Micro-grid system layered and distributed optimal scheduling method considering psychological factors of users

Abstract

The embodiment of the invention discloses a layered and distributed optimal scheduling method for a micro-grid system considering psychological factors of a user. On the basis of considering benefit demands of a micro-grid operator and users participating in demand response, a micro-grid double-layer scheduling model based on a master-slave game is constructed, a user side load model serving as a follower model depicts the influence of psychological factors of the users on the demand response, and a user side load model serving as a follower model depicts the influence of the psychological factors of the users on the demand response. The micro-grid operator model as the leader model optimizes the operation of each device and formulates the excitation price of the demand response, so as to achieve the balance of supply and demand and pursue the maximization of benefits. And layered and distributed optimization solution is performed on the microgrid double-layer scheduling model by adopting an alternating direction multiplier method, so that user side participation demand response can be effectively mined, and resources such as wind power, light and the like in the microgrid system can be fully absorbed. Therefore, on the basis of pursuing optimal operation economy of the micro-grid system and giving consideration to the benefits of the user side, multi-party benefit balance in the micro-grid system is realized.

Description

Technical Field

[0001] This application relates to the research field of microgrid energy management, and involves, but is not limited to, a hierarchical distributed optimization scheduling method for microgrid systems that takes into account user psychological factors. Background Technology

[0002] Building a new power system with new energy sources as the main body is an important task in the energy transition process. The penetration rate of distributed power sources, represented by distributed photovoltaic and wind turbines, in the power grid is increasing day by day. Ensuring the safe and stable operation of the power system while promoting the consumption of new energy sources is a problem that the power grid needs to solve.

[0003] Among them, microgrids are an effective way to solve the grid connection of distributed energy equipment and the local consumption of new energy. Microgrids manage a regional micro power system consisting of a series of equipment such as source-grid-load-storage within a certain geographical area, thereby realizing the local consumption of distributed power sources.

[0004] In the operation of microgrids, to cope with fluctuations in renewable energy output, microgrid operators need to provide certain economic incentives to guide the internal loads of the microgrid to actively participate in demand response. However, users and microgrid operators generally belong to different stakeholders. Therefore, how to achieve a balance of interests among all parties while pursuing optimal system operation economy in microgrids is a technical problem that existing technologies urgently need to solve. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this application provides a hierarchical distributed optimization scheduling method for microgrid systems that takes into account user psychological factors. The aim is to achieve a balance of interests among multiple parties within the microgrid system while pursuing optimal economic operation of the microgrid system and taking into account the interests of the user side.

[0006] The specific technical solution of this application embodiment is as follows: This application provides a hierarchical distributed optimization scheduling method for microgrid systems that takes into account user psychological factors, including: Based on the operating data of user-side energy-consuming equipment in the microgrid system during a preset time period and the probability coefficients taking into account user psychology, a user-side load model is constructed under the constraints of electricity purchase price and heat purchase price, with the objective function of minimizing the purchase cost taking into account user psychological factors. Based on the operational data of distributed energy resources within a microgrid system over a preset time period, a microgrid operator model is constructed under multiple constraints with profit maximization as the objective function. Based on Stackelberg game theory, a two-layer scheduling model for microgrids is obtained by combining user-side load model and microgrid operator model. The alternating direction multiplier method is used to perform hierarchical distributed optimization solution on the microgrid two-level scheduling model, and the optimized values ​​of the traded electricity, traded heat, electricity trading price and heat trading price of the microgrid system in a preset time period are obtained.

[0007] Optionally, the user-side load model includes: a user-side electrical load model and a user-side thermal load model; the operating data of the user-side energy-consuming equipment during the preset time period includes: the actual electrical response, predicted electrical load, and actual thermal response of the user-side energy-consuming equipment during the preset time period. Based on the operating data of user-side energy-consuming equipment within a microgrid system over a preset time period and probability coefficients considering user psychology, a user-side load model is constructed under constraints of electricity and heat purchase prices, with the objective function of minimizing the purchase cost while taking into account user psychological factors. This model includes: Substituting the probability coefficient that takes into account user psychology into the formula for calculating the probability coefficient of user psychological factors, we obtain the probability coefficient of user psychological factors. Based on the probability coefficient of user psychological factors, the actual electrical response and the predicted electrical load, the user electricity satisfaction rate taking into account user psychological factors is determined within a preset time period. A user-side electricity load model is constructed under the constraint of electricity purchase price, with the objective function of maximizing user electricity satisfaction and minimizing electricity purchase cost while taking into account user psychological factors. A user-side heat load model is constructed under the constraint of heat purchase price, with the objective function of minimizing the heat purchase cost corresponding to the actual heat response.

[0008] Optionally, the probability coefficients that take into account user psychology include: the holder effect probability coefficient, which characterizes the user's holder effect. And the probability coefficient of environmental awareness, which represents users' environmental awareness. The formula for calculating the probability coefficient of user psychological factors is: in, This represents the probability coefficient of user psychological factors.

[0009] Optionally, based on the probability coefficient of user psychological factors, the actual electrical response, and the predicted electrical load, the user electricity satisfaction rate taking into account user psychological factors is determined within a preset time period, including: The declared electricity amount, taking into account user psychological factors, is obtained by multiplying the preset user declared electricity amount probability coefficient, user psychological factor probability coefficient, and predicted electricity load amount within a preset time period. A proportional demand response incentive mechanism model is adopted to analyze the declared electricity volume and actual electricity response volume, taking into account user psychological factors, and to obtain the response subsidy coefficient provided by the microgrid system to the user side within a preset time period. By substituting the response subsidy coefficient and the actual electricity response into the user utility value function, the user electricity satisfaction rate, taking into account user psychological factors, is obtained within a preset time period.

[0010] Alternatively, the proportional demand response incentive mechanism model is as follows: in, For microgrid systems within a preset time period The response subsidy coefficient provided to the user side; For user-side power-consuming devices during preset time periods The actual electrical response quantity; To be within a preset time period The declared electricity volume takes into account user psychological factors; and Representing the response ratio The left and right boundaries; and The minimum and maximum subsidy coefficients are provided to the user side for the microgrid system, respectively.

[0011] Optionally, the user utility value function is: in, To be within a preset time period User satisfaction with electricity consumption should take into account psychological factors. The base unit price for demand response subsidies; For microgrid systems within a preset time period The response subsidy coefficient provided to the user side; For user-side power-consuming devices during preset time periods The actual electrical response quantity; The holder effect penalty coefficient; It represents the external inconvenience caused to users by participating in demand response; Environmental awareness coefficient; and All are inconvenience coefficients.

[0012] Optionally, the multiple constraints include: the partial Bruker opportunity constraint based on Wasserstein distance, the operator's electricity sales constraint for the microgrid system, the operator's heat sales constraint for the microgrid system, the heat power balance constraint, and the electrical power balance constraint.

[0013] Optionally, the microgrid operator model includes at least: a combined heat and power unit model, a gas boiler model, and an energy storage device model.

[0014] Optionally, based on Stackelberg game theory, a two-layer dispatch model for microgrids is obtained by combining the user-side load model and the microgrid operator model, including: Based on Stackelberg game theory, an interactive coupling relationship is established between the user-side load model and the microgrid operator model. The interactive coupling relationship includes: the microgrid operator model formulating and transmitting electricity and heat prices to the user-side load model based on the received electricity and heat consumption strategies, and the user-side load model adjusting its electricity and heat consumption strategies according to the electricity and heat prices. Based on the interactive coupling relationship, a two-layer scheduling model for microgrids is constructed, with the user-side load model as the lower-level follower model and the microgrid operator model as the upper-level leader model.

[0015] Optionally, the alternating direction multiplier method is used to perform hierarchical distributed optimization solutions on the microgrid two-layer scheduling model, obtaining the optimized values ​​of traded electricity, traded heat, electricity trading price, and heat trading price for the microgrid system within a preset time period, including: Based on the objective functions of the user-side load model and the microgrid operator model, local dummy variables corresponding to the variables to be optimized are determined, and consistency constraints between the local dummy variables and the variables to be optimized are established. Among them, the variables to be optimized include: electricity trading power, heat trading power, electricity trading price, and heat trading price. Based on consistency constraints, the initial user-side subproblem of the user-side load model and the initial microgrid operator subproblem of the microgrid operator model are decomposed and constructed. The objective functions of the initial user-side subproblem and the initial microgrid operator subproblem both include Lagrange multipliers and quadratic penalty terms used to enforce consistency constraints. By using the McCormick envelope method, the nonlinear terms in the initial user-side subproblem and the initial microgrid operator subproblem are linearly relaxed to obtain the linearized target user-side subproblem and the linearized target microgrid side problem. The linearized target user side problem and the linearized target microgrid side problem are solved iteratively, and the Lagrange multipliers are updated in each iteration until the consistency constraint converges, so as to obtain the optimized values ​​of the traded electricity, traded heat, electricity trading price and heat trading price of the microgrid system in a preset time period.

[0016] The beneficial effects of the technical solutions provided in this application include at least the following: The hierarchical distributed optimization scheduling method for microgrid systems that takes into account user psychological factors provided in this application embodiment firstly constructs a user-side load model with the objective function of minimizing the purchase cost, taking into account user psychological factors, based on the operating data of user-side energy-consuming equipment in the microgrid system during a preset time period and the probability coefficients considering user psychology, under the constraints of electricity purchase price and heat purchase price. Secondly, based on the operating data of distributed energy resources in the microgrid system during the preset time period, a microgrid operator model is constructed with the objective function of maximizing profit under multiple constraints. Then, based on Stackelberg game theory, a two-layer scheduling model for the microgrid is obtained by combining the user-side load model and the microgrid operator model. Finally, the alternating direction multiplier method is used to perform hierarchical distributed optimization solution on the two-layer scheduling model of the microgrid to obtain the optimized values ​​of traded electricity, traded heat, electricity trading price, and heat trading price of the microgrid system during the preset time period. Thus, on the one hand, while balancing the interests of microgrid operators and users participating in demand response, a two-layer microgrid scheduling model based on master-slave game theory is constructed. The user-side load model, acting as a follower, characterizes the impact of user psychology on demand response, while the microgrid operator model, acting as the leader, achieves supply-demand balance and maximizes efficiency by optimizing the operation of various devices and setting incentive prices for demand response. On the other hand, the alternating direction multiplier method is used to perform hierarchical distributed optimization of the two-layer microgrid scheduling model, effectively tapping into user-side participation in demand response and fully utilizing wind, solar, and other resources within the microgrid system. In this way, while pursuing optimal economic operation of the microgrid system, the interests of users are also considered, achieving a balance of interests among multiple parties within the microgrid system.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the technical solutions provided in the embodiments of the present invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 A flowchart illustrating a hierarchical distributed optimization scheduling method for microgrid systems that takes into account user psychological factors, provided as an embodiment of this application; Figure 2 This is a schematic diagram illustrating the process of constructing a user-side load model in the hierarchical distributed optimization scheduling method for microgrid systems provided in this application embodiment; Figure 3 A flowchart illustrating the process of solving the two-layer scheduling model of a microgrid system in the hierarchical distributed optimization scheduling method for microgrid systems provided in this application embodiment; Figure 4 A schematic diagram illustrating the worst-case scenario for renewable processing provided to operators within a microgrid system and the variation of the user's initial electrical load over time; Figure 5 This is a schematic diagram showing how the outdoor temperature on the user side changes over time. Figure 6 This is a schematic diagram illustrating the iterative convergence results of the total cost for operators (microgrid operators) and the total cost for users within a microgrid system. Figure 7 A diagram illustrating the changing energy trading volume between microgrid operators and users over time; Figure 8 A diagram illustrating the changing energy trading prices between microgrid operators and users over time; Figure 9 A schematic diagram of the optimal power operation strategy for microgrid operators; Figure 10 A schematic diagram of the optimal thermal power operation strategy for microgrid operators; Figure 11 A diagram illustrating the worst-case lower bound for renewable energy (renewable processing) for microgrid operators; Figure 12 This diagram illustrates the comparison of various uncertainty optimization methods. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] It should be noted that the terms "first, second, and third" used in the embodiments of this application are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0021] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0022] Example 1 like Figure 1 The diagram shown is a flowchart illustrating a hierarchical distributed optimization scheduling method for microgrid systems that takes into account user psychological factors, provided in an embodiment of this application. Step 101: Based on the operating data of user-side energy-consuming equipment in the microgrid system during a preset time period and the probability coefficients taking into account user psychology, construct a user-side load model with the objective function of minimizing the purchase cost, taking into account user psychological factors, under the constraints of electricity purchase price and heat purchase price.

[0023] In some embodiments of this application, the user-side energy-consuming devices in the microgrid system include, but are not limited to: industrial production lines, commercial facilities, residential equipment (such as lighting equipment, air conditioners, and charging piles), electric vehicles, etc.

[0024] Here, the preset time period can refer to a day, a week, or a month, and this application does not make any limitation in this regard.

[0025] In some embodiments of this application, a microgrid system is an autonomous power system that can be self-controlled, protected, and managed. It integrates distributed energy resources, loads, energy storage devices, and control systems, and is an autonomous system capable of self-control, protection, and management. Its core components include: distributed energy resources (i.e., the power plants and heat plants of the microgrid, such as wind turbines, combined heat and power units, and gas boilers), energy storage devices, and loads (i.e., the user side, which includes the electricity users and heat users of the microgrid system).

[0026] In some embodiments of this application, the probability coefficients that take into account user psychology include: the holder effect probability coefficient representing the user's holder effect and the environmental awareness probability coefficient representing the user's environmental awareness.

[0027] In some embodiments of this application, the user-side load model can be divided into a user-side electrical load model and a user-side thermal load model based on the different needs of user-side energy-consuming devices. The operating data of the user-side energy-consuming devices over a preset time period includes: the actual electrical response, the predicted electrical load, and the actual thermal response of the user-side energy-consuming devices over the preset time period. Correspondingly, as... Figure 2As shown, step 101 above can be implemented by following steps 1011 to 1014: Step 1011: Substitute the probability coefficient that takes into account user psychology into the formula for calculating the probability coefficient of user psychological factors to obtain the probability coefficient of user psychological factors.

[0028] In some embodiments of this application, users' decisions when participating in demand response are not always entirely rational. In addition to the incentive price and mechanism of demand response, users' psychological factors also affect the actual level of participation. Among these psychological factors, the main ones include the holder effect and environmental awareness, which are the two main psychological factors that lead to irrational behavior in the demand response process.

[0029] Here, the probability coefficients that take into account user psychology include the holder effect probability coefficient, which characterizes the user's holder effect. And the probability coefficient of environmental awareness, which represents users' environmental awareness. For example, the probability coefficient of the holder effect. and the probability coefficient of environmental awareness By inputting the user psychological factor probability coefficient calculation formula as shown in formula (1), the user psychological factor probability coefficient can be obtained. : Formula (1); in, and Both have a value range of [0, 1].

[0030] Step 1012: Based on the probability coefficient of user psychological factors, the actual electrical response, and the predicted electrical load, determine the user electricity satisfaction rate taking into account user psychological factors within a preset time period.

[0031] In some embodiments of this application, for the user-side electrical load model, the probability coefficient of user psychological factors, the actual electrical response and the predicted electrical load in a preset time period can be analyzed to obtain the user's electricity satisfaction taking into account user psychological factors in the preset time period.

[0032] Here, step 1012 above can be achieved through the following steps A1 to A3: Step A1: Based on the product of the preset user-reported electricity probability coefficient, the user psychological factor probability coefficient, and the predicted electricity load, obtain the reported electricity volume taking into account user psychological factors within the preset time period.

[0033] In some embodiments, a preset probability coefficient for user-reported electricity consumption can be determined based on consumer psychology principles. This assumes that the user's (consumer's) decision-making process is completely rational; the higher the compensation amount for their demand response, the greater the benefit the user gains from participating in the demand response, and thus, the more electricity they report. Conversely, if the compensation amount is low, the user's enthusiasm for participation will decrease; and when the compensation amount is too high, reaching a saturation point, the user's reported electricity consumption will no longer increase. Therefore, the preset probability coefficient for user-reported electricity consumption can be characterized based on the following formula (2): Formula (2); in, The preset probability coefficient for user-reported electricity consumption is the probability that a perfectly rational user will report electricity consumption rationally, which is equal to the user-reported amount / load. and These represent the minimum and maximum user response probabilities, respectively. For user-reported coefficients; and These are the upper and lower bound unit prices of the base unit price for demand response subsidies, respectively. The base unit price for demand response subsidies.

[0034] Here, regarding the demand response adjustability of some nodes in the distribution network within a microgrid system, users can choose to reduce some loads while ensuring their own electricity needs are met. However, during off-peak hours, there may be a certain degree of rebound, and the corresponding formula can be expressed as shown in the following formula (3): Formula (3); In the formula: For user-side power consumption equipment During the preset time period The actual electrical response (the amount of electrical load reduction); During the preset time period User-side power consumption equipment The maximum percentage of load reduction; User-side power consumption equipment During the preset time period The predicted electrical load.

[0035] Here, you can directly use the preset user-reported electricity consumption probability coefficient, user psychological factor probability coefficient, and predicted electricity load (here, predicted electricity load is the total power-consuming equipment on the user side within a preset time period). Predicted electrical load The product between ) is determined as the product within the preset time period. The reported electricity volume taking into account user psychological factors That is, as shown in formula (4): Formula (4).

[0036] Step A2: Using a proportional demand response incentive mechanism model, analyze the declared electricity volume and actual electricity response volume that take into account user psychological factors, and obtain the response subsidy coefficient provided by the microgrid system to the user side within a preset time period.

[0037] In some embodiments of this application, traditional incentive-based demand response schemes typically rely on fixed compensation amounts. This approach often fails to effectively motivate user participation within the microgrid system and may lead to significant discrepancies between actual response power and pre-declared power. This imbalance between power supply and demand becomes particularly severe during extreme heat events. Therefore, this application provides a differentiated demand response incentive mechanism that determines the subsidy unit price based on the individual circumstances of users within the microgrid system.

[0038] Specifically, the initial subsidy coefficient for users within a microgrid system is based on their past demand response participation history; the higher the historical participation, the higher the initial subsidy coefficient, and vice versa. Meanwhile, to regulate user-side response behavior within the microgrid system and prevent response deviations caused by excessively low or high participation, minimum and maximum limits on the access capacity for demand response can be set, and a maximum coefficient for demand response subsidies is specified to limit the maximum subsidy price. Finally, the final subsidy coefficient for demand response is determined based on the ratio of the actual electricity response to the declared electricity volume by users within the microgrid system during the preset time period. If the deviation between the actual response volume and the declared volume is large, the subsidy coefficient will be reduced accordingly. Correspondingly, the proportional demand response incentive mechanism model can be represented by the following formula (5): Formula (5); in, For microgrid systems within a preset time period The response subsidy coefficient provided to the user side; For user-side power-consuming devices during preset time periods The actual electrical response quantity; To be within a preset time period The declared electricity volume takes into account user psychological factors; and Representing the response ratio The left and right boundaries; and These provide the minimum and maximum subsidy coefficients to the user side for the microgrid system. Here, we can... and They were set to 80% and 120% respectively.

[0039] Step A3: Substitute the response subsidy coefficient and the actual electricity response into the user utility value function to calculate the user electricity satisfaction rate taking into account user psychological factors within a preset time period.

[0040] In some embodiments of this application, a user utility value function is established to quantify actual user satisfaction with electricity consumption, taking into account the holder effect and environmental awareness. This user utility value function is used only to measure user satisfaction with electricity consumption and is not part of the distribution network operation objective function. Here, the user utility value function can be represented by the following formula (6): Formula (6); in, To be within a preset time period User satisfaction with electricity consumption should take into account psychological factors. The base unit price for demand response subsidies; For microgrid systems within a preset time period The response subsidy coefficient provided to the user side; For user-side power-consuming devices during preset time periods The actual electrical response quantity; The holder effect penalty coefficient; It represents the external inconvenience caused to users by participating in demand response; Environmental awareness coefficient; and All are inconvenience coefficients.

[0041] It should be noted that, Let be the holder effect penalty coefficient, representing the sum of psychological discomfort and external inconvenience, and let be the external inconvenience. times; It measures the inconvenience users experience due to participating in demand response, and the inconvenience increases significantly with the increase in response volume; and All are inconvenience coefficients, determined by external factors that influence user response behavior.

[0042] Step 1013: Construct a user-side electricity load model under the constraint of electricity purchase price, with the objective function of maximizing user electricity satisfaction and minimizing electricity purchase cost while taking into account user psychological factors.

[0043] In some embodiments of this application, the electricity purchase price constraint may include: the real-time electricity price constraint and the average electricity price constraint as shown in the following formula (7): Formula (7); in, For the user side within a preset time period The electricity purchase price; and For microgrid system operators, during a preset time period The lower and upper limits of the electricity sales price offered; The upper limit on the average electricity price given to operators of microgrid systems; Preset time period Number of internal regulation cycles.

[0044] It should be noted that the user-side electricity load model, which takes into account user psychological factors, aims to maximize user electricity satisfaction and minimize electricity purchase costs. In this model, efforts should be made to balance user electricity satisfaction (maximum) and electricity purchase costs (minimum) that take into account user psychological factors.

[0045] Step 1014: Construct a user-side heat load model under the constraint of heat purchase price, with the objective function being to minimize the heat purchase cost corresponding to the actual heat response.

[0046] In some embodiments of this application, the heat purchase price constraint may also include: real-time heat price constraint and average heat price constraint, the specific idea of ​​which is the same as that of the real-time electricity price constraint and average electricity price constraint mentioned above, and will not be repeated here.

[0047] In some embodiments of this application, the user's heat load mainly appears in the form of a smart building. Correspondingly, the user-side heat and electricity load model can be based on the smart building and consider a preset time period. The model was constructed based on the actual thermal response within the space.

[0048] Step 102: Based on the operation data of distributed energy resources in the microgrid system within a preset time period, construct a microgrid operator model with profit maximization as the objective function under multiple constraints.

[0049] In some embodiments of this application, the distributed energy resources within the microgrid system include at least: combined heat and power (CHP) units, gas-fired boilers, and energy storage devices. Correspondingly, the operating data of the distributed energy resources within the microgrid system during a preset time period may include: the thermal power and electrical power output of the CHP units during the preset time period; the thermal energy provided by the gas-fired boilers during the preset time period; and the electrical energy storage capacity and discharge amount of the energy storage devices during the preset time period.

[0050] Here, taking the distributed energy resources within a microgrid system as at least including: cogeneration units, gas-fired boilers, and energy storage devices as an example, the corresponding microgrid operator model includes at least: a cogeneration unit model, a gas-fired boiler model, and an energy storage device model.

[0051] In some embodiments of this application, multiple constraints may include: a bibliophilic chance constraint based on Wasserstein distance, an operator electricity sales constraint for the microgrid system, an operator heat sales constraint for the microgrid system, a heat power balance constraint, and an electrical power balance constraint.

[0052] Here, corresponding to the description above, the operator's electricity sales constraints, the operator's heat sales constraints, the heat power balance constraints, and the electrical power balance constraints of the microgrid system can each include their respective real-time constraints and average constraints.

[0053] It should be noted that by introducing a partial Bruker chance constraint based on Wasserstein distance, the uncertainty of renewable energy output within a microgrid system can be addressed.

[0054] In addition, multiple constraints can further include: electrical interaction constraints between the microgrid system operator and the user side, thermal interaction constraints between the microgrid system operator and the user side, etc.

[0055] Step 103: Based on Stackelberg game theory, a two-layer scheduling model for the microgrid is obtained by combining the user-side load model and the microgrid operator model.

[0056] Here, the core of Stackelberg's game theory lies in the impact of decision order on market equilibrium.

[0057] In some embodiments of this application, step 103 can be implemented by steps 1031 and 1032. Figure 1 (not shown in the image) Step 1031: Based on Stackelberg game theory, establish the interactive coupling relationship between the user-side load model and the microgrid operator model.

[0058] The interactive coupling relationship includes: the microgrid operator model formulating and transmitting electricity and heat prices to the user-side load model based on the received electricity and heat consumption strategies, and the user-side load model adjusting the electricity and heat consumption strategies according to the electricity and heat prices.

[0059] Step 1032: Based on the interactive coupling relationship, construct a two-layer microgrid scheduling model with the user-side load model as the lower-level follower model and the microgrid operator model as the upper-level leader model.

[0060] Thus, the established two-layer microgrid dispatch model is a two-layer game optimization model. The microgrid operator model, acting as the upper-layer leader, can achieve supply and demand balance and maximize efficiency by optimizing the operation of various distributed energy resources and setting incentive prices for demand response. Meanwhile, the user-side load model, acting as the lower-layer follower, considers the influence of user psychological factors on demand response decisions and can effectively characterize the degree of demand response.

[0061] Step 104: Using the alternating direction multiplier method, the microgrid two-layer scheduling model is solved by hierarchical distributed optimization to obtain the optimized values ​​of the traded electricity, traded heat, electricity trading price, and heat trading price of the microgrid system within a preset time period.

[0062] In some embodiments of this application, the Alternating Direction Multiplier Method (ADMM) is an important method for solving convex optimization problems with separable structures. It has the ability to perform efficient parallel computation in solving large-scale distributed problems and is currently the mainstream distributed algorithm.

[0063] In some embodiments of this application, such as Figure 3 As shown, step 104 above can be implemented by following steps 1041 to 1044: Step 1041: Based on the objective function of the user-side load model and the objective function of the microgrid operator model, determine the local dummy variables corresponding to the variables to be optimized, and establish consistency constraints between the local dummy variables and the variables to be optimized.

[0064] The variables to be optimized include: electricity trading power, heat trading power, electricity trading price, and heat trading price.

[0065] Step 1042: Based on consistency constraints, decompose the initial user-side sub-problem of the user-side load model and the initial microgrid operator sub-problem of the microgrid operator model.

[0066] The objective functions of the initial user-side subproblem and the initial microgrid operator subproblem both include Lagrange multipliers and quadratic penalty terms used to enforce consistency constraints.

[0067] Step 1043: Using the McCormick envelope method, linear relaxation is performed on the nonlinear terms in the initial user-side subproblem and the initial microgrid operator subproblem to obtain the linearized target user-side subproblem and the linearized target microgrid side problem.

[0068] Step 1044: Iteratively solve the linearized target user side sub-problem and the linearized target microgrid side sub-problem, and update the Lagrange multipliers in each iteration until the consistency constraint converges, to obtain the optimized values ​​of the traded electricity, traded heat, electricity trading price and heat trading price of the microgrid system in the preset time period.

[0069] This application discloses a hierarchical distributed optimization scheduling method for microgrid systems that considers user psychological factors. First, based on the operating data of user-side energy-consuming equipment within the microgrid system over a preset time period and probability coefficients taking into account user psychology, a user-side load model is constructed, with the objective function of minimizing purchase costs while considering user psychological factors, under constraints of electricity and heat purchase prices. Then, based on the operating data of distributed energy resources within the microgrid system over a preset time period, a microgrid operator model is constructed, with the objective function of maximizing profits under multiple constraints. Next, based on Stackelberg game theory, a two-layer scheduling model for the microgrid is obtained by combining the user-side load model and the microgrid operator model. Finally, the alternating direction multiplier method is used to perform hierarchical distributed optimization on the two-layer scheduling model, yielding optimized values ​​for traded electricity, traded heat, electricity trading prices, and heat trading prices for the microgrid system over a preset time period. Thus, on the one hand, while balancing the interests of microgrid operators and users participating in demand response, a two-layer microgrid scheduling model based on master-slave game theory is constructed. The user-side load model, acting as a follower, characterizes the impact of user psychology on demand response, while the microgrid operator model, acting as the leader, achieves supply-demand balance and maximizes efficiency by optimizing the operation of various devices and setting incentive prices for demand response. On the other hand, the alternating direction multiplier method is used to perform hierarchical distributed optimization of the two-layer microgrid scheduling model, effectively tapping into user-side participation in demand response and fully utilizing wind, solar, and other resources within the microgrid system. In this way, while pursuing optimal economic operation of the microgrid system, the interests of users are also considered, achieving a balance of interests among multiple parties within the microgrid system.

[0070] The hierarchical distributed optimization scheduling method for microgrid systems that takes into account user psychological factors provided in this application embodiment can be used in practical applications to establish a master-slave game two-layer optimization model (i.e., the microgrid two-layer scheduling model provided in this application embodiment) between the microgrid operator (MGO) and users (i.e., the master-slave game two-layer optimization model) in the Matlab compilation environment using the Yalmip toolbox. The ADMM algorithm and the Cplex solver can be used to solve the problem of energy trading volume and energy trading price between the microgrid operator and users.

[0071] The basic data and system structure can be described first: the microgrid operator model mentioned in this application embodiment can be directly described as a combined heat and power (CHP) model; the operator within the microgrid system can be configured with: a renewable energy unit (wind and solar power), one CHP unit, one gas-fired boiler, and one energy storage device. The electricity purchase and sale price from the external grid and the gas purchase price from the gas grid can be found in Table 1. Table 1. Electricity purchase price from external power grid and gas purchase price from gas grid Correspondingly, such as Figure 4 The diagram shown illustrates the worst-case scenario for renewable energy processing provided by the operator within a microgrid system and the variation of the user's initial electrical load over time. Figure 5 This diagram illustrates how the outdoor temperature on the user's side changes over time.

[0072] Here, because this application uses the ADMM algorithm to solve the two-level scheduling model of the microgrid in a distributed manner, it obtains the energy trading volume and energy trading price between operators and users within the microgrid system. Correspondingly, in practical applications, it can be as follows: Figure 6 The diagram shows the iterative convergence results of the total cost for operators (microgrid operators) and the total cost for users within a microgrid system. Figure 6 This demonstrates the iterative convergence of the total cost for microgrid operators and users, assuming the algorithm's convergence accuracy is set to 0.1. Figure 6 As can be seen, the ADMM algorithm converges after 28 iterations, with a computation time of 25.5 seconds. The final convergence values ​​of the total cost for microgrid operators and users are -11865.45 yuan and 4918.80 yuan, respectively. This demonstrates that the ADMM algorithm has good convergence for solving bi-level optimization models.

[0073] Furthermore, this application relates to the microgrid two-layer scheduling model constructed based on master-slave game theory. See here for more information. Figure 7 and Figure 8 The figures show: the energy transaction volume between microgrid operators and users over time (specifically represented by the user's purchased electricity and heat), and the energy transaction price over time (also represented by the user's purchased electricity and heat prices). Figure 7 and Figure 8 It can be seen that during the period from 00:00 to 04:00, users' electricity and heat purchases are at a low point, and the prices set by microgrid operators are relatively low during this time to incentivize users to purchase energy. From 05:00 to 07:00, as daily life and production activities resume, users' electricity purchases gradually increase. At this time, in order to maximize their own revenue from energy sales, operators set electricity prices accordingly. From 08:00 to 18:00, combined with... Figure 4Analysis shows that during this period, user electricity load gradually increases. For microgrid operators, to maximize profits, prices also increase with the increase in electricity load. However, it's worth noting that between 12:00 and 13:00, due to the higher electricity prices charged by microgrid operators, users reduce their electricity purchases from them based on their own needs. Therefore, operators lower their electricity prices to users between 14:00 and 15:00, resulting in an increase in user electricity purchases during this period. Between 19:00 and 24:00, user electricity purchases gradually decrease, but do not return to the low levels of the early morning. This is because heating and lighting needs still exist at night, thus keeping the load at a moderate level. For user heat purchases, the fluctuations in both the purchased heat volume and the price of heat provided by operators are relatively stable within a dispatch cycle, reflecting the stability of user demand for heat.

[0074] In practical applications, this application linearizes the objective function of the microgrid two-layer scheduling model, thus achieving scheduling optimization. Here, an example is provided using the optimal operating strategy for the microgrid operator's electrical and thermal power; further details can be found in the references below. Figure 9 and Figure 10 As shown. By Figure 9 It can be seen that within a dispatch cycle, the renewable energy sources within the microgrid operator all contribute to varying degrees and undertake the main power supply, especially during the period from 10:00 to 15:00, when the sunlight intensity is high. Therefore, for photovoltaics, the output is high during this period, which can better meet the demand for electricity load and sell the excess electricity to the upper-level grid to obtain revenue, thereby maximizing its own interests. Meanwhile, during periods of relatively low electricity prices (1:00-4:00, 15:00-16:00, and 23:00-24:00), the microgrid system's energy storage devices will charge during these times, and then discharge during periods of higher electricity demand (06:00, 12:00, 14:00, and 20:00-21:00), employing a "high-charge, low-discharge" operating mode to reduce system operating costs. For cogeneration (CHP) units, generating electricity during periods of 12:00-14:00, 16:00, and 18:00-22:00, combined with renewable energy generation and energy storage, can effectively maintain the system's electricity load balance. Figure 10 It can be seen that the cogeneration unit generates heat during the periods of 12:00-14:00, 16:00 and 18:00-22:00, while the steam turbine unit outputs power outside of the above periods to supplement the system's heat energy; among them, the steam turbine unit mainly undertakes the supply of heat load within the system, and the two work together to achieve heat load balance within the system.

[0075] Correspondingly, the operating costs for microgrid operators and users are shown in Table 2. As shown in Table 2, for microgrid operators, revenue is mainly generated by selling electricity and heat to users, with revenues of RMB 5858.63 and RMB 6982.16 respectively. Internally, considering that the CHP units require natural gas combustion for heating and power generation during commissioning, this incurs a gas purchase cost of RMB 4283.08. Furthermore, since microgrid operators have internal energy storage devices, their degradation also incurs costs, which are also considered in the overall optimization problem. Including this cost also helps avoid the generation of 0-1 variables during the modeling process.

[0076] Table 2 Operating Costs for Microgrid Operators and Users For users, energy is primarily obtained by purchasing electricity and heat from microgrid operators. They also possess flexible demand response capabilities, allowing them to adjust their load status according to market electricity price fluctuations. The resulting cost is 206.12 yuan, while the costs of purchasing electricity and heat are 3376.57 yuan and 1745.54 yuan, respectively. In summary, the two-layer optimization model (microgrid two-layer dispatch model) proposed in this application can effectively achieve cost optimization for both microgrid operators and users, ultimately balancing their interests and demonstrating significant practical value.

[0077] also, Figure 11 The paper also presents the worst-case lower bound for renewable energy (renewable processing) for microgrid operators. Due to the uncertainty of renewable output within microgrid operators, this paper employs a Wasserstein-based Distributed Robust Chance Constraints (DRCC) method to determine the worst-case lower bound for renewable output. Here, an example is given with 5 historical samples of renewable output and a confidence level of 70%. Figure 11 It can be seen that the worst lower bound of renewable output always lies between the maximum and minimum output values ​​of each period in the historical sample.

[0078] Meanwhile, in practical applications, using Monte Carlo simulations with 5000 renewable power output samples, the sample performance of the method proposed in this application embodiment was compared with that of the Stochastic Optimization (SO) method and the Robust Optimization (RO) method. The comparison results are as follows: Figure 12As shown. For the SO model, the Sample Average Approximation (SAA) method is used, setting the radius of the Wasserstein sphere to zero; for the RO model, the corresponding RO-SAA method is used. Figure 12 It is evident that the RO-SAA method yields lower returns than the SAA and Distributed Robust Optimization (DRO) methods. This is because the RO-SAA method only uses the lower and upper bounds of uncertainty for decision-making, resulting in overly conservative solutions. Another observation is that the returns of both the SAA and DRO methods increase with the number of samples. This is because the more uncertain samples there are, the more accurate the probability distribution of uncertainty becomes. In particular, it is observed that within the sample range, the DRO method's returns exceed the cost of the SAA method, further demonstrating that the hierarchical distributed optimization scheduling method for microgrid systems, which considers user psychological factors, provided in this application is superior to the SAA method.

[0079] Based on the above description, this application proposes a two-layer optimal scheduling model for microgrids based on master-slave game theory, which aims to achieve optimal economic efficiency in microgrid operation while also considering the interests of users. Furthermore, a distributed boolean chance constraint based on Wasserstein distance is used for processing. To protect the data privacy of different stakeholders, the ADMM algorithm is employed for hierarchical distributed solution of the two-layer optimal scheduling model. The following conclusions are drawn through numerical examples: 1) This application establishes a two-layer game theory optimization model (microgrid two-layer dispatch model). The upper-layer microgrid operator achieves supply and demand balance and pursues maximum benefits by optimizing the operation of various equipment and setting incentive prices for demand response. The lower layer considers the influence of user psychological factors on demand response decisions, effectively characterizing the degree of demand response. Through case analysis, it is verified that this model is conducive to balancing the interests of all parties, effectively tapping into user-side participation in demand response, and fully absorbing wind and solar resources in the system.

[0080] 2) This application proposes a collaborative hierarchical distributed algorithm. By introducing a bibliometric chance constraint based on Wasserstein distance to handle the uncertainty of renewable energy output in the microgrid, the ADMM algorithm is used to solve the two-layer optimization scheduling model in a hierarchical distributed manner. The effectiveness of the proposed algorithm is verified by comparing different optimization methods, and the data privacy of different stakeholders is protected.

[0081] The modules in the aforementioned hierarchical distributed optimization scheduling method for microgrid systems that takes into account user psychological factors can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0082] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0083] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0084] In the several embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways. The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined to obtain new method embodiments without conflict. The features disclosed in the several method embodiments provided in this application can be arbitrarily combined to obtain new method embodiments without conflict.

[0085] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any modifications or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A hierarchical distributed optimization scheduling method for microgrid systems that takes into account user psychological factors, characterized in that, include: Based on the operating data of user-side energy-consuming equipment in the microgrid system during a preset time period and the probability coefficients taking into account user psychology, a user-side load model is constructed under the constraints of electricity purchase price and heat purchase price, with the objective function of minimizing the purchase cost taking into account user psychological factors. Based on the operational data of distributed energy resources within a microgrid system over a preset time period, a microgrid operator model is constructed under multiple constraints with profit maximization as the objective function. Based on Stackelberg game theory, a two-layer scheduling model for microgrids is obtained by combining user-side load model and microgrid operator model. The alternating direction multiplier method is used to perform hierarchical distributed optimization solution on the microgrid two-level scheduling model, and the optimized values ​​of the traded electricity, traded heat, electricity trading price and heat trading price of the microgrid system in a preset time period are obtained.

2. The hierarchical distributed optimization scheduling method for microgrid systems according to claim 1, characterized in that, The user-side load model includes: the user-side electrical load model and the user-side thermal load model; the operating data of the user-side energy-consuming equipment during the preset time period includes: the actual electrical response, the predicted electrical load, and the actual thermal response of the user-side energy-consuming equipment during the preset time period. Based on the operating data of user-side energy-consuming equipment within a microgrid system over a preset time period and probability coefficients considering user psychology, a user-side load model is constructed under constraints of electricity and heat purchase prices, with the objective function of minimizing the purchase cost while taking into account user psychological factors. This model includes: Substituting the probability coefficient that takes into account user psychology into the formula for calculating the probability coefficient of user psychological factors, we obtain the probability coefficient of user psychological factors. Based on the probability coefficient of user psychological factors, the actual electrical response and the predicted electrical load, the user electricity satisfaction rate taking into account user psychological factors is determined within a preset time period. A user-side electricity load model is constructed under the constraint of electricity purchase price, with the objective function of maximizing user electricity satisfaction and minimizing electricity purchase cost while taking into account user psychological factors. A user-side heat load model is constructed under the constraint of heat purchase price, with the objective function of minimizing the heat purchase cost corresponding to the actual heat response.

3. The hierarchical distributed optimization scheduling method for microgrid systems according to claim 2, characterized in that, The probability coefficients that take into account user psychology include: the holder effect probability coefficient, which characterizes the user's holder effect. And the probability coefficient of environmental awareness, which represents users' environmental awareness. The formula for calculating the probability coefficient of user psychological factors is: in, This represents the probability coefficient of user psychological factors.

4. The hierarchical distributed optimal scheduling method for microgrid systems according to claim 2 or 3, characterized in that, Based on the probability coefficient of user psychological factors, actual electrical response, and predicted electrical load, determine user electricity satisfaction taking into account user psychological factors within a preset time period, including: The declared electricity amount, taking into account user psychological factors, is obtained by multiplying the preset user declared electricity amount probability coefficient, user psychological factor probability coefficient, and predicted electricity load amount within a preset time period. A proportional demand response incentive mechanism model is adopted to analyze the declared electricity volume and actual electricity response volume, taking into account user psychological factors, and to obtain the response subsidy coefficient provided by the microgrid system to the user side within a preset time period. By substituting the response subsidy coefficient and the actual electricity response into the user utility value function, the user electricity satisfaction rate, taking into account user psychological factors, is obtained within a preset time period.

5. The hierarchical distributed optimization scheduling method for microgrid systems according to claim 4, characterized in that, The proportional demand response incentive mechanism model is as follows: in, For microgrid systems within a preset time period The response subsidy coefficient provided to the user side; For user-side power-consuming devices during preset time periods The actual electrical response quantity; To be within a preset time period The declared electricity volume takes into account user psychological factors; and Representing the response ratio The left and right boundaries; and The minimum and maximum subsidy coefficients are provided to the user side for the microgrid system, respectively.

6. The hierarchical distributed optimization scheduling method for microgrid systems according to claim 4, characterized in that, The user utility value function is: in, To be within a preset time period User satisfaction with electricity consumption should take into account psychological factors. The base unit price for demand response subsidies; For microgrid systems within a preset time period The response subsidy coefficient provided to the user side; For user-side power-consuming devices during preset time periods The actual electrical response quantity; The holder effect penalty coefficient; It represents the external inconvenience caused to users by participating in demand response; Environmental awareness coefficient; and All are inconvenience coefficients.

7. The hierarchical distributed optimal scheduling method for microgrid systems according to claim 1, characterized in that, The multiple constraints include: the bibliometric chance constraint based on Wasserstein distance, the operator's electricity sales constraint for the microgrid system, the operator's heat sales constraint for the microgrid system, the heat power balance constraint, and the electrical power balance constraint.

8. The hierarchical distributed optimization scheduling method for microgrid systems according to claim 1, characterized in that, Microgrid operator models include at least: combined heat and power unit models, gas boiler models, and energy storage equipment models.

9. The hierarchical distributed optimal scheduling method for microgrid systems according to claim 1, characterized in that, Based on Stackelberg game theory, and combining user-side load models and microgrid operator models, a two-layer dispatch model for microgrids is obtained, including: Based on Stackelberg game theory, an interactive coupling relationship is established between the user-side load model and the microgrid operator model. The interactive coupling relationship includes: the microgrid operator model formulating and transmitting electricity and heat prices to the user-side load model based on the received electricity and heat consumption strategies, and the user-side load model adjusting its electricity and heat consumption strategies according to the electricity and heat prices. Based on the interactive coupling relationship, a two-layer scheduling model for microgrids is constructed, with the user-side load model as the lower-level follower model and the microgrid operator model as the upper-level leader model.

10. The hierarchical distributed optimal scheduling method for microgrid systems according to claim 1, characterized in that, The alternating direction multiplier method is used to perform hierarchical distributed optimization solutions on the two-level dispatch model of the microgrid, obtaining the optimized values ​​of traded electricity, traded heat, electricity trading price, and heat trading price for the microgrid system within a preset time period, including: Based on the objective functions of the user-side load model and the microgrid operator model, local dummy variables corresponding to the variables to be optimized are determined, and consistency constraints between the local dummy variables and the variables to be optimized are established. Among them, the variables to be optimized include: electricity trading power, heat trading power, electricity trading price, and heat trading price. Based on consistency constraints, the initial user-side subproblem of the user-side load model and the initial microgrid operator subproblem of the microgrid operator model are decomposed and constructed. The objective functions of the initial user-side subproblem and the initial microgrid operator subproblem both include Lagrange multipliers and quadratic penalty terms used to enforce consistency constraints. By using the McCormick envelope method, the nonlinear terms in the initial user-side subproblem and the initial microgrid operator subproblem are linearly relaxed to obtain the linearized target user-side subproblem and the linearized target microgrid side problem. The linearized target user side problem and the linearized target microgrid side problem are solved iteratively, and the Lagrange multipliers are updated in each iteration until the consistency constraint converges, so as to obtain the optimized values ​​of the traded electricity, traded heat, electricity trading price and heat trading price of the microgrid system in a preset time period.

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