Energy storage configuration optimization method and system of independent micro-grid

Abstract

The invention discloses an energy storage configuration optimization method for an independent micro-grid. The method comprises the following steps: determining an independent variable describing weather; based on historical data of the target independent micro-grid, a cumulative density function of weather is obtained through kernel density estimation; performing Latin hypercube sampling on the cumulative density function; performing inverse transformation on the cumulative density function to obtain an inverse cumulative density function; mapping the inverse cumulative density function to generate a corresponding weather scene; cutting down the obtained weather scenes to obtain a weather scene set; and according to the weather scene set, adopting a particle swarm optimization algorithm to carry out energy storage configuration optimization of the target independent micro-grid. The invention also discloses a system for realizing the energy storage configuration optimization method of the independent micro-grid. According to the method, the energy storage configuration optimization of the independent micro-grid can be completed, the influence of extreme weather scenes is considered, the reliability is higher, and the accuracy is better.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and in particular relates to an energy storage configuration optimization method and system for an independent microgrid. Background Art

[0002] With the development of economy and technology and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] At present, environmental problems are becoming more and more serious, extreme weather events are frequent, and the stability and security of power systems, especially independent microgrid systems, are facing unprecedented challenges. As an effective means of energy regulation, energy storage technology has been widely used in power systems and independent microgrid systems, which has brought great guarantees for the safe and reliable operation of power systems and independent microgrid systems.

[0004] In the event of extreme weather, the steady-state operation boundary of the independent microgrid system is breached. In order to ensure the safe and stable operation of the independent microgrid system under extreme weather conditions, the energy storage configuration of the independent microgrid must be optimized. However, the existing energy storage configuration optimization schemes for independent microgrids are generally based on conventional factors such as power demand fluctuations and equipment failures, and do not consider the adverse effects of extreme weather. Therefore, the existing optimization schemes are no longer applicable to the current operation requirements of independent microgrids. Summary of the invention

[0005] One of the purposes of the present invention is to provide an energy storage configuration optimization method for an independent microgrid with high reliability and good accuracy.

[0006] A second objective of the present invention is to provide a system for implementing the energy storage configuration optimization method of the independent microgrid.

[0007] The energy storage configuration optimization method of the independent microgrid provided by the present invention comprises the following steps:

[0008] S1. Identify independent variables that describe the weather;

[0009] S2. Based on the historical data of the target independent microgrid and the independent variables determined in step S1, the cumulative density function of the weather is obtained by using kernel density estimation;

[0010] S3. Perform Latin hypercube sampling on the cumulative density function obtained in step S2;

[0011] S4. Perform an inverse transformation on the cumulative density function obtained in step S3 to obtain an inverse cumulative density function;

[0012] S5. Mapping the inverse cumulative density function obtained in step S4 to generate a corresponding weather scene;

[0013] S6. Reduce the weather scenes obtained in step S5 to obtain a set of weather scenes;

[0014] S7. Based on the weather scenario set obtained in step S6, a particle swarm optimization algorithm is used to optimize the energy storage configuration of the target independent microgrid.

[0015] The step S1 of determining the independent variable describing the weather specifically includes the following steps:

[0016] Identify the independent variables that describe weather, including temperature, humidity, wind speed, precipitation, and radiation intensity.

[0017] The step S2 is based on the historical data of the target independent microgrid, and according to the independent variables determined in step S1, the cumulative density function of the weather is obtained by using kernel density estimation, which specifically includes the following steps:

[0018] According to the independent variable determined in step S1, corresponding historical data is obtained;

[0019] According to the historical data obtained, the corresponding probability density function is estimated using the following formula:

[0020]

[0021] Where f(t) is the probability density estimate at t; n is the total number of samples; h is the bandwidth; K() is the kernel function; T i is the i-th sample data;

[0022] Normalizing the probability density function, we get the cumulative density function of the independent variable of weather: Where f(t) is the probability density estimate at t, and Δt is the sampling interval.

[0023] The step S3 of performing Latin hypercube sampling on the cumulative density function obtained in step S2 specifically includes the following steps:

[0024] Determine the number of factors to be sampled and the number of samples to be sampled;

[0025] Divide each cumulative density function into a number of intervals of equal probability;

[0026] Randomly select a sample point from each interval;

[0027] Arrange and combine the samples of each factor quantity to ensure that the samples of each factor quantity cover all intervals;

[0028] Shuffle the order of all samples to increase the randomness of the samples;

[0029] Finally, Latin hypercube sampling is completed.

[0030] The step S4 performs an inverse transformation on the cumulative density function obtained in step S3 to obtain an inverse cumulative density function, which specifically includes the following steps:

[0031] Transform the cumulative density function to map the uniformly distributed random variables to the target probability distribution, thereby obtaining a random sample that conforms to the target probability distribution;

[0032] Get the sample value corresponding to the uniform random number, expressed as Where ICDF is the inverse cumulative density function, is the inverse function of the cumulative density function, and u is the value of the cumulative probability density.

[0033] Step S5 maps the inverse cumulative density function obtained in step S4 to generate a corresponding weather scene, which specifically includes the following steps:

[0034] A random number is obtained, and according to the inverse cumulative density function obtained in step S4, the obtained random number is mapped to a specific scene, so as to obtain a corresponding weather scene.

[0035] Step S6 of reducing the weather scenes obtained in step S5 to obtain a weather scene set specifically includes the following steps:

[0036] For each weather scenario i , use the following formula to calculate s i With other scenes j The minimum distance D i,min :

[0037] D i,min =minρ i d(s i ,s j )

[0038] Where ρ i For weather scenes i The probability of occurrence; d(s i ,s j ) is s i With s j distance;

[0039] For the probability density function, Latin hypercube sampling is performed to randomly generate scenarios; within the Latin hypercube sampling interval, the scenario with the lowest probability is deleted;

[0040] Increase the probability of deleted scenes to the closest scenes;

[0041] Repeat the above steps until the number of scenes within the 0% to 10% sampling interval or the 90% to 100% sampling interval is 1;

[0042] Finally, a set of weather scenes is obtained;

[0043] The scenario probability within the 0-10% sampling interval or the 90%-100% sampling interval is taken as the risk factor.

[0044] According to the weather scenario set obtained in step S6, the particle swarm optimization algorithm is used to optimize the energy storage configuration of the target independent microgrid, which specifically includes the following steps:

[0045] According to the weather scenario set obtained in step S6 and the obtained risk factor, a particle swarm optimization algorithm is used to optimize the energy storage configuration of the target independent microgrid;

[0046] In the process of optimization using the particle swarm optimization algorithm, the following formula is used to calculate the local learning factor c1 and the total learning factor c2:

[0047]

[0048] In the formula is the termination value of the local learning factor; is the starting value of the local learning factor; is the termination value of all learning factors; is the starting value of all learning factors; is an intermediate variable and N is the total number of samples, is the current local optimal solution, is the current global optimal solution.

[0049] The present invention also provides a system for implementing the energy storage configuration optimization method of the independent microgrid, comprising a variable determination module, a function determination module, a sampling module, an inverse transformation module, a mapping module, a reduction module and a configuration optimization module; the variable determination module, the function determination module, the sampling module, the inverse transformation module, the mapping module, the reduction module and the configuration optimization module are connected in series in sequence; the variable determination module is used to determine the independent variables describing the weather, and upload the data information to the function determination module; the function determination module is used to obtain the cumulative density function of the weather by using kernel density estimation based on the received data information, based on the historical data of the target independent microgrid, and based on the independent variables determined in step S1, and upload the data information to the sampling module; the sampling module is used to obtain the cumulative density function of the weather based on the received data information The cumulative density function obtained is sampled using Latin hypercube, and the data information is uploaded to the inverse transformation module; the inverse transformation module is used to perform an inverse transformation on the obtained cumulative density function according to the received data information to obtain an inverse cumulative density function, and upload the data information to the mapping module; the mapping module is used to map the obtained inverse cumulative density function according to the received data information to generate corresponding weather scenarios, and upload the data information to the reduction module; the reduction module is used to reduce the obtained weather scenarios according to the received data information to obtain a weather scenario set, and upload the data information to the configuration optimization module; the configuration optimization module is used to optimize the energy storage configuration of the target independent microgrid according to the received data information and the obtained weather scenario set using a particle swarm optimization algorithm.

[0050] The energy storage configuration optimization method and system for an independent microgrid provided by the present invention generates extreme weather scenarios through historical meteorological data and Latin hypercube sampling, models extreme weather scenarios in combination with kernel density estimation and inverse cumulative density function, and uses a particle swarm optimization algorithm to perform the final energy storage configuration optimization; therefore, the present invention can not only complete the energy storage configuration optimization of the independent microgrid, but also consider the impact of extreme weather scenarios, with higher reliability and better accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The figure is a schematic diagram of the method flow of the present invention.

[0052] Figure 2 Schematic diagram of the functional modules of the system of the present invention. DETAILED DESCRIPTION

[0053] like Figure 1 The method flow chart of the method of the present invention is shown as follows: the energy storage configuration optimization method of the independent microgrid disclosed in the present invention comprises the following steps:

[0054] S1. Determine the independent variables that describe the weather; specifically, the following steps are included:

[0055] Identify independent variables that describe weather, including temperature, humidity, wind speed, precipitation, and radiation intensity;

[0056] S2. Based on the historical data of the target independent microgrid and the independent variables determined in step S1, the cumulative density function of the weather is obtained by using kernel density estimation; specifically, the following steps are included:

[0057] According to the independent variable determined in step S1, corresponding historical data is obtained;

[0058] According to the historical data obtained, the corresponding probability density function is estimated using the following formula:

[0059]

[0060] Where f(t) is the probability density estimate at t; n is the total number of samples; h is the bandwidth; K() is the kernel function; T i is the i-th sample data;

[0061] Normalizing the probability density function, we get the cumulative density function of the independent variable of weather: Where f(t) is the probability density estimate at t, and Δt is the sampling interval;

[0062] S3. Performing Latin hypercube sampling on the cumulative density function obtained in step S2; specifically comprising the following steps:

[0063] Determine the number of sampling factors (the number of factors is the number of dimensions) and the number of samples;

[0064] Divide each cumulative density function into a number of intervals of equal probability;

[0065] Randomly select a sample point from each interval;

[0066] Arrange and combine the samples of each factor quantity to ensure that the samples of each factor quantity cover all intervals;

[0067] Shuffle the order of all samples to increase the randomness of the samples;

[0068] Finally, Latin hypercube sampling is completed;

[0069] S4. Perform an inverse transformation on the cumulative density function obtained in step S3 to obtain an inverse cumulative density function; specifically comprising the following steps:

[0070] Transform the cumulative density function to map the uniformly distributed random variables to the target probability distribution, thereby obtaining a random sample that conforms to the target probability distribution;

[0071] Get the sample value corresponding to the uniform random number, expressed as Where ICDF is the inverse cumulative density function, is the inverse function of the cumulative density function, and u is the value of the cumulative probability density;

[0072] S5. Mapping the inverse cumulative density function obtained in step S4 to generate a corresponding weather scene; specifically comprising the following steps:

[0073] Obtain a random number, and map the obtained random number to a specific scene according to the inverse cumulative density function obtained in step S4, so as to obtain a corresponding weather scene;

[0074] S6. Reduce the weather scenes obtained in step S5 to obtain a weather scene set; specifically comprising the following steps:

[0075] For each weather scenario i , use the following formula to calculate s i With other scenes j The minimum distance D i,min :

[0076] D i,min =minρ i d(s i ,s j )

[0077] Where ρ i For weather scenes i The probability of occurrence; d(s i ,s j ) is s i With s j distance;

[0078] For the probability density function, Latin hypercube sampling is performed to randomly generate scenarios; within the Latin hypercube sampling interval, the scenario with the lowest probability is deleted; in specific implementation, the probability density function describes the probability of occurrence of different scenarios. When applying these scenarios to practice, it is necessary to generate scenarios based on the probability density function, so sampling is required to randomly generate scenarios to ensure the objectivity and universality of the generated scenarios; common sampling methods include Monte Carlo sampling and Latin hypercube sampling. Since the probability of occurrence of extreme scenarios is low, Monte Carlo sampling may result in no scenarios in low-probability areas or a large sample space, resulting in a sharp increase in the amount of calculation. Latin hypercube uniformly stratifies the cumulative density function to ensure the occurrence of scenarios in low-probability areas, and does not require too many samples, so Latin hypercube sampling is selected; "within the Latin hypercube sampling interval" means within the stratification interval; probability is the probability of occurrence of random scenarios; in order to minimize a certain probability distance between the probability distribution of the retained scenario and the probability of the initial scenario set, the probability with a small probability is reduced and added to the scenario with the closest probability distance to its scenario. i,min, used to calculate this probability distance;

[0079] Increase the probability of deleted scenes to the closest scenes;

[0080] Repeat the above steps until the number of scenes within the 0% to 10% sampling interval or the 90% to 100% sampling interval is 1;

[0081] Finally, a set of weather scenes is obtained;

[0082] The scenario probability within the 0-10% sampling interval or the 90%-100% sampling interval is taken as the risk factor;

[0083] S7. Based on the weather scenario set obtained in step S6, the particle swarm optimization algorithm is used to optimize the energy storage configuration of the target independent microgrid; specifically, the following steps are included:

[0084] According to the weather scenario set obtained in step S6 and the obtained risk factor, a particle swarm optimization algorithm is used to optimize the energy storage configuration of the target independent microgrid;

[0085] In the process of optimization using the particle swarm optimization algorithm, the following formula is used to calculate the local learning factor c1 and the total learning factor c2:

[0086]

[0087] In the formula is the termination value of the local learning factor; is the starting value of the local learning factor; is the termination value of all learning factors; is the starting value of all learning factors; is an intermediate variable and N is the total number of samples, is the current local optimal solution, is the current global optimal solution.

[0088] The present invention incorporates extreme weather risk factors into the optimization process of the energy storage system. By analyzing historical meteorological data, the Latin hypercube sampling method is used to generate extreme weather scenarios, and the weather risk modeling is performed in combination with kernel density estimation and inverse cumulative density function. This allows the configuration of the energy storage system to more comprehensively consider the impact of extreme weather on system operation, thereby improving the resilience and reliability of the system. The present invention generates extreme weather risk factors by using the Latin hypercube sampling method, breaking through the limitations of random sampling in traditional methods, and can cover the entire risk space more evenly and efficiently, avoiding the problem that the distribution of extreme weather samples is biased towards small probability intervals. The combined scenario reduction and shortest distance calculation strategy optimizes the representativeness and rationality of extreme weather scenarios, improves the accuracy and reliability of scenario sampling, and provides a more scientific risk input for subsequent energy storage system optimization; the present invention dynamically adjusts the learning factor of particles in the particle swarm optimization algorithm, realizes rapid exploration in the early stage of search and precise convergence in the later stage, improves the attractiveness of the global optimal solution, ensures that the particle swarm can eventually accurately find the global optimal solution, avoids missing the best configuration due to premature convergence, and effectively responds to some constraint violations and multi-objective optimization problems in the independent microgrid energy storage configuration optimization process under the influence of extreme weather risk factors.

[0089] like Figure 2 The functional module diagram of the system of the present invention is shown as follows: the system for realizing the energy storage configuration optimization method of the independent microgrid disclosed in the present invention comprises a variable determination module, a function determination module, a sampling module, an inverse transformation module, a mapping module, a reduction module and a configuration optimization module; the variable determination module, the function determination module, the sampling module, the inverse transformation module, the mapping module, the reduction module and the configuration optimization module are connected in series in sequence; the variable determination module is used to determine the independent variables describing the weather, and upload the data information to the function determination module; the function determination module is used to obtain the cumulative density function of the weather by using kernel density estimation based on the received data information and the historical data of the target independent microgrid and the independent variables determined in step S1, and upload the data information to the sampling module; the sampling module is used to obtain the cumulative density function of the weather based on the received The data information is used to perform Latin hypercube sampling on the obtained cumulative density function, and upload the data information to the inverse transformation module; the inverse transformation module is used to perform an inverse transformation on the obtained cumulative density function according to the received data information to obtain an inverse cumulative density function, and upload the data information to the mapping module; the mapping module is used to map the obtained inverse cumulative density function according to the received data information to generate corresponding weather scenes, and upload the data information to the reduction module; the reduction module is used to reduce the obtained weather scenes according to the received data information to obtain a weather scene set, and upload the data information to the configuration optimization module; the configuration optimization module is used to optimize the energy storage configuration of the target independent microgrid according to the received data information and the obtained weather scene set using a particle swarm optimization algorithm.

Claims

1. A method for optimizing energy storage configuration of an independent microgrid, comprising the following steps: S1. Identify independent variables that describe the weather; S2. Based on the historical data of the target independent microgrid and the independent variables determined in step S1, the cumulative density function of the weather is obtained by using kernel density estimation; S3. Perform Latin hypercube sampling on the cumulative density function obtained in step S2; S4. Perform an inverse transformation on the cumulative density function obtained in step S3 to obtain an inverse cumulative density function; S5. Mapping the inverse cumulative density function obtained in step S4 to generate a corresponding weather scene; S6. Reduce the weather scenes obtained in step S5 to obtain a set of weather scenes; S7. Based on the weather scenario set obtained in step S6, a particle swarm optimization algorithm is used to optimize the energy storage configuration of the target independent microgrid.

2. The energy storage configuration optimization method for an independent microgrid according to claim 1 is characterized in that The step S1 of determining the independent variable describing the weather specifically includes the following steps: Identify the independent variables that describe weather, including temperature, humidity, wind speed, precipitation, and radiation intensity.

3. The energy storage configuration optimization method for an independent microgrid according to claim 2 is characterized in that The step S2 is based on the historical data of the target independent microgrid, and according to the independent variables determined in step S1, the cumulative density function of the weather is obtained by using kernel density estimation, which specifically includes the following steps: According to the independent variable determined in step S1, corresponding historical data is obtained; According to the historical data obtained, the corresponding probability density function is estimated using the following formula: Where f(t) is the probability density estimate at t; n is the total number of samples; h is the bandwidth; K() is the kernel function; T i is the i-th sample data; Normalizing the probability density function, we get the cumulative density function of the independent variable of weather: Where f(t) is the probability density estimate at t, and Δt is the sampling interval.

4. The energy storage configuration optimization method for an independent microgrid according to claim 3 is characterized in that The step S3 of performing Latin hypercube sampling on the cumulative density function obtained in step S2 specifically includes the following steps: Determine the number of factors to be sampled and the number of samples to be sampled; Divide each cumulative density function into a number of intervals of equal probability; Randomly select a sample point from each interval; Arrange and combine the samples of each factor quantity to ensure that the samples of each factor quantity cover all intervals; Shuffle the order of all samples to increase the randomness of the samples; Finally, Latin hypercube sampling is completed.

5. The energy storage configuration optimization method for an independent microgrid according to claim 4 is characterized in that The step S4 performs an inverse transformation on the cumulative density function obtained in step S3 to obtain an inverse cumulative density function, which specifically includes the following steps: Transform the cumulative density function to map the uniformly distributed random variables to the target probability distribution, thereby obtaining a random sample that conforms to the target probability distribution; Get the sample value corresponding to the uniform random number, expressed as Where ICDF is the inverse cumulative density function, is the inverse function of the cumulative density function, and u is the value of the cumulative probability density.

6. The energy storage configuration optimization method for an independent microgrid according to claim 5 is characterized in that Step S5 maps the inverse cumulative density function obtained in step S4 to generate a corresponding weather scene, which specifically includes the following steps: A random number is obtained, and according to the inverse cumulative density function obtained in step S4, the obtained random number is mapped to a specific scene, so as to obtain a corresponding weather scene.

7. The energy storage configuration optimization method for an independent microgrid according to claim 6 is characterized in that Step S6 of reducing the weather scenes obtained in step S5 to obtain a weather scene set specifically includes the following steps: For each weather scenario i , use the following formula to calculate s i With other scenes j The minimum distance D i,min : D i,min =minρ i d(s i ,s j ) Where ρ i For weather scenes i The probability of occurrence; d(s i ,s j ) is s i With s j distance; For the probability density function, Latin hypercube sampling is performed to randomly generate scenarios; within the Latin hypercube sampling interval, the scenario with the lowest probability is deleted; Increase the probability of deleted scenes to the closest scenes; Repeat the above steps until the number of scenes within the 0% to 10% sampling interval or the 90% to 100% sampling interval is 1; Finally, a set of weather scenes is obtained; The scenario probability within the 0-10% sampling interval or the 90%-100% sampling interval is taken as the risk factor.

8. The energy storage configuration optimization method for an independent microgrid according to claim 7 is characterized in that According to the weather scenario set obtained in step S6, the particle swarm optimization algorithm is used to optimize the energy storage configuration of the target independent microgrid, which specifically includes the following steps: According to the weather scenario set obtained in step S6 and the obtained risk factor, a particle swarm optimization algorithm is used to optimize the energy storage configuration of the target independent microgrid; In the process of optimization using the particle swarm optimization algorithm, the following formula is used to calculate the local learning factor c1 and the total learning factor c2: In the formula is the termination value of the local learning factor; is the starting value of the local learning factor; is the termination value of all learning factors; is the starting value of all learning factors; is an intermediate variable and N is the total number of samples, is the current local optimal solution, is the current global optimal solution.

9. A system for implementing the energy storage configuration optimization method of an independent microgrid according to any one of claims 1 to 8, characterized in that It includes a variable determination module, a function determination module, a sampling module, an inverse transformation module, a mapping module, a reduction module and a configuration optimization module; the variable determination module, the function determination module, the sampling module, the inverse transformation module, the mapping module, the reduction module and the configuration optimization module are connected in series in sequence; the variable determination module is used to determine the independent variables describing the weather, and upload the data information to the function determination module; the function determination module is used to obtain the cumulative density function of the weather by using kernel density estimation based on the received data information, based on the historical data of the target independent microgrid and the independent variables determined in step S1, and upload the data information to the sampling module; The sampling module is used to perform Latin hypercube sampling on the obtained cumulative density function according to the received data information, and upload the data information to the inverse transformation module; the inverse transformation module is used to perform inverse transformation on the obtained cumulative density function according to the received data information, obtain the inverse cumulative density function, and upload the data information to the mapping module; The mapping module is used to map the obtained inverse cumulative density function according to the received data information to generate the corresponding weather scene, and upload the data information to the reduction module; The reduction module is used to reduce the obtained weather scenarios according to the received data information, obtain a weather scenario set, and upload the data information to the configuration optimization module; the configuration optimization module is used to optimize the energy storage configuration of the target independent microgrid according to the received data information and the obtained weather scenario set using a particle swarm optimization algorithm.

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