A method for configuring optical storage and wood based on energy cost and power supply loss probability

Abstract

The application discloses a kind of based on energy cost and power supply loss probability's light storage firewood configuration method, belong to microgrid technical field, including, input microgrid parameter obtains the power generation demand curve of microgrid to its own distributed energy;Set basic scene parameter;Comprehensively consider energy cost and power supply loss probability, set the objective function of microgrid planning as microgrid comprehensive benefit;According to random sample optimization algorithm, the microgrid comprehensive benefit of sample is calculated, compared and constantly iterated, and the optimal configuration of the number of photovoltaic, energy storage battery and diesel generator is obtained.The application discloses a kind of based on energy cost and power supply loss probability's light storage firewood configuration method, can effectively configure the resources in microgrid, under the premise of guaranteeing power supply reliability as far as possible, reduce energy cost, and the overall benefit is optimal.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid, and particularly relates to a photovoltaic-battery-diesel configuration method based on energy cost and power supply loss probability. BACKGROUND

[0002] Distributed power generation has the characteristics of intermittency, volatility, island protection, etc., however, the power quality of distributed power generation is poor, and the utilization rate is low. The micro-grid is a new organization mode and structure of distributed energy proposed to integrate the advantages of distributed power generation, weaken the impact and negative effects of distributed power generation on the power grid, and effectively improve the deficiencies of poor power quality and low equipment utilization rate of distributed power generation.

[0003] The micro-grid directly links the distributed power generation unit, the power network and the terminal user together in a local area through the integration of the relationship between the distributed power generation unit and other energy units, can conveniently optimize the structure configuration and power scheduling, improve the energy utilization efficiency, reduce the impact of the energy power system on the environment, promote the distributed power supply to the grid, reduce the burden of the large power grid, improve the reliability and safety, and promote the society to develop in the direction of green, environmental protection and energy saving.

[0004] The existing micro-grid generally has the problems of low internal power supply quality, large power supply loss probability, weak utilization rate and connection of various resources, and high energy cost. SUMMARY

[0005] To solve the above problems, the present application provides a photovoltaic-battery-diesel configuration method based on energy cost and power supply loss probability.

[0006] To achieve the above purpose, the technical scheme of the present application is as follows:

[0007] A photovoltaic-battery-diesel configuration method based on energy cost and power supply loss probability, comprising,

[0008] Step S1, inputting micro-grid parameters to obtain a power generation demand curve of the micro-grid for its own distributed energy;

[0009] Step S2, setting basic scene parameters;

[0010] Step S3, comprehensively considering the energy cost and the power supply loss probability, setting a target function of the micro-grid planning as the comprehensive benefit of the micro-grid;

[0011] Step S4, according to a random sample optimization algorithm, calculating, comparing and continuously iterating the comprehensive benefit of the sample micro-grid, and solving to obtain the optimal configuration of the number of photovoltaic, energy storage battery and diesel generator.

[0012] The micro-grid parameters include load type, load power, and power grid power loss.

[0013] The above basic scenario parameters include a quantity limit of photovoltaics, a quantity limit of diesel generators, a quantity limit of energy storage, a cost of energy limit, and a loss of supply probability limit.

[0014] The quantity limit of photovoltaics, the quantity limit of diesel generators, and the quantity limit of energy storage are

[0015]

[0016] wherein N pv,max , N bat,max , and N ge,max are upper limits of the number of photovoltaics, energy storage batteries, and diesel generators, respectively.

[0017] The cost of energy limit and the loss of supply probability limit are

[0018]

[0019] wherein COE max and LPSP max are upper limits of the cost of energy COE and the loss of supply probability LPSP, respectively.

[0020] The objective function f is

[0021] f = w1COE + w2LPSP,

[0022] wherein COE is the cost of energy, LPSP is the loss of supply probability, w1 is the weight of the cost of energy, and w2 is the weight of the loss of supply probability.

[0023] The calculation methods of the cost of energy COE and the loss of supply probability LPSP are

[0024]

[0025] wherein N pv , N bat , and N ge are the number of photovoltaics, energy storage batteries, and diesel generators, respectively.

[0026] The step S4 specifically includes

[0027] Step S41, setting sample parameters, more specifically, setting the number of samples to N, the number of iterations to M, the weight w1 of the cost of energy COE, and the weight w2 of the loss of supply probability LPSP;

[0028] Step S42, setting a random sample set, more specifically, under the constraints of the upper and lower limits of photovoltaic, energy storage battery and diesel generator, a random number generation method is used to set an X group of random sample sets, the Euclidean distance of the two nearest samples is calculated, and 1 / y of the distance is taken as the unit distance;

[0029] Step S43, calculating the optimal sample, more specifically, under the initial operating environment, the micro-grid comprehensive benefit of each sample is calculated based on the target function, the micro-grid comprehensive benefit is selected as the highest, and the distance vector of other samples from the optimal sample is calculated, and the sample moving speed is set as moving a unit distance per time;

[0030] Step S44, updating the sample solution set, for other samples, the sample position at the next time is calculated according to the direction of the optimal sample and the sample moving speed; for the optimal sample, the sample is randomly moved b unit distance in a random direction around the sample, as the sample position at the next time, and all sample positions are rounded to integer positions;

[0031] Step S45, iterative calculation of the scene, repeating steps S43 to S44 until the micro-grid comprehensive benefit difference of the samples in the adjacent two iterations is within the calculation threshold ε, then it is considered that the iteration of the sample meets the requirement of the calculation threshold, at this time the optimal sample no longer changes, and at this time the number of photovoltaic, energy storage battery and diesel generator is the optimal configuration.

[0032] In a preferred embodiment of the application, the weight w1 of the energy cost COE is 60, and the weight w2 of the power supply loss probability LPSP is 1.

[0033] In a preferred embodiment of the application, y=100.

[0034] Beneficial effects, the application discloses a photovoltaic, energy storage battery and diesel generator configuration method based on energy cost and power supply loss probability, which can effectively configure the resources in the micro-grid, reduce the energy cost under the premise of ensuring the power supply reliability, and the overall benefit is optimal.

[0035] In order to make the above-mentioned features and advantages of the application more obvious and easy to understand, the following embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The flowchart of the application is a photovoltaic, energy storage battery and diesel generator configuration method based on energy cost and power supply loss probability.

[0037] Figure 2 For Figure 1 The specific flowchart of step S4 in the application.

[0038] Figure 3An iteration result according to the method of the present application in an embodiment. DETAILED DESCRIPTION

[0039] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0040] Figure 1 A flowchart of a photovoltaic, energy storage and diesel generator configuration method based on energy cost and power supply loss probability, optimizes resource capacity configuration of photovoltaic, energy storage and diesel generator in an independent microgrid, so that the user minimizes the energy cost on the basis of ensuring a small power supply loss probability. Figure 1 As shown in the figure, the photovoltaic, energy storage and diesel generator configuration method based on energy cost and power supply loss probability comprises,

[0041] Step S1, inputting microgrid parameters to obtain a power generation demand curve of the microgrid for its own distributed energy, wherein the microgrid parameters include load type, load power, grid power loss, etc.

[0042] Step S2, setting basic scenario parameters, more specifically, setting parameters such as quantity limit of photovoltaic, diesel generator and energy storage, energy cost limit and power supply loss probability limit in the microgrid.

[0043] The quantity limit of each resource is

[0044]

[0045] Wherein, N pv,max , N bat,max , N ge,max are the upper limits of the number of photovoltaic, energy storage battery and diesel generator respectively.

[0046] The energy cost limit and power supply loss probability limit are

[0047]

[0048] Wherein, COE max and LPSP max are the upper limits of energy cost COE and power supply loss probability LPSP respectively.

[0049] Step S3, comprehensively considering the energy cost and power supply loss probability, setting the objective function of the microgrid planning as the comprehensive benefit of the microgrid.

[0050] The objective function f is

[0051] f = w1COE + w2LPSP

[0052] Wherein, COE is the energy cost, LPSP is the probability of power supply loss, and w1 and w2 are the weights of the two, respectively.

[0053] The calculation methods for the energy cost (COE) and the power supply loss probability (LPSP) are as follows:

[0054]

[0055] Where, N pv N bat N ge These represent the quantities of photovoltaic cells, energy storage batteries, and diesel generators, respectively.

[0056] Step S4: Based on the random sample optimization algorithm, the comprehensive benefits of the microgrid for the sample are calculated, compared and iterated continuously to obtain the optimal configuration of the number of photovoltaic, energy storage batteries and diesel generators.

[0057] Step S4 specifically includes, for example: Figure 2 As shown,

[0058] Step S41: Set the sample parameters. More specifically, set the number of samples to N, the number of iterations to M, the weight of COE w1, and the weight of LPSP w2.

[0059] Step S42: Set up a random sample set. More specifically, under the constraints of the upper and lower limits of photovoltaic, energy storage battery and diesel generator, use the random number generation method to set up X sets of random samples, calculate the Euclidean distance between the two farthest adjacent samples, and take 1 / y of their distance as the unit distance.

[0060] Step S43: Calculate the optimal sample. More specifically, under the initial operating environment, based on the objective function, calculate the comprehensive microgrid benefit of each sample, and select the sample with the highest comprehensive microgrid benefit as the optimal sample. Calculate the distance vectors of other samples from the optimal sample, and set the movement speed as 'a' units per moment.

[0061] Step S44: Update the sample solution set. For other samples, calculate their positions at the next time step according to their direction of movement from the optimal sample and their moving speed. For the optimal sample, move it randomly in a surrounding direction by a distance of b units, and use this as its position at the next time step. All sample positions are rounded to integer values.

[0062] Step S45: Iterate through the scenario and repeat steps S43 to S44 until the difference in the comprehensive benefits of the microgrid between two adjacent iterations is within the calculation threshold ε. It is then considered that the iteration of the sample has met the calculation threshold requirement. At this point, the optimal sample no longer changes. The number of photovoltaic, energy storage batteries, and diesel generators at this point is the optimal configuration to ensure energy cost and power supply quality.

[0063] In one specific embodiment, the sample size N is 50, the number of iterations M is 100, the weight w1 of COE is 60, and the weight w2 of LPSP is 1; the number of random sample sets X is 50, y = 100; a = 1, b = 1; and the calculation threshold ε is 0.5%. Following the iterative method of photovoltaic-storage-diesel configuration based on energy cost and power supply loss probability according to the present invention, the results are as follows... Figure 3 As shown, after 32 iterations, LPSP and COE can converge to the optimal result.

[0064] In this embodiment, the following three scenarios are also set for verification:

[0065] (1) Considering only the lowest energy cost;

[0066] (2) The probability of power supply loss is lowest when considering only the power supply loss;

[0067] (3) The configuration method proposed in this invention.

[0068] The overall benefits of microgrids in the above scenarios are shown in Table 1.

[0069] Table 1. Overall benefits of microgrids in various scenarios

[0070]

[0071] As can be seen from the table above, the photovoltaic-storage-diesel configuration method of the present invention, based on energy cost and power supply loss probability, can reduce energy cost and achieve optimal overall benefits while ensuring power supply reliability as much as possible.

[0072] Although the present invention has been disclosed above by way of embodiments, it is not intended to limit the present invention. Anyone skilled in the art can make some modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A photovoltaic-storage-diesel configuration method based on energy cost and power supply loss probability, characterized in that, include, Step S1: Input microgrid parameters to obtain the microgrid's power generation demand curve for its own distributed energy resources; Step S2: Set basic scene parameters; Step S3: Taking into account both energy costs and power supply loss probability, a target function for microgrid planning is set as the overall benefit of the microgrid. f for ， in, COE For energy costs, LPSP For the probability of power supply loss, w 1 represents the weight of energy costs. w 2 represents the weight of the probability of power supply loss; Step S4: Based on the random sample optimization algorithm, the comprehensive benefits of the microgrid samples are calculated, compared, and iterated continuously to obtain the optimal configuration of the number of photovoltaic cells, energy storage batteries, and diesel generators. Step S4 specifically includes: Step S41, set the sample parameters, more specifically, set the sample size to... N , the number of iterations is M Second, energy costs COE weight w 1. Probability of power supply loss LPSP weight w 2; Step S42: Set a random sample set. More specifically, under the constraints of upper and lower limits for photovoltaics, energy storage batteries, and diesel generators, use a random number generation method to set... X Given a set of random samples, calculate the Euclidean distance between the two farthest adjacent samples, and use 1 / y of their distance as the unit distance; Step S43: Calculate the optimal sample. More specifically, under the initial operating environment, based on the objective function, calculate the comprehensive benefit of each sample in the microgrid, select the sample with the highest comprehensive benefit in the microgrid as the optimal sample, calculate the distance vector of other samples from the optimal sample, and set the sample moving a unit distance at each time as the sample moving speed. Step S44: Update the sample solution set. For other samples, calculate the sample position at the next time step according to the direction away from the optimal sample and the moving speed of the sample. For the optimal sample, move it randomly in the surrounding random direction by b units, and use it as the position of the sample at the next time step. All sample positions are rounded to the nearest integer. Step S45: Iterate through the scenario and repeat steps S43 to S44 until the difference in the comprehensive benefits of the microgrid between two adjacent iterations is within the calculation threshold ε. Then, it is considered that the iteration of the sample has met the requirements of the calculation threshold. At this point, the optimal sample no longer changes, and the number of photovoltaic, energy storage batteries and diesel generators at this point is the optimal configuration.

2. The photovoltaic-storage-diesel configuration method based on energy cost and power supply loss probability as described in claim 1, characterized in that, The microgrid parameters include load type, load power consumption, and grid power loss.

3. The photovoltaic-storage-diesel configuration method based on energy cost and power supply loss probability as described in claim 2, characterized in that, The basic scenario parameters include limitations on the number of photovoltaic units, diesel generator units, energy storage units, energy cost, and power supply loss probability.

4. The photovoltaic-storage-diesel configuration method based on energy cost and power supply loss probability as described in claim 3, characterized in that, The quantity limits for photovoltaic cells, diesel generators, and energy storage are as follows: ， in, , , These are the maximum quantities of photovoltaic panels, energy storage batteries, and diesel generators, respectively.

5. The photovoltaic-storage-diesel configuration method based on energy cost and power supply loss probability as described in claim 4, characterized in that, The energy cost limit and the power supply loss probability limit are: ， in, and These are energy costs COE With power supply loss probability LPSP The upper limit.

6. The photovoltaic-storage-diesel configuration method based on energy cost and power supply loss probability as described in claim 5, characterized in that, Energy cost COE With the power supply loss probability LPSP The calculation methods are as follows: in, , , These represent the quantities of photovoltaic cells, energy storage batteries, and diesel generators, respectively.

7. The photovoltaic-storage-diesel configuration method based on energy cost and power supply loss probability as described in claim 1, characterized in that, Energy costs COE weight w 1 is 60, the probability of power loss LPSP weight w 2 is 1.

8. The photovoltaic-storage-diesel configuration method based on energy cost and power supply loss probability as described in claim 1, characterized in that, y=100。

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