Flexible load decoupling regulation and control optimization method and system considering network constraints

Abstract

The invention discloses a flexible load decoupling regulation and control optimization method and system considering network constraints, and relates to the technical field of electric power systems, and the method comprises the steps: building a target function according to the dispatching requirements of an electric power system, taking the minimum cost of controllable load participation regulation as a target, adding constraint conditions, and constructing an optimization model; and solving the optimization model by using a COPT solver, carrying out power flow verification on the solved model, and judging whether a result is out of limit or not. According to the flexible load decoupling regulation and control optimization method considering the network constraints, various constraint conditions such as scheduling instruction requirements, load regulation capability limit values, power balance constraints, network constraints and line constraints sent by a power system scheduling center are considered, various flexible resource characteristics in a power system are comprehensively considered, and an optimization model is established. Meanwhile, for the optimization model, rapid load flow calculation is carried out, whether a result is out of limit is judged, line network constraints are added for an out-of-limit line, and the solving speed is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular, to a flexible load decoupling regulation optimization method and system considering network constraints. Background Art

[0002] With the large-scale access of new energy and flexible loads to the power grid, all controllable resources in the power grid have become power generation resources, resulting in a large number of adjustable resources on the load side. However, under the current traditional dispatching control mode, the elasticity and regulation potential of flexible loads have not been fully utilized, causing a waste of the actual regulation resources of the entire network. Therefore, it is necessary to conduct research on the optimal dispatching of different controllable loads on the power grid load side, cooperate with the power generation side of the power grid to jointly maintain the balance of the power system, and achieve the optimal operation of the power source, grid, load, and storage.

[0003] Existing solutions establish a load dispatching model that takes into account both dispatching costs and the response reliability of power loads by analyzing the response reliability of loads with different response properties and the dynamic response reliability of different users, and use the PSO-DE optimization algorithm to solve the model; or calculate the multi-objective load information entropy index of the distribution network, and construct a multi-objective load mathematical model of the distribution network based on this index to complete the design of the multi-objective load dispatching model of the distribution network; or propose a collaborative optimization method considering "power source, grid, and load", with the goal of minimizing network losses, and analyze aspects such as power output configuration, optimal access points of energy storage, optimal access capacity, and peak load calculation. Or study an optimal dispatching model for active distribution networks considering the participation of various resources to improve the economy of the distribution system and the consumption capacity of renewable energy. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that in the process of flexible load decoupling regulation optimization, most studies are focused on the regulation optimization of a certain type of controllable load. However, how to conduct regulation optimization analysis on various controllable loads in the system on the premise of deeply considering the energy correlation of various controllable loads in the power system is a current research difficulty. At the same time, the flexible load decoupling optimization regulation problem is a multi-variable, multi-objective, non-linear dynamic stochastic control problem, which requires complex calculations and optimizations. However, as the scale of the solution increases, the optimization solution speed will be greatly reduced, making it difficult to meet the application requirements; at the same time, the decoupling optimization regulation of flexible loads needs to consider various factors such as users' electricity consumption willingness, response capacity, control cost, and dispatching accuracy, with a large optimization regulation difficulty, which may lead to incomplete regulation optimization of different types of controllable loads and the inability to fully utilize the flexibility resources on the demand side of the power grid.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A decoupled regulation and optimization method for flexible loads considering network constraints, including:

[0007] According to the power system dispatching requirements, with the goal of minimizing the regulation cost of controllable loads participating in regulation, establish an objective function, add constraint conditions, and construct an optimization model;

[0008] Use the COPT solver to solve the optimization model, conduct a power flow verification on the solved model, and determine whether the results are out of limits;

[0009] If there is an out-of-limit situation, consider the impact of the line network, and add corresponding constraints to the out-of-limit lines; if there is no line out-of-limit situation, output the calculation results.

[0010] As a preferred solution of the decoupled regulation and optimization method for flexible loads considering network constraints according to the present invention, wherein: the objective function is expressed as,

[0011]

[0012] where N is the number of types of controllable resources participating in regulation; i represents the i-th type of controllable resource participating in regulation; T is the time of participation in regulation; t is the current moment; N DR is the number of demand response entities, P DRi，t is the adjustment value of the demand response entity DR i at time t; cost DRi，t is the regulation cost of the adjustable load of the demand response entity DR i at time t.

[0013] As a preferred solution of the decoupled regulation and optimization method for flexible loads considering network constraints according to the present invention, wherein: the constraint conditions include power balance constraints, load capacity constraints, upper and lower limits of load adjustment constraints, load quotation constraints, and line constraints.

[0014] As a preferred solution of the decoupled regulation and optimization method for flexible loads considering network constraints according to the present invention, wherein: the power flow verification is the Newton-Raphson method.

[0015] As a preferred solution of the decoupled regulation and optimization method for flexible loads considering network constraints according to the present invention, wherein: the Newton-Raphson method includes giving the parameters of nodes, branches, generators, and loads;

[0016] Form a nodal admittance matrix;

[0017] Given the initial values of the voltage magnitudes and phase angles of each node U (0) , θ (0) , calculate the active power errors ΔP of PQ and PV nodes （0)and the reactive power error ΔQ of the PQ node (0) ;

[0018] Solve the Jacobian matrix using the LU decomposition method. According to U (0) , θ (0) Use to calculate the Jacobian matrix J (0) , construct and solve the correction equation to obtain Δθ (0) and ΔU (0) , and calculate the new node voltage amplitude and phase angle;

[0019] Calculate the active power error ΔP (1) and the reactive power error ΔQ (1) , and determine whether the power error converges.

[0020] As a preferred solution of the flexible load decoupling regulation and optimization method considering network constraints according to the present invention, wherein: the determination of whether the power error converges includes calculating the branch power flow if it converges; if it does not converge, perform the next iteration: calculate the new Jacobian matrix J (1) , solve the correction equation again to obtain Δθ (1) and ΔU (1) , and calculate θ (2) , U (2) , calculate ΔP (2) , ΔQ (2) , until the power error meets the convergence condition;

[0021] After stopping the iteration, obtain the node voltage and phase angle at this time as the initial power flow value for sensitivity analysis.

[0022] As a preferred solution of the flexible load decoupling regulation and optimization method considering network constraints according to the present invention, wherein: adding corresponding constraints to the over-limit lines includes, when considering the influence of network constraints, converting the network power flow constraints into linear constraints through sensitivity analysis;

[0023] The network constraints considered include apparent power constraints and node voltage constraints.

[0024] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the flexible load decoupling regulation and optimization method considering network constraints as described above.

[0025] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the flexible load decoupling regulation and optimization method considering network constraints as described above.

[0026] Advantages of the present invention: The flexible load decoupling regulation and optimization method considering network constraints provided by the present invention includes various flexibility resources such as adjustable loads in the model, and considers various constraint conditions such as the dispatching instruction requirements sent by the power system dispatching center, the load regulation capacity limit, power balance constraints, network constraints, and line constraints. It comprehensively considers the characteristics of various flexibility resources in the power system and establishes an optimization model. At the same time, for the optimization model, a fast power flow calculation is performed to determine whether the results are out of limits. For the out-of-limit lines, line network constraints are added to accelerate the solution speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0028] Figure 1 It is the overall flowchart of a flexible load decoupling regulation and optimization method considering network constraints provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will give a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0030] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0031] Embodiment 1

[0032] Refer to Figure 1 , which is an embodiment of the present invention, and provides a flexible load decoupling regulation and optimization method considering network constraints, including:

[0033] Step 1: According to the dispatching requirements of the power system, with the goal of minimizing the cost of adjustable loads participating in regulation, establish an objective function, and add constraint conditions such as power constraints, load bid constraints, and capacity constraints to construct an optimization model.

[0034] Step 2: Use the COPT solver to solve the optimization model, and perform power flow verification on the solved model to determine whether the results are out of limits;

[0035] Step 3: If there is an out-of-limit situation, consider the impact of the line network. Based on the sensitivity analysis method, obtain the network power flow constraints and node voltage constraint conditions, add corresponding constraints to the out-of-limit lines, and repeat Step 2; if there is no line out-of-limit situation, output the calculation results.

[0036] In Step 1 of the embodiment of the present invention: Establishment of the flexible load decoupling regulation optimization model

[0037] The flexible load decoupling regulation problem is, under the condition of knowing the grid structure of the transmission and distribution network and the load demand on the transmission network side, by establishing an optimization model, to solve the optimal power distribution values of different types of controllable loads in the distribution network system, so as to achieve the goal of minimizing the regulation cost. This model considers constraint conditions such as power balance constraints, line constraints, and network constraints. This section introduces the flexible load decoupling regulation optimization model, and the specific description is as follows:

[0038] (1) Objective function

[0039] According to the requirements of the power dispatching center, select the controllable loads that need to be regulated. In order to better enable the demand side to cooperate with the power generation side of the power grid to jointly maintain the balance of the power system and enhance the flexibility of the load side, with the goal of minimizing the regulation cost, establish the objective function:

[0040]

[0041] In the formula, N is the number of types of controllable resources participating in the regulation; i represents the i-th type of controllable resource participating in the regulation; T is the time participating in the regulation; t is the current moment; N DR is the number of demand-side response entities, P DRi,t is the adjustment value of the demand-side response entity DR i at time t; cost DRi,t is the regulation cost of the adjustable load of the demand-side response entity DR i at time t.

[0042] (2) Constraint conditions

[0043] ① Power balance constraint

[0044] According to the instructions issued by the dispatching center, the regulation amount of the controllable resources needs to be equal to the instruction value issued by the dispatching center, as shown in the following formula:

[0045]

[0046] In the formula, P c is the instruction value issued by the dispatching center. In this formula, Pc is a known quantity.

[0047] ② Load capacity constraint

[0048] The adjustment amount of the flexible load plus the base value of the load should satisfy the maximum and minimum load constraints.

[0049] P min,i,t ≤ P DRi,t + P base,i,t ≤ P max,i,t (3)

[0050] In the formula, P base，i，t is the base capacity of the flexible load entity i at time t; P min，i，t is the lower limit of the base capacity of the flexible load entity i at time t, and P max，i，t is the maximum capacity of the flexible load entity at time t.

[0051] ③ Load adjustment upper and lower limit constraints:

[0052] 1) Corresponding load increase model:

[0053] 0 ≤ P DRi,t ≤ UL i,t (4)

[0054] Among them, UL i,t is the upper limit of the adjustment amount of the load entity i at time t.

[0055] 2) Corresponding load decrease model:

[0056] -DL i,t ≤ P DRi,t ≤ 0 (5)

[0057] Among them, DL i,t is the lower limit of the adjustment amount of the load entity i at time t.

[0058] ④ Load quotation constraint

[0059] The quantity of the flexible load quotation should not exceed the upper limit of the quotation.

[0060] 1) Corresponding load increase model:

[0061] 0 ≤ P DRi,t ≤ P max (6)

[0062] Among them, P max is the maximum adjustment value of the load entity i at time t.

[0063] 2) Corresponding load decrease model:

[0064] -P max ≤ PDRi,t ≤0 (7)

[0065] Among them, P max is the maximum adjustment value of the load i subject at time t.

[0066] According to the principle of "price priority, capacity priority", the lowest price is taken as the overall goal in the objective function. When the bids of flexible load subjects are the same, the load subject with a larger capacity will reduce its bid by 0.00001 yuan / MW to obtain a preferential dispatching order under the same bid.

[0067] The price of the winning bid load subsidy for this time is determined through centralized bidding and marginal clearing. The market clearing price at each time period and each node is the Lagrange multiplier of the power balance constraint of that node generated during the dispatching optimization process of that time period.

[0068] LMP t = λ t (8)

[0069] In the formula, λ t is the Lagrange multiplier of the system load balance constraint at time period t.

[0070] ⑤ Line constraint

[0071] The regulation ability of flexible load is restricted by lines and voltage as follows:

[0072]

[0073] Among them, S line,0 is the apparent power transmission on the line at the current moment; S max is the maximum apparent power allowed to be transmitted by the line; ΔS line is the change value of the apparent power of the line after the adjustable load participates in the regulation at time t; U min is the minimum allowable operating voltage of the network node; U max is the maximum allowable operating voltage of the network node; U node,0 is the voltage of the network node at the current moment; ΔU node is the change value of the voltage of the network node after the controllable resource participates in the regulation at time t.

[0074] In step 2 of the embodiment of the present invention: the AC power flow calculation step based on the Newton-Raphson method

[0075] Due to the large number of resources on the demand side, when adjusting different loads in the distribution network, the impact of load changes on line transmission needs to be considered. At the same time, in the distribution network, since R is not much larger than X, the influence of resistance cannot be ignored. Therefore, when adjusting adjustable loads, it is also necessary to consider that the active power and voltage value of network transmission do not exceed the transmission limit, and the number of network constraints involved is more and more complex. To maintain safe operation, the line transmission power cannot exceed its limit. Therefore, when considering the network transmission limit in the dispatching process, it is necessary to perform power flow analysis on the distribution network. This involves calculating the power flow of all lines in the model to determine whether the transmission power of the line exceeds the limit, aiming to reduce the interference of network constraints on the optimal dispatching model. Among them, the steps of power flow calculation are as follows:

[0076] ① Give the parameters of nodes, branches, generators, and loads; for PQ nodes, given active and reactive powers; for PV nodes, given active power and voltage amplitude; for the slack node, given voltage amplitude and other information.

[0077] ② Form the nodal admittance matrix:

[0078] ③ Given the initial values of voltage amplitude and phase angle U (0) 、θ (0) of each node, calculate the active power error ΔP (0) of PQ and PV nodes and the reactive power error ΔQ (0) of PQ nodes according to Equation (10):

[0079]

[0080] where ΔP i 、ΔQ i respectively represent the change in injected active power and reactive power of node i; P i and Q i respectively represent the net injected active power and reactive power of node i; θ ij is the voltage phase angle difference between nodes i and j; U i 、U j are the voltage values of nodes i and j; g ij is the conductance of line ij; b ij is the susceptance of line ij.

[0081] ④ Use the LU decomposition method to solve the Jacobian matrix, calculate the Jacobian matrix J (0) according to U (0) 、θ (0) using Equations (13) and (14), construct and solve the correction equation according to Equation (11) to obtain Δθ (0) and ΔU (0) , and calculate the new node voltage amplitude and phase angle: θ (1) =θ(0) +Δθ (0) 、U (1) =U (0) +ΔU (0) 。

[0082]

[0083] 1) For the off - diagonal elements (i≠j) of each block matrix in the Jacobian matrix, there are:

[0084]

[0085] 2) For the diagonal elements (i = j) of each block matrix, there are:

[0086]

[0087] ⑤ Calculate the active power error ΔP (1) and the reactive power error ΔQ (1) , and determine whether the power error converges.

[0088]

[0089] If it converges, calculate the branch power flow; if not, return to ④ for the next iteration: calculate the new Jacobian matrix J (1) , and solve the correction equation again to obtain Δθ (1) and ΔU (1) , and calculate θ (2) 、U (2) , calculate ΔP (2) 、ΔQ (2) , until the power error meets the convergence condition.

[0090] After stopping the iteration, obtain the node voltage and phase angle at this time as the initial power flow value for sensitivity analysis.

[0091] In step 3 of the embodiment of the present invention: the network constraint verification method based on the sensitivity analysis method

[0092] When considering the influence of network constraints, through the sensitivity analysis method, the network power flow constraints are transformed into linear constraints. The network constraints considered include apparent power constraints and node voltage constraints. The steps of transforming the network power flow constraints into linear constraints through the sensitivity analysis method are as follows:

[0093] 1) Combine the power flow calculation program to calculate the correction equation:

[0094]

[0095] Wherein, the orders of ΔP and Δθ are n - 1, and the orders of ΔQ and ΔU are m; H, N, M, and L are sub - block matrices of the Jacobian matrix, and their orders are (n - 1)×(n - 1), (n - 1)×m, m×(n - 1), and (m×m) respectively. (m is the number of PQ nodes)

[0096] 2) For the elements of each sub - block matrix in the Jacobian matrix, there are

[0097]

[0098] 3) Taking the inverse of the Jacobian matrix from Equation (16), we can obtain:

[0099]

[0100] Wherein, A, B, C, and D are sub - block matrices of the inverse matrix of the Jacobian matrix respectively. From the sub - block matrices, we can get:

[0101] ΔU = CΔP (19)

[0102] Therefore, for the load node i, there is:

[0103] ΔU i =C i1 ΔP 1 +C i2 ΔP 2 +...+C i，n-1 ΔP n-1 (20)

[0104] That is: ΔU i，line =C i1 P DR1，t +C i2 P DR2，t +.....+C iN P DRN，t (21)

[0105] Wherein, N is the number of adjustable loads; P DRN,t is the active power adjustment value of the demand - side response entity DR i at time t; ΔU i,line is the change value of the network node voltage after the controllable resources participate in the regulation.

[0106] 4) The branch power flow equation can also be processed by the above - mentioned method, and the relationship between the power of each branch and the node injection power can be analyzed as follows:

[0107]

[0108] Wherein, P ij and Q ij are the active and reactive power magnitudes transmitted from node i to node j respectively; t ijis the transformer turn ratio on branch i-j; if the branch is only a wire, then t ij is 0; if it contains a transformer, then t ij is the ratio of the reference voltages at the beginning and end of the bus; b ij0 is half of the susceptance of branch i-j.

[0109] 5) Perform a Taylor expansion on Equation (22) and neglect the higher-order terms to obtain:

[0110]

[0111] In the formula, ΔP l , ΔQ l are the active and reactive powers transmitted by each branch respectively; ΔP and ΔQ are the active and reactive powers injected into each node respectively; M is the sensitivity coefficient matrix between the power of each branch and the phase angle voltage of each node; E, F, G, K are the sub-block matrices of the sensitivity matrix, representing the sensitivity relationships between the active and reactive powers transmitted by each branch and the active and reactive powers injected into each node. The orders are t×(n - 1), t×m, t×(n - 1), t×m respectively. The total order of the sensitivity matrix is 2t×(n + m - 1), where t is the total number of branches, n is the number of system nodes, and m is the number of PQ nodes.

[0112] For each element in the sensitivity matrix, there are:

[0113]

[0114]

[0115] Among them, t is the total number of branches, n is the number of system nodes, and m is the number of PQ nodes.

[0116] 6) From Equation (23), we can obtain:

[0117]

[0118] According to the calculation formula of apparent power: We can obtain:

[0119]

[0120] Among them, ΔS line is the change value of the line apparent power after the adjustable load of the node participates in the regulation.

[0121] 7) According to Equation (19), the node voltage constraint condition can be obtained:

[0122] U min ≤U node,0 +ΔU node ≤U max (28)

[0123] That is: U min ≤U node,0 +CP DRi,t ≤U max (29)

[0124] Expanding Equation (29) gives:

[0125] U min,1 ≤U 1node,0 +C 11 P DR1,t +C 12 P DR2,t +...+C m,n-1 P DRn-1,t ≤U 1,max

[0126] U min,2 ≤U 2node,0 +C 21 P DR1,t +C 22 P DR2,t +...+C m,n-1 P DRn-1,t ≤U 2,max ...

[0128] U m,min ≤U mnode,0 +C m1,1 P DR1,t +C m2,2 P DR2,t +...+C m,m P DRm,t ≤U max,m (30)

[0129] 8) According to Equation (27), the line apparent power constraint condition is obtained:

[0130]

[0131] Squaring both sides of Equation (31) gives:

[0132] 0≤(RP DRi,t ) 2 +(LP DRi,t ) 2 ≤(S max -S line,0 ) 2 (32)

[0133] Expanding Equation (32) gives:

[0134] (R 11 P DR1,t +....+R 1,n-1 PDRn-1,t ) 2 +(W 11 P DR1,t +...+W 1,n-1 P DRn-1,t ) 2 ≤(S max,1 -S 1,0,1 ) 2 ....

[0136] (R t,1 P DR1,t +...+R t,n-1 P DRn,t ) 2 +(W t,1 P DR1,t +...+W t,n-1 P DRn-1,t ) 2 ≤(S max,t -S 1,0,t ) 2 (33)

[0137] Among them, P DR1,t...... P DRn-1,t is the active power adjustment value of the demand-side response entity DR i at time t; t is the total number of system branches; n is the number of system nodes.

[0138] 9) According to the above analysis, the active power regulation amount of the load is subject to the network constraint as:

[0139]

[0140] Among them, S line,0 is the apparent power transmission on the line at the current moment;, S max is the maximum allowable apparent power transmission of the line, U min is the minimum allowable operating voltage of the network node; U max is the maximum allowable operating voltage of the network node, U node,0 is the network node voltage at the current moment. Among them, S line,0 and U node,0 are obtained through power flow calculation before adding line constraints.

[0141] Embodiment 2

[0142] An embodiment of the present invention provides a flexible load decoupling control and optimization system considering network constraints, including:

[0143] A model construction module, which is used to establish an objective function according to the dispatching requirements of the power system, with the goal of minimizing the regulation cost of controllable loads participating, adding constraint conditions, and constructing an optimization model;

[0144] A model solving module, which is used to solve the optimization model using a COPT solver, perform power flow verification on the solved model, and judge whether the results are out of limits;

[0145] A regulation and optimization module, which is used to consider the influence of the line network if there is an out-of-limit situation and add corresponding constraints to the out-of-limit lines; if there is no line out-of-limit situation, the calculation results are output.

[0146] Embodiment 3

[0147] An embodiment of the present invention, which is different from the previous two embodiments in that:

[0148] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0149] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0150] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0151] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0152] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A flexible load decoupling control optimization method considering network constraints, characterized in that: include: According to the dispatching requirements of the power system, the objective function is established with the goal of minimizing the cost of controllable load participation in regulation, and constraints are added to construct an optimization model; The COPT solver is used to solve the optimization model, and the power flow is verified on the solved model to determine whether the result exceeds the limit; If there is an over-limit situation, the influence of the line network is considered and corresponding constraints are added to the over-limit line; if there is no line over-limit situation, the calculation result is output.

2. The flexible load decoupling control optimization method considering network constraints according to claim 1 is characterized in that: The objective function is expressed as, Where N is the number of controllable resources involved in the regulation; i represents the i-th controllable resource involved in the regulation; T is the time of participating in the regulation; t is the current time; N DR is the number of demand-side responding entities, P DRi,t DR is the demand side response entity i Adjust the value in time period t; cost DRi,t DR is the demand side response entity in period t i The adjustment cost of the adjustable load.

3. The flexible load decoupling control optimization method considering network constraints according to claim 2 is characterized in that: The constraints include power balance constraints, load capacity constraints, load adjustment upper and lower limit constraints, load quotation constraints and line constraints.

4. The flexible load decoupling control optimization method considering network constraints as claimed in claim 3 is characterized by: The power flow verification is the Newton-Raphson method.

5. The flexible load decoupling control optimization method considering network constraints according to claim 4 is characterized in that: The Newton-Raphson method includes providing parameters of nodes, branches, generators, and loads; Forming a node admittance matrix; Given the voltage amplitude and initial phase angle U of each node (0) ,θ (0) , calculate the active power error ΔP of PQ and PV nodes (0) And the reactive power error ΔQ of the PQ node (0) ; Use LU decomposition method to solve Jacobi matrix, according to U (0) ,θ (0) Using the calculation of the Jacobian matrix J (0) , construct the correction equation and solve it to get Δθ (0) and ΔU (0) , and calculate the new node voltage amplitude and phase angle; Calculate the active power error ΔP (1) and reactive power error ΔQ (1) , determine whether the power error converges.

6. The method for optimizing the flexible load decoupling control considering network constraints according to claim 5, characterized in that: The determination of whether the power error has converged includes calculating the branch power flow if it has converged; and performing the next iteration if it has not converged: calculating the new Jacobian matrix J (1) , solve the modified equation again to get Δθ (1) and ΔU (1) , and calculate θ (2) , U (2) , calculate ΔP (2) , ΔQ (2) , until the power error meets the convergence condition; After the iteration is stopped, the node voltage and phase angle at this time are obtained as the initial value of the power flow for sensitivity analysis.

7. The method for optimizing the flexible load decoupling control considering network constraints according to claim 6, characterized in that: The adding of corresponding constraints to the over-limit lines includes converting the network power flow constraints into linear constraints by using a sensitivity analysis method when considering the influence of network constraints; The network constraints considered include apparent power constraints and node voltage constraints.

8. A system using the flexible load decoupling control optimization method considering network constraints as described in any one of claims 1 to 7, characterized in that: include: The model building module is used to establish the objective function and add constraints to build the optimization model according to the power system dispatching requirements and the goal of minimizing the cost of controllable load participation in regulation; The model solving module is used to solve the optimization model using the COPT solver, perform power flow verification on the solved model, and determine whether the result exceeds the limit; The control optimization module is used to consider the impact of the line network if there is an over-limit situation and add corresponding constraints to the over-limit line; if there is no line over-limit situation, the calculation results are output.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the flexible load decoupling control optimization method considering network constraints described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the flexible load decoupling control optimization method considering network constraints described in any one of claims 1 to 7 are implemented.

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