Micro-grid day-ahead-intra-day optimization scheduling method and system considering air conditioner user decision

Abstract

The invention discloses a micro-grid day-ahead-intra-day collaborative scheduling method and system considering air conditioner user decisions. The method comprises the steps that a variable frequency air conditioner virtual energy storage model considering air conditioner user dynamic decisions is established; establishing a day-ahead scheduling model with the aim of minimizing the total cost of day-ahead scheduling; establishing an intra-day real-time rolling optimization model based on the day-ahead scheduling model; in consideration of the reducible electric quantity deviation between the day-ahead and the day, an intra-day rolling correction model which minimizes the intra-day rolling correction scheduling cost of the micro-grid is established, and the scheduling plan is corrected in real time through the rolling strategy, so that the scheduling optimization accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid system operation optimization, and in particular to a microgrid day-ahead and intra-day optimization scheduling method and system taking into account air-conditioning user decisions. Background Art

[0002] With the continuous increase in global energy demand and the rapid development of renewable energy, the power system is facing unprecedented challenges. The traditional power supply model is difficult to meet the increasingly complex power demand, especially during peak hours, the contradiction between power supply and demand is becoming more and more prominent. In this context, the virtual energy storage of dispatching air conditioners to participate in demand response as an effective load management strategy has gradually received attention.

[0003] Prior art 1 - The paper "A data-driven rolling optimization control approach for building energy systems that integrate virtual energy storage systems" (Applied Energy, Mu Y, Xu Y, Zhang J, et al. [J]., 2023, 346: 121362, May 29, 2023) proposes a data-driven rolling optimization control method for integrated virtual energy storage systems. The team constructed a state space model of a building energy system that integrates a virtual energy storage system, and combined support vector machines with rolling optimization while targeting a smaller sample size of building energy system data to correct the day-ahead quantization error of the adjustable potential of the virtual energy storage system and improve the economic operation and thermal comfort of the building energy system that integrates the virtual energy storage system in an uncertain environment. Prior art 2 - The paper "An intraday dispatch strategy for demand-side flexible resources based on two-stage optimization" (Frontiers in Energy Research, Wang L, Liu H, Wang Q, et al., 2024, 12: 1343728, June 14, 2024) proposes an intraday dispatch strategy for demand-side flexible resources based on two-stage optimization. Based on the generalized energy storage model, the adjustable flexibility of demand-side flexible resources is modeled. At the same time, in order to suppress the uncertainties related to the market and demand-side flexible resources, rolling optimization is used for optimal bidding, and real-time scheduling optimization is performed based on the bidding decision to deal with system operation deviations caused by forecast errors. This method can significantly improve the economy of system operation.

[0004] The above literature considers the impact of multi-time scale rolling optimization scheduling on the system model, but ignores the uncertainty of user participation in demand response decision-making. This neglect may cause the scheduling results to deviate from the actual demand, thereby affecting the reliability of the system. Nowadays, there is still a gap in the day-ahead and intraday collaborative optimization scheduling of microgrids considering air conditioning user decisions. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a microgrid day-ahead and intraday optimization scheduling method and system taking into account the decision of air-conditioning users, which takes into account the decision of air-conditioning users and improves the accuracy and reliability of optimization scheduling.

[0006] The present invention adopts the following technical solution.

[0007] A first aspect of the present invention provides a microgrid day-ahead and intraday optimization scheduling method taking into account air conditioning user decisions, comprising:

[0008] S1: Based on the variable frequency air conditioner thermal dynamic model, a virtual energy storage model of the variable frequency air conditioner load is established considering the dynamic decision of air conditioner users;

[0009] S2: Considering the dynamic fluctuation of air-conditioning user decisions during the day and the day, an air-conditioning user decision model is constructed. The air-conditioning user decision model is used to quantify the adjustable capacity of the demand side during the scheduling process;

[0010] S3: Establish a day-ahead dispatch model with the goal of minimizing the total cost of day-ahead dispatch, and consider the deviation of the amount of electricity that can be reduced between the day-ahead and the day. Based on the day-ahead dispatch model, establish a real-time rolling optimization model in the day-ahead optimization stage;

[0011] S4: Establishing an intraday rolling correction model that minimizes the microgrid's intraday rolling correction dispatch cost, wherein the intraday rolling correction model obtains the amount of electricity that can be reduced during each rolling optimization process using the real-time rolling model, and updates the air-conditioning user decision model according to the amount of electricity that can be reduced;

[0012] S5: Update the air conditioning load virtual energy storage capacity, real-time rolling optimization model and intraday rolling correction model in the virtual energy storage model according to the updated air conditioning user decision model, and dispatch the microgrid according to the solution result of the intraday rolling correction model updated last time;

[0013] Among them, during the day-ahead scheduling, intraday real-time rolling optimization and intraday rolling correction processes, the power reduction deviation is required to be less than or equal to the virtual energy storage capacity of the air-conditioning load in the virtual energy storage model.

[0014] Optionally, in S1, a virtual energy storage model of the variable frequency air conditioning load is constructed according to the following formula:

[0015] E t =C(Tmax -T t )

[0016] Among them, E t is the virtual energy storage capacity of the air conditioning load at time t; C is the equivalent heat capacity of the air conditioning load; T t is the indoor temperature at time t, T max is the maximum indoor temperature.

[0017] Optionally, in S2, considering the dynamic fluctuation of air conditioner user decision-making before and during the day, an air conditioner user decision model is constructed according to the following formula:

[0018]

[0019] Among them, P j,t is the decision probability of the air-conditioning user with response level j at time t; β is the internal characteristic parameter of the user; ω k,t is the user's satisfaction with decision-making at time t in the kth period; n is the number of periods.

[0020] Optionally, the user's satisfaction with participating in decision-making is calculated according to the following formula:

[0021]

[0022] Among them, ω t is the user's satisfaction with decision-making at time t, P cut1,t P is the power load that the air conditioner can reduce at time t, cut，0 The air conditioner can reduce the electrical load by setting the initial setting for the day-ahead scheduling.

[0023] Optionally, in S5, the air conditioning load virtual energy storage capacity, the real-time rolling optimization model and the intraday rolling correction model in the virtual energy storage model are updated according to the updated air conditioning user decision model, including:

[0024] According to the updated air-conditioning user decision model P j,t Update the virtual energy storage model of the variable frequency air conditioning load according to the following formula:

[0025] E t ′=C(T max -T t ) j,t

[0026] Among them, E t ′ is the virtual energy storage of variable frequency air conditioning load considering user decision, P j,t is the decision probability of the air-conditioning user with response level j at time t, C is the equivalent heat capacity of the air-conditioning load; T t is the indoor temperature at time t, T max is the maximum value of the indoor temperature;

[0027] The load compensation fee can be reduced according to the following formula:

[0028]

[0029] 0≤P cut,t Δt≤E t ′≤E max

[0030] Among them, C DR1 is the load compensation fee that can be reduced after rolling optimization within the day, λ e is the unit compensation coefficient of the electric load, P cut,t is the electric load that can be reduced, T is the dispatching period, P j,t is the decision probability of the air conditioner user with response level j at time t, E max is the maximum capacity of virtual energy storage for air conditioning load, Δt is the time variation, E t ′ is the virtual energy storage of variable frequency air conditioning load considering user decision;

[0031] The day-ahead dispatch model, real-time rolling optimization model and intraday rolling correction model are updated according to the updated compensation fee for the reducible load.

[0032] Optionally, in S3, the day-ahead scheduling model with the goal of minimizing the day-ahead total scheduling cost is expressed as follows:

[0033] C T =min(C Oper +C Gird +C DR )

[0034]

[0035] Among them, C T is the total scheduling cost on the day before; C Oper is the total operating cost of the microgrid system; C Gird is the interaction cost between microgrid and large grid; C DR To reduce the load compensation cost, λ e is the unit compensation coefficient of the electric load; P cut,t is the electric load that can be reduced, and T is the scheduling period.

[0036] Optionally, in S3, based on the day-ahead dispatch model, a real-time rolling optimization model is established in the intraday optimization stage according to the following formula, with the goal of minimizing the intraday rolling optimization dispatch cost of the microgrid:

[0037] F1=min[C T1 +C P1 ]

[0038] C P1=ΔP Bat1 λ bat +ΔP Gird1 λ gird +ΔP cut1 λ cut

[0039]

[0040] Among them, F1 is the daily rolling optimization dispatching cost of the microgrid; C T1 and C P1 Operation and maintenance costs and penalty costs for intraday rolling optimization; ΔP Bat1 , ΔP Gird1 and ΔP cut1 are the total amount of adjustment of the battery, grid interaction, and curtailable daily electricity compared with the day-ahead dispatch model; bat , gird and λ cut are the penalty adjustment coefficients for battery, grid interaction and reducible power, respectively; P Bat1,t , P Gird1,t and P cut1,t They are battery, grid interaction, and daily electric power that can be reduced at time t, and T is the scheduling period.

[0041] Optionally, in S4, an intraday rolling correction model is established to minimize the microgrid intraday rolling correction dispatch cost according to the following formula:

[0042] F2=min[C T2 +C P2 ]

[0043] Among them, F2 is the daily rolling correction dispatch cost of the microgrid; C T2 and C P2 is the operation and maintenance cost and penalty cost of the intraday rolling correction, where C T2 , C T1 The formula expression and C T Same, C P2 The formula expression and C P1 Same.

[0044] Optionally, after updating the air conditioning load virtual energy storage capacity, the real-time rolling optimization model and the intraday rolling correction model in the virtual energy storage model according to the updated air conditioning user decision model, the method further includes:

[0045] Based on the air-conditioning user decision model, the update can reduce the load compensation costs, the total operating costs of the microgrid system, the interaction costs between the microgrid and the large grid, and the constraints, and enter the next rolling optimization.

[0046] A second aspect of the present invention provides a scheduling system, comprising: a first establishing module, a second establishing module, a third establishing module, a fourth establishing module, a fifth establishing module and an updating module.

[0047] Among them, the first establishment module is used to establish a virtual energy storage model of the variable frequency air conditioning load taking into account the dynamic decision of the air conditioning user based on the variable frequency air conditioning thermal dynamic model;

[0048] The second building module is used to consider the dynamic fluctuations of air-conditioning user decisions during the day and the day, and to build an air-conditioning user decision model, wherein the air-conditioning user decision model is used to quantify the adjustable capacity of the demand side during the scheduling process;

[0049] The third establishment module is used to establish a day-ahead scheduling model with the goal of minimizing the total cost of day-ahead scheduling;

[0050] The fourth establishment module is used to consider the deviation of the amount of electricity that can be reduced between the day-ahead and the day-ahead, and to establish a real-time rolling optimization model in the day-ahead optimization stage based on the day-ahead scheduling model of the third establishment module;

[0051] The fifth establishment module is used to establish an intraday rolling correction model that minimizes the microgrid's intraday rolling correction dispatch cost. The intraday rolling correction model obtains the amount of electricity that can be reduced during each rolling optimization process using the real-time rolling model, and updates the air-conditioning user decision model according to the amount of electricity that can be reduced.

[0052] The updating module is used to update the air conditioning load virtual energy storage capacity, real-time rolling optimization model and intraday rolling correction model in the virtual energy storage model according to the updated air conditioning user decision model, and to dispatch the microgrid according to the solution result of the intraday rolling correction model updated last time.

[0053] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is loaded into the processor, the method for optimizing the scheduling of a microgrid before and after the day, taking into account the decisions of air-conditioning users, is implemented.

[0054] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned microgrid day-ahead and intraday optimization scheduling method taking into account the decisions of air-conditioning users.

[0055] Compared with the prior art, the beneficial effects of the present invention include at least:

[0056] The present invention discloses a microgrid day-ahead-intraday optimization scheduling method taking into account the decision of air-conditioning users, and constructs a virtual energy storage model of variable frequency air-conditioning by considering the dynamic decision of air-conditioning users. Next, a day-ahead scheduling model with the goal of minimizing the total cost of day-ahead scheduling is proposed, and a real-time rolling optimization model is established in the intraday optimization stage. Considering the deviation of the amount of electricity that can be reduced between the day-ahead and the intraday, an intraday rolling correction method for minimizing the microgrid intraday rolling correction scheduling cost is proposed, and the scheduling plan is corrected in real time through this rolling strategy. Finally, the optimal operation method of the microgrid system day-ahead-intraday coordination is obtained, which effectively reduces the energy cost of the microgrid system and improves the accuracy of the system's scheduling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0058] Figure 1 A schematic diagram of the system architecture of a microgrid day-ahead and intra-day optimization scheduling method taking into account air-conditioning user decisions provided in an embodiment of the present application;

[0059] Figure 2 A schematic diagram of a microgrid day-ahead-day optimization scheduling method for a microgrid taking into account air-conditioning user decisions provided in an embodiment of the present application;

[0060] Figure 3 A schematic diagram of microgrid day-ahead data of a microgrid day-ahead and intra-day optimization scheduling method taking into account air-conditioning user decisions provided in an embodiment of the present application;

[0061] Figure 4 A schematic diagram of intraday data of a microgrid provided in an embodiment of the present application for a microgrid day-ahead-intraday optimization scheduling method taking into account air-conditioning user decisions;

[0062] Figure 5 A schematic diagram of a microgrid day-ahead scheduling result of a microgrid day-ahead and intra-day optimization scheduling method taking into account air-conditioning user decisions provided in an embodiment of the present application;

[0063] Figure 6 A schematic diagram of a microgrid intraday scheduling result of a microgrid day-ahead-intraday optimization scheduling method taking into account air-conditioning user decisions provided in an embodiment of the present application;

[0064] Figure 7 A schematic diagram of an air conditioner charging and discharging curve of a microgrid day-ahead and intraday optimization scheduling method taking into account air conditioner user decisions provided in an embodiment of the present application;

[0065] Figure 8 A schematic diagram of the dynamic decision probability of air-conditioning users in a microgrid day-ahead and intraday optimization scheduling method taking into account the decisions of air-conditioning users provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0067] The present invention is applied to Figure 1 The microgrid system shown in the figure includes multiple devices such as photovoltaics, wind turbines, batteries, air conditioners, and electrical loads. By quickly and accurately regulating these flexible resources, the system can achieve a balance between supply and demand and meet diversified energy supply needs.

[0068] Combination Figure 2 As shown, based on the above microgrid system, embodiment 1 of the present invention provides a day-ahead and intra-day collaborative optimization scheduling method taking into account the decision of air-conditioning users, including the following steps:

[0069] S1: Based on the air conditioning thermal dynamic model, a virtual energy storage model of variable frequency air conditioning load is established considering the dynamic decision of air conditioning users.

[0070] S1 includes:

[0071] S101: The thermal dynamic model of the variable frequency air conditioner is:

[0072]

[0073] Where C is the equivalent heat capacity of the air conditioning load; T t is the indoor temperature; t is the time; T o is the outdoor temperature; R is the equivalent impedance of the air conditioning load; Q is the cooling capacity of the variable frequency air conditioner.

[0074] S102: The relationship between the electric power and cooling capacity of the variable frequency air conditioner and the frequency is:

[0075]

[0076] Among them, P AC is the electric power of the variable frequency air conditioner; Q AC is the cooling capacity of the variable frequency air conditioner, a1, a2, b1, b2 are constant coefficients; f is the frequency of the variable frequency air conditioner compressor.

[0077] S103: The variable frequency air conditioning load stores electrical energy in the form of heat energy in the building, and its storage capacity is related to the indoor and outdoor temperature difference. Assume that the indoor temperature is [T min ,T max ] changes, the maximum capacity of the virtual energy storage for air conditioning load and the capacity at time t can be obtained by formula (1):

[0078] E max =C(T max -T min ) (3)

[0079] E t =C(T max -T t ) (4)

[0080] Among them, E max E is the maximum capacity of virtual energy storage for air conditioning load; t is the virtual energy storage capacity of air conditioning load at time t, T t is the indoor temperature at time t, C is the equivalent heat capacity of the air conditioning load, T max is the maximum value of indoor temperature. Formula (4) is the constructed virtual energy storage model of variable frequency air conditioning load.

[0081] S104: The indoor temperature at time t is:

[0082]

[0083] Among them, T k is the indoor temperature at the initial time of the kth period, T o is the outdoor temperature, Q AC is the cooling capacity of the variable frequency air conditioner, C is the equivalent heat capacity of the air conditioning load, and C is the equivalent heat capacity of the air conditioning load.

[0084] S2: Considering the dynamic fluctuations of air-conditioning user decisions during the day and the day, a control and adjustment user decision model is constructed. The air-conditioning user decision model is used to quantify the adjustable capacity of the demand side during the scheduling process.

[0085] Specifically, the setting of the reference electricity price determines the degree of user response, so indirectly, the cost of the air conditioner user will affect the user's response decision. This cost is reflected in the size of the air conditioner user's electricity power.

[0086] In demand response, changes in a user's electricity consumption are often closely related to increases in energy costs. When overall energy demand rises, especially during peak load periods, users' energy costs rise significantly, which is particularly evident in the use of high-energy-consuming devices. During these peak periods, the surge in electricity demand not only leads to increased electricity consumption, but also makes users' energy expenses more expensive. This situation forces users to reconsider their electricity behavior and strategies to cope with the increasing energy costs.

[0087] S201: Construct a quantitative model of user participation satisfaction:

[0088]

[0089] Among them, ω t is the user's satisfaction with decision-making at time t, P cut1,t is the daily load that the air conditioner can reduce at time t, P cut，0 The air conditioner can reduce the electrical load by setting the initial setting for the day-ahead scheduling.

[0090] Through effective management of air conditioning load, the user's satisfaction when participating in the demand response program can be calculated, thus providing a basis for further strategy adjustment and optimization.

[0091] S202: Considering the dynamic fluctuation of air conditioner user decision-making during the day before and during the day, an air conditioner user decision model is constructed according to the following formula:

[0092]

[0093] Among them, P j,t is the decision probability of the air-conditioning user with response level j at time t; ω k,t is the user's satisfaction with decision-making at time t in the kth period; β is the user's internal characteristic parameter; n is the number of periods, which can be 96. The user's decision probability represents the probability of the user's dynamic decision-making. Here, dynamic refers to the change of probability over time, and the corresponding probability at different times is different.

[0094] When the deviation of power reduction is larger, the user's decision probability will be correspondingly more biased. This means that when users are faced with power demand response, their participation willingness and behavior may be more strongly affected, leading to hesitation and uncertainty in choosing whether to participate in the response.

[0095] S3: Establish a day-ahead dispatch model with the goal of minimizing the total cost of day-ahead dispatch, and consider the deviation of the amount of electricity that can be reduced between the day-ahead and the day. Based on the day-ahead dispatch model, establish a real-time rolling optimization model in the day-ahead optimization stage.

[0096] S3 specifically includes:

[0097] S301: The optimization goal of the day-ahead scheduling model is to minimize the total cost of day-ahead scheduling. The mathematical model is as follows:

[0098] C T =min(C Oper +C Gird +C DR ) (8)

[0099] Among them, C T is the total scheduling cost on the day before; C Oper is the total operating cost of the microgrid system; C Gird is the interaction cost between microgrid and large grid; C DR To compensate for the reduced load.

[0100] Specifically, the total operating cost of the microgrid system includes daily operation and maintenance costs, which include the operation and maintenance costs of renewable energy and the energy storage life loss costs generated during the battery charging and discharging process, as shown in the following formula:

[0101]

[0102] Among them, γ1 and γ2 are the operation and maintenance cost coefficients of photovoltaic and wind power; P PV,t and P WT,t is the power generation of photovoltaic and wind power at time t; P Bat,t is the charging and discharging power of the battery at time t, and P Bat,t When the discharge is positive, P Bat,t Negative; K Bat is the life loss cost coefficient caused by the battery charge and discharge; Δt is the time change, which is taken as 1h during the day-ahead scheduling process; T is the scheduling period.

[0103] Furthermore, the cost of interacting with the grid is:

[0104]

[0105] Among them, σ Grid is the cost coefficient for interaction with the power grid; P Grid,t represents the interaction power between the microgrid and the grid at time t. When P Grid,t When it is positive, it means the microgrid purchases electricity from the grid, otherwise it sells electricity. Δt is the time change, which is 1h in the day-ahead dispatch process; T is the dispatch period.

[0106] Furthermore, the load compensation fee can be reduced to:

[0107]

[0108] Among them, λ e is the unit compensation coefficient of the electric load; P cut,tis the electric load that can be reduced, and T is the scheduling period.

[0109] S302: The day-ahead scheduling satisfies constraints, where the constraints include at least one of the following: power balance constraints, renewable energy power constraints, energy storage operation constraints, interconnection line transmission power constraints, and curtailable load constraints.

[0110] Furthermore, the power balance constraint is:

[0111] P PV,t +P WF,t +P Bat,t +P Grid,t =P e,t +P cut,t (12)

[0112] Among them, P PV,t is the photovoltaic power generation at time t, P WT,t is the wind power generation power at time t, P Bat,t is the charge and discharge power of the battery at time t, P Grid,t is the interaction power between the microgrid and the grid at time t, P e,t is the load power of the microgrid at time t, P cut,t The electrical load can be reduced.

[0113] Furthermore, the renewable energy power constraint is:

[0114]

[0115] Among them, P PV,t is the photovoltaic power generation at time t, P WT,t is the wind power generation power at time t, and The maximum and minimum values ​​of photovoltaic output; and are the maximum and minimum values ​​of wind power output.

[0116] Furthermore, the energy storage operation constraints are:

[0117]

[0118] Among them, P Bat,C,t and P Bat,D,t P is the charging power and discharging power of the battery during period t; Bat,C,max and P Bat,D,max They are the maximum charging and discharging power of the battery during period t; SOC t is the capacity of the battery during period t; C Bat Indicates the rated capacity of the battery; P Bat,E Indicates the rated power of the battery; SOC maxis the maximum capacity of the battery. To ensure the continuity of the dispatch cycle, the battery storage capacity at the beginning and end is kept consistent.

[0119] Furthermore, the transmission power constraint of the tie line is:

[0120]

[0121] Among them, P Grid,t is the interaction power between the microgrid and the grid at time t, and are the maximum and minimum values ​​of the transmission power of the tie line.

[0122] Furthermore, the load constraint that can be reduced is:

[0123] 0≤P cut,t Δt≤E t ′≤E max (16)

[0124] Among them, P cut,t The electrical load that can be reduced, E max is the maximum capacity of the virtual energy storage for air conditioning load, Δt is the time variation, which is taken as 1h in the day-ahead dispatch process; the load that can be reduced at time t cannot be greater than the maximum capacity of the virtual energy storage for air conditioning load, E t ′ is the virtual energy storage of variable frequency air conditioning load considering user decision.

[0125] It should be noted that the scheduling during the intraday optimization phase also meets the above constraints. The above constraints need to be met in each scheduling or optimization process.

[0126] S303: Considering the difference in the amount of electricity that can be reduced between the day-ahead and the day-ahead, a real-time rolling optimization model is established in the day-ahead optimization stage based on the day-ahead scheduling model, including:

[0127] Based on the day-ahead dispatch model, a real-time rolling optimization model is established in the intraday optimization stage according to the following formula, with the goal of minimizing the intraday rolling optimization dispatch cost of the microgrid:

[0128] F1=min[C T1 +C P1 ] (17)

[0129] C P1 =ΔP Bat1 λ bat +ΔP Gird1 λ gird +ΔP cut1 λ cut (18)

[0130]

[0131] Among them, F1 is the daily rolling optimization dispatching cost of the microgrid; C T1 and C P1 Operation and maintenance costs and penalty costs for intraday rolling optimization; ΔP Bat1 , ΔP Gird1 and ΔP cut1 are the total amount of battery, grid interaction, and curtailable daily electricity compared to the day-ahead dispatch model, that is, the curtailable electricity deviation between the day-ahead and the day-ahead, λ bat , gird and λ cut are the penalty adjustment coefficients for battery, grid interaction and reducible power, respectively; P Bat1,t , P Gird1,t and P cut1,t They are battery, grid interaction, and daily electric power that can be reduced at time t, and T is the scheduling period.

[0132] In this embodiment, considering that the long-term error of the day-ahead scheduling forecast may lead to the deviation between the optimized scheduling and the actual operation, the intraday scheduling needs to be adjusted according to the latest forecast data. In order to maximize the use of the day-ahead scheduling plan, the goal of the intraday rolling optimization is to minimize the adjustment amount of the day-ahead scheduling plan, and use the adjustment amount as the penalty item in the objective function, thereby improving the economy and stability of the system operation.

[0133] Furthermore, the load compensation fee that can be reduced is updated as follows:

[0134]

[0135] Among them, C DR1 is the compensation cost for load reduction after rolling optimization within the day, i.e., the demand response compensation cost, λ e is the unit compensation coefficient of the electric load, P cut1,t is the daily electricity load that can be reduced, T is the dispatching period, P j,t is the decision probability of the air-conditioning user with response level j at time t.

[0136] It should be noted that in the day-ahead scheduling, since there is no deviation in the amount of reduction that can be made, the compensation cost for the load that can be reduced is as shown in formula (11). At this time, the air-conditioning user decision model is not introduced. In the first rolling optimization of the intraday real-time rolling optimization stage, since the user's reduction amount is obtained after optimization, the user decision probability based on the power reduction deviation cannot be updated in time. At this time, the day-ahead user decision probability P j,t is a fixed value, which can be set to the average of the user decision probability on the previous day or any initial value. After the intraday rolling correction, the user decision probability P j,tThe value of is updated, which can reduce the load compensation cost value update, and then after each round of real-time rolling and rolling correction, the user decision probability P j,t The values ​​of are all changed until the last rolling correction, when the scheduling method is obtained and scheduling is performed.

[0137] Furthermore, the power balance constraint is changed to:

[0138] P PV1,t +P WF1,t +P Bat1,t +P Grid1,t =P e1,t +P cut1,t (twenty one)

[0139] Among them, P PV1,t , P WF1,t are the daily power of photovoltaic power and wind power at time t, P e1,t is the daily load power of the microgrid at time t, P Bat1,t is the daily charge and discharge power of the battery at time t, P Grid1,t is the intraday interaction power between the microgrid and the grid at time t, P e1,t is the daily load power of the microgrid at time t, P cut1,t The daily electrical load that can be reduced.

[0140] Understandably, due to E t ′ has changed, so the reducible electric load obtained by the load constraint has also changed. Other load powers obtained according to the constraint conditions may change, and then the scheduling method changes. After the last rolling optimization, the latest scheduling method is obtained, and the optimized scheduling is performed according to the latest scheduling method.

[0141] It should be noted that the remaining objective functions and constraints are further updated according to the changes in formula (21), which will not be repeated here.

[0142] In one embodiment, in order to prevent the problem of poor adaptability of scheduling results due to large long-term forecast data, the intraday scheduling model uses more accurate short-term wind power, photovoltaic, and load data, which are updated every 15 minutes, and 4 hours of forecast data are issued each time. For example, if the entire scheduling cycle is one day, the real-time rolling optimization model optimizes according to the optimization objectives and constraints of intraday scheduling, and only optimizes for 4 hours each time, and optimizes every 15 minutes, and then rolls forward.

[0143] S4: Considering the deviation of the amount of electricity that can be reduced between the day before and the day, an intraday rolling correction model is established to minimize the penalty cost of the microgrid. The intraday rolling correction model updates the air-conditioning user decision model during each rolling optimization process using the real-time rolling model.

[0144] Specifically, the intraday rolling correction dispatch model that minimizes the microgrid penalty cost is established according to the following formula:

[0145] F2=min[C T2 +C P2 ] (twenty two)

[0146] Among them, F2 is the daily rolling correction dispatch cost of the microgrid; C T2 and C P2 is the operation and maintenance cost and penalty cost of the intraday rolling correction. It should be noted that, among them, C T2 , C T1 The formula expression and C T Same, C P2 The formula expression and C P1 The same, except that all parameter values ​​are the values ​​of the previous rolling revision, which will not be repeated here.

[0147] S5: Update the air conditioning load virtual energy storage capacity, real-time rolling optimization model and intraday rolling correction model in the virtual energy storage model according to the updated air conditioning user decision model, and dispatch the microgrid according to the solution result of the intraday rolling correction model updated last time.

[0148] S5 includes:

[0149] According to the updated air-conditioning user decision model P j,t Update the virtual energy storage model of the variable frequency air conditioning load according to the following formula:

[0150] E t ′=C(T max -T t ) j,t (twenty three)

[0151] Among them, E t ′ is the virtual energy storage of variable frequency air conditioning load considering user decision, P j,t is the decision probability of the air-conditioning user with response level j at time t, C is the equivalent heat capacity of the air-conditioning load; T t is the indoor temperature at time t, T max is the maximum indoor temperature.

[0152] The load compensation fee can be reduced according to the following formula:

[0153]

[0154] 0≤P cut,t Δt≤E t ′≤E max

[0155] Among them, C DR1is the load compensation fee that can be reduced after rolling optimization within the day, λ e is the unit compensation coefficient of the electric load, P cut,t is the electric load that can be reduced, T is the dispatching period, P j,t is the decision probability of the air conditioner user with response level j at time t, E max is the maximum capacity of virtual energy storage for air conditioning load, Δt is the time variation, E t ′ is the virtual energy storage of variable frequency air conditioning load considering user decision.

[0156] The day-ahead dispatch model, real-time rolling optimization model and intraday rolling correction model are updated according to the updated compensation fee for the reducible load.

[0157] Furthermore, the curtailable load compensation fee is further modified:

[0158]

[0159] Among them, C DR2 is the compensation cost of the load that can be reduced after rolling correction within the day, that is, the demand response compensation cost. It should be noted that according to equations (8) to (16), the other objective functions and constraints are further modified, which will not be repeated here.

[0160] In this embodiment, by timely updating the user's decision probability in each rolling optimization, the user's behavior changes and preference adjustments can be accurately captured. This not only helps to optimize the scheduling plan, but also improves the flexibility and adaptability of the overall system, improves the reliability of scheduling decisions, and ensures better load management and resource allocation in a dynamic environment.

[0161] Embodiment 2 of the present invention provides a scheduling system, which runs the method of embodiment 1, and the system includes: a first establishment module, a second establishment module, a third establishment module, a fourth establishment module, a fifth establishment module and an update module. Among them, the first establishment module is used to establish a virtual energy storage model of the variable frequency air conditioning load considering the dynamic decision of the air conditioning user based on the variable frequency air conditioning thermal dynamic model. The second establishment module is used to consider the dynamic fluctuations of the air conditioning user decision during the day and the day, and construct the air conditioning user decision model, which is used to quantify the adjustable capacity of the demand side during the scheduling process. The third establishment module is used to establish a day-ahead scheduling model with the goal of minimizing the total cost of day-ahead scheduling. The fourth establishment module is used to consider the deviation of the amount of electricity that can be reduced between the day-ahead and the day, and establish a real-time rolling optimization model in the day-ahead optimization stage based on the day-ahead scheduling model of the third establishment module. The fifth establishment module is used to establish an intra-day rolling correction model that minimizes the intra-day rolling correction scheduling cost of the microgrid, and the intra-day rolling correction model obtains the amount of electricity that can be reduced in each rolling optimization process using the real-time rolling model, and updates the air conditioning user decision model according to the amount of electricity that can be reduced. The update module is used to update the air conditioning load virtual energy storage capacity, real-time rolling optimization model and intraday rolling correction model in the virtual energy storage model according to the updated air conditioning user decision model, and to dispatch the microgrid according to the solution result of the intraday rolling correction model updated last time.

[0162] Embodiment 3 of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, the method for optimizing the microgrid day-ahead and day-intraday scheduling taking into account the decisions of air-conditioning users as described in Embodiment 1 is implemented.

[0163] Embodiment 4 of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for optimizing the microgrid day-ahead and intraday scheduling taking into account the decision of air-conditioning users according to embodiment 1 is implemented.

[0164] Combination Figures 2 to 8 As shown, Embodiment 5 of the present invention provides a verification embodiment of a microgrid day-ahead and day-intraday optimization scheduling method taking into account the decisions of air-conditioning users.

[0165] With the continuous improvement of the forecast accuracy of wind energy, photovoltaic power and load, the forecast data of the next day is used for optimization in the day-ahead dispatch model, and the dispatch cycle is 24 hours, and the time scale of the forecast data is 1 hour. At the same time, in order to promote the two-way interaction between the energy supply side and the energy consumption side, a demand response mechanism is introduced. However, considering that the hourly error of the day-ahead forecast is still larger than the minute-level forecast error, the optimization dispatch of a single time scale is difficult to give full play to the advantages of multi-time scale dispatch. Therefore, a multi-time scale optimization method is used to adjust the day-ahead dispatch results. Specifically, the intraday dispatch cycle is set to 4 hours and the time scale is 15 minutes. The dispatch plan is updated through the real-time rolling optimization model and the intraday rolling correction model, thereby improving the flexibility and accuracy of the dispatch. The day-ahead dispatch, intraday rolling optimization and correction dispatch process are as follows: Figure 2 shown.

[0166] Specifically, the photovoltaic output, wind power output, and power load curves are as follows: Figure 3 As shown in the figure, the photovoltaic output, wind power output and electric load curves within a day are as follows: Figure 4 shown.

[0167] The following is an analysis of the microgrid day-ahead dispatch results.

[0168] The microgrid day-ahead dispatch results are as follows: Figure 5 As shown. By comparison, it can be clearly seen that before the implementation of the present invention, the electric load curve shows a large peak-to-valley difference. During peak hours, the sharp increase in electric load puts a large pressure on the power grid, while during valley hours, the load drops significantly. This volatility not only reflects the inflexibility of users' electricity usage habits, but also poses a challenge to the stable operation of the power grid.

[0169] The following is an analysis of the microgrid’s intraday rolling optimization and correction scheduling results.

[0170] In the first rolling optimization process of the microgrid within the day, the user's reduction amount is obtained based on the optimization results. As a result, at this stage, the user's decision probability is not updated in time and remains at a fixed value. Since the user's reduction amount is closely related to its decision probability, the lack of dynamic adjustment will affect the optimization effect and thus fail to accurately reflect the actual power consumption. Therefore, in order to overcome this shortcoming, the embodiment of the present invention adopts a strategy of two rolling optimizations. The first is a conventional rolling optimization, which aims to schedule according to preliminary load forecasts and user responses. However, the user's decision probability fails to reflect the changes in the reduction amount in real time, resulting in a deviation between the scheduling result and the actual demand.

[0171] Figure 6 The main goal of this stage is to dynamically update the user's decision probability based on the latest user power reduction data to more accurately reflect their actual power consumption behavior.

[0172] Figure 7 This is the charge and discharge curve of the air conditioner. Figure 7 The obvious changes after day-ahead scheduling and intraday rolling correction are shown. In the day-ahead scheduling stage, the charging and discharging plan of the air conditioner is based on the pre-set load forecast and power generation capacity. However, since the actual load situation often deviates from the forecast, the charging and discharging efficiency of the air conditioner may be affected. With the implementation of intraday rolling correction, the charging and discharging strategy of the air conditioner has been optimized, improving the accuracy and reliability of air conditioner scheduling.

[0173] Figure 8 The dynamic decision probability of air-conditioning users is determined by this dynamic adjustment mechanism, which effectively improves the reliability and adaptability of scheduling and provides strong support for the stable operation of the microgrid.

[0174] The economic feasibility of the day-ahead and intraday collaborative optimization of microgrids is analyzed below.

[0175] Case 1: Day-ahead dispatch, without considering comprehensive demand response, has a total cost of RMB 16,574.

[0176] Case 2: Day-ahead dispatch, taking into account comprehensive demand response, the total cost is 14,901 yuan.

[0177] Case 3: Intraday scheduling, using rolling optimization scheduling method, the total cost is 15,375 yuan.

[0178] Case 4: Intraday scheduling, using rolling optimization and correction scheduling methods, with a total cost of 15,407 yuan.

[0179] In case 1, the total cost of day-ahead dispatch without considering integrated demand response was RMB 16,574, while in case 2, after considering integrated demand response, the total cost dropped to RMB 14,901, a decrease of 10.1%. This shows that integrated demand response plays an important role in optimizing resource allocation and reducing costs.

[0180] Further comparing Case 3 and Case 4, the total cost of using the real-time rolling model to optimize the scheduling method is 15,375 yuan, achieving a 7.7% correction to the day-ahead scheduling; while the cost of using the real-time rolling optimization model and the intraday rolling correction model scheduling method is 15,407 yuan, achieving a 7.5% correction to the day-ahead scheduling. Although the cost difference between the two is not large, the intraday rolling correction model has a more accurate scheduling capability through continuous adjustment and optimization. The two-stage optimization strategy not only improves scheduling accuracy, but also enhances the system's ability to respond to changes in user behavior, providing reliable support for subsequent decision-making.

[0181] Compared with the prior art, the beneficial effects of the present invention include at least:

[0182] The present invention discloses a day-ahead-intraday collaborative optimization scheduling method taking into account the decision of air-conditioning users. Taking into account the dynamic decision of air-conditioning users, a virtual energy storage model of variable frequency air-conditioning is constructed. Next, a day-ahead scheduling model with the goal of minimizing the total cost of day-ahead scheduling is proposed, and a real-time rolling optimization model is established in the intraday optimization stage. Taking into account the deviation of the amount of electricity that can be reduced between the day-ahead and the intraday, an intraday rolling correction method for minimizing the intraday rolling correction scheduling cost of the microgrid is proposed, and the scheduling plan is corrected in real time through this rolling strategy. Finally, the optimal operation method of the day-ahead-intraday collaboration of the microgrid system is obtained, which effectively reduces the energy cost of the microgrid system and improves the accuracy of the system's scheduling process.

[0183] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0184] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0185] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0186] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0187] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A microgrid day-ahead and intraday optimization scheduling method taking into account air conditioning user decisions, characterized in that: include: S1: Based on the variable frequency air conditioner thermal dynamic model, a virtual energy storage model of the variable frequency air conditioner load is established considering the dynamic decision of air conditioner users; S2: Considering the dynamic fluctuation of air-conditioning user decisions during the day and the day, an air-conditioning user decision model is constructed. The air-conditioning user decision model is used to quantify the adjustable capacity of the demand side during the scheduling process; S3: Establish a day-ahead dispatch model with the goal of minimizing the total cost of day-ahead dispatch, and consider the deviation of the amount of electricity that can be reduced between the day-ahead and the day. Based on the day-ahead dispatch model, establish a real-time rolling optimization model in the day-ahead optimization stage; S4: Establishing an intraday rolling correction model that minimizes the microgrid's intraday rolling correction dispatch cost, wherein the intraday rolling correction model obtains the amount of electricity that can be reduced during each rolling optimization process using the real-time rolling model, and updates the air-conditioning user decision model according to the amount of electricity that can be reduced; S5: Update the air conditioning load virtual energy storage capacity, real-time rolling optimization model and intraday rolling correction model in the virtual energy storage model according to the updated air conditioning user decision model, and dispatch the microgrid according to the solution result of the intraday rolling correction model updated last time; Among them, during the day-ahead scheduling, intraday real-time rolling optimization and intraday rolling correction processes, the power reduction deviation is required to be less than or equal to the virtual energy storage capacity of the air-conditioning load in the virtual energy storage model.

2. The microgrid day-ahead and intra-day optimization scheduling method taking into account air conditioning user decisions according to claim 1 is characterized by: In S1, the virtual energy storage model of the variable frequency air conditioning load is constructed according to the following formula: E t =C(T max -T t ) Among them, E t is the virtual energy storage capacity of the air conditioning load at time t; C is the equivalent heat capacity of the air conditioning load; T t is the indoor temperature at time t, T max is the maximum indoor temperature.

3. The microgrid day-ahead and intra-day optimization scheduling method taking into account air conditioning user decisions according to claim 2 is characterized by: In S2, considering the dynamic fluctuation of air-conditioning user decisions during the day before and during the day, the air-conditioning user decision model is constructed according to the following formula: Among them, P j,t is the decision probability of the air-conditioning user with response level j at time t; β is the internal characteristic parameter of the user; ω k,t is the user's satisfaction with decision-making at time t in the kth period; n is the number of periods.

4. The microgrid day-ahead and intra-day optimization scheduling method taking into account air conditioning user decisions according to claim 3 is characterized by: The satisfaction of user participation in decision-making is calculated according to the following formula: Among them, ω t is the user's satisfaction with decision-making at time t, P cut1,t P is the power load that the air conditioner can reduce at time t, cut，0 The air conditioner can reduce the electrical load by setting the initial setting for the day-ahead scheduling.

5. The microgrid day-ahead and intra-day optimization scheduling method taking into account air conditioning user decisions according to claim 3 is characterized by: In S5, the air conditioning load virtual energy storage capacity, the real-time rolling optimization model and the intraday rolling correction model in the virtual energy storage model are updated according to the updated air conditioning user decision model, including: According to the updated air-conditioning user decision model P j,t Update the virtual energy storage model of the variable frequency air conditioning load according to the following formula: E t ′=C(T max -T t )P j,t Among them, E t ′ is the virtual energy storage of variable frequency air conditioning load considering user decision, P j,t is the decision probability of the air-conditioning user with response level j at time t, C is the equivalent heat capacity of the air-conditioning load; T t is the indoor temperature at time t, T max is the maximum value of the indoor temperature; The load compensation fee can be reduced according to the following formula: 0≤P cut,t Δt≤E t ′≤E max Among them, C DR1 is the load compensation fee that can be reduced after rolling optimization within the day, λ e is the unit compensation coefficient of the electric load, P cut,t is the electric load that can be reduced, T is the dispatching period, P j,t is the decision probability of the air conditioner user with response level j at time t, E max is the maximum capacity of virtual energy storage for air conditioning load, Δt is the time variation, E t ′ is the virtual energy storage of variable frequency air conditioning load considering user decision; The day-ahead dispatch model, real-time rolling optimization model and intraday rolling correction model are updated according to the updated compensation fee for the reducible load.

6. The microgrid day-ahead and intra-day optimization scheduling method taking into account air conditioning user decisions according to claim 1 is characterized by: In S3, the day-ahead scheduling model with the goal of minimizing the total day-ahead scheduling cost is expressed as follows: C T =min(C Oper +C Gird +C DR ) Among them, C T is the total scheduling cost on the day before; C Oper is the total operating cost of the microgrid system; C Gird is the interaction cost between microgrid and large grid; C DR To reduce the load compensation cost, λ e is the unit compensation coefficient of the electric load; P cut,t is the electric load that can be reduced, and T is the scheduling period.

7. The microgrid day-ahead and intra-day optimization scheduling method taking into account air conditioning user decisions according to claim 6 is characterized by: In S3, based on the day-ahead dispatch model, a real-time rolling optimization model is established in the intraday optimization stage according to the following formula, with the goal of minimizing the intraday rolling optimization dispatch cost of the microgrid: F1=min[C T1 +C P1 ] C P1 =ΔP Bat1 l bat +ΔP Gird1 l gird +ΔP cut1 l cut Among them, F1 is the daily rolling optimization dispatching cost of the microgrid; C T1 and C P1 Operation and maintenance costs and penalty costs for intraday rolling optimization; ΔP Bat1 , ΔP Gird1 and ΔP cut1 are the total amount of adjustment of the battery, grid interaction, and curtailable daily electricity compared with the day-ahead dispatch model; bat , gird and λ cut are the penalty adjustment coefficients for battery, grid interaction and reducible power, respectively; P Bat1,t , P Gird1,t and P cut1,t They are battery, grid interaction, and daily electric power that can be reduced at time t, and T is the scheduling period.

8. The microgrid day-ahead and intra-day optimization scheduling method taking into account air-conditioning user decisions according to claim 7 is characterized by: In S4, the intraday rolling correction model is established to minimize the microgrid intraday rolling correction dispatch cost according to the following formula: F2=min[C T2 +C P2 ] Among them, F2 is the daily rolling correction dispatch cost of the microgrid; C T2 and C P2 is the operation and maintenance cost and penalty cost of the intraday rolling correction, where C T2 , C T1 The formula expression and C T Same, C P2 The formula expression and C P1 Same.

9. The microgrid day-ahead and intra-day optimization scheduling method taking into account air-conditioning user decisions according to any one of claims 6 to 8, characterized in that: After updating the air conditioning load virtual energy storage capacity, the real-time rolling optimization model and the intraday rolling correction model in the virtual energy storage model according to the updated air conditioning user decision model, the method further includes: Based on the air-conditioning user decision model, the update can reduce the load compensation costs, the total operating costs of the microgrid system, the interaction costs between the microgrid and the large grid, and the constraints, and enter the next rolling optimization.

10. A dispatching system using the microgrid day-ahead and intra-day optimization dispatching method according to any one of claims 1 to 9 and air-conditioning user decision-making, characterized in that: include: The first establishment module is used to establish a virtual energy storage model of the variable frequency air conditioning load taking into account the dynamic decision of the air conditioning user based on the variable frequency air conditioning thermal dynamic model; The second building module is used to consider the dynamic fluctuations of air-conditioning user decisions during the day and the day, and to build an air-conditioning user decision model, wherein the air-conditioning user decision model is used to quantify the adjustable capacity of the demand side during the scheduling process; The third establishment module is used to establish a day-ahead scheduling model with the goal of minimizing the total cost of day-ahead scheduling; The fourth establishment module is used to consider the deviation of the amount of electricity that can be reduced between the day-ahead and the day-ahead, and to establish a real-time rolling optimization model in the day-ahead optimization stage based on the day-ahead scheduling model of the third establishment module; The fifth establishment module is used to establish an intraday rolling correction model that minimizes the microgrid's intraday rolling correction dispatch cost. The intraday rolling correction model obtains the amount of electricity that can be reduced during each rolling optimization process using the real-time rolling model, and updates the air-conditioning user decision model according to the amount of electricity that can be reduced. The updating module is used to update the air conditioning load virtual energy storage capacity, real-time rolling optimization model and intraday rolling correction model in the virtual energy storage model according to the updated air conditioning user decision model, and to dispatch the microgrid according to the solution result of the intraday rolling correction model updated last time.

11. An electronic device, comprising a processor and a storage medium, characterized in that: The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the microgrid day-ahead and intra-day optimization scheduling method for air-conditioning user decision-making according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the microgrid day-ahead and intra-day optimization scheduling method for air-conditioning user decision-making as described in any one of claims 1 to 9 are implemented.

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