Load aggregator demand response dynamic assessment optimization method and system

Abstract

The embodiment of the invention provides a load aggregator demand response dynamic assessment optimization method and system, and belongs to the technical field of demand response and load management. The method comprises the steps of obtaining load information of demand response; according to the load information, obtaining an assessment affirmation effective response amount; obtaining a response score according to the effective response amount determined by the assessment; and completing load aggregator demand response dynamic assessment optimization according to the response score. According to the method, the accuracy and rationality of an existing assessment method can be improved, directional excitation of high-quality demand response users is realized, and the redundancy in an invitation process is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of demand response and load management, and in particular to a method and system for dynamically evaluating and optimizing demand response of a load aggregator. Background Art

[0002] The main function of the dynamic assessment and optimization method of load aggregator demand response is to improve the stability and efficiency of the power system. Through the dynamic assessment of load aggregators, their response capabilities can be evaluated and adjusted in real time, thereby optimizing demand response strategies, reducing the imbalance of power supply, reducing operating costs, and improving the overall reliability of the power market. This method helps to accurately manage and predict power demand when load demand changes, thereby achieving more efficient resource allocation.

[0003] However, the demand response assessment method is prone to the problem of one-size-fits-all judgment criteria, and does not evaluate important indicators that reflect the response quality, such as fluctuations in the response user's load level. In addition, the incentive level is directly linked to the amount of invitation responses. These problems can easily lead to excessive redundancy in invitations on the grid side, excessively high incentive costs, and a lack of accurate assessment of the response quality of individual users. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to provide a method and system for dynamic assessment and optimization of demand response of a load aggregator.

[0005] The technical solution adopted by the present invention is:

[0006] On the one hand, an embodiment of the present invention provides a method for dynamically evaluating and optimizing a load aggregator's demand response, the method comprising the following steps:

[0007] Obtain load information for demand response;

[0008] According to the load information, obtaining an effective response quantity for assessment and certification;

[0009] Determine the effective response amount according to the assessment and obtain a response score;

[0010] Based on the response score, dynamic assessment and optimization of load aggregator demand response is completed.

[0011] Furthermore, the obtaining of the load information of the demand response comprises the following steps:

[0012] Obtaining the maximum level of actual load during the demand response period;

[0013] Obtaining a maximum value of a load baseline during a demand response period;

[0014] Obtain the baseline average level of load during the demand response period;

[0015] Obtain the actual average load level during the demand response period;

[0016] Obtain the load that the user promises to reduce or transfer during the demand response period in the invitation contract;

[0017] Obtain the actual load during the current triggering demand response period;

[0018] Obtain the load baseline for the current demand response period;

[0019] The load information includes the maximum level of actual load during the demand response period, the maximum value of the load baseline during the demand response period, the average level of the load baseline during the demand response period, the average level of actual load during the demand response period, the load that the user promises to reduce or transfer during the demand response period in the invitation contract, the actual load during the currently triggered demand response period, and the load baseline during the current demand response period.

[0020] Furthermore, the formula used to obtain the effective response amount for assessment and certification according to the load information includes:

[0021]

[0022] in, represents the maximum level of actual load during the demand response period;

[0023] represents the maximum value of the load baseline during the demand response period;

[0024] represents the baseline average level of load during the demand response period;

[0025] Indicates the actual average load level during the demand response period;

[0026] It indicates the load that the user promises to reduce or transfer during the demand response period in the invitation contract;

[0027] It indicates the difference between the average load baseline level and the average actual load level of the users participating in the invitation during the demand response period;

[0028] Indicates the effective response quantity recognized by the current assessment rules;

[0029] α1 and α2 represent constants of proportion, and 0<α1<1≤α2;

[0030] Formula (1) is the demand response evaluation model.

[0031] Furthermore, determining the effective response amount according to the assessment and obtaining the response score includes the following steps:

[0032] According to the demand response evaluation model, the effective response quantity recognized by the current assessment rules is obtained;

[0033] Determine the effective response amount according to the assessment, and construct a static effective response amount;

[0034] According to the static effective response amount, a static score is obtained;

[0035] According to the load information, a dynamic index for evaluating the effectiveness of the response from a frequency perspective is obtained;

[0036] According to the assessment, the effective response amount is determined to obtain a dynamic indicator of effectiveness from a time perspective;

[0037] Evaluate the dynamic index of response effectiveness from the frequency perspective and the dynamic index of effectiveness from the time perspective to obtain a dynamic effective response amount;

[0038] According to the dynamic effective response amount, a dynamic score is obtained;

[0039] A response score is obtained according to the static score and the dynamic score.

[0040] Furthermore, the effective response amount is determined according to the assessment, and a static effective response amount is constructed, and the formula used includes:

[0041]

[0042] Among them, Γ s,i A dummy variable indicating whether demand response is effective at the static level;

[0043] λ s,i is the static effective response.

[0044] Furthermore, the static score is obtained according to the static effective response amount, and the formula used includes:

[0045]

[0046] Among them, β1,β2,…β m represents m constants;

[0047] 0,θ i,s,1 ,…θ i,s,m-1 It means that when the ratio of static effective response to invitation response is β1,β2,…β m Static scores at different intervals.

[0048] Furthermore, the dynamic index for evaluating the effectiveness of the response from a frequency perspective is obtained based on the load information, and the formula used includes:

[0049]

[0050] Among them, Γ d,t,i A dummy variable indicating whether the dynamic determination of demand response is effective;

[0051] Indicates the actual load during the current demand response triggering period;

[0052] represents the load baseline for the current demand response period;

[0053] T represents the number of all time periods during the day when users participate in demand response;

[0054] λ d1,i Represents a dynamic indicator for evaluating the effectiveness of a response from a frequency perspective.

[0055] On the other hand, an embodiment of the present invention further provides a load aggregator demand response dynamic assessment optimization system, the system comprising:

[0056] The first module is used to obtain load information for demand response;

[0057] The second module is used to obtain the effective response quantity for assessment and certification according to the load information;

[0058] The third module is used to determine the effective response amount according to the assessment and obtain the response score;

[0059] The fourth module is used to complete the dynamic assessment and optimization of the load aggregator's demand response according to the response score.

[0060] On the other hand, an embodiment of the present invention also provides a load aggregator demand response dynamic assessment and optimization device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the load aggregator demand response dynamic assessment and optimization method as described above.

[0061] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method described above.

[0062] The embodiments of the present application include at least the following beneficial effects: The present application provides a method and system for optimizing dynamic assessment of demand response of load aggregators. The present invention obtains load information of demand response; obtains the effective response quantity recognized by assessment based on the load information; obtains the response score based on the effective response quantity recognized by assessment; and completes dynamic assessment optimization of demand response of load aggregators based on the response score. The present invention can improve the accuracy and rationality of existing assessment methods, realize targeted incentives for high-quality demand response users, and reduce redundancy in the invitation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of a method for dynamically evaluating and optimizing a load aggregator's demand response provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0065] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0066] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0068] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0069] 1) PSO algorithm, namely Particle Swarm Optimization, is an algorithm used to solve optimization problems.

[0070] The present invention takes into account the scientific assessment method, which is not only a reasonable basis for the power grid and load aggregators to determine the economic benefits paid to the responding users, but also the basis for fair and trustful cooperation between the dispatcher and the users. Therefore, it is necessary to build a more accurate and scientific demand response assessment method based on the assessment details given by the current competent authorities, enhance the incentives for high-quality demand response, and achieve consistency between the evaluation process and results.

[0071] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0072] On the one hand, the embodiment of the present invention provides a load aggregator demand response dynamic assessment optimization method, referring to Figure 1 , the method comprises the following steps:

[0073] S100, obtaining load information of demand response;

[0074] S200, obtaining an effective response quantity for assessment and certification according to load information;

[0075] S300, determining the effective response amount according to the assessment, and obtaining the response score;

[0076] S400. Based on the response score, complete the dynamic assessment and optimization of the load aggregator's demand response.

[0077] The embodiment of the present invention discloses step S100 of acquiring load information of demand response, including the following steps:

[0078] S110, obtaining a maximum level of actual load during a demand response period;

[0079] S120, obtaining a maximum value of the load baseline during the demand response period;

[0080] S130, obtaining a load baseline average level during a demand response period;

[0081] S140, obtaining the actual average load level during the demand response period;

[0082] S150, obtaining the load amount that the user promises to reduce or transfer during the demand response period in the invitation contract;

[0083] S160, obtaining the actual load during the current triggering demand response period;

[0084] S170, obtaining a load baseline for the current demand response period;

[0085] S180, the load information includes the maximum level of actual load during the demand response period, the maximum value of the load baseline during the demand response period, the average level of load baseline during the demand response period, the average level of actual load during the demand response period, the load that the user promises to reduce or transfer during the demand response period in the invitation contract, the actual load during the current triggered demand response period, and the load baseline of the current demand response period.

[0086] The embodiment of the present invention discloses that step S200 obtains the effective response amount for assessment and certification according to the load information, and the formula used includes:

[0087]

[0088] in, represents the maximum level of actual load during the demand response period;

[0089] represents the maximum value of the load baseline during the demand response period;

[0090] represents the baseline average level of load during the demand response period;

[0091] Indicates the actual average load level during the demand response period;

[0092] It indicates the load that the user promises to reduce or transfer during the demand response period in the invitation contract;

[0093] It indicates the difference between the average load baseline level and the average actual load level of the users participating in the invitation during the demand response period;

[0094] Indicates the effective response quantity recognized by the current assessment rules;

[0095] α1 and α2 represent constants of proportion, and 0<α1<1≤α2;

[0096] Formula (1) is the demand response evaluation model.

[0097] The embodiment of the present invention discloses step S300 of obtaining a response score according to the assessment and determination of the effective response amount, including the following steps:

[0098] S310, according to the demand response evaluation model, obtaining the effective response quantity recognized by the current assessment rules;

[0099] S320, according to the assessment, the effective response quantity is determined and a static effective response quantity is constructed;

[0100] S330, obtaining a static score according to the static effective response amount;

[0101] S340, obtaining a dynamic index for evaluating response effectiveness from a frequency perspective based on the load information;

[0102] S350, according to the assessment and determination of the effective response amount, a dynamic index of effectiveness from a time perspective is obtained;

[0103] S360, evaluating the dynamic index of response effectiveness from a frequency perspective and the dynamic index of effectiveness from a time perspective to obtain a dynamic effective response amount;

[0104] S370, obtaining a dynamic score according to the dynamic effective response amount;

[0105] S380. Obtain a response score based on the static score and the dynamic score.

[0106] As an optional implementation, in step S310, the embodiment of the present invention calculates the effective response amount during the demand response period by analyzing the user load data based on the current grid-side demand response evaluation model.

[0107] In step S320, according to the static effective response quantity index defined in the evaluation model, an effective response quantity reflecting the user under static conditions is constructed.

[0108] In step S330, the static effective response quantity indicator is used to calculate the user's score at the static level.

[0109] In step S340, the effectiveness of the user response is evaluated from the frequency perspective.

[0110] In step S350 , the effectiveness of the user response is evaluated from a time perspective.

[0111] In step S360, the dynamic indicators from the frequency angle and the time angle are combined to calculate the user's dynamic effective response amount.

[0112] In step S370, the dynamic effective response quantity index is used to calculate the user's score under dynamic conditions.

[0113] In step S380, the static score and the dynamic score are combined to obtain an overall score of the user's single demand response.

[0114] The embodiment of the present invention discloses that step S320 constructs a static effective response amount according to the assessment and determination of the effective response amount, and the formula used includes:

[0115]

[0116] Among them, Γ s,i A dummy variable indicating whether demand response is effective at the static level;

[0117] λ s,i is the static effective response.

[0118] The embodiment of the present invention discloses that step S330 obtains a static score according to the static effective response amount, and the formula used includes:

[0119]

[0120] Among them, β1,β2,…β m represents m constants;

[0121] 0,θ i,s,1 ,…θ i,s,m-1 It means that when the ratio of static effective response to invitation response is β1,β2,…β m Static scores at different intervals.

[0122] The embodiment of the present invention discloses that step S340 obtains a dynamic index for evaluating the effectiveness of a response from a frequency perspective according to load information, and the formula used includes:

[0123]

[0124] Among them, Γ d,t,i A dummy variable indicating whether the dynamic determination of demand response is effective;

[0125] Indicates the actual load during the current demand response triggering period;

[0126] represents the load baseline for the current demand response period;

[0127] T represents the number of all time periods during the day when users participate in demand response;

[0128] λ d1,i Represents a dynamic indicator for evaluating the effectiveness of a response from a frequency perspective.

[0129] The purpose of the present invention is to construct a dynamic, multi-dimensional demand response effect assessment method for load aggregators on the basis of existing demand response assessment methods, so as to improve the accuracy and rationality of existing assessment methods, achieve targeted incentives for high-quality demand response users, and reduce redundancy in the invitation process.

[0130] As an optional embodiment, the present invention relates to a method for dynamically evaluating and optimizing the demand response of a load aggregator, which comprises the following steps: (1) a dynamic evaluation model for the effectiveness of demand response; (2) a comprehensive evaluation method for user demand response; and (3) a benefit model of the aggregator, objective function setting and model solving.

[0131] 1. Dynamic evaluation model of demand response effectiveness

[0132] The model will be based on the current grid-side evaluation method, so the current evaluation method will be described in the model. Since the methods of various places basically use the load baseline extreme value and average value as the hard judgment standard, the difference is only in the specific indicator value. The specific value is replaced by a parameter. The current grid-side demand response evaluation model is expressed as

[0133]

[0134] In the above formula, Indicates the maximum level of actual load of users participating in the invitation during the demand response triggering period; It represents the maximum value of the user's load baseline during the demand response period. The first formula is the condition for determining whether the demand response is effective; It indicates the difference between the average load baseline level and the average actual load level of the users participating in the invitation during the demand response period; It indicates the baseline average level of load during the aforementioned period; Indicates the average level of actual user load during the aforementioned period; Indicates the current effective response quantity of the assessment; It represents the load that the user promises to reduce or transfer during the demand response period in the invitation contract; α1 and α2 are constants representing the proportion, and 0<α1<1≤α2.

[0135] As mentioned above, it can be seen from formula (1) that the current demand response assessment criteria are prone to one-size-fits-all, without examining the quality of the response process and with high incentive levels.

[0136] The present invention constructs the following indicators to reflect the static level and the effective demand response recognition level of the invited users:

[0137]

[0138] Among them, Γ s,i is a dummy variable indicating whether the demand response is effective at the static level; s,i The indicator set in the model to represent the effective demand response quantity of inviting users at the static level can be named static effective response quantity.

[0139] According to the static effective response quantity, under the current grid-side rules, the response scoring standard of the responding user is expressed as

[0140]

[0141] Among them, β1,β2,…β m Represents m constants, and the specific values ​​are subject to the detailed rules of various places;

[0142] 0,θ i,s,1 ,…θ i,s,m-1 It means that when the ratio of static effective response volume to invitation response volume falls within β1, β2, …β m For different intervals, the specific value of the response score will be determined by the local demand response assessment rules.

[0143] Based on the above settings, the present invention adds a dynamic index to evaluate the effectiveness of inviting user demand responses, and comprehensively judges whether the user load change is consistent with the conditions under static conditions from the perspective of frequency and time. Among them, the dynamic index that characterizes the effectiveness of user response load from the perspective of frequency is

[0144]

[0145] Among them, Γ d,t,i A virtual variable that dynamically determines whether the demand response is effective. The judgment condition is the comparison between the actual load level of users during the demand response period and the baseline level. The reason why the maximum value judgment is not used is that it can better reflect the actual situation during the time process; T represents the number of time periods in which users participate in demand response within a day; λ d1,i It represents a dynamic indicator for evaluating the effectiveness of responses from a frequency perspective; Indicates the actual load during the current demand response triggering period; Represents the load baseline for the current demand response period.

[0146] The dynamic index that characterizes the effectiveness of user response load in terms of time is

[0147]

[0148] in, The meaning and calculation method of are still consistent with formula (1).

[0149] The dynamic indicators given by the above two formulas are respectively regarded as the average number of effective responses of users throughout the day and the average effective load. The two indicators are combined to obtain the effective response amount of users under dynamic conditions:

[0150] λ d,i =λ d1,i ·λ d2,i (6)

[0151] According to the dynamic effective response quantity index, the user's single demand response evaluation score can also be given, as follows:

[0152]

[0153] Among them, β`1,β`2,…β` m Indicates the segmentation boundary of the ratio of the dynamic effective response volume to the invitation response volume, and the specific value can be determined according to the actual situation; 0,θ s,1 ,…θ s,m-1 Represents β`1,β`2,…β` m The specific score value corresponding to the segment.

[0154] Combining the static score with the dynamic score, we can get the overall score of the effectiveness of a single user response, as follows:

[0155]

[0156] Among them, θ i It represents the overall score of the effectiveness of user i's single demand response; and Represents the weight coefficient.

[0157] After a single demand response is completed, the load aggregator obtains an overall score of the effectiveness of the response of all invited users based on the above formulas, and arranges the scores from high to low, which will be used as a reference for the priority of the next invitation.

[0158] In the above model, the effectiveness index of demand response helps to determine whether the response provided by the invited user has played a role; in the scoring process, the ratio of the effective response volume to the invited response volume evaluates the performance level of the invited user. The above model helps aggregators identify "high-quality users", and the effectiveness score of a user's single response can be used as the basis for the priority of clearing the supply of its response volume, thereby improving the overall quality of demand response through the above mechanism.

[0159] However, since the scoring in the above mechanism adopts a step-by-step progressive incentive method, it still does not solve the problem that users are incentivized to provide more demand response bids, resulting in a large redundancy on the invitation side. Therefore, the following model is needed to establish a more reasonable and scientific incentive mechanism.

[0160] 2. Comprehensive assessment method for user demand response

[0161] The assessment of the quality of user demand response is mainly to determine a reasonable incentive level based on the quality of the demand response provided by the user. This will not reduce the enthusiasm of users to participate in demand response, but also eliminate the unnecessary fees paid by aggregators under unreasonable incentive mechanisms. From the perspective of social benefits, this part of the expenditure is actually a loss of social welfare.

[0162] Based on the above objectives, the present invention will select indicators from the dimensions of aggregator benefits, user response quality, and social benefits to assess user demand response.

[0163] ① Aggregator Benefit Dimension

[0164] From the perspective of aggregator benefits, since aggregators are willing to participate in the obligation to pay compensation to demand response users, referring to the current grid-side rules, the fulfillment of the payment obligation depends on the effectiveness of the user's response, which has been explained in the first part, but it has not been linked to the subsidy level of the aggregator. Therefore, the dynamic effectiveness of a user's single response can be used as an indicator to assess users from the perspective of aggregator benefits. The specific expression is as follows

[0165]

[0166] Secondly, from the perspective of the aggregator's benefits, the marginal contribution of a single invited user to its benefits can be used as the second assessment indicator, which can be expressed by the following formula:

[0167]

[0168] Among them, Ω represents the set of all invited users; R i∈Ω represents the total benefit of the aggregator when user i participates in the invitation; It represents the total benefit of the aggregator when user i does not participate in the invitation.

[0169] ② Social benefit dimension

[0170] Social benefit dimension indicators include utility consistency indicators and user satisfaction indicators.

[0171] Utility consistency means that the utility changes brought about by inviting users to participate in demand response should be consistent with the changes in the overall social utility. The goal of demand response is to guide users to adjust or change their electricity consumption behavior, improve the safety and stability of the entire network, and thus improve the overall social utility. In this process, users will sacrifice their current utility level by reducing or transferring loads, so they should be financially compensated. Demand response should ensure that the overall utility of users before and after participating in demand response increases, so as to increase the overall welfare of society.

[0172] When a user violates his electricity usage habits and the amount of demand response bid differs from his normal electricity consumption, it indicates that he may have defrauded subsidies or his electricity cost is abnormal. Therefore, the assessment method should increase the incentive level of users who participate in demand response to improve their own utility, and reduce the incentive level of users with abnormal bids. The utility consistency index of users participating in demand response is expressed as

[0173]

[0174] Where σ represents the standard deviation of the daily load level of the invited users in each period; EMA(L i,t ) represents the exponentially weighted moving average of the user's daily load level.

[0175] The user satisfaction index refers to the change in user satisfaction after the user participates in the demand response, which is expressed by the difference between the economic compensation utility and the comfort utility loss. Among them, a large number of studies have shown that the user's comfort utility loss is roughly a quadratic function relationship with the load change. To simplify the analysis, the present invention omits the linear term and constant term in the quadratic function; the economic compensation utility here does not include the utility increase brought by the aggregator paying the economic compensation, but the utility change brought by the change in the time-of-use electricity price before and after participating in the demand response. Because the compensation only occurs after the settlement, the economic compensation utility is measured by the rate of change of the time-of-use electricity price before and after the demand response.

[0176] In summary, the user satisfaction index is expressed as

[0177]

[0178] Among them, ξ i represents the sensitivity coefficient of user i's comfort level to load changes; ε i The comfort elasticity coefficient of utility is the change in user utility caused by a unit comfort change; Δt represents the duration of demand response; ρ t represents the time-of-use electricity price before demand response; represents the time-of-use electricity price after demand response; and Represents the weight coefficient.

[0179] ③User response quality dimension

[0180] The quality of user response is characterized by the accuracy and flexibility of the user response.

[0181] The accuracy index of user response is

[0182]

[0183] in, represents the load level of user i at the start of demand response; Indicates the average load level of users during the demand response period; Indicates the number of load bids accepted by the user.

[0184] Demand response is divided into emergency response and economic response from the perspective of timeliness. Emergency response has requirements on the user's response speed, while economic response does not assess the user's response speed. Therefore, the flexibility index of the response is expressed as

[0185]

[0186] Among them, τ end represents the time when the demand response period t ends; τ dr Indicates the time when the user accepts the invitation and starts to adjust the load; τ pub Indicates the time when the aggregator publishes the invitation; i,2 When it is 0, it means that the economic indicators are not assessed at this time.

[0187] After determining the indicators of the above dimensions, the specific weight parameters of each indicator are obtained through methods such as hierarchical analysis. The process is not repeated here. The final assessment and evaluation indicators are expressed as follows:

[0188]

[0189] Among them, I i represents the comprehensive dynamic assessment index of the aggregator for user i; [w1,w2,…w6] represents the final weight coefficient of each index.

[0190] 3. The benefit model of aggregators

[0191] Assuming that the total demand response period in which users participate is T, according to the current grid-side rules, the expenditure that the aggregator should subsidize users is expressed as

[0192] C s,i =λ s,i ·δ s ·T (16)

[0193] Among them, C s,i represents the total demand response subsidy paid by the aggregator to user i in period T according to the current rules; δ s represents the price of demand response subsidies under the current rules.

[0194] In order to make the incentive level under the current rules more reasonable, a dynamic assessment mechanism is introduced to replace the original C s,i Part of is used for dynamic incentives, then the static subsidy expenditure of the aggregator to user i under the new mechanism is

[0195] C` s,i =(1-κ)C s,i (17)

[0196] Where κ represents C d,i Total original expenditure C s,i The proportion of C d,i represents the subsidy amount that the aggregator should pay to user i in period T under the dynamic assessment mechanism. Through the above formulas, we can get C d,i The expression is

[0197]

[0198] Under the new mechanism, the total subsidy expenditure that the aggregator should pay to user i is expressed as

[0199] `

[0200] C i =C s,i +C d,i (19)

[0201] From the perspective of the entire source-grid-load, the aggregator obtains economic compensation from the grid side. This part of the compensation minus the part that the aggregator should pay to the user is its gross profit. As mentioned above, the current incentive level and mechanism are somewhat unreasonable. Under the current rules, part of the profit of the aggregator and the subsidy received by the user may overlap to a certain extent. The present invention introduces a dynamic incentive mechanism to share the interests of the aggregator and the user to enhance the consistency of interests between the aggregator and the user.

[0202] Assuming that the aggregator and the user agree that the partial sharing ratio of dynamic incentives is χ:1, the dynamic incentive fee that the aggregator should eventually pay is expressed as

[0203] C` d,i =χC d,i =κχC s,i (20)

[0204] It can be expressed as the objective function in the form of

[0205] F=minC` d,i =minκχC s,i (twenty one)

[0206] In addition, considering that the introduction of this method does not increase the cost of the aggregator, in order to achieve Pareto improvement, it should be ensured that after the introduction of the new method, at the set confidence level, no less than a proportion of users ζ obtain benefits greater than or equal to the original benefits under the new method. Therefore, the constraint condition is expressed as

[0207]

[0208] Where f(·) represents the probability function; Indicates an event The cumulative probability of occurrence; ζ represents the proportion.

[0209] Substitute the historical data of participants in demand response and other known data into (δ d ,κ,χ) as decision variables, the optimal solutions of the above equations are obtained through the PSO algorithm.

[0210] The present invention adopts the above technical solution and has the following advantages:

[0211] It overcomes the defects of the current grid-side demand response evaluation mechanism, such as the one-size-fits-all approach, lack of assessment of process quality, and unreasonable incentive level, and solves the problems of excessive redundancy and insufficient response quality on the response invitation side of the current aggregator. It adds a dynamic evaluation mechanism as a supplement to the demand response effectiveness evaluation, so that aggregators can identify users with high compliance and response effectiveness, and give priority to inviting high-quality response users. In the assessment mechanism, by adding multi-dimensional assessment indicators, the incentive level of demand response is made more reasonable, and the enthusiasm of high-quality users is enhanced.

[0212] On the other hand, an embodiment of the present invention further provides a load aggregator demand response dynamic assessment optimization system, the system comprising:

[0213] The first module is used to obtain load information for demand response;

[0214] The second module is used to obtain the effective response quantity for assessment and certification based on the load information;

[0215] The third module is used to determine the effective response amount according to the assessment and obtain the response score;

[0216] The fourth module is used to complete the dynamic assessment and optimization of load aggregator demand response based on the response score.

[0217] On the other hand, an embodiment of the present invention also provides a load aggregator demand response dynamic assessment and optimization device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the load aggregator demand response dynamic assessment and optimization method as described above.

[0218] The load aggregator demand response dynamic assessment and optimization device of the embodiment of the present invention includes a memory and a processor.

[0219] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0220] The memory may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage. Among them, ROM can store static data or instructions required by the processor or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0221] The memory stores executable codes, and when the executable codes are processed by the processor, the processor can execute part or all of the methods described above.

[0222] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above method.

[0223] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0224] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A method for dynamic assessment and optimization of demand response of load aggregators, characterized in that: The method comprises the following steps: Obtain load information for demand response; According to the load information, obtaining an effective response quantity for assessment and certification; Determine the effective response amount according to the assessment and obtain a response score; Based on the response score, dynamic assessment and optimization of load aggregator demand response is completed.

2. The method according to claim 1, characterized in that: The step of obtaining the load information of the demand response comprises the following steps: Obtaining the maximum level of actual load during the demand response period; Obtaining a maximum value of a load baseline during a demand response period; Obtain the baseline average level of load during the demand response period; Obtain the actual average load level during the demand response period; Obtain the load that the user promises to reduce or transfer during the demand response period in the invitation contract; Obtain the actual load during the current demand response triggering period; Obtain the load baseline for the current demand response period; The load information includes the maximum level of actual load during the demand response period, the maximum value of the load baseline during the demand response period, the average level of the load baseline during the demand response period, the average level of actual load during the demand response period, the load that the user promises to reduce or transfer during the demand response period in the invitation contract, the actual load during the currently triggered demand response period, and the load baseline during the current demand response period.

3. The method according to claim 1, characterized in that The formula used to obtain the effective response amount for assessment and certification according to the load information includes: in, represents the maximum level of actual load during the demand response period; represents the maximum value of the load baseline during the demand response period; represents the baseline average level of load during the demand response period; Indicates the actual average load level during the demand response period; It indicates the load that the user promises to reduce or transfer during the demand response period in the invitation contract; It indicates the difference between the average load baseline level and the average actual load level of the users participating in the invitation during the demand response period; Indicates the effective response quantity recognized by the current assessment rules; α1 and α2 represent constants of proportion, and 0<α1<1≤α2; Formula (1) is the demand response evaluation model.

4. The method according to claim 1, characterized in that The step of determining the effective response amount according to the assessment and obtaining the response score comprises the following steps: Determine the effective response amount according to the assessment, and construct a static effective response amount; According to the static effective response amount, a static score is obtained; According to the load information, a dynamic index for evaluating the effectiveness of the response from a frequency perspective is obtained; According to the assessment, the effective response amount is determined to obtain a dynamic indicator of effectiveness from a time perspective; Evaluate the dynamic index of response effectiveness from the frequency perspective and the dynamic index of effectiveness from the time perspective to obtain a dynamic effective response amount; According to the dynamic effective response amount, a dynamic score is obtained; A response score is obtained according to the static score and the dynamic score.

5. The method according to claim 4, characterized in that The effective response amount is determined according to the assessment, and a static effective response amount is constructed, and the formula used includes: Among them, Γ s,i A dummy variable indicating whether demand response is effective at the static level; λ s,i is the static effective response.

6. The method according to claim 4, characterized in that The static score is obtained according to the static effective response amount, and the formula used includes: Among them, β1,β2,…β m represents m constants; 0,θ i,s,1 ,…θ i,s,m-1 It means that when the ratio of static effective response to invitation response is β1,β2,…β m Static scores at different intervals.

7. The method according to claim 4, characterized in that The dynamic index for evaluating the effectiveness of the response from a frequency perspective is obtained based on the load information, and the formula used includes: Among them, Γ d,t,i A dummy variable indicating whether the dynamic determination of demand response is effective; Indicates the actual load during the current demand response triggering period; represents the load baseline for the current demand response period; T represents the number of all time periods during the day when users participate in demand response; λ d1,i Represents a dynamic indicator for evaluating the effectiveness of a response from a frequency perspective.

8. A load aggregator demand response dynamic assessment and optimization system, characterized in that: The system comprises: The first module is used to obtain load information for demand response; The second module is used to obtain the effective response quantity for assessment and certification according to the load information; The third module is used to determine the effective response amount according to the assessment and obtain the response score; The fourth module is used to complete the dynamic assessment and optimization of the load aggregator's demand response according to the response score.

9. A device for dynamically evaluating and optimizing the demand response of a load aggregator, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for dynamically evaluating and optimizing the demand response of a load aggregator is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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