A demand response optimization scheduling method and system

Abstract

The present invention discloses a demand response optimization scheduling method and system, comprising: obtaining the grid's incentive level for users to participate in demand response based on a pre-constructed user behavior model and the minimum threshold of all users' comprehensive willingness to participate at each moment; optimizing the grid's demand response scheduling cost minimization based on the grid's incentive level for users to participate in demand response and the predicted total capacity of demand response scheduling load at each moment, and obtaining a load resource scheduling method when the cost is minimized; the user behavior model is constructed based on the relationship between the probability of users participating in demand response and the grid's incentive level. The present invention takes into account the uncertainty of users' participation in demand response and achieves the purpose of optimizing grid operating costs based on the relationship between the probability of users participating in demand response and incentives.

Description

Technical Field

[0001] The present invention relates to the technical field of power system demand side management, and in particular to a demand response optimization scheduling method and system. Background Art

[0002] As a key component of the many links in the operation of the source-grid-load interactive system, demand response carries the important responsibility of demand-side management. Especially during daily peak power consumption, how the dispatch center considers integrating the load resources of demand-side power users, backup rotating generators, and distributed power sources to achieve load balancing has always been a key issue in current power system planning. The controllable load of power users, especially industrial users, will participate in the power grid's demand response as a high-quality resource. The main reasons are: first, their large controllable capacity has a significant effect on peak load shaving and valley filling for the power grid; second, their load usage is relatively stable and easy to predict over a long period of time.

[0003] Numerous scholars and engineering teams have proposed numerous insights into resource integration and integrated planning and scheduling during demand response implementation. These approaches include considering network losses, voltage and frequency stability, and the most economical resource scheduling. However, it is clear that these studies and engineering applications assume that demand-side resource owners, namely, electricity users, will actively participate in demand response scheduling. However, in reality, not all electricity users actively respond to the grid's demand response scheduling requirements. Faced with varying grid incentives, electricity users exhibit varying degrees of response probability, which reflects the uncertainty of their willingness to participate. To offset this uncertainty, dispatch centers have traditionally adopted a one-size-fits-all approach, directly requiring electricity users to participate in demand response through high incentive subsidies, contracts, or administrative mandates. The inventors have discovered that this approach results in high incentive subsidies for electricity users, thereby increasing the economic cost of demand response scheduling. Furthermore, the use of administrative mandates can degrade the user experience, which does not meet current requirements for improving user experience. Summary of the Invention

[0004] In order to solve the above-mentioned deficiencies in the prior art, the present invention provides a demand response optimization scheduling method, comprising:

[0005] Based on the pre-built user behavior model and the minimum threshold of all users' comprehensive participation willingness at each moment, the grid's incentive level for users to participate in demand response is obtained;

[0006] Based on the incentive level of the power grid for users to participate in demand response and the total predicted capacity of demand response dispatch load at each moment, optimizing the minimum cost of power grid dispatch demand response to obtain the load resource dispatch method with the minimum cost;

[0007] The user behavior model is constructed based on the relationship between the probability of users participating in demand response and the grid incentive level.

[0008] Preferably, the construction of the user behavior model includes:

[0009] The user participation probability under different historical incentive levels is fitted to obtain the incentive dead zone critical point and actual saturation incentive level under the ideal response probability;

[0010] Based on the critical point of the excitation dead zone under the ideal response probability and the actual saturation excitation level, an ideal response probability curve of the power grid to the user is constructed;

[0011] Based on the real-time collected user participation in demand response at different incentive levels, the incentive dead zone critical point and the actual saturation incentive level under the actual response probability are determined;

[0012] Constructing an actual response probability curve of the user to the power grid during operation based on the excitation dead zone critical point under the actual response probability and the actual saturation excitation level;

[0013] Based on the predicted ideal response probability curve of the power grid to the user and the actual response probability curve of the user to the power grid during operation, the relationship between the probability of the user participating in the demand response and the power grid incentive level is determined.

[0014] Preferably, obtaining the grid's incentive level for users to participate in demand response based on a pre-built user behavior model and a minimum threshold of all users' comprehensive participation willingness at each moment includes:

[0015] determining the uncertainty of each user's participation in demand response under the same incentive level based on the user behavior model;

[0016] Determining the probability of users as a whole participating in demand response based on the uncertainty of each user's participation in demand response under the same incentive level;

[0017] Based on the relationship between the probability of the users as a whole participating in demand response and the minimum threshold of the comprehensive participation willingness of all users, the incentive level of the power grid for users to participate in demand response is obtained.

[0018] Preferably, the incentive level of the power grid for users to participate in demand response is calculated as follows:

[0019]

[0020] Where: Γ is the probability of users participating in demand response as a whole; K is the user with the potential to participate in demand response; μ u,k,t is the state of user k participating in demand response at time t; Γ k,1 For the same excitation level λ u,k,t The uncertainty of each user's participation in demand response is as follows; Fset is the confidence threshold with the lowest probability of users within the jurisdiction participating in demand response;

[0021] Among them, the same excitation λ u,k,t The uncertainty of each user's participation in demand response is Γ k,1 , calculated as follows:

[0022]

[0023] Where: u,k,t is the incentive level of the power grid for users to participate in demand response; f(λ u,k,t ) at the excitation level λ u,k,t When , the probability of users participating in demand response under ideal response; g(λ u,k,t ) at the excitation level λ u,k,t When , the probability of users participating in demand response under actual response; λ 2,k For the same probability of participating in demand response, the incentive level corresponding to the ideal response; 3,k For the same probability of participating in demand response, the incentive level corresponding to the actual response.

[0024] Preferably, the excitation level is λ u,k,t When the user participates in the demand response under the ideal response, the probability f(λ u,k,t ), calculated as follows:

[0025]

[0026] Where: 0,k is the critical point of the excitation dead zone under the ideal response probability; 4,k is the actual saturation incentive level;

[0027] The excitation level is λ u,k,t When the actual response is given, the probability g(λ u,k,t ), calculated as follows:

[0028]

[0029] Where: 1,k is the critical point of the excitation dead zone under the actual response probability; 4,k is the actual saturation incentive level.

[0030] Preferably, the method of optimizing the cost of demand response dispatching by the power grid based on the incentive level of the power grid for users to participate in demand response and the total predicted capacity of demand response dispatching load at each moment to obtain the load resource dispatching mode with the minimum cost includes:

[0031] Determining the total incentive subsidy cost of the power grid to users participating in demand response based on the incentive level of the power grid for users participating in demand response, the users participating in demand response, and the load response amount provided by the users participating in demand response;

[0032] Determining the cost of all standby rotating generator sets participating in demand response based on the grid subsidy for connecting each standby rotating generator set to the grid, the standby rotating generator sets participating in demand response, and the power generation capacity provided by the standby rotating generator sets participating in demand response;

[0033] Determining the cost of all distributed power sources participating in demand response based on the grid's subsidy for connecting distributed power sources to the grid, the distributed power sources participating in demand response, and the output provided by the distributed power sources participating in demand response;

[0034] Optimizing the sum of the total incentive subsidy cost of the power grid to users participating in demand response, the cost of all standby rotating generator sets participating in demand response, and the cost of all distributed power sources participating in demand response to minimize the cost, and obtaining a load resource scheduling method with minimum cost;

[0035] The total demand response dispatch load capacity includes: the load response amount provided by all users participating in the demand response, the power generation capacity provided by all standby rotating generator sets participating in the demand response, and the output provided by all distributed power sources participating in the demand response.

[0036] Preferably, the total incentive subsidy cost of the power grid to users participating in demand response is calculated as follows:

[0037]

[0038] Where: F1 is the total incentive subsidy cost of the power grid to users who participate in demand response; K is the total number of users with potential to participate in demand response within the jurisdiction of the power grid; λ u,k,t is the incentive level for user k to participate in demand response at time t; μ u,k,t is the state of user k participating in demand response at time t; T is the duration of each user's participation; The load response provided to user k at time t.

[0039] Preferably, the cost of all the standby rotating generator sets participating in demand response is calculated as follows:

[0040]

[0041] Where: F2 is the cost of all standby rotating generators participating in demand response; T G,E The cut-off time for the standby rotating generator set to generate electricity; T G,S is the start time of power generation of the standby rotating generator set; L is the number of standby rotating generator sets; μ G,l,t is the grid-connected state of standby generator set l at time t; a×O G,l,t 2 +b×O G,l,t +c is the power generation cost of the standby rotating generator set, O G,l,t The power generation capacity provided by the standby rotating generator set l at time t; G,l,t It is the subsidy paid by the power grid to the grid-connected distributed power sources.

[0042] Preferably, the cost of all distributed power sources participating in demand response is calculated as follows:

[0043]

[0044] Where: F3 is the cost of all distributed power sources participating in demand response; T E is the power generation cut-off time of the distributed power source; T S is the start time of power generation of distributed power source; μ w,t is the grid-connected state of the distributed power source w at time t; w,t The subsidy for grid connection of distributed power generation unit electricity; w,t is the output provided by the distributed power source w at time t.

[0045] Preferably, the load resource scheduling method for minimizing the cost includes:

[0046] At each moment, the state of each user participating in demand response, the grid-connected power generation state of each standby rotating generator set and the power generation capacity of the standby rotating generator set, as well as the grid-connected state of each distributed power source and the output of the distributed power source.

[0047] Based on the same inventive concept, the present invention also provides a demand response optimization scheduling system, comprising:

[0048] The incentive level determination module is used to determine the grid's incentive level for users to participate in demand response based on a pre-built user behavior model and the minimum threshold of all users' comprehensive participation willingness at each moment;

[0049] An optimization calculation module is used to optimize the calculation of the minimum cost of the grid dispatching demand response based on the grid's incentive level for users to participate in demand response and the total predicted capacity of the demand response dispatch load at each moment, and obtain a load resource dispatching method with the minimum cost;

[0050] The user behavior model is constructed based on the relationship between the probability of users participating in demand response and the grid incentive level.

[0051] Preferably, the system further includes a user behavior model building module, specifically for:

[0052] The user participation probability under different historical incentive levels is fitted to obtain the incentive dead zone critical point and actual saturation incentive level under the ideal response probability;

[0053] Based on the critical point of the excitation dead zone under the ideal response probability and the actual saturation excitation level, an ideal response probability curve of the power grid to the user is constructed;

[0054] Based on the real-time collected user participation in demand response at different incentive levels, the incentive dead zone critical point and the actual saturation incentive level under the actual response probability are determined;

[0055] Constructing an actual response probability curve of the user to the power grid during operation based on the excitation dead zone critical point under the actual response probability and the actual saturation excitation level;

[0056] Based on the predicted ideal response probability curve of the power grid to the user and the actual response probability curve of the user to the power grid during operation, the relationship between the probability of the user participating in the demand response and the power grid incentive level is determined.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The technical solution provided by the present invention obtains the incentive level of the power grid for users participating in demand response based on a pre-built user behavior model and the minimum threshold of the comprehensive participation willingness of all users at each moment; based on the incentive level of the power grid for users participating in demand response and the total predicted capacity of the demand response scheduling load at each moment, the minimum cost of the power grid scheduling demand response is optimized and calculated to obtain the load resource scheduling method when the cost is minimized; the present invention constructs a user behavior model according to the relationship between the probability of users participating in demand response and the incentive level of the power grid, and uses the user behavior model to take the incentive level corresponding to the minimum threshold of the comprehensive participation willingness of all users as the basis for the power grid's incentive subsidy for users participating in demand response, thereby determining the uncertainty of users participating in demand response, reducing the economic cost of the power grid when performing demand response scheduling, and improving the electricity consumption experience of power users. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of a demand response optimization scheduling method in the present invention;

[0060] Figure 2 A detailed flow chart of a demand response optimization scheduling method in the implementation of the present invention;

[0061] Figure 3 The mathematical model between the user k participation rate and the grid incentive level is used as an example to illustrate the implementation of the present invention;

[0062] Figure 4 It is the logical relationship between various devices in the scheduling system in the implementation of the present invention. DETAILED DESCRIPTION

[0063] In order to better understand the present invention, the present invention is further described below with reference to the accompanying drawings and examples.

[0064] Example 1: Figure 1 As shown, the present invention provides a demand response optimization scheduling method, comprising:

[0065] Step 1: Based on the pre-built user behavior model and the minimum threshold of all users' comprehensive participation willingness at each moment, the grid's incentive level for users to participate in demand response is obtained;

[0066] Step 2: Based on the grid's incentive level for users to participate in demand response and the predicted total capacity of demand response dispatch load at each moment, optimizing the cost of grid dispatching demand response to obtain a load resource dispatching method with minimum cost;

[0067] The user behavior model is constructed based on the relationship between the probability of users participating in demand response and the grid incentive level.

[0068] In this embodiment, the user is an electricity user.

[0069] Figure 2 Shown Figure 1 The specific steps of the demand response optimization scheduling method shown are: 101: The power grid demand response server collects information from the power user's energy management system to predict the user behavior model. Based on the principles of consumer psychology, it is determined that different incentives provided by the power grid will have a fundamental impact on the probability of power users participating in DR. As a result, power user behavior will show a certain response and saturation range to the power grid incentive level.

[0070] by Figure 3 The mathematical model between the user k participation rate and the grid incentive level is shown as an example. Figure 3 The horizontal axis λ is shown in u,k,trepresents the incentive level ρ given by the power grid to user k at time t, and the vertical axis represents the probability y of user k participating in DR. u,k,t ) represents the ideal response probability curve of the power grid to user k. g(λ u,k,t ) represent the actual probability curves of user responses to the grid during operation. The first curve is obtained by fitting historical data before the grid company implements demand response, while the second curve is calculated based on real-time data collected by users when the grid company begins implementing demand response.

[0071] ρ k Characterizes the optimal participation probability of user k under linear programming, λ 0,k is the critical point of the incentive dead zone under the ideal response probability determined by the power grid company. Below this incentive level, users will not respond to the grid demand response request. Similarly, λ 1,k is the critical point of the excitation dead zone of the actual user's actual response; 2,k is the optimal participation probability ρ for user k k The ideal response incentive level expected by the power grid under 3,k is the optimal participation probability ρ for user k k The actual response of the grid under the desired incentive level, λ 4,k The actual saturation incentive for users when the response probability is 100%. It should be clarified that the incentive level is converted into the subsidy fee per unit load response (kW), and the probability of participating in DR is the probability of the user's willingness to participate.

[0072] It can be found that there is a response dead zone before the user responds to the grid DR request. The incentive level corresponding to the dead zone is [0,λ 1,k ], the user is in [0,λ 1,k ] interval to respond to the grid DR request is ρ = 0. When the grid incentive level λ>λ 1,k When , the probability of user response grid DR begins to be active. The incentive level is [λ 1,k ,λ 4,k ] interval, the probability of user response becomes larger. At this time, the probability of user actual response is y=g(λ u,k,t ) function, the probability of the grid expecting the user to respond is y = f(λ u,k,t ). In [λ 4,k ,∞] interval users’ participation probability no longer changes with the increase of the incentive level based on the grid.

[0073]

[0074]

[0075] User response probability curve g(λ u,k,t) and the probability function curve f(λ u,k,t ) has a certain deviation compared to the Figure 1 Medium 0,k to λ 2,k The light grey area between is the user’s response uncertainty due to the incentive level. u,k,t The uncertainty Γ of user k under k,1 Available 2,k to λ 4,k The dark grey area between them represents:

[0076]

[0077] When the incentive level reaches λ u,k,t =λ 4,k When the actual incentive saturation critical point is reached, the user response rate becomes ρ = 100%, and the user response and uncertainty no longer change. Assuming that there are K power industrial users with the potential to participate in DR in the power grid, if the power grid needs to dispatch power users to participate in demand response at time t, in order to ensure that the overall participation probability Γ is as large as possible, the optimization objective can be defined as:

[0078]

[0079] Where μ u,k,t μ represents the state of user k participating in demand response at time t, which is expressed in Boolean form. u,k,t The value is determined by ρ and ρ k Determine, when ρ>ρ k When the power grid determines that the user is in the confidence interval, the subsidy incentive level is λ u,k,t =g(λ),μ u,k,t =1 basically means that the user will participate in grid demand response. Equal to 0 means that the user will not participate. At this time, λ u,k,t Less than ρ k Fset is the confidence threshold for the lowest overall participation rate of the power grid company.

[0080] from Figure 4It can be seen that the main participants in the large-scale source-grid-load interaction include local generators, distributed power sources and industrial power users. Industrial power users, by participating in DR, combine local generators to absorb distributed power sources nearby, and ultimately ensure the goal of stable economic operation of the power grid. The power generation cost of local generators and the subsidy cost of power users participating in DR are minimized, and the load fluctuation of the power grid is minimized. Therefore, in addition to the formula (4) that represents the optimization of the subsidy cost of power users participating in DR, this application also needs to optimize the power generation cost of local generators, which needs to meet the constraints such as power balance, that is, the total capacity of the power grid's demand response dispatch load at each moment. Equal to the load capacity of electricity users participating in demand response Standby rotating generators participating in demand response and grid-connected wind power capacity

[0081]

[0082] Step 102: Determine the total incentive subsidy cost of the power grid to users who participate in demand response based on the information of power users who are potential participants in demand response. The information comes from the power user energy management system and includes the incentive dead zone critical point λ under the actual response probability. 1,k , the actual saturation excitation level λ 4,k , each user's participation time T, each user's load capacity that can participate in demand response regulation in each period and the base load capacity of a single user

[0083] It is stipulated that the distribution period of the historical time t of each user k (k∈[0,K]) participating in DR is T(t∈[T s ,T E ]).

[0084] The load capacity O of user k at each time point t in a day u,k,t Size:

[0085]

[0086] In formula (6) and They represent the basic load and controllable load of user k at time t. Includes interruptible, transferable and curtailable loads. Controllable loads are resources that can participate in DR configuration. Under normal circumstances, the basic load When the grid DR initiates a request, no changes or adjustments are made. This part of the load is used to meet the daily life or production needs of power industry users. This value is the minimum value of historical power load statistics.

[0087]

[0088] Once the power grid determines the best response probability ρ within the set confidence interval k , the corresponding incentive λ can be calculated u,k,t It can enable user k to participate in DR. At this time, it is determined that user k will follow the best-effort service principle. That is, when the power grid determines that the user response probability reaches a certain range during the linear programming process, it is basically confirmed that the power user will participate in DR, and the power user's load adjustment will meet the best-effort service principle (Best-Effort), and do its best to meet the grid configuration adjustment of its controllable load within a certain range. Finally, the total load capacity of the K power industrial users of the entire lower level of the power grid responding to the grid demand response at time t is It is expressed as follows:

[0089]

[0090] Therefore, based on the above analysis, the total incentive subsidy cost for users is:

[0091]

[0092] Where: F1 is the total incentive subsidy cost of the power grid to users participating in demand response; μ u,k,t μ represents the state of user k participating in DR at time t, which is expressed in Boolean form. u,k,t The value is determined by ρ k Determine, when ρ>ρ k When the power grid determines that the user is in the confidence interval, the subsidy incentive level is λ u,k,t =g(λ),μ u,k,t =1 basically means that the user will participate in grid demand response. Equal to 0 means that the user will not participate. At this time, λ u,k,t Less than the set incentive level. T is the duration of each user's participation; The load response provided by user k at time t; the load response provided by all users participating in demand response at time t Equal to the total load capacity of power industry users responding to grid demand at time t

[0093] Step 103: Determine the cost of all standby rotating generator sets participating in demand response based on the information of the standby rotating generator sets that can participate in demand response. The information comes from the standby rotating generator set monitoring module, and the information includes the number L of standby rotating generator sets, the power generation capacity O of each generator set per time period, and the number of standby rotating generator sets. G,l,t , power generation limit O G,max , the lower limit is O G,min , Minimum operating time of generator set Tr , power output change ΔO G,l And the operating cost coefficients a, b, c. Assume that the number of standby rotating generator sets is L, and when the generating capacity of each generator set l(l∈[0,L]) at time t is O G,l,t There are upper and lower limits, the upper limit is O G,max , and the lower limit is O G,min :

[0094] O G,min ≤Q G,l,t ≤O G,max (11)

[0095] The constraint relationship of the ramp rate of generator set 1 is:

[0096] -ΔO G,l ≤Q G,l,t -Q G,l,t-1 ≤+ΔO G,l (12)

[0097] Where ΔO G,l The power output of generator set l can be increased or decreased within a fixed time period. Considering the economic and stability issues, there is a certain power generation time limit for the generator set. That is, once the grid clearly activates the standby rotating generator set, its startup time T in the source-grid-load interaction process is G,s To downtime T G,E The time length ΔT between them should satisfy the following relationship (12).

[0098] |T G,s -T G,E |=ΔT≥T r (13)

[0099] At this time, the duration of the rotating standby generator set ΔT must be greater than or equal to the specified minimum operating time T of the generator set. r The load capacity of standby rotating generators participating in demand response for:

[0100]

[0101] The cost of participating in DR is as follows: It is equal to the difference between the power generation cost and the grid connection profit;

[0102]

[0103] Where μ G,l,t Indicates whether the standby generator set l generates power and is connected to the grid at time t. By default, it participates in demand response when generating power and connecting to the grid. G,l,t =1 means participation, otherwise not participation. G,l,t2 +b×O G,l,t +c is equal to the cost of power generation, O G,l,t The power generation capacity provided by the standby rotating generator set l at time t; the power generation capacity provided by all standby rotating generator sets at time t is equal to the load capacity of the standby rotating generator sets participating in demand response λ G,l,t It is the subsidy paid by the power grid to the grid-connected distributed power sources, that is, the on-grid electricity price per unit of electricity.

[0104] Step 104: Determine the cost of all distributed power sources participating in demand response based on the distributed power source information participating in demand response. The information comes from the distributed power source energy management system, and the information includes the maximum output. w,max Distributed power sources are used to assist the power generation and distribution systems of power grid companies in smoothing out fluctuations in peak and valley loads.

[0105] The output of distributed generation at time t is O w,t Less than or equal to the maximum output forecast value of distributed power generation O w,max .

[0106] 0≤Q w,t ≤O w,max (16)

[0107] Load capacity participating in demand response when distributed generation is connected to the grid for:

[0108]

[0109] Where: T E is the power generation cut-off time of the distributed power source; T S is the start time of power generation of the distributed power source;

[0110] Ultimately, the cost of distributed generation in participating in DR is equal to its grid connection cost:

[0111]

[0112] Where μ w,t Represents the state of the distributed power grid connection, and its value is Boolean. When it is equal to 1, it means that the distributed power grid is connected, otherwise it is not connected. w,t Indicates the grid connection subsidy cost per unit of distributed power generation electricity; O w,t The output provided by the distributed power source w at time t is equal to the load capacity participating in the demand response when the distributed power source is connected to the grid.

[0113] Step 105: The power grid determines the final optimization objective function:

[0114] min{F1+F2+F3} (19)

[0115] The final optimization objective function is to minimize the sum of the total incentive cost F1 of the power user, the cost F2 of the standby rotating generator set participating in DR, and the cost F3 of the distributed power generation. The parameters to be determined include the state μ of each user κ participating in DR at time τ u,k,t , subsidy incentive level λ u,k,t , participation probability ρ, generating capacity O G,l,t , power generation grid-connected status μ G,l,t , wind power output at time t O w,t , wind power grid connection status μ w,t The value of ρ is determined by f(μ u,k,t ) is calculated, and after solving the above parameters, the information is sent to the power user energy management system, the standby rotating generator set monitoring module, and the distributed power supply energy management system. Other parameters not described in the embodiments of the present invention are defaulted to known constants.

[0116] Any method that can obtain the objective function result can be selected, such as a mixed integer nonlinear programming method.

[0117] In the technical solution provided by the embodiment of the present invention, the relationship between the probability of user participation in demand response and the grid incentive level is first established. Then, the grid sets the minimum probability of user participation in demand response by predicting the total capacity of the demand response dispatch load at each moment, and then determines the incentive level. This is completely different from determining the incentive price based on the historical user response load and the incentive level. The latter has nothing to do with the user participation rate or willingness to participate, and is basically determined by the correspondence between the incentive price and the user response load in the historical data. For example, if the grid company gives the maximum subsidy price A, the maximum response load of the user is obtained through the historical load data of the grid and is defined as max{D t During the scheduling process, if the user response load is D t , then the subsidy price that the user can get is A×D t / max{D t Therefore, compared with determining the incentive price only by the maximum response load of historical users, the technical solution provided by this embodiment reduces the subsidy cost of the power grid to users participating in demand response while ensuring that the total capacity of the demand response scheduling load forecast at each moment is met, and determines the user's uncertain participation willingness.

[0118] In addition, compared with the existing application entitled "User-side Optimization Control Method Considering Uncertainty of Demand Response", the technical solution provided by the embodiment of the present invention determines the subsidy price through the participation rate. However, the relationship between the participation rate and the subsidy price in the "User-side Optimization Control Method Considering Uncertainty of Demand Response" is obtained through power grid prediction, and the predicted value is directly used for demand response scheduling without any correction. The technical solution provided by the embodiment of the present application uses the predicted value f and the actual observation value g as the basis for participation to perform uncertain demand response scheduling, and corrects the predicted value through the actual observation value, thereby improving the accuracy of the relationship between the participation rate and the subsidy price.

[0119] Example 2: The present invention also provides an optional embodiment to specifically explain the implementation of the above technical solution. The optional embodiment implements an uncertainty demand response optimization scheduling method that considers user participation willingness through a power grid demand response server, a power user energy management system, a standby rotating generator set monitoring module, and a distributed power supply energy management system, specifically including:

[0120] The grid demand response server uses a short-term load forecasting method to determine the time of peak power consumption in the future period and the peak load power consumption in the corresponding period, and determines the behavior model of the power users under its jurisdiction who may potentially participate in demand response; and

[0121] The power user energy management system reports load information to the grid demand response server; and

[0122] The standby rotating generator set monitoring module sends information to the grid demand response server; and

[0123] The distributed generation energy management system sends information to the grid demand response server; and

[0124] The grid demand response server integrates the information of all participating demand response entities to perform mixed integer nonlinear programming to solve the optimal load resource scheduling method.

[0125] In an embodiment, the electricity user behavior model reflects the relationship between the probability of users participating in demand response and the incentive level. The model includes:

[0126] The ideal response probability curve f(λ u,k,t ) and the actual response probability curve g(λ u,k,t );

[0127] In the embodiment, the ideal response probability curve f(λ u,k,t ) Information includes:

[0128] The critical point λ of the excitation dead zone under the ideal response probability0,k , the actual saturation excitation level λ 4,k .

[0129] In the embodiment, the actual response probability curve g(λ u,k,t ) Information includes:

[0130] The critical point λ of the excitation dead zone under the actual response probability 1,k , the actual saturation excitation level λ 4,k .

[0131] In an embodiment, the load information reported by the power user energy management system includes:

[0132] The critical point λ of the excitation dead zone under the actual response probability 1,k , the actual saturation excitation level λ 4,k , each user's participation time T, each user's load capacity that can participate in demand response regulation in each period and the base load capacity of a single user

[0133] In an embodiment, the information sent by the standby rotating generator set monitoring module includes:

[0134] The number of standby rotating generator sets L, the generating capacity of each generator set per period O G,l,t , power generation limit O G,max , the lower limit is O G,min , Minimum operating time of generator set T r , power output change ΔO G,l and operating cost coefficients a, b, and c.

[0135] In an embodiment, the information sent by the distributed power supply energy management system includes:

[0136] Maximum output of distributed power generation O w,max .

[0137] In an embodiment, the power grid demand response server integrates information of all entities participating in the demand response and its own existing information to perform mixed integer nonlinear programming to obtain information on scheduling of different entities participating in the demand response under the optimal load resource scheduling target;

[0138] In an embodiment, the existing information of the power grid itself includes:

[0139] The critical point λ of the excitation dead zone under the ideal response probability 0,k , the actual saturation excitation level λ 4,k , the number of potential power users K participating in demand response under the power grid company, the total demand response dispatching load capacity of the power grid at each moment The grid company sets the confidence value F with the lowest probability of overall participation set , subsidy cost for each standby rotating generator set λ w,t , subsidy costs for distributed power grid connection.

[0140] In an embodiment, the scheduling objectives obtained by the mixed integer nonlinear programming include:

[0141] The dispatch information of different entities participating in demand response is obtained by minimizing the sum of the total incentive cost F1 of power users, the cost F2 of standby rotating generator sets participating in DR, and the cost F3 of distributed power sources.

[0142] In an embodiment, the scheduling information of different entities participating in demand response is parameters obtained by mixed integer nonlinear programming, and the parameters include:

[0143] The state μ of each power user k participating in DR at time t u,k,t , subsidy incentive level λ υ，κ，τ , participation probability ρ, generating capacity O G,l,t , power generation grid-connected status μ G,l,t , wind power output at time τ O w,t , wind power grid connection status μ w,t .

[0144] Example 3: Based on the same inventive concept, an embodiment of the present invention further provides a demand response optimization scheduling system, including:

[0145] The incentive level determination module is used to determine the grid's incentive level for users to participate in demand response based on a pre-built user behavior model and the minimum threshold of all users' comprehensive participation willingness at each moment;

[0146] An optimization calculation module is used to optimize the calculation of the minimum cost of the grid dispatching demand response based on the grid's incentive level for users to participate in demand response and the total predicted capacity of the demand response dispatch load at each moment, and obtain a load resource dispatching method with the minimum cost;

[0147] The user behavior model is constructed based on the relationship between the probability of users participating in demand response and the grid incentive level.

[0148] In an embodiment, the system further includes a user behavior model building module, specifically for:

[0149] The participation probability of power users under different historical incentive levels is fitted to obtain the incentive dead zone critical point and actual saturation incentive level under the ideal response probability;

[0150] Based on the critical point of the excitation dead zone under the ideal response probability and the actual saturation excitation level, an ideal response probability curve of the power grid to the user is constructed;

[0151] Based on the real-time data collected on the status of power users participating in demand response under different incentive levels, the critical point of the incentive dead zone and the actual saturation incentive level under the actual response probability are determined;

[0152] Constructing an actual response probability curve of the user to the power grid during operation based on the excitation dead zone critical point under the actual response probability and the actual saturation excitation level;

[0153] Based on the predicted ideal response probability curve of the power grid to the power user and the actual response probability curve of the user to the power grid during operation, the relationship between the probability of the user participating in the demand response and the incentive level of the power grid is determined.

[0154] In an embodiment, the module for determining the incentive level includes:

[0155] an uncertainty determination submodule, configured to determine the uncertainty of each power user's participation in demand response under the same incentive level based on the user behavior model;

[0156] a demand response participation probability submodule, configured to determine the probability of the power users as a whole participating in the demand response based on the uncertainty of each power user's participation in the demand response under the same incentive level;

[0157] The incentive level determination submodule is used to obtain the grid's incentive level for users to participate in demand response based on the relationship between the probability of the power users as a whole participating in demand response and the minimum threshold of the comprehensive participation willingness of all power users.

[0158] In the embodiment, the incentive level of the power grid for users to participate in demand response is calculated as follows:

[0159]

[0160] Where: Γ is the probability of electricity users participating in demand response as a whole; K is the user with the potential to participate in demand response; μ u,k,t is the state of electricity user k participating in demand response at time t; Γ k,1 For the same excitation level λ u,k,t The uncertainty of each power user's participation in demand response is given below; Fset is the confidence threshold with the lowest probability of power users within the jurisdiction participating in demand response;

[0161] Among them, the same excitation λ u,k,t The uncertainty of each power user's participation in demand response is Γ k,1 , calculated as follows:

[0162]

[0163] Where:u,k,t is the incentive level of the power grid for users to participate in demand response; f(λ u,kt ) at the excitation level λ u,k,t When , the probability of power users participating in demand response under ideal response; g(λ u,k,t ) at the excitation level λ u,k,t When , the probability of power users participating in demand response under actual response; λ 2,k For the same probability of participating in demand response, the incentive level corresponding to the ideal response; 3,k For the same probability of participating in demand response, the incentive level corresponding to the actual response.

[0164] In the embodiment, the excitation level is λ u,k,t When the ideal response is given, the probability f(λ u,k,t ), calculated as follows:

[0165]

[0166] Where: 0,k is the critical point of the excitation dead zone under the ideal response probability; 4,k is the actual saturation incentive level;

[0167] The excitation level is λ u,k,t When the actual response is u,k,t ), calculated as follows:

[0168]

[0169] Where: 1,k is the critical point of the excitation dead zone under the actual response probability; 4,k is the actual saturation incentive level.

[0170] In the embodiment, the optimization calculation module is specifically used to:

[0171] Determining the total incentive subsidy cost of the power grid to users participating in demand response based on the incentive level of the power grid for users participating in demand response, the users participating in demand response, and the load response amount provided by the users participating in demand response;

[0172] Determining the cost of all standby rotating generator sets participating in demand response based on the grid subsidy for connecting each standby rotating generator set to the grid, the standby rotating generator sets participating in demand response, and the power generation capacity provided by the standby rotating generator sets participating in demand response;

[0173] Determining the cost of all distributed power sources participating in demand response based on the grid's subsidy for connecting distributed power sources to the grid, the distributed power sources participating in demand response, and the output provided by the distributed power sources participating in demand response;

[0174] Optimizing the sum of the total incentive subsidy cost of the power grid to users participating in demand response, the cost of all standby rotating generator sets participating in demand response, and the cost of all distributed power sources participating in demand response to minimize the cost, and obtaining a load resource scheduling method with minimum cost;

[0175] The total demand response dispatch load capacity includes: the load response amount provided by all users participating in the demand response, the power generation capacity provided by all standby rotating generator sets participating in the demand response, and the output provided by all distributed power sources participating in the demand response.

[0176] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0177] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0178] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0179] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0180] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A demand response optimization scheduling method, characterized in that: include: Based on the pre-built user behavior model and the minimum threshold of all users' comprehensive participation willingness at each moment, the grid's incentive level for users to participate in demand response is obtained; Based on the incentive level of the power grid for users to participate in demand response and the total predicted capacity of demand response dispatch load at each moment, optimizing the minimum cost of power grid dispatch demand response to obtain the load resource dispatch method with the minimum cost; The user behavior model is constructed based on the relationship between the probability of users participating in demand response and the grid incentive level; The method of optimizing the minimum cost of demand response dispatching by the power grid based on the incentive level of the power grid for users to participate in demand response and the total predicted capacity of demand response dispatching load at each moment to obtain the load resource dispatching mode with the minimum cost includes: Determining the total incentive subsidy cost of the power grid to users participating in demand response based on the incentive level of the power grid for users participating in demand response, the users participating in demand response, and the load response amount provided by the users participating in demand response; Determining the cost of all standby rotating generator sets participating in demand response based on the grid subsidy for connecting each standby rotating generator set to the grid, the standby rotating generator sets participating in demand response, and the power generation capacity provided by the standby rotating generator sets participating in demand response; Determining the cost of all distributed power sources participating in demand response based on the grid's subsidy for connecting distributed power sources to the grid, the distributed power sources participating in demand response, and the output provided by the distributed power sources participating in demand response; Optimizing the sum of the total incentive subsidy cost of the power grid to users participating in demand response, the cost of all standby rotating generator sets participating in demand response, and the cost of all distributed power sources participating in demand response to minimize the cost, and obtaining a load resource scheduling method with minimum cost; The total load forecast capacity of the demand response dispatch includes: the load response amount provided by all users participating in the demand response, the power generation capacity provided by all standby rotating generator sets participating in the demand response, and the output provided by all distributed power sources participating in the demand response; The total incentive subsidy cost of the power grid to users participating in demand response is calculated as follows: Where: F1 is the total incentive subsidy cost of the power grid to users who participate in demand response; K is the total number of users with potential to participate in demand response within the jurisdiction of the power grid; l u,k,t is the incentive level of user k to participate in demand response at time t; m u,k,t is the state of user k participating in demand response at time t; T is the duration of each user's participation; The load response provided to user k at time t; The cost of all the standby rotating generators participating in demand response is calculated as follows: Where: F2 is the cost of all standby rotating generators participating in demand response; T G,E The cut-off time for the standby rotating generator set to generate electricity; T G,S is the start time of power generation of the standby rotating generator set; L is the number of standby rotating generator sets; m G,l,t is the grid-connected state of standby generator set l at time t; a×O G,l,t 2 +b×O G,l,t +c is the power generation cost of the standby rotating generator set, O G,l,t The power generation capacity provided by the standby rotating generator set l at time t; l G,l,t Subsidy for the grid to connect distributed power sources; The cost of all distributed power sources participating in demand response is calculated as follows: Where: F3 is the cost of all distributed power sources participating in demand response; T E is the power generation cut-off time of the distributed power source; T S is the start time of power generation of distributed power source; m w,t is the grid-connected state of distributed power source w at time t; l w,t The subsidy for grid connection of distributed power generation unit electricity; w,t is the output provided by the distributed power source w at time t.

2. The method according to claim 1, wherein The construction of the user behavior model includes: The user participation probability under different historical incentive levels is fitted to obtain the incentive dead zone critical point and actual saturation incentive level under the ideal response probability; Based on the critical point of the excitation dead zone under the ideal response probability and the actual saturation excitation level, an ideal response probability curve of the power grid to the user is constructed; Based on the real-time collected user participation in demand response at different incentive levels, the incentive dead zone critical point and the actual saturation incentive level under the actual response probability are determined; Constructing an actual response probability curve of the user to the power grid during operation based on the excitation dead zone critical point under the actual response probability and the actual saturation excitation level; Based on the predicted ideal response probability curve of the power grid to the user and the actual response probability curve of the user to the power grid during operation, the relationship between the probability of the user participating in the demand response and the power grid incentive level is determined.

3. The method according to claim 1, wherein The incentive level of the power grid for users to participate in demand response is obtained based on the pre-built user behavior model and the minimum threshold of the comprehensive participation willingness of all users at each moment, including: determining the uncertainty of each user's participation in demand response under the same incentive level based on the user behavior model; Determining the probability of users as a whole participating in demand response based on the uncertainty of each user's participation in demand response under the same incentive level; Based on the relationship between the probability of the users as a whole participating in demand response and the minimum threshold of the comprehensive participation willingness of all users, the incentive level of the power grid for users to participate in demand response is obtained.

4. The method according to claim 3, wherein The incentive level of the power grid for users to participate in demand response is calculated as follows: Where: Γ is the probability of users participating in demand response as a whole; K is the user with the potential to participate in demand response; μ u,k,t is the state of user k participating in demand response at time t; Γ k,1 For the same excitation level λ u,k,t The uncertainty of each user's participation in demand response is as follows; Fset is the confidence threshold with the lowest probability of users within the jurisdiction participating in demand response; Among them, the same excitation λ u,k,t The uncertainty of each user's participation in demand response is Γ k,1 , calculated as follows: Where: u,k,t is the incentive level of the power grid for users to participate in demand response; f(λ u,k,t ) at the excitation level λ u,k,t When , the probability of users participating in demand response under ideal response; g(λ u,k,t ) at the excitation level λ u,k,t When , the probability of users participating in demand response under actual response; λ 2,k For the same probability of participating in demand response, the incentive level corresponding to the ideal response; 3,k For the same probability of participating in demand response, the incentive level corresponding to the actual response.

5. The method according to claim 4, wherein The excitation level is λ u,k,t When the user participates in the demand response under the ideal response, the probability f(λ u,k,t ), calculated as follows: Where: l 0,k is the critical point of the excitation dead zone under the ideal response probability; l 4,k is the actual saturation incentive level; The excitation level is λ u,k,t When the actual response is given, the probability g(λ u,k,t ), calculated as follows: Where: l 1,k is the critical point of the excitation dead zone under the actual response probability; l 4,k is the actual saturation incentive level.

6. The method according to claim 1, wherein The load resource scheduling method when the cost is minimized includes: At each moment, the state of each user participating in demand response, the grid-connected power generation state of each standby rotating generator set and the power generation capacity of the standby rotating generator set, as well as the grid-connected state of each distributed power source and the output of the distributed power source.

7. A system for implementing the demand response optimization scheduling method according to claim 1, characterized in that: The system comprises: The incentive level determination module is used to determine the grid's incentive level for users to participate in demand response based on a pre-built user behavior model and the minimum threshold of all users' comprehensive participation willingness at each moment; An optimization calculation module is used to optimize the calculation of the minimum cost of the grid dispatching demand response based on the grid's incentive level for users to participate in demand response and the total predicted capacity of the demand response dispatch load at each moment, and obtain a load resource dispatching method with the minimum cost; The user behavior model is constructed based on the relationship between the probability of users participating in demand response and the grid incentive level.

8. The system according to claim 7, wherein: The system also includes a user behavior model building module, specifically for: The user participation probability under different historical incentive levels is fitted to obtain the incentive dead zone critical point and actual saturation incentive level under the ideal response probability; Based on the critical point of the excitation dead zone under the ideal response probability and the actual saturation excitation level, an ideal response probability curve of the power grid to the user is constructed; Based on the real-time collected user participation in demand response at different incentive levels, the incentive dead zone critical point and the actual saturation incentive level under the actual response probability are determined; Constructing an actual response probability curve of the user to the power grid during operation based on the excitation dead zone critical point under the actual response probability and the actual saturation excitation level; Based on the predicted ideal response probability curve of the power grid to the user and the actual response probability curve of the user to the power grid during operation, the relationship between the probability of the user participating in the demand response and the power grid incentive level is determined.

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