Air conditioner load aggregator demand response optimization method and device based on user credit

Abstract

The application discloses an air conditioner load aggregator demand response optimization method and device based on user credit, which comprises the following steps: constructing a user credit evaluation model according to user subscription performance indicators, evaluating the response behavior performance of users, and obtaining user credit evaluation indicators; constructing a building room temperature thermal dynamic model based on a thermal resistance-thermal capacity network model; describing the building room temperature thermal dynamic process through the building room temperature thermal dynamic model; constructing a load aggregator demand response double-layer optimization model based on the user credit evaluation indicators; solving the optimization model through a set solving strategy, and obtaining an optimal load aggregator dispatching strategy; constructing a credit updating model; and updating the user credit evaluation indicators through the credit updating model according to the user response performance indicators after the power grid demand response, and obtaining the updated user credit evaluation indicators. The application can improve the effect of demand response and realize fine dispatching.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and power system optimization, and in particular to a method and device for optimizing demand response of an air conditioning load aggregator based on user credit. Background Art

[0002] As a crucial component of load-side resources, HVAC systems, due to their flexibility and ease of control, are currently a key area of ​​research for improving building energy efficiency and grid stability. Peak HVAC loads pose challenges to the grid's power balance, and optimizing and regulating them can effectively alleviate peak load pressures. However, due to the small scale and limited response capabilities of individual users, decentralized optimization and regulation by individual users is unlikely to yield significant results. Therefore, load aggregators are established between users and the grid, generating revenue by aggregating load-side resources and selling flexibility services to the electricity market. Demand response, a key regulatory mechanism in the power system, guides users to adjust their electricity consumption through price signals or incentives, thereby improving the grid's supply-demand balance. Users can earn financial subsidies by actively participating in the response, while the grid leverages the flexibility of load-side resources to improve operational reliability and economic efficiency. Therefore, there is an urgent need for load aggregators to optimize and integrate HVAC loads from multiple types of users and effectively participate in demand response.

[0003] Currently, a large number of scholars are working on user participation in demand response, focusing on incentive mechanism design, optimized scheduling models, and user behavior modeling. However, current research focuses primarily on industrial and commercial user load resources, lacking in-depth research on demand response for HVAC load resources within building clusters. This research fails to fully consider the load characteristics and user behavior complexity of building clusters. Furthermore, users' actual response willingness and contract performance are crucial to the effectiveness of demand response. However, existing models inadequately assess the actual degree of response during the user response phase, relying primarily on crude quantification of user credit and lacking a multidimensional, dynamic credit assessment system. Furthermore, limited research exists on how to further enhance the potential of demand response and user participation while ensuring user comfort and satisfaction.

[0004] Therefore, how to invent a demand response optimization method for air conditioning load aggregators based on user credit, which can improve the effect of demand response and refined scheduling, has become an urgent problem to be solved. Summary of the Invention

[0005] To this end, the present invention provides a method and device for optimizing demand response of air-conditioning load aggregators based on user credit. The method proposes a two-layer optimization model of demand response based on credit evaluation indicators from the perspective of the aggregator. The credit evaluation indicators regulate the response behavior of the building, realize the efficient and reliable aggregation of the building's adjustable potential, and construct a two-layer optimization model of load aggregator revenue and building user subsidies, thereby improving the effect of demand response and refined scheduling.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing demand response of air conditioning load aggregators based on user credit, comprising:

[0007] Constructing a user credit evaluation model based on user subscription performance indicators; evaluating the user's response behavior performance through the user credit evaluation model to obtain a user credit evaluation indicator;

[0008] Based on the thermal resistance-heat capacity network model, a building room temperature thermal dynamic model is constructed; and the building room temperature thermal dynamic process is described by the building room temperature thermal dynamic model;

[0009] Based on the user credit evaluation index, a two-layer optimization model for load aggregator demand response is constructed;

[0010] Solving the load aggregator demand response two-layer optimization model by setting a solution strategy to obtain the load aggregator demand response optimal dispatch strategy;

[0011] A credit update model is constructed; after the grid demand response, the load aggregator updates the user credit evaluation index through the credit update model according to the user response performance index to obtain the user's updated credit evaluation index.

[0012] As a preferred solution of the user credit-based air conditioning load aggregator demand response optimization method, in the process of describing the building room temperature thermal dynamic process through the building room temperature thermal dynamic model, the heat balance constraint of a single cooling area is:

[0013]

[0014]

[0015] Where, (j=2,3,4,5; m=1,2,…,N) are the heat capacity, temperature and thermal resistance of the wall between two nodes; N is the total number of building clusters with different insulation performance; is the indoor air temperature of cooling zone 1; is the indoor temperature of other cooling areas adjacent to cooling area 1. If the adjacent cooling area is outdoor, it is the outdoor temperature; t 1,jWhen (j=2,3,4,5) is 1, it means that the wall is exposed to sunlight, otherwise it is 0; ε 1,j 、 are the heat storage rate and surface area of ​​the four walls respectively; is the outdoor light intensity; is the heat capacity of cooling area 1; R win is the window thermal resistance; T out is the outdoor temperature; Q win The light intensity received by the window; are the HVAC cooling capacity and internal heat generation of cooling zone 1 respectively; win 、S win are the window transmittance and window surface area, respectively.

[0016] As a preferred solution of the air conditioning load aggregator demand response optimization method based on user credit, the calculation formula of the user credit evaluation index is:

[0017] SPI=1-δ t

[0018]

[0019] Where SPI is the value of the user credit evaluation index, which ranges from (0, 1); t is the relative deviation in period t. When the user completes the target load reduction in period t, then δ t =0; on the contrary, the greater the deviation, the greater the t The closer or greater than 1; is the baseline power value corresponding to period t; is the actual operating power value of the user during period t; is the power reduction amount in period t as contracted between the user and the load aggregator; t1 and t2 correspond to the start time and end time respectively.

[0020] As a preferred solution of the air conditioning load aggregator demand response optimization method based on user credit, the calculation formula of the user's updated credit evaluation index is:

[0021]

[0022]

[0023] Where, CEI t is the updated credit score after period t; CEI t-1 The credit score calculated for the previous period; is the dynamic weight coefficient; when the recent credit score volatility is large, becomes larger, more dependent on the credit score at that time; on the contrary, Smaller, more focused on historical response credit scores; rec Calculate the standard deviation of the credit score after the recent response; s all The standard deviation of the credit scores calculated for all responses.

[0024] As a preferred solution of the air conditioning load aggregator demand response optimization method based on user credit, in the process of solving the load aggregator demand response two-layer optimization model through the set solution strategy, the upper model in the load aggregator demand response two-layer optimization model optimizes the subsidy incentive price for users with the goal of maximizing the subsidy benefits obtained by the load aggregator participating in the grid demand response: the objective function expression of the upper model is:

[0025]

[0026] Where, Sub agg Total response subsidies paid to aggregators for the grid; is the total actual response of all building users in the demand response phase; W market The number of bids won by aggregators for demand response; value is the list price provided by the grid; n is the number of building users participating in demand response integrated by the aggregator; σ and It is the segmented assessment coefficient of demand response subsidy;

[0027] The convergence condition of the objective function of the upper model is:

[0028]

[0029] Where, Sub i is the response subsidy obtained by building user i; tolerance is the convergence tolerance to ensure that the aggregator's revenue is balanced with the total subsidy of building users;

[0030] The lower model in the load aggregator demand response two-layer optimization model comprehensively considers the user's electricity purchase cost and maximizes the benefits of the response subsidy within the comfort range, and optimizes the HVAC operating power of each building. The objective function expression of the lower model is:

[0031]

[0032] Where C buy The unit price for building users to purchase electricity from the power grid company; Comfort i The comfort loss cost caused by reducing electricity consumption during the demand response phase for the i-th building user;

[0033] The objective function constraints of the lower model are:

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Where, is the HVAC cooling capacity of cooling area i in period t; c air is the heat capacity of air; is the air quality of the mth building in period t; T room is the indoor temperature; is the air quality of the mth building; ρ room is the indoor air density; is the volume of the mth building; a and b are the quadratic coefficient and linear coefficient of the comfort loss cost respectively; D k is the assessment coefficient matrix.

[0043] The present invention also provides an air conditioning load aggregator demand response optimization device based on user credit, based on the above air conditioning load aggregator demand response optimization method based on user credit, including:

[0044] A user credit evaluation model construction and processing module is used to construct a user credit evaluation model based on the user subscription performance index; evaluate the user's response behavior performance through the user credit evaluation model to obtain the user credit evaluation index;

[0045] A building room temperature thermal dynamic model construction module is used to construct a building room temperature thermal dynamic model based on a thermal resistance-heat capacity network model; the building room temperature thermal dynamic process is described by the building room temperature thermal dynamic model;

[0046] A load aggregator demand response two-tier optimization model construction module, configured to construct a load aggregator demand response two-tier optimization model based on the user credit evaluation index;

[0047] A load aggregator demand response two-layer optimization model solving module, configured to solve the load aggregator demand response two-layer optimization model by setting a solving strategy to obtain an optimal dispatching strategy for the load aggregator demand response;

[0048] The credit update model construction and processing module is used to construct a credit update model; after the grid demand response, the load aggregator updates the user credit evaluation index through the credit update model based on the user response performance index to obtain the user's updated credit evaluation index.

[0049] As a preferred solution of the user credit-based air conditioning load aggregator demand response optimization device, in the building room temperature thermal dynamic model construction module, in the process of describing the building room temperature thermal dynamic process through the building room temperature thermal dynamic model, the heat balance constraint of a single cooling zone is:

[0050]

[0051]

[0052] Where, are the heat capacity, temperature, and thermal resistance of the wall between two nodes; N is the total number of building clusters with different insulation performance; is the indoor air temperature of cooling zone 1; is the indoor temperature of other cooling areas adjacent to cooling area 1. If the adjacent cooling area is outdoor, it is the outdoor temperature; t 1,j When (j=2,3,4,5) is 1, it means that the wall is exposed to sunlight, otherwise it is 0; ε 1,j 、 (j=2, 3, 4, 5) are the heat storage rate and surface area of ​​the four walls respectively; is the outdoor light intensity; is the heat capacity of cooling area 1; R win is the window thermal resistance; T out is the outdoor temperature; Q win The light intensity received by the window; are the HVAC cooling capacity and internal heat generation of cooling zone 1 respectively; win 、S win are the window transmittance and window surface area, respectively.

[0053] As a preferred solution of the air conditioning load aggregator demand response optimization device based on user credit, in the user credit evaluation model construction and processing module, the calculation formula of the user credit evaluation index is:

[0054] SPI=1-δ t

[0055]

[0056] Where SPI is the value of the user credit evaluation index, which ranges from (0, 1); tis the relative deviation in period t. When the user completes the target load reduction in period t, then δ t =0; on the contrary, the greater the deviation, the greater the t The closer or greater than 1; is the baseline power value corresponding to period t; is the actual operating power value of the user during period t; is the power reduction amount in period t as contracted between the user and the load aggregator; t1 and t2 correspond to the start time and end time respectively.

[0057] As a preferred solution of the air conditioning load aggregator demand response optimization device based on user credit, in the credit update model construction and processing module, the calculation formula of the user's updated credit evaluation index is:

[0058]

[0059]

[0060] Where, CEI t is the updated credit score after period t; CEI t-1 The credit score calculated for the previous period; is the dynamic weight coefficient; when the recent credit score volatility is large, becomes larger, more dependent on the credit score at that time; on the contrary, Smaller, more focused on historical response credit scores; rec Calculate the standard deviation of the credit score after the recent response; s all The standard deviation of the credit scores calculated for all responses.

[0061] As a preferred solution of the air conditioning load aggregator demand response optimization device based on user credit, in the load aggregator demand response two-layer optimization model solving module, in the process of solving the load aggregator demand response two-layer optimization model by using the set solving strategy, the upper model in the load aggregator demand response two-layer optimization model optimizes the subsidy incentive price for users with the goal of maximizing the subsidy benefits obtained by the load aggregator participating in the grid demand response: the objective function expression of the upper model is:

[0062]

[0063] Where, Sub agg the total response subsidy paid to aggregators for the grid; is the total actual response of all building users in the demand response phase; W market The number of bids won by aggregators for demand response; valueis the list price provided by the grid; n is the number of building users participating in demand response integrated by the aggregator; σ and It is the segmented assessment coefficient of demand response subsidy;

[0064] The convergence condition of the objective function of the upper model is:

[0065]

[0066] Where, Sub i is the response subsidy obtained by building user i; tolerance is the convergence tolerance to ensure that the aggregator's revenue is balanced with the total subsidy of building users;

[0067] The lower model in the load aggregator demand response two-layer optimization model comprehensively considers the user's electricity purchase cost and maximizes the benefits of the response subsidy within the comfort range, and optimizes the HVAC operating power of each building. The objective function expression of the lower model is:

[0068]

[0069] Where C buy The unit price for building users to purchase electricity from the power grid company; Comfort i The comfort loss cost caused by reducing electricity consumption during the demand response phase for the i-th building user;

[0070] The objective function constraints of the lower model are:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] Where, is the HVAC cooling capacity of cooling area i in period t; c air is the heat capacity of air; is the air quality of the mth building in period t; T room is the indoor temperature; is the air quality of the mth building; ρ room is the indoor air density; is the volume of the mth building; a and b are the quadratic coefficient and linear coefficient of the comfort loss cost respectively; D k is the assessment coefficient matrix.

[0080] The present invention has the following advantages: It constructs a user credit evaluation model based on user subscription performance indicators; evaluates the user's response behavior performance using the user credit evaluation model to obtain a user credit evaluation index; constructs a building room temperature thermal dynamic model based on a thermal resistance-heat capacitance network model; describes the building room temperature thermal dynamic process using the building room temperature thermal dynamic model; constructs a load aggregator demand response two-layer optimization model based on the user credit evaluation index; solves the load aggregator demand response two-layer optimization model by setting a solution strategy to obtain the load aggregator's optimal demand response scheduling strategy; constructs a credit update model; and after the grid demand response, the load aggregator updates the user credit evaluation index using the credit update model based on the user response performance indicator to obtain the user's updated credit evaluation index. From the perspective of the aggregator, the present invention proposes a demand response two-layer optimization model based on credit evaluation indicators. The credit evaluation indicators regulate the building's response behavior, achieve efficient and reliable aggregation of the building's adjustable potential, and construct a two-layer optimization model of load aggregator revenue and building user subsidies, thereby improving the effectiveness of demand response and refined scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0082] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0083] Figure 1 This is a flow chart of the method for optimizing the demand response of air conditioning load aggregators based on user credit provided in Example 1 of the present invention;

[0084] Figure 2This is a schematic diagram of a specific implementation process of the air conditioning load aggregator demand response optimization method based on user credit provided in Example 1 of the present invention;

[0085] Figure 3 A schematic diagram of a curve showing changes in solar radiation intensity and outdoor temperature in a possible embodiment provided in Example 1 of the present invention;

[0086] Figure 4 Schematic diagram of the iterative process of the optimization algorithm in a possible embodiment provided in Example 1 of the present invention; wherein (a) is the response amount iteration curve; (b) is the incentive subsidy iteration curve;

[0087] Figure 5 This is a schematic diagram of the architecture of the air conditioning load aggregator demand response optimization device based on user credit provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0088] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0089] Example 1

[0090] See also Figure 1 and Figure 2 Embodiment 1 of the present invention provides a method for optimizing demand response of an air conditioning load aggregator based on user credit, comprising the following steps:

[0091] S1. Constructing a user credit evaluation model based on user subscription performance indicators; evaluating the user's response behavior performance using the user credit evaluation model to obtain a user credit evaluation indicator;

[0092] S2. Constructing a building room temperature thermal dynamic model based on a thermal resistance-heat capacity network model; describing the building room temperature thermal dynamic process by using the building room temperature thermal dynamic model;

[0093] S3. Building a two-tier optimization model for load aggregator demand response based on the user credit evaluation index;

[0094] S4. Solve the load aggregator demand response two-layer optimization model by setting a solution strategy to obtain the load aggregator demand response optimal scheduling strategy;

[0095] S5. Construct a credit update model. After the grid demand response, the load aggregator updates the user credit evaluation index through the credit update model based on the user response performance index to obtain the user's updated credit evaluation index.

[0096] In this embodiment, in step S1, a user credit evaluation model is constructed based on the user subscription performance index; the user's response behavior performance is evaluated and processed using the user credit evaluation model to obtain the user credit evaluation index;

[0097] Specifically, the load aggregator's actual response to a user's demand response period is reflected by the ratio of the actual response volume to the reported response volume. The traditional subscription performance metric is the ratio of the user's average load reduction during the demand response period to the agreed reduction volume contracted with the user before the response. Based on the user subscription performance metrics, a user credit evaluation model is constructed. This user credit evaluation model is used to evaluate the performance of the user's response behavior. The deviation between the actual response volume and the agreed response volume is used as a metric to evaluate the user's response performance, thereby obtaining a user credit evaluation index. Subsidy prices are calculated based on the user credit evaluation index of the building cluster. Users with higher credit scores receive higher subsidy prices, thereby incentivizing them to participate more actively in demand response. The user credit evaluation index dynamically assesses the degree to which subsidy incentives are affected by past user response performance, including compliance with contracts and consistency between response volumes and agreed volumes.

[0098] The calculation formula of the user credit evaluation index is:

[0099] SPI=1-δ t

[0100]

[0101] Where SPI is the value of the user credit evaluation index, which ranges from (0, 1); t is the relative deviation in period t. When the user completes the target load reduction in period t, then δ t =0; on the contrary, the greater the deviation, the greater the t The closer or greater than 1; is the baseline power value corresponding to period t; is the actual operating power value of the user during period t; is the power reduction amount in period t as contracted between the user and the load aggregator; t1 and t2 correspond to the start time and end time respectively.

[0102] In this embodiment, in step S2, a building room temperature thermal dynamic model is constructed based on a thermal resistance-heat capacity network model; the building room temperature thermal dynamic process is described by the building room temperature thermal dynamic model;

[0103] Specifically, a building room temperature thermal dynamic model is constructed based on a resistor-capacitor (RC) network model. This model describes the dynamic process of building room temperature. Specifically, by constructing an RC network, the heat exchange process of different buildings can be simulated. The RC model calculates internal building temperature changes and simulates HVAC energy consumption, demonstrating the adjustable potential of different buildings and providing a basis for allocating demand response.

[0104] In this example, differences in building wall structure and materials are equivalent to differences in network parameters. For example, in cooling zone 1 of a building within a cluster, node 1 represents the indoor air, with a temperature of Tr. The other air nodes surrounding cooling zone 1 (nodes 2, 3, 4, and 5) have temperatures of T2, T3, T4, and T5, respectively. Users control HVAC operating power by adjusting the HVAC supply air temperature to maintain a comfortable indoor temperature.

[0105] Among them, the heat balance constraint of a single cooling area is:

[0106]

[0107]

[0108] Where, (j=2,3,4,5; m=1,2,…,N) are the heat capacity, temperature and thermal resistance of the wall between two nodes; N is the total number of building clusters with different insulation performance; is the indoor air temperature of cooling zone 1; is the indoor temperature of other cooling areas adjacent to cooling area 1. If the adjacent cooling area is outdoor, it is the outdoor temperature; t 1,j When (j=2,3,4,5) is 1, it means that the wall is exposed to sunlight, otherwise it is 0; ε 1,j 、 (j=2, 3, 4, 5) are the heat storage rate and surface area of ​​the four walls respectively; (j=2,3,4,5) is the outdoor light intensity; is the heat capacity of cooling area 1; R win is the window thermal resistance; T out is the outdoor temperature; Q win The light intensity received by the window; are the HVAC cooling capacity and internal heat generation of cooling zone 1 respectively; win 、S win are the window transmittance and window surface area, respectively.

[0109] In this embodiment, in step S3, a load aggregator demand response two-layer optimization model is constructed based on the user credit evaluation index;

[0110] Specifically, in order to ensure the maximum profit of load aggregators, a two-layer optimization model of load aggregator demand response is established based on user credit evaluation indicators.

[0111] In this embodiment, in step S4, the load aggregator demand response two-layer optimization model is solved by setting a solution strategy to obtain the load aggregator demand response optimal scheduling strategy;

[0112] Specifically, in the two-layer optimization process, the core of the iterative solution is the joint optimization of the upper and lower layers. The upper layer aims to maximize the benefits of the load aggregator, while the lower layer aims to optimize the user's response behavior to achieve the maximum economic benefits of the building cluster. In the upper layer, the load aggregator allocates response subsidies based on market electricity prices and user credit, and determines the subsidy price for each user to optimize its own benefits. In the lower layer, to ensure that the comfort level at the time of response remains within an acceptable range, the optimization system adjusts the HVAC operating power according to the given subsidy price to produce an operating power curve, while maximizing the economic benefits of the user.

[0113] In this embodiment, in the process of solving the load aggregator demand response two-layer optimization model by using the set solution strategy, the upper model in the load aggregator demand response two-layer optimization model optimizes the subsidy incentive price for users with the goal of maximizing the subsidy benefits obtained by the load aggregator participating in the grid demand response: the objective function expression of the upper model is:

[0114]

[0115] Where, Sub agg the total response subsidy paid to aggregators for the grid; is the total actual response of all building users in the demand response phase; W market The number of bids won by aggregators for demand response; value is the list price provided by the grid; n is the number of building users participating in demand response integrated by the aggregator; σ and It is the segmented assessment coefficient of demand response subsidy;

[0116] The convergence condition of the objective function of the upper model is:

[0117]

[0118] Where, Sub i is the response subsidy obtained by building user i; tolerance is the convergence tolerance to ensure that the aggregator's revenue is balanced with the total subsidy of building users;

[0119] The lower model in the load aggregator demand response two-layer optimization model comprehensively considers the user's electricity purchase cost and maximizes the benefits of the response subsidy within the comfort range, and optimizes the HVAC operating power of each building. The objective function expression of the lower model is:

[0120]

[0121] Where C buy The unit price for building users to purchase electricity from the power grid company; Comfort i The comfort loss cost caused by reducing electricity consumption during the demand response phase for the i-th building user;

[0122] The objective function constraints of the lower model are:

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] Where, is the HVAC cooling capacity of cooling area i in period t; c air is the heat capacity of air; is the air quality of the mth building in period t; T room is the indoor temperature; is the air quality of the mth building; ρ room is the indoor air density; is the volume of the mth building; a and b are the quadratic coefficient and linear coefficient of the comfort loss cost respectively; D k is the assessment coefficient matrix.

[0132] In this embodiment, in step S5, a credit update model is constructed; after the grid demand response, the load aggregator updates the user credit evaluation index through the credit update model based on the user response performance index to obtain the user's updated credit evaluation index.

[0133] Specifically, a credit update model is constructed. After a grid demand response, the credit update model uses exponential smoothing to update the user's credit rating based on the user's response performance indicators and historical response performance indicators. For users who perform well, their credit rating will increase; for users who fail to fulfill their commitments, their credit rating will decrease. This effectively incentivizes users to fulfill their commitments in future demand responses, thereby improving the reliability of the overall response and reducing the risk of default.

[0134] The calculation formula for the updated credit rating index of the user is:

[0135]

[0136]

[0137] Where, CEI t is the updated credit score after period t; CEI t-1 The credit score calculated for the previous period; is the dynamic weight coefficient; when the recent credit score volatility is large, becomes larger, more dependent on the credit score at that time; on the contrary, Smaller, more focused on historical response credit scores; rec Calculate the standard deviation of the credit score after the recent response; s all The standard deviation of the credit scores calculated for all responses.

[0138] In a possible embodiment, a specific optimization example is provided as follows:

[0139] Three types of buildings with different thermal parameters were selected, namely office buildings, apartment buildings, and commercial buildings. The building envelopes and thermal parameters are shown in Table 1. The initial credit scores CEI were 70, 80, and 90 respectively.

[0140] Building complex <![CDATA[ε[W / (m 2 ·K)]]]> <![CDATA[S wall (m 2 )]]> <![CDATA[λ win [W / (m 2 ·K)]]]> <![CDATA[V room [m 3 ]]]> A 1.092 1000 2.8 5400 B 0.908 2400 2.75 24000 C 1.146 1500 2.8 12000

[0141] Table 1 Building thermal parameters

[0142] The scene is a typical day in summer in northern China, with solar radiation intensity and outdoor temperature curves. Figure 3 As shown. The comfortable temperature range is set to 22-26℃. Air density ρ room and the specific heat capacity of air C room Take 1.2kg / m3 and 1000J / (kg·℃) respectively.

[0143] The iterative process is a simulation of demand response, e.g. Figure 4As shown in the figure, at the beginning of the iteration, the base subsidy price was low, resulting in low response volumes across the building cluster and low enthusiasm for response. Comfort levels were chosen over response subsidies. As the aggregator increased its subsidy incentives for users, response volumes gradually converged to their respective decomposed quantities. Building users were willing to reduce their comfort requirements in exchange for response subsidies, and demand response was successfully implemented. The final response volumes for users A, B, and C were 29.5, 33, and 37.5 kWh, respectively, and the subsidies they received were 13, 17, and 20 yuan.

[0144] In summary, the present invention constructs a user credit evaluation model based on user subscription performance indicators; evaluates the user's response behavior performance through the user credit evaluation model to obtain a user credit evaluation index; constructs a building room temperature thermal dynamic model based on a thermal resistance-heat capacitance network model; describes the building room temperature thermal dynamic process through the building room temperature thermal dynamic model; constructs a load aggregator demand response two-layer optimization model based on the user credit evaluation index; solves the load aggregator demand response two-layer optimization model by setting a solution strategy to obtain the load aggregator demand response optimal scheduling strategy; constructs a credit update model; after the grid demand response, the load aggregator updates the user credit evaluation index based on the user response performance indicator through the credit update model to obtain the user's updated credit evaluation index. From the perspective of the aggregator, the present invention proposes a demand response two-layer optimization model based on credit evaluation indicators. The credit evaluation indicators regulate the response behavior of the building, achieve efficient and reliable aggregation of the building's adjustable potential, and construct a two-layer optimization model of load aggregator revenue and building user subsidies, thereby improving the effectiveness of demand response and refined scheduling.

[0145] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0146] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0147] Example 2

[0148] See also Figure 5 Embodiment 2 of the present invention further provides an air conditioning load aggregator demand response optimization device based on user credit, including:

[0149] User credit evaluation model construction and processing module 001 is used to construct a user credit evaluation model based on user subscription performance indicators; evaluate the user's response behavior performance through the user credit evaluation model to obtain user credit evaluation indicators;

[0150] The building room temperature thermal dynamic model construction module 002 is used to construct a building room temperature thermal dynamic model based on a thermal resistance-heat capacity network model; the building room temperature thermal dynamic process is described by the building room temperature thermal dynamic model;

[0151] A load aggregator demand response two-tier optimization model construction module 003 is used to construct a load aggregator demand response two-tier optimization model based on the user credit evaluation index;

[0152] A load aggregator demand response two-layer optimization model solving module 004 is used to solve the load aggregator demand response two-layer optimization model by setting a solution strategy to obtain the load aggregator demand response optimal scheduling strategy;

[0153] The credit update model construction and processing module 005 is used to construct a credit update model; after the grid demand response, the load aggregator updates the user credit evaluation index through the credit update model based on the user response performance index to obtain the user's updated credit evaluation index.

[0154] In this embodiment, in the building room temperature thermal dynamic model construction module 002, in the process of describing the building room temperature thermal dynamic process by using the building room temperature thermal dynamic model, the heat balance constraint of a single cooling zone is:

[0155]

[0156]

[0157] Where, (j=2,3,4,5; m=1,2,…,N) are the heat capacity, temperature and thermal resistance of the wall between two nodes; N is the total number of building clusters with different insulation performance; is the indoor air temperature of cooling zone 1; is the indoor temperature of other cooling areas adjacent to cooling area 1. If the adjacent cooling area is outdoor, it is the outdoor temperature; t 1,j When (j=2,3,4,5) is 1, it means that the wall is exposed to sunlight, otherwise it is 0; ε 1,j 、 (j=2, 3, 4, 5) are the heat storage rate and surface area of ​​the four walls respectively; (j=2,3,4,5) is the outdoor light intensity; is the heat capacity of cooling area 1; R win is the window thermal resistance; T out is the outdoor temperature; Q win The light intensity received by the window; are the HVAC cooling capacity and internal heat generation of cooling zone 1 respectively; win 、S win are the window transmittance and window surface area, respectively.

[0158] In this embodiment, in the user credit evaluation model construction and processing module 001, the calculation formula of the user credit evaluation index is:

[0159] SPI=1-δ t

[0160]

[0161] Where SPI is the value of the user credit evaluation index, which ranges from (0, 1); t is the relative deviation in period t. When the user completes the target load reduction in period t, then δ t =0; on the contrary, the greater the deviation, the greater the t The closer or greater than 1; is the baseline power value corresponding to period t; is the actual operating power value of the user during period t; is the power reduction amount in period t as contracted between the user and the load aggregator; t1 and t2 correspond to the start time and end time respectively.

[0162] In this embodiment, in the credit update model construction and processing module 005, the calculation formula of the user's updated credit evaluation index is:

[0163]

[0164]

[0165] Where, CEI t is the updated credit score after period t; CEI t-1 The credit score calculated for the previous period; is the dynamic weight coefficient; when the recent credit score volatility is large, becomes larger, more dependent on the credit score at that time; on the contrary, Smaller, more focused on historical response credit scores; rec Calculate the standard deviation of the credit score after the recent response; sall The standard deviation of the credit scores calculated for all responses.

[0166] In this embodiment, in the load aggregator demand response two-layer optimization model solving module 004, in the process of solving the load aggregator demand response two-layer optimization model using the set solution strategy, the upper-layer model in the load aggregator demand response two-layer optimization model optimizes the subsidy incentive price for users with the goal of maximizing the subsidy benefits obtained by the load aggregator participating in the grid demand response: the objective function expression of the upper-layer model is:

[0167]

[0168] Where, Sub agg Total response subsidies paid to aggregators for the grid; is the total actual response of all building users in the demand response phase; W market The number of bids won by aggregators for demand response; value is the list price provided by the grid; n is the number of building users participating in demand response integrated by the aggregator; σ and It is the segmented assessment coefficient of demand response subsidy;

[0169] The convergence condition of the objective function of the upper model is:

[0170]

[0171] Where, Sub i is the response subsidy obtained by building user i; tolerance is the convergence tolerance to ensure that the aggregator's revenue is balanced with the total subsidy of building users;

[0172] The lower model in the load aggregator demand response two-layer optimization model comprehensively considers the user's electricity purchase cost and maximizes the benefits of the response subsidy within the comfort range, and optimizes the HVAC operating power of each building. The objective function expression of the lower model is:

[0173]

[0174] Where C buy The unit price for building users to purchase electricity from the power grid company; Comfort i The comfort loss cost caused by reducing electricity consumption during the demand response phase for the i-th building user;

[0175] The objective function constraints of the lower model are:

[0176]

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184] Where, is the HVAC cooling capacity of cooling area i in period t; c air is the heat capacity of air; is the air quality of the mth building in period t; T room is the indoor temperature; is the air quality of the mth building; ρ room is the indoor air density; is the volume of the mth building; a and b are the quadratic coefficient and linear coefficient of the comfort loss cost respectively; D k is the assessment coefficient matrix.

[0185] It should be noted that the information interaction, execution process, etc. between the modules of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and no further details will be given here.

[0186] Example 3

[0187] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which the program code of the air conditioning load aggregator demand response optimization method based on user credit is stored. The program code includes instructions for executing the air conditioning load aggregator demand response optimization method based on user credit of embodiment 1 or any possible implementation thereof.

[0188] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0189] Example 4

[0190] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0191] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the user credit-based air conditioning load aggregator demand response optimization method of Example 1 or any possible implementation thereof.

[0192] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0193] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0194] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0195] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. An optimization method for demand response of air conditioning load aggregators based on user credit, characterized in that: include: Constructing a user credit evaluation model based on user subscription performance indicators; evaluating the user's response behavior performance through the user credit evaluation model to obtain a user credit evaluation indicator; Based on the thermal resistance-heat capacity network model, a building room temperature thermal dynamic model is constructed; and the building room temperature thermal dynamic process is described by the building room temperature thermal dynamic model; Based on the user credit evaluation index, a two-layer optimization model for load aggregator demand response is constructed; Solving the load aggregator demand response two-layer optimization model by setting a solution strategy to obtain the load aggregator demand response optimal dispatch strategy; Build a credit renewal model; After the grid demand response, the load aggregator updates the user credit evaluation index through the credit update model based on the user response performance index to obtain the user's updated credit evaluation index; In the process of solving the load aggregator demand response two-layer optimization model by using the set solution strategy, the upper model in the load aggregator demand response two-layer optimization model optimizes the subsidy incentive price for users with the goal of maximizing the subsidy benefits obtained by the load aggregator participating in the grid demand response: the objective function expression of the upper model is: Where, Sub agg the total response subsidy paid to aggregators for the grid; The total actual response of all building users during the demand response phase; W market The number of bids won by aggregators for demand response; value is the list price provided by the grid; n is the number of building users participating in demand response integrated by the aggregator; σ and It is the segmented assessment coefficient of demand response subsidy; is the actual operating power value of the user during period t; The convergence condition of the objective function of the upper model is: Where, Sub i is the response subsidy obtained by building user i; tolerance is the convergence tolerance to ensure that the aggregator's revenue is balanced with the total subsidy of building users; The lower model in the load aggregator demand response two-layer optimization model comprehensively considers the user's electricity purchase cost and maximizes the benefits of the response subsidy within the comfort range, and optimizes the HVAC operating power of each building. The objective function expression of the lower model is: Where C buy The unit price for building users to purchase electricity from the power grid company; Comfort i The comfort loss cost caused by reducing electricity consumption during the demand response phase for the i-th building user.

2. The air conditioning load aggregator demand response optimization method based on user credit according to claim 1 is characterized in that: In the process of describing the building room temperature thermal dynamic process by using the building room temperature thermal dynamic model, the heat balance constraint of a single cooling area is: Where, are the heat capacity, temperature, and thermal resistance of the wall between two nodes; N is the total number of building clusters with different insulation performance, j = 2, 3, 4, 5; m = 1, 2, ..., N; is the indoor air temperature of cooling zone 1; is the indoor temperature of other cooling areas adjacent to cooling area 1. If the adjacent cooling area is outdoor, it is the outdoor temperature; t 1,j When it is 1, it means that the wall is exposed to sunlight, otherwise it is 0, j = 2, 3, 4, 5; ε 1,j 、 are the heat storage rate and surface area of ​​the four walls, j = 2, 3, 4, 5; is the outdoor light intensity, j = 2, 3, 4, 5; is the heat capacity of cooling area 1; R win is the window thermal resistance; T out is the outdoor temperature; Q win The light intensity received by the window; are the HVAC cooling capacity and internal heat generation of cooling zone 1 respectively; win 、S win are the window transmittance and window surface area, respectively.

3. The air conditioning load aggregator demand response optimization method based on user credit according to claim 2 is characterized in that: The calculation formula of the user credit evaluation index is: SPI=1-δ t Where SPI is the value of the user credit evaluation index, which ranges from (0, 1); t is the relative deviation in period t. When the user completes the target load reduction in period t, then δ t =0; On the contrary, the larger the deviation, the greater the t The closer or greater than 1; is the baseline power value corresponding to period t; is the power reduction amount in period t as contracted between the user and the load aggregator; t1 and t2 correspond to the start time and end time respectively.

4. The method for optimizing demand response of air conditioning load aggregators based on user credit according to claim 3 is characterized in that: The calculation formula of the user's updated credit evaluation index is: Where, CEI t is the updated credit score after period t; CEI t-1 The credit score calculated for the previous period; is the dynamic weight coefficient; when the recent credit score volatility is large, becomes larger, more dependent on the credit score at that time; on the contrary, Smaller, more focused on historical response credit scores; rec Calculate the standard deviation of the credit score after the recent response; s all The standard deviation of the credit scores calculated for all responses.

5. The method for optimizing demand response of air conditioning load aggregators based on user credit according to claim 4 is characterized in that: The objective function constraints of the lower model are: Where, is the HVAC cooling capacity of cooling area i in period t; c air is the heat capacity of air; is the air quality of the mth building in period t; T room is the indoor temperature; is the air quality of the mth building; ρ room is the indoor air density; is the volume of the mth building; a and b are the quadratic coefficient and linear coefficient of the comfort loss cost respectively; D k is the assessment coefficient matrix.

6. An air conditioning load aggregator demand response optimization device based on user credit, adopting the air conditioning load aggregator demand response optimization method based on user credit according to any one of claims 1 to 5, characterized in that: include: A user credit evaluation model construction and processing module is used to construct a user credit evaluation model based on the user subscription performance index; evaluate the user's response behavior performance through the user credit evaluation model to obtain the user credit evaluation index; A building room temperature thermal dynamic model construction module is used to construct a building room temperature thermal dynamic model based on a thermal resistance-heat capacity network model; the building room temperature thermal dynamic process is described by the building room temperature thermal dynamic model; A load aggregator demand response two-tier optimization model construction module, configured to construct a load aggregator demand response two-tier optimization model based on the user credit evaluation index; A load aggregator demand response two-layer optimization model solving module, configured to solve the load aggregator demand response two-layer optimization model by setting a solving strategy to obtain an optimal dispatching strategy for the load aggregator demand response; Credit update model construction and processing module, used to build a credit update model; After the grid demand response, the load aggregator updates the user credit evaluation index through the credit update model based on the user response performance index to obtain the user's updated credit evaluation index; In the load aggregator demand response two-layer optimization model solving module, in the process of solving the load aggregator demand response two-layer optimization model by using the set solving strategy, the upper-layer model in the load aggregator demand response two-layer optimization model optimizes the subsidy incentive price for users with the goal of maximizing the subsidy benefits obtained by the load aggregator participating in the grid demand response: the objective function expression of the upper-layer model is: Where, Sub agg Total response subsidies paid to aggregators for the grid; The total actual response of all building users during the demand response phase; W market The number of bids won by aggregators for demand response; value is the list price provided by the grid; n is the number of building users participating in demand response integrated by the aggregator; σ and It is the segmented assessment coefficient of demand response subsidy; is the actual operating power value of the user during period t; The convergence condition of the objective function of the upper model is: Where, Sub i is the response subsidy obtained by building user i; tolerance is the convergence tolerance to ensure that the aggregator's revenue is balanced with the total subsidy of building users; The lower model in the load aggregator demand response two-layer optimization model comprehensively considers the user's electricity purchase cost and maximizes the benefits of the response subsidy within the comfort range, and optimizes the HVAC operating power of each building. The objective function expression of the lower model is: Where C buy The unit price for building users to purchase electricity from the power grid company; Comfort i The comfort loss cost caused by reducing electricity consumption during the demand response phase for the i-th building user.

7. The air conditioning load aggregator demand response optimization device based on user credit according to claim 6, characterized in that: In the building room temperature thermal dynamic model construction module, in the process of describing the building room temperature thermal dynamic process by using the building room temperature thermal dynamic model, the heat balance constraint of a single cooling area is: Where, are the heat capacity, temperature, and thermal resistance of the wall between two nodes; N is the total number of building clusters with different insulation performance, j = 2, 3, 4, 5; m = 1, 2, ..., N; is the indoor air temperature of cooling zone 1; is the indoor temperature of other cooling areas adjacent to cooling area 1. If the adjacent cooling area is outdoor, it is the outdoor temperature; t 1,j When it is 1, it means that the wall is exposed to sunlight, otherwise it is 0, j = 2, 3, 4, 5; ε 1,j 、 are the heat storage rate and surface area of ​​the four walls, j = 2, 3, 4, 5; is the outdoor light intensity, j = 2, 3, 4, 5; is the heat capacity of cooling area 1; R win is the window thermal resistance; T out is the outdoor temperature; Q win The light intensity received by the window; are the HVAC cooling capacity and internal heat generation of cooling zone 1 respectively; win 、S win are the window transmittance and window surface area, respectively.

8. The air conditioning load aggregator demand response optimization device based on user credit according to claim 7 is characterized in that: In the user credit evaluation model construction and processing module, the calculation formula of the user credit evaluation index is: SPI=1-δ t Where SPI is the value of the user credit evaluation index, which ranges from (0, 1); t is the relative deviation in period t. When the user completes the target load reduction in period t, then δ t =0; On the contrary, the larger the deviation, the greater the t The closer or greater than 1; is the baseline power value corresponding to period t; is the actual operating power value of the user during period t; is the power reduction amount in period t as contracted between the user and the load aggregator; t1 and t2 correspond to the start time and end time respectively.

9. The air conditioning load aggregator demand response optimization device based on user credit according to claim 8, characterized in that: In the credit update model construction and processing module, the calculation formula for the user's updated credit evaluation index is: Where, CEI t is the updated credit score after period t; CEI t-1 The credit score calculated for the previous period; is the dynamic weight coefficient; when the recent credit score volatility is large, becomes larger, more dependent on the credit score at that time; on the contrary, Smaller, more focused on historical response credit scores; rec Calculate the standard deviation of the credit score after the recent response; s all The standard deviation of the credit scores calculated for all responses.

10. The air conditioning load aggregator demand response optimization device based on user credit according to claim 9, characterized in that: In the load aggregator demand response two-layer optimization model solving module, the objective function constraint of the lower layer model is: Where, is the HVAC cooling capacity of cooling area i in period t; c air is the heat capacity of air; is the air quality of the mth building in period t; T room is the indoor temperature; is the air quality of the mth building; ρ room is the indoor air density; is the volume of the mth building; a and b are the quadratic coefficient and linear coefficient of the comfort loss cost respectively; D k is the assessment coefficient matrix.

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