation systems, such as wind power and photovoltaics, are typical clean energy systems, producing no pollution emissions during power generation and possessing environmentally friendly characteristics. However, these clean energy sources are significantly affected by environmental factors, with their power generation closely related to factors such as sunlight intensity and climate. These environmental factors are inherently imprecise and uncertain, resulting in a degree of randomness and fluctuation in power generation. This randomness and fluctuation have a significant impact on the safe operation of the power grid. To prevent such uncertainties from affecting the safe and stable operation of the power system and the reliability of power supply, it is necessary to increase reserve capacity to address these uncertainties or increase the power of other equipment, thus preventing large-scale "wind curtailment" and "solar curtailment." Therefore, how to increase the proportion of renewable energy consumption while ensuring the safe and stable operation of the power grid, and ensuring the reliability of grid operation under high renewable energy penetration, is currently a key issue.

[0003] Distributed power generation, as one of the main ways to consume renewable energy locally, mainly consists of renewable energy systems such as photovoltaic and wind power, as well as energy storage systems. In distributed power generation, energy storage systems mainly serve as buffers for power sources and loads. Through the reasonable operation and scheduling of energy storage systems, the safe and stable operation of the power grid can be achieved. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a distributed power supply control method with uncertainty, comprising:

[0006] Establish a distributed power source model;

[0007] Establish renewable energy output forecasting models, load forecasting models, and uncertainty models;

[0008] Based on system flow under multiple uncertainties, optimize energy storage operation strategies.

[0009] As a preferred embodiment of the distributed power source control method with uncertainty described in this invention, the step of establishing the distributed power source model includes:

[0010] First, determine the internal equipment composition of the distributed power system;

[0011] Secondly, based on the above equipment composition, a distributed power system internal equipment characteristic model is established for all equipment within the system.

[0012] Finally, by analyzing the connection relationships between devices within the distributed energy system, an energy flow model for the distributed power system is established.

[0013] As a preferred embodiment of the distributed power generation control method with uncertainty described in this invention, the establishment of the renewable energy output prediction model, load prediction model, and uncertainty model includes:

[0014] Data was collected, and correlation analysis was used to analyze influencing factors;

[0015] Based on the collected data, renewable energy output forecasting models, load forecasting models, and their uncertainty models are established.

[0016] As a preferred embodiment of the distributed power supply control method with uncertainty described in this invention, the optimization of energy storage operation strategy based on system power flow under multiple uncertainties includes:

[0017] Establish renewable energy power generation models for renewable energy output and load under different uncertain conditions;

[0018] For different energy storage charging and discharging strategies, transformer taps are used to determine the optimal control strategy for distributed power sources.

[0019] As a preferred embodiment of the distributed power source control method with uncertainty described in this invention, wherein: determining the optimal control strategy for the distributed power source includes:

[0020] For renewable energy output and load power under deterministic conditions, we establish optimized control strategies for distributed power sources under different energy storage charging and discharging strategies and transformer tap settings. Specifically, we analyze system power flow under different energy storage charging and discharging strategies (charging during off-peak hours, discharging during peak hours, charging at night, discharging during daytime, charging when power is plentiful, discharging when power is insufficient, etc.) and different transformer tap settings, calculate operating costs, and select the charging and discharging strategy with lower costs, i.e., the transformer tap configuration.

[0021] As a preferred embodiment of the distributed power supply control method with uncertainty described in this invention, the use of correlation analysis is expressed as follows:

[0022]

[0023] Where cov is the covariance, σ is the standard deviation, and V and W are two variables to be judged, v i with w i These are the i-dimensional elements of variables V and W, respectively, and n is the total number of dimensions.

[0024] As a preferred embodiment of the distributed power source control method with uncertainty described in this invention, the renewable power source model includes:

[0025] The horizontal and vertical components of voltage drop, power loss, and system power flow are calculated using the PQ decomposition method. When using the PQ decomposition method for power flow calculation, nodes are categorized into PQ nodes, PV nodes, and slack nodes based on their known parameter types. The calculation formula is as follows:

[0026]

[0027] θ ij =θ i -θ j

[0028] In the formula, P i With Q i Injecting power into the node, U i For node voltage, G ij With B ij These are the real and imaginary parts of the nodal admittance matrix, respectively, θ i With θ i The voltages at nodes i and j are given. Furthermore, the power flow calculation results must satisfy certain constraints, such as upper and lower limits for node voltages and generator injected power. During the calculation process, the initial values ​​of the node voltages to be calculated are first set to 0, and then iterative solutions are performed.

[0029] Secondly, the present invention provides a distributed power supply control system with uncertainty, comprising:

[0030] Power modeling module, prediction and evaluation module, energy storage optimization module.

[0031] Thirdly, the present invention provides an electronic device, comprising:

[0032] Memory and processor;

[0033] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of a distributed power control method with indeterminism.

[0034] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the aforementioned indeterminate distributed power control method.

[0035] Compared with existing technologies, the beneficial effects of this invention are as follows: Based on a distributed power system model, and considering the uncertainties in renewable energy output and load, this invention proposes an optimal operating strategy for the energy storage system and an optimal transformer tap changer control method for the power system, under the premise of safe and stable system operation, to address such uncertainties. Specifically, the improved method of this invention has the following beneficial effects:

[0036] (1) Increase the penetration rate of renewable energy in the power grid and improve the environmental performance of the system operation.

[0037] (2) Improve the stability, security and reliability of system operation. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is an algorithm flowchart of a distributed power supply control method with uncertainty according to an embodiment of the present invention. Detailed Implementation

[0040] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0041] Example 1, an embodiment of the present invention, provides a distributed power supply control method with indeterminism, comprising:

[0042] S1: Establish a distributed power source model;

[0043] For distributed power systems, characteristic models of each device and energy flow relationship models between devices are established based on the types of devices within the distributed power system.

[0044] S2: Based on historical renewable energy data and historical load data, establish renewable energy output forecasting models and load forecasting models, as well as their uncertainty models;

[0045] Distributed power sources typically include renewable energy sources such as solar and wind power, which are highly random and volatile, significantly impacting node voltage and system power flow. Therefore, it is necessary to predict the output of solar and wind power systems. Furthermore, distributed power systems usually supply power to the nearest region, thus requiring forecasting of regional load as well.

[0046] S3: Based on the system power flow under multiple uncertainties, select transformer taps and optimize energy storage operation strategies to achieve effective control of distributed power sources;

[0047] Based on the output uncertainty and load uncertainty of the renewable energy system, the system power flow under different uncertainty conditions is calculated, the system power flow under different energy storage charging and discharging strategies and transformer taps is determined, and the optimal charging and discharging strategy of the energy storage system is selected.

[0048] It should be noted that S1 establishes a distributed generation model to address the randomness and volatility issues of renewable energy power generation. By establishing accurate models of distributed generation sources such as photovoltaics and wind power, including models of the characteristics of each device and the energy flow relationship between devices, it provides a deeper understanding of the impact of environmental factors on the output of distributed generation sources, accurately simulates the power output characteristics of distributed generation sources under different environmental conditions, and provides a basis for predicting their power generation behavior. This reduces the uncertainty caused by the volatility of renewable energy power generation and improves the grid's ability to accommodate distributed generation sources.

[0049] S2's distributed generation planning method based on uncertainty prediction addresses the challenge of considering the uncertainties of renewable energy in power grid planning and operation. By quantitatively analyzing the uncertainties of renewable energy output and load demand, it generates various typical forecast scenarios, providing more comprehensive information for power grid planning and operation. This supports risk avoidance and the early development of countermeasures, ultimately improving the efficiency and reliability of power grid operation.

[0050] Based on system power flow under multiple uncertainties, S3 selects transformer taps and optimizes energy storage operation strategies to achieve effective control of distributed power sources, resolving the contradiction between grid safety and stable operation and renewable energy consumption. Building upon the models and predictions established in the first two modules, and considering the multiple uncertainties in renewable energy output and load demand, S3 calculates system power flow under different uncertainty conditions. By selecting transformer taps and optimizing the charging and discharging strategies of the energy storage system, it adjusts the grid's voltage level and power balance, improving the grid's adaptability to renewable energy fluctuations, reducing wind and solar curtailment, and increasing the utilization rate of renewable energy.

[0051] Example 2, Reference Figure 1As one embodiment of the present invention, based on the above embodiment, a distributed power supply control method with uncertainty is provided.

[0052] S1: Establish a distributed power generation model, including photovoltaic, wind power system, and energy storage models. The specific processing flow is A1-A3:

[0053] A1: Determine the internal equipment composition of the distributed power system;

[0054] A2: Establish a characteristic model of the internal equipment of the distributed power system based on the composition of the internal equipment;

[0055] A3: Establish an energy flow model for a distributed power system.

[0056] The specific implementation methods for steps A1-A3 are as follows:

[0057] A characteristic model for the power generation of the photovoltaic system is determined, as well as a model relating the power generation of the photovoltaic system to the irradiance deviation ΔG and the photovoltaic panel temperature deviation ΔT. The specific model is as follows:

[0058]

[0059] In the formula, P and P ref These represent the actual power generation and the photovoltaic system power generation under standard conditions, respectively; G and Gref represent the actual irradiance and the standard irradiance, respectively, with Gref set to 1000 W / m²; T is the photovoltaic panel temperature; T ref The temperature of the photovoltaic panel under standard conditions is 25℃; a, b, and c are the power generation coefficients.

[0060] Establish a wind power system model, namely, establish a model of the relationship between wind power and wind speed, establish an energy storage system model, and establish a model of the charging and discharging power of the energy storage system and the system state of charge.

[0061] Based on the energy flow relationships among energy storage systems, wind power systems, and photovoltaic systems, the correlation between wind power systems and energy flow models are established.

[0062] S2: Based on historical renewable energy data and load data, establish renewable energy output forecasting models and load forecasting models, along with their uncertainty models. Specific processing steps B1-B2:

[0063] B1: Conduct data collection, use correlation analysis, and analyze influencing factors;

[0064] B2: Based on the collected data, establish renewable energy output forecasting models, load forecasting models, and their uncertainty models.

[0065] In this embodiment, the correlation analysis in step A1 uses Pearson correlation analysis. The specific implementation process is as follows: historical power generation data of photovoltaic systems and wind power systems are collected, relevant weather data is collected, and Pearson correlation analysis is used to analyze the key influencing factors of photovoltaic and wind power generation respectively.

[0066]

[0067] In the formula, cov is the covariance, σ is the standard deviation, and V and W are the two variables to be judged, respectively. i with w i These are the i-dimensional elements of variables V and W, respectively, and n is the total number of dimensions.

[0068] Collect load data, including data from holidays and relevant weather data, and determine the key influencing factors of the load using the method shown in step 2.1).

[0069] Based on the collected data, renewable energy output forecasting models, load forecasting models, and their uncertainty models are established.

[0070] In an optional embodiment, the correlation analysis may also employ Spearman correlation analysis, which enhances the robustness of the results when the data distribution is skewed or there is a nonlinear relationship.

[0071] In another alternative embodiment, the correlation analysis can also be combined with partial correlation analysis to eliminate other interfering variables and improve the accuracy of the analysis when analyzing the relationship between photovoltaic or wind power and a single factor.

[0072] S3: Based on system power flow under multiple uncertainties, select transformer taps, optimize energy storage operation strategies, and achieve effective control of distributed power sources. The specific processing flow is as follows:

[0073] To address renewable energy output and load under varying uncertainties, a renewable energy source model is established to calculate the horizontal and vertical components of voltage drop, power loss, and system power flow. The PQ decomposition method is used for power flow calculation. When using the PQ decomposition method, nodes are categorized into PQ nodes, PV nodes, and slack nodes based on their known parameter types. The calculation formula is as follows:

[0074]

[0075]

[0076] θ ij =θ i -θ j

[0077] In the formula, P i With Q i Injecting power into the node, U i For node voltage, G ij With B ij These are the real and imaginary parts of the nodal admittance matrix, respectively, θ i With θ i The voltages at nodes i and j are given. Furthermore, the power flow calculation results must satisfy certain constraints, such as upper and lower limits for node voltages and generator injected power. During the calculation process, the initial values ​​of the node voltages to be calculated are first set to 0, and then iterative solutions are performed.

[0078] For different energy storage charging and discharging strategies, transformer taps are used to determine the optimal control strategy for distributed power sources. Based on whether the voltage is within a suitable range, the optimal charging and discharging strategy for the energy storage system is selected. The voltage fluctuation range for each node is ±5% of the system's rated voltage.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0080] Example 3 illustrates a schematic scheme for a distributed power source control method with indeterminism. It should be noted that the technical solution of this system with indeterminism control and the technical solution of the aforementioned distributed power source control method belong to the same concept. Details not described in detail in this embodiment of the distributed power source control system with indeterminism can be found in the description of the aforementioned distributed power source control method with indeterminism.

[0081] This embodiment also provides a distributed power supply control system with uncertainty, including:

[0082] The power modeling module is used to establish distributed power source models.

[0083] The forecasting and assessment module establishes renewable energy output forecasting models, load forecasting models, and uncertainty models.

[0084] The energy storage optimization module optimizes energy storage operation strategies based on system power flow under multiple uncertainties.

[0085] This embodiment also provides an electronic device applicable to a situation with indeterminate distributed power control, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for implementing indeterminate distributed power control as proposed in the above embodiment.

[0086] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a distributed power control method with indeterminism as proposed in the above embodiments.

[0087] The storage medium proposed in this embodiment and the method for implementing a distributed power control method with indeterminism proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0088] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed power source control method with uncertainty, characterized in that, include: Establish a distributed power source model; Establish renewable energy output forecasting models, load forecasting models, and uncertainty models; Based on system flow under multiple uncertainties, optimize energy storage operation strategies.

2. The distributed power supply control method with uncertainty as described in claim 1, characterized in that, The establishment of the distributed power model includes: Determine the internal equipment composition of the distributed power system; Based on the equipment composition, a distributed power system internal equipment characteristic model is established for all equipment within the system. By analyzing the connection relationships between devices within a distributed energy system, an energy flow model for the distributed power system is established.

3. The distributed power supply control method with uncertainty as described in claim 2, characterized in that, The establishment of renewable energy output forecasting models, load forecasting models, and uncertainty models includes: Data was collected, and correlation analysis was used to analyze influencing factors; Based on the collected data, renewable energy output forecasting models, load forecasting models, and their uncertainty models are established.

4. The distributed power supply control method with uncertainty as described in claim 3, characterized in that, The optimization of energy storage operation strategies based on system power flow under multiple uncertainties includes: Establish renewable energy power generation models for renewable energy output and load under different uncertain conditions; For different energy storage charging and discharging strategies, transformer taps are used to determine the optimal control strategy for distributed power sources.

5. A distributed power supply control method with uncertainty as described in claim 4, characterized in that, The determination of the distributed power source optimization control strategy includes: For renewable energy output and load power under deterministic conditions, we establish optimized control strategies for distributed power sources under different energy storage charging and discharging strategies and transformer tapping conditions.

6. The distributed power supply control method with uncertainty as described in claim 5, characterized in that, The use of correlation analysis is expressed as follows: Where cov is the covariance, σ is the standard deviation, and V and W are two variables to be judged, v i With w i These are the i-dimensional elements of variables V and W, respectively, and n is the total number of dimensions.

7. A distributed power supply control method with uncertainty as described in claim 6, characterized in that, The renewable energy model includes, i ij =θ i -θ j In the formula, P i With Q i Injecting power into the node, U i For node voltage, G ij With B ij These are the real and imaginary parts of the nodal admittance matrix, θ and θ', respectively. i With θ i Let be the voltages at nodes i and j.

8. A distributed power supply control system with uncertainty, employing the method described in any one of claims 1-7, characterized in that, include: Power modeling module, prediction and evaluation module, energy storage optimization module.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the distributed power control method with indeterminism as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the indeterminate distributed power control method according to any one of claims 1 to 7.

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