Cloud energy storage operation mechanism based on thermal inertia of urban building air conditioning system

Abstract

The invention discloses a cloud energy storage operation mechanism based on thermal inertia of an urban building air conditioning system, which relates to the field of energy storage and comprises the following steps: (1) establishing a fixed-frequency air conditioner virtual energy storage monomer model and an aggregation model based on the thermal inertia of the urban building air conditioning system; (2) designing a cloud energy storage operation mechanism taking air conditioner aggregation virtual energy storage as a main leasing resource, and establishing a cloud energy storage operator model, a cloud energy storage user model and a transaction pricing strategy; and (3) solving the cloud energy storage operation model by taking the minimum power consumption cost of the user as a target, and distributing and evaluating the social total welfare brought by the cloud energy storage mechanism. According to the energy storage operation mechanism provided by the invention, the electricity consumption cost of the subscribing user can be effectively reduced, and the early-stage investment and the later-stage operation and maintenance cost of an operator are reduced; and under the dual-carbon background, the strategy is also beneficial to the real-time consumption of new energy on the user side.

Description

Technical Field

[0001] The present invention relates to an energy storage operation, in particular to a cloud energy storage operation mechanism based on the thermal inertia of an urban building air conditioning system. Background Art

[0002] Guided by the "dual carbon" goal, my country is striving to build a new power system with new energy as the main body. Energy storage will play a vital role in the future high-proportion new energy power system with its flexible adjustment capabilities. In recent years, the continuous development of the "sharing economy" has spawned a new energy storage investment and application model, namely the "cloud energy storage" model. Cloud energy storage is a shared energy storage service based on the existing power grid structure. It aggregates the scattered physical energy storage capacity on the user side to the cloud and centrally manages it in the form of virtual energy storage capacity, thereby providing distributed energy storage services to a large number of users, effectively making up for the high investment price, low utilization rate and limited installation site of traditional energy storage.

[0003] At the same time, with the global warming and the continuous evolution of urbanization, the demand for air conditioning use by Chinese residents has increased year by year. The load increase caused by the operation of a large number of air conditioners will bring great challenges to the safe and stable operation of the entire power grid. The traditional view regards air conditioners as simple electrical equipment, and the temperature and humidity are adjusted by changing their operation control strategies; but thanks to the heat storage capacity of urban buildings, urban building air conditioning systems have thermal inertia and can convert electrical energy into thermal energy storage in a relatively short time, so they have certain energy storage characteristics. From this perspective, if the building air conditioning system is converted into a virtual energy storage device in the form of an equivalent thermal parameter model, its energy storage characteristics can be used to achieve active regulation of energy and power. Through aggregation technology, a considerable number of distributed air conditioning systems are aggregated into virtual energy storage resources, and unified integration and reuse through cloud energy storage technology can produce considerable regulation capabilities. Summary of the invention

[0004] The purpose of the present invention is to provide a cloud energy storage operation mechanism based on the thermal inertia of urban building air-conditioning systems to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A cloud energy storage operation mechanism based on thermal inertia of urban building air conditioning systems, the mechanism comprising the following steps:

[0007] (1) Establish a single-unit virtual energy storage model and an aggregate model of fixed-frequency air conditioning based on the thermal inertia of urban building air conditioning systems;

[0008] (2) Design a cloud energy storage operation mechanism with air-conditioning aggregated virtual energy storage as the main leasing resource, and establish a cloud energy storage operator model, a cloud energy storage user model and a transaction pricing strategy;

[0009] (3) Solve the cloud energy storage operation model with the goal of minimizing user electricity costs, and distribute and evaluate the overall social benefits brought by the cloud energy storage mechanism.

[0010] Specifically, the technical solution of the fixed-frequency air-conditioning virtual energy storage monomer model and aggregation model based on the thermal inertia of the urban building air-conditioning system in step (1) is described in detail below:

[0011] According to the heat storage characteristics of urban buildings, the thermal inertia and control method of the fixed-frequency air-conditioning system, the difference between the real-time power and the baseline power of the air-conditioning system is defined as the charging and discharging power of the virtual energy storage of the fixed-frequency air-conditioning, and its virtual energy storage capacity is defined as follows:

[0012]

[0013]

[0014] EN j represents the rated capacity of the virtual energy storage model of the fixed-frequency air conditioner, C j,a represents the indoor equivalent heat capacity of air conditioner j; δ j Indicates the temperature dead zone width of air conditioner j; COP j represents the energy efficiency ratio of air conditioner j; E j,t represents the storage capacity of the virtual energy storage of the fixed-frequency air conditioner in period t; T j,max It represents the upper limit of indoor temperature when air conditioner j operates normally; Tin j,t represents the indoor temperature during period t.

[0015] Considering that the air-conditioning loads in the same building complex have great similarity, the above-mentioned fixed-frequency air-conditioning virtual energy storage monomer models are aggregated to avoid the problem of large-scale air-conditioning load decentralized scheduling, and a fixed-frequency air-conditioning virtual energy storage aggregation model with considerable regulation potential is established. The state of charge variables of the fixed-frequency air-conditioning virtual energy storage aggregation model are defined as follows:

[0016]

[0017] Eagg t is the remaining storage capacity of the fixed-frequency air-conditioning virtual energy storage aggregation model in period t; E agg,N is the rated capacity of the fixed-frequency air-conditioning virtual energy storage aggregation model.

[0018] Specifically, the technical solution of the cloud energy storage operation mechanism based on the thermal inertia of the urban building air conditioning system in step (2) is described in detail below:

[0019] First, establish a cost and revenue settlement model for cloud energy storage operators.

[0020] In this technical solution, cloud energy storage operators provide energy storage services to users by leasing air conditioning systems to aggregate virtual energy storage resources. Therefore, the costs of cloud energy storage operators include energy storage leasing costs and operation and maintenance costs, and the revenue is the service fees charged to users. In addition, transactions between energy storage operators and power grids will also generate corresponding electricity purchase and sales costs. The model constraint is the air conditioning aggregated energy storage constraint.

[0021] Secondly, establish a cost and benefit settlement model for cloud energy storage users.

[0022] Among them, the user's electricity cost is the service fee paid for subscribing to cloud energy storage services; since the user side may have renewable energy, in order to further improve the absorption capacity of renewable energy, additional transactions will also occur between users and operators. The model constraints are user-side power balance constraints and virtual energy storage constraints.

[0023] The completion of additional transactions between cloud energy storage users and operators consists of two parts: transactions between users and operators, and transactions between operators and power grids: users can sell renewable energy power generation that exceeds their own needs to the power grid, and vice versa, they need to purchase electricity from the power grid. This transaction is carried out by the operator, and the operator and the power grid first settle the accounts; with the help of a dynamic pricing mechanism, cloud energy storage operators trade electricity with users, thereby helping users absorb renewable resources and achieving effective arbitrage. When formulating a dynamic pricing mechanism, operators need to analyze the supply and demand ratio of energy storage services, and the pricing calculation formula is:

[0024]

[0025]

[0026] λsell t and λbuy t are the unit prices of electricity sold and purchased by cloud energy storage users from operators, respectively; ebuyt and esell t are the real-time electricity purchase and sale prices on the grid side, respectively; Usd t represents the energy storage supply and demand ratio between operators and users.

[0027] It should be noted that the dynamic transaction pricing set by the operator must be between the power purchase and sales prices on the grid side and must satisfy the economic settlement balance.

[0028] Specifically, the technical solution of solving the cloud energy storage operation model with the goal of minimizing the user's electricity cost and allocating and evaluating the overall social welfare brought by the cloud energy storage mechanism is described in detail below:

[0029] The mixed integer linear programming method is used to solve the above cloud energy storage operation model. The model aims to minimize the electricity cost of the user group:

[0030]

[0031] Based on this, the cloud energy storage operation model is first decomposed into user model and operator model sub-problems, and the global variables and user original data in the operator model are initialized. The user decision variables can be effectively solved using commercial solvers. The operator decision variables are then solved through data transmission, and the revenue is settled based on this, analyzing the renewable energy consumption on the user side and the distribution of the overall social welfare.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] (1) Compared with centralized physical energy storage investment, the cloud energy storage operation mechanism based on the thermal inertia of urban building air conditioning systems proposed in this invention uses fixed-frequency air conditioning aggregated virtual energy storage as the operator's main energy storage resource. By leasing air conditioning virtual energy storage, on the one hand, the operator's initial investment and subsequent operation and maintenance costs are greatly reduced, and on the other hand, the increase in air conditioning load can be effectively regulated;

[0034] (2) For users who have renewable energy and need energy storage, subscribing to cloud energy storage services can improve their energy consumption capacity and alleviate the phenomenon of abandoned solar and wind power. For ordinary users who do not have renewable energy, participating in cloud energy storage can reduce electricity costs by shifting load peaks.

[0035] (3) The use of a dynamic pricing mechanism in additional transactions between cloud energy storage operators and users can enhance users’ enthusiasm for subscribing to cloud energy storage services while protecting their interests, increase the revenue of cloud energy storage operators to a certain extent, and improve the overall welfare of society. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the interactive relationship between cloud energy storage operators, cloud energy storage users and the power grid.

[0037] Figure 2 Schematic diagram of the cloud energy storage operation mechanism based on the thermal inertia of urban building air-conditioning systems.

[0038] Figure 3 Solve the flow chart for the cloud energy storage model. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0040] See also Figure 2 In an embodiment of the present invention, a cloud energy storage operation mechanism based on thermal inertia of an urban building air conditioning system includes the following steps:

[0041] (1) The fixed-frequency air conditioning virtual energy storage monomer model and aggregation model based on the thermal inertia of urban building air conditioning systems are described in detail as follows:

[0042] 1) Virtual energy storage monomer model of fixed-frequency air conditioner in urban buildings;

[0043] Taking the fixed-frequency air conditioner in urban buildings as the virtual energy storage research object, its control strategy is as follows:

[0044]

[0045] s j,t represents the start and stop status of air conditioner j in period t, 1 represents on and 0 represents off; Tin j,t and Tout j,t represent the indoor and outdoor temperatures of air conditioner j in period t, respectively. j,max 、T j,min They are the upper and lower limits of the indoor temperature when the air conditioner j is operating normally, and must meet the comfort requirements of the air conditioner users.

[0046] Since fixed-frequency air conditioners can only operate at rated frequency, the indoor temperature will be j,min ,T j,max ] range, and presents the following relationship with the air conditioning set temperature Tset j:

[0047]

[0048] δ j is the temperature dead zone width of air conditioner j.

[0049] Known air conditioning energy efficiency ratio COP j and the indoor equivalent heat capacity R of air conditioner j j,a , the air conditioning baseline power Pbase j can be obtained as:

[0050]

[0051] Define the real-time power of fixed-frequency air conditioner Pac j,t:

[0052]

[0053] Based on the thermal inertia of the urban building air conditioning system, the difference between Pac j,t and Pbase j is defined as the charging and discharging power Pch j,t and Pdis j,t of the air conditioning virtual energy storage:

[0054]

[0055] Define the virtual energy storage capacity of the air conditioner:

[0056]

[0057]

[0058] Where: ENj is the rated capacity of the virtual energy storage model of the fixed-frequency air conditioner; E j,t is the amount of electricity stored in period t.

[0059] Based on the above definition, the virtual energy storage model of fixed-frequency air conditioner is as follows:

[0060]

[0061]

[0062]

[0063] The calculation formulas for the parameters in the virtual energy storage model established by equations (8) to (10) are as follows:

[0064]

[0065] Pcmax j and Pdmax j are the upper limits of the charging and discharging power of the virtual energy storage monomer model of air conditioner j; Uch j and Udis j are the charging and discharging states of the virtual energy storage monomer model of air conditioner j, respectively, and both are Boolean variables.

[0066] 2) Virtual energy storage aggregation model for fixed-frequency air conditioners in urban buildings

[0067] As a typical distributed resource, air conditioning load has the characteristics of wide distribution range and large cardinality. In practical applications, considering the similarities between air conditioning models, outdoor environment and building structure in the same building complex, an air conditioning virtual energy storage aggregation model that is convenient for macro-control can be established through parameter aggregation.

[0068] To represent the aggregated virtual energy storage model of fixed-frequency air conditioners in urban buildings, perform the following operations:

[0069]

[0070]

[0071] Define the storage capacity Eaggt and rated capacity E of the aggregated virtual energy storage agg,N And the charge and discharge power Pagg,ch t and Pagg,dis t are as follows:

[0072]

[0073] The fixed-frequency air-conditioning virtual energy storage aggregation model is as follows:

[0074]

[0075]

[0076]

[0077]

[0078] Eagg t is the remaining storage capacity of the fixed-frequency air-conditioning virtual energy storage aggregation model in period t; E agg,N is the rated capacity of the virtual energy storage aggregation model of the fixed-frequency air conditioner; Pagg,ch t and Pagg,dis t are the charging and discharging power of the virtual energy storage aggregation model of the fixed-frequency air conditioner in period t; Pcmax agg and Pdmax agg are the upper and lower limits of the charging and discharging power of the virtual energy storage aggregation model of the fixed-frequency air conditioner; Uagg,ch t and Uagg,dis t are the charging and discharging states of the virtual energy storage aggregation model of the fixed-frequency air conditioner, both of which are Boolean variables; Emax agg is the upper limit of the storage capacity of the virtual energy storage aggregation model; N FFAC is the total number of fixed-frequency air conditioners; α and γ are the relevant conversion parameters of the fixed-frequency air conditioner virtual energy storage aggregation model.

[0079] Define the state of charge variable SOCagg t of the air conditioning virtual energy storage aggregation model:

[0080]

[0081] Correspondingly, the calculation formulas for the state of charge conversion parameters j and l of the virtual energy storage aggregation model are as follows:

[0082]

[0083] (2) Design a cloud energy storage operation mechanism with air conditioning aggregated virtual energy storage as the main rental resource, as detailed below:

[0084] 1) Cloud energy storage operator revenue settlement model

[0085] The responsibility of the cloud energy storage operator is to jointly configure energy storage resources (specifically, the aggregated virtual energy storage of fixed-frequency air conditioners in urban buildings in this invention) and provide energy storage leasing and trading services for cloud energy storage users. Taking into account the scale benefits of centralized energy storage in terms of initial construction and later operation and maintenance, cloud energy storage operators are considering adopting a centralized energy storage capacity sharing model to divide centralized energy storage resources into energy storage partitions of different capacities, which are allocated to subscribed users in the form of leasing. In this mode, users have the right to use the capacity of the leased partition and can charge and discharge the cloud energy storage as needed, and the operator has no right to interfere. For cloud energy storage operators, minimizing costs and maximizing profits are the fundamental starting points for formulating their operating strategies. The main source of income for cloud energy storage operators is the service fees paid by subscribed users, and their costs include the leasing and operation and maintenance costs of energy storage.

[0086] The operator's revenue settlement model is as follows:

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] C sp represents the daily revenue of the cloud energy storage operator; C s represents the daily service fee paid by the user to the operator; Ce i represents the fee generated by the additional transaction between user i and the operator; K represents the number of users; C rent represents the average daily cost of operating and leasing virtual energy storage; C op represents the average daily operation and maintenance cost; Cgrid sp represents the cost generated by the transaction between the operator and the power grid; Cs i is the service fee paid by user i for purchasing cloud energy storage services; Pbuy i,t and Psell i,t are the power purchased and sold by user i at the power grid during period t; λbuy t and λsell t are the unit purchase and sale prices of user i at period t; c e is the daily rental cost per unit of energy storage capacity; c p is the daily rental cost per unit power; σ is the energy storage capacity retention rate of the energy storage provider; Emax agg is the maximum energy storage capacity owned by the energy storage provider; Pcmax agg and Pdmax agg are the maximum charge and discharge powers of the energy storage respectively; c op is the daily operation and maintenance cost paid by the operator under the unit charging and discharging power of energy storage; Pcmax agg and Pdmax agg are the maximum charging and discharging powers of energy storage respectively; Pbuy sp,t and Psell sp,t are the purchase and sale power of the cloud energy storage operator on the grid side in period t respectively; ebuy t and esell t are the unit purchase and sale prices on the grid side in period t respectively.

[0094] It should be noted that the unit electricity price between operators and users is different from the price of electricity purchased and sold on the grid. Since cloud energy storage users may have renewable energy, in order to promote its consumption, additional electricity transactions will occur between users and operators: users can sell the power generation that exceeds their own needs to the grid, and vice versa, purchase electricity from the grid. This transaction is carried out by the operator, and the settlement process consists of two parts: the settlement between the grid and the operator, and the settlement between the operator and the user. In the settlement process between operators and users, with the help of a dynamic pricing mechanism, cloud energy storage operators can achieve effective arbitrage while helping users consume renewable resources. When formulating a dynamic pricing mechanism, operators need to analyze the supply and demand ratio of energy storage services. The pricing calculation formula is:

[0095]

[0096]

[0097] λsell t and λbuy t are the unit prices of electricity sold and purchased by cloud energy storage users from operators, respectively; ebuyt and esell t are the real-time electricity purchase and sale prices on the grid side, respectively; Usd t represents the energy storage supply and demand ratio between operators and users, which is calculated as follows.

[0098]

[0099]

[0100]

[0101]

[0102] Pts t and Ptd t are the total power supply and power demand of users and operators in period t, respectively; Pnet i,t is the net load of user i in period t; I 1 The user satisfies Pnet i,t<0; I 2 The users in satisfy Pnet i,t>0.

[0103] 2) Operator-side fixed-frequency air conditioning aggregation virtual energy storage power constraints

[0104]

[0105]

[0106]

[0107] Eagg,sp t is the energy storage capacity of the FFAC virtual aggregated energy storage leased by the cloud energy storage operator in period t; Eagg,Nsp is the total capacity of the FFAC virtual energy storage leased by the cloud energy storage operator; SOCagg min and SOCagg max are the maximum and minimum charge states of the virtual energy storage, respectively; Pmax ch / dis is the upper limit of the cloud energy storage charging and discharging power.

[0108] 3) Operator-side fixed-frequency air conditioning aggregated virtual energy storage charge state constraints

[0109]

[0110]

[0111]

[0112] 4) Cloud energy storage user electricity bill settlement model

[0113] The goal of users who subscribe to cloud energy storage services is, on the one hand, to reduce electricity costs under the current peak-valley electricity price mechanism through energy storage charging and discharging, and on the other hand, to reasonably consume their own renewable energy output. The expenses of cloud energy storage subscribers are mainly composed of cloud energy storage service fees and the cost of purchasing electricity from cloud energy storage operators. Users need to purchase electricity from cloud energy storage operators if the electricity consumption exceeds their own capacity, and vice versa, they can also sell electricity to operators.

[0114]

[0115]

[0116]

[0117] C i represents the daily electricity cost of user i to maintain normal operation of the load; Cs i represents the daily service fee paid by user i to the operator; Ce i represents the cost of additional transactions between user i and the operator; K represents the number of users; and are the service fees per unit capacity and per unit power respectively; Emax i and Pmax i are the energy storage capacity and maximum charging and discharging power of energy storage obtained by user i when purchasing cloud energy storage services.

[0118] 5) User-side power balance constraints

[0119]

[0120]

[0121]

[0122] Pload i,t is the load power of user i in period t; Pe i,t is the output power of user i’s own energy in period t; Pch i,t and Pdis i,t are the charge and discharge power of the energy storage block purchased by user i in period t respectively; Pcmax i and Pdmaxi are the upper and lower limits of the charge and discharge power respectively.

[0123] 6) User-side energy storage constraints

[0124]

[0125] E i,t is the cloud energy storage capacity of user i in period t.

[0126] In the above model, the decision variables of the cloud energy storage operator model are the charge state of the fixed-frequency air-conditioning virtual energy storage, the maximum capacity of the leased virtual energy storage, the power of electricity purchase and sale, and the charging and discharging power of the aggregated energy storage; the decision variables on the user side include the power of electricity purchase and sale and the charging and discharging power of the energy storage.

[0127] (3) The cloud energy storage operation model is solved with the goal of minimizing the user's electricity cost, and the overall social welfare brought by the cloud energy storage mechanism is distributed and evaluated. The detailed description is as follows:

[0128] Determine the cloud energy storage operation mechanism based on the thermal inertia of the urban building air conditioning system, and the cloud energy storage operation mechanism takes minimizing the electricity cost of the user group as the objective function:

[0129]

[0130] The constraints of the cloud energy storage operation model include: (21)-(38), (40)-(45). The model solution process is as follows:

[0131] 1) Decompose the cloud energy storage operation model into user sub-problems and operator sub-problems;

[0132] 2) Initialize the global variables of the model, including the operator's unit capacity leasing standard and User daily electricity load data Pload i,t, user new energy output data Pe i,t, etc.;

[0133] 3) Input the original data and solve the user decision variables Pbuy i,t, Psell i,t, Pch i,t, Pdis i,t;

[0134] 4) Data transmission at the user judgment layer to solve the operator's decision variables Pcmax agg, Pdmax agg, Eagg, N sp;

[0135] 5) Based on the data results of the user judgment layer and the operator judgment layer, the revenue settlement is carried out, and the overall social welfare brought by the cloud energy storage mechanism is distributed and evaluated.

[0136] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0137] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A cloud energy storage operation mechanism based on the thermal inertia of urban building air conditioning systems, characterized in that: The mechanism includes the following steps: (1) Establish a single-unit virtual energy storage model and an aggregate model of fixed-frequency air conditioning based on the thermal inertia of urban building air conditioning systems; (2) Design a cloud energy storage operation mechanism that uses air-conditioning aggregated virtual energy storage as a rental resource, and establish a cloud energy storage operator model, a cloud energy storage user model, and a transaction pricing strategy; (3) Solve the cloud energy storage operation model with the goal of minimizing user electricity costs, and distribute and evaluate the overall social benefits brought by the cloud energy storage mechanism.

2. According to claim 1, a cloud energy storage operation mechanism based on thermal inertia of urban building air conditioning systems is characterized in that: The establishment of a virtual monomer model and aggregation model of a fixed-frequency air conditioner based on thermal inertia of an urban building air conditioning system specifically includes: The establishment of a virtual energy storage model for urban building air conditioning systems first involves building thermodynamic models and air conditioning system control strategies. Based on the equivalent thermal parameter method, a first-order thermodynamic model of urban buildings is established to predict and describe future room temperature changes. Combined with the fixed-frequency air conditioning control strategy, a mathematical model is established to represent the relationship between air conditioning power and start-stop status and cooling capacity. With the help of the heat storage capacity of urban buildings, the air conditioning system relies on thermal inertia to convert electrical energy into thermal energy within a specific time to achieve the purpose of energy storage. The difference between the real-time power of the air conditioner and the baseline power is used as the charging and discharging power of the energy storage. The process of users adjusting the set temperature of the air conditioner is regarded as the energy storage charging and discharging process, and a fixed-frequency air conditioner virtual energy storage monomer model is established. In order to carry out macro-control of urban building air-conditioning systems, a fixed-frequency air-conditioning virtual energy storage aggregation model is established through parameter aggregation, which solves the decentralized scheduling problem and forms controllable virtual energy storage resources.

3. The cloud energy storage operation mechanism based on thermal inertia of urban building air conditioning systems according to claim 1 is characterized in that: The cloud energy storage operation mechanism and corresponding model using air conditioning aggregated virtual energy storage as leasing resources specifically include: A. The revenue settlement formula for cloud energy storage operators is: C sp represents the daily revenue of the cloud energy storage operator; C s represents the daily service fee paid by the user to the operator; Ce i represents the fee generated by the additional transaction between user i and the operator; K represents the number of users; C rent represents the average daily cost of operating and leasing virtual energy storage; C op represents the average daily operation and maintenance cost; Cgrid sp represents the cost generated by the transaction between the operator and the power grid; The constraints of the cloud energy storage operator model include virtual energy storage constraints for fixed-frequency air conditioners; B. The formula for settling electricity costs for cloud energy storage users is: C i represents the daily electricity cost of user i to maintain normal operation of the load; Cs i represents the daily service fee paid by user i to the operator; Ce i represents the cost of additional transactions between user i and the operator; K represents the number of users; The constraints of the cloud energy storage user model include power balance constraints and energy storage operation constraints; C. Cloud energy storage users have renewable energy. To promote its consumption, it is considered that additional electricity transactions will occur between users and operators: users can sell the power generation that exceeds their own needs to the power grid, and vice versa, they can purchase electricity from the power grid. This transaction is carried out by the operator, and the settlement process consists of two parts: the settlement between the power grid and the operator, and the settlement between the operator and the user. In the settlement process between the operator and the user, with the help of the dynamic pricing mechanism, the cloud energy storage operator can achieve effective arbitrage while helping users consume the output of renewable resources. When formulating the dynamic pricing mechanism, the operator needs to analyze the supply and demand ratio of energy storage services. The pricing calculation formula is: λsell t and λbuy t are the unit prices of electricity sold and purchased by cloud energy storage users from operators, respectively; ebuy t and esell t are the real-time electricity purchase and sale prices on the grid side, respectively; Usd t represents the energy storage supply and demand ratio between operators and users.

4. The cloud energy storage operation mechanism based on thermal inertia of urban building air conditioning systems according to claim 1 is characterized in that: The method for allocating and evaluating the overall social welfare brought by the cloud energy storage mechanism specifically includes: The cloud energy storage operation mechanism model is decomposed into user sub-problems and operator sub-problems. The cloud energy storage model is solved using the mixed integer linear programming method with the minimum user expenditure as the objective function to obtain the optimal charging and discharging strategy on the user side and the revenue settlement results of each participant in the cloud energy storage mechanism. The specific steps include: Decompose the cloud energy storage operation mechanism model into user sub-problems and operator sub-problems; Initialize the global variables of the operator model, including the operator's unit capacity leasing standard, unit capacity service fee, unit power service fee, user daily power load data, and user new energy output data; Input raw data to solve user decision variables; Data transfer at the user judgment layer to solve the operator's decision variables; Based on the data results of the user judgment layer and the operator judgment layer, the revenue settlement and overall social welfare distribution results, as well as the analysis of the renewable energy consumption situation on the user side are carried out.

5. A cloud energy storage operation mechanism based on thermal inertia of urban building air conditioning systems according to claim 1 or 2, characterized in that: The modeling method of the fixed-frequency air-conditioning virtual energy storage monomer model of the thermal inertia of the urban building air-conditioning system is as follows: Taking the fixed-frequency air conditioner in urban buildings as the virtual energy storage research object, its control strategy is as follows: s j,t represents the start and stop status of air conditioner j in period t, 1 represents on and 0 represents off; Tin j,t and Tout j,t represent the indoor and outdoor temperatures of air conditioner j in period t, respectively. j,max 、T j,min are the upper and lower limits of the indoor temperature when air conditioner j is operating normally; Fixed frequency air conditioner can only work at rated frequency, and the indoor temperature will be j,min ,T j,max ] range, and presents the following relationship with the air conditioning set temperature Tset j: δ j is the temperature dead zone width of air conditioner j; Known air conditioning energy efficiency ratio COP j and the indoor equivalent heat capacity R of air conditioner j j,a , the air conditioning baseline power Pbase j can be obtained as: Define the real-time power of fixed-frequency air conditioner Pac j,t: Based on the thermal inertia of the urban building air conditioning system, the difference between Pac j,t and Pbase j is defined as the charging and discharging power Pch j,t and Pdis j,t of the air conditioning virtual energy storage: Define the virtual energy storage capacity of the air conditioner: Where: ENj is the rated capacity of the virtual energy storage model of the fixed-frequency air conditioner; E j,t is the amount of electricity stored in period t; Based on the above definition, the virtual energy storage model of fixed-frequency air conditioner is as follows: The calculation formulas for the parameters in the virtual energy storage model established by equations (8) to (10) are as follows: Pcmax j and Pdmax j are the upper limits of the charging and discharging power of the virtual energy storage monomer model of air conditioner j; Uch j and Udis j are the charging and discharging states of the virtual energy storage monomer model of air conditioner j, respectively, and both are Boolean variables.

6. A cloud energy storage operation mechanism based on thermal inertia of urban building air conditioning systems according to claim 1 or 3, characterized in that: The modeling method of the fixed-frequency air-conditioning virtual energy storage aggregation model of the thermal inertia of the urban building air-conditioning system is as follows: To represent the aggregated virtual energy storage model of fixed-frequency air conditioners in urban buildings, perform the following operations: Define the storage capacity Eaggt and rated capacity E of the aggregated virtual energy storage agg,N And the charge and discharge power Pagg,ch t and Pagg,dis t are as follows: The fixed-frequency air-conditioning virtual energy storage aggregation model is as follows: Eagg t is the remaining storage capacity of the fixed-frequency air-conditioning virtual energy storage aggregation model in period t; E agg,N is the rated capacity of the virtual energy storage aggregation model of the fixed-frequency air conditioner; Pagg,ch t and Pagg,dis t are the charging and discharging power of the virtual energy storage aggregation model of the fixed-frequency air conditioner in period t; Pcmax agg and Pdmax agg are the upper and lower limits of the charging and discharging power of the virtual energy storage aggregation model of the fixed-frequency air conditioner; Uagg,ch t and Uagg,dis t are the charging and discharging states of the virtual energy storage aggregation model of the fixed-frequency air conditioner, both of which are Boolean variables; Emax agg is the upper limit of the storage capacity of the virtual energy storage aggregation model; N FFAC is the total number of fixed-frequency air conditioners; α and γ are the relevant conversion parameters of the fixed-frequency air conditioner virtual energy storage aggregation model; Define the state of charge variable SOCagg t of the air conditioning virtual energy storage aggregation model: Correspondingly, the calculation formulas for the state of charge conversion parameters j and l of the virtual energy storage aggregation model are as follows:

7. The cloud energy storage operation mechanism based on thermal inertia of urban building air conditioning systems according to claim 6 is characterized in that: The method for designing a cloud energy storage operation mechanism using air conditioning aggregated virtual energy storage as a rental resource is as follows: 1) Cloud energy storage operator revenue settlement model The responsibility of cloud energy storage operators is to jointly configure energy storage resources, provide energy storage leasing and trading services to cloud energy storage users, divide centralized energy storage resources into energy storage partitions of different capacities, and allocate them to subscribed users in the form of leasing. In this model, users have the right to use the capacity of the leased partitions and can charge and discharge cloud energy storage as needed. The revenue settlement model for cloud energy storage operators is as follows: C sp represents the daily revenue of the cloud energy storage operator; C s represents the daily service fee paid by the user to the operator; Ce i represents the fee generated by the additional transaction between user i and the operator; K represents the number of users; C rent represents the average daily cost of operating and leasing virtual energy storage; C op represents the average daily operation and maintenance cost; Cgrid sp represents the cost generated by the transaction between the operator and the power grid; Cs i is the service fee paid by user i for purchasing cloud energy storage services; Pbuy i,t and Psell i,t are the power purchased and sold by user i at the power grid during period t; λbuy t and λsell t are the unit purchase and sale prices of user i at period t; c e is the daily rental cost per unit of energy storage capacity; c p is the daily rental cost per unit power; σ is the energy storage capacity retention rate of the energy storage provider; Emax agg is the maximum energy storage capacity owned by the energy storage provider; Pcmax agg and Pdmax agg are the maximum charge and discharge powers of the energy storage respectively; c op is the daily operation and maintenance cost paid by the operator under the unit charging and discharging power of energy storage; Pcmax agg and Pdmax agg are the maximum charging and discharging power of energy storage respectively; Pbuy sp,t and Psell sp,t are the power purchased and sold by the cloud energy storage operator on the grid side during period t respectively; ebuyt and esell t are the unit purchase and sale prices on the grid side during period t respectively; When formulating a dynamic pricing mechanism, cloud energy storage operators analyze the supply and demand ratio of energy storage services. The pricing calculation formula is: λsell t and λbuy t are the unit prices of electricity sold and purchased by cloud energy storage users from operators respectively; ebuy t and esell t are the real-time electricity purchase and sale prices on the grid side respectively; Usd t represents the energy storage supply and demand ratio between operators and users, which is calculated as follows: Pts t and Ptd t are the total power supply and power demand of users and operators in period t, respectively; Pnet i,t is the net load of user i in period t; users in I1 satisfy Pnet i,t<0; users in I2 satisfy Pnet i,t>0; 2) Constraints on the aggregated virtual energy storage power of fixed-frequency air conditioners on the cloud energy storage operator side Eagg, sp t is the energy storage capacity of the FFAC virtual aggregated energy storage leased by the cloud energy storage operator in period t; Eagg, N sp is the total capacity of the FFAC virtual energy storage leased by the cloud energy storage operator; SOCagg min and SOCagg max are the maximum and minimum charge states of the virtual energy storage, respectively; Pmax ch / dis is the upper limit of the cloud energy storage charge and discharge power; 3) Cloud energy storage operator-side fixed-frequency air conditioning aggregation virtual energy storage charge state constraints 4) Cloud energy storage user electricity bill settlement model C i represents the daily electricity cost of user i to maintain normal operation of the load; Cs i represents the daily service fee paid by user i to the operator; Ce i represents the fee generated by additional transactions between user i and the operator; K represents the number of users; and are the service fees per unit capacity and per unit power respectively; Emax i and Pmax i are the energy storage capacity and maximum charging and discharging power of energy storage obtained by user i when purchasing cloud energy storage services; 5) User-side power balance constraints Pload i,t is the load power of user i in period t; Pe i,t is the output power of user i's own energy in period t; Pchi,t and Pdis i,t are the charge and discharge power of the energy storage block purchased by user i in period t; Pcmax i and Pdmax i are the upper and lower limits of the charge and discharge power respectively; 6) User-side energy storage constraints E i,t is the cloud energy storage capacity of user i in period t; In the above model, the decision variables of the cloud energy storage operator model are the charge state of the fixed-frequency air-conditioning virtual energy storage, the maximum capacity of the leased virtual energy storage, the power of electricity purchase and sale, and the charging and discharging power of the aggregated energy storage; the decision variables on the user side include the power of electricity purchase and sale and the charging and discharging power of the energy storage.

8. A cloud energy storage operation mechanism based on thermal inertia of urban building air conditioning systems according to claim 1 or 4, characterized in that: The method for solving the cloud energy storage operation model with the goal of minimizing the user's electricity cost and allocating and evaluating the overall social welfare brought by the cloud energy storage mechanism is as follows: Determine the cloud energy storage operation mechanism based on the thermal inertia of the urban building air conditioning system, and the cloud energy storage operation mechanism takes minimizing the electricity cost of the user group as the objective function: The constraints of the cloud energy storage operation model include: (21)-(38), (40)-(45), and the model solution process is as follows: 1) Decompose the cloud energy storage operation model into user sub-problems and operator sub-problems; 2) Initialize the global variables of the model, including the operator's unit capacity leasing standard and User daily electricity load data Pload i,t, user new energy output data Pe i,t; 3) Input the original data and solve the user decision variables Pbuy i,t, Psell i,t, Pch i,t, Pdis i,t; 4) Data transmission at the user judgment layer to solve the operator's decision variables Pcmax agg, Pdmax agg, Eagg, N sp; 5) Based on the data results of the user judgment layer and the operator judgment layer, the revenue settlement is carried out, and the overall social welfare brought by the cloud energy storage mechanism is distributed and evaluated.

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