Electric power spot market operation effect evaluation method considering energy storage system

Abstract

The invention relates to the technical field of electric power, in particular to an electric power spot market operation effect evaluation method considering an energy storage system, and is used for solving the problems of low efficiency and poor flexibility of a traditional power generation resource scheduling power supply market. The evaluation method comprises the steps that an evaluation index system for the market operation effect and an evaluation index system for the energy storage operation effect are established respectively, and then comprehensive evaluation of the electric power spot market operation effect considering the energy storage system is carried out through multiple comprehensive evaluation methods. By comprehensively evaluating the multi-dimensional influence of the energy storage system on the power grid safety, market economy and green low-carbon benefit, the energy storage configuration strategy is optimized, the power grid operation cost is reduced, the market transaction efficiency is improved, the clean energy utilization rate is enhanced, the participation enthusiasm of a market subject is further improved, and the economic benefit is improved. And the healthy and sustainable development of the electric power spot market is boosted.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method for evaluating the operation effect of an electric power spot market considering an energy storage system. Background Art

[0002] Renewable energy sources such as wind and solar continue to increase their share of power systems. The intermittent and volatile nature of these renewable energy sources poses challenges to the stability and reliability of power systems. To address these challenges, energy storage systems have become a key tool for enhancing power system flexibility. Energy storage systems can smooth out fluctuations in renewable energy and release excess energy during peak demand periods, reducing power system operating costs while improving safety.

[0003] The existing electricity spot market primarily relies on traditional generation resources for power supply and dispatch. With technological advancements, energy storage systems are becoming increasingly important players in the electricity market. Energy storage systems not only store and release electricity but also enhance market efficiency and grid flexibility by participating in spot market dispatch. The participation and dispatch of energy storage systems depends not only on their storage capacity but also on multiple factors, including economics, technology, and market regulations. Therefore, accurately evaluating the operational effectiveness of energy storage systems in the electricity spot market requires comprehensive consideration of their impact on market price fluctuations, supply and demand balance, and system stability. Existing evaluation methods in the power technology field lack comprehensive evaluation methods that comprehensively consider the operational effectiveness of energy storage systems in the electricity spot market. These methods are unable to assess the multi-dimensional impact of energy storage systems on grid security, market economics, and green and low-carbon benefits. Furthermore, these methods cannot be used to optimize energy storage configuration strategies based on these multi-dimensional impacts, reduce grid operating costs, improve market transaction efficiency, and enhance clean energy utilization. Consequently, these methods fail to further enhance market participation and promote the healthy and sustainable development of the electricity spot market. Summary of the Invention

[0004] In order to solve the current lack of technical problems that can evaluate the impact of energy storage systems on power grid market price fluctuations, process balance, system stability, etc., and then comprehensively consider the evaluation of the energy storage system's impact on the power spot market operation effect, the present invention provides a power spot market operation effect evaluation method considering the energy storage system. The evaluation method first constructs an electricity market operation effect evaluation index system and an energy storage operation effect evaluation system respectively, and then uses a variety of comprehensive evaluation methods to evaluate the power spot market operation effect considering the energy storage system, comprehensively measures the market operation effect, optimizes energy storage configuration, provides strong support for market optimization, policy formulation and corporate decision-making, reduces power grid operation costs, improves market transaction rates, enhances the utilization rate of clean renewable energy, enhances the participation enthusiasm of market players, and further promotes the healthy and sustainable development of the power spot market.

[0005] The present invention solves the technical problem using a technical solution: a method for evaluating the operational effect of an electricity spot market taking into account an energy storage system, characterized by comprising the following steps:

[0006] Step 1. Construct an evaluation index system for the effectiveness of power market operations. The evaluation index system for the effectiveness of power market operations consists of three indicators: grid production and operation, power market operation, and comprehensive social benefits.

[0007] Step 2: Construct an evaluation index system for energy storage operation effects. This evaluation index system consists of two parts: energy storage itself and energy storage benefits.

[0008] Step 3. Use multiple comprehensive evaluation methods to evaluate the effectiveness of electricity spot market operations that take energy storage systems into account. Based on the evaluation index system from Steps 1 and 2, collect the indicator data required for the comprehensive evaluation of electricity market operations. Based on different energy storage capacity ratios, generate a reference sample sequence and a sample sequence to be evaluated, and normalize the raw data.

[0009] Conduct a comprehensive evaluation of the indicators in step 1 and step 2. The methods used in the comprehensive evaluation include analytic hierarchy process, grey relational analysis, weighted method and superior and inferior solution distance method;

[0010] Comprehensive evaluation process: First, the indicators of power market operation effect and energy storage operation effect are standardized; the entropy weight method is used to calculate the objective weight of each indicator, and the hierarchical analysis method is combined to determine the subjective weight to improve the rationality of the weight calculation; the relative closeness of different energy storage capacity ratio schemes is calculated using the good and bad solution distance method, and the grey correlation analysis method is used to evaluate the advantages and disadvantages of different energy storage capacity ratio schemes; the comprehensive score is used to determine the optimal energy storage configuration scheme to support the optimized operation of the power market.

[0011] This invention evaluates the effectiveness of electricity market operations and energy storage operations separately to obtain corresponding indicator data. Combining the power market's grid production, power market operations, and overall social benefits with the energy storage itself and its benefits, it comprehensively evaluates the advantages and disadvantages of different energy storage capacity ratio schemes and ultimately determines the optimal energy storage configuration, thereby providing support for optimized electricity market operations. The aforementioned four evaluation methods, including the Analytic Hierarchy Process (AHP), Grey Relational Analysis (GRA), EW, and Top-of-the-Place Distance Method (TOPSIS), are all commonly used comprehensive evaluation methods for multivariate comprehensive evaluation.

[0012] As a further improvement and supplement to the above technical solution, the present invention adopts the following technical measures:

[0013] The indicators of the power grid production and operation part include power generation indicators, production efficiency indicators and power grid security indicators;

[0014] (1) Power generation indicators, including the peak output P in a specified area in a specified season max and output valley value P min And the output mean P avg During use, separate evaluations can be performed by specifying a region and a season. For example, the power generation curve in winter or summer can be evaluated to obtain the peak and valley output values and average output values in the typical power curve, thereby analyzing the power grid output level in a specific region in a specific season.

[0015] (2) Production efficiency indicators, including network loss rate, section congestion, congestion cost, power fluctuation variance and peak-to-valley difference;

[0016] Network loss rate η loss =P loss / P supply , where P loss is the total power loss or electricity loss of the power grid, which includes line loss and transformer loss; P supply is the total power supply (or total power supply), that is, the power delivered to the grid from the power supply side;

[0017] Section blockage C B =P trans / P max , where P trans is the actual power transmission through the section; P max is the maximum power transmission capacity of the section; (Section congestion is used to describe the situation in which the load exceeds the line limit in the power network in a given area. The line set including the energy storage node can be selected as the reference section, and the line flow and the basic line transmission limit power are used for calculation.)

[0018] Congestion cost C = P·(λ1-λ2), where λ1 is the marginal electricity price of the node in the case of congestion; λ2 is the marginal electricity price of the node in the case of no congestion. This describes the economic loss caused by congestion to the power grid and can be calculated using the node electricity price.

[0019] Power fluctuation variance Where P i is the power at the i-th moment; is the average power value; considering the volatility of renewable energy output such as wind and solar power, the power fluctuation variance is used to describe the stability of the power grid output. It is calculated using the variance of the typical curve's unit time output value and the output mean.

[0020] Peak-to-valley difference: calculates the difference between the peak output value and the valley output value of the typical curve. It is used to describe the flexibility of the power grid output level. The difference between the peak output value and the valley output value of the typical curve is calculated.

[0021] (3) Grid safety indicators, including bus voltage, main transformer capacity-load ratio, and line maximum load rate;

[0022] Bus voltage: Obtain bus voltage at nodes including energy storage, major load points, and major power source equipment, and count the number of voltage limit violations, direction of violations, and value of violations. The aforementioned content mainly reflects the grid voltage limit violations.

[0023] Main transformer capacity-load ratio, select the ratio of the total capacity of the main transformer under different voltage levels to the corresponding power supply load

[0024] R=P bmax / P bn , where the numerator and denominator are the total capacity of the main transformer and the corresponding power supply load respectively;

[0025] Maximum load rate of the line, the ratio of the maximum load on the line to the maximum load capacity of the line itself

[0026] R l =P lmax / P ln , where the numerator and denominator are the maximum load and the maximum load capacity of the line itself respectively.

[0027] Preferably, the indicators of the power market operation part include market subject indicators, market transaction indicators, market concentration indicators and market economic indicators;

[0028] (1) Market entity indicators, including installed capacity, market power, and diversity of market entities;

[0029] The installed capacity of market entities is described by the installed capacity ratio of the preset planning target using the installed capacity of the market entities;

[0030] The market power of market entities describes the ability of power generators to manipulate market price changes. It calculates the ratio of the successful transaction volume of each market entity on a typical day / month to the total successful transaction volume of electricity in the electricity market on a typical day / month. Here, "typical day (month)" refers to a time period that can represent the operating characteristics of the electricity market. Typical days can be selected based on load characteristics (high load, low load, normal load) or market trading activity (market trading peak, trough). The specific selection method can be combined with historical data, using cluster analysis, quantile screening or market trading statistics to ensure that the selected time period can effectively reflect the trading behavior of market entities and market volatility characteristics. The proportion of successful transactions initiated by power generators representing market entities to the total successful transactions in the market is evaluated by calculating the proportion. The higher the proportion, the stronger the power generator's ability to manipulate the market, and vice versa.

[0031] Market player diversity describes the number and types of market players actually participating in the market.

[0032] (2) Market transaction indicators, including total market transaction volume, proportion of electricity spot market transaction volume, proportion of large and small users in the market, and electricity price fluctuation variance;

[0033] The total market transaction volume is calculated by calculating the ratio of the total transaction volume of the electricity market to the total installed capacity actually participating in the market, describing the relationship between the total transaction volume of the electricity market and the total installed capacity actually participating in the market;

[0034] The proportion of electricity spot market trading volume is calculated by calculating the ratio of electricity spot market trading volume to the total power generation on the day, which represents the total market trading volume. The electricity spot market trading volume and the total power generation on the day represent the total market trading volume. Both quantitative indicators are obtained from the corresponding data of the same day, and the ratio is then calculated as the proportion result.

[0035] The proportion of large and small users in the market is divided into large and small users by setting a threshold value, and users below the threshold are marked as small users. The threshold value can be set based on a preset formula or dynamic calculation, using a relative calculation method. The first method is to divide by percentage of the total market volume, such as classifying the top 10% of large users and the remaining 90% as small users. This method dynamically adjusts the threshold value based on the power distribution of users in the market. The second method is to determine a reasonable dividing point by sorting the power consumption or load of users over a period of time based on historical data and market demand.

[0036] The price fluctuation variance is calculated for the electricity seller based on the node marginal electricity price on a typical day.

[0037]

[0038] Where P t is the node electricity price or market electricity price in the tth period; is the arithmetic mean of electricity prices.

[0039] (3) Market concentration indicators, including the Lerner index, Top-m index, HHI index, and excess supply capacity index;

[0040] The Lerner index describes the deviation of the electricity market clearing price from marginal cost;

[0041]

[0042] Where: P is the clearing price of the electricity market (i.e., the price traded in the market); MC is the marginal cost of electricity production (i.e., the additional cost of increasing output by one unit).

[0043] The Top-m index describes the proportion of power generation accounted for by the largest m power generators in the market. The power generators are ranked according to their power generation and the top m largest power generators are selected.

[0044]

[0045] Where G i is the power generation of the i-th power generator, arranged in descending order; G total is the total power generation of all power generators in the market; N is the total number of power generators;

[0046] The HHI index describes the sum of the squares of the percentage of total industry revenue or total assets held by market entities. Two corresponding HHI indices are calculated based on the total market revenue or total market assets. The market entities are power generators in the market, corresponding to the various companies or power generators that provide electricity in the power market.

[0047]

[0048] Where S i is the percentage of total market revenue or total assets held by the ith market entity; N is the number of market entities corresponding to the number of companies in the market;

[0049] The residual supply capacity index describes the importance of a certain market player to the supply and demand balance. The residual supply capacity index, also known as the residual supply index (RSI), is an existing technical content. It reflects the percentage of the power generation capacity provided by other power generation companies to the total market demand after deducting the capacity of a certain power generation company. The higher the index, the smaller the impact of the power generation of the deducted power generation company on the proportion of total market demand. The conclusion is that the power generation company is less important to the supply and demand balance.

[0050] (4) Market economic indicators, including node electricity price difference, average electricity purchase cost, and price-cost index;

[0051] Node electricity price difference, comparing the average node electricity price of all grid nodes with the node electricity price difference of the energy storage node; (in use, it is necessary to compare the difference between the average node electricity price of all grid nodes and the sum of the electricity prices of all energy storage nodes one by one, for example, the average node electricity price of all N nodes in the grid and the node electricity price difference of each energy storage node, and finally obtain the N node electricity price differences);

[0052] The average electricity purchase cost is analyzed by combining the production cost, the proportion of electricity purchase costs, and the total amount of energy storage configuration costs with the total amount of electricity purchased, as determined by a questionnaire administered to electricity users. In practice, the different cost items, such as electricity purchase costs, energy storage configuration costs, and production costs, are aggregated based on actual conditions, and the average electricity purchase cost is calculated based on the total amount of electricity purchased. Production costs refer to the costs incurred during the electricity production process, typically including fuel costs, equipment maintenance costs, and labor costs, which affect the price setting of power generation companies. The proportion of electricity purchase costs refers to the proportion of electricity costs paid by users to power suppliers, typically including the supplier's electricity price and transmission fees. Total electricity purchases refer to the total amount of electricity purchased by users over a certain period of time, typically measured in kilowatt-hours (kWh). Energy storage configuration costs refer to the investment, maintenance, and operating costs of the energy storage system, which typically include the capital expenditure, operation, and maintenance costs of the energy storage equipment itself.

[0053] Price-cost indicator, where the quoted price represents the simulated system marginal price; the quoted price in use represents the simulated system marginal price, which is the system marginal price calculated directly from the market quotation, or under certain simple assumptions, the quoted price is regarded as the system marginal price.

[0054] Preferably, the indicators of the social comprehensive benefit part include environmental friendly benefit indicators and social economic benefit indicators;

[0055] (1) Environmentally friendly benefit indicators include clean energy utilization rate, emission reduction penalties, environmental governance impact, and carbon emissions;

[0056] Clean energy utilization rate describes the level of clean energy development by measuring the proportion of clean energy in the power system to the total electricity consumption of the society. Clean energy refers to renewable energy such as wind power and solar power in the background technology, and the proportion here refers to the proportion within a specific period.

[0057] Emission reduction penalties describe the penalty costs that market entities will incur as a result of reducing emissions; market entities are those participating in the electricity market, such as power generators;

[0058] Environmental governance impact, by governing the impact of noise, electromagnetic radiation and water environment on the environment, describes the friendly index of market entities in governing the environment; the environmental friendly index is usually used to quantify the management level of market entities such as power generation companies on environmental impacts during operations. It can comprehensively consider different environmental factors such as noise, electromagnetic radiation, water environment and other governance conditions to give a comprehensive "environmental friendliness" score. Although the index itself may vary in different fields and applications, its core idea is to summarize the governance effects of various environmental factors into a specific quantitative indicator through some calculation method; Environmental impact assessment: Evaluate the impact of noise, electromagnetic radiation, water environment, etc. The evaluation criteria can be based on relevant laws and regulations, environmental protection standards or industry standards. For example, noise can be measured in decibels ( dB), electromagnetic radiation can be assessed according to radiation intensity, and water environmental impact can be measured by water quality standards; different environmental factors are given different weights according to their impact on ecology and society. The distribution of weights can be determined based on the severity of the environmental impact, the difficulty of governance, and public attention. For example, noise governance may have a smaller weight because, although noise has an impact, it is usually easier to control; electromagnetic radiation can be given a higher weight based on its possible impact on human health; the impact of water pollution is more serious and may be given the greatest weight; various environmental impacts can be standardized first, and the impact data of different magnitudes can be converted into standardized values to facilitate comparison and weighting with other factors, such as noise from 0 to 100 points, electromagnetic radiation from 0 to 100 points, and water quality from 0 to 100 points;

[0059] Carbon emissions, using carbon emissions to describe the carbon emission intensity of market entities in the power generation process; carbon emissions = fuel consumption × carbon emission factor.

[0060] (2) Social and economic benefit indicators include unit energy supply cost, reduction of power outage losses in important places, and delay in grid construction;

[0061] Unit energy supply cost refers to the cost of producing one kilowatt-hour (kWh) of electricity, which includes the fixed costs and variable costs of the power generation company. (Usually, the fixed costs and variable costs of power generation companies include fuel, equipment operation, maintenance, labor, etc.

[0062] Reduce power outage losses in important locations and describe the economic losses caused by power outages at important loads. Economic losses can be the estimated losses at custom-defined important power-consuming locations due to power outages.

[0063] Delaying grid construction describes the economic losses caused by underutilized grid investments mitigated by energy storage. Energy storage can mitigate underutilized grid investments in the following ways: 1. Excessive grid investment: Grid facilities built to meet peak loads are chronically underutilized, meaning they are not fully utilized, resulting in unnecessary investment. 2. Poor return on investment for energy storage systems: If grid construction fails to fully utilize the potential of energy storage, the return on investment for energy storage systems may be reduced because energy storage fails to achieve maximum economic benefits. 3. Wasted operating and maintenance costs: Low utilization of grid facilities can also lead to additional maintenance and operating costs, such as maintaining underutilized lines and substation equipment, which increases unnecessary costs.

[0064] Preferably, the index content of the energy storage body part includes an energy storage cost index and an energy storage nameplate value index;

[0065] Energy storage cost indicators are used to evaluate the system costs of the same energy storage technology in capacity-based and power-based scenarios. For capacity-based scenarios, it is the system energy cost (10,000 yuan / (MW·h)), and for power-based scenarios, it is the system power cost (10,000 yuan / (MW)).

[0066] Energy storage nameplate value indicators include the energy storage capacity nameplate value and the energy storage power nameplate value. The energy storage capacity nameplate value directly determines the maximum output capability of the energy storage system, representing the maximum output power per unit time. The ratio of the energy storage capacity nameplate value to the energy storage power nameplate value can measure the continuous output capability of the energy storage system.

[0067] Preferably, the index content of the energy storage benefit part includes energy storage market index and energy storage operation income index;

[0068] (1) Energy storage market indicators consist of energy storage peak-valley arbitrage, energy storage quotation upper and lower limits, and energy storage entry thresholds;

[0069] Energy storage peak-valley arbitrage, through the energy storage peak-valley arbitrage income, combined with the energy storage cost to describe the economic feasibility of energy storage in the power market; due to the current imperfect profit mechanism of energy storage participating in the frequency regulation market, the main profit method of participating in the electricity market transaction is peak-valley arbitrage, so the energy storage peak-valley arbitrage income is combined with relevant indicators such as energy storage cost to describe the economic feasibility of energy storage participating in the power market. When calculating the income of peak-valley arbitrage, it is assumed that the electricity price in the power market varies in different time periods. The energy storage equipment can be charged in the off-peak period and discharged in the peak period to carry out peak-valley arbitrage. The specific income calculation is as follows: Arbitrage income = Ecapacity×(ηcharge×Ppeak-ηdischarge×Pvalley); The following methods can be used to calculate the economic feasibility of energy storage participating in the power market: 1. Net present value method (NPV), net present value is an important indicator for evaluating the economic feasibility of investment projects, taking into account the cash flow and time value of the project; 2. Internal rate of return method (IRR) Internal rate of return (IRR) is the discount rate that makes the net present value (NPV) equal to zero. It is an important indicator for measuring the rate of return of a project. When the IRR is higher than the cost of capital, it indicates that the project is economically sound. 3. Payback period: The payback period refers to the time it takes for the initial investment of an investment project to be recovered through profits. A shorter payback period means a lower project risk and a faster return on investment. 4. Benefit-Cost Ratio (BCR): The BCR measures the economic benefits of an investment by dividing the total revenue by the total cost.

[0070] Energy storage price limits: Statistics on the price limits of energy storage in different types of markets. By comparing these price limits with the market limits, we can analyze the price range and competitiveness of energy storage.

[0071] Energy storage entry thresholds: Calculate the entry thresholds for energy storage of different sizes to participate in different types of markets and take the average to determine the market's acceptance of energy storage of different sizes.

[0072] (2) The energy storage operation benefit indicator consists of the dynamic investment payback period and the low-carbon benefits of energy storage;

[0073] The dynamic payback period is the time required for the initial investment cost to be gradually recovered through the net cash flow of the energy storage project over the next few years. The net cash flow includes both benefits and costs. Based on the dynamic payback period, the depreciation rate and net present value are taken into account to calculate the energy storage cost per kilowatt-hour after cumulative present value discounts for each year.

[0074] Energy storage's low-carbon benefits are used to describe the emission reduction benefits of energy storage combined with thermal power units or industrial users. These benefits are typically expressed as a reduction in carbon emissions, demonstrating the energy storage system's contribution to emissions reduction.

[0075] Preferably, the steps of the analytic hierarchy process are as follows:

[0076] 1) Establish a hierarchical structure model

[0077] ① The highest level: There is only one element, which is the predetermined goal and ideal result of the analysis problem;

[0078] ② Middle layer: includes the intermediate links involved in achieving the goal, including several consideration indicators and criteria;

[0079] ③The bottom layer: contains various options available to achieve the goal.

[0080] 2) Construct all judgment matrices at each level

[0081] Set a weight. Since the weights of each criterion in the criterion layer may be different, a weight needs to be set.

[0082] ① Compare the meaning of the elements of the discriminant matrix. Suppose we want to compare the influence of n factors on a factor Z. Use pairwise comparison to establish a comparative discriminant matrix, x i with x j The ratio of the impact on Z is a ij , in turn x j with x i The influence ratio is a ji =1 / a ij ;

[0083] ② Compare the definition of the discriminant matrix;

[0084] ③ Determination of comparative discriminant matrix elements.

[0085] 3) Hierarchical single sorting and consistency test

[0086] ①Calculate the consistency index CI;

[0087] ②Query the average random consistency index RI, corresponding to the RI values when n=1 to 9. Here, RI is a set of standard indicators generated by a random method;

[0088] ③ Calculate the consistency ratio CR. When CR < 0.1, the consistency of the matrix is considered acceptable.

[0089] ④ Hierarchical total ranking and consistency test: when CR < 0.1, the hierarchical total ranking result is considered to have satisfactory consistency and the analysis result is accepted.

[0090] Preferably, the steps of the grey relational analysis method are as follows:

[0091] 1) Determine the analysis series;

[0092] Parent sequence (also known as reference series, parent index): a data sequence that can reflect the behavioral characteristics of the system—> similar to the dependent variable Y;

[0093] Subsequence (also called comparison series, sub-index): a data sequence composed of factors that affect system behavior -> similar to the independent variable X;

[0094] 2) Preprocess the variables. Preprocessing includes removing dimensions, reducing the range of variables, and simplifying calculations. First, find the mean of each indicator column, and then divide each element of the indicator column by the mean of the indicator column.

[0095] 3) Subtract the element in the same row of the corresponding parent sequence from each element in the child sequence and take the absolute value to obtain a new matrix new_X;

[0096] 4) In the new matrix, let a be the smallest element in the matrix, b be the largest element in the matrix, and the resolution coefficient r o is 0.5, and the correlation coefficient of each element corresponding to the parent sequence is a+r o ×b / (new_X+r o ×b), and then calculate the mean of each column of the obtained correlation coefficient matrix, and the final result gamma is the grey correlation degree of each indicator to the parent sequence.

[0097] Preferably, the steps of the entropy weight method are as follows:

[0098] 1) Data standardization;

[0099] 2) Calculate the ratio of each indicator under each scheme;

[0100] 3) Calculate the information entropy of each indicator;

[0101] 4) Determine the weight of each indicator: calculate the weight of each indicator by information entropy or by calculating the information redundancy;

[0102] 5) Calculate the comprehensive score.

[0103] Preferably, the steps of the superior-inferior solution distance method are as follows:

[0104] 1) Data standardization

[0105]

[0106] 2) Find the ratio of each indicator under each scheme

[0107] After processing, the data matrix R can be constructed as follows: ij ) m×n , for a certain indicator r j , information entropy is E j

[0108]

[0109] in:

[0110]

[0111] The weights are:

[0112]

[0113] Let the element of the standardized data matrix be r ij , from the above we can get the data matrix element after the index is positively converted to x′ ij :

[0114] r ij =w j x ij '

[0115] 3) Get positive ideal solution and negative ideal solution

[0116] After processing, the data matrix R can be constructed as follows: ij ) m×n

[0117] ① Define each indicator, that is, the maximum value of each column is a positive ideal solution

[0118]

[0119] ② Define each indicator, that is, the maximum value of each column is the negative ideal solution

[0120]

[0121] 4) Calculate the distance between each solution and the positive / negative ideal solution

[0122] ① Define the distance between the i-th object and the maximum value as the positive ideal solution

[0123]

[0124] ② Define the distance between the i-th object and the maximum value as the negative ideal solution

[0125]

[0126] 5) Calculate the comprehensive evaluation value

[0127] The score is:

[0128]

[0129] In the calculation of the comprehensive evaluation value of the good and bad solution distance method, it can be clearly seen that 0≤Score i≤1, when Score i The bigger it is, The smaller it is, the closer the indicator is to the maximum value.

[0130] Beneficial technical effects of the present invention: The present invention comprehensively evaluates different energy storage capacity ratio schemes based on the entropy weight method (EW), the analytic hierarchy process (AHP), the superior and inferior solution distance method (TOPSIS) and the grey relational analysis method (GRA); the evaluation indicators cover system marginal price, levelized cost of electricity, total power generation cost, market revenue, net income, carbon emissions and carbon emission intensity, etc., and finally the comprehensive score is used to determine the best energy storage configuration scheme to match the electricity spot market operation, providing support for the optimized operation of the electricity market; the application of multiple comprehensive evaluation methods can also be the combination of various comprehensive evaluation methods, such as the analytic hierarchy process + superior and inferior solution distance method, the grey relational analysis method + superior and inferior solution distance method, the entropy weight method + superior and inferior solution distance method, by calculating the weight and comprehensive score of each indicator, analyzing the impact of energy storage capacity and configuration type on the effectiveness of electricity market operation, and determining the direction and benefit of energy storage capacity optimization by comparing the reference sample and the sample to be evaluated. Compared with existing technologies, the present invention can optimize energy storage configuration strategies, reduce grid operating costs, improve market transaction efficiency, enhance clean energy utilization, and thereby increase the enthusiasm of market players to participate, further helping to promote the healthy and sustainable development of the electricity spot market. BRIEF DESCRIPTION OF THE DRAWINGS

[0131] Figure 1 : Schematic diagram of the present invention.

[0132] Figure 2 : Schematic diagram of the evaluation index system of the present invention. DETAILED DESCRIPTION

[0133] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0134] like Figure 1 As shown, the present invention is a method for evaluating the operation effect of the electricity spot market considering the energy storage system, which includes the following three steps.

[0135] 1. Construct an evaluation index system for the operation effect of the power market (i.e. Figure 2 Market operation results in

[0136] (1) Power grid production and operation

[0137] 1) Power generation index

[0138] Power generation indicators include peak and valley output and average output in typical power curves for different seasons in a given region, which are used to analyze the power output level of the power grid in a given region.

[0139] ① Peak output P max

[0140] ② Output mean P avg

[0141] ③ Output valley value P min

[0142] 2) Production efficiency indicators

[0143] The production efficiency of the power grid is mainly considered from the aspects of power loss and line congestion, mainly including five aspects: network loss rate, section congestion, congestion cost, power fluctuation variance and peak-to-valley difference.

[0144] ① Network loss rate, which describes the amount of energy lost in the power network within a given area and is calculated using the line parameter matrix and the power network flow data within the area.

[0145] η loss =P loss / P supply

[0146] Where: P loss is the total power loss (or power loss) of the power grid, including line loss and transformer loss; P supply It is the total power supply (or total power supply), that is, the power delivered to the grid from the power supply side.

[0147] ② Section blocking, which describes the situation where the load exceeds the line limit in the power network in a given area. A set of lines including energy storage nodes can be selected as the reference section, and the line flow and basic line transmission limit power are used for calculation.

[0148] C B =P trans / P max

[0149] Where: P trans is the actual power transmission through the section; P max is the maximum power transmission capacity of the section.

[0150] ③ Congestion cost, which describes the economic loss caused by congestion to the power grid and is calculated using node electricity prices

[0151] C=P·(λ1-λ2)

[0152] Where: λ1 is the marginal electricity price of the node under congestion; λ2 is the marginal electricity price of the node under non-congestion.

[0153] ④ Power fluctuation variance: Taking into account the volatility of renewable energy output such as wind and solar power, this indicator describes the stability of the power grid output and is calculated using the variance of the typical curve's unit time output value and the output mean.

[0154]

[0155] Where: P i is the power at the i-th moment; is the average value of power.

[0156] ⑤ Peak-to-valley difference, which describes the flexibility of the power grid output level and is calculated using the peak and valley output values of the typical curve.

[0157] 3) Grid security indicators

[0158] The grid safety indicators are mainly described from three aspects: grid voltage over-limit situation, main transformer capacity load ratio and line load rate.

[0159] ① Bus voltage: Obtain the bus voltage at nodes including energy storage, main load points, and main output source equipment, and count the number of voltage limit violations, direction of violation, and value of violation.

[0160] ② Main transformer capacity-load ratio, select the ratio of the total capacity of the main transformer under different voltage levels to the corresponding power supply load

[0161] R=P bmax / P bn

[0162] ③ Maximum load rate of the line, which describes the ratio of the maximum load on the line to the maximum load capacity of the line itself

[0163] R l =P lmax / P ln

[0164] (2) Power market operation

[0165] 1) Market entity indicators

[0166] ① The installed capacity of market entities is described by the proportion of the installed capacity of market entities to the planned target installed capacity.

[0167] ② Market power of market players, which describes the ability of power generators to manipulate market price changes, is calculated using the successful transaction volume of each market player on a typical day (month) and the total successful transaction volume of electricity in the electricity market on a typical day (month).

[0168] ③ Diversity of market players, describing the number and types of market players actually participating in the market.

[0169] 2) Market trading indicators

[0170] ① Total market transaction volume, which describes the relationship between the total transaction volume of the electricity market and the total installed capacity actually participating in the market, and is calculated using the ratio of the two data.

[0171] ② The proportion of electricity spot market trading volume. The total power generation on the day represents the total market trading volume.

[0172] ③ The proportion of large and small users in the market. Users below the threshold value are marked as small users.

[0173] ④ The electricity price fluctuation variance is for the electricity seller, which requires the node marginal electricity price on a typical day

[0174]

[0175] Where: P t is the node electricity price or market electricity price in the tth period; is the arithmetic mean of electricity prices.

[0176] 3) Market concentration index

[0177] ① Lerner index, which describes the deviation between the electricity market clearing price and marginal cost.

[0178] ②Top-m index, which describes the proportion of power generation accounted for by the largest m power generators in the market

[0179]

[0180] Where: G i is the power generation of the i-th power generator, arranged in descending order; G total is the total power generation of all power generators in the market; N is the total number of power generators.

[0181] ③HHI index, which describes the sum of the squares of the percentage of total industry revenue or total assets held by market entities

[0182]

[0183] Where: S i is the percentage of total market revenue or total assets held by the i-th market entity; N is the number of entities in the market (i.e., the number of companies in the market).

[0184] ④ The residual supply capacity index describes the importance of a certain market entity to the supply and demand balance.

[0185] 4) Market economy indicators

[0186] ① Node electricity price difference, which compares the average node electricity price of all nodes in the grid and the node electricity price difference of the node where the energy storage is located.

[0187] ② Average electricity purchase cost, which is analyzed by the sum of the production cost set by the questionnaire for electricity users, the ratio of electricity purchase cost, and the energy storage configuration cost and the total electricity purchase amount.

[0188] ③ Price-cost indicator, represented by the quotation to simulate the marginal price of the system.

[0189] Comprehensive social benefits

[0190] 1) Environmentally friendly benefit indicators

[0191] ① Clean energy utilization rate, which describes the proportion of clean energy in the power system to the total electricity consumption of the society and describes the level of clean energy development.

[0192] ② Emission reduction penalties, which describe the penalty costs that market entities will reduce due to emission reductions.

[0193] ③ Manage environmental impacts such as noise, electromagnetic radiation, and water environment, and describe the friendly index of market entities in managing the environment.

[0194] ④ Carbon emissions, describing the carbon emission intensity of market entities in the power generation process.

[0195] 2) Social and economic benefit indicators

[0196] ① Unit energy supply cost, which describes the share of the market entity's electricity generation cost per kilowatt-hour in the marginal cost of the total market power generation.

[0197] ② Reduce power outage losses in important places and describe the economic losses caused by power outages of important loads.

[0198] ③ Delaying grid construction, describing how energy storage can alleviate the economic losses caused by grid investment with low utilization rates.

[0199] 2. Construct an evaluation index system for energy storage operation effects (i.e. Figure 2 Energy storage operation results in the

[0200] (1) Energy storage body

[0201] 1) Energy storage cost indicators

[0202] Energy storage costs are divided into system energy costs (10,000 yuan / (MW·h)) and system power costs (10,000 yuan / (MW)), which are used to evaluate the system costs of the same energy storage technology applied in capacity-based and power-based scenarios respectively.

[0203] 2) Energy storage nameplate value index

[0204] The important nameplate values of energy storage are the capacity nameplate value and the power nameplate value of energy storage. The sum of these values gives the maximum output capacity of the energy storage system.

[0205] (2) Energy storage benefits

[0206] 1) Energy Storage Market Indicators

[0207] ① Energy storage peak-valley arbitrage. Due to the imperfect profit mechanism of energy storage participating in the frequency regulation market, the main profit method of participating in the electricity market transaction is peak-valley arbitrage. The economic feasibility of energy storage participating in the electricity market is described by using the energy storage peak-valley arbitrage income and combining it with relevant indicators such as energy storage costs.

[0208] ② The upper and lower limits of energy storage quotations are calculated by statistically analyzing the upper and lower limits of energy storage quotations in different types of markets. This indicator data is compared with the upper and lower limits stipulated by the market to analyze the quotation range and competitiveness of energy storage.

[0209] ③ Energy storage entry threshold: Calculate the entry threshold for energy storage of different sizes to participate in different types of markets, take the average, and judge the market's acceptance of energy storage of different sizes.

[0210] 2) Energy storage operation income indicators

[0211] ① Dynamic investment recovery period: considering the depreciation rate and net present value, calculate the energy storage electricity cost after the cumulative discounted value in each year.

[0212] ② Low-carbon benefits of energy storage, describing the emission reduction benefits brought about by energy storage combined with thermal power units or industrial users.

[0213] 3. Use multiple comprehensive evaluation methods to evaluate the effectiveness of electricity spot market operations taking into account energy storage systems. Collect the indicator data required for the comprehensive evaluation of electricity market operations based on the evaluation indicator system of steps 1 and 2. Based on different energy storage capacity ratios, form a reference sample sequence and a sample sequence to be evaluated, and normalize the original data. The indicator data used in this embodiment refers to the indicator data required for comprehensive evaluation, covering system marginal price, levelized cost of electricity, total power generation cost, market revenue, net income, carbon emissions, and carbon emission intensity. These are only some examples. Normalizing the original data can ensure the comparability of the evaluation results.

[0214] A variety of comprehensive evaluation methods including hierarchical analysis method, grey correlation analysis method, weighted method and superior-inferior solution distance method are used to evaluate the operational effects of the electricity spot market considering the energy storage system for the indicators of step 1 and step 2.

[0215] Comprehensive evaluation process: First, the indicators of power market operation effect and energy storage operation effect are standardized; the entropy weight method is used to calculate the objective weight of each indicator, and the hierarchical analysis method is combined to determine the subjective weight to improve the rationality of the weight calculation; the relative closeness of different energy storage capacity ratio schemes is calculated using the good and bad solution distance method, and the grey correlation analysis method is used to evaluate the advantages and disadvantages of different energy storage capacity ratio schemes; the comprehensive score is used to determine the optimal energy storage configuration scheme to support the optimized operation of the power market. The present invention comprehensively evaluates different energy storage capacity ratio schemes based on the entropy weight method (EW), the analytic hierarchy process (AHP), the superior and inferior solution distance method (TOPSIS) and the grey relational analysis method (GRA); the evaluation indicators cover system marginal price, levelized cost of electricity, total power generation cost, market revenue, net income, carbon emissions and carbon emission intensity, etc., and finally the comprehensive score is used to determine the best energy storage configuration scheme to match the electricity spot market operation, providing support for the optimized operation of the electricity market; the application of multiple comprehensive evaluation methods can also be the analytic hierarchy process + superior and inferior solution distance method, the grey relational analysis method + superior and inferior solution distance method, the entropy weight method + superior and inferior solution distance method, by calculating the weight and comprehensive score of each indicator, analyzing the impact of energy storage capacity and configuration type on the effectiveness of electricity market operation, and determining the direction and benefit of energy storage capacity optimization by comparing the reference sample and the sample to be evaluated.

[0216] The introduction and use of the hierarchical analysis method, grey relational analysis method, weighted method and superior and inferior solution distance method are as follows:

[0217] (1) Analytical Hierarchy Process

[0218] The Analytic Hierarchy Process (AHP) is a simple method for making decisions on complex and ambiguous issues, particularly those that are difficult to fully analyze quantitatively. It was proposed by American operations researcher Professor T.L.Saaty in the early 1970s as a simple, flexible, and practical multi-criteria decision-making method.

[0219] The specific steps are:

[0220] 1) Establish a hierarchical structure model

[0221] ① The highest level: There is only one element, which is usually the predetermined goal and ideal result of the analysis problem;

[0222] ② Middle layer: includes the intermediate links involved in achieving the goal, mainly some consideration indicators and some criteria;

[0223] ③The bottom layer: contains various options available to achieve the goal.

[0224] 2) Construct all judgment matrices at each level

[0225] Since the weights of each criterion in the criterion layer may be different, a weight should be set.

[0226] ① Compare the meaning of the elements of the discriminant matrix. Suppose we want to compare the influence of n factors on a factor Z, and use pairwise comparison to establish a comparative discriminant matrix, x i with x j The ratio of the impact on Z is a ij , in turn x j with x i The influence ratio is a ji =1 / a ij ;

[0227] ② Compare the definition of the discriminant matrix;

[0228] ③ Determination of comparative discriminant matrix elements.

[0229] 3) Hierarchical single sorting and consistency test

[0230] ①Calculate the consistency index CI;

[0231] ②Query the average random consistency index RI, corresponding to n = 1 to 9, what are the RI values? This is a set of standard indicators generated by random methods;

[0232] ③ Calculate the consistency ratio CR. When CR < 0.1, the consistency of the matrix is considered acceptable.

[0233] ④ Hierarchical total ranking and consistency test: when CR < 0.1, the hierarchical total ranking result is considered to have satisfactory consistency and the analysis result is accepted.

[0234] (2) Grey correlation analysis method

[0235] Grey correlation analysis makes up for the shortcomings of using mathematical statistics for system analysis. It is applicable to both the size of the sample and whether there is a regularity. It also has a small amount of calculation and is very convenient. It will not cause the quantitative results to be inconsistent with the qualitative analysis results.

[0236] The basic idea of grey relational analysis is to judge whether the relationship is close based on the similarity of the geometric shapes of the sequence curves. The closer the curves are, the greater the correlation between the corresponding sequences, and vice versa.

[0237] To analyze an abstract system or phenomenon, the first step is to select a data sequence that accurately reflects the system's behavioral characteristics. This is called finding the mapping quantity of the system's behavior, and using the mapping quantity to indirectly characterize the system's behavior. For example, the average number of years of education per citizen can be used to reflect the level of educational development, the crime rate can be used to reflect social security and order, and the number of hospital registrations can be used to reflect the public's health level. With data on the system's behavioral characteristics and related factors, it is possible to create graphs of each sequence for intuitive analysis.

[0238] The specific steps are:

[0239] 1) Determine the analysis series;

[0240] Parent sequence (also known as reference series, parent index): a data sequence that can reflect the behavioral characteristics of the system—> similar to the dependent variable Y

[0241] Subsequence (also called comparison series, sub-index): a data sequence composed of factors that affect system behavior -> similar to the independent variable X

[0242] 2) Preprocess the variables (de-dimension, reduce the range of variables, simplify calculations), first find the mean of each indicator column, and then divide each element of the indicator column by the mean of the indicator column;

[0243] 3) Subtract the element in the same row of the corresponding parent sequence from each element in the child sequence and take the absolute value to obtain a new matrix new_X.

[0244] 4) Let a be the smallest element in the matrix, b be the largest element in the matrix, and the resolution coefficient ro is usually 0.5. Then the correlation coefficient of each element corresponding to the parent sequence is a+r o ×b / (new_X+r o ×b), then we calculate the mean of each column of the obtained correlation coefficient matrix, and the final result gamma is the grey correlation degree of each indicator to the parent sequence.

[0245] (3) Entropy Weight Method

[0246] The entropy weighting method determines indicator weights based on the degree of variation in each indicator's value. This objective weighting method avoids bias caused by human factors. Compared to subjective weighting methods, it offers greater precision and objectivity, allowing for better interpretation of the results. The entropy weighting method is generally best when the number of indicators is smaller than the number of objects. It can be used to determine indicator weights in any evaluation problem and to eliminate indicators from the index system that contribute little to the evaluation results. It can be used in any process requiring weight determination and can be combined with other methods.

[0247] The specific steps are:

[0248] 1) Data standardization;

[0249] 2) Calculate the ratio of each indicator under each scheme;

[0250] 3) Calculate the information entropy of each indicator;

[0251] 4) Determine the weight of each indicator: calculate the weight of each indicator by information entropy or by calculating the information redundancy;

[0252] 5) Calculate the comprehensive score.

[0253] (4) TOPSIS method

[0254] The TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) can be translated as the method of ranking close to the ideal solution. In China, it is often referred to as the superior and inferior solution distance method. The TOPSIS method is a commonly used comprehensive evaluation method that can fully utilize the information of the original data and its results can accurately reflect the gap between the evaluation solutions. Its calculation steps are as follows:

[0255] 1) Data standardization

[0256]

[0257] 2) Find the ratio of each indicator under each scheme

[0258] After processing, the data matrix R can be constructed as follows: ij ) m×n , for a certain indicator r j , information entropy is E j

[0259]

[0260] in:

[0261]

[0262] The weights are:

[0263]

[0264] Let the element of the standardized data matrix be r ij , from the above we can get the data matrix element after the index is positively converted to x′ ij :

[0265] r ij =w j x ij '

[0266] 3) Get positive ideal solution and negative ideal solution

[0267] After processing, the data matrix R can be constructed as follows: ij ) m×n

[0268] ① Define each indicator, that is, the maximum value of each column is a positive ideal solution

[0269]

[0270] ② Define each indicator, that is, the maximum value of each column is the negative ideal solution

[0271]

[0272] 4) Calculate the distance between each solution and the positive (negative) ideal solution

[0273] ① Define the distance between the i-th object and the maximum value as the positive ideal solution

[0274]

[0275] ② Define the distance between the i-th object and the maximum value as the negative ideal solution

[0276]

[0277] 5) Calculate the comprehensive evaluation value

[0278] The score is:

[0279]

[0280] It is obvious that 0≤Score i ≤1, when Score i The bigger it is, The smaller it is, the closer the indicator is to the maximum value.

Claims

1. A method for evaluating the operational effectiveness of the electricity spot market considering energy storage systems, characterized by: The following steps are involved: Step 1. Construct an evaluation index system for the effectiveness of power market operations. The evaluation index system for the effectiveness of power market operations consists of three indicators: grid production and operation, power market operation, and comprehensive social benefits. Step 2: Construct an evaluation index system for energy storage operation effects. This evaluation index system consists of two parts: energy storage itself and energy storage benefits. Step 3. Use multiple comprehensive evaluation methods to evaluate the effectiveness of electricity spot market operations that take energy storage systems into account. Based on the evaluation index system from Steps 1 and 2, collect the indicator data required for the comprehensive evaluation of electricity market operations. Based on different energy storage capacity ratios, generate a reference sample sequence and a sample sequence to be evaluated, and normalize the raw data. Conduct a comprehensive evaluation of the indicators in step 1 and step 2. The methods used in the comprehensive evaluation include analytic hierarchy process, grey relational analysis, weighted method and superior and inferior solution distance method; Comprehensive evaluation process: First, the indicators of power market operation effect and energy storage operation effect are standardized; the entropy weight method is used to calculate the objective weight of each indicator, and the hierarchical analysis method is combined to determine the subjective weight to improve the rationality of the weight calculation; the relative closeness of different energy storage capacity ratio schemes is calculated using the good and bad solution distance method, and the grey correlation analysis method is used to evaluate the advantages and disadvantages of different energy storage capacity ratio schemes; the comprehensive score is used to determine the optimal energy storage configuration scheme to support the optimized operation of the power market.

2. The method for evaluating the operation effect of the electricity spot market considering the energy storage system according to claim 1 is characterized by: The indicators of the power grid production and operation part include power generation indicators, production efficiency indicators and power grid security indicators; (1) Power generation indicators, including the peak output P in a specified area in a specified season max and output valley value P min And the output mean P avg ; (2) Production efficiency indicators, including network loss rate, section congestion, congestion cost, power fluctuation variance and peak-to-valley difference; Network loss rate η loss =P loss / P supply , where P loss is the total power loss or power loss of the power grid; P supply is the total power supply or total power supply; Section blockage C B =P trans / P max , where P trans is the actual power transmission through the section; P max is the maximum power transmission capacity of the section; Congestion cost C = P·(λ1-λ2), where λ1 is the marginal electricity price of the node under congestion; λ2 is the marginal electricity price of the node under non-congestion; Power fluctuation variance Where P i is the power at the i-th moment; is the average value of power; Peak-to-valley difference, calculate the difference between the peak value and the valley value of the typical curve (3) Grid security indicators, including bus voltage, main transformer capacity-load ratio, and line maximum load rate; Bus voltage: Obtain bus voltage at nodes including energy storage, major load points, and major power source equipment, and count the number of voltage limit violations, direction of violations, and value of violations; Main transformer capacity-load ratio, select the ratio of the total capacity of the main transformer under different voltage levels to the corresponding power supply load R=P bmax / P bn , where the numerator and denominator are the total capacity of the main transformer and the corresponding power supply load respectively; Maximum load rate of the line, the ratio of the maximum load on the line to the maximum load capacity of the line itself R l =P lmax / P ln , where the numerator and denominator are the maximum load and the maximum load capacity of the line itself respectively.

3. The method for evaluating the operation effect of the electricity spot market considering the energy storage system according to claim 1 is characterized by: The indicators of the power market operation part include market entity indicators, market transaction indicators, market concentration indicators and market economic indicators; (1) Market entity indicators, including installed capacity, market power, and diversity of market entities; The installed capacity of market entities is described by the installed capacity ratio of the preset planning target using the installed capacity of the market entities; Market power of market players, which describes the ability of power generators to manipulate market price changes, is calculated by calculating the ratio of the successful trading volume of each market player to the total successful trading volume of electricity in the electricity market on a typical day / month; Diversity of market players, describing the number and types of market players actually participating in the market; (2) Market transaction indicators, including total market transaction volume, proportion of electricity spot market transaction volume, proportion of large and small users in the market, and electricity price fluctuation variance; The total market transaction volume is calculated by calculating the ratio of the total transaction volume of the electricity market to the total installed capacity actually participating in the market, describing the relationship between the total transaction volume of the electricity market and the total installed capacity actually participating in the market; The proportion of electricity spot market trading volume is calculated as the ratio of electricity spot market trading volume to the total power generation on the day representing the total market trading volume; The proportion of large and small users in the market is divided into large and small users by setting a threshold value, and users below the threshold are marked as small users; The price fluctuation variance is calculated for the electricity seller based on the node marginal electricity price on a typical day. Where P t is the node electricity price or market electricity price in the tth period; is the arithmetic mean of electricity prices; (3) Market concentration indicators, including the Lerner index, Top-m index, HHI index, and excess supply capacity index; The Lerner index describes the deviation of the electricity market clearing price from marginal cost; Where: P is the clearing price of the electricity market; MC is the marginal cost of electricity production The Top-m index describes the proportion of power generation accounted for by the largest m power generators in the market. The power generators are ranked according to their power generation and the top m largest power generators are selected. Where G i is the power generation of the i-th power generator, arranged in descending order; G total is the total power generation of all power generators in the market; N is the total number of power generators; The HHI index describes the sum of the squares of the percentage of total industry revenue or total assets held by market entities. Two corresponding HHI indices are calculated based on the total market revenue or total market assets. The market entities are power generators in the market, corresponding to the various companies or power generators that provide electricity in the power market. Where S i is the percentage of total market revenue or total assets held by the ith market entity; N is the number of market entities corresponding to the number of companies in the market; The residual supply capacity index describes the importance of a certain market entity to the balance of supply and demand; (4) Market economic indicators, including node electricity price difference, average electricity purchase cost, and price-cost index; Node electricity price difference, which compares the average node electricity price of all nodes in the grid and the node electricity price difference of the node where the energy storage is located; The average electricity purchase cost is analyzed by analyzing the ratio of production cost, electricity purchase cost, and the sum of energy storage configuration cost and total electricity purchase volume through a questionnaire set up for electricity users; Price-cost indicator, represented by the quoted price, simulates the marginal price of the system.

4. The method for evaluating the operation effect of the electricity spot market considering the energy storage system according to claim 1 is characterized by: The indicators of the social comprehensive benefit part include environmental friendly benefit indicators and social economic benefit indicators; (1) Environmentally friendly benefit indicators include clean energy utilization rate, emission reduction penalties, environmental governance impact, and carbon emissions; Clean energy utilization rate describes the level of clean energy development by measuring the proportion of clean energy in the power system to the total electricity consumption of the society; Emission reduction penalties, which describe the penalty costs that market entities will reduce due to emission reductions; Environmental governance impact, which describes the environmental friendliness index of market entities by managing the impact of noise, electromagnetic radiation and water environment on the environment; Carbon emissions, which describes the carbon emission intensity of market entities in the power generation process through carbon emissions; (2) Social and economic benefit indicators include unit energy supply cost, reduction of power outage losses in important places, and delay in grid construction; Unit energy supply cost, which is the cost required to produce one kilowatt-hour (kWh) of electricity, including the fixed and variable costs of the power generation company; Reduce power outage losses in important places and describe the economic losses caused by power outages of important loads; Delaying grid construction and describing how energy storage can alleviate the economic losses caused by low grid investment utilization.

5. The method for evaluating the operation effect of the electricity spot market considering the energy storage system according to claim 1 is characterized by: The index content of the energy storage body part includes energy storage cost index and energy storage nameplate value index; Energy storage cost indicators are used to evaluate the system costs of the same energy storage technology in capacity-based and power-based scenarios. For capacity-based scenarios, it is the system energy cost (10,000 yuan / (MW·h)), and for power-based scenarios, it is the system power cost (10,000 yuan / (MW)). Energy storage nameplate value indicators include energy storage capacity nameplate value and energy storage power nameplate value.

6. The method for evaluating the operation effect of the electricity spot market considering the energy storage system according to claim 1 is characterized by: The indicators of the energy storage benefits section include energy storage market indicators and energy storage operation income indicators; (1) Energy storage market indicators consist of energy storage peak-valley arbitrage, energy storage quotation upper and lower limits, and energy storage entry thresholds; Energy storage peak-valley arbitrage, which describes the economic feasibility of energy storage in the power market by combining the energy storage peak-valley arbitrage income with the energy storage cost; Energy storage price limits: Statistics on the price limits of energy storage in different types of markets. By comparing these price limits with the market limits, we can analyze the price range and competitiveness of energy storage. Energy storage entry thresholds: Calculate the entry thresholds for energy storage of different sizes to participate in different types of markets and take the average to determine the market's acceptance of energy storage of different sizes; (2) The energy storage operation benefit indicator consists of the dynamic investment payback period and the low-carbon benefits of energy storage; The dynamic payback period is the time required for the initial investment cost to be gradually recovered through the net cash flow of the energy storage project over the next few years. The net cash flow includes both benefits and costs. Based on the dynamic payback period, the depreciation rate and net present value are taken into account to calculate the energy storage cost per kilowatt-hour after cumulative present value discounts for each year. The low-carbon benefits of energy storage are used to describe the emission reduction benefits brought about by energy storage combined with thermal power units or industrial users.

7. The method for evaluating the operation effect of the electricity spot market considering the energy storage system according to any one of claims 1 to 6, characterized in that The steps of the AHP are as follows: 1) Establish a hierarchical structure model ① The highest level: There is only one element, which is the predetermined goal and ideal result of the analysis problem; ② Middle layer: includes the intermediate links involved in achieving the goal, including several consideration indicators and criteria; ③The bottom layer: contains various options available to achieve the goal; 2) Construct all judgment matrices at each level Set a weight; ① Compare the meaning of the elements of the discriminant matrix. Suppose we want to compare the influence of n factors on a factor Z. Use pairwise comparison to establish a comparative discriminant matrix, x i with x j The ratio of the impact on Z is a ij , in turn x j with x i The influence ratio is a ji =1 / a ij ; ② Compare the definition of the discriminant matrix; ③ Determination of comparative discriminant matrix elements; 3) Hierarchical single sorting and consistency test ①Calculate the consistency index CI; ②Query the average random consistency index RI, and find the RI values corresponding to n=1 to 9; ③ Calculate the consistency ratio CR. When CR < 0.1, the consistency of the matrix is considered acceptable. ④ Hierarchical total ranking and consistency test: when CR < 0.1, the hierarchical total ranking result is considered to have satisfactory consistency and the analysis result is accepted.

8. The method for evaluating the operation effect of the electricity spot market considering the energy storage system according to any one of claims 1 to 6, characterized in that The steps of the grey relational analysis method are as follows: 1) Determine the analysis series; Parent sequence: a data sequence that can reflect the behavioral characteristics of the system—>similar to the dependent variable Y; Subsequence: A data sequence consisting of factors that affect system behavior—>similar to the independent variable X; 2) Preprocess the variables. Preprocessing includes removing dimensions, reducing the range of variables, and simplifying calculations. First, find the mean of each indicator column, and then divide each element of the indicator column by the mean of the indicator column. 3) Subtract the element in the same row of the corresponding parent sequence from each element in the child sequence and take the absolute value to obtain a new matrix new_X; 4) In the new matrix, let a be the smallest element in the matrix, b be the largest element in the matrix, and the resolution coefficient r o is 0.5, and the correlation coefficient of each element corresponding to the parent sequence is a+r o ×b / (new_X+r o ×b), and then calculate the mean of each column of the obtained correlation coefficient matrix, and the final result gamma is the grey correlation degree of each indicator to the parent sequence.

9. The method for evaluating the operation effect of the electricity spot market considering the energy storage system according to any one of claims 1 to 6, characterized in that The steps of the entropy weight method are as follows: 1) Data standardization; 2) Calculate the ratio of each indicator under each scheme; 3) Calculate the information entropy of each indicator; 4) Determine the weight of each indicator: calculate the weight of each indicator by information entropy or by calculating the information redundancy; 5) Calculate the comprehensive score.

10. The method for evaluating the operation effect of the electricity spot market considering the energy storage system according to any one of claims 1 to 6, characterized in that The steps of the superior-inferior solution distance method are as follows: 1) Data standardization 2) Find the ratio of each indicator under each scheme After processing, the data matrix R can be constructed as follows: ij ) m×n , for a certain indicator r j , information entropy is E j in: The weights are: Let the element of the standardized data matrix be r ij , from the above we can get the data matrix element after the index is positively converted to x′ ij : r ij =w j x′ ij 3) Get positive ideal solution and negative ideal solution After processing, the data matrix R can be constructed as follows: ij ) m×n ① Define each indicator, that is, the maximum value of each column is a positive ideal solution ② Define each indicator, that is, the maximum value of each column is the negative ideal solution 4) Calculate the distance between each solution and the positive / negative ideal solution ① Define the distance between the i-th object and the maximum value as the positive ideal solution ② Define the distance between the i-th object and the maximum value as the negative ideal solution 5) Calculate the comprehensive evaluation value and divide it into:

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