Efficiency economic evaluation method considering participation of energy storage in multi-scale adjustment of power system

Abstract

The invention provides an efficiency economic evaluation method considering participation of energy storage in multi-scale adjustment of an electric power system, and aims at a novel electric power system adjustment demand, and a multi-type energy storage power station network adjustment performance evaluation system is constructed based on a regional power grid rule: firstly, a subjective weight is set by combining a unit capacity configuration standard and a rule requirement; and correcting index correlation and data noise influence through a CRITIC method to form an improved CRITIC evaluation model. The model is not only suitable for a traditional pumped storage scene, but also has good applicability to performance evaluation of novel energy storage technologies such as electrochemical energy storage, and provides scientific method support for accurate efficiency evaluation and optimized operation of multi-type energy storage power stations.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage efficiency evaluation. Specifically, for areas with high wind power penetration, an efficiency economic evaluation method that considers energy storage participating in multi-scale regulation of power systems is proposed. Background Art

[0002] In recent years, with the continuous advancement of the "carbon peak and carbon neutrality" goals, the installed capacity of new energy has increased significantly. The wind power industry is the main clean power source for achieving the "dual carbon" goals. Unlike traditional units, the output of wind turbines is easily affected by the natural environment and has great randomness and volatility. Therefore, with the large-scale development of wind power, the peak regulation situation of the power grid has become more severe, and the participation of energy storage effectively balances the mismatch between source and load in time and space. With the increasing number and types of energy storage, there is an urgent need for an effective economic efficiency evaluation method for energy storage to address the selection and systematic evaluation of energy storage, so as to better meet the system regulation needs under the premise of economy.

[0003] Therefore, a new technical solution is urgently needed in the existing technology to solve this problem. Summary of the Invention

[0004] The purpose of the present invention is to conduct technical performance evaluation research on existing multi-type energy storage power stations, which mostly focus on the single dimension of peak regulation or primary and secondary frequency regulation, involve limited indicators, and it is difficult to comprehensively evaluate the technical performance of the power station. In addition, the evaluation data mostly uses experimental data, which cannot accurately reflect its actual performance in participating in the operation of the power grid. Based on the sorting out of the peak regulation and frequency regulation assessment indicators of power grids in different regions, the present invention constructs a technical performance evaluation system for multi-type energy storage power stations participating in power grid regulation. Subjective weights are determined according to the peak regulation and frequency regulation capacity allocation of units and the requirements of rules; in order to address the problems of indicator correlation and messy data distribution, the CRITIC weighting method is used to make corrections and obtain comprehensive weights, and then a multi-type energy storage peak regulation and frequency regulation technical performance evaluation model based on the improved CRITIC weighting method is constructed. According to the evaluation results, the quality of each unit is obtained, and the relevant operating parameters are collected for high-quality units, and then the current operating parameters are maintained. For low-quality units, a comprehensive investigation is carried out to determine whether there is a fault and optimize the operating status.

[0005] The present invention provides an efficiency economic evaluation method considering energy storage participating in multi-scale regulation of a power system, which includes the following steps, which are performed in sequence:

[0006] Step 1: Design a performance evaluation model for the pumped storage unit participating in grid regulation technology based on actual operating data.

[0007] 1) Performance evaluation system for peak-shaving and frequency-regulating technology of pumped storage units,

[0008] 2) CRITIC Empowerment Act,

[0009] 3) TOPSIS evaluation method,

[0010] Step 2: Construction of a comprehensive performance evaluation system for multiple types of energy storage power stations.

[0011] 1) Construction of a comprehensive performance evaluation system for multi-timescale regulation of power grids using multiple types of energy storage;

[0012] 2) Weights and evaluation methods of multi-energy storage system evaluation indicators

[0013] As a preferred embodiment, the peak-shaving and frequency-regulating technical performance evaluation system of the pumped storage unit in step 1) is:

[0014] The calculation method for each indicator in the evaluation system is:

[0015] Peak regulation deviation:

[0016]

[0017] Where P(t) and P'(t) represent the actual output of each unit in the power station and the command output value issued by the system on a time scale of 15 minutes, respectively.

[0018] One frequency modulation response time:

[0019] C Δt =t2-t1 (2)

[0020] Primary frequency modulation rate:

[0021]

[0022] Primary frequency modulation power response index:

[0023]

[0024] Where, P(t) is the actual output of the unit, P t The output of the unit before the frequency exceeds the frequency regulation dead zone, δ n is the rated differential coefficient of the unit. The numerator on the right side of the equal sign represents the actual action integral power, f n ·δ n The denominator represents the theoretical action integral power.

[0025] Adjustment coefficient:

[0026]

[0027] Among them, P(t3) is the output extreme value after the large disturbance occurs, f3 is the corresponding frequency at this time, P nThe rated output of the unit. Generally speaking, the smaller the value, the stronger the primary frequency regulation performance of the unit.

[0028] AGC Compatibility:

[0029]

[0030] Adjustment rate:

[0031]

[0032] Response time:

[0033] C ΔT =T1-T0 (8)

[0034] As a preferred embodiment, the CRITIC weighting method in step 1) 2) is:

[0035] Construct the initial matrix as shown below:

[0036]

[0037] Formula a ij It represents the jth index value of the i-th unit.

[0038] According to the above initial matrix, each element is dimensionless, and the j-th index value of the i-th unit is processed as follows:

[0039]

[0040] To calculate the variability and conflict of a certain indicator, we select the data in the jth column for calculation. The variability of the jth indicator is:

[0041]

[0042] The conflict of the jth indicator is:

[0043]

[0044] Where: r ij is the correlation coefficient between indicator i and indicator j. The stronger the correlation, the weaker the conflict.

[0045] According to the required variability and conflict, the information content of the jth indicator can be obtained as:

[0046] C j =S j ×R j (14)

[0047] Then the objective weight of indicator j is obtained as:

[0048]

[0049] If the subjective weight of indicator j obtained above is defined as follows, the final comprehensive weight is:

[0050]

[0051] As a preferred embodiment, the TOPSIS evaluation method of step 1 (3) is:

[0052] Combined with the comprehensive weights of various indicators obtained in the previous article, the initial evaluation matrix A m×n Perform weighted standardization to obtain the weighted standard matrix Z m×n :

[0053]

[0054] All indicators are divided into two categories: benefit indicators (the larger the value, the better) and cost indicators (the smaller the value, the better), and the optimal and worst values ​​of each indicator are obtained respectively. + and J - are sets of benefit-type indicators and cost-type indicators respectively, then:

[0055]

[0056] Where, and They represent the best and worst values ​​in the same group of data respectively.

[0057] Then calculate the geometric distance between all indicators of m units and the corresponding optimal and worst values and Finally, the relative proximity C of the power station is calculated i This is the rating of the unit’s technical performance:

[0058]

[0059] The one with the largest relative proximity value is the best, and the units are ranked according to their quality.

[0060] As a preferred embodiment, the comprehensive performance evaluation system of multi-type energy storage participating in multi-time-scale regulation of power grid in step 2) 1) is as follows:

[0061] The calculation formulas for the regulation accuracy, equivalent utilization coefficient, power station availability coefficient, and unplanned outage coefficient in the regulation capability index are given as follows:

[0062]

[0063] Where P(t) and P'(t) represent the real-time output of the power station and the command output value issued by the system, respectively. c and E d are the charging and discharging amounts of the power station during the statistical period, P N is the rated operating power, T ava is the available time, T un Unplanned downtime. Regarding energy efficiency level indicators, the following are the calculation formulas for power station energy storage loss rate, station power consumption rate, and comprehensive efficiency:

[0064]

[0065] Where, E off is the off-grid power consumption, E s is the electricity consumption of the station, E on For Internet access electricity.

[0066] Regarding environmental protection indicators, the calculation formulas for toxic and harmful gas emission reduction, power and energy density are given below:

[0067]

[0068] Where G is the amount of coal saved by energy storage participating in system operation during the statistical period, A represents the carbon dioxide produced by burning unit mass of coal. N1 and A S1 is the nitrogen and sulfur content in coal, A N2 and A S2 is the conversion efficiency of nitrogen oxides and sulfur oxides, m is the ratio of nitrogen oxides converted from nitrogen in coal to total nitrogen oxides (including nitrogen converted from air during combustion), n N and n S is the denitrification and desulfurization efficiency, and S is the power plant area.

[0069] The energy storage medium pollution index is calculated using a value between 0 and 1, with the closer to 1, the more severe the pollution. Regarding economic indicators, the following formulas are given for calculating the investment payback period, return on investment, and life cycle cost per kilowatt-hour:

[0070]

[0071] The investment payback period is the year when the net cash flow of each year in the project calculation period is converted to the initial period according to the standard discount rate, and the cumulative net income covers the initial investment cost. It is calculated using the net present value equation solution method. 1,n and B o,n are the revenue and cost of year n respectively, r is the discount rate, B p and B EThey are the unit power cost and unit capacity cost in the initial investment respectively; the rate of return on investment is the ratio of the average annual net income over the life cycle of the power station to the initial investment cost.

[0072] As a preferred embodiment, the weights and evaluation methods of the multi-energy storage system evaluation indicators in step 2) are as follows:

[0073] Combining the experience of experts and according to the importance of the indicators, the mutual weight relationship of the remaining 7 indicators is supplemented to obtain a complete subjective weight vector:

[0074]

[0075] The weight vector is:

[0076]

[0077] By applying the CRITIC weighting method, a complete objective weight vector can be obtained:

[0078]

[0079] Finally, the improved weight vector can be obtained:

[0080]

[0081] The final weight vector of the indicator is obtained as follows:

[0082]

[0083] After determining the final weight vector, the TOPSIS evaluation method is used to calculate the score.

[0084] Through the above design scheme, the present invention can bring the following beneficial effects:

[0085] The present invention is a construction of a technical performance evaluation system for multiple types of energy storage power stations participating in grid regulation. First, based on the peak-shaving and frequency regulation assessment index system of power grids in different regions, a full-dimensional technical performance evaluation framework covering multiple types of energy storage power stations participating in grid regulation is constructed. Through the deep coupling of the peak-shaving and frequency regulation capacity allocation of the units and the requirements of relevant rules, the subjective weights of the indicators are scientifically determined; in response to the common problems of indicator correlation interference and data distribution disorder in traditional evaluations, the CRITIC weighting method is introduced for objective correction to form a comprehensive weighting system that integrates subjective and objective factors, and finally a technical performance evaluation model for multiple types of energy storage peak-shaving and frequency regulation based on the improved CRITIC weighting method is constructed. Simulation analysis and comparison of dynamic control effects based on the above methods demonstrate the orderliness and scientific nature of this method. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 Performance evaluation system for peak-shaving and frequency-regulating technologies of pumped storage units

[0087] Figure 2 Schematic diagram of primary frequency regulation of pumped storage unit

[0088] Figure 3 Schematic diagram of AGC frequency regulation output of pumped storage unit

[0089] Figure 4 Comprehensive performance evaluation system for multi-type energy storage participating in wide-time-scale peak and frequency regulation of power grids

[0090] Figure 5 Complete calculation flow chart of the comprehensive performance evaluation model for multi-type energy storage with wide time scale peak and frequency regulation. DETAILED DESCRIPTION

[0091] The present invention will be further described in detail below in conjunction with specific embodiments. The following examples are used to illustrate the present invention, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0092] The present invention is a construction of a technical performance evaluation system for multiple types of energy storage power stations participating in grid regulation, including: a technical performance evaluation system for peak and frequency regulation of pumped storage units, the weighting of evaluation indicators and evaluation methods, the construction of a comprehensive performance evaluation system for multiple types of energy storage participating in multi-timescale grid regulation, the weighting of evaluation indicators and evaluation methods, and simulation analysis using a large amount of actual operating data from a pumped storage power station in North China. The specific steps are:

[0093] Step 1: Pumped storage unit peak and frequency regulation technical performance evaluation system,

[0094] The first step is to understand the performance evaluation indicators of regional power grid peak and frequency regulation.

[0095] Regarding the primary frequency regulation performance, the regional power grid stipulates that when the difference is within ±2%, it meets the assessment requirements of the power grid. If it exceeds this range, it will be punished. Figure 1 The following is a technical performance evaluation system for peak-shaving and frequency regulation of pumped-storage units (also applicable to electrochemical energy storage power plants). This evaluation system covers the technical performance of pumped-storage units in three areas: primary frequency regulation, AGC frequency regulation, and peak regulation. The time scale for primary frequency regulation is in the second order (milliseconds for electrochemical energy storage), the time scale for AGC frequency regulation is in the second to minute order (seconds for electrochemical energy storage), and the time scale for peak regulation is in the 15-minute to hour order. This evaluation system fully reflects the operational characteristics of pumped storage and electrochemical energy storage in participating in grid peak-shaving and frequency regulation.

[0096] The calculation method of each indicator in the second step evaluation system is:

[0097]

[0098] Where P(t) and P'(t) represent the actual output of each unit in the power station and the command output value issued by the system on a time scale of 15 minutes, respectively.

[0099] The indicator calculation formulas for primary frequency regulation and AGC frequency regulation are given in combination with the unit output operation schematic diagram to facilitate subsequent indicator calculation based on the actual operating data of the pumped storage unit.

[0100] Figure 2 In the equation, t0 is the time when the system failure occurs. Subsequently, the frequency deviates from normal operation, reaching a point where the frequency fluctuation exceeds the unit's frequency regulation dead zone (±0.05Hz for thermal power units and ±0.03Hz for hydropower units). However, the unit does not immediately adjust its output at this point; there is usually a delay. t2 is the time when the unit begins adjusting its output, identified by the unit's output crossing the initial output threshold. t3 is the time when the unit's output reaches its maximum value, and t4 is the time when the frequency returns to stability. The following describes the calculation methods for each indicator during a frequency regulation process.

[0101] One frequency modulation response time:

[0102] C Δt =t2-t1 (31)

[0103] Primary frequency modulation rate:

[0104]

[0105] Primary frequency modulation power response index:

[0106]

[0107] Where, P(t) is the actual output of the unit, P t The output of the unit before the frequency exceeds the frequency regulation dead zone, δ n is the rated differential coefficient of the unit. The numerator on the right side of the equal sign represents the actual action integral power, f n ·δ n The denominator represents the theoretical action integral power.

[0108] Adjustment coefficient:

[0109]

[0110] Among them, P(t3) is the output extreme value after the large disturbance occurs, f3 is the corresponding frequency at this time, P n The rated output of the unit. Generally speaking, the smaller the value, the stronger the primary frequency regulation performance of the unit.

[0111] Figure 3 In the equation, T0 is the moment when the unit receives the P2 output command from the EMS system. Before T0, the unit's output fluctuates around P1. The unit begins to change its output after receiving the system command. T1 is the moment when the unit's AGC output crosses the P1 frequency regulation dead zone: P(T1)-P1≥P d1 , P d1 is the set value of the response dead zone corresponding to P1. T2 is the moment when the unit AGC output enters the P2 frequency regulation dead zone: P2-P(T2)≤P d2 ,P d2 is the set value of the response dead zone corresponding to P2. T3 is the moment when the unit receives the output command from the EMS system; T4 is the moment when the unit AGC output crosses the frequency regulation dead zone: P2-P(T4)≥P d2 T5 is the moment when the AGC output of the unit enters the P3 frequency regulation dead zone: P(T5)-P3≤P d3 , where P d3 It is the setting value of the response dead zone corresponding to P3.

[0112] AGC Compatibility:

[0113]

[0114] Adjustment rate:

[0115]

[0116] Response time:

[0117] C ΔT =T1-T0 (37)

[0118] Step 2: Weights of evaluation indicators and evaluation methods,

[0119] The first step is the CRITIC empowerment law.

[0120] In this embodiment, referring to grid regulations, the weight coefficients for peak shaving, primary frequency regulation, and AGC frequency regulation can be temporarily set to 5 / 10, 1 / 10, and 4 / 10 based on the distribution of unit capacity. The weights for AGC regulation rate, AGC compatibility, and AGC response time are set to 2 / 4, 1 / 4, and 1 / 4, respectively. Referring to the weights of the AGC frequency regulation performance indicators, the weight of the primary frequency regulation rate indicator is set to twice that of the other three indicators. Through the above analysis, the subjective weights of the eight indicators can be determined, as shown in Table 1.

[0121] Table 1 Subjective weights of peak and frequency regulation indicators of pumped storage units

[0122]

[0123] The CRITIC weighting method was chosen for correction, as it reflects the numerical complexity while also reflecting the correlation of indicators. The calculation process of the CRITIC weighting method is as follows: Assuming that the evaluation object includes w pumped storage units and n evaluation indicators, the initial matrix is ​​constructed as follows:

[0124]

[0125] Formula a ij It represents the jth index value of the i-th unit.

[0126] According to the above initial matrix, each element is dimensionless, and the j-th index value of the i-th unit is processed as follows:

[0127]

[0128] To calculate the variability and conflict of a certain indicator, we select the data in the jth column for calculation. The variability of the i-th indicator is:

[0129]

[0130] Where:

[0131]

[0132] The conflict of the jth indicator is:

[0133]

[0134] Where: r ij is the correlation coefficient between indicator j. The stronger the correlation, the weaker the conflict.

[0135] According to the required variability and conflict, the information content of the jth indicator can be obtained as:

[0136] C j =S j ×R j (43)

[0137] Then the objective weight of indicator j is obtained as:

[0138]

[0139] If the subjective weight of indicator j obtained above is defined as p j , the final comprehensive weight is:

[0140]

[0141] The second step is TOPSIS evaluation method,

[0142] Combined with the comprehensive weights of various indicators obtained in the previous article, the initial evaluation matrix A m×n Perform weighted standardization to obtain the weighted standard matrix Z m×n :

[0143]

[0144] All indicators are divided into two categories: benefit indicators (the larger the value, the better) and cost indicators (the smaller the value, the better), and the optimal and worst values ​​of each indicator are obtained respectively. + and J - are sets of benefit-type indicators and cost-type indicators respectively, then:

[0145]

[0146]

[0147] Where, and They represent the best and worst values ​​in the same group of data respectively.

[0148] Then calculate the geometric distance between all indicators of m units and the corresponding optimal and worst values and Finally, the relative proximity C of the power station is calculated i This is the rating of the unit’s technical performance:

[0149]

[0150] The one with the largest relative proximity value is the best, and the units are ranked according to their quality.

[0151] Step 3: Construction of a comprehensive performance evaluation system for multiple types of energy storage power stations.

[0152] The first step is to establish a comprehensive efficiency evaluation system for multi-type energy storage participating in multi-time scale regulation of the power grid.

[0153] The "National Standard for Energy Storage" (GB / T36549-2018), implemented in 2018, assesses electrochemical energy storage from three aspects: charging and discharging capacity, energy efficiency level, and equipment operating status, and sets weights for each indicator, as shown in Table 2.

[0154]

[0155] Based on the assessment indicators of various types of energy storage power stations in the above-mentioned industry documents, and taking into account the characteristics and advantages of various types of energy storage, a comprehensive assessment system is established from four aspects: regulation capacity, energy efficiency, environmental protection and economy. Figure 4The comprehensive performance evaluation system shown covers static, second, minute, month, year, and full life cycle time scales, fully reflecting the peak-shaving and frequency-regulating characteristics of multiple types of energy storage power stations participating in grid operation (the four-level indicators in the evaluation system provide the corresponding time scales).

[0156] In the regulation capability index, the power station's chargeable and dischargeable power (D1) represents the instantaneous maximum power that the station can achieve during the charging and discharging process; the station's maximum peak-shaving capacity (D2) and the station's maximum frequency-shaving capacity (D3) represent the rated capacity of the station used for peak-shaving and frequency-shaving, respectively. The following formulas are used to calculate the regulation accuracy, equivalent utilization coefficient, station availability coefficient, and unplanned outage coefficient in the regulation capability index:

[0157]

[0158] Where P(t) and P'(t) represent the real-time output of the power station and the command output value issued by the system, respectively. c and E d are the charging and discharging amounts of the power station during the statistical period, P N is the rated operating power, T ava is the available time, T un Unplanned downtime. Regarding energy efficiency level indicators, the following are the calculation formulas for power station energy storage loss rate, station power consumption rate, and comprehensive efficiency:

[0159]

[0160] Where, E off is the off-grid power consumption, E s is the electricity consumption of the station, E on For Internet access electricity.

[0161] Regarding environmental protection indicators, the calculation formulas for toxic and harmful gas emission reduction, power and energy density are given below:

[0162]

[0163] Where G is the amount of coal saved by energy storage participating in system operation during the statistical period, A represents the carbon dioxide produced by burning unit mass of coal. N1 and A S1 is the nitrogen and sulfur content in coal, A N2 and A S2 is the conversion efficiency of nitrogen oxides and sulfur oxides, m is the ratio of nitrogen oxides converted from nitrogen in coal to the total nitrogen oxides generated, n N and n S is the denitrification and desulfurization efficiency, and S is the power plant area.

[0164] In the calculation of the energy storage medium pollution degree index, a value between 0 and 1 is used to represent it. The closer it is to 1, the more serious the pollution.

[0165] Regarding economic indicators, the following are the calculation formulas for investment recovery period, investment return rate, and life cycle electricity cost:

[0166]

[0167] The investment payback period is the year when the net cash flow of each year in the project calculation period is converted to the initial period according to the standard discount rate, and the cumulative net income covers the initial investment cost. It is calculated using the net present value equation solution method. 1,n and B o,n are the revenue and cost of year n respectively, r is the discount rate, B p and B E They are the unit power cost and unit capacity cost in the initial investment respectively; the rate of return on investment is the ratio of the average annual net income over the life cycle of the power station to the initial investment cost.

[0168] The second step is to evaluate the weights of the indicators and the evaluation methods.

[0169] An improved CRITIC weighting method is proposed to weight the 21 indicators, but the specific operation process is different from the weighting method of the technical performance indicators mentioned above. Referring to the capacity ratio of 5:4:1 for peak regulation, AGC frequency regulation and primary frequency regulation mentioned in the previous technical performance evaluation, the mutual weight relationship of some peak regulation and frequency regulation indicators in the regulation capacity index can be determined; combined with the "National Standard for Energy Storage" and the official documents of the domestic power grid in different regions for the weight requirements of relevant indicators, the mutual weight relationship of the first 14 indicators in the indicator system can be determined. However, at this time, it is still impossible to determine the weight of each of the 14 indicators in the total 21 indicators. Therefore, combined with the experience of experts, according to the degree of importance between the indicators, the mutual weight relationship of the remaining 7 indicators is supplemented, and a complete subjective weight vector can be obtained:

[0170]

[0171] In the subjective weight vector, the weights of the first 14 indicators in the indicator system are the basic weights. From the above analysis, it can be directly concluded that this part of the basic weight is the final weight of the first 14 indicators. The weight vector is:

[0172]

[0173] Next, we need to determine the weights for the last seven indicators. Taking into account the correlation between some indicators (such as the power plant's chargeable and dischargeable power and toxic and hazardous gas emission reduction, power density, and energy density), as well as the complexity of the indicator values ​​(different types of energy storage power plants have significant differences in some indicators), and to eliminate subjective influences, we use the CRITIC weighting method to make corrections. By applying the CRITIC weighting method, we can derive a complete objective weight vector:

[0174]

[0175] According to the above formula, the sum of the basic weights of the first 14 indicators can be calculated. Let x be x, then the sum of the weights of the last 7 indicators is (1-x). The mutual matching relationship of the last 7 indicators can be obtained according to the conventional formula. According to the sum of the weights and mutual matching relationship of the last 7 indicators, the improved weight vector can be finally obtained:

[0176]

[0177] The final weight vector of the indicator is obtained as follows:

[0178]

[0179] After determining the final weight vector, the TOPSIS evaluation method is used to calculate the score. The complete calculation process of the comprehensive performance evaluation model of multi-type energy storage power stations participating in grid regulation is as follows: Figure 5 shown.

[0180] Step 4: Use the analysis method of the present invention to perform corresponding simulation analysis on a specific example.

[0181] Five multi-type energy storage power stations participating in the North China Power Grid were selected for simulation analysis. The raw performance indicators for the five multi-type energy storage stations are shown in Table 3. Station A is a pumped storage power station (4 × 100 MW), while stations B, C, D, and E are sodium-sulfur power stations (45 MW, 90 MWh), lead-acid power stations (15 MW, 30 MWh), lithium-ion power stations (80 MW, 160 MWh), and all-vanadium liquid flow power stations (60 MW, 120 MWh), respectively.

[0182] Table 3 Original data of indicators of various types of energy storage power stations

[0183]

[0184] According to the data presented in Table 3, the weight of each indicator is calculated using the improved CRITIC weighting method mentioned above. The results are shown in Tables 4 and 5.

[0185] Table 4 Comprehensive index weights of various types of energy storage power stations

[0186]

[0187]

[0188] Table 5 Secondary indicator weights for various types of energy storage power stations

[0189]

[0190] Based on the initial data and the weights of each indicator obtained using the improved CRITIC weighting method, the TOPSIS evaluation method was used for evaluation and scoring. The scores of each secondary indicator and the comprehensive performance score of the five power plants are shown in Table 6.

[0191] Table 6 Comprehensive performance evaluation results of various types of energy storage power stations

[0192]

[0193] Case studies confirm that pumped storage, with its significant economic and environmental advantages, ranks first in the overall efficiency score. Its large capacity makes its regulation performance comparable to electrochemical energy storage. However, due to the inherent pumping losses during actual operation, pumped storage exhibits lower energy efficiency. Compared to pumped storage, lithium-ion and vanadium liquid flow, two new electrochemical energy storage elements, while significantly inferior in environmental and economic performance, offer significant advantages in response time and regulation rate, resulting in excellent overall regulation performance. Combined with their high energy efficiency, their overall efficiency score is comparable to that of pumped storage. While lithium-ion and vanadium liquid flow offer similar ratings in regulation, energy efficiency, and environmental performance, vanadium liquid flow offers superior economic performance and is currently more suitable for commercialization. Sodium sulfur and lead acid are two electrochemical energy storage power sources with a long application time. Compared with lithium ion and all-vanadium liquid flow, they have certain disadvantages in terms of regulation ability and energy efficiency level. However, their cost per kilowatt-hour is low and the return on investment is high. If the economic requirements are high, these two types of energy storage are also optional solutions. In the comprehensive performance evaluation model of the pumping / storage power station established by the present invention, the indicator time scale of the evaluation system covers static, seconds, minutes, months, years and the entire life cycle, and covers four major aspects: regulation ability, energy efficiency level, environmental protection and economy, which can fully reflect the difference between the comprehensive performance of pumping / storage power stations; the proposed weighting method not only combines the official documents in the power grid industry, but also fully considers the correlation between indicators and the chaos of numerical distribution, so that the final evaluation results are true and reliable.

[0194] The embodiments of the present invention are not exhaustive and do not limit the scope of protection of the claims. Those skilled in the art can conceive of other substantially equivalent alternatives based on the inspiration gained from the embodiments of the present invention without creative work, and all of them are within the scope of protection of the present invention.

Claims

1. An efficiency economic evaluation method considering energy storage participating in multi-scale regulation of power systems, characterized by: include: Establish a technical performance evaluation system for pumped storage units participating in peak load regulation and frequency regulation; The CRITIC weighting method is used to determine the comprehensive weight of each indicator in the technical performance evaluation system; Based on the comprehensive weight under the technical performance evaluation system, the TOPSIS evaluation method is used to obtain the first relative closeness characterizing the technical performance of the unit; Establish a comprehensive performance evaluation system for multiple types of energy storage power stations participating in peak and frequency regulation; For each indicator under the regulation capacity dimension and energy efficiency level dimension in the comprehensive performance evaluation system, the mutual weight relationship is determined according to the peak and frequency regulation capacity allocation of the units and the preset rules to obtain the basic weight of each indicator; For each indicator under the environmental dimension and economic dimension in the comprehensive performance evaluation system, the improved CRITIC weighting method is used to obtain the improved weight of each indicator; Obtaining a comprehensive weight under the comprehensive performance evaluation system based on the basic weight and the improved weight, so as to further obtain a second relative closeness representing the comprehensive performance of the unit; The quality of each unit is evaluated according to the first relative closeness and the second relative closeness, and feedback adjustment is performed on the power station units according to the quality.

2. The method for efficiency economic evaluation considering energy storage participating in multi-scale regulation of power system according to claim 1 is characterized in that: The various indicators under the technical performance evaluation system include peak regulation deviation, primary frequency regulation response time, primary frequency regulation rate, primary frequency regulation power response index, regulation coefficient, AGC fit, regulation rate and response time.

3. The method for efficiency economic evaluation considering energy storage participating in multi-scale regulation of power system according to claim 1, characterized in that: The comprehensive weights of the various indicators of the technical performance evaluation system determined by the CRITIC weighting method include: Constructing an initial evaluation matrix for the indicator values ​​of each indicator of the technical performance evaluation system; Performing dimensionless processing on each element of the initial evaluation matrix; And calculate the variability and conflict of each indicator to calculate the information content; For each indicator, the objective weight is calculated based on the amount of information, and then the comprehensive weight is determined by combining it with the subjective weight determined based on preset rules.

4. The method for efficiency economic evaluation considering energy storage participating in multi-scale regulation of power system according to claim 3 is characterized in that: The initial evaluation matrix is ​​expressed as: Where a ij It represents the jth index value of the i-th unit; The dimensionless processing of the jth index of the i-th unit is expressed as: The variability of the jth indicator is: In the formula, m represents the number of units; The conflict of the jth indicator is: Where: r ij is the correlation coefficient between the i-th indicator and the j-th indicator. The stronger the correlation, the weaker the conflict. The amount of information of the jth indicator is: C j =S j ×R j The objective weight of the jth indicator is: The comprehensive weight of the jth indicator is: Where p j is the subjective weight of the j-th indicator.

5. The method for efficiency economic evaluation considering energy storage participating in multi-scale regulation of power system according to claim 4 is characterized in that: The first relative closeness of the unit technical performance obtained by using the TOPSIS evaluation method includes: Performing weighted standardization processing on the initial evaluation matrix using the comprehensive weight to obtain a weighted standard matrix; Divide the various indicators under the technical performance evaluation system into benefit-based indicators and cost-based indicators; Based on the weighted standard matrix, for the benefit-type indicator and the cost-type indicator, the optimal value and the worst value are calculated according to the corresponding strategies respectively; Calculate the geometric distances between all indicators of multiple units and their corresponding optimal and worst values; The first relative closeness characterizing the technical performance of the power plant units is calculated.

6. The method for efficiency economic evaluation considering energy storage participating in multi-scale regulation of power system according to claim 5 is characterized in that: For the initial evaluation matrix A m×n Perform weighted standardization to obtain the weighted standard matrix Z m×n Expressed as: The calculation formulas for the optimal and worst values ​​of benefit-based indicators and cost-based indicators are: Where, J + and J - are respectively a collection of benefit-type indicators and cost-type indicators; The calculation formula for the geometric distances between all indicators of multiple units and the corresponding optimal and worst values ​​and the first relative closeness that characterizes the technical performance of the units of the power plant is: Where m represents the number of units.

7. The method for efficiency economic evaluation considering energy storage participating in multi-scale regulation of power system according to claim 1, characterized in that: The multi-type energy storage power stations include pumped storage power stations and electrochemical energy storage power stations.

8. The method for efficiency economic evaluation considering energy storage participating in multi-scale regulation of power system according to claim 7 is characterized in that: The various indicators of the unit in the comprehensive performance evaluation system include: Regulation capability dimensions include charge and discharge capability indicators and equipment operating status indicators. The charge and discharge capability indicators include the power station's charge and discharge power (static), the power station's maximum peak-shaving capacity (static), the power station's maximum frequency regulation capacity (static), the primary frequency regulation response time (seconds), the AGC regulation rate (seconds), the ACC regulation accuracy (seconds), and the peak-shaving deviation (minutes). The equipment operating status indicators include the equivalent utilization coefficient (month), the power station availability coefficient (month), the regulation response success rate (month), and the unplanned outage coefficient (month). Energy efficiency level dimension, including loss rate index and operation energy efficiency index. The loss rate index includes power station energy storage loss rate (monthly) and plant power consumption rate (monthly), and the operation energy efficiency index includes comprehensive efficiency (monthly); Environmental protection dimensions, including pollutant emission reduction indicators and unit floor area indicators. The pollutant emission reduction indicators include the reduction in toxic and harmful gas emissions (monthly), and the unit floor area indicators include power density (static) and energy disk density (static); The economic dimension includes investment return indicators and cost indicators. The investment return indicators include the investment recovery period (years), and the cost indicators include the investment return rate (years) and the unit electricity cost (full life cycle).

9. The method for efficiency economic evaluation considering energy storage participating in multi-scale regulation of power system according to claim 8, characterized in that: The complete subjective weight vector in the comprehensive effectiveness evaluation system is: In the subjective weight vector, the weights of the first 14 indicators in the indicator system are the basic weights and serve as the final weights of the first 14 indicators. The weight vectors of the first 14 indicators are: The CRITIC weighting method is used to determine the weights of the last seven indicators to obtain a complete objective weight vector: Calculate the sum of the basic weights of the first 14 indicators and set it as x. Then the sum of the weights of the last 7 indicators is (1-x). According to the sum of the weights of the last 7 indicators and the predetermined mutual matching relationship, the improved weight vector is obtained. The final weight vector composed of comprehensive weights is obtained according to the improved weight vector:

10. The method for efficiency economic evaluation considering energy storage participating in multi-scale regulation of power system according to claim 9, characterized in that: The second relative closeness is calculated as follows: determining a weighted normalization matrix according to the comprehensive weights; Determining the optimal value and the worst value of each indicator according to the weighted normalization matrix; The geometric distances between the optimal and worst values ​​corresponding to the various indicators of each power station unit are calculated to further determine the second relative closeness.

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