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

Abstract

The invention discloses an air conditioner load aggregator demand response optimization method and device based on user credit, and the method comprises the steps: building a user credit evaluation model according to a user subscription performance index, carrying out the evaluation processing of the response behavior performance of a user, and obtaining a user credit evaluation index; based on the thermal resistance-thermal capacity network model, constructing a building room temperature thermal dynamic model; describing the building room temperature thermal dynamic process through the building room temperature thermal dynamic model; based on the user credit evaluation indexes, constructing a load aggregator demand response double-layer optimization model; solving the optimization model by setting a solving strategy to obtain an optimal scheduling strategy of the load aggregator; constructing a credit updating model; and after the power grid demand response, updating the user credit evaluation index through the credit updating model according to the user response performance index, and obtaining the updated credit evaluation index of the user. According to the invention, the demand response effect can be improved and fine scheduling can be realized.

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 an important component of load-side resources, HVAC has become a key object in the current research on improving the energy efficiency of buildings and the stability of power grids due to its strong flexibility and easy control. The peak load of HVAC poses a challenge to the power balance of the power grid. The peak load pressure of the power grid can be effectively alleviated by optimizing and regulating it. However, due to the small scale of load resources of a single user and limited response capacity, it is difficult to achieve significant results by relying on the decentralized participation of a single user in optimization and regulation. Therefore, a load aggregator agent is set up between the user and the power grid to obtain revenue by selling flexibility services to the power market by integrating load-side resources. Demand response, as a key regulatory mechanism in the power system, guides users to adjust their own electricity consumption behavior through the implementation of price signals or incentives, thereby improving the balance of supply and demand in the power grid. Among them, users can obtain economic subsidies by actively participating in the response, and the power grid can improve the reliability and economy of operation by relying on the flexibility of load-side resources. Therefore, it is urgent for load aggregators to optimize and integrate the HVAC loads of multiple types of users and effectively participate in demand response.

[0003] At present, in the study of user participation in demand response, a large number of scholars have carried out work in the areas of incentive mechanism design, optimization scheduling model and user behavior modeling. The current research focuses more on industrial and commercial user load resources, lacks in-depth research on the demand response of HVAC load resources in building clusters, and fails to fully consider the load characteristics of building clusters and the complexity of user behavior; at the same time, the actual response willingness and performance of users are crucial to the effectiveness of demand response, but the existing models do not adequately evaluate the actual response degree of the user response stage, mostly relying on user credit for rough quantification, and lack a multi-dimensional dynamic credit evaluation system. In addition, there is little research on how to further enhance the potential of demand response and user participation willingness under the premise of 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 to 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 effect of demand response and refined scheduling.

[0006] In order to achieve the above object, the present invention provides the following technical solution: an air conditioning load aggregator demand response optimization method based on user credit, comprising:

[0007] According to the user subscription performance index, a user credit evaluation model is constructed; the user's response behavior performance is evaluated and processed by the user credit evaluation model to obtain the user credit evaluation index;

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

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

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

[0011] Constructing a credit update model; 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 air conditioning load aggregator demand response optimization method based on user credit, 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] In the formula, (j=2,3,4,5; m=1,2,…,N) is 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 refrigeration areas adjacent to refrigeration area 1. If the adjacent refrigeration area is outdoor, it is the outdoor temperature; t 1,jWhen (j=2,3,4,5) takes 1, it means that the wall is exposed to sunlight, otherwise it takes 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] In the formula, 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 the period t; is the actual operating power value of the user during period t; It is the power reduction amount in period t signed by 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 updated credit evaluation index of the user 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;s rec Calculate the standard deviation of the credit score for the most recent response; 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 by setting the solution strategy, the upper model in the load aggregator demand response two-layer optimization model aims to maximize the subsidy income obtained by the load aggregator participating in the grid demand response, and optimizes the subsidy incentive price for the user: the objective function expression of the upper model is:

[0025]

[0026] Where, Sub agg Total response subsidies paid to aggregators for grids; is the total actual response of all building users in the demand response phase; W market The number of bids won by aggregators participating in 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 segment 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 the maximization of the response subsidy benefits within the comfort range, and optimizes the HVAC operating power of each building; the objective function expression of the lower model is:

[0031]

[0032] In the formula, C buy The unit price for building users to purchase electricity from the power grid company; Comfot 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] In the formula, 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 the linear coefficient of the comfort loss cost respectively; D k is the test 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, which is used to construct a building room temperature thermal dynamic model based on a thermal resistance-heat capacity network model; and to describe the building room temperature thermal dynamic process through the building room temperature thermal dynamic model;

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

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

[0048] The credit update model construction and processing module is used to construct a credit update model; after the power 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.

[0049] As a preferred solution of the air conditioning load aggregator demand response optimization device based on user credit, 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 area is:

[0050]

[0051]

[0052] In the formula, 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 refrigeration areas adjacent to refrigeration area 1. If the adjacent refrigeration area is outdoor, it is the outdoor temperature; t 1,j When (j=2,3,4,5) takes 1, it means that the wall is exposed to sunlight, otherwise it takes 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] In the formula, 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 larger the deviation, the greater the t The closer or greater than 1; is the baseline power value corresponding to the period t; is the actual operating power value of the user during period t; It is the power reduction amount in period t signed by 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 credit evaluation index after the user is updated 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;s rec Calculate the standard deviation of the credit score for the most recent response; 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 aims to maximize the subsidy income obtained by the load aggregator participating in the power grid demand response, and optimizes the subsidy incentive price for the user: the objective function expression of the upper model is:

[0062]

[0063] Where, Sub agg Total response subsidies paid to aggregators for grids; 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 segment 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 the maximization of the response subsidy benefits within the comfort range, and optimizes the HVAC operating power of each building; the objective function expression of the lower model is:

[0068]

[0069] In the formula, C buy The unit price for building users to purchase electricity from the power grid company; Comfot 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] In the formula, 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 the linear coefficient of the comfort loss cost respectively; D k is the test coefficient matrix.

[0080] The present invention has the following advantages: the present invention constructs a user credit evaluation model according to the user subscription performance index; the user's response behavior performance is evaluated and processed through the user credit evaluation model to obtain the user credit evaluation index; based on the thermal resistance-heat capacitance network model, a building room temperature thermal dynamic model is constructed; the building room temperature thermal dynamic process is described through the building room temperature thermal dynamic model; based on the user credit evaluation index, a load aggregator demand response double-layer optimization model is constructed; the load aggregator demand response double-layer optimization model is solved by setting a solution strategy to obtain the load aggregator demand response optimal scheduling strategy; a credit update model is constructed; after the power 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. The present invention proposes a demand response double-layer optimization model based on credit evaluation indicators from the perspective of the aggregator, and the credit evaluation indicators regulate the response behavior of the building to achieve efficient and reliable aggregation of the building's adjustable potential, and construct a double-layer optimization model of load aggregator revenue and building user subsidies, which improves the effect of demand response and refined scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0082] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

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

[0086] Figure 4 Schematic diagram of the iterative process of the optimization algorithm in a possible embodiment provided in Embodiment 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 is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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 air conditioning load aggregators based on user credit, comprising the following steps:

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

[0092] S2. Based on the thermal resistance-heat capacity network model, a thermal dynamic model of the building room temperature is constructed; and the thermal dynamic process of the building room temperature is described by the thermal dynamic model of the building room temperature;

[0093] S3. Based on the user credit evaluation index, a two-layer optimization model for load aggregator demand response is constructed;

[0094] S4, 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;

[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 according to 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 according to the user subscription performance index; the user's response behavior performance is evaluated and processed by the user credit evaluation model to obtain the user credit evaluation index;

[0097] Specifically, the actual response degree of the load aggregator to the user's demand response period is reflected by the ratio of the actual response amount to the response declaration amount. The traditional subscription performance indicator is the ratio of the average load reduction of the user during the demand response period to the agreed reduction amount signed with the user before the response. According to the user subscription performance indicator, a user credit evaluation model is constructed; the user's response behavior performance is evaluated and processed by the user credit evaluation model, and the deviation degree between the actual response amount and the agreed response amount is used as a measurement basis to evaluate the user's response behavior performance, and obtain the user credit evaluation index. The subsidy price is calculated according to the user credit evaluation index of the building cluster. The higher the credit score of the user, the higher the subsidy price will be, thereby encouraging these users to participate in demand response more actively. The user credit evaluation index is based on the user's performance in the past response, including whether the contract is fulfilled, whether the response amount is consistent with the agreed amount, etc., to dynamically evaluate the degree of influence on the subsidy incentive.

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

[0099] SPI=1-δ t

[0100]

[0101] In the formula, 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 the period t; is the actual operating power value of the user during period t; It is the power reduction amount in period t signed by 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 thermal dynamic model of the room temperature of a building is constructed based on a thermal resistance-heat capacitance network model; the thermal dynamic process of the room temperature of a building is described by the thermal dynamic model of the room temperature of a building;

[0103] Specifically, based on the Resistor-Capacitor (RC) network model, a building room temperature thermal dynamic model is constructed; the building room temperature thermal dynamic process is described by the building room temperature thermal dynamic model. Specifically, by constructing an RC network, the heat exchange process of different buildings can be simulated. With the help of the RC model, the change of the internal temperature of the building is calculated, and the energy consumption of the HVAC of the building is simulated, showing the adjustable potential of different buildings to provide a basis for demand response allocation response.

[0104] In this embodiment, the differences in building wall structures and materials are equivalent to differences in network parameters. Take the cooling area 1 of a building in a cluster as an example: Node 1 represents its indoor air, with a temperature of Tr; the other air nodes around the cooling area 1 (nodes 2, 3, 4, and 5) have temperatures of T2, T3, T4, and T5 respectively. Users control the HVAC operating power by adjusting the HVAC supply air temperature to keep the indoor temperature within a comfortable range.

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

[0106]

[0107]

[0108] In the formula, (j=2,3,4,5; m=1,2,…,N) is 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 refrigeration areas adjacent to refrigeration area 1. If the adjacent refrigeration area is outdoor, it is the outdoor temperature; t 1,j When (j=2,3,4,5) takes 1, it means that the wall is exposed to sunlight, otherwise it takes 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 goal of the upper layer is to maximize the benefits of the load aggregator, while the goal of the lower layer is to optimize the response behavior of the user in order to achieve the maximum economic benefits of the building cluster. In the upper stage, the load aggregator will allocate response subsidies based on the market electricity price and user credit, and determine the subsidy price for each user to optimize its own benefits. In the lower stage, to ensure that the comfort level during response remains within an acceptable range, the optimization system will adjust the HVAC operating power according to the given subsidy price to give 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 setting the solution strategy, the upper model in the load aggregator demand response two-layer optimization model aims to maximize the subsidy benefits obtained by the load aggregator participating in the grid demand response, and optimizes the subsidy incentive price for the user: the objective function expression of the upper model is:

[0114]

[0115] Where, Sub agg Total response subsidies paid to aggregators for grids; 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 segment 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 the maximization of the response subsidy benefits within the comfort range, and optimizes the HVAC operating power of each building; the objective function expression of the lower model is:

[0120]

[0121] In the formula, C buy The unit price for building users to purchase electricity from the power grid company; Comfot 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] In the formula, 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 the linear coefficient of the comfort loss cost respectively; D k is the test 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 according to the user response performance index to obtain the user's updated credit evaluation index.

[0133] Specifically, a credit update model is constructed; after the grid demand response, the credit update model is used to update the user's credit evaluation index based on the user response performance index and the historical response performance index using the exponential smoothing method. For users with good performance, the credit evaluation index will be improved; for users who fail to fulfill their commitments, the credit evaluation index will decrease. It can effectively motivate users to fulfill their obligations in future demand responses, thereby improving the reliability of the overall response and reducing the risk of default.

[0134] The calculation formula of the updated credit evaluation 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;s rec Calculate the standard deviation of the credit score for the most recent response; 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 northern China in summer, 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 air specific heat 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 basic subsidy price was low, the response volume of the building group was low, and the response enthusiasm was poor. Comfort was chosen between the measurement of comfort and response subsidy. As the subsidy incentives of the aggregator to users increased, the response volume gradually tended to their respective decomposition volumes. Building users were willing to reduce their own comfort requirements in exchange for response subsidies, and demand response was successfully implemented. The final response volumes of the three types of users A, B, and C were 29.5, 33, and 37.5 kwh, respectively, and the subsidy costs 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 user credit evaluation indicators; constructs a building room temperature thermal dynamic model based on the 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 double-layer optimization model based on the user credit evaluation indicators; solves the load aggregator demand response double-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 power grid demand response, the load aggregator updates the user credit evaluation indicator through the credit update model according to the user response performance indicator to obtain the user's updated credit evaluation indicator. The present invention proposes a demand response double-layer optimization model based on credit evaluation indicators from the perspective of the aggregator. The credit evaluation indicators regulate the response behavior of the building to achieve efficient and reliable aggregation of the building's adjustable potential, and constructs a double-layer optimization model of load aggregator revenue and building user subsidies, which improves the effect of demand response and refined scheduling.

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

[0146] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some 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 also provides an air conditioning load aggregator demand response optimization device based on user credit, including:

[0149] The user credit evaluation model construction and processing module 001 is used to construct a user credit evaluation model according to the user subscription performance index; the user's response behavior performance is evaluated and processed through the user credit evaluation model to obtain the user credit evaluation index;

[0150] The building room temperature thermal dynamic model construction module 002 is used to construct the building room temperature thermal dynamic model based on the 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-layer optimization model construction module 003 is used to construct a load aggregator demand response two-layer 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 solving strategy to obtain an optimal dispatching strategy for the load aggregator demand response;

[0153] The credit update model construction and processing module 005 is used to construct a credit update model; after the power 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.

[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 the building room temperature thermal dynamic model, the thermal balance constraint of a single cooling area is:

[0155]

[0156]

[0157] In the formula, (j=2,3,4,5; m=1,2,…,N) is 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 refrigeration areas adjacent to refrigeration area 1. If the adjacent refrigeration area is outdoor, it is the outdoor temperature; t 1,j When (j=2,3,4,5) takes 1, it means that the wall is exposed to sunlight, otherwise it takes 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] In the formula, 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 the period t; is the actual operating power value of the user during period t; It is the power reduction amount in period t signed by 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;s rec Calculate the standard deviation of the credit score for the most recent response;all 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 by using the set solving strategy, the upper model in the load aggregator demand response two-layer optimization model aims to maximize the subsidy income obtained by the load aggregator participating in the grid demand response, and optimizes the subsidy incentive price for the user: the objective function expression of the upper model is:

[0167]

[0168] Where, Sub agg Total response subsidies paid to aggregators for grids; is the total actual response of all building users in the demand response phase; W market The number of bids won by aggregators participating in 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 segment 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 the maximization of the response subsidy benefits within the comfort range, and optimizes the HVAC operating power of each building; the objective function expression of the lower model is:

[0173]

[0174] In the formula, C buy The unit price for building users to purchase electricity from the power grid company; Comfot 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] In the formula, 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 the linear coefficient of the comfort loss cost respectively; D k is the test coefficient matrix.

[0185] It should be noted that the information interaction, execution process and other contents 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 will not be repeated here.

[0186] Example 3

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

[0188] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[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 air conditioning load aggregator demand response optimization method based on user credit in 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 implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be 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 a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0194] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by a program code executable by a computing system, so that they can be stored in a storage system and executed by the computing system, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0195] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. The air conditioning load aggregator demand response optimization method based on user credit is characterized by: include: According to the user subscription performance index, a user credit evaluation model is constructed; the user's response behavior performance is evaluated and processed by the user credit evaluation model to obtain the user credit evaluation index; Based on the thermal resistance-heat capacity network model, a thermal dynamic model of the building room temperature is constructed; and the thermal dynamic process of the building room temperature is described by the thermal dynamic model of the building room temperature; Based on the user credit evaluation index, a two-layer optimization model for load aggregator demand response is constructed; The load aggregator demand response two-layer optimization model is solved by setting a solution strategy to obtain the load aggregator demand response optimal dispatch strategy; Building a credit renewal model; After the power 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.

2. The method for optimizing demand response of air conditioning load aggregators based on user credit according to claim 1, characterized in that: In the process of describing the thermal dynamic process of the building room temperature by using the building room temperature thermal dynamic model, the heat balance constraint of a single cooling area is: In the formula, 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 refrigeration areas adjacent to refrigeration area 1. If the adjacent refrigeration area is outdoor, it is the outdoor temperature; t 1,j When (j=2,3,4,5) takes 1, it means that the wall is exposed to sunlight, otherwise it takes 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.

3. The method for optimizing demand response of air conditioning load aggregators based on user credit according to claim 2 is characterized in that: The calculation formula of the user credit evaluation index is: In the formula, 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 t The closer or greater than 1; is the baseline power value corresponding to the period t; is the actual operating power value of the user during period t; It is the power reduction amount in period t signed by 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 updated credit evaluation index of the user 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; conversely, Smaller, more focused on historical response credit scores;s rec Calculate the standard deviation of the credit score for the most recent response; 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: In the process of solving the load aggregator demand response two-layer optimization model by setting the solution strategy, the upper model in the load aggregator demand response two-layer optimization model aims to maximize the subsidy benefits obtained by the load aggregator participating in the grid demand response and optimizes the subsidy incentive price for users: the objective function expression of the upper model is: Where, Sub agg Total response subsidies paid to aggregators for grids; It is the total actual response of all building users during the demand response phase; W market The number of bids won by aggregators participating in 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 segment assessment coefficient of demand response subsidy; 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 the maximization of the response subsidy benefits within the comfort range, and optimizes the HVAC operating power of each building; the objective function expression of the lower model is: In the formula, C buy The unit price for building users to purchase electricity from the power grid company; Comfot i The comfort loss cost caused by reducing electricity consumption during the demand response phase for the i-th building user; The objective function constraints of the lower model are: In the formula, 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 the linear coefficient of the comfort loss cost respectively; D k is the test 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-layer optimization model construction module, used to construct a load aggregator demand response two-layer optimization model based on the user credit evaluation index; A load aggregator demand response two-layer optimization model solving module, used to solve the load aggregator demand response two-layer optimization model by setting a solving strategy to obtain the load aggregator demand response optimal dispatching strategy; A credit update model building and processing module, used to build a credit update model; After the power 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.

7. The air conditioning load aggregator demand response optimization device based on user credit according to claim 6 is 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 the building room temperature thermal dynamic model, the heat balance constraint of a single cooling area is: In the formula, 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 refrigeration areas adjacent to refrigeration area 1. If the adjacent refrigeration area is outdoor, it is the outdoor temperature; t 1,j When (j=2,3,4,5) takes 1, it means that the wall is exposed to sunlight, otherwise it takes 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.

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 In the formula, 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 t The closer or greater than 1; is the baseline power value corresponding to the period t; is the actual operating power value of the user during period t; It is the power reduction amount in period t signed by 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 of the updated credit evaluation index of the user 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;s rec Calculate the standard deviation of the credit score for the most recent response; 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, 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 aims to maximize the subsidy benefits obtained by the load aggregator participating in the power grid demand response, and optimizes the subsidy incentive price for the user: the objective function expression of the upper model is: Where, Sub agg Total response subsidies paid to aggregators for grids; It is 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 segment assessment coefficient of demand response subsidy; 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 the maximization of the response subsidy benefits within the comfort range, and optimizes the HVAC operating power of each building; the objective function expression of the lower model is: In the formula, C buy The unit price for building users to purchase electricity from the power grid company; Comfot i The comfort loss cost caused by reducing electricity consumption during the demand response phase for the i-th building user; The objective function constraints of the lower model are: In the formula, 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 the linear coefficient of the comfort loss cost respectively; D k is the test coefficient matrix.

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