Demand response excitation method suitable for sub-load directrix

Abstract

The invention discloses a demand response excitation method suitable for a sub-load directrix, which spans three levels of an independent system operator ISO, a load aggregator and a registered user, and comprises the following steps: the ISO formulates a CDL and issues the CDL and a first excitation signal to the aggregator; the aggregator decomposes a load directrix into a plurality of SCDLs according to the historical power consumption behaviors of the user, and issues the SCDLs and a second excitation signal to the user; the ISO, the aggregator and the main pole of each level of the user establish income models based on interaction between different levels; and solving the income model of each subject to obtain an optimized first excitation signal and an optimized second excitation signal. According to the invention, guidance can be provided for users with different power utilization characteristics, so that the adjustment cost of the adjustable users is reduced and the resource flexibility is fully released; in addition, each main body establishes an income model based on interaction among different levels, and a benefit equilibrium state is finally achieved through gaming.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical engineering and automation thereof, and in particular relates to a demand response incentive method applicable to a sub-load criterion. Background Art

[0002] In order to achieve the "net zero emissions" goal of the Paris Agreement, renewable energy is increasingly replacing fossil fuels and developing rapidly, with an increasing share in electricity production. Therefore, China has proposed a new power system with greater capacity to accommodate renewable energy. However, the uncertainty of renewable energy limits the regulation capacity, resulting in obvious supply-side regulation shortages, which poses a major challenge to the balance of the new power system.

[0003] The lack of regulation capacity on the supply side forces the demand side to seek solutions. In fact, the demand side contains a large number of users who can change their electricity consumption behavior according to the goals of the power grid. Demand response (DR) seeks regulation capacity from flexible loads, provides regulation services for the power system, and maintains the power balance on the supply and demand sides. So far, large-scale DR experiments have been carried out in many regions of China, proving that DR is a promising way to solve the above problems. In order to further promote the widespread adoption of disaster tolerance, scholars have proposed that disaster tolerance in new power systems should have four characteristics: scalability, standardization, lightweight implementation, and user participation. However, there are several problems in the promotion of DR: on the one hand, the large user base, small individual loads, and diverse electricity consumption behaviors mean that regulation requirements often conflict with users' energy consumption patterns. In other words, users have to pay a large regulation cost, such as the loss of electricity comfort, which has a certain impact on normal life. On the other hand, the DR target is too single, and only a few users meet the regulation requirements. Most users choose not to participate because the regulation target is inconsistent with their own electricity consumption behavior. It is difficult to provide personalized guidance for different users. Therefore, it is urgent to design a personalized disaster recovery plan based on user energy consumption behavior and establish an interactive disaster recovery mechanism between users and power grids.

[0004] Existing technologies attempt to solve these problems from different perspectives. Currently, the widely used DR solutions can be divided into price-based DR (PDR) and incentive-based DR (IDR). For PDR, package pricing, as an innovative way to reform the power market, may become a future development trend. For example, some literature optimizes the user's electricity consumption curve by identifying user behavior and issuing customized electricity price packages. Some literature designs multiple retail price packages based on customer preferences in a two-tier market to maximize revenue. However, these scholars focus on the optimization of electricity price packages, while ignoring the user's autonomous choice and interaction process. In addition, traditional single-objective optimization is difficult to resolve the conflict of interests between sales companies and users. To solve this problem, game theory provides a new approach. Some literature establishes an electricity price game model between power sales companies and users to increase their income and reduce peak-valley differences. However, package pricing is difficult to implement widely. First, China's power market does not allow arbitrary adjustment of electricity prices. Multiple electricity price mechanisms will bring great pressure to market mechanism supervision. In addition, the current research on package pricing mainly discusses the interest game between power sales companies and users. It does not analyze the impact of different users on the choice of price packages, nor does it consider whether it is possible to guide users by adjusting price packages, promote interaction with the power grid, and achieve a win-win situation. In order to achieve IDR, a customer baseline (CBL) is established to represent the power consumption behavior of each user. The level of user participation is then evaluated by comparing the user's power curve with the CBL. Many scholars have debated the fairness and effectiveness of CBL, pointed out its limitations, and opposed its widespread adoption.

[0005] In recent years, scholars have proposed a demand response mechanism based on the Customer Directrix Load Line (CDL), that is, the "adjustable resources" on the demand side are balanced with the "non-adjustable resources" on the grid side. CDL defines the required load profile, guides users to participate in DR, and simplifies the support for the construction of new power systems. However, the current methods do not effectively reduce the user regulation costs, nor do they fully stimulate user responses. Scholars have proposed a sub-user directrix load (SCDL) based on user consumption behavior, which provides a new solution to reduce the DR regulation costs. Nevertheless, the practical application of demand response based on SCDL is hindered by the lack of a complete and feasible demand response framework and incentive mechanism. Developing DR mechanisms and incentive methods suitable for SCDL is crucial to meet these challenges. Summary of the invention

[0006] The present invention provides a demand response incentive method applicable to sub-load guidelines, which aims to achieve a win-win situation at all levels through coordination and game between entities by spanning three levels: demand response center, load aggregator and registered user.

[0007] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0008] A demand response incentive method applicable to a sub-load line, comprising:

[0009] The independent system operator formulates an expected load curve, i.e., a load benchmark, and publishes the load benchmark and a first incentive signal to the load aggregator; wherein the first incentive signal refers to the purchase fee of demand response paid by the independent system operator to the load aggregator, which is used to encourage each load aggregator to submit optimized power consumption data;

[0010] The load aggregator decomposes the load criterion into multiple sub-load criterions according to the user's historical electricity consumption behavior, and publishes the sub-load criterion and the second incentive signal to the user; the second incentive signal refers to the purchase fee of demand response paid by the load aggregator to the user, which is used to encourage the user to select the sub-load criterion as a guiding target to adjust the electricity consumption mode, thereby obtaining optimized power consumption data for various users;

[0011] Based on the load standard line, the first incentive signal and the power consumption data submitted by each load aggregator, a revenue model of the independent system operator is constructed with the goal of minimizing the operating cost of the independent system operator; wherein the power consumption data submitted by the load aggregator is the total power consumption data of all users after the load aggregator predicts the participation of the demand response incentive;

[0012] Based on the load criterion, the first incentive signal, the sub-load criterion, the second incentive signal and the power consumption data optimized by the user, a profit model of the load aggregator is constructed with the goal of maximizing the profit of the load aggregator; wherein the power consumption data uploaded by the user is the power consumption data predicted after each user participates in the demand response incentive;

[0013] Based on the second incentive signal and the user's own power data, a user-side profit model is constructed with the goal of maximizing the benefits obtained from participating in demand response and the utility of power consumption;

[0014] Solve the revenue models of independent system operators, load aggregators and user-side entities to obtain optimized first incentive signals and second incentive signals.

[0015] Furthermore, the load aggregator decomposes the load benchmark into multiple sub-load benchmarks based on the user's historical electricity consumption behavior, specifically:

[0016] Obtain historical load curves of several users and perform normalization processing, then cluster the normalized load curves to obtain multiple typical power consumption patterns;

[0017] The objective function is constructed to minimize the demand response cost of adjustable loads in each power consumption mode:

[0018]

[0019] In the formula, is the load at time t in the load curve corresponding to the i-th power consumption mode, represents the time in the ith sub-load line The load value; is the set of time points in the load curve, It is a collection of multiple typical electricity consumption modes; is the proportion of electricity consumption of users in the i-th electricity consumption mode;

[0020] Create constraints:

[0021]

[0022] In the formula, is the load guideline published by the independent system operator at time The value of .

[0023] Furthermore, the revenue model of the independent system operator is constructed, specifically:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] In the formula, Represents the operating costs of the independent system operator, Representing independent system operators to aggregators The first excitation signal provided, Is the independent system operator to the aggregator Demand response services provided; is a collection of aggregators; For Aggregators At the moment Power consumption; Euclidean distance Used to describe aggregators The similarity between the power curve and the load line, Represents the correction coefficient of similarity; Payments on behalf of independent system operators for regulation costs to conventionally regulated units; Representing Aggregators At the moment Power consumption With load alignment The Euclidean distance between , and Represents the cost factor.

[0030] Furthermore, a revenue model for load aggregators is constructed, specifically:

[0031]

[0032]

[0033]

[0034] In the formula, Representing Aggregators of income, Representing independent system operators to aggregators The first excitation signal provided, Is the independent system operator to the aggregator Demand response services provided; On behalf of the aggregator The second stimulus signal provided by the user, Is the aggregator to Demand response services provided to users of this category, is a collection of user categories; Indicates Class user at time The power consumption, Used to describe the Power curve for class users Alignment with sub-load The similarity between Represents the correction coefficient of similarity, Indicates Sub-load guidelines for class users At the moment power consumption.

[0035] Furthermore, a user-side revenue model is constructed, specifically:

[0036]

[0037]

[0038]

[0039]

[0040] In the formula, Representative The benefits of class users, On behalf of the aggregator The second stimulus signal provided by the user, Is the aggregator to Demand response services provided to users of this category, Representative Class user at time The power consumption, Indicates Class user at time The utility obtained from electricity consumption, Indicates the maximum load power consumption capability on the user side. Indicates Class users in time period The total power consumption within and User dependent parameters.

[0041] Further, the types of load aggregators include industrial, commercial and residential.

[0042] Beneficial Effects

[0043] The present invention considers three independent entities at different levels, including ISO, load aggregators and DR users. First, ISO issues CDL as the regulation demand of the entire system based on the balance of supply and demand of the power system. CDL provides guidance for different load aggregators as the regulation target. Secondly, the load aggregator decomposes CDL into different SCDLs according to the different characteristics of the users under its jurisdiction, which are used to provide guidance for users with different electricity consumption characteristics, thereby reducing the regulation cost of adjustable users and fully releasing resource flexibility. Thirdly, each level has to pay the regulation cost and earn monetary incentives in the process of implementing demand response. The various subjects of the present invention establish a profit model based on the interaction between different levels, and then finally reach a state of interest equilibrium through game theory. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is the demand response mechanism architecture applicable to the sub-load criterion of the present invention;

[0045] Figure 2 The demand response implementation process applicable to the sub-load standard line described in the embodiment of the present invention;

[0046] Figure 3 is a schematic diagram of interest analysis of different entities described in an embodiment of the present invention;

[0047] Figure 4 This is the response effect of the case 1 described in the embodiment of the present invention where the power grid issues a unified CDL to users with four power consumption modes, where (a), (b), (c), and (d) correspond to four different power consumption modes respectively;

[0048] Figure 5It is the SCDL response result of the aggregator to the four power usage modes of the DR user agent in Case 2 of the embodiment of the present invention, where (a), (b), (c), and (d) correspond to four different power usage modes respectively;

[0049] Figure 6 It is the SCDL response result of the aggregator to the four power usage modes of the DR user agent in Case 3 of the embodiment of the present invention, where (a), (b), (c), and (d) correspond to four different power usage modes respectively;

[0050] Figure 7 is the regulation cost of the ISO to the three groups of load aggregators in different cases;

[0051] Figure 8 is the revenue of the load aggregator in different cases;

[0052] Fig. 9 is the benefit to the demand response users in different cases;

[0053] Fig.10 is the demand response adjustment cost of the demand response users in different cases. DETAILED DESCRIPTION

[0054] The following is a detailed description of an embodiment of the present invention. This embodiment is based on the technical solution of the present invention, and provides a detailed implementation method and a specific operation process to further explain the technical solution of the present invention.

[0055] The embodiment of the present invention provides a demand response incentive method applicable to sub-load lines, spanning three levels of independent system operators (demand response centers), load aggregators, and registered users, and aims to achieve win-win results at all levels through coordination and game between entities at the three levels. Specifically, the independent system operator ISO issues CDL as the regulation demand of the entire system based on the balance of supply and demand of the power system, and CDL provides guidance for different load aggregators as a regulation target. The load aggregator decomposes the CDL into different SCDLs according to the different characteristics of the users under its jurisdiction. SCDL provides guidance for users with different electricity consumption characteristics, aiming to reduce the regulation cost of adjustable users as much as possible and thus fully release resource flexibility. Each level has to pay the regulation cost and earn monetary incentives during the DR implementation process. Each subject at different levels eventually reaches a state of interest equilibrium through game.

[0056] 1. Independent system operators (ISOs) formulate and publish load guidelines and incentive signals

[0057] In recent years, some scholars have proposed using "adjustable resources" on the demand side to balance the regulation demand from non-adjustable resources and calculate the expected load curve of adjustable users, namely the load directive line (CDL). CDL, as the regulation target of demand response users, can measure the contribution of "adjustable resources" to the balance of supply and demand.

[0058] 2. ISO Revenue Model

[0059] After the grid entity (belonging to the independent system operator) calculates the load benchmark according to the supply and demand balance, it realizes the demand response power balance by purchasing DR services from the aggregator (LA) entity. It should be noted that if the aggregator cannot provide complete DR regulation services, that is, the total load curve after the aggregator's integration still deviates from the load benchmark, then the grid center will smooth out the remaining deviation by dispatching adjustable units. Since it is costly to mobilize adjustable units to smooth out non-adjustable fluctuations, the goal of the grid center is to achieve the regulation target as much as possible on the basis of fully tapping the flexibility resources of the aggregator.

[0060] Therefore, the goal of the power grid center is to minimize the operating cost, including the power generation cost of conventional adjustable units and the cost of purchasing DR services from load aggregators. As shown in formula (6):

[0061] (6) (7) (8) (9) (10)

[0062] In the formula, represents the operating cost of the independent system operator; the first term of formula (6) represents the cost of the grid center purchasing DR services from the aggregator, and the second term represents the regulation cost paid by the grid center to the conventional adjustable units, in the widely used form of formula (10);

[0063] Representing independent system operators to aggregators providing a first excitation signal; Is the independent system operator to the aggregator The demand response service provided can be obtained by formula (7); is a collection of aggregators; For Aggregators At the moment Power consumption; Euclidean distance Used to describe aggregators The similarity between the power curve and the load line; The correction coefficient representing the similarity is usually 500-1000; Representing Aggregators At the moment Power consumption With load alignment The Euclidean distance between , and Represents the cost factor.

[0064] 3. Load Aggregator (LA) Decomposition Calculation Sub-Load Line

[0065] Load aggregators with flexible resources play a dual role, both as DR service providers for ISOs and as purchasers of DR services for adjustable users. Load aggregators can be understood as large DR users. Aggregators include a large number of flexible loads such as industrial and commercial users, and can optimize the overall load power curve by dispatching the rich flexible adjustable resources within their footprints according to the regulation targets issued by ISOs. Once the guidance signal and incentive signal issued by ISO are received, the aggregator first evaluates the response potential of the adjustable loads under its jurisdiction, and then decomposes CDL according to the power consumption characteristics of different adjustable loads to formulate SCDL that minimizes the demand response regulation cost of adjustable loads. SCDL is issued to user groups as a regulation signal for adjustable loads to guide flexible users within its jurisdiction to adjust their power consumption behavior in response. In addition, load aggregators pay the purchase fee of DR services for user groups with different power consumption behaviors according to their proximity to the sub-pivot, that is, the incentive signal issued to the user group. The income of load aggregators mainly includes DR incentives obtained from ISOs and DR purchase costs paid to user groups.

[0066] The CDL formulated by ISO does not take into account the characteristics of users' electricity consumption behavior. In the actual implementation of demand response, due to the different electricity consumption characteristics of many users, the consumption patterns on the demand side are also different. Therefore, the deviation between the user's load curve and the CDL leads to significant demand response regulation costs. When the load curves of all users are fully matched with the CDL, the changes from renewable energy (RE) and inelastic loads are minimized. The electric energy between the user's load curve and the CDL, representing the power consumption required to approach the CDL, is defined as the regulation cost. This cost is quantified by calculating the norm of the difference between the vectors of the two curves. Due to the increase in marginal costs, the 2 norm is used. The mathematical definition of the user's demand response regulation cost is shown in formula (1).

[0067] (1)

[0068] in, and Respectively represent the load line CDL and user load curve at time The power value at the moment.

[0069] When the unified load criterion is used as the target response, the response results of large-scale adjustable loads may deviate greatly from the unified load criterion. Only a small number of users can achieve the system's regulation target by slightly adjusting the load curve, and most choose not to participate in the response due to the high regulation cost. In order to fully mobilize the user's response enthusiasm, the demand response center has to issue higher incentives to loads with weak enthusiasm, which greatly weakens the economic efficiency of the system while failing to guarantee the response effect.

[0070] Therefore, affected by the differences in electricity consumption characteristics on the demand side, the unified load standard line cannot guide the adjustable load to participate in demand response in a personalized manner. The load aggregator (LA) of the present invention decomposes the CDL based on the user's historical load curve to obtain several sub-load standards (SCDL), each SCDL represents a typical electricity consumption mode.

[0071] First, define the shape of the load curve: is a load curve, in the period The total amount of electricity inside is written as formula (2).

[0072] (2)

[0073] Total power For the benchmark Normalize to get ,Right now After standardization algorithm The value after is written as formula (3).

[0074] (3)

[0075] The load power characteristics clustering method is used to find the standardized historical load curve Power consumption mode, Typical load curves under different power consumption modes .in, It is the per-unit value after removing the dimension, which only retains the curve shape characteristics of the power consumption mode. The proportion of electricity consumption of users in this electricity consumption mode is , represents the power consumption weight under the i-th power consumption mode.

[0076] After obtaining typical load curves under various typical power consumption modes through clustering, the sub-load curves under various typical power consumption modes are solved by constructing the objective function and establishing constraints:

[0077] (1) Objective function: The decomposition objective is to minimize the demand response cost of adjustable loads under each type of power consumption mode. That is, the weighted sum of the squared Euclidean distance between the typical curve under each type of power consumption mode and the SCDL obtained by its decomposition is minimized, as expressed in formula (4). This is equivalent to dividing user curves with the same power consumption mode into a category, in which the user curve selects the corresponding SCDL to achieve the minimum regulation cost. Under the guidance of SCDL, the shape of the total load curve after response will be much closer to CDL.

[0078] (4)

[0079] In the formula, is the load at time t in the typical load curve corresponding to the i-th power consumption mode; is the set of time points in a typical load curve, It is a collection of multiple electricity usage modes; is the proportion of electricity consumption of users in the i-th electricity consumption mode; It represents the load value at time t in the i-th sub-load criterion, which is sent by the power grid entity to the user entity under its jurisdiction, in response to the user's rational decision-making on that day to select the SCDL that conforms to its own power consumption pattern as the guidance target.

[0080] (2) Constraints: The constraints are shown in formula (5). During the decomposition process, it is ensured that The SCDL value and the corresponding power consumption mode category power weight The sum after multiplication is equal to the time The CDL value. The SCDL is equivalent to the CDL in terms of guidance. The value of is between [0,1] and the cumulative sum is 1.

[0081] (5)

[0082] In the formula, is the load guideline published by the independent system operator at time The value of .

[0083] 4. Load Aggregator Revenue Model

[0084] For the aggregator entity, on the one hand, it is necessary to maximize the incentives it obtains from the power grid, which is determined by the similarity between the aggregator's total power consumption curve and the load criterion. On the other hand, the aggregator should minimize the incentive cost issued to the DR user entity, so the aggregator's revenue function is shown in formula (11).

[0085] (11)

[0086] (12)

[0087] (13)

[0088] The first term of formula (11) represents the cost of purchasing DR services from the aggregator on behalf of the power grid center, and the second term represents the aggregator Incentive fees issued to the user groups under jurisdiction.

[0089] In the formula, Representing Aggregators of income, Representing independent system operators to aggregators The first excitation signal provided, Is the independent system operator to the aggregator Demand response services provided; On behalf of the aggregator The second stimulus signal provided by the user, Is the aggregator to Demand response services provided to users of this category, is a collection of user categories; Indicates Class user at time The power consumption, Used to describe the Power curve for class users Alignment with sub-load The similarity between Indicates Sub-load guidelines for class users At the moment power consumption.

[0090] 5. Users and their revenue model

[0091] User aggregators with flexible resources provide DR regulation services. User-level entities such as virtual power plants, industrial and commercial users can be certified as DR users through registration, while unregistered users are regarded as non-adjustable loads. After receiving the SCDL issued by the aggregator, the user rationally chooses a sub-line closest to his own electricity consumption behavior as the regulation target to participate in the response, thereby reducing the regulation cost. In terms of benefits, the user group mainly obtains the incentive signals issued by the aggregator and the electricity benefits generated from its own energy consumption.

[0092] The goal of user decision-making is to maximize its power benefits, including the benefits obtained from participating in DR and the utility of power consumption under corresponding constraints, as shown below:

[0093] (14)

[0094] (15)

[0095] (16)

[0096] In the formula, Representative The benefits of class users, On behalf of the aggregator The second stimulus signal provided by the user, Is the aggregator to Demand response services provided to users of this category, Representative Class user at time The power consumption, Indicates Class user at time The utility obtained from electricity consumption, Indicates the maximum load power consumption capability on the user side. Indicates Class users in time period The total power consumption within.

[0097] The decrease in power consumption utility indicates an increase in user comfort loss. Without loss of generality, the power consumption utility of this embodiment adopts a piecewise quadratic utility function, as shown in formula (17):

[0098] (17)

[0099] In the formula, and The user-dependent parameters with different values ​​in different time periods describe the user's time-varying power consumption preferences.

[0100] 6. Analysis of Subject Interests

[0101] By analyzing the objective function of each subject's profit model, we can find a compromise between each cost and benefit in the objectives of each optimization subject. For the power grid center, in order to minimize the operating cost, that is, to meet the system's regulation needs as much as possible by dispatching flexible resources, it is necessary to increase the incentives for load aggregators to improve the load regulation performance, which will also lead to higher fees for load aggregators. For load aggregators, in order to maximize their profits, they need to increase their own profits from the power grid center as much as possible, while reducing the payment of DR service fees to users, but increasing the payment of DR service fees to users can also increase the profits obtained from the power grid center. Similarly, users also need to make a trade-off between their power consumption utility and the benefits they get from aggregators, because choosing sub-pivots to participate in the response to subsequent performance will increase their income, but it will also lead to utility losses.

[0102] Therefore, the incentives provided by the power grid center will affect the regulation performance of users under the control of the load aggregator, while the incentives provided by the load aggregator will affect the flexibility provided by users. Conversely, the user's response will affect the load aggregator's incentive adjustment, thereby affecting the DR purchase fee and incentive signal paid by the power grid center to the aggregator. The interaction between the above entities can be summarized as follows: the power grid center issues incentive signals to each load aggregator , each aggregator submits the optimized total power consumption vector In response to receiving the incentive signal, the aggregator also issues an incentive signal to the user group. , the user publishes the optimized self-power consumption vector according to the selected sub-line as the guiding target In response to receiving an excitation signal.

[0103] 7. Response implementation process

[0104] The present invention is applicable to the demand response incentive method of sub-load lines. First, the ISO formulates a unified load line CDL based on the renewable energy and non-adjustable load curves in the system, and the ISO issues the CDL to the load aggregator. The aggregator manages many types of DR user resources. It predicts the adjustable load curve according to the adjustment capabilities of the users under its jurisdiction and formulates the SCDL according to the historical electricity consumption information of the users. The aggregator reports the predicted user adjustable power curve to the ISO and publishes the SCDL to the user group. The ISO issues subsidies for the reporting of the aggregator, and the aggregator issues subsidies according to the user's response. In this process, the shape of the curve reported by the aggregator and the user's response are changing dynamically. At the same time, the incentives issued by the ISO to the aggregator and the incentives issued by the aggregator to the user group are also dynamically adjusted, and finally a three-level interest equilibrium is achieved.

[0105] 8. Experimental comparison

[0106] Using real data from the United States, a city-level test system was developed to verify the proposed DR scheme. It includes three large load aggregators (LA) for industrial, commercial, and residential users. The user parameters are extended to different time intervals to form a 1-hour power consumption sequence for 100 users for a total of 24 hours per day for 31 days in 2022. The SCDL was developed using the data from the first 30 days, and the SCDL-based demand response deployment was tested using the data from the 31st day. Numerical tests were performed using MATLAB 2021a and gurobi 9.0 on a laptop with an Intel Core i7-8565U 1.80 GHz CPU and 8 GB of memory.

[0107] On the basis of developing the test system, the effects of the DR mechanism and incentive method based on SCDL are discussed through simulation tests of the urban system. The performance comparison analysis of the method proposed in this paper is carried out, and the effects of reducing the cost of user demand response adjustment and the solutions for balancing the interests of different subjects are compared from three levels.

[0108] The effectiveness of the demand response incentive method for SCDL of the present invention is verified through three examples. In Case 1, ISO directly issues CDL to the user entity (no load aggregator formulates SCDL or incentive signal). In Case 2, ISO issues CDL to the load aggregator entity, and the load aggregator issues SCDL to the user entity without an incentive signal. Case 3 adopts the method proposed by the present invention.

[0109] 8.1 SCDL Response Results

[0110] Figure 4 The example shows the response effect of the power grid issuing a unified CDL to users with four electricity consumption modes in case 1. Figure 5 , Figure 6 The SCDL results of the four electricity consumption patterns of the aggregator for DR user agents in Case 2 and Case 3 are shown respectively. From the characteristics of electricity consumption patterns, the curves of Mode 1 and Mode 4 fluctuate weakly throughout the day, while the curves of Mode 2 and Mode 3 fluctuate greatly throughout the day.

[0111] Comparing the DR regulation cost of users in different cases, we can find a large area between the load power usage and CDL in Case 1, which is especially obvious in Modes 2 and 3, indicating that it is costly for users to keep consistent with CDL. Cases 2 and 3 use SCDL as a guide from LA1 to users. Figure 7 and Figure 8 It shows a significant reduction in the shaded area compared to Case 1, indicating that the use of SCDL effectively reduces the user's DR regulation cost. In Case 3, the user's load profile is closer to SCDL than Case 2, which shows that under the proposed incentive method, the user further optimizes their load profile. This can improve the response efficiency in each power consumption mode.

[0112] 8.2 Classification of power consumption patterns

[0113] The following gives the interests of different entities across the three layers, including the costs and benefits of ISO, load aggregators, and users. LA1, LA2, and LA3 represent different types of users in the urban system. By analyzing the power consumption behavior of users, the users represented by LA1 are divided into 4 power consumption patterns, and the users represented by LA2 and LA3 are divided into 2 and 3 historical power consumption patterns, respectively.

[0114] (1) ISO benefit analysis

[0115] Figure 7 The regulation costs paid by the ISO to three load aggregators under three scenarios are shown. Comparing Case 1, Case 2, and Case 3, it is obvious that the demand response regulation costs of the ISO in Case 3 are significantly reduced. This reduction is due to the ISO's incentives to load aggregators, which in turn encourages them to explore and utilize user flexibility. By issuing SCDL and providing incentives to users, load aggregators can more closely align the comprehensive load power consumption with the ISO's overall regulation target CDL. As a result, the ISO needs to spend less on thermal power unit regulation and achieve better DR effects at a lower cost.

[0116] It is clear from the fees paid by the ISO to LA1, LA2, and LA3 that the ISO pays less for LA1 and more for LA3. This indicates that the electricity consumption behavior of users in LA1 is closest to the ISO's regulation target. In contrast, users in LA3 are the least similar to CDL, which results in higher regulation costs for the ISO.

[0117] (2) Analysis of the benefits of load aggregators

[0118] Figure 8 The revenues of different load aggregators (LAs) for the three cases are shown. The revenues of LAs consist of two parts: the cost paid by the ISO for the DR service and the incentives provided by the LAs to users. Comparing the three cases, it is clear that only the LAs generate revenue in Case 2 and Case 3, while the revenue is higher in Case 3. This shows that, using the approach proposed in this paper, the ISO issues incentives to the load aggregators, who then issue SCDLs and provide incentives to users. This coordination among the three layers balances the interests, which is reflected in the higher revenue of the load aggregators in Case 3.

[0119] (3) User benefit analysis

[0120] Fig. 9 The income of users represented by different LAs in three cases is shown. Comparing the two cases, it is obvious that users in case 3 have higher benefits in all LAs. As shown in equation (13), the benefit includes the benefits of participating in DR and power consumption efficiency. Users represented by LA1 and LA2 have higher income than users represented by LA3, indicating that their electricity consumption behavior is closer to the regulatory target. In contrast, users represented by LA3, whose behavior is not very similar to the target, have lower income.

[0121] Fig.10The DR regulation costs of users represented by different LAs in the three cases are shown. Compared with the two cases, users in case 3 have higher DR regulation costs in all LAs. Users represented by LA1 and LA2 have lower regulation costs than users represented by LA3, indicating that their behavior is more consistent with the regulation goal. This inverse relationship between DR regulation costs and revenue corresponds to the previous analysis.

[0122] The above embodiments are preferred embodiments of the present application. Ordinary technicians in this field can also make various changes or improvements on this basis. Without departing from the overall concept of the present application, these changes or improvements should fall within the scope of protection required by the present application.

Claims

1. A demand response incentive method applicable to a sub-load line, characterized in that: include: The independent system operator formulates an expected load curve, i.e., a load benchmark, and publishes the load benchmark and a first incentive signal to the load aggregator; wherein the first incentive signal refers to the purchase fee of demand response paid by the independent system operator to the load aggregator, which is used to encourage each load aggregator to submit optimized power consumption data; The load aggregator decomposes the load criterion into multiple sub-load criterions according to the user's historical electricity consumption behavior, and publishes the sub-load criterion and the second incentive signal to the user; the second incentive signal refers to the purchase fee of demand response paid by the load aggregator to the user, which is used to encourage the user to select the sub-load criterion as a guiding target to adjust the electricity consumption mode, thereby obtaining optimized power consumption data for various users; Based on the load standard line, the first incentive signal and the power consumption data submitted by each load aggregator, a revenue model of the independent system operator is constructed with the goal of minimizing the operating cost of the independent system operator; wherein the power consumption data submitted by the load aggregator is the total power consumption data of all users after the load aggregator predicts the participation of the demand response incentive; Based on the load criterion, the first incentive signal, the sub-load criterion, the second incentive signal and the power consumption data optimized by the user, a profit model of the load aggregator is constructed with the goal of maximizing the profit of the load aggregator; wherein the power consumption data uploaded by the user is the power consumption data predicted after each user participates in the demand response incentive; Based on the second incentive signal and the user's own power data, a user-side profit model is constructed with the goal of maximizing the benefits obtained from participating in demand response and the utility of power consumption; Solve the revenue models of independent system operators, load aggregators and user-side entities to obtain optimized first incentive signals and second incentive signals.

2. The demand response incentive method according to claim 1, characterized in that: The load aggregator decomposes the load benchmark into multiple sub-load benchmarks based on the user's historical electricity consumption behavior, specifically: Obtain historical load curves of several users and perform normalization processing, then cluster the normalized load curves to obtain multiple typical power consumption patterns; The objective function is constructed to minimize the demand response cost of adjustable loads in each power consumption mode: ； In the formula, is the load at time t in the load curve corresponding to the i-th power consumption mode, represents the time in the ith sub-load line The load value; is the set of time points in the load curve, It is a collection of multiple typical electricity consumption modes; is the proportion of electricity consumption of users in the i-th electricity consumption mode; Create constraints: ； In the formula, is the load guideline published by the independent system operator at time The value of .

3. The demand response incentive method according to claim 1, characterized in that: Construct an independent system operator's revenue model, specifically: ； ； ； ； ； In the formula, Represents the operating costs of the independent system operator, Representing independent system operators to aggregators The first excitation signal provided, Is the independent system operator to the aggregator Demand response services provided; is a collection of aggregators; For Aggregators At the moment Power consumption; Euclidean distance Used to describe aggregators The similarity between the power curve and the load line, Represents the correction coefficient of similarity; Payments on behalf of independent system operators for regulation costs to conventionally regulated units; Representing Aggregators At the moment Power consumption With load alignment The Euclidean distance between , and Represents the cost factor.

4. The demand response incentive method according to claim 1, characterized in that: Construct a revenue model for load aggregators, specifically: ； ； ； In the formula, Representing Aggregators of income, Representing independent system operators to aggregators The first excitation signal provided, Is the independent system operator to the aggregator Demand response services provided; On behalf of the aggregator The second stimulus signal provided by the user, Is the aggregator to Demand response services provided to users of this category, is a collection of user categories; Indicates Class user at time The power consumption, Used to describe the Power curve for class users Alignment with sub-load The similarity between Represents the correction coefficient of similarity, Indicates Sub-load guidelines for class users At the moment power consumption.

5. The demand response incentive method according to claim 1, characterized in that: Construct a user-side revenue model, specifically: ； ； ； ； In the formula, Representative The benefits of class users, On behalf of the aggregator The second stimulus signal provided by the user, Is the aggregator to Demand response services provided to users of this category, Representative Class user at time The power consumption, Indicates Class user at time The utility obtained from electricity consumption, Indicates the maximum load power consumption capability on the user side. Indicates Class users in time period The total power consumption within and User dependent parameters.

6. The demand response incentive method according to claim 1, characterized in that: Types of load aggregators include industrial, commercial and residential.

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