Power distribution network dispatching method, system, equipment and medium

Abstract

The invention relates to a power distribution network dispatching method, system and device and a medium. The power distribution network dispatching method comprises the following steps: acquiring user participation degree data; constructing a power distribution network demand response model based on the user participation degree data; constructing a power distribution network dispatching model based on the power distribution network demand response model; and solving the power distribution network scheduling model to obtain a scheduling result. According to the method, the scheduling result is obtained by solving the problem of the power distribution network scheduling model, the power distribution network scheduling model is established on the basis of the power distribution network demand response model, and the power distribution network demand response model is established according to the user participation degree, so that the power distribution network scheduling model is established in combination with the demand side response technology; therefore, the efficiency of the power grid is improved, and the requirements of users are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and particularly relates to a distribution network scheduling method, system, device and medium. Background Art

[0002] With the rapid development of new energy and smart grids, the scale of the distribution network has been continuously expanding. During the peak period of grid demand, due to the high user peak value, the load pressure on the grid is relatively large, resulting in complex and difficult distribution network scheduling, and thus the user demand cannot be better met. Summary of the Invention

[0003] The purpose of the present invention is to provide a distribution network scheduling method, system, device and medium to solve the above problems.

[0004] The present invention realizes the above purpose through the following technical solutions:

[0005] A distribution network scheduling method includes the following steps:

[0006] Obtain user participation data;

[0007] Construct a distribution network demand response model based on the user participation data;

[0008] Construct a distribution network scheduling model based on the distribution network demand response model;

[0009] Solve the distribution network scheduling model to obtain a scheduling result.

[0010] As a further optimization scheme of the present invention, the user participation data includes price-based demand response user participation and incentive-based demand response user participation.

[0011] As a further optimization scheme of the present invention, the distribution network demand response model includes a distribution network price-based demand response model and / or a distribution network incentive-based demand response model.

[0012] As a further optimization scheme of the present invention, the distribution network price-based demand response model is constructed according to the price-based demand response user participation and the electricity price;

[0013] The distribution network incentive-based demand response model is constructed according to the incentive-based demand response user participation and the response power.

[0014] As a further optimization scheme of the present invention, before solving the distribution network scheduling model to obtain a scheduling result, the method further includes:

[0015] Construct a first sub-objective function for minimizing the distribution network scheduling operation cost;

[0016] Construct the second sub-objective function for minimizing voltage deviation;

[0017] Construct the objective function according to the first sub-objective function and the second sub-objective function;

[0018] Determine the constraint conditions according to at least one of the network operation constraints, the controllable micro gas turbine operation constraints, the energy storage operation constraints, the demand response constraints, and the reactive power compensation equipment constraints, where the demand response constraints are determined based on the distribution network demand response model;

[0019] Establish the distribution network scheduling model according to the objective function and the constraint conditions.

[0020] As a further optimization scheme of the present invention, the distribution network scheduling operation cost includes at least one of the following:

[0021] Power purchase cost;

[0022] Power generation cost;

[0023] Energy storage operation cost;

[0024] Demand response cost;

[0025] Static var compensator operation cost;

[0026] Shunt capacitor operation cost.

[0027] As a further optimization scheme of the present invention, constructing the objective function according to the first sub-objective function and the second sub-objective function includes:

[0028] Use a weighted minimum norm evaluation function to combine and process the first sub-objective function and the second sub-objective function to construct the objective function.

[0029] As a further optimization scheme of the present invention, solving the problem of the distribution network scheduling model to obtain the scheduling result includes:

[0030] Use an improved particle swarm optimization algorithm to solve the problem of the distribution network scheduling model to obtain the scheduling result.

[0031] A distribution network scheduling system includes:

[0032] A data acquisition module for acquiring user participation data;

[0033] A model construction module for constructing a distribution network demand response model based on the user participation data; constructing a distribution network scheduling model based on the distribution network demand response model;

[0034] A model solving module for solving the distribution network scheduling model to obtain the scheduling result.

[0035] An electronic device includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete their mutual communication through the communication bus;

[0036] The memory is used to store computer programs;

[0037] The processor, when executing the programs stored in the memory, implements the distribution network scheduling method.

[0038] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the distribution network scheduling method is implemented.

[0039] The beneficial effects of the present invention are as follows:

[0040] The present invention obtains a scheduling result by solving problems of the distribution network scheduling model. Among them, the distribution network scheduling model is established based on the distribution network demand response model, and the distribution network demand response model is constructed according to the user participation degree. In this way, the distribution network scheduling model is established by combining the demand-side response technology, reducing the load pressure on the power grid, thereby improving the power grid efficiency and meeting the user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic flow chart of the distribution network scheduling method of the present invention;

[0042] Figure 2 It is a schematic diagram of the price-based demand response curve provided by an embodiment of the present invention;

[0043] Figure 3 It is a schematic diagram of the demand response participation degree curve provided by an embodiment of the present invention;

[0044] Figure 4 It is a schematic flow chart of one implementation manner of the method described in an embodiment of the present invention;

[0045] Figure 5 It is a schematic structural diagram of the distribution network scheduling system provided by an embodiment of the present invention;

[0046] Figure 6 It is a schematic structural diagram of the device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The following further describes the present application in detail with reference to the drawings. It is necessary to point out here that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the protection scope of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application according to the above application content.

[0048] As shown in Figure 1 the figure, a distribution network scheduling method includes:

[0049] Obtaining user participation data;

[0050] Constructing a distribution network demand response model based on the user participation data;

[0051] Constructing a distribution network scheduling model based on the distribution network demand response model;

[0052] Solving the distribution network scheduling model to obtain a scheduling result.

[0053] The user participation data includes price-based demand response user participation and incentive-based demand response user participation.

[0054] The distribution network demand response model includes a distribution network price-based demand response model and / or a distribution network incentive-based demand response model.

[0055] The distribution network price-based demand response model is constructed according to the price-based demand response user participation and the electricity price;

[0056] The distribution network incentive-based demand response model is constructed according to the incentive-based demand response user participation and the response power.

[0057] Before solving the distribution network scheduling model to obtain a scheduling result, the method further includes:

[0058] Constructing a first sub-objective function for minimizing the distribution network scheduling operation cost;

[0059] Constructing a second sub-objective function for minimizing the voltage deviation;

[0060] Constructing an objective function according to the first sub-objective function and the second sub-objective function;

[0061] Determining constraint conditions according to at least one of network operation constraints, controllable micro gas turbine operation constraints, energy storage operation constraints, demand response constraints, and reactive power compensation equipment constraints, where the demand response constraints are determined based on the distribution network demand response model;

[0062] Establishing the distribution network scheduling model according to the objective function and the constraint conditions.

[0063] The distribution network scheduling operation cost includes at least one of the following:

[0064] Power purchase cost;

[0065] Power generation cost;

[0066] Energy storage operation cost;

[0067] Demand response cost

[0068] Static var compensator operation cost

[0069] Parallel capacitor operation cost

[0070] Construct an objective function according to the first sub-objective function and the second sub-objective function, including:

[0071] Use a weighted minimum norm evaluation function to combine and process the first sub-objective function and the second sub-objective function to construct the objective function

[0072] Solve the problem of the distribution network scheduling model to obtain a scheduling result, including:

[0073] Use an improved particle swarm optimization algorithm to solve the problem of the distribution network scheduling model to obtain the scheduling result

[0074] In this embodiment, specifically, a distribution network scheduling method includes:

[0075] Step 101: Solve the problem of the distribution network scheduling model to obtain a scheduling result, where the distribution network scheduling model is established based on a distribution network demand response model, and the distribution network demand response model is constructed according to user participation

[0076] It should be noted that the distribution network scheduling model of this application embodiment is established by combining DR (Demand Response) technology. DR refers to adjusting the electricity consumption behavior of users during the peak demand period of the power grid to reduce the peak electricity consumption and the load pressure on the power grid, so as to meet the user demand

[0077] In this application embodiment, optionally, the distribution network demand response model includes a distribution network price-based demand response model and / or a distribution network incentive-based demand response model

[0078] Among them, the distribution network price-based demand response model is constructed according to the participation of price-based demand response users

[0079] The distribution network incentive-based demand response model is constructed according to the participation of incentive-based demand response users

[0080] It should be noted that the distribution network price-based demand response model and the distribution network incentive-based demand response model are established after considering the demand response participation of users, and the distribution network price-based demand response model and the distribution network incentive-based demand response model are established under the ideal condition that users can use electricity rationally. However, in real life, the demand response behavior of users is affected by factors such as the publicity degree of demand response policies and the behavior of user groups

[0081] Among them, the price elasticity of demand can describe the response change of electricity consumption demand caused by the change of electricity price. The price-based demand response curve is as Figure 2 shown, and the approximate relationship between the demand response participation degree and the information intensity is as Figure 3 shown.

[0082] Based on the influence of publicity factors, the approximate relationship between the demand response participation degree and the information intensity includes three types of response regions.

[0083] The first type of response region is the dead zone: when the information intensity r is less than the minimum information intensity r min , the demand response participation degree basically does not respond to the information intensity, and the demand response participation degree is basically always 0.

[0084] The second type of response region is the linear region: when the information intensity r exceeds the minimum information intensity r min , the demand response participation degree is affected by the information intensity and shows a positive correlation.

[0085] The second type of response region is the saturation region: when the information intensity r exceeds the maximum information intensity r max , the demand response participation degree reaches 1 and will no longer change.

[0086] In an embodiment of the present application, optionally, the distribution network price-based demand response model is constructed according to the price-based demand response user participation degree and the electricity price.

[0087] It should be noted that the elasticity coefficient can describe the response change of electricity consumption demand caused by the change of electricity price. In real life, multi-period electricity price response is usually adopted. The electricity consumption of a user at a certain moment is affected by the electricity price change at that moment and also by the electricity price changes at other moments. Specifically, the elasticity coefficient is divided into self-elasticity coefficient and cross-elasticity coefficient, and the following calculation formulas are adopted:

[0088]

[0089]

[0090] ΔD i =D i -D i0 ;

[0091] Δp i =p i -p i0 ;

[0092] Δp j =p j -p j0 ;

[0093] In the formula, eii is the self-elasticity coefficient. If the electricity price decreases in the i-th period, the electricity consumption of users increases; e ij is the cross-elasticity coefficient. If the electricity price increases in the j-th period, the electricity consumption of users in the j-th period decreases and transfers to the i-th period, and the electricity consumption of users in the i-th period increases, that is, e ii <0, e ij > 0; D i0 、D i are the initial electricity price and the changed electricity price in the i-th period; P j0 、p j are the initial electricity price and the changed electricity price in the j-th period; p i0 、p i are the initial electricity price and the changed electricity price in the i-th period.

[0094] The price-based demand response model of the distribution network is as follows:

[0095]

[0096] In the formula, u pR is the participation degree of price-based demand response users.

[0097] In an embodiment of the present application, optionally, the incentive demand response model of the distribution network is constructed according to the participation degree of incentive demand response users and the response power.

[0098] It should be noted that incentive demand response adjusts the load size by signing economic compensation or reward contracts with users, and the application of interruptible load is the most extensive.

[0099] The compensation cost of incentive-based demand response is as follows:

[0100]

[0101] In the formula, T is the scheduling duration; Ω IL is the set of ILs; is the response power of the i-th IL at time t; c IL is the compensation cost per unit of IL.

[0102] The incentive demand response model of the distribution network is as follows:

[0103]

[0104] In the formula, u IL is the participation degree of incentive demand response users; is the maximum power of the interruptible load at time t.

[0105] In one implementation, optionally, before step 101, the method further includes:

[0106] Construct the first sub-objective function for minimizing the dispatching operation cost of the distribution network;

[0107] Construct the second sub-objective function for minimizing the voltage deviation;

[0108] Construct the objective function according to the first sub-objective function and the second sub-objective function;

[0109] Determine the constraint conditions according to at least one of the network operation constraints, the controllable micro gas turbine operation constraints, the energy storage operation constraints, the demand response constraints, and the reactive power compensation equipment constraints, where the demand response constraints are determined based on the distribution network demand response model;

[0110] Establish the distribution network dispatching model according to the objective function and the constraint conditions.

[0111] In the embodiment of the present application, optionally, the distribution network dispatching operation cost includes at least one of the following:

[0112] Power purchase cost;

[0113] Power generation cost;

[0114] Energy storage operation cost;

[0115] Demand response cost;

[0116] SVC (Static Var Compensator) operation cost;

[0117] CB (Shunt Capacitor) operation cost.

[0118] In this embodiment of the present application, construct the first sub-objective function for minimizing the dispatching operation cost of the distribution network. This first sub-objective function can be called the economic objective function and is expressed by the following formula:

[0119]

[0120] In the formula, in the first item, C is the total operation cost of the system during the entire dispatching period; C G is the power purchase cost from the upper-level power grid; C DG is the power generation cost of the controllable DG (micro gas turbine). C OD is the energy storage operation cost. C T is the demand response cost of the time-of-use electricity price; C IL is the interruptible cost.

[0121] The second item is the SVC operation cost; Q svc (t) is the reactive power compensation power of the SVC; c svc is the unit operation cost of the SVC.

[0122] The third item is the economic cost generated by the switching times of the CB; cCB is the average adjustment cost for one CB operation; N C is the number of CB switching operations in the entire cycle.

[0123] Construct the second sub-objective function for minimizing voltage deviation, which can be called the security objective function, and is expressed by the following formula:

[0124]

[0125] In the formula, N bus is the number of nodes, U i (t) is the voltage of node i at time t; is the upper limit of the voltage deviation of node i at time t; U iN is the rated voltage of node i.

[0126] Therefore, the objective function is constructed by using the above first objective sub-function and second objective sub-function. The objective function of this application embodiment combines multiple objectives to achieve multi-objective scheduling of the distribution network.

[0127] Furthermore, a distribution network scheduling model is established according to the objective function and constraint conditions.

[0128] Among them, the constraint conditions can include at least one of the following:

[0129] (1) Network operation constraints, including system power balance constraints, node voltage constraints, branch current constraints, etc.

[0130] The system power balance constraint is as follows:

[0131]

[0132] In the formula, P pci (t), Q pci (t) are the active and reactive power injected into the tie line of node i at time t, P DGi (t), Q DGi (t) are the active and reactive power injected by DG or energy storage at node i at time t; other reactive variables in the power grid are regarded as constants; P Li (t), Q Li (t) are the active and reactive power demands of the load at node i at time t; U i (t) and U k (t) are the voltages of nodes i and k at time t; B ik , G ij are the susceptance and conductance of the branch; θ ik is the voltage phase angle difference between nodes i and j. Q ci (t) is the reactive power output of the reactive power compensation device at node i during time period t.

[0133] The node voltage constraint is as follows:

[0134] U imin ≤U i ≤U imax ;

[0135] In the formula, U imax and U imin are the upper and lower limit values of U i respectively.

[0136] The branch current constraint is as follows:

[0137] I j ≤I jmax

[0138] In the formula, I jmax is the upper limit value of the current allowed to flow through branch j.

[0139] (2) The operation constraints of controllable DG, including the output power constraint of controllable DG, the ramp rate constraint of controllable DG, etc.

[0140] The output power constraint of controllable DG is as follows:

[0141] P DG,i,min ≤P DG,i (t) ≤ P DG,i,max ;

[0142] In the formula, P DG,i (t) is the active power output of DG at time t; P DG,i,min and P DG,i,max are the upper and lower limit values of the active power output of DG.

[0143] The output power adjustment of controllable DG requires a certain amount of time. Therefore, the ramp rate constraint of controllable DG is as follows:

[0144]

[0145] In the formula, U DG,i and D DG,i are the upper and lower limits of the ramp rate of the i-th distributed power source; P DG,i (t), P DG,i (t - 1) are the powers of the i-th controllable DG at times t - 1 and t.

[0146] (3) Energy storage operation constraints

[0147]

[0148] In the formula, P ESSch,max and P ESSdis,max represent the maximum charging power and maximum discharging power of the energy storage at time t.

[0149] (4) Demand response constraints, including demand - side electricity price response constraints, interruptible load constraints, etc.

[0150] The demand - side electricity price response constraint is as follows:

[0151]

[0152] In the formula, p f is the adjusted electricity price during the peak period; p p is the adjusted electricity price during the normal period; p max is the maximum value of the adjusted electricity price; p min is the minimum value of the adjusted electricity price; p v is the adjusted electricity price during the off - peak period; ζ is the peak - valley electricity price ratio; ζ min , ζ max are the minimum and maximum peak - valley electricity price ratios.

[0153] The interruptible load constraint is as follows:

[0154] 0 ≤ P IL,j (t) ≤ P IL,j,max ;

[0155] In the formula, P IL,j (t) is the interrupted electricity quantity of user j at time t; P IL,j,max is the maximum interrupted electricity quantity of user j.

[0156] (5) The reactive power compensation equipment constraint is as follows:

[0157] Q CBi,min ≤ Q CBi (t) ≤ Q CBi,max ;

[0158] 0 ≤ n CB,i (t) ≤ n CB,i,max ;

[0159] Q SVCi,min ≤ Q SVCi (t) ≤ Q SVCi,max ;

[0160] In the formula, Q CBi (t) is the reactive power output of the CB at node i at time t; Q CBi,min , Q CBi,max are the upper limits of the reactive power of the CB at node i; n CB,i (t) is the number of groups of the i - th CB put into operation in time period t; n CB,i,max is the upper limit value of n CB,i (t); Q SVCi (t) is the reactive power output of the SVC at node i at time t; Q SVCi,min , Q SVCi,maxThey are the upper and lower limits of the CB reactive power output at node i.

[0161] Therefore, the distribution network scheduling method described in the embodiments of the present application considers the demand response participation rate for optimal scheduling. By coordinating and optimizing the control of distributed power sources, energy storage, and flexible loads, the operation cost of the distribution network is minimized, the network loss is better reduced, and the voltage quality of the system is improved, ensuring the safe and stable operation of the power grid.

[0162] In one implementation, optionally, constructing the objective function according to the first sub-objective function and the second sub-objective function includes:

[0163] Using a weighted minimum norm evaluation function to combine the first sub-objective function and the second sub-objective function to construct the objective function.

[0164] In this embodiment of the present application, a weighted minimum norm evaluation function is used to combine each sub-objective function, as shown in the following formula:

[0165]

[0166] In the formula, is the ideal point of the first sub-objective function F 1 , the second sub-objective function F 2 ; w 1 , w 2 are the weights of the economic index and the safety index to be scheduled, and satisfy w 1 +w 2 =1.

[0167] It should be noted that using a weighted minimum norm evaluation function to combine each sub-objective function can solve the problem of inconsistent units and dimensions between different sub-objective functions, and the weights can be flexibly set according to the differences in the value preferences of decision-makers.

[0168] In one implementation, optionally, step 101 includes:

[0169] Using an improved particle swarm algorithm to solve the distribution network scheduling model to obtain the scheduling result.

[0170] As Figure 4 is a schematic flow diagram of one implementation of the method described in the embodiments of the present application. The following will be specifically described in conjunction with Figure 4 for specific illustration.

[0171] The control variables in the distribution network scheduling model are the output of controllable DG, the charge and discharge power of energy storage, the scheduling plan of flexible loads, the reactive power output of CB, and the reactive power output of SVC. The forward-backward substitution method is used for power flow calculation. The reactive power output of CB is a discrete variable. In the optimization iteration of the improved particle swarm algorithm, the following processing needs to be done on the position of the reactive power output of CB:

[0172] x i,b (k)=round{(x i,b (k - 1)+v i,b (k) / Q C,1 )}.Q C,1 ;

[0173] In the formula, is the reactive power output of a single group of CB; x i,b (k) is the position parameter of CB at node i in the particle swarm algorithm; v i,b (k) is the speed parameter of CB at node i in the particle swarm algorithm.

[0174] To meet the power balance constraint, the connection point between the distribution network and the upper-level power grid is regarded as a slack node.

[0175] For the value of controllable DG, when its output exceeds the upper limit, the maximum value of controllable DG is taken; when its output exceeds the lower limit, the minimum value of controllable DG is taken, as specifically expressed by the following formula:

[0176] If P DG,i (t)>P DG,i (t - 1)+U DG,i , then its upper limit value P DG,i (t - 1)+U DG,i is taken;

[0177] If P DG，i (t)<P DG，i (t - 1)-D DG，i , then its lower limit value P DG,i (t - 1)-D DG,i is taken.

[0178] Therefore, the controllable DG ramp rate constraint is processed as the following formula:

[0179] P DG,i (t)=max[min(P DG,i (t),P DG,i (t - 1)+U DG,i ),P DG,i (t - 1)-D DG,i ;

[0180] To avoid excessive charge and discharge of energy storage, in the charging state, if SOC i (t + 1)>SOCmax,i , then take its upper SOC limit max,i , and adjust its output through the following formula:

[0181]

[0182] In the discharge state, if SOC i (t + 1) < SOC min,i , then take its lower SOC limit min,i , and adjust its output through the following formula.

[0183] P ESS,i (t) = E ess,i (SOC i (t) - SOC max,i ) · η d,i ;

[0184] For other constraint conditions, use the penalty function method to incorporate them into the original objective function and handle the scheduling schemes that violate the constraints:

[0185]

[0186] In the formula, K is the total number of constraints processed by the penalty function method; C is the penalty factor, which is a relatively large positive number; δ i is the penalty flag, which is 0 when the constraint is satisfied and 1 when the constraint is violated. Add the penalty function to the system power balance constraint to exclude the schemes that violate the constraints.

[0187] Optionally, the steps for solving the problem of the distribution network scheduling model using the improved particle swarm algorithm are as follows:

[0188] (1) Input parameters. Input relevant original parameters, such as the structural parameters of the active distribution network, wind power, photovoltaic power generation data values, load data values, and demand response parameters, etc.

[0189] (2) Solve the economic dispatch ideal point. Construct an economic objective function (i.e., the first sub-objective function) that minimizes the operation cost of the distribution network scheduling, and use the improved particle swarm algorithm to optimize and solve the economic objective function to find the optimal solution of the economic objective function F 1 (i.e., the economic dispatch ideal point).

[0190] (3) Solve the security dispatch ideal point. Construct a security objective function (i.e., the second sub-objective function) that minimizes the voltage deviation, use the improved particle swarm algorithm as the calculation tool to solve the voltage deviation problem, and find the optimal solution of the security objective function F 2 (i.e., the security dispatch ideal point).

[0191] (4) Solve the scheduling scheme for the multi-objective optimization of the active distribution network. Use the weighted minimum norm evaluation function to comprehensively calculate the objective function. The power flow calculation adopts the forward-backward substitution method, and an improved algorithm is used to solve the model, so as to solve the optimal scheduling scheme.

[0192] In summary, by using the distribution network scheduling method described in the embodiments of the present application, a distribution network scheduling model is established by combining the demand-side response technology and the multi-objective optimization theory. By adjusting the electricity consumption behavior of users and optimizing the scheduling strategy, the user peak value is reduced, thereby reducing the load pressure on the power grid. It can effectively achieve the balance of the power grid load and optimize the energy scheduling, improve the power grid efficiency and enhance the user's electricity consumption experience, and meet the user's needs.

[0193] As Figure 5 shown, the embodiments of the present disclosure provide a distribution network scheduling system, including:

[0194] A data acquisition module, configured to acquire user participation data;

[0195] A model construction module, configured to construct a distribution network demand response model based on the user participation data; construct a distribution network scheduling model based on the distribution network demand response model;

[0196] A model solving module, configured to solve the distribution network scheduling model to obtain a scheduling result.

[0197] For the implementation process of the functions and roles of each module in the above system, please refer to the implementation process of the corresponding steps in the above method for details, which will not be elaborated here.

[0198] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The system embodiments described above are only illustrative. The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present invention. Those of ordinary skill in the art can understand and implement without creative efforts.

[0199] In the above embodiments, any number of all the modules can be combined and implemented in one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. At least one of all the modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on substrate, a system on package, an application specific integrated circuit (ASIC), or can be implemented by any other reasonable means such as hardware or firmware for integrating or packaging circuits, or can be implemented in any one of the three implementation manners of software, hardware, and firmware or in an appropriate combination of any several of them. Alternatively, at least one of all the modules can be at least partially implemented as a computer program module, and when the computer program module runs, it can execute the corresponding functions.

[0200] See Figure 6 , the electronic device provided by the embodiments of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130, and a communication bus 1140. Among them, the processor 1110, the communication interface 1120, and the memory 1130 complete communication with each other through the communication bus 1140;

[0201] The memory 1130 is used to store computer programs;

[0202] When the processor 1110 is used to execute the program stored on the memory 1130, the power distribution network scheduling method shown below is implemented.

[0203] The above-mentioned communication bus 1140 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0204] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0205] The memory 1130 can include a random access memory (RAM), and can also include a non-volatile memory, such as at least one disk memory. Optionally, the memory 1130 can also be at least one storage device located far from the aforementioned processor 1110.

[0206] The above-mentioned processor 1110 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0207] Embodiments of the present disclosure also provide a computer-readable storage medium. A computer program is stored on the above-mentioned computer-readable storage medium, and when the computer program is executed by a processor, the distribution network scheduling method as described above is implemented.

[0208] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; it may also exist alone without being assembled into the device / apparatus. The above-mentioned computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the distribution network scheduling method according to the embodiments of the present disclosure is implemented.

[0209] According to the embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or device.

[0210] The above-described embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. A distribution network scheduling method, characterized in that, it includes the following steps: Obtain user participation data; Construct a distribution network demand response model based on the user participation data; Construct a distribution network scheduling model based on the distribution network demand response model; Solve the distribution network scheduling model to obtain a scheduling result.

2. The distribution network scheduling method according to claim 1, characterized in that, the user participation data includes price-based demand response user participation and incentive-based demand response user participation.

3. The distribution network scheduling method according to claim 2, characterized in that, the distribution network demand response model includes a distribution network price-based demand response model and / or a distribution network incentive-based demand response model.

4. The distribution network scheduling method according to claim 3, characterized in that, the distribution network price-based demand response model is constructed according to the price-based demand response user participation and the electricity price; the distribution network incentive-based demand response model is constructed according to the incentive-based demand response user participation and the response power.

5. The distribution network scheduling method according to claim 1, characterized in that, before solving the distribution network scheduling model to obtain a scheduling result, the method further includes: Construct a first sub-objective function for minimizing the operation cost of the distribution network scheduling; Construct a second sub-objective function for minimizing the voltage deviation; Construct an objective function according to the first sub-objective function and the second sub-objective function; Determine constraint conditions according to at least one of network operation constraints, controllable micro gas turbine operation constraints, energy storage operation constraints, demand response constraints, and reactive power compensation device constraints, and the demand response constraints are determined based on the distribution network demand response model; Establish the distribution network scheduling model according to the objective function and the constraint conditions.

6. The distribution network scheduling method according to claim 5, characterized in that, the operation cost of the distribution network scheduling includes at least one of the following: Power purchase cost; Power generation cost; Energy storage operation cost; Demand response cost; Static var compensator operation cost; Shunt capacitor operation cost.

7. The distribution network scheduling method according to claim 5, characterized in that, constructing an objective function according to the first sub-objective function and the second sub-objective function includes: Using a weighted minimum norm evaluation function to combine and process the first sub-objective function and the second sub-objective function to construct the objective function.

8. The distribution network scheduling method according to claim 1, characterized in that, solving the distribution network scheduling model to obtain a scheduling result includes: Using an improved particle swarm optimization algorithm to solve the distribution network scheduling model to obtain the scheduling result.

9. A distribution network scheduling system, characterized in that, it includes: A data acquisition module for acquiring user participation data; A model construction module for constructing a distribution network demand response model based on the user participation data; constructing a distribution network scheduling model based on the distribution network demand response model; A model solving module, configured to solve the distribution network scheduling model to obtain a scheduling result.

10. An electronic device, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; The memory is used for storing computer programs; The processor, when executing the programs stored in the memory, implements the distribution network scheduling method according to any one of claims 1-8.

11. A computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, it implements the distribution network scheduling method according to any one of claims 1-8.

Images