Power demand response excitation method and system based on three-layer optimization

Abstract

The invention relates to the technical field of electric power systems and electric power markets, in particular to an electric power demand response excitation method and system based on three-layer optimization, and the method comprises the steps: building a three-layer optimization model through cost and income analysis by considering the cost and income of a load aggregator, a user and a power grid company; the model optimizes the income maximization problem of a power grid company, a load aggregator and a user, and ensures that the optimal economic benefit is realized in a demand response process; the method comprises the following steps: firstly, analyzing incentive cost of a load aggregator, response cost of a user and power transmission and distribution cost of a power grid company; secondly, solving a decision problem of a load aggregator and a user by taking benefit maximization of a power grid company as a target; and finally, designing a solution algorithm, proving the uniqueness of the algorithm, and verifying that the model can promote energy conservation and emission reduction and reduce the operation cost of a power grid company while ensuring that a load aggregator obtains reasonable benefits through experiments, thereby providing an efficient solution for a power demand response incentive mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems and power markets, and particularly to a power demand response incentive method and system based on three-layer optimization. Background Art

[0002] With the continuous development of the power market, the balance between power supply and demand has become increasingly important. Traditional power systems mainly rely on regulation on the power generation side. However, with the increasing proportion of new energy and renewable energy, the volatility of the power system has also increased. Currently, large transferable loads have entered the power market, but there are still few relevant studies and applications regarding the entry of small and medium-sized transferable loads into the power market. As an emerging entity in the power market, load aggregators have gradually become an effective means of regulation in power system demand response. They can adjust users' electricity consumption behaviors by aggregating small and medium-sized transferable loads to balance the power output of the power grid. As an important technical means, demand response encourages users to reduce electricity consumption during peak demand periods or increase electricity consumption during low demand periods through economic incentives, thereby achieving peak shaving and valley filling in the power system and increasing the flexibility and stability of the power system.

[0003] Domestic and foreign scholars have conducted a large number of studies on the grid incentive mechanism and proposed a variety of innovative solutions to improve the stability and efficiency of the power system. In terms of load-side demand response, researchers believe that fully mobilizing transferable loads to participate in grid demand response is the key method to address the volatility brought by the high proportion of energy grid connection. Some studies have constructed an optimal incentive model under information asymmetry based on the principal-agent theory, effectively solving this problem. To address challenges such as small response scale, low user response willingness, and multiple uncertainties in the initial stage of demand response pilots, a stochastic optimization model of a stepped demand response incentive mechanism considering the instability of user response temperature intensity has been proposed, increasing the benefits of grid companies while enhancing users' enthusiasm for participation.

[0004] For industrial and commercial adjustable load resources, the virtual aggregator demand response mode realizes load aggregation through a response subsidy mechanism based on user credit and constructs a double-layer optimization model for user response subsidies, improving the accuracy of demand response. In the context of coal shortages, some studies have proposed a precise evaluation and dynamic incentive method considering user contribution degrees, which not only improves the quality of demand response but also reduces the redundancy of invitations. The home energy efficiency management model promotes users to actively adjust their electricity consumption patterns by comprehensively considering factors such as user groups, electricity consumption structures, electricity price incentives, and user satisfaction, optimizing the operation of the power system.

[0005] In terms of the dispatching of regional load aggregators, researchers have proposed a three-stage optimized dispatching strategy, which improves the coordination and effectiveness of demand response. To ensure the scientificity and effectiveness of the incentive mechanism, a load curve perception model applicable to load aggregators is proposed to evaluate the user response performance in detail. Existing research has also designed a credit-based incentive mechanism and established a two-layer optimization model for the demand-side incentive price to regulate user response behavior and enhance the reliability of demand response. Finally, a three-party interactive master-slave game model in which load aggregators act on behalf of users to participate in demand response provides a theoretical basis for the design of the incentive mechanism. In short, these studies optimize the grid incentive mechanism through various methods and models, aiming to improve the efficiency and stability of the power system, promote the utilization of new energy, and encourage users to actively participate.

[0006] The present invention comprehensively evaluates the cost-benefits of different entities, namely power grid companies, load aggregators, and users. Taking power grid companies as the main research object, a three-layer optimization model of power grid-load aggregator-user is established under the premise of limited costs of power grid companies, providing a solution for the load dispatching incentive mechanism under limited costs for power grid companies.

[0007] At present, a variety of technologies in the power system have been widely used to achieve more efficient power management.

[0008] Smart Grids: Smart grids integrate advanced communication, control, and computing technologies to achieve real-time monitoring and optimized dispatching of the power system, thereby improving the supply efficiency and reliability of electricity.

[0009] Microgrids: A microgrid is an independent power system that can continue to supply electricity when problems occur in the main grid. It realizes more efficient power management by optimizing power production and consumption.

[0010] Distributed Generation: Distributed generation realizes more flexible and efficient power supply by dispersedly installing small-scale power production equipment in the power system.

[0011] The possible technical drawbacks of the current mechanism in practical applications are as follows: The three-layer optimization structure introduces more variables and constraint conditions, increasing the complexity of the model, which poses higher requirements for computing power and algorithm efficiency; The three-layer optimization structure introduces more variables and constraint conditions, increasing the complexity of the model, which poses higher requirements for computing power and algorithm efficiency. Summary of the Invention

[0012] In view of the problems existing in the above-mentioned prior art, the present invention is proposed.

[0013] Therefore, the technical problem to be solved by the present invention is as follows: Power demand response is a power system management strategy that prompts power users to change their power consumption patterns through price signals or incentive mechanisms to address the imbalance between power grid supply and demand. With the deepening of the power market reform, emerging market players represented by load aggregators have gradually participated in power market transactions. Load aggregators can enable small and medium-sized users to directly participate in demand response by offering incentive prices. Therefore, in order to better promote demand response to ensure the stable operation of the power system, the present invention comprehensively considers the cost-benefits of power grid companies, load aggregators, and users. Based on the previous research and the limited total incentive price of power grid companies, a three-layer optimization model is designed to help power grid companies incentivize the response capabilities of load aggregators and users to maximize the demand response electricity volume. While proving the existence and uniqueness of the optimal solution, a simple method is provided to solve the model. Finally, according to the demand response results of numerical experiments, the effectiveness of the model is verified.

[0014] To solve the above technical problem, the present invention provides the following technical solution, a power demand response incentive method based on three-layer optimization, including: confirming the basic assumptions of the model; evaluating the costs and benefits of load aggregators in participating in demand response; calculating the cost-benefits of users in participating in demand response; analyzing the calculated costs and benefits; establishing a three-layer optimization model based on the cost-benefit analysis; designing a solution algorithm, and verifying the uniqueness of the algorithm by optimizing and solving the model layer by layer.

[0015] As a preferred solution of the power demand response incentive method based on three-layer optimization according to the present invention, wherein: the basic assumptions of the model include that the users represented by the load aggregator are stable and will not transfer; the power grid issues independent demand response requirements to the load aggregator, stipulating the response capacity and response time; the power grid understands the electricity consumption cost data of the load aggregator and the represented users.

[0016] As a preferred solution of the power demand response incentive method based on three-layer optimization according to the present invention, wherein: the costs and benefits include analyzing and considering the incentive fees given to the represented users and the profit compensation fees provided by the power grid; for load aggregator j, the cost of participating in demand response is the incentive fee C given to the represented users 1,j , and the benefits include the profit compensation fee C provided by the power grid 2,j .

[0017] As a preferred solution of the power demand response incentive method based on three-layer optimization according to the present invention, wherein: the incentive fee is expressed as:

[0018]

[0019] wherein, q i,j,kDenote the responsive power of user k, which is agented by load aggregator j, in period i; p LA,i,j Denote the incentive price given by load aggregator j to agent user k in period i; N denotes the total number of users agented by load aggregator j;

[0020] The profit compensation cost is expressed as:

[0021]

[0022] where p G,i,j Denote the profit compensation price given by the power grid company to load aggregator j in period i.

[0023] As a preferred solution of the power demand response incentive method based on three-layer optimization described in the present invention, wherein: the analysis of the calculated costs and benefits includes considering dissatisfaction, obtained incentive costs, reduced power cost expenses, psychological value based on emission reduction awareness, and responsive power penalty terms;

[0024] wherein, dissatisfaction is expressed as:

[0025]

[0026] where α k and β k Denote constants greater than 0;

[0027] wherein, the obtained incentive cost is expressed as:

[0028] C 3,k = p LA,i,j q i,j,k

[0029] wherein, the reduced power cost expense is expressed as:

[0030] C 4,k = τ i,k q i,j,k

[0031] where τ i,k Denote the electricity price of user k in period i;

[0032] wherein, the psychological value based on emission reduction awareness is expressed as:

[0033] C 5,k = ω k q i,j,k

[0034] where the carbon reduction coefficient ω k The numerical value of depends on the user's own awareness of energy conservation and emission reduction. If the value of ω k increases, it indicates that the user attaches importance to energy conservation and emission reduction;

[0035] Among them, the response power penalty term is expressed as:

[0036] punish k = log(θ k q i,j,k + c)

[0037] Among them, θ k represents the non - response coefficient of user k, and c represents a constant offset term greater than 0.

[0038] As a preferred solution of the power demand response incentive method based on three - layer optimization described in the present invention, among them: the three - layer optimization model includes taking the maximum profit of the power grid company as the objective function, and based on the cost - benefit analysis of the power grid company, constructing a power grid decision - making model, which is expressed as:

[0039]

[0040] Adjusting the incentive price to guide users to participate in demand response with the maximization of their own interests as the goal, and combining the cost - benefit analysis of the load aggregator, establishing a load aggregator decision - making model, which is expressed as:

[0041]

[0042] Among them, the parameter k j represents the competition coefficient of load aggregator j. When the value increases, the competition advantage increases, manifested as an increase in the demand response ability;

[0043] Taking the maximization of the user's own income as the goal, according to the cost - benefit analysis of the user, establishing a user decision - making model, which is expressed as:

[0044]

[0045] As a preferred solution of the power demand response incentive method based on three - layer optimization described in the present invention, among them: the method of solving the model through layer - by - layer optimization includes ensuring the maximization of the power grid company's interests, considering the third - layer optimization problem, the derivative function of the objective function is a fractional function, and its positive and negative nature is determined by the concave quadratic function of the numerator; regarding p LA,i,j as a parameter, since the objective function has a maximum value on a bounded closed interval, then q i,j,k is a function of p LA,i,j ;

[0046] Considering the second - layer optimization problem, calculating q i,j,k based on q i,j , and substituting it into the objective function; since p G,i,j ≥0, substituting the constraint into the objective function, in the case of a given p G,i , since the objective function has a maximum value on a bounded closed interval, there exists a unique optimal solution;

[0047] Considering the first-layer optimization problem, since p in the algorithm G,i takes values in the finite set, there exists a maximum benefit I G .

[0048] Another object of the present invention is to provide a power demand response incentive system based on three-layer optimization, which can effectively evaluate and optimize the costs and benefits of all parties in the power demand response process, ensure the maximization of the interests among the power grid company, the load aggregator, and the users, and guarantee the uniqueness and optimality of the system solution through a layer-by-layer optimization algorithm.

[0049] To solve the above technical problems, the present invention provides the following technical solutions: A power demand response incentive system based on three-layer optimization, comprising: a model assumption module, a benefit evaluation module, a cost-benefit module, a benefit analysis module, a three-layer optimization module, and an algorithm verification module;

[0050] The model assumption module confirms the basic assumptions of the model;

[0051] The benefit evaluation module evaluates the costs and benefits of the load aggregator in participating in the demand response;

[0052] The cost-benefit module calculates the cost-benefit of the user in participating in the demand response;

[0053] The benefit analysis module analyzes the calculated costs and benefits;

[0054] The three-layer optimization module establishes a three-layer optimization model based on the cost-benefit analysis;

[0055] The algorithm verification module designs a solution algorithm and verifies the uniqueness of the algorithm by solving the model layer by layer.

[0056] A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the power demand response incentive method based on three-layer optimization as described above are implemented.

[0057] A computer-readable storage medium, on which a computer program is stored, and is characterized in that when the computer program is executed by a processor, the steps of the power demand response incentive method based on three-layer optimization as described above are implemented.

[0058] Advantages of the present invention: The present invention mainly studies the incentive mechanism of power grid companies facing load aggregators with different response capabilities under the constraint of limited costs. By designing a three-layer optimization model, it details how to incentivize load aggregators with different response capabilities to maximize the demand response electricity volume when the total incentive price of the power grid company is limited. The research results show that this model can not only ensure that each load aggregator obtains reasonable economic benefits during the demand response process, but also effectively promote energy conservation and emission reduction and reduce the operating costs of power grid companies.

[0059] Specifically, the model classifies the response capabilities of load aggregators and designs an incentive price strategy according to their different response capabilities. This strategy can not only incentivize strong load aggregators to actively participate in demand response, but also fully mobilize the response enthusiasm of weak load aggregators with relatively small incentive expenditures, thereby maximizing the demand response electricity volume. Numerical experiments further verify the effectiveness and practicality of the model. The experimental results show that during the demand response process, each load aggregator can ensure its own profitability, which is consistent with the actual situation. At the same time, under the constraint of limited costs, the power grid company can not only achieve the goal of energy conservation and emission reduction through a differentiated incentive strategy, but also effectively reduce its own operating costs. In addition, the research of the present invention also provides valuable references for policy making. Through reasonable incentive mechanism design, it can further guide the demand side to participate in demand response and promote the stable operation and sustainable development of the power system. Future research can further optimize the demand response model based on this, combined with smart grid technology and big data analysis, to improve the response efficiency and accuracy.

[0060] In summary, the three-layer optimization model proposed by the present invention provides an effective solution for the incentive mechanism design of power grid companies in demand response under limited costs, which not only has theoretical significance but also strong practical application value. Through further research and practice, it can provide more powerful support for the optimal operation of the power system and energy conservation and emission reduction. Description of the Drawings

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0062] Figure 1 It is a flowchart of a power demand response incentive method based on three-layer optimization provided for an embodiment of the present invention.

[0063] Figure 2Schematic diagram of the power demand response incentive mechanism for the power demand response incentive method based on three - layer optimization provided by an embodiment of the present invention.

[0064] Figure 3 Diagram showing the response situations of all parties under the maximum benefit of the power grid company for the power demand response incentive method based on three - layer optimization provided by an embodiment of the present invention.

[0065] Figure 4 Diagram showing the revenue situation of the power grid company for the power demand response incentive method based on three - layer optimization provided by an embodiment of the present invention.

[0066] Figure 5 Diagram showing the revenue situation of the weak load aggregator for the power demand response incentive method based on three - layer optimization provided by an embodiment of the present invention.

[0067] Figure 6 Diagram showing the revenue situation of the strong load aggregator for the power demand response incentive method based on three - layer optimization provided by an embodiment of the present invention.

[0068] Figure 7 Diagram showing the demand response situation of the weak load aggregator for the power demand response incentive method based on three - layer optimization provided by an embodiment of the present invention.

[0069] Figure 8 Diagram showing the demand response situation of the strong load aggregator for the power demand response incentive method based on three - layer optimization provided by an embodiment of the present invention. Detailed implementation manners

[0070] To make the above - mentioned objects, features and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0071] Example 1, referring to Figure 1 - Figure 2 , which is an embodiment of the present invention. This embodiment provides a power demand response incentive method based on three - layer optimization, including:

[0072] S1: Confirm the basic assumptions of the model.

[0073] It should be noted that, as shown in S1 of Figure 1 , it includes that the users represented by the load aggregator agent are stable and will not transfer; the power grid company issues independent demand response requirements to the load aggregator, stipulating the response capacity and response time; the power grid company fully understands the electricity cost data of the load aggregator and the agent users.

[0074] Further, model establishment:

[0075] Basic assumptions: In this study, it is assumed that the users represented by the load aggregator are stable and will not transfer; the power grid company issues independent demand response requirements to the load aggregator, specifying the response capacity and response time; the power grid company fully understands the electricity cost data of the load aggregator and the represented users.

[0076] Furthermore, under this assumption, "no user transfer" enables the study to focus on the demand response mechanism itself rather than the uncertainty of user behavior. In the short term, the possibility of user transfer is small, so this assumption helps to focus on analyzing how the load aggregator responds to the power grid company's demand; "only the load aggregator participates in demand response" can more clearly observe and analyze how the load aggregator optimizes the electricity use of its represented users and how they interact with the power grid company by excluding other participants; "the power grid company understands the electricity cost data" can help the power grid company predict the possible response behaviors of the load aggregator and users, thus designing more effective incentive mechanisms.

[0077] S2: Evaluate the costs and benefits of the load aggregator in participating in demand response.

[0078] It should be noted that, as Figure 1 shown in S2, the cost-benefit analysis of the load aggregator considers the incentive fees given to the represented users and the profit compensation fees provided by the power grid company; for load aggregator j, the cost of participating in demand response is mainly the incentive fee C 1,j given to the users it represents, and the benefits mainly include the profit compensation fee C 2,j .

[0079] S3: Calculate the cost-benefit of users in participating in demand response.

[0080] It should be noted that, as Figure 1 shown in S3, in order to smooth the load curve and obtain a larger scale of user response electricity, the load aggregator should give an attractive incentive price to the represented users:

[0081]

[0082] In the formula, q i,j,k is the response electricity of user k represented by load aggregator j at time period i; p LA,i,j is the incentive price given by load aggregator j to user k at time period i; N is the total number of users represented by load aggregator j;

[0083] Profit compensation fees provided by the power grid company:

[0084]

[0085] In the formula, p G,i,j is the profit compensation price given by the power grid company to the load aggregator j during period i;

[0086] Combining the above analysis, it can be seen that the revenue function I of the load aggregator j 1,j is:

[0087]

[0088] For each load aggregator, the incentive cost it can give to users should have upper and lower limits, that is, it should satisfy the following constraints:

[0089]

[0090] S4: Analyze the calculated costs and revenues.

[0091] It should be noted that, as Figure 1 shown in S4, analyzing the calculated costs and revenues includes considering dissatisfaction, obtained incentive costs, reduced power consumption costs, psychological value based on emission reduction awareness, and response power penalty terms;

[0092] For individual users, the cost-benefit factors considered in the present invention mainly include: user dissatisfaction Dissatisfaction caused by responding to power consumption k , incentive cost C given by the load aggregator 3,k , power consumption cost C saved by responding to power consumption 4,k and quantified value C obtained based on emission reduction awareness 5,k ; in addition, in order to enable the model to reflect the actual situation that some users do not participate in demand response at all, a response power penalty term punish is additionally added to each user k .

[0093] Furthermore, from the perspective of economics, the user utility function is assumed to be a convex function and monotonically non-decreasing with respect to power consumption. Therefore, the dissatisfaction caused by reducing power consumption should be a concave function and monotonically non-decreasing with respect to the response power. Therefore, the dissatisfaction of user k caused by responding to power consumption during period i is defined by the following quadratic function:

[0094]

[0095] where, α k and β k are both constants greater than 0, and their values are determined by multiple factors such as the user's own power consumption habits and user sensitivity;

[0096] The incentive price given by the load aggregator is the main motivation for users to participate in demand response. The cost for this part of time period i is as follows:

[0097] C 3,k = p LA,i,j q i,j,k

[0098] After the user decides to respond in time period i, the user's electricity consumption will be reduced. The cost savings of this part of the electricity should be regarded as part of the benefits in this demand response process, which is defined as follows:

[0099] C 4,k = τ i,k q i,j,k

[0100] where τ i,k is the electricity price of user k in time period i;

[0101] In the process of actively participating in demand response, users obtain psychological satisfaction based on the awareness of carbon emission reduction. This psychological value should also be regarded as part of the benefits of users; introduce the carbon reduction coefficient ω k to describe the level of users' awareness of carbon emission reduction, and quantify the psychological value of carbon emission reduction obtained by users participating in demand response in time period i as:

[0102] C 5,k = ω k q i,j,k

[0103] where the carbon reduction coefficient ω k The numerical value of depends on the users' own awareness of energy conservation and emission reduction. The larger the value of ω k , the more the user attaches importance to energy conservation and emission reduction, and the stronger their response willingness;

[0104] In actual situations, there are often users who do not participate in demand response. In order to realistically depict this group of people in the model, consider defining the response electricity penalty term as follows:

[0105] punish k = log(θ k q i,j,k + c)

[0106] where θ k represents the non-response coefficient of user k. When the non-response coefficient θ k takes a large value, in the case of considering the maximum optimization problem, this term can make the response electricity q i,j,k tend to 0, and c is a constant offset term greater than 0.

[0107] Furthermore, based on the above cost-benefit analysis, it can be seen that the benefit function of user k participating in demand response in time period i is:

[0108] I 2,k =-Dissatisfaction k +C 3,k +C 4,k +C 5,k -punish k

[0109] =-1 / 2(q i,j,k ) 2 +(p LA,i,j +τ i,k -β k +ω k )q i,j,k -log(θ k q i,j,k +c)

[0110] Limited by its own load volume, the response power q of user k in period i i,j,k cannot exceed its maximum response capacity in this period, that is, it satisfies the following constraints:

[0111]

[0112] Cost-benefit analysis of the power grid company: The benefits of demand response generally include the benefits on the power grid side and the power generation side. When only considering demand response, the benefits on the power generation side do not need to be included in the cost-benefit analysis of the power grid company. From the perspective of the power grid side, the costs of the power grid company to carry out demand response mainly include the reduced transmission and distribution revenue C 6 and the profit compensation cost C 7 for the load aggregator. The benefits mainly include the avoidable transmission and distribution capacity cost C 8 and the avoidable transmission and distribution network loss cost C 9 ;

[0113] As the implementing entity of the demand response project, the power grid company will reduce the following transmission and distribution revenues by carrying out demand response:

[0114]

[0115] In the formula, q i,j is the response power of load aggregator j in period i, which is equal to the sum of the response powers of the users it represents; M is the number of load aggregators participating in the response; τ is the transmission and distribution price of the power grid company during the demand response implementation period. By default, in the demand response cycle of the same year, the transmission and distribution price remains unchanged;

[0116] The power grid company needs to give appropriate incentives to encourage load aggregators to actively participate in demand response, and its costs are as follows:

[0117]

[0118] Among them, p G,i is the profit compensation fee given by the power grid company to all load aggregators during period i;

[0119] The implementation of demand response effectively reduces the peak load, resulting in a corresponding reduction in the network loss during the power transmission and distribution process. This part of the cost should be included in the demand response revenue of the power grid company:

[0120]

[0121] In the formula, τ a is the average market trading electricity price; α is the power grid transmission and distribution loss coefficient;

[0122] The peak shaving effect brought by demand response helps to delay the investment in power grid transmission and distribution facilities. The avoidable capacitance cost generated should be regarded as the revenue of the power grid company;

[0123]

[0124] Δp = R i / (1 - α)

[0125]

[0126] In the formula, c c is the unit cost of avoidable transmission and distribution capacity; Δp is the actually avoidable transmission and distribution capacity; R i is the total demand response power of all load aggregators during period i; t con,i is the duration of a single peak load period during period i; for simplicity of calculation, it is assumed here that the response power of each user remains unchanged within each response period;

[0127] Based on the above cost - benefit analysis, it can be known that the revenue function of the power grid company is:

[0128]

[0129] For the power grid company, it should have an expected range for the compensation fees given to all load aggregators, that is, to meet the following constraints:

[0130]

[0131] S5: Based on the cost - benefit analysis, establish a three - layer optimization model.

[0132] It should be noted that, such as Figure 1As shown in S5, the establishment of the three - layer optimization model: The load aggregator plays an important role in demand response. During the process of the load aggregator participating in the power grid demand response, the power grid company issues the demand response capacity requirement for a certain time period i to the load aggregator according to the actual demand, and provides a certain economic compensation p to the load aggregator. G,i The limited power dispatching cost of the power grid company results in the limited economic compensation p provided to the load aggregator. G,i Therefore, taking the maximization of the power grid company's revenue as the objective function, based on the cost - benefit analysis of the power grid company, the following decision - making model of the power grid company is constructed:

[0133]

[0134] Furthermore, after the load aggregator receives the fixed economic compensation p and the demand response electricity requirement given by the power grid company, considering the incentive cost for guiding users to participate in demand response, it adjusts the incentive price to guide users to participate in demand response with the maximization of its own interests as the goal. Combining the cost - benefit analysis of the load aggregator, the following decision - making model of the load aggregator is established: G,i

[0135]

[0136] Among them, the parameter k j represents the competition coefficient of load aggregator j. The larger the value, the greater the competitive advantage, which is manifested as the greater the demand response ability.

[0137] Even further, when users receive the incentive price from the load aggregator, based on the benefits that can be obtained from demand response, the electricity cost saved by reducing electricity consumption, the quantified psychological value of emission reduction, and the penalty for not participating in demand response, combined with the goal of maximizing the users' own benefits, they make the behavior of participating in demand response and the strategy of the specific response capacity. According to the cost - benefit analysis of users, the following decision - making model of users is established:

[0138]

[0139] S6: Design a solution algorithm, and verify the uniqueness of the algorithm by optimizing the solution model layer by layer.

[0140] It should be noted that, as shown in S6 Figure 1 , the solution of the model:

[0141] ​As a "bridge" for users to participate in the power grid demand response, the load aggregator aggregates small and medium-sized loads on the user side to participate in the power grid demand response. Under the limited cost constraints of the power grid company, both the load aggregator and users formulate demand response strategies with the goal of maximizing their own benefits. The load aggregator guides users to achieve the demand response load of the target capacity within the specified time period by adjusting the incentive price given to users. Users then determine the final load volume participating in the demand response based on the incentive price and their own losses after adjusting their electricity consumption behaviors due to participating in the demand response.

[0142] Furthermore, algorithm introduction: Since the decision-making models of the power grid company, load aggregator, and users established are of relatively high complexity, the present invention proposes a simple model solving algorithm.

[0143] Consider a simple method to solve the model. The core idea is to ensure the maximization of the power grid company's interests. Its algorithm flow is as shown in Table 1 below:

[0144] Table 1 Algorithm Flow

[0145]

[0146]

[0147] Even further, algorithm discussion: Under the assumptions of the present invention, it is not difficult to see that the solution of the model has existence and uniqueness; specifically, first consider the third-layer optimization problem. The derivative function of the objective function is a fractional function, and its positive and negative nature is only determined by the concave quadratic function of the numerator; regarding p LA,i,j as a parameter, since the objective function must have a maximum value on a bounded closed interval, then q i,j,k is a function of p LA,i,j .

[0148] Then consider the second-layer optimization problem. Calculate q i,j,k according to q i,j , and substitute it into the objective function. Since p G,i,j ≥0, the equality constraint can be directly substituted into the objective function. Given p G,i , similarly, since the objective function must have a maximum value on a bounded closed interval, there exists a unique optimal solution.

[0149] Finally, consider the first-layer optimization problem. Since p G,i takes values in a finite set in the algorithm, there must exist a maximum benefit I G .

[0150] The above is a schematic solution of a power demand response incentive method based on three - layer optimization in this embodiment. It should be noted that the technical solution of the system of the power demand response incentive method based on three - layer optimization belongs to the same concept as the technical solution of the above - mentioned power demand response incentive method based on three - layer optimization. For the details not described in detail in the technical solution of the power demand response incentive system based on three - layer optimization in this embodiment, reference can be made to the description of the technical solution of the above - mentioned power demand response incentive method based on three - layer optimization.

[0151] The power demand response incentive system based on three - layer optimization in this embodiment is characterized in that it includes: a model assumption module, a revenue evaluation module, a cost - benefit module, a revenue analysis module, a three - layer optimization module, and an algorithm verification module;

[0152] The model assumption module confirms the basic assumptions of the model;

[0153] The revenue evaluation module evaluates the costs and revenues of the load aggregator in participating in demand response;

[0154] The cost - benefit module calculates the cost - benefit of users in participating in demand response;

[0155] The revenue analysis module analyzes the calculated costs and revenues;

[0156] The three - layer optimization module establishes a three - layer optimization model based on cost - benefit analysis;

[0157] The algorithm verification module designs a solution algorithm and verifies the uniqueness of the algorithm by solving the model layer by layer;

[0158] This embodiment also provides a computing device applicable to the case of the power demand response incentive method based on three - layer optimization, including:

[0159] A memory and a processor; the memory is used to store computer - executable instructions, and the processor is used to execute the computer - executable instructions to implement the power demand response incentive method based on three - layer optimization as proposed in the above - mentioned embodiment.

[0160] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the power demand response incentive method based on three - layer optimization as proposed in the above - mentioned embodiment.

[0161] The storage medium proposed in this embodiment and the power demand response incentive method based on three - layer optimization proposed in the above - mentioned embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above - mentioned embodiment, and this embodiment has the same beneficial effects as the above - mentioned embodiment.

[0162] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0163] The logics and / or steps described otherwise herein, for example, can be regarded as a defined sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0164] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGA), field-programmable gate arrays (FPGA), etc.

[0165] Embodiment 2, refer to Figure 3 - Figure 8 , an embodiment of the present invention provides a power demand response incentive method based on three-layer optimization. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0166] Carry out numerical experiments to verify the effectiveness of the model proposed above. First, set a basic scenario based on the characteristics of the model; then, solve the model to obtain basic results; finally, summarize and analyze the results.

[0167] Basic scenario description and parameter settings: For the three-layer optimization model established above, the basic scenario involves three parties: the power grid company, the load aggregator, and the users it represents. The power grid company is the organizer of the demand response market and the issuer of demand response projects. To simplify the calculation, the response period is selected as one period. It is assumed that only two load aggregators participate in the demand response, and one has a competitive advantage over the other. Each load aggregator represents 8 types of users, and the types are determined by the dissatisfaction, emission reduction awareness, and response willingness introduced above.

[0168] Regarding parameter settings, the present invention refers to some data. The present invention sets the competition coefficients of the two load aggregators as k 1 = 1 and k 2 = 1.5; the total incentive price pG of the power grid company based on each load aggregator is set in the range of 5.00 yuan / kWh - 20.00 yuan / kWh; the unit cost c of the avoidable transmission and distribution capacity c = 100 yuan / (kW·year); the power grid transmission and distribution loss coefficient ɑ = 0.06; the electricity price τ of users during this period i,k = 1.00 yuan / kWh; the average market trading electricity price τ a = 0.85 yuan / kWh; the power grid company's transmission and distribution price τ = 0.15 yuan / kWh; the duration t of a single peak load period con,i = 1h; the constant offset term c = 1.

[0169] Table 2 User-related parameters

[0170]

[0171]

[0172] The relevant parameters of various types of users during this period are given in Table 2, including the dissatisfaction parameters α k and β k , the carbon reduction coefficient ω k and the response coefficient θ k . Each type of coefficient is mainly set with two values, divided into two levels of "high" and "low" according to the value size.

[0173] The maximum response electricity quantity Q of each type of user j,kIt will be randomly generated. Considering the maximum response power range of a single user is [2000, 3000] mW. For the weak load aggregator, the number of users in each category is set to 5 here, that is, the maximum response power range of each category of users is [10000, 15000] mW; for the strong load aggregator, the number of users in each category is set to 10 here, that is, the maximum response power range of each category of users is [20000, 30000] mW. The values of various parameters can vary according to specific situations. Next, the Matlab built-in solver is used to optimize and solve the model.

[0174] Experimental results: In the optimal case, the response situations of the three parties are as Figure 3 shown.

[0175] Under different total profit compensation amounts, the revenue situation of the power grid company is as Figure 4 shown.

[0176] Under different incentive levels, the revenue situations of the two load aggregators are as Figure 5 and Figure 6 shown.

[0177] Under different incentive prices, the demand response situations of users are as Figure 7 and Figure 8 shown.

[0178] Among them, Figure 4 - Figure 8 the red dots in

[0179] all correspond to the optimal situation of the power grid company.

[0180] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. The power demand response incentive method based on three-layer optimization is characterized by: include: Confirm the basic assumptions of the model; Assess the costs and benefits of load aggregators in participating in demand response; Calculate the cost-benefit of users when participating in demand response; Analyze the calculated costs and benefits; Based on cost-benefit analysis, a three-layer optimization model is established; Design a solution algorithm, optimize the solution model layer by layer, and verify the uniqueness of the algorithm.

2. The power demand response incentive method based on three-layer optimization according to claim 1, characterized in that: The basic assumptions of the model include that the users represented by load aggregators are stable and will not be transferred; the power grid issues independent demand response requirements to load aggregators and stipulates the response capacity and response time; the power grid understands the electricity cost data of load aggregators and proxy users.

3. The power demand response incentive method based on three-layer optimization according to claim 2, characterized in that: The costs and benefits include the incentive fees given to the proxy users and the profit compensation fees provided by the power grid. For load aggregator j, the cost of participating in demand response is the incentive fee C given to the proxy users. 1,j , the income includes the profit compensation fee C provided by the power grid 2，j .

4. The power demand response incentive method based on three-layer optimization according to claim 3 is characterized in that: The incentive fee is expressed as: Among them, q i，j，k represents the response power of user k represented by load aggregator j in time period i; p LA，i，j represents the incentive price given by load aggregator j to proxy user k in time period i; N represents the total number of users represented by load aggregator j; The profit compensation fee is expressed as: Among them, p G，i，j It represents the profit compensation price that the power grid gives to load aggregator j in time period i.

5. The power demand response incentive method based on three-layer optimization according to claim 4, characterized in that: The analysis of the calculated costs and benefits includes considering dissatisfaction, incentive costs obtained, reduced electricity costs, psychological value based on emission reduction awareness, and response electricity penalty items; Among them, dissatisfaction is expressed as: Among them, α k and β k Represents a constant greater than 0; Among them, the incentive fee obtained is expressed as: C 3，k =p LA，i，j q i，j，k The reduced electricity cost is expressed as: C 4，k =t i，k q i，j，k Among them, τ i，k represents the electricity price of user k in time period i; Among them, the psychological value based on emission reduction awareness is expressed as: C 5，k =ω k q i，j，k Among them, the carbon reduction coefficient ω k The value of depends on the user's own energy conservation and emission reduction awareness. k If the value of increases, it means that users attach importance to energy conservation and emission reduction; Among them, the response power penalty term is expressed as: punish k =log(θ k q i，j，k +c) Among them, θ k represents the non-response coefficient of user k, and c represents a constant offset term greater than 0.

6. The power demand response incentive method based on three-layer optimization according to claim 5, characterized in that: The three-layer optimization model includes taking the maximization of power grid revenue as the objective function and constructing a power grid decision model based on power grid cost-benefit analysis, which is expressed as: The incentive price is adjusted with the goal of maximizing its own interests to guide users to participate in demand response. Combined with the cost-benefit analysis of load aggregators, the decision model of load aggregators is established as follows: Among them, the parameter k j represents the competition coefficient of load aggregator j. When the value increases, the competitive advantage increases, which is realized as an increase in demand response capability; With the goal of maximizing the user's own benefits, a user decision model is established based on the user's cost-benefit analysis, which is expressed as:

7. The power demand response incentive method based on three-layer optimization according to claim 6, characterized in that: The model is solved by optimizing layer by layer, including ensuring the maximization of the benefits of the power grid. Considering the third-layer optimization problem, the derivative of the objective function is a fractional function, and the positive or negative is determined by the concave quadratic function of the numerator; p LA,i，j As a parameter, since the objective function has a maximum value on a bounded closed interval, q i,j，k For about p LA,i，j Function of Consider the second-level optimization problem, according to q i，j，k Calculate q i，j , and substitute it into the objective function; since p G,i，j ≥0, substitute the constraint into the objective function, given p G，i In the case of , since the objective function has a maximum value on a bounded closed interval, there is a unique optimal solution; Consider the first-level optimization problem. Since p G，i In the existence of a finite set, there exists a maximum benefit I G .

8. A system for power demand response incentive based on three-layer optimization according to any one of claims 1 to 7, characterized in that: include: Model hypothesis module, benefit evaluation module, cost-benefit module, benefit analysis module, three-layer optimization module and algorithm verification module; The model assumption module confirms the basic assumptions of the model; The benefit evaluation module evaluates the costs and benefits of the load aggregator in participating in demand response; The cost-benefit module calculates the cost-benefit of users when participating in demand response; The benefit analysis module analyzes the calculated costs and benefits; The three-layer optimization module establishes a three-layer optimization model based on cost-benefit analysis; The algorithm verification module designs a solution algorithm and verifies the uniqueness of the algorithm by optimizing the solution model layer by layer.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power demand response incentive method based on three-layer optimization according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power demand response incentive method based on three-layer optimization according to any one of claims 1 to 7 are implemented.

Images