An incentive method for demand response of multiple aggregators facing overlapping users and application thereof

Abstract

The application discloses a multi-aggregator demand response incentive method for overlapping users, which comprises the following steps: establishing a demand response model of users, a dissatisfaction model of users participating in demand response, and an optimal incentive model of aggregators implementing demand response; establishing a multi-aggregator cooperation game model considering overlapping users, and formulating a negotiation set in the cooperation game; regarding the overlapping users as users participating in the demand response plan of each aggregator, obtaining an incentive strategy S1 of each aggregator according to the response model of the users; regarding the overlapping users as users not participating in the demand response plan of each aggregator, obtaining an incentive strategy S2 of each aggregator according to the response model of the users; judging the optimal result of the negotiation set according to S1 and S2, and finally obtaining the best incentive of each aggregator under the condition of the overlapping users.

Description

Technical Field

[0001] This invention relates to the field of demand-side power demand response incentive strategy optimization, and in particular to a multi-aggregator demand response incentive method for overlapping users and its application. Background Technology

[0002] With rapid socio-economic development and technological progress, demand-side controllable resources such as distributed power sources, electric vehicles, distributed energy storage, air conditioning, and electric boilers are growing rapidly, greatly enriching the types and capacity of demand response resources. Furthermore, with the grid connection of a large amount of renewable energy with uncertain output and the further widening of peak-valley differences, electricity demand response, especially incentive-based demand response, has become an important means of addressing supply and demand balance. However, existing research on incentive-based demand response has not considered overlapping users. Therefore, demand response incentive strategies for overlapping users are a pressing issue that needs to be addressed, with the development of incentives for overlapping users by multiple aggregators being particularly important.

[0003] Existing electricity demand response methods mainly include price-based demand response and incentive-based demand response. Price-based demand response strategies are further divided into time-of-use pricing, peak-hour pricing, and real-time pricing. Time-of-use pricing is a common pricing strategy in China, effectively reflecting the cost differences in power supply during different time periods. Its main measures are to appropriately increase prices during peak hours and appropriately decrease prices during off-peak hours, reducing the peak-to-valley load difference, improving user electricity consumption, and achieving peak shaving and valley filling. However, because the time periods for time-of-use pricing are fixed, and the price ratios for each time period are also fixed, it can only reflect the statistical regularity of daily load and power supply costs over a period of time, and cannot accurately reflect the changes in load and power supply costs at different times of the day. Incentive-based demand response involves market participants releasing subsidy signals to users to guide them to increase or decrease electricity consumption during specific periods, thereby achieving supply and demand balance. In existing research, Ghazvinia, MAF et al. published "A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers" in the journal Appl. Energy. This model models incentive-based demand response programs to effectively address the risks of market price and load fluctuations, determining the optimal hourly incentive to be provided to end users to minimize peak demand. Yu, M. et al. published "An incentive-based demand response model considering composited DR resources" in the journal IEEE Trans. Ind. Electron. This model proposes an incentive-based demand response model for intraday scheduling by power grid companies, mitigating system imbalances at the lowest cost by offering different incentive prices to large industrial and residential customers. Lu, R. et al. published "Incentive-based demand response for smart grid with reinforcement learning and deep neural network" in the journal Appl. Energy. This model proposes a real-time incentive-based demand response model that helps service providers purchase energy resources from different customers while considering the profits of both service providers and users, and uses reinforcement learning to obtain the optimal incentive rate for different customers. While the above studies have taken into account consumer diversity, they have overlooked the impact of overlapping users on the pricing strategies of multiple aggregators.

[0004] Currently, the uncertainty of user responses in incentive-based demand response (DR) is a thorny issue for aggregators. Regarding user uncertainty in DR: Kwac, Rajagopal et al. published "Data-driven targeting of customers for demand response" in IEEE Trans. Smart Grid, using a stochastic expected value model to describe user uncertainty. Kwac, Kim et al. published "Efficient customer selection process for various DR objectives" in IEEE Trans. Smart Grid, investigating how to effectively select users for specific types of incentive-based demand response plans with different objectives based on hourly energy consumption data. While these studies consider the uncertainty of user participation in incentive-based demand response, they neglect the uncertainty arising from overlapping users when related aggregators (aggregators with the same users) maintain their pricing strategies. To address this uncertainty, cooperation among related aggregators is necessary.

[0005] Furthermore, most studies model demand response planning as a multi-agent, multi-objective, and multi-level optimization problem. Therefore, applying game theory to demand response modeling is very appropriate. Regarding game theory-based interaction models in demand response: Lu, Q., Lü et al. published "A nash-stackelberg game approach in regional energy market considering users' integrated demand response" in the journal *Energy*, modeling the interaction mechanism between two retailers and two users as a Nash-Stackelberg game model with two leaders and two followers. This model considers the game between the two retailers, but only one user can participate in the integrated demand response, and the two types of retailers have different priorities in the energy market. Shinde, P., Swarup, KS et al. published "Stackelberg game-based demand response in multiple utility environments for electric vehicle charging" in the journal *IETElectr.Syst.Transp*, proposing a Stackelberg game-based demand response model in a multi-power company environment, considering the non-cooperative game between multiple power companies. However, this model is based on price-based demand response rather than the more flexible incentive-based demand response. In summary, existing literature does not study the game behavior between different aggregators targeting overlapping users.

[0006] Finally, existing technologies, such as Chinese patent application CN110728410A, involve an economic dispatch method for load aggregators that takes into account the flexibility and uncertainty of demand response. This method employs multi-period dispatch and utilizes renewable energy incentive prices and its own flexibility to arrange dispatch, thereby maximizing the revenue of load aggregators. However, this method models the day-ahead economic scheduling among load aggregators as a completely informational, static, non-cooperative game problem. In reality, it is impossible for aggregators to achieve complete information disclosure in a non-cooperative context. This method also ignores the existence of overlapping users among load aggregators and does not consider the uncoordinated pricing strategies caused by these overlapping users. Existing technologies, such as Chinese patent application CN111952978A, involve a demand response incentive method and system considering user response characteristics. It establishes a two-layer interaction model with the optimal incentive model for aggregators as the upper layer and the user demand response model as the lower layer. However, it only analyzes the case of a single aggregator and multiple users, ignoring the game behavior between multiple aggregators and failing to consider the existence of overlapping users among aggregators. Existing technologies, such as Chinese patent application CN114048911A, propose a non-cooperative game-based optimization scheduling method based on load aggregator classification. It establishes a non-cooperative game optimization model based on load aggregator classification according to game behavior, and establishes objective functions on the grid side and load aggregator side, based on NSGA-I. This method combines multi-objective optimization algorithms and interior point generation algorithms to solve for the optimal configuration between Nash equilibrium and unit output status. While classifying load aggregators into commercial and residential types, it neglects the existence of overlapping users among aggregators of the same type, leading to a deviation between the constructed model and the actual situation. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention discloses a multi-aggregator demand response incentive method for overlapping users, the technical solution of which is as follows:

[0008] A multi-aggregator demand response incentive method for overlapping users, characterized by the following steps:

[0009] Step 1: Establish a user demand response model, a user dissatisfaction model for participating in demand response, and an optimal incentive model for aggregators to implement demand response;

[0010] Step 2: Consider the multi-aggregator cooperative game model with overlapping users, and formulate the negotiation set model in the cooperative game;

[0011] Step 3: Each aggregator first considers overlapping users as participants in its demand response plan, and based on the user response model, derives the minimum incentive strategy for each aggregator.

[0012] Step 4: Each aggregator treats overlapping users as users who do not participate in their own demand response plan, and derives the maximum incentive strategy for each aggregator based on the user response model.

[0013] Step 5: According to and The optimal result of the negotiation set is determined, and the best incentive for each aggregator is obtained when there are overlapping users.

[0014] The present invention also discloses a non-volatile storage medium, characterized in that the non-volatile storage medium includes a stored program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute the above-described method.

[0015] The present invention also discloses an electronic device, characterized in that it comprises a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the computer-readable instructions execute the method described above.

[0016] Beneficial effects

[0017] To address the issue of uncoordinated pricing strategies among multiple aggregators when overlapping consumers join an incentive-based demand response program, this invention proposes a multi-aggregator demand response incentive method for overlapping users and constructs a negotiating cooperative game model among multiple aggregators for overlapping users.

[0018] The innovation and beneficial effects of this invention mainly lie in the establishment of a multi-aggregator cooperative game model and corresponding negotiation set model considering overlapping users in step 2 of the technical solution. Although game theory has been widely applied in different demand response scenarios, existing game models do not consider the uncoordination and uncertainty scenarios caused by overlapping users. This patent solves the above problems by establishing a negotiating cooperative game model among multiple aggregators oriented towards overlapping users. In the game, multiple aggregators cooperate based on a negotiation set. The negotiation set benefits all participants, standardizes the pricing mechanism among multiple aggregators considering overlapping users, and solves the problem of uncoordination pricing strategies among multiple aggregators. Furthermore, the participants' pricing strategies are public to the collaborators, thus eliminating the uncertainty caused by incomplete information. Attached Figure Description

[0019] Figure 1 A schematic diagram illustrating the specific application of the multi-aggregator demand response incentive method for overlapping users;

[0020] Figure 2 A flowchart for calculating incentive strategies for multi-aggregator demand response to overlapping users. Detailed Implementation

[0021] This invention provides a multi-aggregator demand response incentive method for overlapping users, as shown in the appendix. Figure 1-2 The method includes:

[0022] Step 1: Establish a user demand response model, a user dissatisfaction model for participating in demand response, and an optimal incentive model for aggregators to implement demand response;

[0023] 1) User demand response model

[0024]

[0025] In the formula: U i,t (π i,t ,x i,t Let π be the effect function of user i at time t; i,t x is the unit incentive that the aggregator provides to user i at time t; i,t Let ω be the response of user i at time t; i,t The weighting factor is used to reflect the degree of importance that user i attaches to energy satisfaction. For the response quantity x i,t The function for user dissatisfaction; x i,t The lower limit;

[0026] 2) User dissatisfaction model in demand response

[0027] User dissatisfaction is quantified by the following formula:

[0028]

[0029] In the formula: where θ i and λ i These are user-related parameters that reflect users' willingness to participate in incentive-based demand response programs. θ i Users with smaller values ​​than θ i Users with higher values ​​are willing to provide more and demand decreases. i This represents the minimum incentive price at which a user is willing to participate in the aggregator's incentive-based demand response. Equation (4) represents the dissatisfaction cost for users when reducing their demand.

[0030] 3) Optimal incentive model for aggregators to implement demand response:

[0031]

[0032] in: The incentive subsidy given to users by aggregator k; ΔL t The target power reduction for aggregator k during time period t.

[0033] Based on the modeling of users and aggregators, aggregators were randomly combined, with multiple aggregators equivalent to two. A system with two aggregators and 200 users was then analyzed. (Users are...) and The three types of users' electricity consumption parameters are shown in Table 1. The total reduction required by the two aggregators is 400kW, of which the target reduction power of aggregator 1 is ΔL1=400×Λ%, and the target reduction power of aggregator 2 is ΔL2=400×(1-Λ%), where Λ is the response task allocation ratio coefficient issued by the power grid company to the aggregators.

[0034] Table 1 User Parameters of All Types

[0035]

[0036] Step 2: Consider the multi-aggregator cooperative game model with overlapping users, and formulate the negotiation set model in the cooperative game.

[0037] 1) Multi-aggregator cooperative game model:

[0038]

[0039] Where: Γ represents the core of the cooperative game; {π1}, {π2} are the game strategies of the aggregator; {U DRA,1},{U DRA,2} is the objective function of the aggregator; s i It is a Boolean variable indicating whether overlapping users respond to the aggregator; These represent the sets of non-overlapping users managed by aggregators 1 and 2, respectively. These represent the sets of overlapping users managed by aggregators 1 and 2, respectively.

[0040] 2) Negotiation set model for cooperative game

[0041]

[0042] Where: G represents the negotiation set in the cooperative game; It is the minimum incentive price for aggregator 1. It is the maximum incentive price for aggregator 1; It is the minimum incentive price for aggregator 2; It is the maximum incentive price for aggregator 2; π coop This refers to the incentive price for cooperation between aggregators, excluding the first two scenarios of negotiation.

[0043] Step 3: Each aggregator first considers overlapping users as participants in its demand response plan, and based on the user response model, derives the minimum incentive strategy for each aggregator.

[0044]

[0045] In the formula, The minimum incentive strategies for aggregators 1 and 2, respectively, together constitute the minimum incentive strategies for all aggregators. Indicates that there is Each user will only have the opportunity to participate in the aggregator's incentive-based demand response program. Indicates that there is Each user only has the opportunity to participate in the Aggregator 2 Incentive Demand Response Program, while Indicates that there is One overlapping user gains the opportunity to participate in two aggregator incentive-based demand response programs.

[0046] Step 4: Each aggregator treats overlapping users as users who do not participate in their own demand response plan, and derives the maximum incentive strategy for each aggregator based on the user response model.

[0047]

[0048] In the formula, The maximum incentive strategies for aggregators 1 and 2, respectively, together constitute the maximum incentive strategies for all aggregators. This indicates that overlapping users did not have the opportunity to participate in the incentive-based demand response programs of both aggregators.

[0049] Step 5: Based on and The optimal result of the negotiation set is determined, and the best incentive strategy for each aggregator is obtained when there are overlapping users.

[0050]

[0051] Equation (8) is a further improvement on the cooperative game negotiation set of Equation (5) based on the calculation of the maximum and minimum incentive strategies of aggregators 1 and 2 in Equations (6) and (7).

[0052] In step 1, this invention establishes a user demand response model, a user dissatisfaction model for participating in demand response, and an optimal incentive model for aggregators to implement demand response. It then provides the original data for each user's participation in demand response and the response task data for aggregators after market clearing, giving the user and aggregator basic models established in step 1 practical significance. Step 2, based on step 1, further considers the uncoordinated impact of overlapping users on the formulation of incentive strategies among aggregators and the impact of the uncertainty of overlapping user responses on aggregator costs. It establishes a multi-aggregator cooperative game model considering overlapping users and formulates a negotiation set model in the cooperative game. Steps 3 and 4 provide the specific solution process for the maximum and minimum incentive strategies of aggregators in the negotiation set of step 2, further improving the construction of the negotiation set in step 2. Step 5 uses the minimum incentive strategy calculated in steps 1-4. With maximum incentive strategy The optimal result of the negotiation set is determined, and the best incentive strategy for each aggregator is obtained when there are overlapping users.

[0053] To make the conclusions of this invention more convincing, the following is a proof that the Nash equilibrium exists in the multi-merchant negotiating cooperative game proposed in this patent. In negotiating cooperative games, the construction of the negotiation set is very important; it must be beneficial to all participants, otherwise cooperation cannot be achieved.

[0054] The most obvious benefit of collaboration among aggregators is that it resolves the uncertainty caused by overlapping users, ensuring zero deviation in the aggregator's target reduction power. Furthermore, it addresses the issue of incompatible incentive prices between two aggregators. Finally, collaboration can reduce the total cost for each aggregator, as demonstrated below.

[0055] If the two aggregators do not cooperate, the strategies to avoid penalizing the two aggregators are shown in equations (9) and (10).

[0056]

[0057]

[0058] In the formula ε D,1 ε D,2 These are the deviations between the target power reduction and the actual power reduction caused by the uncertainty of overlapping users. When ε D,1 ,ε D,2 When ε > 0, aggregators 1 and 2 must pay a penalty fee because the reduction is insufficient. D,1 ,ε D,2 When the deviation is less than 0, aggregators 1 and 2, although not penalized, still need to pay users additional reduction fees. Therefore, the optimal strategy for aggregators 1 and 2 is to make the deviation zero, i.e., ε. D,1 ,εD,2 =0.

[0059] exist In this scenario, when the two aggregators do not cooperate, aggregator 2's actual power reduction will be greater than its target power reduction because... Their total cost can be expressed as:

[0060]

[0061]

[0062] However, in In this scenario, if the two aggregators cooperate according to the negotiation set in equation (8), the total cost of the two aggregators can be expressed as:

[0063]

[0064]

[0065] Subtracting equation (13) from equation (11) and subtracting equation (14) from equation (12) yields the following equation:

[0066]

[0067]

[0068] According to equation (11), Therefore, we can obtain ΔC. DRA,1,s1 =0≥0. From equation (12), we can see that, Therefore, ΔC DRA,2,s1 >0. Furthermore, Equation (16) shows that the actual incentive price of aggregator 2 is higher than the optimal incentive price. If the two aggregators do not cooperate with each other, the reduction power of overlapping users will be wasted.

[0069] As stated above, it can be concluded that the negotiation set is suitable for the scenario. Both aggregators in the scenario benefit (although aggregator 1's cost is higher in the scenario). The cost remains unchanged in the standard scenario, but decreases in other scenarios. Similarly, in scenarios... This conclusion can also be proved using a similar method.

[0070] exist In this scenario, due to The total cost when the two aggregators do not cooperate can be expressed by equations (17) and (18):

[0071]

[0072]

[0073] If two aggregators cooperate according to the negotiation set in equation (8), the total cost of the two aggregators can be expressed as:

[0074]

[0075]

[0076] Since cooperation can reduce the aggregator's target power reduction deviation to zero, we can obtain:

[0077]

[0078]

[0079] It is easy to see that the incentive price for cooperation must be less than the maximum incentive price of the two aggregators, that is... Therefore, we can obtain and

[0080] As mentioned above, we can conclude that in the scenario Under these conditions, the negotiation set is advantageous to both aggregators. Furthermore, this conclusion holds true in scenario... A similar proof can be obtained in [the following context]. In summary, the negotiation set can benefit all participating aggregators.

[0081] This invention proposes a multi-aggregator demand response incentive method for overlapping users, constructing a negotiating cooperative game model among aggregators with overlapping users. In the game, the negotiation set is designed as an unbiased agreement, benefiting all participants. The proposed method can effectively solve the problems of inconsistent pricing strategies among related aggregators and uncertain responses from overlapping users.

[0082] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-aggregator demand response incentive method for overlapping users, characterized in that: Includes the following steps: Step 1: Establish a user demand response model, a user dissatisfaction model for participating in demand response, and an optimal incentive model for aggregators to implement demand response; Step 2: Establish a multi-aggregator cooperative game model that considers overlapping users, and formulate the negotiation set in the cooperative game; Step 3: Each aggregator first considers overlapping users as participants in its own demand response plan, and based on the user response model, derives the minimum incentive strategy for each aggregator. Step 4: Each aggregator treats overlapping users as users who do not participate in their own demand response plan, and derives the maximum incentive strategy for each aggregator based on the user response model. Step 5: According to and Determine the optimal result of the negotiation set, and finally obtain the best incentive for each aggregator when there are overlapping users; 1) User demand response model: In the formula: U i,t (π i,t ,x i,t Let π be the effect function of user i at time t; i,t x is the unit incentive that the aggregator provides to user i at time t; i,t Let ω be the response of user i at time t; i,t The weighting factor is used to reflect the degree of importance that user i attaches to energy satisfaction. For the response quantity x i,t The function for user dissatisfaction; For x i,t The upper limit; 2) User dissatisfaction model in demand response: User dissatisfaction is quantified by the following formula: In the formula: where θ i and λ i These are user-related parameters that reflect users' willingness to participate in incentive-based demand response programs; θ i Users with smaller values ​​than θ i Users with higher values ​​are willing to provide more and demand decreases; λ i This indicates the minimum incentive price at which a user is willing to participate in the aggregator's incentive-based demand response; 3) Optimal incentive model for aggregators to implement demand response: in: The incentive subsidy given to users by aggregator k; ΔL k,t Reduce the target power for aggregator k during time period t; 1) Multi-aggregator cooperative game model considering overlapping users Where: Γ represents the core of the cooperative game; {π1}, {π2} are the game strategies of the aggregator; {U DRA,1 },{U DRA,2 } represents the objective function of the aggregator; s i It is a Boolean variable indicating whether overlapping users respond to the aggregator; These represent the sets of non-overlapping users managed by aggregators 1 and 2, respectively. These represent the sets of overlapping users managed by aggregators 1 and 2, respectively. 2) Negotiation set model for cooperative game Where: G represents the negotiation set in the cooperative game; It is the minimum incentive price for aggregator 1; It is the maximum incentive price for aggregator 1; It is the minimum incentive price for aggregator 2; It is the maximum incentive price for aggregator 2; π coop The incentive price for cooperation between aggregators, excluding the first two scenarios of the negotiation set, is as follows: The minimum incentive strategies for aggregators 1 and 2 are as follows: In the formula, The minimum incentive strategies for aggregators 1 and 2, respectively, together constitute the minimum incentive strategies for all aggregators. Indicates that there is Each user will only have the opportunity to participate in the aggregator's incentive-based demand response program. Indicates that there is Each user only has the opportunity to participate in the Aggregator 2 Incentive Demand Response Program, while Indicates that there is One overlapping user gains the opportunity to participate in two aggregator incentive-based demand response programs; In the formula, The maximum incentive strategies for aggregators 1 and 2, respectively, together constitute the maximum incentive strategies for all aggregators. This indicates that overlapping users did not have the opportunity to participate in the incentive-based demand response programs of either aggregator; the optimal incentive strategy for each aggregator is: Equation (8) is a further improvement on the cooperative game negotiation set of Equation (5) based on the calculation of the maximum and minimum incentive strategies of aggregators 1 and 2 in Equations (6) and (7).

2. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute the method of claim 1.

3. An electronic device, characterized in that, It includes a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the computer-readable instructions, when executed, perform the method of claim 1.

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