Load side resource market declaration behavior analog simulation method and system

Abstract

The invention discloses a load side resource market declaration behavior simulation method and system, and the method comprises the steps: building a load side resource market declaration behavior simulation model which comprises a user quotation model and a user quantity reporting model; various deduction scenes are set in a control variable mode, simulation parameters of the various deduction scenes are input into the load side resource market declaration behavior simulation model, and declaration prices and declaration amounts of all users under different control variables of the various deduction scenes are generated; a centralized clearing mode is adopted to form load side resource clearing results under various deduction scenes; and evaluating load side resource clearing results in various deduction scenes by using set evaluation indexes to analyze the influence of various parameters of user behavior changes in the future on market clearing results. According to the method, the declaration behavior and clearing result simulation of large-scale load side resources participating in the electricity market are realized, and a reliable basis is provided for analyzing load resource quotation and predicting market clearing electricity price.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and system for simulating and emulating load-side resource market declaration behavior. Background Art

[0002] With the continuous grid connection of volatile clean energy and the continuous increase in load scale, the power grid faces the challenges of new energy consumption and power supply security. The system's flexible adjustment capacity is insufficient, and the participation of massive flexible load resources in the normalized interactive regulation of the power grid has become an important means to improve the flexibility of the power grid. Marketization is the key to the normal interaction of load resources. my country is gradually establishing and improving the demand response price mechanism connected with the power market, encouraging demand response entities to participate in the corresponding electric energy market, ancillary service market, capacity market, etc., and obtain economic benefits according to market rules.

[0003] Relevant scholars have conducted simulation research on the market declaration behavior on the load side. However, in the existing research, the user declaration behavior is based on the maximization of revenue, and the default declaration price is used. The users simulated by this type of method take the maximization of revenue as the optimization goal, and use the optimized user declaration price as the actual declaration price, which makes users of the same size declare the same price in the same period. But in fact, the user declaration behavior is also affected by subjective factors, and the declared prices will be different due to differences in profit-seeking psychology and risk aversion psychology. Therefore, the current research focuses on the connection between the user's declared price and its revenue, ignoring the differences in the declaration behavior of various users participating in multiple types of markets, and cannot simulate the interactive situation of various users participating in market declaration. It is necessary to further optimize the simulation technology of the load-side resource market declaration behavior. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and system for simulating the market declaration behavior of load-side resources, which realizes the declaration behavior and clearing result simulation of large-scale load-side resources participating in the power market, and provides a reliable basis for analyzing load resource quotations and predicting market clearing electricity prices.

[0005] The present invention adopts the following technical solution.

[0006] In a first aspect, the present invention provides a method for simulating load-side resource market declaration behavior, the method comprising:

[0007] Step 1: Establish a load-side resource market declaration behavior simulation model, including a user quotation model and a user quantity declaration model;

[0008] Step 2: various simulation scenarios are set by means of control variables, and the simulation parameters of various simulation scenarios are respectively input into the load-side resource market declaration behavior simulation model of step 1 to generate the declared price and declared quantity of each user under different control variables of various simulation scenarios;

[0009] Step 3: Based on the declared price and declared quantity of each user, a centralized clearing method is used to form the load-side resource clearing results under various simulation scenarios;

[0010] Step 4: Use the set evaluation indicators to evaluate the load-side resource clearing results under various simulation scenarios to analyze the impact of various parameters of future user behavior changes on market clearing results.

[0011] Optionally, in step 1, the user quotation model includes:

[0012] According to the characteristics of each user's historical quotation data, they are divided into rational users and irrational users: the quotations of the irrational users are randomly distributed only within the upper and lower limits of the declared price, and the quotations of the rational users meet the normal distribution;

[0013] According to the bids, the rational users are divided into conservative, general and radical users. The average of the bids of the three types of rational users divides the upper and lower limits of the bids into four equal parts, namely:

[0014]

[0015] Where: μ min is the lower limit of the quotation set; μ max is the upper limit of the quotation set; μ x is the average quote of x-type users, and the superscript x=1, 2, and 3 represent conservative, general, and radical users, respectively.

[0016] Optionally, the expression that the quote of the rational user satisfies the normal distribution is:

[0017] X~N(μ,σ 2 )

[0018]

[0019] Where X is the quotation set; μ is the mean quotation of rational users; δ is the user concentration setting value of rational users.

[0020] Optionally, in step 1, the user reporting model includes:

[0021] According to the size of the reported amount, each user is divided into large, medium and small users. The average of the reported amount of the three types of users divides the upper and lower limits of the reported amount into four equal parts, namely:

[0022]

[0023] In the formula, λ min is the set lower limit of the reported amount; max is the upper limit of the reported amount; y is the mean reported amount of users in category y, and the superscript y=1, 2, 3 represent large, medium, and small users, respectively.

[0024] Optionally, in step 3, the step of using a centralized clearing method to form load-side resource clearing results in various simulation scenarios includes:

[0025] Step 3.1: Sort the prices of each user in descending order to form a price sequence; if the prices are the same, sort them in order of the time of application;

[0026] Step 3.2: The declared price of user i* that satisfies the following conditions is used as the clearing price J i* :

[0027]

[0028] In the formula, λ i is the reported amount of the i-th user, λ tar Clear electricity for the target;

[0029] Step 3.3: Take the first i* users in the price sequence as clearing users, and output the load-side resource clearing results, including the number of clearing users i* and the clearing price J. i* .

[0030] Optionally, in step 4, the set evaluation index includes electricity price volatility, which is calculated as follows:

[0031]

[0032] In the formula, α z is the clearing price when the simulation parameter variable takes the zth value, α z-1 The variable of the simulation parameter is the clearing electricity price at the z-1th value.

[0033] Optionally, in step 2, the various deduction scenarios set include: a user scale impact module, a rational user proportion impact module, a quotation upper limit impact module, a large user proportion impact module, and / or a large user group behavior impact module;

[0034] The simulation parameters of the various simulation scenarios include: user scale, clearance target, proportion of rational users, proportion of rational conservative / general / aggressive users, concentration of rational user quotations, proportion of large / medium / small users, range of user declarations, and range of quotations of rational and irrational users.

[0035] Optionally, in the user scale impact module, the variable parameter is the user scale, and the other simulation parameters are fixed values, and the deduction process includes: as the user scale increases from small to large, a centralized clearing method is used to form load resource clearing electricity price results under different user scales, and its electricity price volatility is calculated;

[0036] In the rational user ratio influence module, the variable parameter is the rational user ratio, and the other simulation parameters are fixed values. The deduction process includes: as the rational user ratio increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under different rational user ratios, and its electricity price volatility is calculated;

[0037] In the bid ceiling impact module, the variable parameters are the bid ceilings of rational and irrational users, and the other simulation parameters are fixed values. The deduction process includes: as the bid ceilings of rational and irrational users increase from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under the bid ceilings of different rational and irrational users, and the electricity price volatility is calculated;

[0038] In the large user proportion module, the variable parameter is the large user proportion, and the other simulation parameters are fixed values. The deduction process includes: as the large user proportion increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price result under the large-scale user proportion, and its electricity price volatility is calculated.

[0039] Optionally, in step 1, the load-side resource market declaration behavior simulation model further includes a cluster bidding behavior model; the cluster bidding behavior model includes:

[0040] The group price of a set proportion of large group users is adjusted to k times the original price, that is, the group price is μ'=kμ, where μ is the normal quotation of the cluster group users, and μ' is the adjusted group quotation of the cluster group users.

[0041] Optionally, in step 2, the set deduction scenario also includes: a large user group behavior impact module; the simulation parameters also include the large user group price and the proportion of large users in the group.

[0042] Optionally, in step 4, the set evaluation index also includes a market power evaluation index;

[0043] The market power evaluation index is expressed by the Herfindahl-Hirschman HHI index, and its calculation formula is as follows:

[0044]

[0045] Where I is the total number of users; S i is the market share of the i-th user, λ tar is the target clearing power, λ iThe electricity amount reported by the i-th user.

[0046] Optionally, two scenarios are included in the large user group behavior impact module:

[0047] In scenario 1, the variable parameter is the proportion of large users in a group, and the other simulation parameters are fixed values. The deduction process includes: as the price of large users in a group changes from small to large, a centralized clearing method is used to form the load resource clearing price results under different proportions of large users in a group, and its price volatility and market power evaluation index are calculated;

[0048] In scenario two, the variable parameter is the group price of large users, and the other simulation parameters are fixed values. The deduction process includes: as the group price of large users increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under different group prices of large users, and its electricity price volatility and market power evaluation indicators are calculated.

[0049] In a second aspect, the present invention provides a load-side resource market declaration behavior simulation system, which runs the steps of any method described in the first aspect of the present invention, and the system includes:

[0050] The simulation model building module is used to build a simulation model of the load-side resource market declaration behavior, including a user quotation model and a user quantity declaration model;

[0051] The simulation scenario setting module is used to set various simulation scenarios by means of control variables, and input the simulation parameters of various simulation scenarios into the load-side resource market declaration behavior simulation model to generate the declared price and declared quantity of each user under different control variables of various simulation scenarios;

[0052] The centralized clearing module is used to form the load-side resource clearing results in various simulation scenarios based on the declared price and declared quantity of each user by adopting a centralized clearing method;

[0053] The clearing result evaluation module is used to evaluate the load-side resource clearing results under various simulation scenarios using the set evaluation indicators, so as to analyze the impact of various parameters of future user behavior changes on the market clearing results.

[0054] In a third aspect, the present invention provides a terminal, including a processor and a storage medium;

[0055] The storage medium is used to store instructions;

[0056] The processor is used to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0057] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0058] The beneficial effects of the present invention are as follows:

[0059] The present invention focuses on the differences in the reporting behaviors of load-side resources. On the basis of exploring the differences in the reporting behaviors of load-side resources, a refined analysis is conducted on the characteristics of the reported quantities, quotations, scales, proportions, etc. of user groups, and the load-side resources are divided into rational and irrational users. The user reporting behaviors are expanded to the normal distribution space, and the rational users are divided into conservative, general, and radical users. The profit-seeking degree of users is simulated, and there are differences in the mean of their quotations. For irrational users, their reported prices are randomly distributed, thereby increasing the uncertainty of the reported prices. This is more in line with the actual situation of each user participating in the market purchase of electricity, thereby achieving an accurate simulation of the reporting behavior of load-side resources participating in the electricity market.

[0060] The present invention adopts a control variable method to set various simulation scenarios and corresponding user market behavior simulation model parameters, including a user scale influencing module, a rational user proportion influencing module, a quotation upper limit influencing module, a large user proportion module and a large user group behavior influencing module. The proposed key simulation parameters, such as user scale, rational user proportion, quotation upper limit, large user proportion and large user group behavior, have an influence on the power market clearing result, and price volatility and market power evaluation indicators are proposed to measure the clearing results. In the application field, the parameter range can be adjusted according to actual conditions to explore the changing trend of the clearing results, and the declaration behavior of large-scale load resources participating in the power market and the simulation of clearing results can be realized, which has supporting significance for analyzing load resource quotations, predicting market clearing electricity prices, and evaluating market power status and power market development trends. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of the simulation method of the load side resource market declaration behavior in the present invention;

[0062] Figure 2 This is a simulation result diagram of the impact of different user scales on the clearing price in the present invention;

[0063] Figure 3 This is a simulation result diagram of the impact of different rational user proportions on the clearing price in the present invention;

[0064] Figure 4 This is a simulation result diagram of the impact of different quotation upper limits on the clearing price in the present invention;

[0065] Figure 5This is a simulation result diagram of the impact of different large user proportions on the clearing price in the present invention;

[0066] Figure 6 This is a simulation result diagram of the impact of different group prices on the clearing price and market power HHI in the present invention;

[0067] Figure 7 This is a simulation result diagram of the impact of different proportions of large group users on the clearing price and market power HHI in the present invention;

[0068] Figure 8 This is a structural principle block diagram of the load-side resource market declaration behavior simulation system in the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0070] Embodiment 1:

[0071] Reference Figure 1 The embodiment of the present invention provides a method for simulating the behavior of the load-side resource market declaration, which specifically includes the following steps:

[0072] Step 1: Establish a load-side resource market declaration behavior simulation model, including a user quotation model and a user quantity declaration model;

[0073] Specifically, in the user quotation model, users are divided into rational users and irrational users according to the characteristics of their historical quotation data: irrational users’ quotations are randomly distributed only within the upper and lower limits of the declared price; rational users’ quotations satisfy the normal distribution X~N(μ,σ 2 ), where X is the quotation set; μ is the mean quotation of rational users; δ is the user concentration setting value of rational users. When the user's quotations are concentrated near the mean, the δ value is larger, otherwise δ is smaller.

[0074] Among them, the rational users are divided into conservative, general and radical users according to the bids. The average of the bids of the three types of rational users divides the upper and lower limits of the bids into four equal parts, namely:

[0075]

[0076] Where: μ min is the lower limit of the quotation set; μ maxis the upper limit of the quotation set; μ x is the average quote of x-type users, and the superscript x=1, 2, and 3 represent conservative, general, and radical users, respectively.

[0077] For example, when the number of users participating in load interaction is set to 10,000, the upper limit of the quotation for each type of user is 900 yuan / MW, and the lower limit of the quotation is 300 yuan / MW, then the average quotations of conservative, general, and aggressive users among rational users are 450 yuan / MW, 600 yuan / MW, and 750 yuan / MW, respectively.

[0078] In the user reporting model, from the perspective of the size of the user's reporting, each user is divided into large, medium and small users according to the size of the reporting amount. The average of the reporting amount of the three types of users divides the upper and lower limits of the reporting amount into four equal parts, namely:

[0079]

[0080] In the formula, λ min is the set lower limit of the reported amount; max is the upper limit of the reported amount; y is the mean reported amount of users in category y, and the superscript y=1, 2, 3 represent large, medium, and small users, respectively.

[0081] For example, when the lower limit of user reported capacity is set to 1MW and the upper limit is set to 10MW, the average reported capacity of small, medium and large users is 3.25MW, 5.5MW and 7.75MW respectively.

[0082] It should be noted that, considering that the user's declaration behavior is easily affected by subjective factors, the declared prices may be different due to differences in profit-seeking psychology, risk aversion psychology, etc. For example, users may declare higher prices to achieve the effect of "high risk and high return"; large users (users whose market share is greater than the set threshold) have a higher winning rate and will also raise prices in groups, that is, some large users will raise the market clearing price through collective price-raising behavior, thereby obtaining higher returns; a preferred but non-restrictive embodiment, the load-side resource market declaration behavior simulation model established in step 1 also includes a cluster quotation behavior model.

[0083] In the cluster quotation behavior model: the group price of a set proportion of large group users is adjusted to k times the original price, that is, the group price is μ'=kμ, where μ is the normal quotation of the cluster group users, and μ' is the adjusted group quotation of the cluster group users. m g is the number of users in the cluster (the number of users with a sharp increase in historical quotation data is observed), m l The total number of large users.

[0084] For example, if the proportion of cluster users is set to 30%, the group price will be 1.5 times the original price.

[0085] Step 2: various simulation scenarios are set by means of control variables, and the simulation parameters of various simulation scenarios are respectively input into the load-side resource market declaration behavior simulation model of step 1 to generate the declared price and declared quantity of each user under different control variables of various simulation scenarios;

[0086] In this implementation, various simulation scenarios set up include: user scale impact module, rational user proportion impact module, quotation upper limit impact module, large user proportion impact module, and / or large user group behavior impact module; the simulation parameters of various simulation scenarios include: user scale, clearance target, rational user proportion, rational conservative / general / aggressive user proportion, rational user quotation concentration, large / medium / small user proportion, user declaration range, and rational and irrational user quotation range.

[0087] In a preferred but non-limiting embodiment, the deduction scenario set in step 2 also includes: a large user grouping behavior impact module; the simulation parameters also include the large user grouping price and the proportion of large users in the group.

[0088] Step 3: Based on the declared price and declared quantity of each user, a centralized clearing method is used to form the load-side resource clearing results under various simulation scenarios;

[0089] Step 3.1: Sort the prices of each user in descending order to form a price sequence; if the prices are the same, sort them in order of the time of application;

[0090] Step 3.2: The declared price of user i* that satisfies the following conditions is used as the clearing price J i* :

[0091]

[0092] In the formula, λ i is the reported amount of the i-th user, λ tar Clear electricity for the target;

[0093] Step 3.3: Take the first i* users in the price sequence as clearing users, and output the load-side resource clearing results, including the number of clearing users i* and the clearing price J. i* Among them, all clearing users are settled according to the clearing price, and the remaining users are settled according to their corresponding declared prices.

[0094] Taking the target clearing power of 30,000 MW as an example, the load resource normal interactive market simulation was carried out using the example data described in step 1. The clearing result was that 5,564 users won the bid and the clearing price was 634.0 yuan / MW.

[0095] Step 4: Use the set evaluation indicators to evaluate the load-side resource clearing results under various simulation scenarios to analyze the impact of various parameters of future user behavior changes on market clearing results.

[0096] The evaluation index set in this embodiment includes electricity price volatility, and its calculation formula is:

[0097]

[0098] In the formula, α z is the clearing price when the simulation parameter variable takes the zth value, α z-1 The variable of the simulation parameter is the clearing electricity price at the z-1th value.

[0099] A preferred but non-limiting embodiment further includes a market power assessment indicator;

[0100] The market power evaluation index is expressed by the Herfindahl-Hirschman HHI index, and its calculation formula is as follows:

[0101]

[0102] Where I is the total number of users; S i is the market share of the i-th user, λ tar is the target clearing power, λ i is the electricity declared by the i-th user; the larger the HHI, the higher the market concentration and the higher the degree of monopoly.

[0103] It is further explained that in the user scale impact module, the variable parameter is the user scale, and the other simulation parameters are fixed values. The deduction process includes: as the user scale increases from small to large, the load resource clearing electricity price results under different user scales are formed by using a centralized clearing method, and the electricity price volatility is calculated;

[0104] In the rational user ratio influence module, the variable parameter is the rational user ratio, and the other simulation parameters are fixed values. The deduction process includes: as the rational user ratio increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under different rational user ratios, and its electricity price volatility is calculated;

[0105] In the bid ceiling impact module, the variable parameters are the bid ceilings of rational and irrational users, and the other simulation parameters are fixed values. The deduction process includes: as the bid ceilings of rational and irrational users increase from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under the bid ceilings of different rational and irrational users, and the electricity price volatility is calculated;

[0106] In the large user proportion module, the variable parameter is the large user proportion, and the other simulation parameters are fixed values. The deduction process includes: as the large user proportion increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price result under the large-scale user proportion, and its electricity price volatility is calculated.

[0107] The module on the impact of large user grouping behavior includes two scenarios:

[0108] In scenario 1, the variable parameter is the proportion of large users in a group, and the other simulation parameters are fixed values. The deduction process includes: as the price of large users in a group changes from small to large, a centralized clearing method is used to form the load resource clearing price results under different proportions of large users in a group, and its price volatility and market power evaluation index are calculated;

[0109] In scenario two, the variable parameter is the group price of large users, and the other simulation parameters are fixed values. The deduction process includes: as the group price of large users increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under different group prices of large users, and its electricity price volatility and market power evaluation indicators are calculated.

[0110] To sum up, the present invention adopts the method of control variables to set each simulation scenario and the corresponding load-side resource market declaration behavior simulation model parameters, outputs the load resource clearing results under different control variables, and evaluates the simulation results of the load-side resource market declaration behavior, which has a certain guiding role in predicting the market clearing results under future changes in user behavior.

[0111] The following is a specific experimental example to verify the effectiveness of the load-side resource market declaration behavior simulation method proposed in the present invention. Let the user concentration δ of rational users be 0.3 for simulation, that is, 30% of users are concentrated in the middle 1 / 3 of the normal distribution, that is, the declared prices of rational users are roughly in the classic normal distribution.

[0112] ① In the user scale impact module, the proportion of rational users is set to 70%, and the proportion of conservative / general / aggressive users among rational users is 20% / 50% / 30% respectively, and the proportion of large / medium / small users is 20% / 50% / 30% respectively. The average user declaration amount is 5MW, and the user declaration amount range is 1-10MW. The quotation distribution of rational users meets the normal distribution with a range of 300-900 yuan / MW, an average of 600 yuan / MW, and a concentration degree of 0.3. The quotation distribution of irrational users meets the random distribution with a range of 300-900 yuan / MW. Only changing the user scale, four clearing targets are set to analyze the unified change trend of the clearing results. The clearing results are as follows: Figure 2 The electricity price volatility is shown in Table 1:

[0113] Table 1 Electricity price volatility

[0114]

[0115]

[0116] From the simulation results data, we can see that:

[0117] 1) Under the same clearing target, the market clearing price is negatively correlated with the number of users (market supply capacity). As the number of users increases, the clearing price gradually tends to be stable. When the number of users is less than 4,000, the market clearing price drops significantly. At this time, the market clearing price is greatly affected by the number of users. When the number of users is greater than 4,000, the market clearing price tends to be stable, roughly maintaining at 400-450 yuan / MW.

[0118] 2) Due to the large user scale, it is less affected by changes in market clearing targets, and the deviations between the fitting curve and the corresponding points are all within 2%.

[0119] 3) The market-clearing price is positively correlated with the clearing target. When the user scale is 1,000, as the market-clearing target increases, the market-clearing price gradually increases from 627 yuan / MW to 777 yuan / MW. When the user scale is larger, the growth rate becomes smaller. For every 500MW increase in the market-clearing target, the market-clearing price also increases by about 12 yuan / MW.

[0120] 4) As the clearing electricity price shows a trend of gradually decreasing, the volatility of electricity prices is basically negative, and its absolute value generally shows a trend of gradually decreasing and tends to 0.

[0121] 5) From the perspective of market operation, the situation where the increase in the number of users leads to a lower clearing price is developing in the direction of better market operation, reducing the dispatching costs on the power grid side and preventing the occurrence of monopoly.

[0122] ② In the rational user proportion impact module, the rational user proportion is set to 70%, the conservative / general / aggressive users among rational users account for 20% / 50% / 30% respectively, the large / medium / small users account for 20% / 50% / 30% respectively, the average user declaration amount is 5MW, the user declaration amount range is 1-10MW, the rational user's quotation distribution meets the range of 300-900 yuan / MW, the average is 600 yuan / MW, the concentration degree is 0.3, and the irrational user quotation distribution meets the range of 300-900 yuan / MW. Random distribution. Only changing the rational user proportion, four clearing targets are set to analyze the unified change trend of the clearing results. The clearing results are as follows: Figure 3 The electricity price volatility is shown in Table 2:

[0123] Table 2 Electricity price volatility

[0124]

[0125]

[0126] From the simulation results data, we can see that:

[0127] 1) Under the same clearing target, when the clearing target increases from 15,000 MW to 22,500 MW, the market clearing price changes from near stability to a decrease in volatility as the proportion of rational users increases.

[0128] 2) The deviation of each point of the fitting curve under different clearance targets is less than 1%.

[0129] 3) Under the same proportion of rational users, the market clearing price is positively correlated with the clearing target. However, as the proportion of rational users increases, the clearing prices under different clearing targets also gradually decrease. The difference in clearing prices between the clearing targets of 22,500MW and 15,000MW drops from 180 yuan / MW to 108 yuan / MW.

[0130] 4) The volatility of electricity prices is relatively small, reflecting the stable characteristics of electricity prices. With the increase of clearing prices, the absolute value of electricity price volatility increases slightly.

[0131] 5) From the perspective of market operation, the increase in the proportion of rational users has led to a slight decrease in the clearing price, which also shows that the market is moving towards better operation. It reduces the dispatching cost on the grid side and prevents the occurrence of monopoly. However, the influence of the proportion of rational users on the clearing price is smaller than the influence of changes in user scale.

[0132] ③ In the quotation upper limit impact module, the proportion of rational users is set to 70%, and the proportion of conservative / general / aggressive users among rational users is 20% / 50% / 30% respectively, and the proportion of large / medium / small users is 20% / 50% / 30% respectively. The average user declaration amount is 5MW, the user declaration amount range is 1-10MW, and the declaration lower limit is 300 yuan / MW. The concentration of rational user quotations is a normal distribution of 0.3, and the mean is the median from the lower limit to the upper limit of the quotation. The distribution of irrational user quotations satisfies the random distribution ranging from the lower limit to the upper limit of the quotation. Only the quotation upper limit of rational users is changed, and four clearing targets are set to analyze the unified change trend of the clearing results. The clearing results obtained are as follows: Figure 4 The electricity price volatility is shown in Table 3:

[0133] Table 3 Electricity price volatility

[0134]

[0135]

[0136] From the simulation results data, we can see that:

[0137] 1) Under the same clearing target, as the upper limit of the declaration increases, the clearing price increases linearly. For every increase of 100 yuan / MW in the upper limit of the declaration, the clearing price increases by about 63 yuan / MW.

[0138] 2) The deviation of each point of the fitting curve under different clearance targets is less than 1%.

[0139] 3) Under the same rational user ratio, the market clearing price is positively correlated with the clearing target. However, as the upper limit of the declaration increases, the clearing prices under different clearing targets gradually increase. The difference in clearing prices between the clearing targets of 22,500MW and 15,000MW increases from 54 yuan / MW to 168 yuan / MW.

[0140] 4) The lowest electricity price volatility is 8.40%, which means that as the reporting upper limit increases, electricity prices increase at a faster rate; under different clearing targets, the growth trends of electricity price volatility are roughly the same.

[0141] 5) From the perspective of market operation, the increase in the declaration limit has led to an increase in the clearing price, indicating that the market is moving towards higher operating costs. When it is found that users are generally raising prices, relevant policies need to be introduced in a timely manner to prevent the intensification of this behavior.

[0142] ④ In the large user proportion influence module, the rational user proportion is set to 70%, and the quotation conforms to the normal distribution. The proportion of conservative / general / aggressive users among rational users is 20% / 50% / 30% respectively, and the proportion of medium users is 0.3 unchanged. The average user declaration amount is 5MW, and the user declaration amount range is 1-10MW. The quotation distribution of rational users meets the normal distribution with a range of 300-900 yuan / MW, an average of 600 yuan / MW, and a concentration degree of 0.3. The quotation distribution of irrational users meets the random distribution with a range of 300-900 yuan / MW. Only the proportion of large users is changed, and four clearing targets are set to analyze the unified change trend of the clearing results. The clearing results are as follows: Figure 5 The electricity price volatility is shown in Table 4:

[0143] Table 4 Electricity price volatility

[0144]

[0145]

[0146] From the simulation results data, we can see that:

[0147] 1) Under the same clearing target, when the proportion of large users increases from 0.1 to 0.9, the market clearing price gradually stabilizes. When the proportion of large users is less than 0.4, the clearing price drops rapidly. When the proportion of large users is greater than 0.4, the clearing price tends to be stable, roughly maintained between 550-700 yuan / MW.

[0148] 2) The deviation between the fitting curve and the actual clearing price is small, less than 1.5%.

[0149] 3) Under the same large user proportion, the market clearing price is positively correlated with the clearing target. For every increase of 5,000MW in clearing demand, the clearing price increases by approximately RMB 38 / MW.

[0150] 4) The volatility of electricity prices is negative and gradually tends to 0; under different clearing targets, the growth trend of electricity price volatility is roughly the same.

[0151] 5) From the perspective of market operation, the increase in the proportion of large users has led to a decrease in clearing prices, indicating that the market is moving towards better operations. By supporting related enterprises, we can reduce grid-side operating costs while promoting their development.

[0152] ⑤ In the module on the impact of large user group behavior, two types of scenarios are set:

[0153] Both scenarios meet the requirement of 10,000 users, of which 4,000 are large, 3,000 are medium and 3,000 are small users respectively. Rational users account for 70% of the total, and conservative / general / aggressive users account for 20% / 50% / 30% of the rational users respectively. The proportion of medium users remains unchanged at 0.3. The average user declared capacity is 5MW, and the range of user declared capacity is 1 to 10MW. The quotation distribution of rational users meets the normal distribution with a range of 300 to 900 yuan / MW, a mean of 600 yuan / MW and a concentration degree of 0.3. The quotation distribution of irrational users meets the random distribution with a range of 300 to 900 yuan / MW.

[0154] Scenario 1: The proportion of large group users is set to 80%, and the group price is adjusted uniformly. The group price / original price is 0.8-2.6, with an interval of 0.2, and the Herfindahl-Hirschman index is selected to calculate the market power, where the market share is represented by the winning bid of each user;

[0155] Scenario 2: The group price / original price is uniformly set to 1.5, the proportion of large group users is 10%-100%, the interval is 10%, and the Herfindahl-Hirschman Index is selected to calculate the market power, where the market share is represented by the winning bid of each user. Among them, the HHI value satisfies that when there is a monopoly of the exclusive enterprise, the value is equal to 1, and when all enterprises are of the same size, the index is equal to 1 / n, so the range of this indicator is [1 / n,1].

[0156] The clearing situation and HHI size of scenario 1 are as follows Figure 6 shown.

[0157] From the simulation results data, it can be seen that:

[0158] 1) Under the same proportion of large users in a group, when the changed price / original price increases from 0.8 to 2.6, the clearing price first gradually increases, then tends to stabilize, and finally stabilizes at about 760 yuan / MW. When these large users raise the bid price to more than 1.6 times, the winning rate will drop to 0, so the clearing price will stabilize;

[0159] 2) As the growth of clearing prices gradually stabilizes, the volatility of electricity prices gradually decreases to zero;

[0160] 3) The HHI value gradually decreases. This is because as the price of large users increases, more small and medium-sized users will win the bid. The market is actually moving in the direction of reducing monopoly. The HHI value finally stabilized at about 0.00019.

[0161] The clearing situation and HHI size of scenario 2 are as follows Figure 7 shown.

[0162] From the simulation results data, we can see that:

[0163] 1) At the same group price / original price, when the proportion of large group users increases from 10% to 100%, the clearing price changes from being roughly stable to gradually increasing, with the turning point being roughly at 60% of the large group users;

[0164] 2) Due to the growth trend of the clearing price, the volatility of electricity prices is non-negative;

[0165] 3) The HHI value gradually decreases because as the proportion of large users in groups increases, more small and medium-sized users will win the bid, and the market is actually developing in the direction of reducing monopoly.

[0166] 5) From the perspective of market operation, the increase in the proportion of large users in groups and the increase in group prices have led to higher clearing prices and reduced monopoly, indicating that the operating costs on the grid side have increased, but it has an anti-monopoly effect. It is necessary to reasonably regulate this behavior to achieve a balance between the two.

[0167] The beneficial effects of the present invention are as follows:

[0168] The present invention focuses on the differences in the reporting behaviors of load-side resources. On the basis of exploring the differences in the reporting behaviors of load-side resources, a refined analysis is conducted on the characteristics of the reported quantities, quotations, scales, proportions, etc. of user groups, and the load-side resources are divided into rational and irrational users. The user reporting behaviors are expanded to the normal distribution space, and the rational users are divided into conservative, general, and radical users. The profit-seeking degree of users is simulated, and there are differences in the mean of their quotations. For irrational users, their reported prices are randomly distributed, thereby increasing the uncertainty of the reported prices. This is more in line with the actual situation of each user participating in the market purchase of electricity, thereby achieving an accurate simulation of the reporting behavior of load-side resources participating in the electricity market.

[0169] The present invention adopts a control variable method to set various simulation scenarios and corresponding user market behavior simulation model parameters, including a user scale influencing module, a rational user proportion influencing module, a quotation upper limit influencing module, a large user proportion module and a large user group behavior influencing module. The proposed key simulation parameters, such as user scale, rational user proportion, quotation upper limit, large user proportion and large user group behavior, have an influence on the power market clearing result, and price volatility and market power evaluation indicators are proposed to measure the clearing results. In the application field, the parameter range can be adjusted according to actual conditions to explore the changing trend of the clearing results, and the declaration behavior of large-scale load resources participating in the power market and the simulation of clearing results can be realized, which has supporting significance for analyzing load resource quotations, predicting market clearing electricity prices, and evaluating market power status and power market development trends.

[0170] Embodiment 2:

[0171] like Figure 8 As shown, the present invention provides a load-side resource market declaration behavior simulation system, the system is used to implement the steps of the method in the above embodiment 1, and the system specifically includes:

[0172] The simulation model building module is used to build a simulation model of the load-side resource market declaration behavior, including a user quotation model and a user quantity declaration model;

[0173] The simulation scenario setting module is used to set various simulation scenarios by means of control variables, and input the simulation parameters of various simulation scenarios into the load-side resource market declaration behavior simulation model to generate the declared price and declared quantity of each user under different control variables of various simulation scenarios;

[0174] The centralized clearing module is used to form the load-side resource clearing results in various simulation scenarios based on the declared price and declared quantity of each user by adopting a centralized clearing method;

[0175] The clearing result evaluation module is used to evaluate the load-side resource clearing results under various simulation scenarios using the set evaluation indicators, so as to analyze the impact of various parameters of future user behavior changes on the market clearing results.

[0176] The load-side resource market declaration behavior simulation system provided in the embodiment of the present invention and the load-side resource market declaration behavior simulation method provided in Example 1 are based on the same technical concept, and can produce the beneficial effects as described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0177] Embodiment three:

[0178] An embodiment of the present invention provides a terminal, including a processor and a storage medium;

[0179] The storage medium is used to store instructions;

[0180] The processor is used to operate according to the instruction to execute the steps of the method according to any one of the embodiments.

[0181] Embodiment 4:

[0182] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of any method described in Embodiment 1 are implemented.

[0183] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0184] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0185] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0186] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for simulating the behavior of the load-side resource market declaration, characterized in that: Methods include: Step 1: Establish a load-side resource market declaration behavior simulation model, including a user quotation model and a user quantity declaration model; Step 2: various simulation scenarios are set by means of control variables, and the simulation parameters of various simulation scenarios are respectively input into the load-side resource market declaration behavior simulation model of step 1 to generate the declared price and declared quantity of each user under different control variables of various simulation scenarios; Step 3: Based on the declared price and declared quantity of each user, a centralized clearing method is used to form the load-side resource clearing results under various simulation scenarios; Step 4: Use the set evaluation indicators to evaluate the load-side resource clearing results under various simulation scenarios to analyze the impact of various parameters of future user behavior changes on market clearing results.

2. The load-side resource market declaration behavior simulation method according to claim 1 is characterized in that: In step 1, the user quotation model includes: According to the characteristics of each user's historical quotation data, they are divided into rational users and irrational users: the quotations of the irrational users are randomly distributed only within the upper and lower limits of the declared price, and the quotations of the rational users meet the normal distribution; According to the bids, the rational users are divided into conservative, general and radical users. The average of the bids of the three types of rational users divides the upper and lower limits of the bids into four equal parts, namely: Where: μ min is the lower limit of the quotation set; μ max is the upper limit of the quotation set; μ x is the average quote of x-type users, and the superscript x=1, 2, and 3 represent conservative, general, and radical users, respectively.

3. The load-side resource market declaration behavior simulation method according to claim 2 is characterized in that: The expression that the rational user's quotation satisfies the normal distribution is: X~N(μ,σ 2 ) Where X is the quotation set; μ is the mean quotation of rational users; δ is the user concentration setting value of rational users.

4. The load-side resource market declaration behavior simulation method according to claim 1 is characterized in that: In step 1, the user reporting model includes: According to the size of the reported amount, each user is divided into large, medium and small users. The average of the reported amount of the three types of users divides the upper and lower limits of the reported amount into four equal parts, namely: In the formula, λ min is the set lower limit of the reported amount; max is the upper limit of the reported amount; y is the mean reported amount of users in category y, and the superscript y=1, 2, 3 represent large, medium, and small users, respectively.

5. The load-side resource market declaration behavior simulation method according to claim 1 is characterized in that: In step 3, the steps of using a centralized clearing method to form load-side resource clearing results under various simulation scenarios include: Step 3.1: Sort the prices of each user in descending order to form a price sequence; if the prices are the same, sort them in order of the time of application; Step 3.2: The declared price of user i* that satisfies the following conditions is used as the clearing price J i* : In the formula, λ i is the reported amount of the i-th user, λ tar Clear electricity for the target; Step 3.3: Take the first i* users in the price sequence as clearing users, and output the load-side resource clearing results, including the number of clearing users i* and the clearing price J. i* .

6. The load-side resource market declaration behavior simulation method according to claim 1 is characterized in that: In step 4, the set evaluation index includes electricity price volatility, and its calculation formula is: In the formula, α z is the clearing price when the simulation parameter variable takes the zth value, α z-1 The variable of the simulation parameter is the clearing electricity price at the z-1th value.

7. The load-side resource market declaration behavior simulation method according to claim 6 is characterized in that: In step 2, the various simulation scenarios set include: user scale impact module, rational user proportion impact module, quotation upper limit impact module, large user proportion impact module, and / or large user group behavior impact module; The simulation parameters of the various simulation scenarios include: user scale, clearance target, proportion of rational users, proportion of rational conservative / general / aggressive users, concentration of rational user quotations, proportion of large / medium / small users, range of user declarations, and range of quotations of rational and irrational users.

8. The load-side resource market declaration behavior simulation method according to claim 7 is characterized in that: In the user scale impact module, the variable parameter is the user scale, and the other simulation parameters are fixed values. The deduction process includes: as the user scale increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under different user scales, and its electricity price volatility is calculated; In the rational user ratio influence module, the variable parameter is the rational user ratio, and the other simulation parameters are fixed values. The deduction process includes: as the rational user ratio increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under different rational user ratios, and its electricity price volatility is calculated; In the bid ceiling impact module, the variable parameters are the bid ceilings of rational and irrational users, and the other simulation parameters are fixed values. The deduction process includes: as the bid ceilings of rational and irrational users increase from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under the bid ceilings of different rational and irrational users, and the electricity price volatility is calculated; In the large user proportion module, the variable parameter is the large user proportion, and the other simulation parameters are fixed values. The deduction process includes: as the large user proportion increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price result under the large-scale user proportion, and its electricity price volatility is calculated.

9. The load-side resource market declaration behavior simulation method according to claim 7 is characterized in that: In step 1, the load-side resource market declaration behavior simulation model also includes a cluster bidding behavior model; the cluster bidding behavior model includes: The group price of a set proportion of large group users is adjusted to k times the original price, that is, the group price is μ'=kμ, where μ is the normal quotation of the cluster group users, and μ' is the adjusted group quotation of the cluster group users.

10. The load-side resource market declaration behavior simulation method according to claim 9 is characterized in that: In step 2, the set deduction scenario also includes: a large user group behavior impact module; the simulation parameters also include the large user group price and the proportion of large users in the group.

11. The load-side resource market declaration behavior simulation method according to claim 10 is characterized in that: In step 4, the set evaluation index also includes a market power evaluation index; The market power evaluation index is expressed by the Herfindahl-Hirschman HHI index, and its calculation formula is as follows: Where I is the total number of users; S i is the market share of the i-th user, λ tar is the target clearing power, λ i The electricity amount reported by the i-th user.

12. The load-side resource market declaration behavior simulation method according to claim 11 is characterized in that: The module on the impact of large user grouping behavior includes two scenarios: In scenario 1, the variable parameter is the proportion of large users in a group, and the other simulation parameters are fixed values. The deduction process includes: as the price of large users in a group changes from small to large, a centralized clearing method is used to form the load resource clearing price results under different proportions of large users in a group, and its price volatility and market power evaluation index are calculated; In scenario two, the variable parameter is the group price of large users, and the other simulation parameters are fixed values. The deduction process includes: as the group price of large users increases from small to large, a centralized clearing method is used to form the load resource clearing electricity price results under different group prices of large users, and its electricity price volatility and market power evaluation indicators are calculated.

13. A load-side resource market declaration behavior simulation system, running the load-side resource market declaration behavior simulation method according to any one of claims 1 to 12, characterized in that: System includes The simulation model building module is used to build a simulation model of the load-side resource market declaration behavior, including a user quotation model and a user quantity declaration model; The simulation scenario setting module is used to set various simulation scenarios by means of control variables, and input the simulation parameters of various simulation scenarios into the load-side resource market declaration behavior simulation model to generate the declared price and declared quantity of each user under different control variables of various simulation scenarios; The centralized clearing module is used to form the load-side resource clearing results in various simulation scenarios based on the declared price and declared quantity of each user by adopting a centralized clearing method; The clearing result evaluation module is used to evaluate the load-side resource clearing results under various simulation scenarios using the set evaluation indicators, so as to analyze the impact of various parameters of future user behavior changes on the market clearing results.

14. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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