Economic evaluation method and system for improving consumption capability of distributed power supply by energy storage system

Abstract

The invention relates to the technical field of electric power systems and energy, and discloses an economic evaluation method and system for improving the consumption capability of a distributed power supply by an energy storage system. The method comprises the following steps: determining key economic indexes, collecting related data, constructing a simulation model and a scoring standard, then determining index weights by using an analytic hierarchy process and a CRITIC weight method, and combining the weights to calculate a comprehensive weight. And finally, constructing an economic evaluation model to evaluate a scheme for improving the distributed energy consumption capability of the energy storage system. According to the technical scheme, the environmental influence of the energy storage system is considered, the evaluation standard of the environmental carbon value is added into the economical efficiency evaluation model, the energy storage system can be promoted to achieve the dual-carbon target, and by evaluating the influence of the energy storage system on environmental carbon emission, the energy storage system can be promoted to develop in the more environment-friendly and more sustainable direction.

Description

Technical Field

[0001] The present invention relates to the field of power systems and energy technologies, and in particular to a method and system for evaluating the economic performance of an energy storage system in improving the ability of a distributed power source to be absorbed. Background Art

[0002] With the proposal of the "dual carbon" goals, the development of new energy has become an important part of the national energy strategy. The inherent characteristics of new energy output, such as intermittent and volatile, have brought many bottleneck problems to the safe and stable operation of the power system. For example, the wind and solar output is too small or too large, which does not match the load demand, resulting in power shortages for a large number of users or new energy utilization problems. Therefore, it is imperative to introduce flexible resources into the new energy power system.

[0003] Energy storage systems, due to their rapid response characteristics, can effectively cope with the challenges brought by the integration of new energy into the power system and promote the high penetration of new energy. In the dispatching and management of new energy, energy storage systems play a key role. Energy storage technology can effectively balance energy supply and demand and improve the stability and reliability of the power grid by storing excess electrical energy and releasing it when needed. At the same time, the application of energy storage technology can also reduce energy waste and improve energy utilization, thereby promoting the sustainable development of clean energy.

[0004] The economic evaluation of energy storage technology is a key factor in determining whether it is feasible in practical applications. Due to the differences in battery chemical materials and manufacturing technologies used in energy storage systems, their performance characteristics are also different, including energy density, response speed, power density, energy efficiency, rate characteristics, cycle life, cost and support response time. Therefore, when energy storage systems with different characteristics participate in power dispatch, their economic benefits are also different, and their economic efficiency needs to be evaluated through appropriate methods.

[0005] The current economic evaluation methods for energy storage systems have problems such as not considering environmental impacts, not adding constraints to the scheme itself, and not removing subjectivity from indicator weights, which results in the economic evaluation being inaccurate and unrepresentative. Summary of the invention

[0006] The present invention provides an economic evaluation method and system for improving the distributed power supply absorption capacity of an energy storage system, which can take into account the constraints of the energy storage system itself, incorporate environmental carbon value evaluation, and use comprehensive weights for evaluation to improve the accuracy of the economic evaluation of the energy storage system.

[0007] The present invention provides an economic evaluation method for improving the distributed energy consumption capacity of an energy storage system, comprising:

[0008] S1: Determine the economic indicators of the energy storage system to improve the distributed energy consumption capacity, the economic indicators include: power quality, environmental carbon value, investment return, and rank the priorities of the economic indicators;

[0009] S2: Collect historical electricity price information, historical power generation data of distributed energy, grid load data, and relevant technical parameters and cost-benefit data of energy storage systems;

[0010] S3: For the indicators of power quality, environmental carbon value, and investment return, corresponding simulation models are constructed based on the historical data collected by MATLAB input, and scoring standards for power quality, environmental carbon value, and investment return are constructed respectively;

[0011] S4: Sort the indicators by priority and use the analytic hierarchy process to determine the subjective weight W of each indicator a,j ;

[0012] S5: Use the CRITIC weighting method to determine the objective weight of each indicator;

[0013] S6: Combine the subjective weight and the objective weight to calculate the comprehensive weight;

[0014] S7: Construct an economic evaluation model for energy storage systems and evaluate solutions for improving the distributed energy absorption capacity of energy storage systems.

[0015] Preferably, in step S3, the scoring standard for power quality is established by the following method:

[0016] The simulation model is used to estimate the three-phase voltage U after the implementation of the scheme a , U b , U c , and calculate the negative sequence voltage unbalance by the following formula

[0017]

[0018] Obtain the nth-order harmonic voltage V by estimating n And fundamental voltage V1, and calculate the harmonic distortion rate THD by the following formula:

[0019]

[0020] By estimating the RMS values ​​U and I of the AC voltage and AC current and obtaining the cycle time T, the power factor PF is calculated:

[0021]

[0022] Among them, P is the active power and S is the apparent power;

[0023] Then formulate the power quality scoring standard: let the power quality score be X1, if it meets THD≤2% and PF≥0.9,

[0024]

[0025] Otherwise X1 is 0.

[0026] Preferably, in step S3, the scoring standard of the environmental carbon value is established by the following method: obtaining the original life cycle carbon value emission C0 of the energy storage system;

[0027] The simulation model is used to estimate the situation after the implementation of the plan, and the carbon emission is calculated using the following formula;

[0028] C t =C1+C2+C3

[0029] Among them C t is the total carbon emissions of the energy storage system throughout its life cycle; C1 is the carbon emissions of the energy storage system during the raw material acquisition stage; C2 is the carbon emissions of the energy storage system during the component production stage; C3 is the carbon emissions of the energy storage system during the use and maintenance stage; all units are kg;

[0030] C1, C2, and C3 are calculated using the following formula:

[0031]

[0032] Where n1 is the total number of main line processes for raw material production, E i The power consumed by each process in the main production line of raw materials, unit: kW·h, K 1i R is the proportion of thermal power in each process of the main raw material production line, e is the carbon emission coefficient of electricity, which can be obtained through query; n2 is the total number of raw materials of the energy storage system, M i is the energy content of the i-th material per unit of building material, in MJ / kg; Q i is the amount of material used, in kg; R m is the conversion coefficient between energy and carbon emissions, in kgCO2 / MJ, with a uniform value; n3 is the total number of production process steps, C wc is the CO2 emission coefficient of aerobic wastewater treatment, unit: kg / m 3 ; W i The amount of wastewater treated in each production process, unit: m 3 ; n4 is the total number of maintenance process steps, E wi K is the power consumed in each step of the maintenance process. 3i is the proportion of thermal power used in the maintenance process, Wwi The amount of wastewater treated for each step of the maintenance process;

[0033] Then formulate the carbon emission scoring standard: let the carbon emission score be X2,

[0034]

[0035] Preferably, in step S3, the scoring criteria for investment returns are established by the following method:

[0036] Calculate historical annual return on investment (ROI) 原 ;

[0037] Use simulation models to estimate the cost-benefit of the solution after implementation and calculate the return on investment (ROI) 新 ;

[0038] The ratio of average annual income to investment cost is defined as the return on investment (ROI), and is calculated as follows:

[0039]

[0040] Where: R c is the net benefit of the energy storage system improving the distributed energy consumption capacity; i0 is the expected rate of return; N is the service life of the energy storage device; Z1 is the initial investment cost of the energy storage device;

[0041]

[0042] Where I1(n) is the charging and discharging operating income of the energy storage system in the nth year, I2(n) is the government subsidy and surplus power income of the energy storage system in the nth year; C1(n) is the investment cost of the energy storage system and distributed power generation equipment in the nth year, which is directly obtained by statistics, and C2(n) is the operation and maintenance cost of the energy storage system and distributed power generation equipment in the nth year;

[0043] The operation and maintenance cost C2(n) is as follows:

[0044] C2(n)=C M (n)Q y (n)+C e (n)

[0045] Among them C M (n) is the operation and maintenance cost of the energy storage system per unit of power generation in the nth year; Q y (n) is the annual power generation of distributed generation equipment in the nth year; C e (n) is the annual operation and maintenance cost of distributed generation equipment in year n;

[0046] The charging and discharging operating income of the energy storage system I1(n) is as follows:

[0047]

[0048] Where N is the number of days in a year, t is in hours, and ρ t is the peak-valley time-of-use electricity price at time t, P load (t) is the original load power demand at time t, P' load (t) is the load power demand after energy storage regulation, η sc is the energy storage system charging efficiency, P sc (t) is the charging electricity price at time t, P pv (t) is the photovoltaic power generation at time t, η sf is the energy storage system discharge efficiency, P sf is the discharge price at time t, P dis (t) is the discharge power of the energy storage system at time t;

[0049] The government subsidies for energy storage systems and the surplus electricity income I2(n) are as follows:

[0050]

[0051] Where N is the number of days in a year, t is in hours, Q p is the power generation of distributed generation equipment in one day under standard test conditions, ρ sp Distributed generation equipment subsidy standard, P cg (t) is the exchange power between the energy storage system and the grid, ρ pp is the electricity price when the energy storage system delivers electricity to the grid, P cg (t) < 0 means that the PV system delivers electricity to the grid;

[0052] Then formulate the profit scoring standard: let the profit score be X3, ROI 原 <ROI 新 If ROI 原 >=ROI 新 , X3=0.

[0053] Preferably, in the step S3, for the power quality index, the simulation model constructed is a mathematical model for simulating the power system, which can simulate the operation of the entire power system and is directly implemented by MATLAB. The model can estimate the three-phase voltage U after the implementation of the scheme. a , U b , U c , harmonic voltage V n And the fundamental voltage V1, the root mean square values ​​U and I of the AC voltage and AC current.

[0054] Preferably, in the step S3, for the environmental carbon value indicator, the simulation model constructed is a mathematical model for simulating the life cycle of the energy storage system, which can simulate the carbon emissions of the entire energy storage system life cycle and is directly implemented by MATLAB. The simulation of the model is based on the energy storage system design of the scheme, the historical data of the selected production enterprise, and the specific steps of the maintenance process.

[0055] Preferably, in the step S3, for the investment return indicator, the simulation model constructed is a mathematical model for simulating the operation process of the energy storage system. The model is directly implemented by MATLAB and can simulate the charging and discharging timing of the energy storage system to calculate the optimal parameters under the selected scheme. The simulation of the model is based on the historical power generation data of distributed energy and the grid load data.

[0056] Preferably, in step S5, the objective weight of each target is determined by using the CRITIC weight method as follows: Assume that there are m objects to be evaluated and n evaluation indicators, forming a data matrix X = (x ij ) m×n , let the element in the data matrix after standardization be x' ij ,

[0057] For positive indicators:

[0058] For negative indicators:

[0059] is the average value of the jth indicator on all evaluation objects

[0060] Calculate the contrast strength of the jth indicator:

[0061] Then calculate the conflict between the evaluation indicators Q, and set the conflict between indicator j and the remaining indicators to be f j ,but:

[0062]

[0063] where r ij It represents the correlation coefficient between indicator i and indicator j, using the Pearson correlation coefficient;

[0064] Calculate the information carrying capacity:

[0065] C j =σ j f j

[0066] Finally calculate the weight:

[0067]

[0068] Preferably, the comprehensive weight calculation formula described in S6 is as follows:

[0069]

[0070] Where n represents the total number of indicators, W a,j is the weight calculated by the hierarchical analysis method for the jth indicator, W b,j is the weight calculated by the CRITIC weight method for the jth indicator;

[0071] This step is used to calculate the weight W1 of the power quality score, the weight W2 of the carbon emission score, and the weight W3 of the benefit score.

[0072] Preferably, the economic evaluation model described in S7 is as follows:

[0073] X=W1X1+W2X2+W3X3

[0074] Among them, X1 is the power quality score, W1 is the weight of the power quality score; X2 is the carbon emission score, W2 is the weight of the carbon emission score; X3 is the benefit score, W3 is the weight of the benefit score.

[0075] The present invention provides an economic evaluation system for improving the distributed energy consumption capacity of an energy storage system, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of an economic evaluation method for improving the distributed energy consumption capacity of an energy storage system described in any one of claims 1-4 or 8-9 are implemented.

[0076] It can be seen from the above technical solution that the present invention has the following innovative features:

[0077] 1. The present invention takes into account the environmental impact of the energy storage system and adds an environmental carbon value assessment standard to the economic evaluation model. Adding an environmental carbon value assessment standard helps promote the energy storage system to achieve the "dual carbon" goal. By evaluating the impact of the energy storage system on environmental carbon emissions, it can promote the development of the energy storage system in a more environmentally friendly and sustainable direction;

[0078] 2. The present invention takes into account the influence of the power quality of the energy storage system, adds the evaluation standard of power quality to the economic evaluation model, and constrains it with the national standard threshold. Those that do not meet the standard are deemed unqualified and scored 0. Including power quality in the scoring standard can ensure the stability and reliability of power supply, reduce power outages and equipment failures caused by power quality problems, and thus improve the stability of the entire power system;

[0079] 3. The present invention introduces the return on investment to evaluate the cost and benefits of the energy storage system, which can better compare the economic benefits of various solutions, making the economic efficiency of the energy storage project more transparent and helping to improve the market competitiveness of the project;

[0080] 4. The present invention takes into account the government subsidies for energy storage systems and surplus electricity revenue when considering the benefits, so that solutions with better absorption capacity can obtain higher scores, which helps to improve the long-term economic sustainability of energy storage projects, create higher social benefits, and ensure that the projects can achieve cost recovery and reasonable profits in long-term operation;

[0081] 5. The present invention adopts a method combining subjective weights and objective weights when determining the weights of each evaluation index. The subjective weight method adopts the hierarchical analysis method to reflect the experts' empirical judgment and preference for different indicators, while the objective weight method adopts the CRITIC weight method to take into account the correlation between indicators while considering the volatility of the data, thereby more comprehensively evaluating the importance of the indicators. The combination of the two can reduce the subjectivity of the hierarchical analysis method weighting, and also reduce the fluctuation of weights caused by data changes, and to a certain extent reduce the arbitrariness caused by single reliance on subjective judgment, making the weight distribution more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0083] Figure 1 It is a flow chart of an economic evaluation method for improving the distributed power supply absorption capacity of an energy storage system according to the present invention. DETAILED DESCRIPTION

[0084] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] The embodiment of the present invention provides an economic evaluation method and system for improving the distributed power supply absorption capacity of an energy storage system, which is described in detail below.

[0086] Embodiment 1

[0087] See also Figure 1The present invention provides an economic evaluation method for improving the absorption capacity of distributed power sources by an energy storage system, comprising:

[0088] S1: Determine the economic indicators of the energy storage system to improve the distributed energy consumption capacity, the economic indicators include: power quality, environmental carbon value, investment return, and rank the priorities of the economic indicators;

[0089] S2: Collect historical electricity price information, historical power generation data of distributed energy, grid load data, and relevant technical parameters and cost-benefit data of energy storage systems;

[0090] S3: For the indicators of power quality, environmental carbon value, and investment return, corresponding simulation models are constructed based on the historical data collected by MATLAB input, and scoring standards for power quality, environmental carbon value, and investment return are constructed respectively;

[0091] S4: Sort the indicators by priority and use the analytic hierarchy process to determine the subjective weight W of each indicator a,j ;

[0092] S5: Use the CRITIC weighting method to determine the objective weight of each indicator;

[0093] S6: Combine the subjective weight and the objective weight to calculate the comprehensive weight;

[0094] S7: Construct an economic evaluation model for energy storage systems and evaluate solutions for improving the distributed energy absorption capacity of energy storage systems.

[0095] Embodiment 2

[0096] Collect historical power generation data of distributed power sources, and use MATLAB to build a simulation mathematical model for simulating the power system. Use this model to simulate the operation of the entire power system after the implementation of the scheme;

[0097] The simulation model is used to estimate the three-phase voltage U after the implementation of the scheme a , U b , U c , nth order harmonic voltage V n , fundamental voltage V1, root mean square value of AC voltage U, root mean square value of AC current I;

[0098] Calculate negative sequence voltage unbalance

[0099]

[0100] Calculate the harmonic distortion THD:

[0101]

[0102] By estimating the RMS values ​​U and I of the AC voltage and AC current and obtaining the cycle time T, the power factor PF is calculated:

[0103]

[0104] S=UI

[0105]

[0106] Among them, P is the active power and S is the apparent power;

[0107] Determine the constraint range and determine the permissible range of negative sequence voltage imbalance, harmonic distortion rate and power factor according to national standards;

[0108] Formulate the power quality scoring standard, set the power quality score as X1, if it meets THD≤2% and PF≥0.9,

[0109]

[0110] Otherwise X1 is 0.

[0111] Embodiment 3

[0112] Collect historical carbon emission data C0 of distributed power and energy storage systems throughout their life cycle, historical use and maintenance stages of energy storage systems, equipment selected by the solution, power usage and production processes of equipment and raw material manufacturers;

[0113] A simulation mathematical model for simulating the life cycle of an energy storage system was built using MATLAB. The carbon emissions of the entire energy storage system life cycle after the implementation of the solution were simulated through this model. The simulation of this model was based on the energy storage system design of the solution, the historical data of the selected production enterprise, and the specific steps of the maintenance process.

[0114] The simulation model is used to estimate the situation after the implementation of the plan and calculate the carbon emission value.

[0115] C t =C1+C2+C3

[0116] Among them C t is the total carbon emissions of the energy storage system throughout its life cycle; C1 is the carbon emissions of the energy storage system during the raw material acquisition stage; C2 is the carbon emissions of the energy storage system during the component production stage; C3 is the carbon emissions of the energy storage system during the use and maintenance stage; all units are kg;

[0117] C1, C2, and C3 are calculated using the following formula:

[0118]

[0119] Where n1 is the total number of main line processes for raw material production, E i The power consumed by each process in the main production line of raw materials, unit: kW·h, K 1i R is the proportion of thermal power in each process of the main raw material production line, e is the carbon emission coefficient of electricity, which can be obtained through query; n2 is the total number of raw materials of the energy storage system, M i is the energy content of the i-th material per unit of building material, in MJ / kg; Q i is the amount of material used, in kg; R m is the conversion coefficient between energy and carbon emissions, in kgCO2 / MJ, with a uniform value; n3 is the total number of production process steps, C wc is the CO2 emission coefficient of aerobic wastewater treatment, unit: kg / m 3 ; W i The amount of wastewater treated in each production process, unit: m 3 ; n4 is the total number of maintenance process steps, E wi K is the power consumed in each step of the maintenance process. 3i is the proportion of thermal power used in the maintenance process, W wi The amount of wastewater treated for each step of the maintenance process;

[0120] Formulate a carbon emission scoring standard, and set the carbon emission score as X2.

[0121]

[0122] Embodiment 4

[0123] Calculate the historical annual return on investment (ROI) based on historical data 原 ;

[0124] A simulation mathematical model for simulating the operation of the energy storage system is built by MATLAB. The optimal charging and discharging timing of the entire energy storage system after the implementation of the simulation scheme is simulated by this model. The simulation of this model is based on the historical power generation data of distributed energy and grid load data.

[0125] Use simulation models to estimate the cost-benefit of the solution after implementation and calculate the return on investment (ROI) 新 :

[0126] The ratio of average annual income to investment cost is defined as the return on investment (ROI), and is calculated as follows:

[0127]

[0128] Where: R cis the net benefit of the energy storage system improving the distributed energy consumption capacity; i0 is the expected rate of return; N is the service life of the energy storage device; Z1 is the initial investment cost of the energy storage device;

[0129]

[0130] Where I1(n) is the charging and discharging operating income of the energy storage system in the nth year, I2(n) is the government subsidy and surplus power income of the energy storage system in the nth year; C1(n) is the investment cost of the energy storage system and distributed power generation equipment in the nth year, which is directly obtained by statistics, and C2(n) is the operation and maintenance cost of the energy storage system and distributed power generation equipment in the nth year;

[0131] The operation and maintenance cost C2(n) is as follows:

[0132] C2(n)=C M (n)Q y (n)+C e (n)

[0133] Among them C M (n) is the operation and maintenance cost of the energy storage system per unit of power generation in the nth year; Q y (n) is the annual power generation of distributed generation equipment in the nth year; C e (n) is the annual operation and maintenance cost of distributed generation equipment in year n;

[0134] The charging and discharging operating income of the energy storage system I1(n) is as follows:

[0135]

[0136] Where N is the number of days in a year, t is in hours, and ρ t is the peak-valley time-of-use electricity price at time t, P load (t) is the original load power demand at time t, P' load (t) is the load power demand after energy storage regulation, η sc is the energy storage system charging efficiency, P sc (t) is the charging electricity price at time t, P pv (t) is the photovoltaic power generation at time t, η sf is the energy storage system discharge efficiency, P sf is the discharge price at time t, P dis (t) is the discharge power of the energy storage system at time t;

[0137] The government subsidies for energy storage systems and the surplus electricity income I2(n) are as follows:

[0138]

[0139] Where N is the number of days in a year, t is in hours, Q pis the power generation of distributed generation equipment in one day under standard test conditions, ρ sp Distributed generation equipment subsidy standard, P cg (t) is the exchange power between the energy storage system and the grid, ρ pp is the electricity price when the energy storage system delivers electricity to the grid, P cg (t)<0 means that the PV system delivers electricity to the grid.

[0140] Then formulate the profit scoring standard: let the profit score be X3,

[0141]

[0142] If ROI 原 >=ROI 新 , X3=0.

[0143] Embodiment 5

[0144] Determine subjective weights using the analytic hierarchy process:

[0145] The ultimate goal of decision-making is to determine the optimal economic performance of the energy storage system, and to determine power quality, carbon emissions and revenue as indicators that affect the economic performance of the energy storage system;

[0146] Each element is compared with each other, and a pairwise comparison matrix is ​​constructed by judging the relative importance of each indicator, and the relative importance is quantified using the 1-9 scale method;

[0147] Calculate the eigenvalues ​​and eigenvectors of the pairwise comparison matrix to determine the weight W of each indicator relative to the economic performance of the energy storage system a,1 , W a,2 , W a,3 , and a consistency test is performed. If the consistency ratio is less than 0.1, the judgment matrix is ​​considered to have satisfactory consistency;

[0148] Use the CRITIC weighting method to determine the objective weight of each goal:

[0149] There are m objects to be evaluated and 3 evaluation indicators, which can form a data matrix X = (x ij ) m×3 , let the element in the data matrix after standardization be x' ij ,

[0150] For positive indicators:

[0151] For negative indicators:

[0152] is the average value of the jth indicator on all evaluation objects

[0153] Calculate the contrast strength of the jth indicator:

[0154] Then calculate the conflict between the evaluation indicators Q, and set the conflict between indicator j and the remaining indicators to be f j ,but:

[0155]

[0156] where r ij It represents the correlation coefficient between indicator i and indicator j, using the Pearson correlation coefficient;

[0157] Calculate the information carrying capacity:

[0158] C j =σ j f j

[0159] Finally calculate the weight:

[0160]

[0161] Obtain the weights W of power quality, carbon emission and benefits b,1 , W b,2 , W b,3 ;

[0162] Determine the overall weight:

[0163]

[0164] This step is used to calculate the weight W1 of the power quality score, the weight W2 of the carbon emission score, and the weight W3 of the benefit score.

[0165] Embodiment 6

[0166] Construct an economic evaluation method for energy storage system to improve the absorption capacity of distributed power sources. Economic evaluation model:

[0167] X=W1X1+W2X2+W3X3

[0168] Among them, X1 is the power quality score, W1 is the weight of the power quality score; X2 is the carbon emission score, W2 is the weight of the carbon emission score; X3 is the benefit score, W3 is the weight of the benefit score.

[0169] Embodiment 7

[0170] An economic evaluation system for improving the distributed energy consumption capacity of an energy storage system includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of any one of the economic evaluation methods for improving the distributed energy consumption capacity of an energy storage system are implemented.

[0171] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which can include: read-only memory, random access memory, disk or CD, etc.

[0172] The above is a detailed introduction to an economic evaluation method for improving the distributed power supply absorption capacity of an energy storage system provided by an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An economic evaluation method for improving the distributed energy consumption capacity of an energy storage system, characterized in that: include: S1: Determine the economic indicators of the energy storage system to improve the distributed energy consumption capacity, the economic indicators include: power quality, environmental carbon value, investment return, and rank the priorities of the economic indicators; S2: Collect historical electricity price information, historical power generation data of distributed energy, grid load data, and relevant technical parameters and cost-benefit data of energy storage systems; S3: For the indicators of power quality, environmental carbon value, and investment return, corresponding simulation models are constructed based on the historical data collected by MATLAB input, and scoring standards for power quality, environmental carbon value, and investment return are constructed respectively; S4: Sort the indicators by priority and use the analytic hierarchy process to determine the subjective weight W of each indicator a,j ; S5: Use the CRITIC weighting method to determine the objective weight of each indicator; S6: Combine the subjective weight and the objective weight to calculate the comprehensive weight; S7: Construct an economic evaluation model for energy storage systems and evaluate solutions for improving the distributed energy absorption capacity of energy storage systems.

2. The economic evaluation method for improving the distributed energy consumption capacity of an energy storage system according to claim 1 is characterized in that: In step S3, the scoring criteria for power quality are established by the following method: The simulation model is used to estimate the three-phase voltage U after the implementation of the scheme a , U b , U c , and calculate the negative sequence voltage unbalance by the following formula Obtain the nth-order harmonic voltage V by estimating n And fundamental voltage V1, and calculate the harmonic distortion rate THD by the following formula: By estimating the RMS values ​​U and I of the AC voltage and AC current and obtaining the cycle time T, the power factor PF is calculated: Among them, P is the active power and S is the apparent power; Then formulate the power quality scoring standard: let the power quality score be X1, if it meets THD≤2% and PF≥0.9, Otherwise X1 is 0.

3. The economic evaluation method for improving the distributed energy consumption capacity of the energy storage system according to claim 1 is characterized by: In step S3, the scoring criteria of the environmental carbon value are established by the following method: obtaining the original life cycle carbon value emission C0 of the energy storage system; The simulation model is used to estimate the situation after the implementation of the plan, and the carbon emission is calculated using the following formula; C t =C1+C2+C3 Among them C t is the total carbon emissions of the energy storage system throughout its life cycle; C1 is the carbon emissions of the energy storage system during the raw material acquisition stage; C2 is the carbon emissions of the energy storage system during the component production stage; C3 is the carbon emissions of the energy storage system during the use and maintenance stage; all units are kg; C1, C2, and C3 are calculated using the following formula: Where n1 is the total number of main line processes for raw material production, E i The power consumed by each process in the main production line of raw materials, unit: kW·h, K 1i R is the proportion of thermal power in each process of the main raw material production line, e is the carbon emission coefficient of electricity, which can be obtained through query; n2 is the total number of raw materials of the energy storage system, M i is the energy content of the i-th material per unit of building material, in MJ / kg; Q i is the amount of material used, in kg; R m is the conversion coefficient between energy and carbon emissions, in kgCO2 / MJ, with a uniform value; n3 is the total number of production process steps, C wc is the CO2 emission coefficient of aerobic wastewater treatment, unit: kg / m 3 ; W i The amount of wastewater treated in each production process, unit: m 3 ; n4 is the total number of maintenance process steps, E wi K is the power consumed in each step of the maintenance process. 3i is the proportion of thermal power used in the maintenance process, W wi The amount of wastewater treated for each step of the maintenance process; Then formulate the carbon emission scoring standard: let the carbon emission score be X2, 4. The economic evaluation method for improving the distributed energy consumption capacity of an energy storage system according to claim 1 is characterized by: In step S3, the scoring criteria for investment returns are established through the following methods: Calculate historical annual return on investment (ROI) 原 ; Use simulation models to estimate the cost-benefit of the solution after implementation and calculate the return on investment (ROI) 新 ; The ratio of average annual income to investment cost is defined as the return on investment (ROI), and is calculated as follows: Where: R c is the net benefit of the energy storage system improving the distributed energy consumption capacity; i0 is the expected rate of return; N is the service life of the energy storage device; Z1 is the initial investment cost of the energy storage device; Where I1(n) is the charging and discharging operating income of the energy storage system in the nth year, I2(n) is the government subsidy and surplus power income of the energy storage system in the nth year; C1(n) is the investment cost of the energy storage system and distributed power generation equipment in the nth year, which is directly obtained by statistics, and C2(n) is the operation and maintenance cost of the energy storage system and distributed power generation equipment in the nth year; The operation and maintenance cost C2(n) is as follows: C2(n)=C M (n)Q y (n)+C e (n) Among them C M (n) is the operation and maintenance cost of the energy storage system per unit of power generation in the nth year; Q y (n) is the annual power generation of distributed generation equipment in the nth year; C e (n) is the annual operation and maintenance cost of distributed generation equipment in year n; The charging and discharging operating income of the energy storage system I1(n) is as follows: Where N is the number of days in a year, t is in hours, and ρ t is the peak-valley time-of-use electricity price at time t, P load (t) is the original load power demand at time t, P' load (t) is the load power demand after energy storage regulation, η sc is the energy storage system charging efficiency, P sc (t) is the charging electricity price at time t, P pv (t) is the photovoltaic power generation at time t, η sf is the energy storage system discharge efficiency, P sf is the discharge price at time t, P dis (t) is the discharge power of the energy storage system at time t; The government subsidies for energy storage systems and the surplus electricity income I2(n) are as follows: Where N is the number of days in a year, t is in hours, Q p is the power generation of distributed generation equipment in one day under standard test conditions, ρ sp Distributed generation equipment subsidy standard, P cg (t) is the exchange power between the energy storage system and the grid, ρ pp is the electricity price when the energy storage system delivers electricity to the grid, P cg (t) < 0 means that the PV system delivers electricity to the grid; Then formulate the profit scoring standard: let the profit score be X3, If ROI 原 >=ROI 新 , X3=0.

5. The economic evaluation method for improving the distributed energy consumption capacity of an energy storage system according to claim 1 or 2, characterized in that: In the step S3, for the power quality index, the simulation model constructed is a mathematical model for simulating the power system, which can simulate the operation of the entire power system and is directly implemented by MATLAB. The model can estimate the three-phase voltage U after the implementation of the scheme. a , U b , U c , harmonic voltage V n And the fundamental voltage V1, the root mean square values ​​U and I of the AC voltage and AC current.

6. The economic evaluation method for improving the distributed energy consumption capacity of an energy storage system according to claim 1 or 3, characterized in that: In the step S3, the simulation model constructed for the environmental carbon value indicator is a mathematical model for simulating the life cycle of the energy storage system. It can simulate the carbon emissions of the entire energy storage system life cycle and is directly implemented by MATLAB. The simulation of the model is based on the energy storage system design of the scheme, the historical data of the selected production enterprise, and the specific steps of the maintenance process.

7. The method for economic evaluation of improving the distributed energy consumption capacity of an energy storage system according to claim 1 or 4, characterized in that: In the step S3, for the investment return indicator, the simulation model constructed is a mathematical model for simulating the operation process of the energy storage system. The model is directly implemented by MATLAB and can simulate the charging and discharging timing of the energy storage system to calculate the optimal parameters under the selected scheme. The simulation of the model is based on the historical power generation data of distributed energy and the grid load data.

8. The method for economic evaluation of improving the distributed energy consumption capacity of an energy storage system according to claim 1, characterized in that: In step S5, the objective weight of each target is determined by using the CRITIC weight method as follows: Suppose there are m objects to be evaluated and n evaluation indicators, forming a data matrix X = (x ij ) m×n , let the element in the data matrix after standardization be x' ij , For positive indicators: For negative indicators: is the average value of the jth indicator on all evaluation objects Calculate the contrast strength of the jth indicator: Then calculate the conflict between the evaluation indicators Q, and set the conflict between indicator j and the remaining indicators to be f j ,but: where r ij It represents the correlation coefficient between indicator i and indicator j, using the Pearson correlation coefficient; Calculate the information carrying capacity: C j =σ j f j Finally calculate the weight:

9. The method for economic evaluation of improving the distributed energy consumption capacity of an energy storage system according to claim 1, characterized in that: The comprehensive weight calculation formula described in S6 is as follows: Where n represents the total number of indicators, W a,j is the weight calculated by the hierarchical analysis method for the jth indicator, W b,j is the weight calculated by the CRITIC weight method for the jth indicator; This step is used to calculate the weight W1 of the power quality score, the weight W2 of the carbon emission score, and the weight W3 of the benefit score.

10. An economic evaluation method for improving the distributed energy consumption capacity of an energy storage system according to any one of claims 1-4 or 8-9, characterized in that: The economic evaluation model described in S7 is as follows: X=W1X1+W2X2+W3X3 Among them, X1 is the power quality score, W1 is the weight of the power quality score; X2 is the carbon emission score, W2 is the weight of the carbon emission score; X3 is the benefit score, W3 is the weight of the benefit score.

11. An economic evaluation system for improving the distributed energy consumption capacity of an energy storage system, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of a method for economically evaluating an energy storage system for improving the distributed energy absorption capacity according to any one of claims 1-4 or 8-9 are implemented.

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