Power grid peak regulation control model based on large compressed air energy storage power generation

Abstract

The invention discloses a power grid peak regulation control model based on large-scale compressed air energy storage power generation, peak regulation control strategy design and valley time energy storage: when the load of a power grid is relatively low, a compressor system is started, and surplus electric energy is converted into compressed air to be stored; the control target is as follows: the compressor operates at the lowest-cost time period to avoid causing a new load peak value to the power grid; when the load of the power grid is high, an expansion machine system is started, the stored compressed air is released, and a power generator is driven to generate power; when the load of the power grid is high, the expansion machine system is started, the stored compressed air is released, and the generator is driven to generate electricity; constructing a control model; and operation scene analysis: daily load adjustment: using an expansion machine to generate power to meet the power demand during peak load in the daytime, and using a compressor to store energy during valley load at night. According to the invention, the compressed air energy storage system (CAES) is used for storing energy at the low ebb of the power grid and releasing energy at the peak, so that the load curve of the power grid is effectively smoothed and the peak-valley difference is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of dental comprehensive treatment machines, and particularly to a grid peak shaving control model based on large-scale compressed air energy storage power generation. Background Art

[0002] With the rapid development of new energy power generation, the volatility and instability of the power grid have become increasingly significant. Especially during the peak electricity consumption period, the load demand of the power grid rises sharply, and the intermittency and uncertainty of new energy power generation make it more difficult to perform grid peak shaving.

[0003] The current peak shaving methods include thermal power peak shaving and hydropower peak shaving. However, thermal power peak shaving and hydropower peak shaving have certain defects and deficiencies:

[0004] 1. The contradiction between the economy and environmental protection of thermal power peak shaving: During thermal power peak shaving, the unit needs to deviate from the rated operating condition, resulting in an increase in energy loss and a decrease in economy. Frequent peak shaving operations may also affect the service life of the unit and increase the maintenance cost. At the same time, the pollutant emissions generated during the thermal power peak shaving process will also have a certain impact on the environment.

[0005] 2. The limitations of hydropower peak shaving: Hydropower peak shaving is restricted by natural conditions such as water resources and topography, and the peak shaving capacity is limited. During the dry season or in arid regions, the effect of hydropower peak shaving may be greatly reduced.

[0006] Large-scale compressed air energy storage power generation technology is an efficient energy storage method. It can convert the excess power into the potential energy of compressed air and store it during the low load period of the power grid, and release this potential energy to generate electricity during the peak load period of the power grid, so as to meet the peak shaving demand of the power grid. This technology can not only improve the stability and economy of the power grid, but also promote the consumption and utilization of new energy. Therefore, it is of great significance to develop a grid peak shaving control model based on large-scale compressed air energy storage power generation. Summary of the Invention

[0007] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a grid peak shaving control model based on large-scale compressed air energy storage power generation.

[0008] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0009] A grid peak shaving control model based on large-scale compressed air energy storage power generation, and the specific steps for implementing the model are as follows:

[0010] S1: Design of peak shaving control strategy, energy storage during valley hours: When the grid load is low, start the compressor system to convert surplus electric energy into compressed air for storage; control objective: The compressor operates during the period with the lowest cost to avoid creating new load peaks on the grid; power generation during peak hours: When the grid load is high, start the expander system to release the stored compressed air and drive the generator to generate electricity; when the grid load is high, start the expander system to release the stored compressed air and drive the generator to generate electricity; The set peak shaving control strategy includes multiple modes such as charging, discharging, and maintaining the energy storage state. During the grid load valley, the compressed air energy storage power generation system is used to absorb the excess electric energy for charging; during the grid load peak, the stored energy is released for discharging to supplement the grid's power demand.

[0011] By real-time monitoring of the grid load and the status of the compressed air energy storage power generation system, dynamically adjust the control strategy to ensure the stable operation of the grid and the optimization of the peak shaving effect.

[0012] S2: Construction of control model, including:

[0013] a. Mathematical model

[0014] Objective function:

[0015] Minimize the operating cost C:

[0016] Where:

[0017] C compress (t): The electricity cost during the compression process.

[0018] C expand (t): The power generation revenue or cost.

[0019] C maintenance (t): The equipment maintenance cost.

[0020] Constraint conditions: P stored (t) ≤ P max

[0021] Gas storage capacity constraint:

[0022] P grid (t) + P CAES (t) = P load (t)

[0023] Grid balance constraint:

[0024] P compress， P expand ∈ [P min , P max .

[0025] By using mathematical models and simulation tools, simulate the operating characteristics of the compressed air energy storage power generation system under different working conditions to provide basic data for the subsequent design of control strategies.

[0026] b. Dynamic scheduling algorithm

[0027] Optimization algorithm: Based on intelligent optimization methods such as genetic algorithms and particle swarm optimization, perform dynamic optimization of energy storage and power generation plans.

[0028] Real-time adjustment: Combine the real-time operation data of the power grid and use model predictive control (MPC) to adjust the output in real time.

[0029] Adopt advanced optimization algorithms such as genetic algorithms and particle swarm algorithms to optimize the peak shaving control strategy. Consider multiple factors such as the economy, technology, and stability of the power grid, construct a multi-objective optimization model, and seek the optimal peak shaving control plan. Through programming and simulation tools, realize the automatic operation and real-time monitoring of the peak shaving control strategy.

[0030] S3: Analysis of operating scenarios, including:

[0031] Daily load regulation: During the daytime peak load, use the expander to generate electricity to meet the electricity demand. During the nighttime low load, use the compressor for energy storage.

[0032] Renewable energy access: When the output of wind power or photovoltaic power generation fluctuates greatly, quickly respond to suppress the power fluctuation.

[0033] Emergency power regulation: When short-term frequency fluctuations occur in the power grid, perform primary frequency modulation by quickly starting the expander.

[0034] Establish an experimental platform or a simulation system to verify and evaluate the proposed peak shaving control model. Compare the peak shaving effects, economy, stability and other indicators under different control strategies, and evaluate the advantages and disadvantages of the model. According to the verification results, make necessary corrections and optimizations to the model to improve its practicality and reliability.

[0035] This solution: First, conduct a detailed modeling of the large-scale compressed air energy storage power generation system, including key parameters such as its working principle, energy conversion efficiency, and operating cost. By using mathematical models and simulation tools, simulate the operating characteristics of the compressed air energy storage power generation system under different working conditions to provide basic data for the subsequent design of control strategies.

[0036] Analysis of power grid peak shaving demand: Analyze the load demand of the power grid, especially the differences and changing trends of peak and valley loads. According to the peak shaving demand of the power grid, determine the target output power and energy storage capacity of the compressed air energy storage power generation system during peak shaving.

[0037] By implementing this technical solution, it is expected to significantly improve the peak shaving capacity of the power grid, reduce the peak shaving cost, and enhance the stability and economy of the power grid. Meanwhile, this technical solution can also make full use of the unique advantages of the compressed air energy storage power generation system to provide strong support for the sustainable development of the power grid.

[0038] The beneficial effects of the present invention are as follows:

[0039] 1. The present invention has an efficient peak shaving capacity for the power grid. By using a compressed air energy storage system (CAES) to store energy during the low load period of the power grid and release energy during the high load period, it effectively smooths the load curve of the power grid and reduces the peak-valley difference.

[0040] 2. The present invention has flexible energy management. The regulation model can, through a dynamic optimization algorithm, adjust the charge-discharge plan according to real-time load prediction and market electricity prices to achieve efficient management of energy storage resources. It also supports multiple operation modes (such as long-term peak shaving and short-term frequency modulation) to adapt to different power grid operation requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 FIG. is a schematic structural diagram of a power grid peak shaving control model based on large-scale compressed air energy storage power generation proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0043] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0044] A power grid peak shaving control model based on large-scale compressed air energy storage power generation, and the specific implementation steps of the model are as follows:

[0045] S1: Design of peak shaving control strategy, energy storage during valley hours: When the power grid load is low, start the compressor system to convert surplus electric energy into compressed air for storage; control objective: The compressor operates during the period with the lowest cost to avoid creating new load peaks for the power grid; power generation during peak hours: When the power grid load is high, start the expander system to release the stored compressed air and drive the generator to generate electricity; when the power grid load is high, start the expander system to release the stored compressed air and drive the generator to generate electricity; The set peak shaving control strategy includes multiple modes such as charging, discharging, and maintaining the energy storage state. During the low load period of the power grid, the compressed air energy storage power generation system is used to absorb the excess electric energy for charging; during the high load period of the power grid, the stored energy is released for discharging to supplement the power demand of the power grid.

[0046] By monitoring the power grid load and the status of the compressed air energy storage power generation system in real time, the control strategy is dynamically adjusted to ensure the stable operation of the power grid and the optimization of the peak shaving effect.

[0047] S2: Construction of the control model, including:

[0048] a. Mathematical model

[0049] Objective function:

[0050] Minimize the operating cost C:

[0051] Where:

[0052] C compress (t): The power cost during the compression process.

[0053] C expand (t): The power generation revenue or cost.

[0054] C msintenance (t): The equipment maintenance cost.

[0055] Constraint condition: P stored (t) ≤ P max

[0056] Gas storage capacity constraint:

[0057] P grid (t) + P CAES (t) = P load (t)

[0058] Power grid balance constraint:

[0059] P compress , P expand ∈ [P min , P max .

[0060] By using the mathematical model and simulation tools, the operating characteristics of the compressed air energy storage power generation system under different working conditions are simulated to provide basic data for the subsequent design of the control strategy.

[0061] b. Dynamic scheduling algorithm

[0062] Optimization algorithm: Based on intelligent optimization methods such as genetic algorithm and particle swarm optimization, the dynamic optimization of energy storage and power generation plans is carried out.

[0063] Real-time regulation: Combining the real-time operation data of the power grid, model predictive control (MPC) is used to adjust the output in real time.

[0064] Adopt advanced optimization algorithms, such as genetic algorithms, particle swarm algorithms, etc., to optimize the peak shaving control strategy. Consider multiple factors such as the economy, technology, and stability of the power grid, construct a multi-objective optimization model, seek the optimal peak shaving control scheme, and realize the automated operation and real-time monitoring of the peak shaving control strategy through programming and simulation tools.

[0065] S3: Operating scenario analysis, including:

[0066] Daily load regulation: During the peak load in the daytime, use the expander to generate electricity to meet the power demand. During the low load at night, use the compressor for energy storage.

[0067] Renewable energy access: When the output of wind power or photovoltaic power generation fluctuates greatly, quickly respond to suppress the power fluctuation.

[0068] Emergency power regulation: When short-term frequency fluctuations occur in the power grid, perform primary frequency modulation by quickly starting the expander.

[0069] Establish an experimental platform or simulation system to verify and evaluate the proposed peak shaving control model. Compare the peak shaving effect, economy, stability and other indicators under different control strategies, and evaluate the advantages and disadvantages of the model. According to the verification results, make necessary corrections and optimizations to the model to improve its practicability and reliability.

[0070] This solution: First, build a detailed model of the large-scale compressed air energy storage power generation system, including its working principle, energy conversion efficiency, operating cost and other key parameters. Through mathematical models and simulation tools, simulate the operating characteristics of the compressed air energy storage power generation system under different working conditions, and provide basic data for the subsequent design of control strategies.

[0071] Analysis of power grid peak shaving demand: Analyze the load demand of the power grid, especially the differences and change trends of peak and valley loads. According to the peak shaving demand of the power grid, determine the target output power and energy storage capacity of the compressed air energy storage power generation system during peak shaving.

[0072] By implementing this technical solution, it is expected to significantly improve the peak shaving capacity of the power grid, reduce the peak shaving cost, and improve the stability and economy of the power grid. At the same time, this technical solution can also make full use of the unique advantages of the compressed air energy storage power generation system to provide strong support for the sustainable development of the power grid.

[0073] For ease of description, spatial relative terms such as "above", "over", "on the upper surface", "upper" etc. can be used here to describe the spatial positional relationship of a device or feature shown in the figures with other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is inverted, a device described as "above" or "over" other devices or structures will then be positioned "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both the orientations of "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the corresponding explanations are made for the spatial relative descriptions used here.

[0074] It should be noted that the terms used here are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used here, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprise" and / or "include" are used in this specification, they specify the presence of features, steps, operations, devices, components and / or their combinations.

[0075] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned figures of the present application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these process, method, product or device.

[0076] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A grid peak shaving control model based on large-scale compressed air energy storage power generation, characterized in that, The specific steps for model implementation are as follows: S1: Design of peak shaving control strategy, energy storage during valley hours: When the grid load is low, start the compressor system to convert surplus electric energy into compressed air for storage; control objective: The compressor operates during the period with the lowest cost to avoid creating new load peaks on the grid; power generation during peak hours: When the grid load is high, start the expander system to release the stored compressed air and drive the generator to generate electricity; When the grid load is high, start the expander system to release the stored compressed air and drive the generator to generate electricity; S2: Construction of the control model, including: a. Mathematical model Objective function: Minimize the operating cost C: Where: C compress (t): The electricity cost during the compression process. C expand (t): Power generation revenue or cost. C maintenance (t): Equipment maintenance cost. Constraint: P stored (t) ≤ P max Gas storage capacity constraint: P grid (t) + P CAES (t) = P load (t) Grid balance constraint: P compress , P expand ∈ [P min , P max . b. Dynamic scheduling algorithm Optimization algorithm: Based on intelligent optimization methods such as genetic algorithm and particle swarm optimization, perform dynamic optimization of energy storage and power generation plans. Real-time adjustment: Combine the real-time operation data of the grid and use model predictive control (MPC) to adjust the output in real time. S3: Analysis of operation scenarios, including: Daily load regulation: During the daytime peak load, use the expander to generate electricity to meet the electricity demand, and during the nighttime valley load, use the compressor for energy storage. Renewable energy access: When the output of wind power or photovoltaic power fluctuates greatly, quickly respond to suppress the power fluctuation. Emergency power regulation: When short-term frequency fluctuations occur in the grid, perform primary frequency modulation by quickly starting the expander.

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