CN113608446B - Building group demand response optimization scheduling method, device, terminal equipment and medium - Google Patents
Building group demand response optimization scheduling method, device, terminal equipment and medium Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
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Abstract
The invention discloses a building group demand response optimization scheduling method, a device, terminal equipment and a medium, wherein the method comprises the following steps: dividing the total response time period of a plurality of buildings in a building group into a plurality of time steps, and acquiring the demand response of the plurality of buildings in the time steps; establishing a scheduling optimization model of each time step according to the demand response of a plurality of buildings; establishing a 0-1 planning model corresponding to the scheduling optimization model, and determining a first constraint condition of the 0-1 planning model; and calculating an optimal solution of the 0-1 planning model by using the first constraint condition, and determining a control strategy of building group demand response optimization scheduling according to the optimal solution. According to the invention, the optimal scheduling problem of the building group in the demand response is extracted into the 0-1 planning problem, and the correlation of the scheduling demands of all the buildings in the building group is considered, so that the established optimal scheduling strategy realizes the stable reduction target, and the control effect of the demand response of the building group is improved.
Description
Technical Field
The invention relates to the technical field of power energy optimization scheduling, in particular to a building group demand response optimization scheduling method, a device, terminal equipment and a medium.
Background
At present, power demand side management is mainly focused on providing response strategies for demands of single buildings, and the method is to convert a demand response optimization problem into a mathematical optimization problem and solve the mathematical optimization problem by using a corresponding mathematical optimization method. For example, in a multi-building power scheduling algorithm with the minimum electricity consumption as a target, the demand response optimization problem is converted into a convex planning problem, and then the problem is solved by using a Lagrange relaxation method. The optimal scheduling problem for the building group is mainly that a model prediction control method of a single building is determined first, and then the model prediction control method is extended and applied to the building group to realize the optimal control of the building group.
However, the reduction of the single building is very small for the power grid, and the method for optimizing and extending the single building to the building group mainly manages the building of the building group as the single building, so that each building of the building group often forms an 'island effect', the relevance of power requirements among each building in the building group cannot be considered during dispatching, and further unified dispatching cannot be realized on the building group level, finally, the problems of unreasonable setting of a dispatching control strategy, unsatisfactory control effect, poor economical efficiency and the like are caused, and meanwhile, the stable operation of the power market is not facilitated.
Disclosure of Invention
The invention aims to provide a building group demand response optimization scheduling method, a device, terminal equipment and a medium, which are used for solving the problems of non-ideal optimization control result and poor economical efficiency caused by island effect in the building group demand response optimization problem in the prior art.
In order to achieve the above object, the present invention provides a building group demand response optimizing and scheduling method, including:
dividing the total response time period of a plurality of buildings in a building group into a plurality of time steps, and acquiring the demand response of the plurality of buildings in the time steps;
establishing a scheduling optimization model of each time step according to the demand response of the plurality of buildings;
establishing a 0-1 planning model corresponding to the scheduling optimization model, and determining a first constraint condition of the 0-1 planning model;
and calculating an optimal solution of the 0-1 planning model by using the first constraint condition, and determining a control strategy of building group demand response optimization scheduling according to the optimal solution.
Preferably, said determining the first constraint of the 0-1 planning model comprises:
determining an objective function and a second constraint condition in the current time step, and solving the objective function by using the second constraint condition;
determining a control scheme of building group demand response optimization scheduling in the current time step according to a solving result, and acquiring a building group demand response after executing the control scheme;
and determining an objective function and a second constraint condition in the next time step according to the building group demand response after the control scheme is executed until the first constraint condition of the 0-1 planning model in all the time steps is determined.
Preferably, said calculating an optimal solution of said 0-1 planning model using said first constraint comprises:
and calculating an optimal solution of the 0-1 planning model according to a branch-and-bound method by using the first constraint condition.
Preferably, the control strategy includes an indirect shutdown strategy, a shut down partial chiller strategy, and a reset chilled water temperature strategy.
The invention also provides a building group demand response optimizing and scheduling device, which comprises:
the response time interval dividing unit is used for dividing the total response time interval of a plurality of buildings in the building group into a plurality of time steps and acquiring the demand response of the plurality of buildings in the time steps;
the scheduling optimization model building unit is used for building a scheduling optimization model of each time step according to the demand response of the plurality of buildings;
the 0-1 planning model construction unit is used for establishing a 0-1 planning model corresponding to the scheduling optimization model and determining a first constraint condition of the 0-1 planning model;
and the control strategy determining unit is used for calculating an optimal solution of the 0-1 planning model by using the first constraint condition and determining a control strategy of building group demand response optimization scheduling according to the optimal solution.
Preferably, the 0-1 planning model construction unit is further configured to:
determining an objective function and a second constraint condition in the current time step, and solving the objective function by using the second constraint condition;
determining a control scheme of building group demand response optimization scheduling in the current time step according to a solving result, and acquiring a building group demand response after executing the control scheme;
and determining an objective function and a second constraint condition in the next time step according to the building group demand response after the control scheme is executed until the first constraint condition of the 0-1 planning model in all the time steps is determined.
Preferably, the control strategy determining unit is further configured to calculate an optimal solution of the 0-1 planning model according to a branch-and-bound method using the first constraint condition.
Preferably, the control strategy includes an indirect shutdown strategy, a shut down partial chiller strategy, and a reset chilled water temperature strategy.
The invention also provides a terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the building group demand response optimization scheduling method of any one of the above.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the building group demand response optimizing scheduling method as defined in any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a building group demand response optimization scheduling method, which comprises the following steps: dividing the total response time period of a plurality of buildings in a building group into a plurality of time steps, and acquiring the demand response of the plurality of buildings in the time steps; establishing a scheduling optimization model of each time step according to the demand response of a plurality of buildings; establishing a 0-1 planning model corresponding to the scheduling optimization model, and determining a first constraint condition of the 0-1 planning model; and calculating an optimal solution of the 0-1 planning model by using the first constraint condition, and determining a control strategy of building group demand response optimization scheduling according to the optimal solution.
According to the building group demand response optimization scheduling method, the optimization scheduling problem of the building group in demand response is refined to be a 0-1 planning problem, and the correlation of the building scheduling demands in the building group is considered, so that the established optimization scheduling strategy achieves a stable reduction target, and the control effect of the building group demand response is improved.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for optimizing and scheduling demand response of a building group according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a group dispatch optimization framework in demand response provided in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a binary tree network with three variables according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an optimized dispatch device for demand response of building groups according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the step numbers used herein are for convenience of description only and are not limiting as to the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1, an embodiment of the present invention provides a building group demand response optimization scheduling method, as shown in fig. 1, including steps S10 to S40, where each step specifically includes:
s10, dividing the total response time period of a plurality of buildings in a building group into a plurality of time steps, and acquiring the demand response of the plurality of buildings in the time steps;
s20, establishing a scheduling optimization model of each time step according to the demand response of the plurality of buildings;
s30, establishing a 0-1 planning model corresponding to the scheduling optimization model, and determining a first constraint condition of the 0-1 planning model;
and S40, calculating an optimal solution of the 0-1 planning model by using the first constraint condition, and determining a control strategy of building group demand response optimization scheduling according to the optimal solution.
In this embodiment, the Demand Response (DR) refers to a short term of power Demand Response, in which when the price of the wholesale market of power increases or the reliability of the system is compromised, after receiving a direct compensation notification of an induced load reduction or a power price increase signal sent by a power supply party, a power consumer changes an inherent habit power consumption mode thereof, and reduces or shifts a power load in a certain period to respond to power supply, thereby ensuring the stability of the power grid and suppressing the short term behavior of the increase of the power price. It is one of the solutions for Demand Side Management (DSM). When the demand response occurs, the power grid side can send out signals about the time and the size of the power gap. The demand side manager dispatches each building in the building group based on these parameters, and the building group is completed with the target reduction amount.
Currently, for building demand response, control strategies that implement building demand response to individual buildings are mostly focused. However, the reduction of the electric load of a single building has very little effect on the whole power grid, and if the correlation of the electric power demands among them is not considered, the island effect is easily caused, so that the effect of the dispatching control strategy is not fit with reality and lacks economic consideration, and meanwhile, the stable operation of the power grid is not favored. It should be noted that, because a building participating in a demand response in an area is often connected to the same power grid, in an actual demand response, a plurality of commercial buildings should be integrated into one large user in a building group, and the large user should be uniformly scheduled and managed by a load integrator (Aggregator), an electric energy server, or a decision maker of the building group at a demand management side, so as to achieve a goal of reducing the demand response. The embodiment of the invention aims to provide an optimized scheduling strategy among buildings, wherein a plurality of commercial buildings participate in demand response as a cluster. Specifically, when the building group participates in the demand response as one large user, the demand management side can determine the buildings participating in the response in each period, and determine which control strategy they should adopt and the time when each building starts to implement the demand response strategy, namely, the overall goal of the demand response is achieved through the optimized scheduling of the building group, so that the economical efficiency and the control effect are both considered.
Further, the typical measure of demand response effect typically includes two metrics: firstly, the cumulative cut-off amount Δqkwh in the response period, and secondly, the average load cut-off Δ PkW at each time. The reduction of the demand response is relative to a specific baseline, and various algorithms of the baseline in the demand response exist, for example, when evaluating an air conditioning system control strategy of a single building, the embodiment is used for describing the baseline load of the system and the load condition after the demand response by establishing an energy consumption model of the system. However, the base line for settlement should have accurate and simple characteristics for the building complex and the power grid. Because the optimization scheduling algorithm and the demand response baseline algorithm are relatively independent, the embodiment preferably adopts a simpler baseline form as a settlement baseline of demand response of the building group, namely, the energy consumption curves of similar days (the same types of days except for the same running condition of equipment in the demand response period and similar outdoor meteorological parameters) of response days are selected as the baselines.
Referring to fig. 2, fig. 2 is a frame of a building group optimization scheduling process in demand response according to an embodiment of the present invention. As shown in FIG. 2, the building group has a total of N (capital letters indicate buildings) building components, and taking building A as an example, in a certain demand response, a response control scheme which can be adopted by the building group has N A Seed (n) A Not less than 1), each scheme has its corresponding power load reduction curve (e.g. strategy n A Corresponding curtailment curve n A '). At this time, the task of the building group decision maker may be further expressed as how to select and optimally combine these load curves, so that the load curves of the building group meet the objective of the demand response, i.e. the task of the "optimal scheduling strategy" part of the building group optimal scheduling solver in fig. 2. The optimized scheduling problem may be translated into a 0-1 programming problem in integer programming. Because in one demand response, each demand response control strategy of each building only has two states of starting and not starting, and the states are respectively indicated by 1 and 0.
It should be noted that, when a building is in a "start-up" state for the first time at a certain moment, it is the time when the building starts to participate in a response. In order to simplify the optimization problem in continuous time, in step S10, the embodiment of the present invention first divides the total response time period of a plurality of buildings in a building group into a plurality of time steps, and obtains the demand response of the plurality of buildings in the time step; i.e. the problem can be limited to every time step while at the same time ensuring that the curtailment of the building complex remains within a certain range. Wherein each time step is a 0-1 scheduling problem participating in the scheduling of the building. Therefore, in step S20, a scheduling optimization model is first built according to the demand response of a plurality of buildings in each time step, and then in step S30, the scheduling optimization model is converted into a corresponding 0-1 planning model, and the first constraint condition of the 0-1 planning model is determined. Finally, when executing step S40, calculating the optimal solution of the 0-1 planning model by using the first constraint condition, and determining the control strategy of building group demand response optimization scheduling according to the optimal solution.
It is emphasized that since each time step is not independent, i.e. the building status at this moment is constrained by the building status at the previous moment and limits the next moment. For example, once a response policy for a building is determined at a certain time, the building maintains the policy until the policy implementation ends, and at most one control scheme can be selected for a building. These constraints form constraints in the optimization process. To avoid this limitation, in a particular embodiment, the determining the first constraint of the 0-1 planning model includes:
1) Determining an objective function and a second constraint condition in the current time step, and solving the objective function by using the second constraint condition;
2) Determining a control scheme of building group demand response optimization scheduling in the current time step according to a solving result, and acquiring a building group demand response after executing the control scheme;
3) And determining an objective function and a second constraint condition in the next time step according to the building group demand response after the control scheme is executed until the first constraint condition of the 0-1 planning model in all the time steps is determined.
For ease of understanding, the following will specifically explain the entire procedure of determining the first constraint in the present embodiment based on fig. 2:
first, assume that a building group consists of N buildings, each building having N A The total duration of the demand response event is t, the time step is deltat, the total step number of mt as the response time period is t/deltat, and the reduction target of each time step is deltaQ m,goal kWh or Δp m,goal kW; then actually cut down to DeltaQ m,real kWh or Δp m,real kW；(m＝1,2,…m t ). Because some states of the previous time step are translated into constraints in the optimization solution of the next time step during the optimization process, the constraints will be different in response to the first time step beginning and the subsequent time steps.
Specifically, building N policy N N The load reduction of the corresponding load curve at a certain time step is thatThe curtailment of the entire building group in a certain time step is:
in the method, in the process of the invention,is a coefficient of the policy curve, x=0 or 1,0 indicating that the policy of the building is not selected, and 1 indicating that the policy of the building is selected.
In the actual demand response event, the reduction amount may not be lower than the target reduction by a certain proportion (for example, > 90%) and the target reduction may be exceeded, but the excess portion is not rewarded (for example, > 110%). Therefore, it is desirable for a decision maker of a building group to be able to approach the reduction target as close as possible after satisfying the reduction amount greater than a certain ratio.
Specifically, the optimization problem for time step 1 can be converted into a 0-1 programming problem, i.e., a 0-1 programming model is built:
the constraint conditions are as follows:
wherein constraint (3) indicates that the total curtailment of the building group must be greater than 90% of the target curtailment; constraint (4) indicates that the total curtailment of the building group is preferably less than 110% of the target curtailment; constraint (5) indicates that at most one control scheme is selected for a single building.
Further, after entering the next time step, some states of the previous time step become constraints of the next time step, namely, the building with the previous time step participating in the response must participate in the response until the implementation of the control scheme is finished, and the mathematical expression is as shown in the following formula (6):
if it isThen->
If the demand response policy of a building is implemented when m=m', the corresponding cut-down amount is then calculatedAt this time->There is no effect on the overall optimization result, so equation (6) is always true. Then from the second time step to the last time step of the demand response, the goals and constraints of the optimal schedule can be written in the form of:
the constraint conditions are as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
it will be appreciated that the constraint equations (8) - (10) and equations (3) - (5) have the same form, indicating that the reduction amount is greater than 90% and less than 110% of the target reduction, respectively, and that only one control scheme can be selected for a building. And constraint (11) indicates that the control strategy involved in the response in the last time step must continue to be implemented in this step. Constraint (11) is analyzed as follows, the product of which is 1 only if and only if the elements at the same position in the sum are 1 at the same time, otherwise 0.Representation->The number of elements in (1). Thus, formula (10) represents->And->The product is the number of 1 and +.>The number of elements 1 in (a) is the same, thus limiting the elements 1 at the previous moment to be still equal to 1 in the next step. While elements that were 0 in the previous step may be 0 or 1 in the next step. In this way, the definition can be accurately performed, specifically:
a) The control strategy of (1) has been started in the last time step, in which step (1) has to be continued,
b) The policy not implemented by the previous step may choose to implement (1) or not implement (0) within this step.
Therefore, equations (3) to (5) and (8) to (10) are mathematical expressions of the building group optimization scheduling problem when the response goal of the building group is "smooth cut". And each time step is a 0-1 programming problem, and the corresponding optimal scheduling control strategy can be determined by solving the 0-1 programming model. It should be noted that the control strategy may include, but is not limited to, an indirect shutdown strategy, a shut down partial chiller strategy, and a reset chilled water temperature strategy.
In an alternative embodiment, to solve the 0-1 planning model, a branch-and-bound method may be used for the solution. It should be noted that branch and bound (branch and bound) is one of the most commonly used algorithms for solving integer programming problems. The method can solve not only pure integer programming, but also mixed integer programming problems.The branch-and-bound method is a search and iteration method, and different branch variables and sub-questions are selected for branching. The main idea is as follows: repeatedly partitioning the total feasible solution space into smaller and smaller subsets, called branches; and a target lower bound (for the minimum problem) is computed for the solution set within each subset, which is called bounding. After each branching, any subset beyond the known feasible solution set target value is not further branched, so many subsets may be disregarded, which is called pruning. Branch and bound methods generate a "search tree" by continually adding a constraint to the problem being solved, a process called branching. In each step "branching", the algorithm will not currently be an integer x j Limiting x in one branch j =0, limiting x in the other branch j ＝1。
In particular, the whole process of the branch-and-bound method can be represented by a binary tree network, as shown in fig. 3. As can be seen from FIG. 3, with the constraints at each node, the algorithm solves one LP-relaxation problem at each node, determining whether to branch or move to another node based on different results. The solution to the LP-relaxation problem serves as a lower bound to the binary integer programming problem. If the LP-relaxation problem is already a binary integer vector, then its solution will be the upper bound on the binary integer programming problem. As more and more nodes of the search tree (search tree) get the limit value using a bounding procedure (bounding), the algorithm continuously updates the upper and lower limits of the objective function. Delimitation is a threshold for the target value, so that unnecessary branches are "cut off". The embodiment of the invention realizes the solution of the 0-1 programming problem by utilizing the bintprog function in MATLAB, and the essence is a branch-and-bound method.
According to the building group demand response optimization scheduling method provided by the embodiment of the invention, the optimization scheduling problem of the building group in the demand response is refined into the 0-1 planning problem, and the correlation of the building scheduling demands in the building group is considered, so that the established optimization scheduling strategy realizes the stable reduction target, and the control effect of the building group demand response is improved.
Referring to fig. 4, an embodiment of the present invention further provides a building group demand response optimization scheduling device, including:
the response time interval dividing unit 100 is configured to divide a total response time interval of a plurality of buildings in a building group into a plurality of time steps, and obtain demand responses of the plurality of buildings in the time steps;
a scheduling optimization model construction unit 200, configured to establish a scheduling optimization model for each of the time steps according to the demand responses of the plurality of buildings;
a 0-1 planning model construction unit 300, configured to establish a 0-1 planning model corresponding to the scheduling optimization model, and determine a first constraint condition of the 0-1 planning model;
the control policy determining unit 400 is configured to calculate an optimal solution of the 0-1 planning model according to the first constraint condition, and determine a control policy of building group demand response optimization scheduling according to the optimal solution.
In a specific embodiment, the 0-1 planning model construction unit 300 is further configured to:
determining an objective function and a second constraint condition in the current time step, and solving the objective function by using the second constraint condition;
determining a control scheme of building group demand response optimization scheduling in the current time step according to a solving result, and acquiring a building group demand response after executing the control scheme;
and determining an objective function and a second constraint condition in the next time step according to the building group demand response after the control scheme is executed until the first constraint condition of the 0-1 planning model in all the time steps is determined.
In a specific embodiment, the control strategy determining unit 400 is further configured to calculate an optimal solution of the 0-1 planning model according to a branch-and-bound method using the first constraint condition.
In a particular embodiment, the control strategy includes an indirect shutdown strategy, a shut down partial chiller strategy, and a reset chilled water temperature strategy.
The building group demand response optimizing and scheduling device provided by the embodiment of the invention is used for executing the building group demand response optimizing and scheduling method according to any one of the embodiments. According to the embodiment of the invention, the optimal scheduling problem of the building group in the demand response is extracted to be a 0-1 planning problem, and the correlation of the scheduling demands of all the buildings in the building group is considered, so that the established optimal scheduling strategy realizes a stable reduction target, and the control effect on the demand response of the building group is improved.
Referring to fig. 5, an embodiment of the present invention provides a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the building group demand response optimization scheduling method as described above.
The processor is used for controlling the overall operation of the terminal equipment to complete all or part of the steps of the building group demand response optimization scheduling method. The memory is used to store various types of data to support operation at the terminal device, which may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk or optical disk.
In an exemplary embodiment, the terminal device may be implemented by one or more application specific integrated circuits (Application Specific 1ntegrated Circuit, abbreviated AS 1C), digital signal processors (Digital Signal Processor, abbreviated DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated DSPD), programmable logic devices (Programmable Logic Device, abbreviated PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated FPGA), controllers, microcontrollers, microprocessors, or other electronic components for executing the building group demand response optimization scheduling method according to any of the above embodiments, and achieving technical effects consistent with the above method.
In another exemplary embodiment, there is also provided a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the building group demand response optimizing scheduling method as described in any one of the embodiments above. For example, the computer readable storage medium may be the above memory including program instructions executable by a processor of the terminal device to perform the building group demand response optimizing scheduling method according to any one of the above embodiments, and achieve technical effects consistent with the above methods.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (10)
1. The building group demand response optimization scheduling method is characterized by comprising the following steps of:
dividing the total response time period of a plurality of buildings in a building group into a plurality of time steps, and acquiring the demand response of the plurality of buildings in the time steps;
establishing a scheduling optimization model of each time step according to the demand response of the plurality of buildings;
establishing a 0-1 planning model corresponding to the scheduling optimization model, and determining a first constraint condition of the 0-1 planning model;
calculating an optimal solution of the 0-1 planning model by using the first constraint condition, and determining a control strategy of building group demand response optimization scheduling according to the optimal solution;
wherein the 0-1 programming model is expressed as:
the constraint conditions are as follows:
wherein f m (x) Representing an objective function of the 0-1 programming model for the mth time step;and->Respectively represent the ith corresponding to the A-th building in the mth time step A Load reduction curves and corresponding coefficients of the individual strategies; n is n A Representing the total number of strategies corresponding to the A-th building in the m-th time step; />And->Representing the ith corresponding to the ith building in the mth time step B Load reduction curves and corresponding coefficients of the individual strategies; n is n B Representing the total number of strategies corresponding to the B-th building in the m-th time step; />And->Representing the ith corresponding to the nth building in the mth time step N Load reduction curves and corresponding coefficients of the individual strategies; n is n N Representing the total number of strategies corresponding to the Nth building in the mth time step; ΔP M, Representing the load shedding goal of the entire building group in the mth time step.
2. The building group demand response optimization scheduling method of claim 1, wherein the determining the first constraint of the 0-1 planning model comprises:
determining an objective function and a second constraint condition in the current time step, and solving the objective function by using the second constraint condition;
determining a control scheme of building group demand response optimization scheduling in the current time step according to a solving result, and acquiring a building group demand response after executing the control scheme;
and determining an objective function and a second constraint condition in the next time step according to the building group demand response after the control scheme is executed until the first constraint condition of the 0-1 planning model in all the time steps is determined.
3. The building group demand response optimization scheduling method of claim 1, wherein the calculating the optimal solution of the 0-1 planning model using the first constraint condition comprises:
and calculating an optimal solution of the 0-1 planning model according to a branch-and-bound method by using the first constraint condition.
4. The building group demand response optimization scheduling method of claim 1, wherein the control strategy comprises an indirect shutdown strategy, a shutdown partial chiller strategy, and a reset chilled water temperature strategy.
5. A building group demand response optimizing and scheduling device, comprising:
the response time interval dividing unit is used for dividing the total response time interval of a plurality of buildings in the building group into a plurality of time steps and acquiring the demand response of the plurality of buildings in the time steps;
the scheduling optimization model building unit is used for building a scheduling optimization model of each time step according to the demand response of the plurality of buildings;
the 0-1 planning model construction unit is used for establishing a 0-1 planning model corresponding to the scheduling optimization model and determining a first constraint condition of the 0-1 planning model;
the control strategy determining unit is used for calculating an optimal solution of the 0-1 planning model by utilizing the first constraint condition and determining a control strategy of building group demand response optimization scheduling according to the optimal solution;
wherein the 0-1 programming model is expressed as:
the constraint conditions are as follows:
wherein f m (x) Representing an objective function of the 0-1 programming model for the mth time step;and->Respectively represent the ith corresponding to the A-th building in the mth time step A Load reduction curves and corresponding coefficients of the individual strategies; n is n A Representing the total number of strategies corresponding to the A-th building in the m-th time step; />And->Representing the ith corresponding to the ith building in the mth time step B Load reduction curves and corresponding coefficients of the individual strategies; n is n B Representing the total number of strategies corresponding to the B-th building in the m-th time step; />And->Representing the ith corresponding to the nth building in the mth time step N Load reduction curves and corresponding coefficients of the individual strategies; n is n N Representing the total number of strategies corresponding to the Nth building in the mth time step; ΔP m,goal Representing the load shedding goal of the entire building group in the mth time step.
6. The building group demand response optimizing scheduling apparatus according to claim 5, wherein the 0-1 planning model construction unit is further configured to:
determining an objective function and a second constraint condition in the current time step, and solving the objective function by using the second constraint condition;
determining a control scheme of building group demand response optimization scheduling in the current time step according to a solving result, and acquiring a building group demand response after executing the control scheme;
and determining an objective function and a second constraint condition in the next time step according to the building group demand response after the control scheme is executed until the first constraint condition of the 0-1 planning model in all the time steps is determined.
7. The building group demand response optimizing scheduling apparatus according to claim 5, wherein the control policy determining unit is further configured to calculate an optimal solution of the 0-1 planning model using the first constraint condition according to a branch-and-bound method.
8. The building group demand response optimizing and scheduling device of claim 5, wherein the control strategy comprises an indirect shutdown strategy, a shut down partial chiller strategy, and a reset chilled water temperature strategy.
9. A terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the building group demand response optimization scheduling method of any one of claims 1-4.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the building group demand response optimizing scheduling method of any one of claims 1-4.
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