Distribution network distributed photovoltaic consumption scheduling optimization method based on demand side response

Abstract

The invention relates to the technical field of photovoltaic consumption, in particular to a distribution network distributed photovoltaic consumption scheduling optimization method based on demand side response, and the method comprises the steps: firstly obtaining a historical output data set and a historical monitoring data set; constructing a power distribution network resource consumption equation according to the historical output data set and the historical monitoring data set; constructing a resource consumption target according to the output constraint condition and a plurality of resource consumptions; and finally, solving the power distribution network resource consumption equation by adopting an optimization algorithm by taking the resource consumption target as a target according to the current power demand, and regulating and controlling the main power supply and the distributed power supply according to the obtained main power supply output indication and the distributed power supply output indication. According to the embodiment of the invention, on the premise of the current electricity demand and the environment monitoring data, the output of the power supply is optimized, so that the output of the distributed power supply is consumed to the maximum extent, and the comprehensive consumption of various resources is smaller.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic consumption technology, and in particular to a distributed photovoltaic consumption scheduling optimization method for a distribution network based on demand-side response. Background Art

[0002] Distributed photovoltaic power generation refers specifically to photovoltaic power generation facilities that are built near user sites, operate in a way that users generate electricity for their own use, access the grid for excess electricity, and are balanced and regulated in the distribution system. Distributed photovoltaic power generation follows the principles of adapting measures to local conditions, clean and efficient, decentralized layout, and nearby utilization, making full use of local solar energy resources to replace and reduce fossil energy consumption. It is a new type of power generation and comprehensive energy utilization method with broad development prospects. It advocates the principles of nearby power generation, nearby grid connection, nearby conversion, and nearby use. It can not only effectively increase the power generation of photovoltaic power stations of the same size, but also effectively solve the problem of power loss in boosting and long-distance transportation.

[0003] The most widely used distributed photovoltaic power generation system is a photovoltaic power generation project built on the roof of urban buildings. Such projects must be connected to the public power grid and supply power to nearby users together with the public power grid.

[0004] Most of the existing research on distribution network dispatching adopts traditional economic objectives, which cannot directly reflect the impact of the demand side on photovoltaic consumption. In order to make full use of renewable energy power generation equipment and reduce power abandonment, the distributed photovoltaic consumption rate should be considered on the basis of full consideration of demand in the optimization objectives of the distribution system. This will promote the low resource consumption rate operation of the distribution network.

[0005] Based on this, it is necessary to develop and design a distributed photovoltaic consumption scheduling optimization method for distribution networks based on demand-side response. Summary of the invention

[0006] The embodiment of the present invention provides a distribution network distributed photovoltaic consumption scheduling optimization method based on demand-side response, which is used to solve the problem that the photovoltaic consumption method in the prior art lacks overall consideration of user needs, resulting in poor photovoltaic consumption effect.

[0007] In a first aspect, an embodiment of the present invention provides a method for optimizing the dispatching of distributed photovoltaic power consumption in a distribution network based on demand-side response, comprising:

[0008] Acquire a historical output data set and a historical monitoring data set, wherein the historical monitoring data set is constructed based on data of factors affecting the power generation of distributed power sources;

[0009] Constructing a distribution network resource consumption equation according to the historical output data set and the historical monitoring data set, wherein the distribution network resource consumption equation represents the relationship between the main power output and the distributed power output and the resource consumption;

[0010] Construct resource consumption targets based on output constraints and multiple resource consumptions;

[0011] According to the current electricity demand and taking the resource consumption target as the goal, an optimization algorithm is used to solve the distribution network resource consumption equation, and the main power supply and the distributed power supply are regulated according to the obtained main power supply output indication and the distributed power supply output indication.

[0012] In a possible implementation, the historical output data set includes a historical main power output queue and a historical distributed power output queue, the historical monitoring data set includes multiple historical monitoring data queues and multiple historical resource consumption queues, and the distribution network resource consumption equation is constructed according to the historical output data set and the historical monitoring data set, including:

[0013] Acquire a relational equation and a plurality of coefficient arrays, wherein the relational equation includes a plurality of undetermined coefficients, and the number of data in the coefficient array is the same as the number of the plurality of undetermined coefficients;

[0014] Substituting the plurality of coefficient arrays into the relational equation to obtain a plurality of first process equations;

[0015] Array construction step: respectively taking out historical main power output data, historical distributed power output data, multiple historical monitoring data and multiple historical resource consumption data of the same period from each queue of the historical output data set and the historical monitoring data set, and constructing the historical main power output data, the historical distributed power output data and the multiple historical monitoring data into an input array, and constructing the multiple historical resource consumption data into a reference array;

[0016] Inputting the input array into each first process equation respectively to obtain a plurality of output arrays, wherein the output arrays are constructed according to a plurality of resource consumption indication data output by the first process equation;

[0017] Determine a plurality of deviation values ​​according to the plurality of output arrays and the reference array, and add the plurality of deviation values ​​into a plurality of deviation queues, respectively, wherein each deviation queue corresponds to a first process equation;

[0018] If there is a queue that meets the convergence condition among the multiple deviation queues, the first process equation corresponding to the queue that meets the convergence condition is used as the distribution network resource consumption equation;

[0019] Otherwise, for each first process equation, the coefficients of the first process equation are adjusted according to the coefficients of the historical optimal first process equation and the coefficients of the current optimal first process equation, and the process jumps to the array construction step, wherein the historical optimal first process equation is the first process equation with the smallest deviation in all previous iterations, and the current optimal first process equation is the first process equation with the smallest deviation among the current multiple first process equations.

[0020] In a possible implementation, the relationship equation is:

[0021]

[0022] Where ROut(i) is the consumption of the i-th resource, rMax is the total number of rows of intermediate nodes, lMax is the total number of columns of intermediate nodes, and wo ir is the rth coefficient of the ith output node, c is the bias coefficient, wm rlk is the kth coefficient of the node in the rth row and lth column, m(r,l) is the output of the middle node in the rth row and lth column, x j is the jth data in the input array, iMax is the total number of resource types, jmax is the total number of data in the input array, l d is the column number where the feeding node is located, and e is a natural constant.

[0023] In a possible implementation, adjusting the coefficients of the first process equation according to the coefficients of the historical optimal first process equation and the coefficients of the current optimal first process equation includes:

[0024] The coefficients of the first process equation are adjusted according to the first formula, the coefficients of the historical optimal first process equation and the current optimal first process equation, wherein the first formula is:

[0025] w (n) =w (n-1) +b (n-1) ·(α1·(w (hopt) -w (n-1) )+α2·(w opt -w (n-1) ))

[0026] In the formula, w (n) is the adjusted coefficient of the first process equation, w (n-1) is the coefficient of the first process equation before adjustment, b (n -1) is the deviation value of the first process equation, α1 is the first compensation coefficient, w (hopt) is the coefficient of the historical optimal first process equation, α2 is the second compensation coefficient, w opt is the coefficient of the current optimal first process equation.

[0027] In a possible implementation, constructing a resource target according to the output constraint and the multiple resource consumptions includes:

[0028] Determining the maximum output of the distributed power source according to a plurality of monitoring data, wherein the monitoring data is data collected according to influencing factors affecting the maximum output of the distributed power source;

[0029] According to the maximum output of the distributed power source and the minimum output of the main power source, respectively constructing a distributed power source output constraint and a main power source output constraint;

[0030] With the goal of minimizing comprehensive resource consumption, a resource target equation is constructed according to the output constraints of the distributed power source and the output constraints of the main power source, wherein the resource target equation is:

[0031]

[0032] Where P DG For distributed power output, P DGMaz is the maximum output of distributed power generation, P M is the main power output, P MMin The minimum output of the main power supply, a i is the weight of the i-th resource, R i is the consumption of the i-th resource, iMax is the total number of resource types, and R is the comprehensive resource consumption.

[0033] In a possible implementation, according to the current power demand, taking the resource consumption target as the goal, using an optimization algorithm to solve the distribution network resource consumption equation to obtain a main power output indication and a distributed power output indication, including:

[0034] Acquire current power demand and a plurality of monitoring data, wherein the plurality of monitoring data are obtained based on factors affecting the output of distributed power sources;

[0035] Generate multiple output arrays according to the current power demand, wherein the output arrays include a first main power supply output indication and a first distributed power supply output indication, and the first main power supply output indication and the first distributed power supply output indication are obtained by decomposing the current power demand;

[0036] Combining the plurality of output arrays with the plurality of monitoring data respectively to obtain a plurality of input arrays;

[0037] Inputting the multiple input arrays into the distribution network resource consumption equation respectively to obtain multiple resource consumption arrays, wherein each resource consumption array corresponds to an input array, and the resource consumption array is obtained according to multiple resource consumption outputs of the distribution network resource consumption equation;

[0038] Determining a plurality of comprehensive resource consumption amounts according to the plurality of resource consumption arrays, wherein each comprehensive resource consumption amount corresponds to an input array;

[0039] Adding the multiple comprehensive resource consumption amounts to multiple resource consumption queues respectively, wherein each resource consumption queue corresponds to an output array;

[0040] Determining the convergence of a target resource consumption queue, wherein the target resource consumption queue is a resource consumption queue corresponding to a minimum comprehensive resource consumption among the multiple comprehensive resource consumptions;

[0041] If the convergence of the target resource consumption queue meets the convergence condition, the output array corresponding to the target resource consumption queue is used as the target array, and the first main power output indication and the first distributed power output indication of the target array are used as the main power output indication and the distributed power output indication respectively;

[0042] Otherwise, for each input array, adjust the first main power supply output indication and the first distributed power supply output indication in the input array according to the multiple resource consumption queues, and jump to the step of inputting the multiple input arrays into the distribution network resource consumption equation respectively to obtain multiple resource consumption arrays.

[0043] In a possible implementation, adjusting the first main power supply output indication and the first distributed power supply output indication in the input array according to the multiple resource consumption queues includes:

[0044] The first main power supply output indication and the first distributed power supply output indication in the input array are adjusted according to the second formula, wherein the second formula is:

[0045]

[0046] In the formula, is the adjusted output indication of the first main power source or the output indication of the first distributed power source, is the output indication of the first main power source or the output indication of the first distributed power source before adjustment, w g is the adjustment coefficient, x opt A first main power supply output indication or a first distributed power supply output indication of an input array corresponding to a minimum value among a plurality of comprehensive resource consumptions, is the first main power output indication or the first distributed power output indication of the input array corresponding to the historical minimum comprehensive resource consumption of the input array, w g is the adjustment coefficient, R(m-1) is the m-1th data in the resource consumption queue, R(m-2) is the m-2th data in the resource consumption queue, and kg Substitute the coefficient.

[0047] In a second aspect, an embodiment of the present invention provides a distribution network distributed photovoltaic consumption scheduling optimization device based on demand side response, which is used to implement the distribution network distributed photovoltaic consumption scheduling optimization method based on demand side response as described in the first aspect or any possible implementation method of the first aspect above, and the distribution network distributed photovoltaic consumption scheduling optimization device based on demand side response includes:

[0048] A data acquisition module, used to acquire a historical output data set and a historical monitoring data set, wherein the historical monitoring data set is constructed based on data of factors affecting the power generation of distributed power sources;

[0049] A resource consumption equation construction module, used to construct a distribution network resource consumption equation based on the historical output data set and the historical monitoring data set, wherein the distribution network resource consumption equation represents the relationship between the main power output and the distributed power output and the resource consumption;

[0050] A target building module is used to build resource consumption targets based on output constraints and multiple resource consumptions;

[0051] as well as,

[0052] The scheduling optimization module is used to solve the distribution network resource consumption equation using an optimization algorithm according to the current electricity demand and the resource consumption target, and to regulate the main power supply and the distributed power supply according to the obtained main power supply output indication and the distributed power supply output indication.

[0053] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0054] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0055] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0056] The embodiment of the present invention discloses a method for optimizing the dispatching of distributed photovoltaic power consumption in a distribution network based on demand-side response, which first obtains a historical output data set and a historical monitoring data set, wherein the historical monitoring data set is constructed based on the factor data affecting the power generation of distributed power sources; then, a distribution network resource consumption equation is constructed based on the historical output data set and the historical monitoring data set, wherein the distribution network resource consumption equation characterizes the relationship between the output of the main power source and the output of the distributed power source and the resource consumption; then, a resource consumption target is constructed based on the output constraint condition and multiple resource consumptions; finally, according to the current power demand, the resource consumption target is taken as the target, and the distribution network resource consumption equation is solved by using an optimization algorithm, and the main power source and the distributed power source are regulated according to the obtained main power source output indication and the distributed power source output indication. The embodiment of the present invention constructs a distribution network resource consumption equation based on the factor data affecting the power generation of distributed power sources, the output of the distributed power source and the output of the main power source, and then determines the output of the main power source and the output of the distributed power source based on the power demand and the current monitoring data, that is, under the premise of the current power demand and the environmental monitoring data, the power output is optimized so that the output of the distributed power source is maximized for consumption, and the comprehensive resource consumption is smaller. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0058] Figure 1 It is a flow chart of a method for optimizing the dispatching of distributed photovoltaic power consumption in a distribution network based on demand-side response provided by an embodiment of the present invention;

[0059] Figure 2 It is a functional block diagram of a distribution network resource consumption equation provided by an embodiment of the present invention;

[0060] Figure 3 It is a functional block diagram of a distributed photovoltaic consumption dispatch optimization device for a distribution network based on demand-side response provided by an embodiment of the present invention;

[0061] Figure 4 It is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0063] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following will be described through specific implementation methods in conjunction with the accompanying drawings.

[0064] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and a specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0065] Figure 1 A flow chart of a method for optimizing the dispatching of distributed photovoltaic power generation in a distribution network based on demand-side response provided in an embodiment of the present invention.

[0066] like Figure 1 As shown, it shows a flow chart of the implementation of the distributed photovoltaic consumption scheduling optimization method of the distribution network based on demand side response provided by the embodiment of the present invention, which is detailed as follows:

[0067] In step 101, a historical output data set and a historical monitoring data set are obtained, wherein the historical monitoring data set is constructed based on data of factors affecting the power generation of distributed power sources.

[0068] In step 102, a distribution network resource consumption equation is constructed according to the historical output data set and the historical monitoring data set, wherein the distribution network resource consumption equation represents the relationship between the main power output and the distributed power output and the resource consumption.

[0069] In some implementations, the historical output data set includes a historical main power output queue and a historical distributed power output queue, the historical monitoring data set includes multiple historical monitoring data queues and multiple historical resource consumption queues, and constructing a distribution network resource consumption equation based on the historical output data set and the historical monitoring data set includes:

[0070] Obtaining a relational equation and a plurality of coefficient arrays, wherein the relational equation includes a plurality of undetermined coefficients, and the number of data in the coefficient array is the same as the number of the plurality of undetermined coefficients;

[0071] Substituting the plurality of coefficient arrays into the relational equation to obtain a plurality of first process equations;

[0072] Array construction step: respectively taking out historical main power output data, historical distributed power output data, multiple historical monitoring data and multiple historical resource consumption data of the same period from each queue of the historical output data set and the historical monitoring data set, and constructing the historical main power output data, the historical distributed power output data and the multiple historical monitoring data into an input array, and constructing the multiple historical resource consumption data into a reference array;

[0073] Inputting the input array into each first process equation respectively to obtain a plurality of output arrays, wherein the output arrays are constructed according to a plurality of resource consumption indication data output by the first process equation;

[0074] Determine a plurality of deviation values ​​according to the plurality of output arrays and the reference array, and add the plurality of deviation values ​​into a plurality of deviation queues, respectively, wherein each deviation queue corresponds to a first process equation;

[0075] If there is a queue that meets the convergence condition among the multiple deviation queues, the first process equation corresponding to the queue that meets the convergence condition is used as the distribution network resource consumption equation;

[0076] Otherwise, for each first process equation, the coefficients of the first process equation are adjusted according to the coefficients of the historical optimal first process equation and the coefficients of the current optimal first process equation, and the process jumps to the array construction step, wherein the historical optimal first process equation is the first process equation with the smallest deviation in all previous iterations, and the current optimal first process equation is the first process equation with the smallest deviation among the current multiple first process equations.

[0077] In some embodiments, the relationship equation is:

[0078]

[0079] Where ROut(i) is the consumption of the i-th resource, rMax is the total number of rows of intermediate nodes, lMax is the total number of columns of intermediate nodes, and wo ir is the rth coefficient of the ith output node, c is the bias coefficient, wm rlk is the kth coefficient of the node in the rth row and lth column, m(r,l) is the output of the middle node in the rth row and lth column, x j is the jth data in the input array, iMax is the total number of resource types, jmax is the total number of data in the input array, l d is the column number where the feeding node is located, and e is a natural constant.

[0080] In some implementations, adjusting the coefficients of the first process equation according to the coefficients of the historical optimal first process equation and the coefficients of the current optimal first process equation includes:

[0081] The coefficients of the first process equation are adjusted according to the first formula, the coefficients of the historical optimal first process equation and the current optimal first process equation, wherein the first formula is:

[0082] w (n) =w (n-1) +b (n-1) ·(α1·(w (hopt) -w (n-1) )+α2·(w opt -w (n-1) ))

[0083] In the formula, w (n) is the adjusted coefficient of the first process equation, w (n-1) is the coefficient of the first process equation before adjustment, b (n -1) is the deviation value of the first process equation, α1 is the first compensation coefficient, w (hopt) is the coefficient of the historical optimal first process equation, α2 is the second compensation coefficient, w opt is the coefficient of the current optimal first process equation.

[0084] Exemplarily, an embodiment of the present invention first obtains historical output data and historical monitoring data, wherein the historical output data includes a main power output data queue and a distributed power output data queue, and the historical monitoring data includes a data queue obtained by monitoring environmental variables and a resource consumption data queue, for example, when photovoltaic power generation is used, the sunshine intensity and temperature, and the resource consumption data queue, for example, the apportionment of some loss parts or maintenance human resources invested in power generation. The embodiment of the present invention is intended to clarify in various scenarios, under the premise of certain electricity demand, and based on the factors affecting distributed power generation, determine a new energy consumption plan with the minimum total resource consumption.

[0085] In order to achieve the above objectives, an embodiment of the present invention constructs a resource consumption equation based on multiple historical output data and historical monitoring data. The equation describes the consumption of multiple resources under different main power output, distributed power output and different scenarios of factors affecting power generation.

[0086] This equation is constructed based on the relationship equation, which is:

[0087]

[0088] Where ROut(i) is the consumption of the i-th resource, rMax is the total number of rows of intermediate nodes, lMax is the total number of columns of intermediate nodes, and wo ir is the rth coefficient of the ith output node, c is the bias coefficient, wm rlkis the kth coefficient of the node in the rth row and lth column, m(r,l) is the output of the middle node in the rth row and lth column, x j is the jth data in the input array, iMax is the total number of resource types, jmax is the total number of data in the input array, l d is the column number where the feeding node is located, and e is a natural constant.

[0089] There are multiple coefficients in this equation that need to be determined. The implementation method of the present invention first randomly initializes multiple coefficient arrays, substitutes these coefficient arrays into the above-mentioned relationship equation, and obtains the first process equation. Then, the input data is extracted from the historical output data set and the historical monitoring data set, and substituted into the first process equation respectively. The obtained results are compared with the actual resource consumption, so as to determine the deviation value of each first process equation. Then, the deviation value is added to the deviation queue. If the last few digits of the deviation queue are less than the deviation threshold, for example, the deviation value of the last five digits is less than the deviation threshold, the first process equation is used as the distribution network resource consumption equation. Otherwise, enter the step of modifying the coefficients.

[0090] In an application scenario, the coefficient of the first process equation is adjusted according to the first formula, the coefficient of the historical optimal first process equation, and the current optimal first process equation, wherein the first formula is:

[0091]

[0092] In the formula, w (n) is the adjusted coefficient of the first process equation, w (n-1) is the coefficient of the first process equation before adjustment, b (n -1) is the deviation value of the first process equation, α1 is the first compensation coefficient, w (hopt) is the coefficient of the historical optimal first process equation, α2 is the second compensation coefficient, w opt is the coefficient of the current optimal first process equation.

[0093] Through the above steps, the construction of the distribution network resource consumption equation is finally completed.

[0094] In step 103, a resource consumption target is constructed according to the output constraint condition and the multiple resource consumptions.

[0095] In some implementations, constructing a resource target according to the output constraint and the plurality of resource consumptions includes:

[0096] Determining the maximum output of the distributed power source according to a plurality of monitoring data, wherein the monitoring data is data collected according to influencing factors affecting the maximum output of the distributed power source;

[0097] According to the maximum output of the distributed power source and the minimum output of the main power source, respectively constructing a distributed power source output constraint and a main power source output constraint;

[0098] With the goal of minimizing comprehensive resource consumption, a resource target equation is constructed according to the output constraints of the distributed power source and the output constraints of the main power source, wherein the resource target equation is:

[0099]

[0100] Where P DG For distributed power output, P DGMax is the maximum output of distributed power generation, P M is the main power output, P MNin The minimum output of the main power supply, a i is the weight of the i-th resource, R i is the consumption of the i-th resource, iMax is the total number of resource types, and R is the comprehensive resource consumption.

[0101] Exemplarily, the embodiment of the present invention constructs a resource consumption target, which mainly includes two parts, namely, operation constraints and resource consumption targets. The constraints include distributed power output constraints and main power constraints, and the resource consumption target is a comprehensive resource consumption target. Therefore, the constructed resource target equation is as follows:

[0102]

[0103] Where P DG For distributed power output, P DGMax is the maximum output of distributed power generation, P M is the main power output, P MMin The minimum output of the main power supply, a i is the weight of the i-th resource, R i is the consumption of the i-th resource, iMax is the total number of resource types, and R is the comprehensive resource consumption.

[0104] In step 104, according to the current electricity demand and taking the resource consumption target as the goal, an optimization algorithm is used to solve the distribution network resource consumption equation, and the main power supply and the distributed power supply are regulated according to the obtained main power supply output indication and the distributed power supply output indication.

[0105] In some implementations, according to the current power demand, taking the resource consumption target as the target, using an optimization algorithm to solve the distribution network resource consumption equation to obtain a main power output indication and a distributed power output indication, including:

[0106] Acquire current power demand and a plurality of monitoring data, wherein the plurality of monitoring data are obtained based on factors affecting the output of distributed power sources;

[0107] Generate multiple output arrays according to the current power demand, wherein the output arrays include a first main power supply output indication and a first distributed power supply output indication, and the first main power supply output indication and the first distributed power supply output indication are obtained by decomposing the current power demand;

[0108] Combining the plurality of output arrays with the plurality of monitoring data respectively to obtain a plurality of input arrays;

[0109] Inputting the multiple input arrays into the distribution network resource consumption equation respectively to obtain multiple resource consumption arrays, wherein each resource consumption array corresponds to an input array, and the resource consumption array is obtained according to multiple resource consumption outputs of the distribution network resource consumption equation;

[0110] Determining a plurality of comprehensive resource consumption amounts according to the plurality of resource consumption arrays, wherein each comprehensive resource consumption amount corresponds to an input array;

[0111] Adding the multiple comprehensive resource consumption amounts to multiple resource consumption queues respectively, wherein each resource consumption queue corresponds to an output array;

[0112] Determining the convergence of a target resource consumption queue, wherein the target resource consumption queue is a resource consumption queue corresponding to a minimum comprehensive resource consumption among the multiple comprehensive resource consumptions;

[0113] If the convergence of the target resource consumption queue meets the convergence condition, the output array corresponding to the target resource consumption queue is used as the target array, and the first main power output indication and the first distributed power output indication of the target array are used as the main power output indication and the distributed power output indication respectively;

[0114] Otherwise, for each input array, adjust the first main power supply output indication and the first distributed power supply output indication in the input array according to the multiple resource consumption queues, and jump to the step of inputting the multiple input arrays into the distribution network resource consumption equation respectively to obtain multiple resource consumption arrays.

[0115] In some implementations, adjusting the first main power supply output indication and the first distributed power supply output indication in the input array according to the multiple resource consumption queues includes:

[0116] The first main power supply output indication and the first distributed power supply output indication in the input array are adjusted according to the second formula, wherein the second formula is:

[0117]

[0118] In the formula, is the adjusted output indication of the first main power source or the output indication of the first distributed power source, is the output indication of the first main power source or the output indication of the first distributed power source before adjustment, w g is the adjustment coefficient, x opt A first main power supply output indication or a first distributed power supply output indication of an input array corresponding to a minimum value among a plurality of comprehensive resource consumptions, is the first main power output indication or the first distributed power output indication of the input array corresponding to the historical minimum comprehensive resource consumption of the input array, w g is the adjustment coefficient, R(m-1) is the m-1th data in the resource consumption queue, R(m-2) is the m-2th data in the resource consumption queue, and k g Substitute the coefficient.

[0119] Exemplarily, the embodiment of the present invention takes the resource target equation as the target, solves the above-mentioned distribution network resource consumption equation, and attempts to find a distribution of output under the premise of satisfying the constraint conditions.

[0120] Therefore, the embodiment of the present invention initializes multiple output arrays, in which the sum of the output of the distributed power source and the output of the main power source is the current power demand, and these distributed power source outputs and the main power source output meet the constraints, and then each output array is combined with multiple monitoring data to obtain an input array. These input arrays are respectively substituted into the distribution network resource consumption equation to obtain multiple resource consumption indication data, and these indication data are then used to obtain the comprehensive resource consumption according to the aforementioned comprehensive resource consumption formula, and finally added to the comprehensive resource consumption queue. By analyzing the convergence of the queue, we can know whether it is close to the minimum comprehensive resource consumption data. If it does not converge, the data of the distributed power source output and the main power source output in the input array are adjusted, and then the above input steps to obtain the output result of the distribution network resource consumption equation are repeated.

[0121] The embodiment of the present invention adjusts the first main power supply output indication and the first distributed power supply output indication in the input array according to the second formula, wherein the second formula is:

[0122]

[0123] In the formula, is the adjusted output indication of the first main power source or the output indication of the first distributed power source, is the output indication of the first main power source or the output indication of the first distributed power source before adjustment, w g is the adjustment coefficient, x optA first main power supply output indication or a first distributed power supply output indication of an input array corresponding to a minimum value among a plurality of comprehensive resource consumptions, is the first main power output indication or the first distributed power output indication of the input array corresponding to the historical minimum comprehensive resource consumption of the input array, w g is the adjustment coefficient, R(m-1) is the m-1th data in the resource consumption queue, R(m-2) is the m-2th data in the resource consumption queue, and k g Substitute the coefficient.

[0124] When the iteration is completed, the data of the distributed power output and the main power output of the input array that meet the convergence conditions are used as the main power output indication and the distributed power output indication to regulate the main power and the distributed power.

[0125] The present invention is based on the implementation method of the distributed photovoltaic consumption dispatch optimization method of the distribution network based on the demand side response, which first obtains the historical output data set and the historical monitoring data set, wherein the historical monitoring data set is constructed based on the factor data affecting the power generation of the distributed power source; then the distribution network resource consumption equation is constructed according to the historical output data set and the historical monitoring data set, wherein the distribution network resource consumption equation characterizes the relationship between the main power output and the distributed power output and the resource consumption; then, according to the output constraint conditions and multiple resource consumptions, the resource consumption target is constructed; finally, according to the current power demand, with the resource consumption target as the target, the distribution network resource consumption equation is solved by using the optimization algorithm, and the main power source and the distributed power source are regulated according to the obtained main power output indication and the distributed power output indication. The implementation method of the present invention is based on the factor data affecting the power generation of the distributed power source, the distributed power output and the main power output, and then based on the power demand and the current monitoring data, the main power output and the distributed power output are determined, that is, under the premise of the current power demand and the environmental monitoring data, the power output is optimized so that the distributed power output is maximized for consumption, and the comprehensive resource consumption is smaller.

[0126] It should be understood that the size of the serial numbers of the steps in the above implementation does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation method of the present invention.

[0127] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0128] Figure 3 This is a functional block diagram of a distributed photovoltaic consumption dispatch optimization device for a distribution network based on demand-side response provided by an embodiment of the present invention, referring to Figure 3The distributed photovoltaic consumption dispatch optimization device for distribution network based on demand side response includes: a data acquisition module 301, a resource consumption equation construction module 302, a target construction module 303 and a dispatch optimization module 304, wherein:

[0129] A data acquisition module 301 is used to acquire a historical output data set and a historical monitoring data set, wherein the historical monitoring data set is constructed based on data of factors affecting the power generation of distributed power sources;

[0130] A resource consumption equation construction module 302 is used to construct a distribution network resource consumption equation based on the historical output data set and the historical monitoring data set, wherein the distribution network resource consumption equation represents the relationship between the main power output and the distributed power output and the resource consumption;

[0131] A target construction module 303 is used to construct a resource consumption target according to the output constraint condition and the multiple resource consumptions;

[0132] The dispatch optimization module 304 is used to solve the distribution network resource consumption equation according to the current power demand and the resource consumption target, and to adjust the main power supply and the distributed power supply according to the obtained main power supply output indication and the distributed power supply output indication.

[0133] Figure 4 is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 400 and a memory 401, wherein the memory 401 stores a computer program 402 that can be run on the processor 400. When the processor 400 executes the computer program 402, the above-mentioned distribution network distributed photovoltaic consumption scheduling optimization method based on demand side response and the steps in the embodiment are implemented, for example Figure 1 Steps 101 to 104 are shown.

[0134] Exemplarily, the computer program 402 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 401 and executed by the processor 400 to implement the present invention.

[0135] The electronic device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will appreciate that Figure 4It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 4 may also include input and output devices, network access devices, buses, etc.

[0136] The processor 400 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0137] The memory 401 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 401 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 401 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 401 is used to store the computer program 402 and other programs and data required by the electronic device 4. The memory 401 may also be used to temporarily store data that has been output or is to be output.

[0138] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0139] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0141] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0143] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0144] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various methods and device implementation methods. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0145] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A distributed photovoltaic consumption dispatch optimization method for distribution network based on demand side response, characterized in that: include: Acquire a historical output data set and a historical monitoring data set, wherein the historical monitoring data set is constructed based on data of factors affecting the power generation of distributed power sources; Constructing a distribution network resource consumption equation according to the historical output data set and the historical monitoring data set, wherein the distribution network resource consumption equation represents the relationship between the main power output and the distributed power output and the resource consumption; Construct resource consumption targets based on output constraints and multiple resource consumptions; According to the current electricity demand and taking the resource consumption target as the goal, an optimization algorithm is used to solve the distribution network resource consumption equation, and the main power supply and the distributed power supply are regulated according to the obtained main power supply output indication and the distributed power supply output indication.

2. The method for optimizing the dispatching of distributed photovoltaic power generation in distribution network based on demand-side response according to claim 1 is characterized in that: The historical output data set includes a historical main power output queue and a historical distributed power output queue, the historical monitoring data set includes multiple historical monitoring data queues and multiple historical resource consumption queues, and the distribution network resource consumption equation is constructed according to the historical output data set and the historical monitoring data set, including: Acquire a relational equation and a plurality of coefficient arrays, wherein the relational equation includes a plurality of undetermined coefficients, and the number of data in the coefficient array is the same as the number of the plurality of undetermined coefficients; Substituting the plurality of coefficient arrays into the relational equation to obtain a plurality of first process equations; Array construction step: respectively taking out historical main power output data, historical distributed power output data, multiple historical monitoring data and multiple historical resource consumption data of the same period from each queue of the historical output data set and the historical monitoring data set, and constructing the historical main power output data, the historical distributed power output data and the multiple historical monitoring data into an input array, and constructing the multiple historical resource consumption data into a reference array; Inputting the input array into each first process equation respectively to obtain a plurality of output arrays, wherein the output arrays are constructed according to a plurality of resource consumption indication data output by the first process equation; Determine a plurality of deviation values ​​according to the plurality of output arrays and the reference array, and add the plurality of deviation values ​​into a plurality of deviation queues, respectively, wherein each deviation queue corresponds to a first process equation; If there is a queue that meets the convergence condition among the multiple deviation queues, the first process equation corresponding to the queue that meets the convergence condition is used as the distribution network resource consumption equation; Otherwise, for each first process equation, the coefficients of the first process equation are adjusted according to the coefficients of the historical optimal first process equation and the coefficients of the current optimal first process equation, and the process jumps to the array construction step, wherein the historical optimal first process equation is the first process equation with the smallest deviation in all previous iterations, and the current optimal first process equation is the first process equation with the smallest deviation among the current multiple first process equations.

3. The method for optimizing the dispatching of distributed photovoltaic power generation in distribution network based on demand-side response according to claim 2 is characterized in that: The relationship equation is: Where ROut(i) is the consumption of the i-th resource, rMax is the total number of rows of intermediate nodes, lMax is the total number of columns of intermediate nodes, and wo ir is the rth coefficient of the ith output node, c is the bias coefficient, wm rlk is the kth coefficient of the node in the rth row and lth column, m(r, l) is the output of the middle node in the rth row and lth column, x j is the jth data in the input array, iMax is the total number of resource types, jmax is the total number of data in the input array, l d is the column number where the feeding node is located, and e is a natural constant.

4. The method for optimizing the dispatching of distributed photovoltaic power generation in distribution network based on demand-side response according to claim 2 is characterized in that: The adjusting the coefficient of the first process equation according to the coefficient of the historical optimal first process equation and the coefficient of the current optimal first process equation includes: The coefficients of the first process equation are adjusted according to the first formula, the coefficients of the historical optimal first process equation and the current optimal first process equation, wherein the first formula is: In (n) =in (n-1) +b (n-1) (α1 (in (hopt) -In (n-1) )+α2 (in opt -In (n-1) )) In the formula, w (n) is the adjusted coefficient of the first process equation, w (n-1) is the coefficient of the first process equation before adjustment, b (n-1) is the deviation value of the first process equation, α1 is the first compensation coefficient, w (hopt) is the coefficient of the historical optimal first process equation, α2 is the second compensation coefficient, w opt is the coefficient of the current optimal first process equation.

5. The method for optimizing the dispatching of distributed photovoltaic power consumption in distribution network based on demand-side response according to claim 1 is characterized in that: The resource target is constructed according to the output constraint condition and the multiple resource consumptions, including: Determining the maximum output of the distributed power source according to a plurality of monitoring data, wherein the monitoring data is data collected according to influencing factors affecting the maximum output of the distributed power source; According to the maximum output of the distributed power source and the minimum output of the main power source, respectively constructing a distributed power source output constraint and a main power source output constraint; With the goal of minimizing comprehensive resource consumption, a resource target equation is constructed according to the output constraints of the distributed power source and the output constraints of the main power source, wherein the resource target equation is: Where P DG For distributed power output, P DGMax is the maximum output of distributed power generation, P M is the main power output, P MMin The minimum output of the main power supply, a i is the weight of the i-th resource, R i is the consumption of the i-th resource, iMax is the total number of resource types, and R is the comprehensive resource consumption.

6. The method for optimizing the dispatching of distributed photovoltaic power generation in distribution network based on demand-side response according to any one of claims 1 to 5, characterized in that: The method of solving the distribution network resource consumption equation according to the current power demand and taking the resource consumption target as the target, and obtaining the main power supply output indication and the distributed power supply output indication includes: Acquire current power demand and a plurality of monitoring data, wherein the plurality of monitoring data are obtained based on factors affecting the output of distributed power sources; Generate multiple output arrays according to the current power demand, wherein the output arrays include a first main power supply output indication and a first distributed power supply output indication, and the first main power supply output indication and the first distributed power supply output indication are obtained by decomposing the current power demand; Combining the plurality of output arrays with the plurality of monitoring data respectively to obtain a plurality of input arrays; Inputting the multiple input arrays into the distribution network resource consumption equation respectively to obtain multiple resource consumption arrays, wherein each resource consumption array corresponds to an input array, and the resource consumption array is obtained according to multiple resource consumption outputs of the distribution network resource consumption equation; Determining a plurality of comprehensive resource consumption amounts according to the plurality of resource consumption arrays, wherein each comprehensive resource consumption amount corresponds to an input array; Adding the multiple comprehensive resource consumption amounts to multiple resource consumption queues respectively, wherein each resource consumption queue corresponds to an output array; Determining the convergence of a target resource consumption queue, wherein the target resource consumption queue is a resource consumption queue corresponding to a minimum comprehensive resource consumption among the multiple comprehensive resource consumptions; If the convergence of the target resource consumption queue meets the convergence condition, the output array corresponding to the target resource consumption queue is used as the target array, and the first main power output indication and the first distributed power output indication of the target array are used as the main power output indication and the distributed power output indication respectively; Otherwise, for each input array, adjust the first main power supply output indication and the first distributed power supply output indication in the input array according to the multiple resource consumption queues, and jump to the step of inputting the multiple input arrays into the distribution network resource consumption equation respectively to obtain multiple resource consumption arrays.

7. The method for optimizing the dispatching of distributed photovoltaic power consumption in distribution network based on demand-side response according to claim 6 is characterized in that: The step of adjusting the first main power supply output indication and the first distributed power supply output indication in the input array according to the plurality of resource consumption queues comprises: The first main power supply output indication and the first distributed power supply output indication in the input array are adjusted according to the second formula, wherein the second formula is: In the formula, is the adjusted output indication of the first main power source or the output indication of the first distributed power source, is the output indication of the first main power source or the output indication of the first distributed power source before adjustment, w g is the adjustment coefficient, x opt The first main power supply output indication or the first distributed power supply output indication of the input array corresponding to the minimum value among the multiple comprehensive resource consumptions, is the first main power output indication or the first distributed power output indication of the input array corresponding to the historical minimum comprehensive resource consumption of the input array, w g is the adjustment coefficient, R(m-1) is the m-1th data in the resource consumption queue, R(m-2) is the m-2th data in the resource consumption queue, and k g Substitute the coefficient.

8. A distributed photovoltaic consumption dispatch optimization device for distribution network based on demand side response, characterized in that: The method for optimizing the dispatching of distributed photovoltaic power generation in a distribution network based on demand-side response according to any one of claims 1 to 7 is used to implement the method, wherein the device for optimizing the dispatching of distributed photovoltaic power generation in a distribution network based on demand-side response comprises: A data acquisition module, used to acquire a historical output data set and a historical monitoring data set, wherein the historical monitoring data set is constructed based on data of factors affecting the power generation of distributed power sources; A resource consumption equation construction module, used to construct a distribution network resource consumption equation based on the historical output data set and the historical monitoring data set, wherein the distribution network resource consumption equation represents the relationship between the main power output and the distributed power output and the resource consumption; A target building module is used to build resource consumption targets based on output constraints and multiple resource consumptions; as well as, The scheduling optimization module is used to solve the distribution network resource consumption equation using an optimization algorithm according to the current electricity demand and the resource consumption target, and to regulate the main power supply and the distributed power supply according to the obtained main power supply output indication and the distributed power supply output indication.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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