Optimization model and method for demand response of variable frequency air conditioner group based on virtual energy storage

Abstract

The present invention discloses a variable frequency air conditioner group demand response optimization model and method based on virtual energy storage in the field of electrical engineering and automation technology, comprising the following steps: a demand response evaluation index based on a load criterion; a variable frequency air conditioner group virtual energy storage demand response optimization model; a variable frequency air conditioner group demand response control strategy based on virtual energy storage; the present invention is based on the study of a demand response method for variable frequency air conditioner virtual energy storage, in order to fully absorb a high proportion of new energy in the power grid, the demand response research is carried out based on the load criterion, a park containing a large number of variable frequency air conditioner loads is taken as the research object, the volatility of the new energy output in the park and the time-varying nature of the air conditioning load model parameters are fully considered, a demand response intraday rolling optimization model is constructed, the new energy output is predicted intraday and the air conditioning load model parameters are identified online, and the fluctuation of the new energy output is suppressed by virtual energy storage while responding to the criterion.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering and automation technology, and in particular to a variable frequency air conditioning group demand response optimization model and method based on virtual energy storage. Background Art

[0002] Considering the virtual energy storage characteristics of air conditioning-building. There have been many studies on virtual energy storage modeling and participation in demand response for air conditioning loads at home and abroad, most of which are based on the first-order ETP model. For the control method of air conditioning load participating in demand response, the direct load control method is currently more commonly used, including switch control, temperature control, periodic pause control and frequency control. Duty cycle control refers to the periodic switching of air conditioning loads, and the load is adjusted by adjusting the duty cycle of operation, which is often used for the control of central air conditioning; frequency control refers to controlling the compressor frequency through the variable frequency terminal, thereby changing the air conditioning power, which is suitable for variable frequency air conditioning; switch control and temperature control are almost applicable to all air conditioning loads, and fixed frequency air conditioning often adopts these two control methods. Each control method has its own advantages and disadvantages. Switch control can quickly adjust the load size, with fast response speed and large capacity, but the adjustable time is short under the comfort constraint; temperature control has a long adjustable time, but due to the hysteresis of actual air conditioning control and temperature changes, the response speed is slow and the adjustable capacity is small. The frequency control for variable frequency air conditioning can not only achieve rapid changes in load, but also change the frequency value to reach a stable state after the temperature reaches the set value.

[0003] However, in actual applications, the installation difficulty and cost of intrusive variable frequency control terminals are high, making them difficult to promote. Therefore, when choosing a control method, comprehensive considerations should be made based on the air conditioning load type, response type, control requirements, and control cost.

[0004] Therefore, it is urgent to design a variable frequency air conditioning group demand response optimization model and method based on virtual energy storage. Summary of the invention

[0005] The purpose of the present invention is to provide a variable frequency air conditioning group demand response optimization model and method based on virtual energy storage to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a variable frequency air conditioning group demand response optimization model and method based on virtual energy storage, comprising the following steps:

[0007] S1: Demand response evaluation index based on load criterion;

[0008] S2: Virtual energy storage demand response optimization model for variable frequency air conditioning group;

[0009] S3: Demand response control strategy for variable frequency air conditioning groups based on virtual energy storage.

[0010] Furthermore, in the above-mentioned variable frequency air conditioning group demand response optimization model and method based on virtual energy storage, the specific steps of S1 are:

[0011] In order to fully absorb the output of renewable energy, the calculation model of the load standard is as follows: Figure 2 As shown in the figure, the load standard is usually issued by the DR center of the power department and defined by shape; the DR center solves the system's minimum operating cost to obtain the shape of CDL based on the operating parameters of the entire network, taking into account the output and climbing constraints of the generator sets, the output range constraints of new energy sources, the power generation cost, and the cost of wind and solar power abandonment; users participating in DR actively adjust the shape of their own load curves to be close to the shape of CDL, respond to DR autonomously, and evaluate the user's DR contribution based on the similarity between the load curve and CDL and provide incentives;

[0012] In the calculation model of the load criterion, the objective function is to minimize the system operating cost, including the output cost of the generator set and the cost of wind and power abandonment, which is set as follows:

[0013]

[0014] Where: T is the total time period length, P G,j (t i ) represents the jth adjustable unit t i Active power in the time period, a j , b j 、c j is the cost coefficient, there are N adjustable units; P R (t i ), P R,max (t i ) is the new energy in t i Output and maximum output during the period, C R is the unit cost of abandoned electricity from renewable energy; by setting C R If the power consumption is a larger value and the proportion of wind and solar power abandonment costs in the total operating costs is increased, the maximum consumption of renewable energy output can be achieved within the adjustable range.

[0015] Assuming that the flexible load regulation capability in the system is strong enough, the adjustable part in the system balances the non-adjustable part, and the power balance constraint is obtained as shown in formula (2):

[0016]

[0017] Where: P D (t i ), P C (t i ) represent t iThe size of controllable and uncontrollable loads in different time periods; in addition to power balance constraints, the output and ramp constraints of a single generator set, the output range of new energy sources, etc. should also be considered when solving the model;

[0018] The demand response based on the load criterion focuses on the shape of the load curve, so the load criterion is normalized and recorded as As shown in formula (3):

[0019]

[0020] In addition to guiding users to participate in DR to assist in fully absorbing new energy, IBDR based on CDL is also a bottom-up organizational method. Each DR user can spontaneously adjust the load curve according to CDL. As long as a good individual response is achieved, the load shape will be close to CDL at the overall system level, thereby achieving self-optimizing operation of the system.

[0021] The DR benefit of the user is calculated based on the similarity between the load curve shape and the CDL. The line similarity index E is defined as follows:

[0022]

[0023] Where: ε is a given coefficient, is the normalized user load curve, d is and The Euclidean distance of ; Based on this, the incentive e for DR users based on the criterion similarity index E is calculated as follows:

[0024]

[0025] In the formula: c represents the electricity price incentive coefficient, and the incentive e for users is equivalent to giving a certain discount rate on the basis of the electricity price; under the premise that the electricity consumption remains unchanged, the larger the line similarity index E of the user's load, the larger the electricity price discount rate, and the higher the incentive obtained.

[0026] Furthermore, in the above-mentioned variable frequency air conditioning group demand response optimization model and method based on virtual energy storage, the specific steps of S2 are:

[0027] Consider a park with a large number of adjustable variable frequency air conditioning loads, non-adjustable rigid loads and a small amount of new energy. When participating in demand response, the variable frequency air conditioning load is equivalent to virtual energy storage, and the optimization goal is to minimize the system operating cost.

[0028] In the optimization model, the time-decoupled charging and discharging control is adopted for the variable frequency air conditioner virtual energy storage, and the variable frequency air conditioner group can be aggregated into a whole virtual energy storage. iThe charging and discharging power can be obtained by calculating the overall charging and discharging power, and then the scheduling can be optimized; for the variable frequency air conditioning load virtual energy storage, there are restrictions on the charging and discharging power of energy storage in different periods:

[0029]

[0030] Where: P ch,max (t i ), P dis,max (t i ) represents t i The maximum value of the virtual energy storage charging and discharging power of the variable frequency air conditioning load during the period (Δt cont Take 1 hour), k represents the air conditioner number, and there are M adjustable variable frequency air conditioner loads;

[0031] By using the user interaction function of the smart power network, variable frequency air conditioner users set demand response parameters, such as whether to participate in demand response, the adjustable temperature range of air conditioners participating in the response, etc. The variable frequency air conditioner load that does not participate in demand response is included in the rigid load, and the power balance constraints in the park are as follows:

[0032]

[0033] Flexible load P D (t i ) is calculated as follows:

[0034]

[0035] The optimization goal is to minimize the park operation cost, which is the electricity purchase cost minus the demand response incentive. This paper chooses the IBDR strategy based on CDL, and the operation cost is calculated as follows:

[0036]

[0037] Where: C ost represents the park operation cost, p ric represents the basic electricity price;

[0038] Taking into account the volatility of renewable energy load, its output forecast may change and adjust continuously during the day; at the same time, the second-order ETP model parameters of air-conditioning load may also change with the environment during the day, resulting in changes in the parameters of the virtual energy storage model that the air conditioner complies with; therefore, the model can be optimized on a rolling basis during the day, and the model parameters can be continuously updated and the results optimized to adjust the virtual energy storage output based on the latest renewable energy output forecast value and the online identified air-conditioning load model parameters.

[0039] Furthermore, in the above-mentioned variable frequency air conditioning group demand response optimization model and method based on virtual energy storage, the specific steps of S3 are:

[0040] In this scheme, the adjustable temperature of the air conditioner participating in demand response is set by the user, and the user can choose whether to accept the regulation. Therefore, the user's willingness to participate in demand response can be reflected by the virtual energy storage charging and discharging power. Under the time decoupling control method of this paper, the variable frequency air conditioner virtual energy storage charging and discharging power P ves Basically not affected by the indoor gas virtual energy storage charge state S OVC,a and solid virtual energy storage charge state S OVC,m The influence of S OVC,a , S OVC,m Unable to reflect the charging and discharging power status of virtual energy storage;

[0041] Therefore, this scheme is directly based on the virtual energy storage charging and discharging power P ves,k For variable frequency air conditioner load sequencing control, priority is given to controlling the air conditioner load with larger virtual energy storage charging and discharging power to reduce the total control times; this paper proposes a daily rolling optimization process for variable frequency air conditioner cluster virtual energy storage to participate in demand response. Figure 3 .

[0042] Furthermore, in the above-mentioned variable frequency air conditioning group demand response optimization model and method based on virtual energy storage, when solving the rolling optimization demand response model, the total period T is 24 hours in a day, so as to minimize the system operation cost C in the T period. ost (P ves ) is the optimization objective;

[0043] It should be noted that the current t i The daily air conditioning load output before time t adopts the historical actual value, and the optimization period is t i The remaining period to 24:00; and the virtual energy storage output optimization result of the rolling optimization model is only in the current t i The virtual energy storage output is equal to the actual virtual energy storage output within one control cycle (1 hour) after the moment, and the virtual energy storage output of the next control cycle is optimized according to the latest rolling optimization model of the next cycle;

[0044] Solve the optimization model to obtain the variable frequency air conditioner virtual energy storage output P ves (t i ), the air conditioners are sequenced and controlled according to the virtual energy storage charging and discharging power priority principle until the total output of the virtual energy storage reaches the optimized value; the variable frequency air conditioner is temperature controlled, the temperature is set using the intelligent infrared terminal, and the variable frequency air conditioner virtual energy storage is time-decoupled for charging and discharging; it can be seen from the example that the actual temperature control of the variable frequency air conditioner has a response delay of about 1 to 3 minutes, which is acceptable compared to a one-hour control cycle; at the same time, the temperature control method is suitable for various types of air conditioners, which is easy to promote.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention is based on the study of the demand response method of variable frequency air-conditioning virtual energy storage. In order to fully absorb a high proportion of new energy in the power grid, the demand response research is carried out based on the load criterion. The park containing a large number of variable frequency air-conditioning loads is taken as the research object, and the volatility of the new energy output in the park and the time-varying nature of the air-conditioning load model parameters are fully considered to construct a demand response intraday rolling optimization model, predict the new energy output within the day and identify the air-conditioning load model parameters online, and respond to the criterion while suppressing the fluctuation of the new energy output through virtual energy storage; that is, the present invention can use the intraday rolling optimization strategy for the virtual energy storage of the variable frequency air-conditioning load to participate in the demand response, which can effectively solve the fluctuation problem of the new energy output within the day, realize the accurate calculation of the virtual energy storage model parameters of the variable frequency air-conditioning, and optimize the demand response effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0048] Figure 1 A schematic diagram of a demand response mechanism based on load baseline of the present invention;

[0049] Figure 2 It is a schematic diagram of the load guideline calculation model of the present invention;

[0050] Figure 3 It is a schematic diagram of the intraday rolling optimization process of the variable frequency air conditioning demand response based on virtual energy storage of the present invention; DETAILED DESCRIPTION

[0051] 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.

[0052] The present invention provides a technical solution:

[0053] A variable frequency air conditioning group demand response optimization model and method based on virtual energy storage, comprising the following steps:

[0054] S1: Demand response evaluation index based on load criterion;

[0055] The participation of variable frequency air conditioner virtual energy storage in DR first requires the definition of corresponding evaluation indicators to measure the effect of its participation in demand response. The existing IBDR (incentive-based demand response) widely adopts the load baseline CBL as the implementation criterion, quantifying the user's contribution to DR by the load forecast value when the user does not participate in DR and the actual load reduction, and using this to calculate the incentive, such as Figure 1 As shown;

[0056] The IBDR based on CBL has a good effect in guiding users to reduce peak load; however, its implementation is based on load forecast values, which cannot fully guarantee accuracy; at the same time, it cannot accurately evaluate the contribution of users to the consumption of new energy, and therefore cannot effectively guide the consumption of a high proportion of new energy;

[0057] In order to fully absorb the output of renewable energy, the calculation model of the load standard is as follows: Figure 2 As shown in the figure, the load standard is usually issued by the DR center of the power department and defined by shape; the DR center solves the system's minimum operating cost to obtain the shape of CDL based on the operating parameters of the entire network, taking into account the output and climbing constraints of the generator sets, the output range constraints of new energy sources, the power generation cost, and the cost of wind and solar power abandonment; users participating in DR actively adjust the shape of their own load curves to be close to the shape of CDL, respond to DR autonomously, and evaluate the user's DR contribution based on the similarity between the load curve and CDL and provide incentives;

[0058] In the calculation model of the load criterion, the objective function is to minimize the system operating cost, including the output cost of the generator set and the cost of wind and power abandonment, which is set as follows:

[0059]

[0060] Where: T is the total time period length, P G,j (t i ) represents the jth adjustable unit t i Active power in the time period, a j , b j 、c j is the cost coefficient, there are N adjustable units; P R (t i ), P R,max (t i ) is the new energy in t i Output and maximum output during the period, C R is the unit cost of abandoned electricity from renewable energy; by setting C R If the power consumption is a larger value and the proportion of wind and solar power abandonment costs in the total operating costs is increased, the maximum consumption of renewable energy output can be achieved within the adjustable range.

[0061] Assuming that the flexible load regulation capability in the system is strong enough, the adjustable part in the system balances the non-adjustable part, and the power balance constraint is obtained as shown in formula (2):

[0062]

[0063] Where: P D (t i ), P C (t i ) represent t i The size of controllable and uncontrollable loads in different time periods; in addition to power balance constraints, the output and ramp constraints of a single generator set, the output range of new energy sources, etc. should also be considered when solving the model;

[0064] The demand response based on the load criterion focuses on the shape of the load curve, so the load criterion is normalized and recorded as As shown in formula (3):

[0065]

[0066] In addition to guiding users to participate in DR to assist in fully absorbing new energy, IBDR based on CDL is also a bottom-up organizational method. Each DR user can spontaneously adjust the load curve according to CDL. As long as a good individual response is achieved, the load shape will be close to CDL at the overall system level, thereby achieving self-optimizing operation of the system.

[0067] The DR benefit of the user is calculated based on the similarity between the load curve shape and the CDL. The line similarity index E is defined as follows:

[0068]

[0069] Where: ε is a given coefficient, is the normalized user load curve, d is and The Euclidean distance of ; Based on this, the incentive e for DR users based on the criterion similarity index E is calculated as follows:

[0070]

[0071] In the formula: c represents the electricity price incentive coefficient, and the incentive e for users is equivalent to giving a certain discount rate on the basis of the electricity price; under the premise that the electricity consumption remains unchanged, the larger the line similarity index E of the user's load, the larger the electricity price discount rate, and the higher the incentive obtained.

[0072] S2: Virtual energy storage demand response optimization model for variable frequency air conditioning group;

[0073] Consider a park with a large number of adjustable variable frequency air conditioning loads, non-adjustable rigid loads and a small amount of new energy. When participating in demand response, the variable frequency air conditioning load is equivalent to virtual energy storage, and the optimization goal is to minimize the system operating cost.

[0074] In the optimization model, the time-decoupled charging and discharging control is adopted for the variable frequency air conditioner virtual energy storage, and the variable frequency air conditioner group can be aggregated into a whole virtual energy storage. i The charging and discharging power can be obtained by calculating the overall charging and discharging power, and then the scheduling can be optimized; for the variable frequency air conditioning load virtual energy storage, there are restrictions on the charging and discharging power of energy storage in different periods:

[0075]

[0076] Where: P ch,max (t i ), P dis,max (t i ) represents t i The maximum value of the virtual energy storage charging and discharging power of the variable frequency air conditioning load during the period (Δt cont Take 1 hour), k represents the air conditioner number, and there are M adjustable variable frequency air conditioner loads;

[0077] By using the user interaction function of the smart power network, variable frequency air conditioner users set demand response parameters, such as whether to participate in demand response, the adjustable temperature range of air conditioners participating in the response, etc. The variable frequency air conditioner load that does not participate in demand response is included in the rigid load, and the power balance constraints in the park are as follows:

[0078]

[0079] Flexible load P D (t i ) is calculated as follows:

[0080]

[0081] The optimization goal is to minimize the park operation cost, which is the electricity purchase cost minus the demand response incentive. This paper chooses the IBDR strategy based on CDL, and the operation cost is calculated as follows:

[0082]

[0083] Where: C ost represents the park operation cost, p ric represents the basic electricity price;

[0084] Taking into account the volatility of renewable energy load, its output forecast may change and adjust continuously during the day; at the same time, the second-order ETP model parameters of air-conditioning load may also change with the environment during the day, resulting in changes in the parameters of the virtual energy storage model that the air conditioner complies with; therefore, the model can be optimized on a rolling basis during the day, and the model parameters can be continuously updated and the results optimized to adjust the virtual energy storage output based on the latest renewable energy output forecast value and the online identified air-conditioning load model parameters.

[0085] S3: Demand response control strategy for variable frequency air conditioning groups based on virtual energy storage.

[0086] There are many studies on the participation of virtual energy storage in demand response control of air conditioning groups, among which the SOVC priority principle is widely adopted. In addition, there are also control methods based on control cost sorting and indoor temperature sorting, which take into account the user's demand response willingness.

[0087] In this scheme, the adjustable temperature of the air conditioner participating in demand response is set by the user, and the user can choose whether to accept the regulation. Therefore, the user's willingness to participate in demand response can be reflected by the virtual energy storage charging and discharging power. Under the time decoupling control method of this paper, the variable frequency air conditioner virtual energy storage charging and discharging power P ves Basically not affected by the indoor gas virtual energy storage charge state S OVC,a and solid virtual energy storage charge state S OVC,m The influence of S OVC,a , S OVC,m Unable to reflect the charging and discharging power status of virtual energy storage;

[0088] Therefore, this scheme is directly based on the virtual energy storage charging and discharging power P ves,k For variable frequency air conditioner load sequencing control, priority is given to controlling the air conditioner load with larger virtual energy storage charging and discharging power to reduce the total control times; this paper proposes a daily rolling optimization process for variable frequency air conditioner cluster virtual energy storage to participate in demand response. Figure 3 .

[0089] When solving the rolling optimization demand response model, the total period T is 24 hours a day, so as to minimize the system operation cost C in the T period. ost (P ves ) is the optimization objective;

[0090] It should be noted that the current t i The daily air conditioning load output before time t adopts the historical actual value, and the optimization period is t i The remaining period to 24:00; and the virtual energy storage output optimization result of the rolling optimization model is only in the current t i The virtual energy storage output is equal to the actual virtual energy storage output within one control cycle (1 hour) after the moment, and the virtual energy storage output of the next control cycle is optimized according to the latest rolling optimization model of the next cycle;

[0091] Solve the optimization model to obtain the variable frequency air conditioner virtual energy storage output P ves (t i ), the air conditioners are sequenced and controlled according to the virtual energy storage charging and discharging power priority principle until the total output of the virtual energy storage reaches the optimized value; the variable frequency air conditioner is temperature controlled, the temperature is set using the intelligent infrared terminal, and the variable frequency air conditioner virtual energy storage is time-decoupled for charging and discharging; it can be seen from the example that the actual temperature control of the variable frequency air conditioner has a response delay of about 1 to 3 minutes, which is acceptable compared to a one-hour control cycle; at the same time, the temperature control method is suitable for various types of air conditioners, which is easy to promote.

[0092] In summary, the intraday rolling optimization strategy adopted in this paper for the participation of variable frequency air-conditioning load virtual energy storage in demand response can effectively solve the fluctuation problem of new energy output within the day, realize the accurate calculation of the parameters of the variable frequency air-conditioning virtual energy storage model, and optimize the demand response effect.

[0093] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0094] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A variable frequency air conditioning group demand response optimization model and method based on virtual energy storage, characterized in that: The following steps are involved: S1: Demand response evaluation index based on load criterion; S2: Virtual energy storage demand response optimization model for variable frequency air conditioning group; The specific steps of S2 are: Consider a park with a large number of adjustable variable frequency air conditioning loads, non-adjustable rigid loads and a small amount of new energy. When participating in demand response, the variable frequency air conditioning load is equivalent to virtual energy storage, and the optimization goal is to minimize the system operating cost. In the optimization model, the time-decoupled charging and discharging control is adopted for the variable frequency air conditioner virtual energy storage, and the variable frequency air conditioner group is aggregated into a whole virtual energy storage. i The charging and discharging power can be obtained by calculating the overall charging and discharging power, and then the scheduling can be optimized; for the variable frequency air conditioning load virtual energy storage, there are restrictions on the charging and discharging power of energy storage in different periods: Where: P ch,max (t i ), P dis,max (t i ) represents t i The maximum value of the virtual energy storage charging and discharging power of the variable frequency air conditioning load during the period; Δt cont Take 1 hour, k represents the air conditioner number, and there are M adjustable variable frequency air conditioner loads; By using the user interaction function of the smart power network, variable frequency air conditioner users set demand response parameters, such as whether to participate in demand response and the adjustable temperature range of air conditioners participating in the response; variable frequency air conditioner loads that do not participate in demand response are included in the rigid load, and the power balance constraints in the park are as follows: Flexible load P D (t i ) is calculated as follows: R (t i ) is the new energy in t i Output during the period, P sta,k (t i ) is the variable frequency air conditioner at t i Power in a period, P D (t i ) and P C (t i ) represent t i The output of controllable load and uncontrollable load in the time period, The optimization goal is to minimize the park operation cost, which is the electricity purchase cost minus the demand response incentive. The IBDR strategy based on CDL is selected, and the operation cost is calculated as follows: Among them, the line similarity index E is defined as follows: Where: C ost represents the park operation cost, p ric represents the basic electricity price; ε is a given coefficient, is the normalized user load curve, d is and The Euclidean distance, c represents the electricity price incentive coefficient. The incentive e for users is equivalent to giving a certain discount rate on the basis of electricity price. Under the premise that the power consumption remains unchanged, the larger the line similarity index E of the user load, the larger the electricity price discount rate, and the higher the incentive obtained. Considering the volatility of new energy load, its output forecast may change and adjust continuously during the day; at the same time, the second-order ETP model parameters of air conditioning load may also change with the environment during the day, resulting in changes in the parameters of the air conditioning virtual energy storage model; therefore, the model can be optimized on a rolling basis during the day, and the model parameters can be updated and optimized based on the latest new energy output forecast value and online identified air conditioning load model parameters, and the virtual energy storage output can be adjusted; S3: Demand response control strategy for variable frequency air conditioning groups based on virtual energy storage.

2. According to claim 1, a variable frequency air conditioning group demand response optimization model and method based on virtual energy storage is characterized in that: The specific steps of S1 are: The load standard is issued by the DR center of the power department and is defined by shape. The DR center calculates the shape of CDL by solving the system's minimum operating cost based on the operating parameters of the entire network, taking into account the output and ramp constraints of the generator sets, the output range constraints of new energy sources, the cost of power generation, and the cost of wind and solar power abandonment. Users participating in DR will actively adjust the shape of their own load curves to be close to the shape of CDL, respond to DR autonomously, and evaluate the user's DR contribution based on the similarity between the load curve and CDL and provide incentives. In the calculation model of the load criterion, the objective function is to minimize the system operating cost, including the output cost of the generator set and the cost of wind and power abandonment, which is set as follows: Where: T is the total time period length, P G,j (t i ) represents the jth adjustable unit t i Active power in the time period, a j 、b j 、c j is the cost coefficient, there are N adjustable units; P R (t i ), P R,max (t i ) is the new energy in t i Output and maximum output during the period, C R is the unit cost of abandoned electricity from renewable energy; by setting C R If the power consumption is a larger value and the proportion of wind and solar power abandonment costs in the total operating costs is increased, the maximum consumption of renewable energy output can be achieved within the adjustable range. Assuming that the flexible load regulation capability in the system is strong enough, the adjustable part in the system balances the non-adjustable part, and the power balance constraint is obtained as shown in formula (2): Where: P D (t i ), P C (t i ) represent t i The size of controllable and uncontrollable loads in the time period; in addition to the power balance constraint, the output and ramp constraints of a single generator set and the output range of new energy sources should also be considered when solving the model; The demand response based on the load criterion focuses on the shape of the load curve, so the load criterion is normalized and recorded as As shown in formula (3): In addition to guiding users to participate in DR to assist in fully absorbing new energy, IBDR based on CDL is also a bottom-up organizational method. Each DR user can spontaneously adjust the load curve according to CDL. As long as a good individual response is achieved, the load shape will be close to CDL at the overall system level, thereby achieving self-optimizing operation of the system. The DR benefit of the user is calculated based on the similarity between the load curve shape and the CDL. The line similarity index E is defined as follows: Where: ε is a given coefficient, is the normalized user load curve, d is and The Euclidean distance of ; Based on this, the incentive e for DR users based on the criterion similarity index E is calculated as follows: In the formula: c represents the electricity price incentive coefficient, and the incentive e for users is equivalent to giving a discount rate based on the electricity price; under the premise that the electricity consumption remains unchanged, the larger the line similarity index E of the user's load, the larger the electricity price discount rate, and the higher the incentive obtained.

3. According to claim 1, a variable frequency air conditioning group demand response optimization model and method based on virtual energy storage is characterized in that: The specific steps of S3 are: The adjustable temperature of the air conditioner participating in demand response is set by the user, and the user can choose whether to accept the regulation. Therefore, the user's willingness to participate in demand response can be reflected by the virtual energy storage charging and discharging power. Under the time decoupling control mode, the variable frequency air conditioner virtual energy storage charging and discharging power P ves Basically not affected by the indoor gas virtual energy storage charge state S OVC,a and solid virtual energy storage charge state S OVC,m The influence of S OVC,a , S OVC,m Unable to reflect the charging and discharging power status of virtual energy storage; Therefore, it is directly based on the virtual energy storage charging and discharging power P ves,k For variable frequency air conditioning load sequencing control, priority is given to controlling air conditioning loads with larger virtual energy storage charging and discharging power to reduce the total control times.

4. According to claim 1, a variable frequency air conditioning group demand response optimization model and method based on virtual energy storage is characterized in that: When solving the rolling optimization demand response model, the total period T is 24 hours a day, so as to minimize the system operation cost C in the T period. ost (P ves ) is the optimization target; It should be noted that the current t i The daily air conditioning load output before time t adopts the historical actual value, and the optimization period is t i The remaining period to 24:00; and the virtual energy storage output optimization result of the rolling optimization model is only in the current t i The virtual energy storage output in the next control cycle is equal to the actual virtual energy storage output. The virtual energy storage output in the next control cycle is optimized according to the latest rolling optimization model in the next cycle. Solve the optimization model to obtain the variable frequency air conditioner virtual energy storage output P ves (t i ), the air conditioners are sequenced and controlled according to the virtual energy storage charging and discharging power priority principle until the total output of the virtual energy storage reaches the optimized value; The variable frequency air conditioner is temperature controlled, the temperature is set using an intelligent infrared terminal, and the variable frequency air conditioner virtual energy storage is time-decoupled for charge and discharge control.

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