CN114596005A - Variable frequency air conditioner group demand response optimization model and method based on virtual energy storage - Google Patents
Variable frequency air conditioner group demand response optimization model and method based on virtual energy storage Download PDFInfo
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Abstract
The invention discloses a variable frequency air conditioner group demand response optimization model and method based on virtual energy storage in the technical field of electrical engineering and automation thereof, and the model comprises the following steps: a demand response evaluation index based on a load guideline; the method comprises the following steps that a virtual energy storage demand response optimization model of the variable frequency air conditioner group is obtained; a variable frequency air conditioner group demand response control strategy based on virtual energy storage; the demand response research is carried out based on a load guideline in order to fully absorb high-proportion new energy in a power grid, a park containing a large number of variable-frequency air-conditioner loads is taken as a research object, the fluctuation of the output of the new energy in the park and the time-varying property of air-conditioner load model parameters are fully considered, a rolling optimization model in a demand response day is constructed, the output of the new energy is predicted in the day, the air-conditioner load model parameters are identified on line, and the response guideline is used for inhibiting the fluctuation of the output of the new energy through virtual energy storage.
Description
Technical Field
The invention relates to the technical field of electrical engineering and automation thereof, in particular to a variable frequency air conditioner group demand response optimization model and method based on virtual energy storage.
Background
The virtual energy storage characteristics of the air-conditioning-building are considered. At home and abroad, virtual energy storage modeling and participation in demand response research aiming at air conditioner load are more, and most of the virtual energy storage modeling and the participation are constructed based on a first-order ETP model. Aiming at a control method for air conditioner load to participate in demand response, a direct load control method is frequently adopted at present, and comprises switch control, temperature control, periodic pause control and frequency control. Duty cycle control refers to periodically switching on and off air conditioning loads, and is commonly used for controlling a central air conditioner by adjusting duty cycle occupied by operation to adjust the loads; the frequency control means that the frequency of the compressor is controlled through the variable frequency terminal, so that the power of the air conditioner is changed, and the variable frequency air conditioner is suitable for the variable frequency air conditioner; the switch control and the temperature control are almost suitable for all air conditioner loads, and the fixed-frequency air conditioner usually adopts the two control modes. Each control method has advantages and disadvantages, the switch control can quickly adjust the load, the response speed is high, the capacity is large, but the adjustable time is short under the comfort degree constraint; the temperature control is adjustable for a long time, but the response speed is slow due to the hysteresis of the actual air conditioning control and the temperature change, and the adjustable capacity is small. The frequency control aiming at the variable frequency air conditioner can not only realize the rapid change of the load, but also change the frequency value to reach a stable state after the temperature reaches a set value.
However, in practical application, the invasive variable frequency control terminal is difficult to install and popularize due to high installation difficulty and cost. Therefore, when the control mode is selected, the control requirement, the control cost and the like are comprehensively considered based on the air conditioner load type and the response type.
Therefore, it is desirable to design a demand response optimization model and method for an inverter air conditioning group based on virtual energy storage.
Disclosure of Invention
The invention aims to provide a virtual energy storage-based variable frequency air conditioner group demand response optimization model and method, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a variable frequency air conditioner group demand response optimization model and method based on virtual energy storage comprises the following steps:
s1: a demand response evaluation index based on a load guideline;
s2: the method comprises the following steps that a virtual energy storage demand response optimization model of the variable frequency air conditioner group is obtained;
s3: and responding to a control strategy based on the demand of the variable frequency air conditioner group of the virtual energy storage.
Further, in the virtual energy storage based demand response optimization model and method for the variable frequency air conditioning group, the specific step of S1 is:
in order to fully absorb the new energy output as much as possible, a calculation model of a load guideline is shown in fig. 2, the load guideline is usually issued by the DR center of the power department and is defined by a shape; the DR center considers the output and climbing constraints of the generator set, the output range constraints of new energy, the power generation cost, the wind and light abandoning costs according to the operation parameters of the whole network, and solves the lowest operation cost of the system to obtain the shape of the CDL; the users participating in DR actively adjust the shape of the load curve of the users to be close to the shape of the CDL, autonomously respond to DR, evaluate the DR contribution degree of the users according to the similarity of the load curve and the CDL and give an incentive;
in the calculation model of the load alignment, the objective function is the minimum system operation cost, including the output cost of the generator set and the cost of wind and electricity abandonment, and is set as follows:
in the formula: t is the total period length, PG,j(ti) Indicating the jth adjustable unit tiActive power of time period, aj、bj、cjN adjustable units are provided for cost coefficient; p isR(ti)、PR,max(ti) For new energy at tiForce applied and maximum force available in time interval, CRThe unit cost of electricity is abandoned for new energy; by setting CRThe ratio of the cost of the abandoned wind and abandoned light to the total running cost is increased to a large value, so that the output of new energy can be furthest absorbed in an adjustable range;
assuming that the flexible load adjusting capacity in the system is strong enough to balance the non-adjustable part by the adjustable part in the system, the power balance constraint is obtained as the following formula (2):
in the formula: pD(ti)、PC(ti) Respectively represents tiControllable load and uncontrollable load of a time interval; besides power balance constraint, the output and climbing constraint of a single generator set, the output range of new energy and the like are considered during model solution;
the demand response based on the load guideline focuses on the shape of the load curve, so the load guideline is unified and recorded as
As shown in formula (3):
besides guiding users to participate in DR to assist in fully absorbing new energy, the CDL-based IBDR is a bottom-up organization method, each DR user can spontaneously adjust a load curve according to the CDL, and as long as good individual response is realized, the load shape is close to the CDL on the whole system level, so that the self-optimization operation of the system is realized;
the DR benefit of the user is calculated according to the similarity between the load curve shape and the CDL, and the alignment similarity index E is defined as follows:
in the formula: epsilon is a given coefficient of the number of bits,
for a per-unit user load curve, d is
And
the Euclidean distance of; accordingly, the incentive E to the DR user based on the guideline similarity index E is calculated as follows:
in the formula: c represents an electricity price incentive coefficient, and the incentive e to the user is equivalent to giving a certain discount rate on the basis of the electricity price; on the premise of no change of the power consumption, the larger the alignment similarity index E of the user load is, the larger the discount rate of the electricity price is, and the higher the obtained incentive is.
Further, in the virtual energy storage based demand response optimization model and method for the variable frequency air conditioning group, the specific step of S2 is:
considering that a lot of adjustable variable frequency air conditioner loads, nonadjustable rigid loads and a small amount of new energy are contained in a park; when participating in demand response, the load of the variable frequency air conditioner is equivalent to virtual energy storage, and optimization is carried out by taking the minimized system operation cost as an optimization target;
in the optimization model, time decoupling charge and discharge control is adopted for virtual energy storage of the variable frequency air conditioners, the variable frequency air conditioner groups can be aggregated into a whole virtual energy storage, and each air conditioner is calculated at tiThe charging and discharging power of (2) can obtain the integral charging and dischargingPower, and then optimizing scheduling; for the virtual energy storage of the load of the variable frequency air conditioner, the charging and discharging power of the energy storage at different time intervals is limited:
in the formula: pch,max(ti)、Pdis,max(ti) Represents tiMaximum value (delta t) of virtual energy storage charging and discharging power of time interval variable frequency air conditioner loadcontTaking 1 hour), wherein k represents the serial number of the air conditioner, and M adjustable variable frequency air conditioner loads are shared;
and setting a demand response parameter by a variable frequency air conditioner user by using a user interaction function of the intelligent power utilization network, wherein the demand response parameter includes whether to participate in demand response, the temperature adjustable range of the air conditioner participating in response and the like. For the variable frequency air conditioner load which does not participate in demand response, the rigid load is included, and the power balance constraint in the park is as follows:
flexible load PD(ti) The calculation is as follows:
the optimization objective is to minimize campus operating costs, specifically electricity purchase costs minus demand response incentives, and a CDL-based IBDR strategy is selected herein, the operating costs being calculated as follows:
in the formula: costRepresenting operating expenses of the park, pricRepresents the base electricity price;
considering the fluctuation of the new energy load, the output prediction of the new energy load can be continuously changed and adjusted within the day; meanwhile, the second-order ETP model parameters of the air conditioner load can change along with the environment in the day, so that the air conditioner conforms to the parameter change of the virtual energy storage model; therefore, rolling optimization can be carried out on the model in the day, the model parameters are updated and the result is optimized continuously according to the latest new energy output predicted value and the air conditioner load model parameters identified on line, and the virtual energy storage output is adjusted.
Further, in the virtual energy storage based demand response optimization model and method for the variable frequency air conditioning group, the specific step of S3 is:
in the scheme, the adjustable temperature of the air conditioning participation and demand response is set by a user independently, and whether regulation and control are accepted or not can be selected independently, so that the willingness of the user to participate in the demand response can be reflected by the virtual energy storage charging and discharging power; under the control mode of time decoupling, the variable frequency air conditioner stores energy and charges and discharges power P virtuallyvesVirtual energy storage state of charge S basically free of indoor gasOVC,aAnd solid virtual energy storage state of charge SOVC,mOf (2), i.e. SOVC,a、SOVC,mThe virtual energy storage charging and discharging power state cannot be reflected;
therefore, the scheme is directly based on the virtual energy storage charging and discharging power Pves,kSequencing and controlling the variable frequency air conditioner loads, preferentially controlling the air conditioner loads with larger virtual energy storage charging and discharging power, and reducing the total control times; the day rolling optimization process for participating in demand response on the virtual energy storage of the variable frequency air conditioner cluster is shown in fig. 3.
Further, in the virtual energy storage based variable frequency air conditioner group demand response optimization model and method, when the demand response model of the rolling optimization is solved, the total time period T is 24 hours in a day so as to minimize the system operation cost C in the T time periodost(Pves) To optimize the goal;
it should be noted that the current tiThe air conditioner load output in the day before the moment adopts a historical actual value, and the optimized time period is tiThe remaining period of time to 24; and the virtual energy storage output optimization result of the rolling optimization model is only at the current tiThe actual virtual energy storage output is equal to the virtual energy storage output in one control period (1 hour) after the moment, and the virtual energy storage output in the next control period is according to the nextOptimizing the latest rolling optimization model in the period;
solving the optimization model to obtain the virtual energy storage output P of the variable frequency air conditionerves(ti) Then, sequencing control is carried out on the air conditioners according to the virtual energy storage charging and discharging power priority principle until the total output of the virtual energy storage reaches an optimized value; the method comprises the following steps of controlling the temperature of the variable frequency air conditioner, setting the temperature by using an intelligent infrared terminal, and performing time decoupling charge and discharge control on the virtual energy storage of the variable frequency air conditioner; as can be seen from the examples, the response delay of the actual temperature controlled inverter air conditioner is about 1 to 3 minutes, which is acceptable compared with the one-hour control period; meanwhile, the temperature control mode is suitable for various air conditioners of different types and is convenient to popularize.
Compared with the prior art, the invention has the beneficial effects that:
the demand response method based on research of virtual energy storage of the variable frequency air conditioner is used for fully absorbing high-proportion new energy in a power grid and developing demand response research based on a load guideline, a park containing a large number of variable frequency air conditioner loads is taken as a research object, the fluctuation of new energy output and the time-varying property of air conditioner load model parameters in the park are fully considered, a rolling optimization model in a demand response day is built, the new energy output is predicted in the day, the air conditioner load model parameters are identified on line, and the new energy output fluctuation is inhibited through virtual energy storage and the guideline is responded; the method can effectively solve the problem of fluctuation of new energy output in the day, realize accurate calculation of the virtual energy storage model parameters of the variable frequency air conditioner, and optimize the demand response effect.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a load baseline based demand response mechanism of the present invention;
FIG. 2 is a schematic view of a load alignment calculation model according to the present invention;
FIG. 3 is a schematic view of a day-in-day rolling optimization process of demand response of the inverter air conditioner based on virtual energy storage according to the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a technical scheme that:
a variable frequency air conditioner group demand response optimization model and method based on virtual energy storage comprises the following steps:
s1: a demand response evaluation index based on a load guideline;
firstly, determining corresponding evaluation indexes for the variable frequency air conditioner to participate in DR to measure the effect of participation in demand response; in the existing IBDR (incentive type demand response), a load baseline CBL is widely adopted as an implementation criterion, the contribution degree of a user in DR is quantified by the load predicted value and the actual load reduction amount of the user not participating in DR, and an incentive is calculated according to the contribution degree, as shown in fig. 1;
the CBL-based IBDR has a good effect in guiding a user to reduce peak load; however, the implementation is based on a load predicted value, and the accuracy cannot be completely guaranteed; meanwhile, the contribution of the user to the new energy consumption cannot be accurately evaluated, so that the high-proportion new energy consumption cannot be effectively guided;
in order to fully absorb the new energy output as much as possible, a calculation model of a load guideline is shown in fig. 2, the load guideline is usually issued by the DR center of the power department and is defined by a shape; the DR center considers the output and climbing constraints of the generator set, the output range constraints of new energy, the power generation cost, the wind and light abandoning costs according to the operation parameters of the whole network, and solves the lowest operation cost of the system to obtain the shape of the CDL; the users participating in DR actively adjust the shape of the load curve of the users to be close to the shape of the CDL, autonomously respond to DR, evaluate the DR contribution degree of the users according to the similarity of the load curve and the CDL and give an incentive;
in the calculation model of the load alignment, the objective function is the minimum system operation cost, including the output cost of the generator set and the cost of wind and electricity abandonment, and is set as follows:
in the formula: t is the total period length, PG,j(ti) Indicating the jth adjustable unit tiActive power of time period, aj、bj、cjN adjustable units are provided for cost coefficient; pR(ti)、PR,max(ti) For new energy at tiForce applied and maximum force available in time interval, CRThe unit cost of new energy power abandonment; by setting CRThe ratio of the cost of the abandoned wind and the abandoned light to the total running cost is increased to a larger value, so that the output of new energy can be maximally absorbed in an adjustable range;
assuming that the flexible load adjusting capacity in the system is strong enough to balance the non-adjustable part by the adjustable part in the system, the power balance constraint is obtained as the following formula (2):
in the formula: pD(ti)、PC(ti) Respectively represents tiControllable load and uncontrollable load of a time interval; besides power balance constraint, the output and climbing constraint of a single generator set, the output range of new energy and the like are considered during model solution;
the demand response based on the load guideline focuses on the shape of the load curve, so the load guideline is unified and recorded as
As shown in formula (3):
besides guiding users to participate in DR to assist in fully absorbing new energy, the CDL-based IBDR is a bottom-up organization method, each DR user can spontaneously adjust a load curve according to the CDL, and as long as good individual response is realized, the load shape is close to the CDL on the whole system level, so that the self-optimization operation of the system is realized;
the DR benefit of the user is calculated according to the similarity between the load curve shape and the CDL, and the alignment similarity index E is defined as follows:
in the formula: epsilon is a given coefficient of the number of bits,
for a per-unit user load curve, d is
And
the Euclidean distance of; accordingly, the incentive E to the DR user based on the guideline similarity index E is calculated as follows:
in the formula: c represents an electricity price incentive coefficient, and the incentive e to the user is equivalent to giving a certain discount rate on the basis of the electricity price; on the premise of no change of the power consumption, the larger the alignment similarity index E of the user load is, the larger the discount rate of the electricity price is, and the higher the obtained incentive is.
S2: a virtual energy storage demand response optimization model of the variable frequency air conditioner group;
considering that a park comprises a large amount of adjustable variable frequency air conditioner load, non-adjustable rigid load and a small amount of new energy; when participating in demand response, the load of the variable frequency air conditioner is equivalent to virtual energy storage, and optimization is carried out by taking the minimized system operation cost as an optimization target;
in the optimization model, time decoupling charge and discharge control is adopted for virtual energy storage of the variable frequency air conditioners, variable frequency air conditioner groups can be aggregated into a whole virtual energy storage, and each air conditioner is calculated at tiThe integral charging and discharging power can be obtained, and then the scheduling is optimized; for the virtual energy storage of the load of the variable frequency air conditioner, the charging and discharging power of the energy storage at different time intervals is limited:
in the formula: p isch,max(ti)、Pdis,max(ti) Represents tiMaximum value (delta t) of virtual energy storage charging and discharging power of time interval variable frequency air conditioner loadcontTaking 1 hour), wherein k represents the serial number of the air conditioner, and M adjustable variable frequency air conditioner loads are shared;
and setting a demand response parameter by a user of the variable frequency air conditioner by utilizing a user interaction function of the intelligent power utilization network, wherein the demand response parameter is whether to participate in demand response or not, and the temperature adjustable range of the air conditioner participating in response is the same as the temperature adjustable range of the air conditioner. For the variable frequency air conditioner load which does not participate in demand response, the rigid load is included, and the power balance constraint in the park is as follows:
flexible load PD(ti) The calculation is as follows:
the optimization objective is to minimize campus operating costs, specifically electricity purchase costs minus demand response incentives, and a CDL-based IBDR strategy is selected herein, the operating costs being calculated as follows:
in the formula: costRepresenting operating expenses of the park, pricRepresents the base electricity price;
considering the fluctuation of the new energy load, the output prediction of the new energy load can be continuously changed and adjusted within the day; meanwhile, the second-order ETP model parameters of the air conditioner load can change along with the environment in the day, so that the air conditioner conforms to the parameter change of the virtual energy storage model; therefore, rolling optimization can be carried out on the model in the day, the model parameters are updated and the result is optimized continuously according to the latest new energy output predicted value and the air conditioner load model parameters identified on line, and the virtual energy storage output is adjusted.
S3: and responding to a control strategy based on the demand of the variable frequency air conditioner group of the virtual energy storage.
The research on the participation of the virtual energy storage of the air conditioning group in demand response control is more, wherein the SOVC priority principle is adopted more; in addition, a control method based on control cost sequencing, sequencing based on indoor temperature and the like are also considered;
in the scheme, the adjustable temperature of the air conditioning participation and demand response is set by a user independently, and whether regulation and control are accepted or not can be selected independently, so that the willingness of the user to participate in the demand response can be reflected by the virtual energy storage charging and discharging power; under the control mode of time decoupling, the variable frequency air conditioner stores energy and charges and discharges power P virtuallyvesVirtual energy storage state of charge S basically free of indoor gasOVC,aAnd solid virtual energy storage state of charge SOVC,mOf (2), i.e. SOVC,a、SOVC,mThe virtual energy storage charging and discharging power state cannot be reflected;
therefore, the scheme is directly based on the virtual energy storage charging and discharging power Pves,kThe load of the variable frequency air conditioner is controlled in sequence and the virtual energy storage is preferentially controlledThe air conditioner load with higher charging and discharging power reduces the total control times; the day rolling optimization process for participating in demand response on the virtual energy storage of the variable frequency air conditioner cluster is shown in fig. 3.
When the demand response model of the rolling optimization is solved, the total time interval T is 24 hours in a day so as to minimize the system operation cost C in the T time intervalost(Pves) To optimize the goal;
it should be noted that the current tiThe air conditioner load output in the day before the moment adopts a historical actual value, and the optimized time period is tiThe remaining period of time to 24; and the virtual energy storage output optimization result of the rolling optimization model is only at the current tiThe virtual energy storage output in the next control period is obtained by optimizing the latest rolling optimization model in the next period, wherein the actual virtual energy storage output is equal to the virtual energy storage output in one control period (1 hour) after the moment;
solving the optimization model to obtain the virtual energy storage output P of the variable frequency air conditionerves(ti) Then, sequencing and controlling the air conditioners according to a virtual energy storage charging and discharging power priority principle until the total output of the virtual energy storage reaches an optimized value; the method comprises the following steps of controlling the temperature of the variable frequency air conditioner, setting the temperature by using an intelligent infrared terminal, and performing time decoupling charge and discharge control on the virtual energy storage of the variable frequency air conditioner; as can be seen from the examples, the response delay of the actual temperature controlled inverter air conditioner is about 1 to 3 minutes, which is acceptable compared with the one-hour control period; meanwhile, the temperature control mode is suitable for various air conditioners of different types and is convenient to popularize.
In conclusion, the intraday rolling optimization strategy adopted for the variable frequency air conditioner load virtual energy storage participation demand response can effectively solve the fluctuation problem of the intraday new energy output, realize accurate calculation of the variable frequency air conditioner virtual energy storage model parameters and optimize the demand response effect.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (5)
1. A variable frequency air conditioner group demand response optimization model and method based on virtual energy storage are characterized by comprising the following steps:
s1: a demand response evaluation index based on a load guideline;
s2: the method comprises the following steps that a virtual energy storage demand response optimization model of the variable frequency air conditioner group is obtained;
s3: and responding to a control strategy based on the demand of the variable frequency air conditioner group of the virtual energy storage.
2. The variable frequency air conditioner group demand response optimization model and method based on virtual energy storage according to claim 1, characterized in that: the specific steps of S1 are as follows:
in order to fully absorb the new energy output as much as possible, a calculation model of a load guideline is shown in fig. 2, the load guideline is usually issued by the DR center of the power department and is defined by a shape; the DR center considers the output and climbing constraints of the generator set, the output range constraints of new energy, the power generation cost, the wind and light abandoning costs according to the operation parameters of the whole network, and solves the lowest operation cost of the system to obtain the shape of the CDL; the users participating in DR actively adjust the shape of the load curve of the users to be close to the shape of the CDL, autonomously respond to DR, evaluate the DR contribution degree of the users according to the similarity of the load curve and the CDL and give an incentive;
in the calculation model of the load alignment, the objective function is the minimum system operation cost, including the output cost of the generator set and the cost of wind and electricity abandonment, and is set as follows:
in the formula: t is the total period length, PG,j(ti) Indicating the jth adjustable unit tiActive power of time period, aj、bj、cjN adjustable units are provided for cost coefficient; pR(ti)、PR,max(ti) For new energy at tiForce applied and maximum force available in time interval, CRThe unit cost of new energy power abandonment; by setting CRThe ratio of the cost of the abandoned wind and the abandoned light to the total running cost is increased to a larger value, so that the output of new energy can be maximally absorbed in an adjustable range;
assuming that the flexible load adjusting capacity in the system is strong enough to balance the non-adjustable part by the adjustable part in the system, the power balance constraint is obtained as the following formula (2):
in the formula: pD(ti)、PC(ti) Respectively represents tiControllable load and uncontrollable load of time interval; besides power balance constraint, the output and climbing constraint of a single generator set, the output range of new energy and the like are considered during model solution;
the demand response based on the load guideline focuses on the shape of the load curve, so the load guideline is unified and recorded as
As shown in formula (3):
besides guiding users to participate in DR to assist in fully absorbing new energy, the CDL-based IBDR is a bottom-up organization method, each DR user can spontaneously adjust a load curve according to the CDL, and as long as good individual response is realized, the load shape is close to the CDL on the whole system level, so that the self-optimization operation of the system is realized;
the DR benefit of the user is calculated according to the similarity between the load curve shape and the CDL, and the alignment similarity index E is defined as follows:
in the formula: epsilon is a given coefficient of the number of bits,
for a per-unit user load curve, d is
And
the Euclidean distance of; accordingly, the incentive E to the DR user based on the guideline similarity index E is calculated as follows:
in the formula: c represents an electricity price incentive coefficient, and the incentive e to the user is equivalent to giving a certain discount rate on the basis of the electricity price; on the premise of no change of the power consumption, the larger the alignment similarity index E of the user load is, the larger the discount rate of the electricity price is, and the higher the obtained incentive is.
3. The variable frequency air conditioner group demand response optimization model and method based on virtual energy storage according to claim 1, characterized in that: the specific steps of S2 are as follows:
considering that a park comprises a large amount of adjustable variable frequency air conditioner load, non-adjustable rigid load and a small amount of new energy; when participating in demand response, the load of the variable frequency air conditioner is equivalent to virtual energy storage, and optimization is carried out by taking the minimized system operation cost as an optimization target;
in the optimization model, time decoupling charge and discharge control is adopted for virtual energy storage of the variable frequency air conditioners, the variable frequency air conditioner groups can be aggregated into a whole virtual energy storage, and each air conditioner is calculated at tiThe integral charging and discharging power can be obtained, and then the scheduling is optimized; for the virtual energy storage of the load of the variable frequency air conditioner, the charging and discharging power of the energy storage at different time intervals is limited:
in the formula: p isch,max(ti)、Pdis,max(ti) Represents tiTime interval variable frequency air conditioner load virtual energy storage charging and discharging power maximum value (delta t)contTaking 1 hour), wherein k represents the serial number of the air conditioner, and M adjustable variable frequency air conditioner loads are shared;
and setting a demand response parameter by a user of the variable frequency air conditioner by utilizing a user interaction function of the intelligent power utilization network, wherein the demand response parameter is whether to participate in demand response or not, and the temperature adjustable range of the air conditioner participating in response is the same as the temperature adjustable range of the air conditioner. For the variable frequency air conditioner load which does not participate in demand response, the rigid load is included, and the power balance constraint in the park is as follows:
flexible load PD(ti) The calculation is as follows:
the optimization objective is to minimize campus operating costs, specifically, electricity purchase costs minus demand response incentives, and a CDL-based IBDR strategy is selected herein, and the operating costs are calculated as follows:
in the formula: costRepresenting operating expenses of the park, pricRepresents the base electricity price;
considering the fluctuation of the new energy load, the output prediction of the new energy load can be continuously changed and adjusted within the day; meanwhile, the second-order ETP model parameters of the air conditioner load can change along with the environment in the day, so that the air conditioner conforms to the parameter change of the virtual energy storage model; therefore, rolling optimization can be carried out on the model in the day, the model parameters are updated and the result is optimized continuously according to the latest new energy output predicted value and the air conditioner load model parameters identified on line, and the virtual energy storage output is adjusted.
4. The variable frequency air conditioner group demand response optimization model and method based on virtual energy storage according to claim 1, characterized in that: the specific steps of S3 are as follows:
in the scheme, the adjustable temperature of the air conditioner participating in the demand response is set by a user independently, and whether the adjustment is accepted or not can be selected independently, so that the willingness of the user participating in the demand response can be reflected by the virtual energy storage charging and discharging power; under the control mode of time decoupling, the variable frequency air conditioner stores energy and charges and discharges power P virtuallyvesVirtual energy storage state of charge S basically free of indoor gasOVC,aAnd solid virtual energy storage state of charge SOVC,mOf (2), i.e. SOVC,a、SOVC,mThe virtual energy storage charging and discharging power state cannot be reflected;
therefore, the scheme is directly based on the virtual energy storage charging and discharging power Pves,kThe load of the variable frequency air conditioner is controlled in sequence, the air conditioner load with larger virtual energy storage charging and discharging power is controlled preferentially, and the total control is reducedMaking times; the day rolling optimization process for participating in demand response on the virtual energy storage of the variable frequency air conditioner cluster is shown in fig. 3.
5. The variable frequency air conditioner group demand response optimization model and method based on virtual energy storage according to claim 4, characterized in that: when the demand response model of the rolling optimization is solved, the total time interval T is 24 hours in a day so as to minimize the system operation cost C in the T time intervalost(Pves) To optimize the goal;
it should be noted that the current tiThe air conditioner load output in the day before the moment adopts a historical actual value, and the optimized time period is tiThe remaining period of time to 24; and the virtual energy storage output optimization result of the rolling optimization model is only at the current tiThe virtual energy storage output in the next control period is obtained by optimizing the latest rolling optimization model in the next period, wherein the actual virtual energy storage output is equal to the virtual energy storage output in one control period (1 hour) after the moment;
solving the optimization model to obtain the virtual energy storage output P of the variable frequency air conditionerves(ti) Then, sequencing and controlling the air conditioners according to a virtual energy storage charging and discharging power priority principle until the total output of the virtual energy storage reaches an optimized value; the method comprises the following steps of controlling the temperature of the variable frequency air conditioner, setting the temperature by using an intelligent infrared terminal, and performing time decoupling charge and discharge control on the virtual energy storage of the variable frequency air conditioner; as can be seen from the examples, the response delay of the actual temperature controlled inverter air conditioner is about 1 to 3 minutes, which is acceptable compared with the one-hour control period; meanwhile, the temperature control mode is suitable for various air conditioners of different types and is convenient to popularize.
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| CN117674168A (en) * | 2024-01-31 | 2024-03-08 | 国网湖北省电力有限公司经济技术研究院 | Regional power low-carbon adjustment method and system considering power demand response |
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| CN115833190A (en) * | 2023-02-20 | 2023-03-21 | 广东电网有限责任公司中山供电局 | Distributed resource edge autonomous control method and system |
| CN117674168A (en) * | 2024-01-31 | 2024-03-08 | 国网湖北省电力有限公司经济技术研究院 | Regional power low-carbon adjustment method and system considering power demand response |
| CN117674168B (en) * | 2024-01-31 | 2024-04-16 | 国网湖北省电力有限公司经济技术研究院 | Regional power low-carbon adjustment method and system considering power demand response |
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