Optimized scheduling management method and system for multiple types of energy storage resources and related equipment

Abstract

The invention is suitable for the technical field of resource scheduling management, and provides an optimal scheduling management method and system for multi-type energy storage resources and related equipment, and the method comprises the steps: obtaining the operation state data of a power grid, the power generation data of renewable energy sources, and the state data of each type of energy storage resources; analyzing power grid stability, renewable energy consumption and economic cost benefits according to the power grid operation state data, the renewable energy power generation data and the state data of various types of energy storage resources; constructing a comprehensive evaluation model, and inputting the analysis results of the power grid stability, the renewable energy consumption and the economic cost benefit into the comprehensive evaluation model to obtain the scheduling priority of each type of energy storage resources; and the optimal scheduling scheme of the energy storage resources is adjusted based on the scheduling priorities of the various types of energy storage resources, so that the scheduling based on the various types of energy storage resources is more comprehensive and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of resource scheduling management, and in particular to an optimization scheduling management method, system and related equipment for multiple types of energy storage resources. Background Art

[0002] With the widespread application of renewable energy, such as solar and wind energy, its inherent intermittency and volatility have brought great challenges to the stable operation of energy systems. At present, different types of energy storage technologies have their own unique performance characteristics, such as energy density, power density, charge and discharge efficiency, cycle life, cost, etc. However, with the access of multiple types of energy storage resources, the structure and operation mode of the energy storage system have become more complex. Different types of energy storage equipment may require different control strategies and management methods. It is necessary to consider the influence of multiple types of energy storage resources on multiple factors. When the operating state of the power grid changes, the scheduling based on multiple types of energy storage resources may not be adjusted in time, resulting in inaccurate and incomplete scheduling and low resource utilization.

[0003] The present invention provides an optimization scheduling management method, system and related equipment for multiple types of energy storage resources. Summary of the invention

[0004] The embodiments of the present application provide a method, system and related equipment for optimizing the scheduling and management of multiple types of energy storage resources, which are used to solve the problem.

[0005] A first aspect of an embodiment of the present application provides a method for optimizing scheduling and management of multiple types of energy storage resources, including:

[0006] Obtain grid operation status data, renewable energy generation data, and various types of energy storage resource status data;

[0007] Analyze grid stability, renewable energy consumption, and economic cost-effectiveness based on the grid operation status data, renewable energy generation data, and various types of energy storage resource status data;

[0008] Constructing a comprehensive evaluation model, and inputting the analysis results of the grid stability, renewable energy consumption, and economic cost-effectiveness into the comprehensive evaluation model to obtain the scheduling priority of various types of energy storage resources;

[0009] An optimized scheduling scheme for energy storage resources is adjusted based on the scheduling priorities of the various types of energy storage resources.

[0010] Furthermore, the analysis of grid stability, renewable energy consumption and economic cost-effectiveness based on the grid operation status data, renewable energy generation data and various types of energy storage resource status data includes:

[0011] Preprocessing the power grid operation status data, performing probability distribution fitting on the preprocessed power grid operation status parameters, and respectively calculating the voltage stability index probability, the frequency stability index probability, and the power stability index probability;

[0012] Preprocessing and standardizing the renewable energy power generation data, performing probability distribution fitting on the preprocessed and standardized renewable energy power generation parameters, and respectively calculating the power generation power prediction probability, the intermittency of renewable energy power generation, and the volatility of renewable energy power generation;

[0013] The investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource are analyzed based on the status data of each type of energy storage resource.

[0014] Furthermore, the preprocessing of the grid operation status data, performing probability distribution fitting on the preprocessed grid operation status parameters, and respectively calculating the voltage stability index probability, the frequency stability index probability and the power stability index probability include:

[0015] Describing the probability distribution of voltage in the power grid operation status data using normal distribution, and calculating the probability of voltage exceeding a preset operation range and the probability of voltage collapse based on the voltage probability distribution;

[0016] Describing the probability distribution of frequency in the power grid operation status data using normal distribution, and calculating the probability of frequency deviation and the probability of frequency fluctuation based on the probability distribution of frequency;

[0017] The normal distribution is used to describe the probability distribution of power in the power grid operation status data, and the probabilities of power overload and power balance deviation are calculated based on the probability distribution of power.

[0018] Furthermore, the renewable energy power generation data is preprocessed and standardized, and probability distribution fitting is performed on the preprocessed and standardized renewable energy power generation parameters to respectively calculate the power generation power prediction probability, the intermittency of renewable energy power generation, and the volatility of renewable energy power generation, including:

[0019] Describing the probability distribution of renewable energy power generation in the renewable energy power generation data using normal distribution, and calculating the power generation prediction probability based on the probability distribution of renewable energy power generation;

[0020] Using Weibull distribution to describe the probability distribution of intermittency of renewable energy generation in the renewable energy generation data, and evaluating the intermittency of renewable energy generation by determining the probability that the generated power is zero or below a certain threshold;

[0021] Statistical indicators are combined with probability distribution to describe the probability distribution of renewable energy power generation volatility in the renewable energy power generation data, and the volatility of renewable energy power generation is evaluated based on the probability distribution of renewable energy power generation volatility.

[0022] Furthermore, the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource are analyzed according to the status data of each type of energy storage resource, including:

[0023] Calculate the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource based on the status data of each type of energy storage resource;

[0024] The benefit-cost ratio of each type of resource is calculated based on the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource.

[0025] Furthermore, the comprehensive evaluation model is constructed, and the analysis results of the grid stability, renewable energy consumption and economic cost-effectiveness are input into the comprehensive evaluation model to obtain the scheduling priority of various types of energy storage resources, including:

[0026] Determine a comprehensive grid stability index based on the voltage stability index probability, the frequency stability index probability and the power stability index probability;

[0027] Determining a comprehensive renewable energy consumption index based on the predicted probability of power generation, the intermittency of renewable energy power generation, and the volatility of renewable energy power generation;

[0028] Determine the comprehensive economic cost-benefit index based on the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource;

[0029] The comprehensive index of grid stability, comprehensive index of renewable energy consumption and comprehensive index of economic cost-benefit are input into the constructed comprehensive evaluation model to obtain the scheduling priority of various types of energy storage resources.

[0030] Furthermore, the grid stability comprehensive index, renewable energy consumption comprehensive index and economic cost-benefit comprehensive index are input into the constructed comprehensive evaluation model to obtain the dispatch priority of various types of energy storage resources, including:

[0031] Calculate the first weights of the comprehensive index of power grid stability, the comprehensive index of renewable energy consumption and the comprehensive index of economic cost-benefit respectively by using the hierarchical analysis method;

[0032] The second weights of the comprehensive index of power grid stability, the comprehensive index of renewable energy consumption and the comprehensive index of economic cost-benefit are calculated respectively by using the entropy weight method;

[0033] A target weight is determined according to the first weight and the second weight, and a scheduling priority of each type of energy storage resource is determined according to the target weight.

[0034] Furthermore, the expression for determining the target weight according to the first weight and the second weight is as follows:

[0035] P = w1S + w2R + w3E;

[0036] Where: S, R and E are the comprehensive indicators of grid stability, renewable energy consumption and economic cost-benefit, respectively; w1, w2 and w3 are the comprehensive weights corresponding to the comprehensive indicators of grid stability, renewable energy consumption and economic cost-benefit, respectively.

[0037] A second aspect of the embodiment of the present application provides an optimization scheduling management system for multiple types of energy storage resources, including:

[0038] A data acquisition unit, used to acquire power grid operation status data, renewable energy generation data, and various types of energy storage resource status data;

[0039] A data analysis unit, configured to analyze grid stability, renewable energy consumption, and economic cost-effectiveness based on the grid operation status data, renewable energy generation data, and various types of energy storage resource status data;

[0040] A dispatch priority determination unit is used to construct a comprehensive evaluation model, and input the analysis results of the grid stability, renewable energy consumption and economic cost-effectiveness into the comprehensive evaluation model to obtain the dispatch priority of each type of energy storage resource;

[0041] The optimized scheduling scheme adjustment unit is used to adjust the optimized scheduling scheme of the energy storage resources based on the scheduling priorities of the various types of energy storage resources.

[0042] A third aspect of an embodiment of the present application provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein when the processor executes the computer-readable instructions, the steps of the method for optimizing scheduling and management of multiple types of energy storage resources as described in any one of the above are implemented.

[0043] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0044] This application analyzes the influencing factors of grid stability, renewable energy consumption, and economic cost-effectiveness of various types of resources based on the acquired data, and then inputs the analysis results of grid stability, renewable energy consumption, and economic cost-effectiveness into the constructed comprehensive evaluation model to obtain the dispatch priority and finally determine the optimized dispatch plan. By evaluating the dispatch priority of various types of energy storage resources based on the three indicators of grid stability, renewable energy, and economic cost-effectiveness, when the grid operation status changes, the dispatch plan can be adjusted in real time according to various influencing factors, making the dispatch based on multiple types of energy storage resources more comprehensive and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of an embodiment of a method for optimizing scheduling and management of multiple types of energy storage resources in the present invention;

[0046] Figure 2 It is a structural block diagram of an embodiment of an optimized scheduling and management system for multiple types of energy storage resources in the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] The optimization scheduling management method of multiple types of energy storage resources in this embodiment is used to adjust the scheduling scheme of multiple types of energy storage resources in real time. The implementation method in this embodiment can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, and the specific details are not clearly limited.

[0049] Embodiment 1

[0050] See also Figure 1 , an embodiment of an optimization scheduling management method for multiple types of energy storage resources in the present invention comprises the following steps:

[0051] S11. Obtaining grid operation status data, renewable energy generation data, and various types of energy storage resource status data;

[0052] In this embodiment, the multiple types of energy storage resources include electrochemical energy storage, pumped storage, compressed air energy storage and flywheel energy storage.

[0053] The grid operation status data includes voltage data, current data, power data and frequency data. The voltage data includes node voltage amplitude, voltage phase angle, voltage fluctuation and flicker data and voltage harmonic content; the current data includes line current amplitude, current phase angle and current harmonic content; the power data includes active power, reactive power and apparent power.

[0054] Renewable energy power generation data includes power generation data, energy resource data, power generation efficiency data, power generation time and power generation data. Power generation data includes active power output (solar photovoltaic power generation, wind power generation, thermal power generation) and reactive power output; energy resource data includes solar illumination, wind speed and direction, as well as water level and flow; power generation efficiency data includes photoelectric conversion efficiency, wind energy utilization coefficient and turbine efficiency; power generation time and power data includes power generation time records and power generation statistics.

[0055] The status data of various types of energy storage resources include the charge state, health status, charge and discharge current and voltage, battery temperature and internal resistance data of electrochemical energy storage; the water levels of upper and lower reservoirs, pumping and power generation flow, unit operation status and unit efficiency data of pumped storage; the air tank pressure, air flow, temperature data and energy storage and release power of compressed air energy storage; the speed data, torque data and temperature data of flywheel energy storage.

[0056] S12. Analyze grid stability, renewable energy consumption and economic cost-effectiveness based on grid operation status data, renewable energy generation data and various types of energy storage resource status data;

[0057] Step S12 includes:

[0058] 1. Preprocess the grid operation status data, perform probability distribution fitting on the preprocessed grid operation status parameters, and calculate the voltage stability index probability, frequency stability index probability, and power stability index probability respectively;

[0059] First, the grid operation status data is preprocessed, including removing obviously erroneous data points (such as outliers caused by sensor failure), which can be judged by setting reasonable thresholds. For example, for voltage data, if the voltage value at a certain moment exceeds the normal range (such as ±20% of the rated voltage) and there is no similar fluctuation in the surrounding data, it can be determined as an outlier and removed. Then the data is standardized to make data of different dimensions (such as voltage amplitude, current size, power, etc.) comparable. The following probability distributions are described by normal distribution respectively:

[0060] Voltage stability index probability:

[0061] The voltage over-limit probability is the probability that the voltage amplitude exceeds the specified operating range. If the voltage amplitude obeys the fitted normal probability distribution, then the voltage over-limit probability can be obtained by integral calculation, which is calculated according to the probability density function of the normal distribution, that is,

[0062] The voltage collapse probability is calculated by establishing a voltage stability analysis model and combining the probability distribution to evaluate the possibility of voltage collapse. According to the probability distribution of system state variables, the voltage collapse probability P is calculated. vc =P(V <V critical ), where V critical is the voltage collapse threshold.

[0063] Frequency stability index: The frequency deviation probability is similar to the voltage over-limit probability, and the frequency fluctuation is to analyze the frequency fluctuation within a certain period of time.

[0064] Power stability index:

[0065] The power overload probability is the transmission power P of the transmission line or transformer t , with rated power P rated If P t Obeying a certain probability distribution, the power overload probability P po For: P po =P(P>P rated ), the overload probability is calculated by integrating the probability distribution obtained by fitting the power data (for example, assuming a gamma distribution whose probability density function is known).

[0066] The power balance deviation probability is considered to be the power generation power P g and load power P i , power balance deviation ΔP=P g ―P i After fitting the probability distribution of power generation and load power respectively, the probability distribution of ΔP is obtained by convolution and other methods, and then the probability of ΔP exceeding the allowable range is calculated as the power balance deviation probability P pb , that is, P pb =1-P(P min <ΔP <P max ).

[0067] 2. Preprocess and standardize the renewable energy power generation data, perform probability distribution fitting on the preprocessed and standardized renewable energy power generation parameters, and calculate the power generation prediction probability, intermittency of renewable energy power generation, and volatility of renewable energy power generation;

[0068] The power generation prediction probability is to use the fitted probability distribution to predict the probability of renewable energy power generation. For example, for solar power generation, the current solar irradiance probability distribution and the power-irradiance characteristic curve of the photovoltaic system are known, and the probability distribution of power generation can be obtained through convolution operation. Assume that the solar irradiance I follows the probability distribution f I (i) The power-irradiance relationship of the photovoltaic system is P = g(I), then the probability distribution of the generated power P is f p(p) can be obtained by f p (p) = ∫f I (i)δ(p―g(i))di calculation. This helps to predict the probability of power generation being in a certain range in the future and provide a reference for applications such as power grid dispatching.

[0069] The intermittency of renewable energy generation is evaluated by calculating the probability that the power generation is zero or below a certain threshold. For example, for wind power generation, the probability that the wind speed is lower than the cut-in wind speed (when the wind turbine does not generate electricity) is calculated. If the wind speed V follows the Weibull distribution W(k,λ), the cut-in wind speed is V cut―in , then the intermittent probability

[0070] Volatility is the use of statistical indicators such as variance, standard deviation or root mean square error combined with probability distribution to quantify the volatility of renewable energy generation. For example, for a solar power generation series over a period of time, the variance of its probability distribution is calculated. The larger the variance, the stronger the volatility. Volatility can also be assessed by analyzing the probability of power changes on different time scales, such as calculating the probability of power generation changing by more than a certain percentage (such as 20%) within 1 hour.

[0071] 3. Analyze the investment cost, operation and maintenance cost, life cost and benefits of each type of energy storage resource based on its status data.

[0072] Specifically, analyzing the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource based on the status data of each type of energy storage energy includes the following steps:

[0073] The investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource are calculated according to the status data of each type of energy storage resource; the benefit-cost ratio of each type of resource is calculated based on the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource.

[0074] The investment cost for electrochemical energy storage includes battery cost, battery management system cost and other supporting equipment cost; operation and maintenance cost includes battery life and replacement cost, energy loss cost and maintenance personnel and equipment maintenance cost. Pumped storage includes civil engineering cost, electromechanical equipment cost, equipment maintenance cost and energy consumption cost. Compressed air energy storage includes gas storage system cost, compression and expansion equipment cost, other supporting costs, equipment maintenance cost and energy consumption cost. Flywheel energy storage includes flywheel body cost, motor and power electronic equipment cost, vacuum cooling system cost, equipment maintenance cost and energy consumption cost. Revenue includes peak-valley electricity price arbitrage revenue, ancillary service revenue, power generation revenue and rapid response revenue, etc.

[0075] The investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource are calculated in turn to calculate the benefit-cost ratio of each type of resource:

[0076] Benefit-cost ratio = total benefit / total cost.

[0077] S13. Construct a comprehensive evaluation model and input the analysis results of grid stability, renewable energy consumption and economic cost-effectiveness into the comprehensive evaluation model to obtain the scheduling priority of various types of energy storage resources;

[0078] 1. Determine the comprehensive grid stability index based on the voltage stability index probability, frequency stability index probability and power stability index probability;

[0079] 2. Determine the comprehensive index of renewable energy consumption based on the power generation prediction probability, the intermittency of renewable energy generation and the volatility of renewable energy generation;

[0080] 3. Determine the comprehensive economic cost-benefit index based on the investment cost, operation and maintenance cost, life cost and benefits of each type of energy storage resource;

[0081] The grid stability index is determined based on the voltage stability index probability, frequency stability index probability and power stability index probability in step S12 above, and each index is given a certain weight to obtain a comprehensive index value. Similarly, the comprehensive index of renewable energy consumption and the comprehensive index of economic cost-effectiveness are also determined based on the index probabilities calculated in the above steps, which will not be repeated here.

[0082] 4. Input the comprehensive indicators of grid stability, renewable energy consumption and economic cost-benefit into the constructed comprehensive evaluation model to obtain the scheduling priority of various types of energy storage resources.

[0083] The steps specifically include the following:

[0084] The first weights of the comprehensive index of grid stability, comprehensive index of renewable energy consumption and comprehensive index of economic cost-benefit are calculated by using the analytic hierarchy process.

[0085] Constructing a judgment matrix: Specifically, invite power system experts to compare the relative importance of the three aspects of grid stability, renewable energy consumption, and economic cost-effectiveness, and use a 1-9 scale (1 means equally important, 9 means extremely important) to construct a judgment matrix A. For example, if the experts believe that grid stability is slightly more important than renewable energy consumption, and renewable energy consumption is much more important than economic cost-effectiveness, then the judgment matrix A may be:

[0086]

[0087] Calculate the weight vector: By calculating the maximum eigenvalue of the judgment matrix and its corresponding eigenvector, the eigenvector is normalized to obtain the weight vector w of each indicator. For example, the maximum eigenvalue of the judgment matrix A is calculated to be 3.0385, and the corresponding eigenvector is [0.6370, 0.2583, 0.1047] T , after normalization, we get the weight vector w = [0.637, 0.258, 0.105], which correspond to the weights of grid stability, renewable energy consumption, and economic cost-effectiveness, respectively.

[0088] Consistency check: Calculate consistency index where λ max is the maximum eigenvalue of the judgment matrix, and n is the order of the matrix. For the above matrix, Then find the average random consistency index RI (for the third-order matrix, RI = 0.58) and calculate the consistency ratio The consistency test shows that the weight distribution is reasonable.

[0089] The second weights of the comprehensive index of power grid stability, the comprehensive index of renewable energy consumption and the comprehensive index of economic cost-benefit are calculated respectively by using the entropy weight method;

[0090] The second weight is calculated by the entropy weight method, and the calculation formula is as follows:

[0091]

[0092] A target weight is determined according to the first weight and the second weight, and a scheduling priority of each type of energy storage resource is determined according to the target weight.

[0093] The final weight is determined by linear combination. The weight vector obtained by analytic hierarchy process is The weight vector obtained by the entropy weight method is: Then the combined weight vector w final =[w1,w2,w3]The calculation formula is Where is the weight distribution coefficient, which is generally around 0.01 and can be adjusted according to actual conditions. For example, assuming that w is obtained through the above calculation AHP =[0.6,0.2,0.2], w EW =[0.5,0.3,0.2], α = 0.5, then w final =[0.55,0.25,0.2].

[0094] According to the calculated comprehensive weight, the grid stability index, renewable energy consumption comprehensive index and economic cost-benefit comprehensive index calculated above are substituted into the comprehensive model to calculate the comprehensive evaluation index of the dispatch priority of each type of energy storage resource:

[0095] P=w1S+w2R+w3E

[0096] Where: S, R and E are the comprehensive indicators of grid stability, renewable energy consumption and economic cost-benefit, respectively; w1, w2 and w3 are the comprehensive weights corresponding to the comprehensive indicators of grid stability, renewable energy consumption and economic cost-benefit, respectively.

[0097] The various types of energy storage resources are ranked according to the size of the P value. The larger the P value, the higher the dispatch priority. The evaluation results are analyzed and interpreted in combination with the actual grid operation and demand. For example, if a certain energy storage technology performs well in grid stability but has poor economic cost-effectiveness, its potential value in key scenarios (such as ensuring power supply to important loads, responding to extreme events, etc.) can be further analyzed to comprehensively consider whether it should be prioritized in specific situations.

[0098] S14. Adjust the optimal scheduling plan of energy storage resources based on the scheduling priorities of various types of energy storage resources.

[0099] Finally, a dispatch strategy for energy storage resources is formulated based on the evaluation results, with priority given to high-priority energy storage resources to achieve stable operation of the power grid, efficient consumption of renewable energy, and optimization of economic benefits. The operation effect of energy storage resources is continuously monitored and evaluated, and the dispatch strategy is adjusted and optimized according to actual conditions.

[0100] Embodiment 2

[0101] See also Figure 2 An embodiment of an optimization scheduling management system for multiple types of energy storage resources of the present invention includes the following steps:

[0102] A data acquisition unit, used to acquire power grid operation status data, renewable energy generation data, and various types of energy storage resource status data;

[0103] A data analysis unit, used to analyze grid stability, renewable energy consumption and economic cost-effectiveness based on grid operation status data, renewable energy generation data and various types of energy storage resource status data;

[0104] The dispatch priority determination unit is used to construct a comprehensive evaluation model and input the analysis results of grid stability, renewable energy consumption and economic cost-effectiveness into the comprehensive evaluation model to obtain the dispatch priority of various types of energy storage resources;

[0105] The optimized scheduling scheme adjustment unit is used to adjust the optimized scheduling scheme of energy storage resources based on the scheduling priorities of various types of energy storage resources.

[0106] Embodiment 3

[0107] The present invention provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0108] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0109] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0110] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0111] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for optimizing the scheduling and management of multiple types of energy storage resources, characterized in that: include: Obtain grid operation status data, renewable energy generation data, and various types of energy storage resource status data; Analyze grid stability, renewable energy consumption, and economic cost-effectiveness based on the grid operation status data, renewable energy generation data, and various types of energy storage resource status data; Constructing a comprehensive evaluation model, and inputting the analysis results of the grid stability, renewable energy consumption, and economic cost-effectiveness into the comprehensive evaluation model to obtain the scheduling priority of various types of energy storage resources; An optimized scheduling scheme for energy storage resources is adjusted based on the scheduling priorities of the various types of energy storage resources.

2. The method for optimizing scheduling and management of multiple types of energy storage resources according to claim 1, characterized in that: The analysis of grid stability, renewable energy consumption and economic cost-effectiveness based on the grid operation status data, renewable energy generation data and various types of energy storage resource status data includes: Preprocessing the power grid operation status data, performing probability distribution fitting on the preprocessed power grid operation status parameters, and respectively calculating the voltage stability index probability, the frequency stability index probability, and the power stability index probability; Preprocessing and standardizing the renewable energy power generation data, performing probability distribution fitting on the preprocessed and standardized renewable energy power generation parameters, and respectively calculating the power generation power prediction probability, the intermittency of renewable energy power generation, and the volatility of renewable energy power generation; The investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource are analyzed based on the status data of each type of energy storage resource.

3. The method for optimizing scheduling and management of multiple types of energy storage resources according to claim 2, characterized in that: The preprocessing of the power grid operation status data, performing probability distribution fitting on the preprocessed power grid operation status parameters, and respectively calculating the voltage stability index probability, the frequency stability index probability, and the power stability index probability include: Describing the probability distribution of voltage in the power grid operation status data using normal distribution, and calculating the probability of voltage exceeding a preset operation range and the probability of voltage collapse based on the voltage probability distribution; Describing the probability distribution of frequency in the power grid operation status data using normal distribution, and calculating the probability of frequency deviation and the probability of frequency fluctuation based on the probability distribution of frequency; The normal distribution is used to describe the probability distribution of power in the power grid operation status data, and the probabilities of power overload and power balance deviation are calculated based on the probability distribution of power.

4. The method for optimizing scheduling and management of multiple types of energy storage resources according to claim 2, characterized in that: The preprocessing and standardizing of the renewable energy power generation data, performing probability distribution fitting on the preprocessed and standardized renewable energy power generation parameters, and respectively calculating the power generation prediction probability, the intermittency of renewable energy power generation, and the volatility of renewable energy power generation, include: Describing the probability distribution of renewable energy power generation in the renewable energy power generation data using normal distribution, and calculating the power generation prediction probability based on the probability distribution of renewable energy power generation; Using Weibull distribution to describe the probability distribution of intermittency of renewable energy generation in the renewable energy generation data, and evaluating the intermittency of renewable energy generation by determining the probability that the generated power is zero or below a certain threshold; Statistical indicators are combined with probability distribution to describe the probability distribution of renewable energy power generation volatility in the renewable energy power generation data, and the volatility of renewable energy power generation is evaluated based on the probability distribution of renewable energy power generation volatility.

5. The method for optimizing scheduling and management of multiple types of energy storage resources according to claim 2, characterized in that: The analyzing the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource according to the status data of each type of energy storage resource includes: Calculate the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource based on the status data of each type of energy storage resource; The benefit-cost ratio of each type of resource is calculated based on the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource.

6. The method for optimizing scheduling and management of multiple types of energy storage resources according to any one of claims 1 to 5, characterized in that: The comprehensive evaluation model is constructed, and the analysis results of the grid stability, renewable energy consumption and economic cost-effectiveness are input into the comprehensive evaluation model to obtain the scheduling priority of various types of energy storage resources, including: Determine a comprehensive grid stability index based on the voltage stability index probability, the frequency stability index probability and the power stability index probability; Determining a comprehensive renewable energy consumption index based on the predicted probability of power generation, the intermittency of renewable energy power generation, and the volatility of renewable energy power generation; Determine the comprehensive economic cost-benefit index based on the investment cost, operation and maintenance cost, life cost and benefit of each type of energy storage resource; The comprehensive index of grid stability, comprehensive index of renewable energy consumption and comprehensive index of economic cost-benefit are input into the constructed comprehensive evaluation model to obtain the scheduling priority of various types of energy storage resources.

7. The method for optimizing scheduling and management of multiple types of energy storage resources according to claim 6, characterized in that: The comprehensive indicators of grid stability, renewable energy consumption and economic cost-benefit are input into the constructed comprehensive evaluation model to obtain the scheduling priority of various types of energy storage resources, including: Calculate the first weights of the comprehensive index of power grid stability, the comprehensive index of renewable energy consumption and the comprehensive index of economic cost-benefit respectively by using the hierarchical analysis method; The second weights of the comprehensive index of power grid stability, the comprehensive index of renewable energy consumption and the comprehensive index of economic cost-benefit are calculated respectively by using the entropy weight method; A target weight is determined according to the first weight and the second weight, and a scheduling priority of each type of energy storage resource is determined according to the target weight.

8. The method for optimizing scheduling and management of multiple types of energy storage resources according to claim 7, characterized in that: The expression for determining the target weight according to the first weight and the second weight is as follows: P = w1S + w2R + w3E; Where: S, R and E are the comprehensive indicators of grid stability, renewable energy consumption and economic cost-benefit, respectively; w1, w2 and w3 are the comprehensive weights corresponding to the comprehensive indicators of grid stability, renewable energy consumption and economic cost-benefit, respectively.

9. An optimization dispatching and management system for multiple types of energy storage resources, characterized in that: include: A data acquisition unit, used to acquire power grid operation status data, renewable energy generation data, and various types of energy storage resource status data; A data analysis unit, configured to analyze grid stability, renewable energy consumption, and economic cost-effectiveness based on the grid operation status data, renewable energy generation data, and various types of energy storage resource status data; A dispatch priority determination unit is used to construct a comprehensive evaluation model, and input the analysis results of the grid stability, renewable energy consumption and economic cost-effectiveness into the comprehensive evaluation model to obtain the dispatch priority of each type of energy storage resource; The optimized scheduling scheme adjustment unit is used to adjust the optimized scheduling scheme of the energy storage resources based on the scheduling priorities of the various types of energy storage resources.

10. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the processor implements the steps of the method for optimizing scheduling and management of multiple types of energy storage resources as described in any one of claims 1 to 8.

Images