A decision-making method for fixed energy storage capacity compensation of renewable energy generation plan

Abstract

The present invention provides a decision-making method for a fixed energy storage capacity compensation new energy power generation plan, including: first, based on the fuzzy entropy theory, evaluate the change trend of load power consumption, determine the load power consumption distribution, and determine the load change trend coefficient of a certain period; second, cyclically and progressively count the load change trend coefficients of all time periods within the day, and combine them into a load trend coefficient array; after counting the load trend coefficient array day by day, form a load trend coefficient matrix; normalize the above-mentioned generated matrix as a decision matrix for energy storage compensation new energy power generation plan control; then, take the load change trend coefficient of each time period as an independent decision attribute, analyze the mapping relationship between its functions, and use this as a scoring function to evaluate the advantages and disadvantages of the new energy power generation plan compensation decision matrix scheme; finally, calculate the intuitive fuzzy weight of the decision attribute as a reference standard for determining the compensation amount of the new energy power generation plan. This method improves the dispatchability of the grid-connected operation of new energy power generation as a whole.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power generation, and in particular to a decision-making method for a fixed energy storage capacity to compensate for a renewable energy power generation plan. Background Art

[0002] With the increase in the penetration rate of new energy, the huge scale of new energy resources has raised the reliability requirements of operators' planned power generation. New energy power generation plans are often affected by some objective factors, and it is inevitable that there will be excess or insufficient planned power generation. If there is excess energy, energy can be consumed through load devices or stored through energy storage facilities; if there is insufficient energy and it is impossible to add other power generation energy sources for joint power generation, energy can only be supplemented by energy storage equipment. Therefore, after reporting the new energy planned power generation curve, it is very important to ensure that the energy storage scheduling energy is sufficient to facilitate accurate implementation.

[0003] Entropy is an evaluation attribute used to measure the uncertainty of the system in information theory. The greater the amount of information, the smaller the uncertainty and the smaller the entropy; conversely, the smaller the amount of information, the greater the uncertainty and the greater the entropy. The entropy value method is an objective weighting method that determines the weight of decision indicators based on the amount of information contained in each indicator value. The data used in this weighting method is the decision matrix, and the determined attribute weight reflects the discrete degree of the attribute value. The new energy power generation plan is mainly formulated objectively based on historical operation data and meteorological conditions data. Considering the regional load characteristics, it can be adjusted subjectively to improve the adaptability of the new energy power generation plan.

[0004] Effective adjustment of renewable energy power generation plans can greatly avoid problems caused by insufficient system energy or imbalance between supply and demand, and can alleviate the energy supply pressure of the public power grid. Relying on the trend change law of load data samples, subjective adjustment of renewable energy power generation plans can improve the reputation and credibility of power plants, and is more conducive to cooperation with power grid companies. This method is based on the traditional concept of entropy weight and extends it to the adjustment of renewable energy power generation plans. It is used for the decision-making plan of energy scheduling during the day for fixed energy storage equipment, which can effectively improve the power production capacity structure and enhance the economic benefits of power generation operators. Summary of the invention

[0005] The purpose of the present invention is to solve the above problems and propose a decision-making method for a fixed energy storage capacity compensation renewable energy power generation plan. Based on load change data, the fuzzy entropy theory is used to evaluate the trend of the load and generate a load trend coefficient matrix as the initial decision matrix. The decision matrix is ​​evaluated by studying the benefit attributes and cost attributes of the load trend coefficient matrix, and the intuitive fuzzy weight is calculated as the final decision-making scheme for the planned compensation amount, thereby effectively improving the economic benefits of renewable energy power generation operators.

[0006] The decision-making method includes: first, based on the fuzzy entropy theory, the load power consumption change trend is evaluated, the load power consumption distribution is determined, and the load change trend coefficient of a certain period is determined; second, the load change trend coefficients of all periods within the day are cyclically and progressively counted to form a load trend coefficient array; after counting the load trend coefficient array day by day, a load trend coefficient matrix is ​​formed; the above-mentioned generated matrix is ​​normalized as a decision matrix for energy storage compensation new energy power generation plan control; then, the load change trend coefficient of each time period is used as an independent decision attribute, the mapping relationship between its functions is analyzed, and this is used as a scoring function to evaluate the advantages and disadvantages of the new energy power generation plan compensation decision matrix scheme; finally, the decision attribute intuitive fuzzy weight is calculated as a reference standard for determining the compensation amount of the new energy power generation plan.

[0007] This method can actively adjust the reporting plan of renewable energy power generation, reasonably consider the response strategy that must be made for the scheduling of renewable energy power generation plans in response to regional load changes, and illustrate the advantages of the renewable energy power generation plan compensation decision matrix in the form of a scoring function. This method improves the dispatchability of renewable energy power generation grid-connected operation as a whole, and improves the economic benefits of renewable energy power generation operators to a certain extent.

[0008] In order to achieve the above objectives, a technical solution adopted by the present invention is: a decision-making method for a fixed energy storage capacity compensation renewable energy power generation plan, comprising:

[0009] Step 1: Based on the fuzzy entropy theory, evaluate the change trend of load power consumption, determine the load power consumption distribution, and determine the load change trend coefficient in a certain period of time;

[0010] Step 2: cyclically and progressively count the load change trend coefficients of all time periods within the day and combine them into a load trend coefficient array; after counting the load trend coefficient array on a daily basis, a load trend coefficient matrix is ​​formed; the above generated matrix is ​​normalized as a decision matrix for energy storage compensation new energy power generation plan control;

[0011] Step 3: Take the load change trend coefficient of each time period as an independent decision attribute, analyze the mapping relationship between its functions, and use it as a scoring function to evaluate the advantages and disadvantages of the compensation decision matrix scheme of the new energy power generation plan;

[0012] Step 4: Calculate the intuitive fuzzy weight of the decision attribute as a reference standard for determining the compensation amount of the new energy power generation plan.

[0013] The specific steps include:

[0014] Step 1: The sampling frequency of load data is minute-level, and the distribution of load changes is counted. The load data is divided into intervals of 15 minutes, and the power consumption trend is evaluated using fuzzy entropy to determine the specific value of the load change trend in a period;

[0015] Step 2: Circularly and progressively count the load change trend coefficients of each time period within the day, and combine them into a 1*24 decision array; count the load change trend coefficient arrays of each day within the month, and combine them into a 30*24 decision matrix; normalize the matrix and use it as the decision attribute matrix for compensating for the new energy power generation plan. Among them, the rows of the decision matrix are the days of the month, and the columns are the number of time periods divided within the day;

[0016] Step 3: Take the load change trend coefficients of all divided time periods within the day as separate decision attributes, and analyze the function mapping relationship. The function mapping relationship mainly includes benefit type and cost type. When studying the decision attributes, the benefit type is mainly considered, and it is used as a scoring function to evaluate the quality of the constructed decision model;

[0017] Step 4: Solve the fuzzy intuitive attribute weight. The fuzzy intuitive attribute weight indicates the allocation share of the compensation power in each period of the day, and the cumulative value of the fuzzy intuitive attribute weight of all periods of the day should be less than 1. Considering the specific value of the fuzzy intuitive weight of each period and the actual capacity of the energy storage equipped, the power value of the final compensation plan for the new energy power generation is obtained, and the new energy power generation plan curve is adjusted. At the same time, according to the changes in the power throughput of the daily energy storage equipment during the compensation of the plan deviation, the reliability of the previously fixed energy storage capacity is re-evaluated.

[0018] The method of using the scoring function to evaluate the quality of the model in steps 3 and 4 and confirming the adjustment amount of the new energy power generation plan by using the fuzzy intuitive attribute weights; specifically includes:

[0019] 1.1 Select relevant data, the data source is load power consumption data; use fuzzy entropy theory to generate a load trend coefficient matrix, with m goals and n decision-making schemes. The matrix is ​​the target eigenvalue matrix, recorded as:

[0020]

[0021] in is the load trend coefficient value, m is the number of days, n is the number of time periods divided within a day, i is the number of rows, and j is the number of columns;

[0022] 1.2 Given the energy storage capacity value of the revised renewable energy power generation plan, the decision matrix defined in 1.1 is normalized and recorded as The calculation is shown in the formula

[0023] x j Benefit-oriented attributes

[0024] x j Cost attribute

[0025] Apparently, The cost attribute values ​​are generally too large, and the purpose is to scale up the attributes so that the difference in cost attributes can be seen intuitively. The benefit attribute values ​​are all small, and the purpose is to scale down the attributes so that programming simulation software can calculate them. Since the control decision matrix is ​​to actively adjust the new energy power generation plan strategy, and the capacity of the energy storage equipment is fixed, the cost attribute function is not considered, so the benefit attribute function is selected as the main scoring function to provide a comparative analysis basis for the advantages and disadvantages of independent attributes in the decision model;

[0026] 1.3 Based on 1.2 Calculate the jth attribute x j The entropy value The calculation formula is:

[0027]

[0028] Among them, the constant k = (lnm) -1 ,The main purpose of step 1.3 is to update the fuzzy entropy value calculated according to traditional theory.

[0029] 1.4 Entropy value obtained based on 1.3 Calculate the jth attribute x j The entropy weight is calculated as follows:

[0030]

[0031] From the above formula It is the fuzzy intuitive attribute weight, and it is also the final decision to determine the specific compensation amount of the new energy power generation plan divided into time periods within the day.

[0032] 1.5 The trend of load data drives the dynamic adjustment of the new energy power generation plan curve. The trend of load data changes can be described by observation and language, but it is difficult to measure with digital quantities. The above 1.1 to 1.5 first implement the update optimization based on traditional fuzzy entropy; then use the benefit-based scoring function to illustrate the performance of the constructed decision model; finally, the energy throughput of the fixed energy storage capacity equipment is determined by the fuzzy intuitive attribute weights, and the new energy power generation plan curve is optimized. At the same time, the fuzzy attribute intuitive weights can also help new energy power generation operators re-evaluate the reliability of the existing energy storage capacity, determine whether the energy storage capacity needs to be adjusted, and configure the specific data of the energy storage capacity and other related issues.

[0033] The above technical solution of the present invention has the following advantages compared with the prior art:

[0034] (1) The decision attribute matrix is ​​generated through fuzzy entropy theory, and the fuzzy intuitive attribute weight is derived to calculate. The final compensation amount of new energy power generation plan can be improved through fuzzy intuitive attribute weight, and the rationality of energy storage capacity can be re-evaluated;

[0035] (2) The model is evaluated through the benefit-based attribute function, namely the scoring function. The output value of the scoring function determines the quality of the compensation amount of the renewable energy power generation plan. The larger the value of the scoring function, the more appropriate the compensation amount of the renewable energy power generation plan. On the other hand, the smaller the value of the scoring function, the greater the deviation of the compensation amount of the renewable energy power generation plan.

[0036] (3) The new energy power generation plan compensation method that considers the load trend coefficient can actively adjust the new energy power generation plan to ensure sufficient power generation energy and balance supply and demand, which has strong practical engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of a decision-making method for compensating a new energy power generation plan with a fixed energy storage capacity according to the present invention;

[0038] Figure 2 It is a schematic diagram showing the energy output ratio of the energy storage device controlled at each time period on a typical day of the present invention;

[0039] Figure 3 It is a schematic diagram showing the energy output ratio of the energy storage device per day in a typical month of the present invention;

[0040] Figure 4 Shown is a schematic diagram of the relationship between time period, power and load adaptability factor of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0042] The embodiment of the present invention discloses a decision-making method for a fixed energy storage capacity compensation renewable energy power generation plan, such as Figure 1 As shown, the decision-making method for compensating for the renewable energy power generation plan under the fixed energy storage equipment capacity has the following steps:

[0043] Step 1: The sampling frequency of load data is minute-level, and the distribution of load changes is counted. The load data is divided into intervals of 15 minutes, and the power consumption trend is evaluated using fuzzy entropy to determine the specific value of the load change trend in a period;

[0044] Step 2: Circularly and progressively count the load change trend coefficients of each time period within the day, and combine them into a 1*24 decision array; count the load change trend coefficient arrays of each day within the month, and combine them into a 30*24 decision matrix; normalize the matrix and use it as the decision attribute matrix for compensating for the new energy power generation plan. Among them, the rows of the decision matrix are the days of the month, and the columns are the number of time periods divided within the day;

[0045] Step 3: Take the load change trend coefficients of all divided time periods within the day as separate decision attributes, and analyze the function mapping relationship. The function mapping relationship mainly includes benefit type and cost type. When studying the decision attributes, the benefit type is mainly considered, and it is used as a scoring function to evaluate the quality of the constructed decision model;

[0046] Step 4: Solve the fuzzy intuitive attribute weight. The fuzzy intuitive attribute weight indicates the allocation share of the compensation power in each period of the day, and the cumulative value of the fuzzy intuitive attribute weight of all periods of the day should be less than 1. Considering the specific value of the fuzzy intuitive weight of each period and the actual capacity of the energy storage equipped, the power value of the final compensation plan for the new energy power generation is obtained, and the new energy power generation plan curve is adjusted. At the same time, according to the changes in the power throughput of the daily energy storage equipment during the compensation of the plan deviation, the reliability of the previously fixed energy storage capacity is re-evaluated.

[0047] The method of using the scoring function to evaluate the quality of the model in steps 3 and 4 and confirming the adjustment amount of the new energy power generation plan by using the fuzzy intuitive attribute weights; specifically includes:

[0048] 1.1 Select relevant data, the data source is load power consumption data; use fuzzy entropy theory to generate a load trend coefficient matrix, with m goals and n decision-making schemes. The matrix is ​​the target eigenvalue matrix, recorded as:

[0049]

[0050] in is the load trend coefficient value, m is the number of days, n is the number of time periods divided within a day, i is the number of rows, and j is the number of columns;

[0051] 1.2 Given the energy storage capacity value of the revised renewable energy power generation plan, the decision matrix defined in 1.1 is normalized and recorded as The calculation is shown in the formula

[0052] x j Benefit-oriented attributes

[0053] x j Cost attribute

[0054] Apparently, The cost attribute values ​​are generally too large, and the purpose is to scale up the attributes so that the difference in cost attributes can be seen intuitively. The benefit attribute values ​​are all small, and the purpose is to scale down the attributes so that programming simulation software can calculate them. Since the control decision matrix is ​​to actively adjust the new energy power generation plan strategy, and the capacity of the energy storage equipment is fixed, the cost attribute function is not considered, so the benefit attribute function is selected as the main scoring function to provide a comparative analysis basis for the advantages and disadvantages of independent attributes in the decision model;

[0055] 1.3 Based on 1.2 Calculate the jth attribute x j The entropy value The calculation formula is:

[0056]

[0057] Among them, the constant k = (lnm) -1 ,The main purpose of step 1.3 is to update the fuzzy entropy value calculated according to traditional theory.

[0058] 1.4 Entropy value obtained based on 1.3 Calculate the jth attribute x j The entropy weight is calculated as follows:

[0059]

[0060] From the above formula It is the fuzzy intuitive attribute weight, and it is also the final decision to determine the specific compensation amount of the new energy power generation plan divided into time periods within the day.

[0061] 1.5 The trend of load data drives the dynamic adjustment of the new energy power generation plan curve. The trend of load data changes can be described by observation and language, but it is difficult to measure with digital quantities. The above 1.1 to 1.5 first implement the update optimization based on traditional fuzzy entropy; then use the benefit-based scoring function to illustrate the performance of the constructed decision model; finally, the energy throughput of the fixed energy storage capacity equipment is determined by the fuzzy intuitive attribute weights, and the new energy power generation plan curve is optimized. At the same time, the fuzzy attribute intuitive weights can also help new energy power generation operators re-evaluate the reliability of the existing energy storage capacity, determine whether the energy storage capacity needs to be adjusted, and configure the specific data of the energy storage capacity and other related issues.

[0062] During specific implementation, the compensation decision for the renewable energy power generation can be made by year, month and day. The decision plan for one year can be statistically analyzed based on the historical load data of the previous year; the decision plan for one month is discussed by situation, according to the quarter. If it is the first month of the first quarter, the historical load data of the previous quarter is selected, and the following months can be analyzed based on the historical load data of the previous month of the current quarter; the decision plan for one day is generally consistent with the decision process for one month, but it is divided into working days and holidays for separate consideration.

[0063] This patent specifically uses a time span of one month for illustration. First, the trend of historical load data is mined, and the fuzzy entropy theory is used to describe the load trend law with specific numerical values ​​to form a matrix, and the matrix is ​​used as a decision-making scheme for controlling the energy exchange of energy storage equipment. Among them, considering the minimum time span of the planned power generation curve of new energy, the sampling point time resolution of the load data is 15 minutes.

[0064] Secondly, the decision matrix for controlling the energy exchange of energy storage equipment is studied. The rows in the decision matrix represent the decision plan for each day, and the columns represent the independent decision attributes for each time period. The mapping function relationship is analyzed. The mapping function mainly includes benefit function and cost function. Considering the system situation, the capacity of the energy storage equipment is fixed, so the benefit function is used as the scoring function of the decision matrix. The larger the scoring function, the better the decision effect; the smaller the scoring function, the meaningless the decision plan.

[0065] Finally, when the energy storage system is actually operating, its own state of charge should be within the safety limit, and the general state of charge is between [0.2, 0.8]. The above step 4 mentions that the fuzzy intuitive attribute weight represents the proportion of the energy delivered by the energy storage device to the energy storage itself in each period. The total energy share of the energy storage output every day should be controlled to be less than 0.8. At the same time, the decision-making plan within the month can also be used to analyze whether the energy storage equipment has effectively compensated for the planned power generation curve of new energy. Here, a pass rate is set to describe the reliability of the decision-making plan.

[0066]

[0067] Where r1 is the pass rate, N is the total number of time periods, k represents the kth time period, B k The specific value of is as follows.

[0068]

[0069] like Figure 2 As shown in the figure, the energy output ratio of energy storage equipment on a typical day is shown, showing the distribution of energy storage equipment to compensate for renewable energy generation in different periods of time. Figure 3As shown in the figure, the proportion of energy storage output power in a month is shown. There are three periods with a proportion exceeding 0.8. According to the above formula, the qualified rate is 90.3%. Figure 4 As shown in the figure, the relationship between time period, power and compensation energy ratio is shown. When the power of renewable energy generation is high, the compensation energy ratio is low; when the power of renewable energy generation is low, the compensation energy ratio is high. Table 1 shows the scoring function for each time period of the decision matrix for one month.

[0070] Table 1 Scoring function values ​​of decision attributes within one month

[0071]

[0072] Among them, day represents the daily decision plan, and the number of time periods is the number of sequential time periods divided into 1-hour intervals. It can be seen from the table that the lowest score function value of all time periods is about 0.98, which generally shows that the decision matrix for controlling energy storage equipment to compensate for renewable energy power generation plans under fixed capacity has a good effect.

[0073] The present invention relates to a decision-making method for compensating a new energy power generation plan with a fixed energy storage capacity. Under the condition of a fixed energy storage capacity device, the method formulates a decision-making scheme matrix for controlling the energy storage throughput according to the demand law of historical load data, can effectively utilize existing energy equipment, realize energy replenishment according to load demand, reduce the probability of the public power grid taking energy dispatching measures to deal with inaccurate new energy power generation plans, and improve the credibility of the public power grid to the new energy power generation operators.

[0074] The above description is only an exemplary embodiment of the present invention, and does not limit the scope of patent protection of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the scope of patent protection of the present invention.

Claims

1. A decision-making method for a fixed energy storage capacity compensation renewable energy power generation plan, characterized in that: include: Step 1: Based on the fuzzy entropy theory, evaluate the change trend of load power consumption, determine the load power consumption distribution, and determine the load change trend coefficient in a certain period of time; Step 2: cyclically and progressively count the load change trend coefficients of all time periods within the day and combine them into a load trend coefficient array; After the load trend coefficient array is counted daily, a load trend coefficient matrix is ​​formed; The above generated matrix is ​​normalized as the decision matrix for energy storage compensation and new energy power generation plan control; Step 3: Take the load change trend coefficient of each time period as an independent decision attribute, analyze the mapping relationship between its functions, and use it as a scoring function to evaluate the advantages and disadvantages of the compensation decision matrix scheme of the new energy power generation plan; Step 4: Calculate the intuitive fuzzy weight of the decision attribute, jointly consider the specific value of the fuzzy intuitive weight in each period and the actual capacity of the energy storage equipped, obtain the power value of the final compensation new energy power generation plan, and adjust the new energy power generation plan curve; The scoring function is used to evaluate the quality of the model, and the adjustment amount of the renewable energy power generation plan is confirmed by fuzzy intuitive attribute weights. The specific steps include: 1.1 Select relevant data, the data source is load power consumption data, and use fuzzy entropy theory to generate a load trend coefficient matrix. There are m goals and n decision-making plans. The matrix is ​​the target eigenvalue matrix, which is recorded as: in is the load trend coefficient value, m is the number of days, n is the number of time periods divided within a day, i is the number of rows, and j is the number of columns; 1.2 Given the energy storage capacity value of the revised renewable energy power generation plan, the decision matrix defined in 1.1 is normalized and recorded as The calculation is shown in the formula x j Benefit-oriented attributes x j Cost attribute Apparently, The cost attribute values ​​are generally too large, and the purpose is to scale up the attributes so that the difference in cost attributes can be seen intuitively; the benefit attribute values ​​are all small, and the purpose is to scale down the attributes so that programming simulation software can calculate them easily; since the control decision matrix is ​​to actively adjust the new energy power generation plan strategy, and the capacity of the energy storage equipment is fixed, the cost attribute function is not considered, so the benefit attribute function is selected as the main scoring function to provide a comparative analysis basis for the advantages and disadvantages of independent attributes in the decision model; 1.3 Based on 1.2 Calculate the jth attribute x j The entropy value The calculation formula is: Among them, the constant k = (lnm) -1 ,The main purpose of step 1.3 is to update the fuzzy entropy value calculated according to traditional theory; 1.4 Entropy value obtained based on 1.3 Calculate the jth attribute x j The entropy weight is calculated as follows: From the above formula It is the fuzzy intuitive attribute weight, and it is also the final decision to determine the specific compensation amount of the new energy power generation plan divided into time periods within the day.

2. A decision-making method for a fixed energy storage capacity compensation renewable energy power generation plan according to claim 1, characterized in that: The specific steps include: Step 1: The sampling frequency of load data is minute-level, and the distribution of load changes is counted. The load data is divided into intervals of 15 minutes, and the power consumption trend is evaluated using fuzzy entropy to determine the specific value of the load change trend in a period; Step 2: cyclically and progressively count the load change trend coefficients of each time period within the day, and combine them into a 1*24 decision array; count the load change trend coefficient arrays of each day within the month, and combine them into a 30*24 decision matrix; normalize the matrix and use it as the decision attribute matrix for compensating for the new energy power generation plan; where the rows of the decision matrix are the days of the month, and the columns are the number of time periods divided within the day; Step 3: Take the load change trend coefficients of all divided time periods within the day as separate decision attributes, and analyze the function mapping relationship. The function mapping relationship mainly includes benefit type and cost type. When studying the decision attributes, the benefit type is mainly considered, and it is used as a scoring function to evaluate the quality of the constructed decision model; Step 4: Solve the fuzzy intuitive attribute weight. The fuzzy intuitive attribute weight indicates the allocation share of the compensation power in each period of the day, and the cumulative value of the fuzzy intuitive attribute weight of all periods of the day should be less than 1. At the same time, according to the change of the power throughput of the daily energy storage equipment during the plan deviation compensation process, re-evaluate the reliability of the previous fixed energy storage capacity.

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