Method and system for accommodating new energy in distribution network considering SOP access and network reconstruction

Abstract

The present invention discloses a method and system for accommodating new energy in a distribution network taking into account SOP access and network reconstruction, the method comprising: taking the maximum amount of new energy consumption as the goal, establishing a medium-voltage distribution network new energy consumption capacity model taking into account SOP access and network reconstruction; converting the medium-voltage distribution network new energy consumption capacity model into a second-order cone programming model; on the basis of the second-order cone programming model, considering the uncertainty of new energy output, establishing a two-stage robust optimization model of the medium-voltage distribution network new energy consumption capacity taking into account SOP access and network reconstruction; solving the two-stage robust optimization model to obtain a corresponding distribution network new energy consumption plan. The present invention can realize the improvement of the new energy consumption capacity by flexible interconnection technology.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy consumption in power grids, and relates to a method and system for consuming new energy in a distribution network taking into account SOP access and network reconstruction. Background Art

[0002] As an important public infrastructure, the distribution network plays an important role in ensuring power supply, supporting economic and social development, and serving to improve people's livelihood. With the advancement of the construction of new power systems, the distribution network is gradually transforming from a network that simply receives and distributes electricity to users to a new distribution network that integrates source, grid, load and storage and is flexibly coupled with the upper-level power grid. Among them, new energy with randomness and volatility is easily affected by the environment during the absorption process, which makes it abandon wind and light in actual operation, wasting energy resources. Therefore, with the rapid popularization of distributed power generation of renewable energy, the absorption of new energy in medium-voltage distribution networks has become a major problem.

[0003] In the context of the gradual intelligent and flexible transformation of distribution networks, the intelligent soft switch (soft open point, SOP) as a typical flexible interconnection device can replace the traditional tie switch to connect the adjacent feeders at both ends, control the active and reactive power flow, enhance the flexibility of the power grid, and has the potential to promote the consumption of new energy. In order to fully consider the uncertainty of the output of new energy and submit an effective and referenceable decision result to the superior, it is also necessary to select a suitable uncertainty processing method. Robust optimization is the mainstream method for dealing with uncertainty at present. It breaks through many assumptions that rely on prior knowledge and obey probability distribution. The scale of solution is relatively small and it has been widely used. The key lies in the establishment of robust optimization model. The model constructed by the existing technology generally does not fully consider the uncertainty of new energy output. It only explores the system's new energy consumption capacity under the determined wind and solar forecast curve. The results obtained often have a certain deviation from the actual situation and cannot be used as an effective reference. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and system for accommodating new energy in a distribution network taking into account SOP access and network reconstruction, establishes a robust optimization model for new energy consumption in a medium-voltage distribution network taking into account intelligent soft switch access and network reconstruction, obtains different consumption schemes by adjusting conservatism, and realizes the improvement of new energy consumption capacity through flexible interconnection technology.

[0005] The present invention adopts the following technical solution.

[0006] A first aspect of the present invention provides a method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction, comprising:

[0007] Step 1: With the goal of maximizing the amount of new energy consumption, a new energy consumption capacity model for the medium-voltage distribution network is established, taking into account SOP access and network reconstruction;

[0008] Step 2: converting the medium voltage distribution network new energy consumption capacity model into a second-order cone programming model;

[0009] Step 3: Based on the second-order cone programming model, considering the uncertainty of renewable energy output, a two-stage robust optimization model for the renewable energy absorption capacity of the medium-voltage distribution network is established, taking into account SOP access and network reconstruction;

[0010] Step 4: Solve the two-stage robust optimization model to obtain the corresponding distribution network new energy consumption plan.

[0011] Preferably, the medium voltage distribution network new energy consumption capacity model considering SOP access and network reconstruction established in step 1 has an objective function as follows:

[0012] (12)

[0013] Where: and They represent the node set containing photovoltaic power and the node set containing wind power respectively;

[0014] and Respectively represent nodes of photovoltaic and wind power in The power consumption at the moment;

[0015] Indicates a time interval;

[0016] T represents the total number of time intervals.

[0017] Preferably, the medium-voltage distribution network new energy absorption capacity model taking into account SOP access and network reconstruction established in step 1 has constraints including distribution network Distflow flow constraints, operation safety constraints, line current carrying capacity constraints, new energy output constraints, SOP constraints and network reconstruction constraints.

[0018] Preferably, the distribution network Distflow flow constraint is:

[0019] (13)

[0020] (14)

[0021] (15)

[0022] (16)

[0023] (17)

[0024] (18)

[0025] (19)

[0026] Where: Representation Node The collection of child nodes;

[0027] Representation Node The parent node set of

[0028] express Time from node Flow to Node Active power of

[0029] and Respectively represent branches resistance and reactance;

[0030] express Time Node Flow to Node The current value;

[0031] express Time from node Flow to Node Reactive power;

[0032] Representation Node exist The voltage amplitude at the moment;

[0033] Representation Node exist Active power injected from the outside at all times;

[0034] express Time from node Flow to Node Active power of

[0035] express Time from node Flow to Node Reactive power;

[0036] Representation Node exist Reactive power injected from the outside at all times;

[0037] express Time Node Flow to Node The current value;

[0038] Representation Node exist The voltage amplitude at the moment;

[0039] Indicates extremely large numbers;

[0040] express Branch within time The state variable, 1 means connected, 0 means disconnected;

[0041] and Respectively represent branches resistance and reactance;

[0042] and express At the node Active power output from photovoltaic and wind power;

[0043] , represent At the node Reactive power output from photovoltaic and wind power;

[0044] , Respectively represent Time SOP to the node Active and reactive power provided;

[0045] and Respectively expressed in Time Node Active and reactive power consumed.

[0046] Preferably, the operational safety constraints are:

[0047] (20)

[0048] (twenty one)

[0049] Where: express The voltage amplitude at the moment;

[0050] express Time Branch the current flowing;

[0051] and Respectively represent the lower and upper limits of voltage;

[0052] Indicates the upper current limit.

[0053] Preferably, the line current carrying capacity constraint is:

[0054] (twenty two)

[0055] Where: express Time Branch Active power flowing through;

[0056] express Time Branch Reactive power flowing through;

[0057] Indicates branch The maximum apparent power allowed to flow.

[0058] Preferably, the new energy output constraint is:

[0059] (twenty three)

[0060] Where: , Respectively represent nodes exist Wind power and photovoltaic power are producing active power at all times;

[0061] , Respectively represent nodes exist The upper limit of wind power and photovoltaic active output at all times.

[0062] Preferably, the SOP constraints include:

[0063] 1) SOP active power constraint:

[0064] (1)

[0065] (2)

[0066] (3)

[0067] Where: and Represents SOP nodes respectively and nodes In Active power output at all times;

[0068] and Represents SOP nodes respectively and nodes exist The power loss generated at each moment;

[0069] and Represents SOP nodes respectively and nodes exist Reactive power output at all times;

[0070] and Representation Node and nodes Loss coefficient of the port;

[0071] 2) SOP reactive power constraint:

[0072] (4)

[0073] (5)

[0074] Where: and Represents SOP nodes respectively and nodes The maximum reactive power that can be output;

[0075] 3) SOP capacity constraints:

[0076] (6)

[0077] (7)

[0078] Where: Indicates the maximum capacity that can be transmitted on the line where the SOP is located.

[0079] Preferably, the network reconstruction constraints include:

[0080] (8)

[0081] (9)

[0082] (10)

[0083] (11)

[0084] Where: express Time from node Flow to child nodes Virtual power of

[0085] express The moment is determined by the parent node Flow to Node Virtual power of

[0086] Represents a set of distribution network nodes;

[0087] express At time 1, the medium voltage distribution network is injected from the head node 1 into its child node Virtual power of

[0088] represents any real number;

[0089] Indicates extremely large numbers;

[0090] express Branch within time The state variable, 1 means connected, 0 means disconnected;

[0091] Represents a collection of medium voltage distribution network branches;

[0092] Representing a collection The number of elements contained in ;

[0093] Representation Node The collection of child nodes;

[0094] Representation Node The parent node collection of .

[0095] Preferably, in step 2, the process of converting the medium voltage distribution network new energy absorption capacity model into a second-order cone programming model is as follows:

[0096] 1) The line current carrying capacity constraint formula (22) of the medium voltage distribution network new energy absorption capacity model is transformed into:

[0097] (twenty four)

[0098] 2) Introduce the auxiliary variable shown in formula (25) and To eliminate the quadratic terms in equations (13), (14), (17), (18), and (19) of the distribution network Distflow constraint:

[0099] (25)

[0100] The corresponding constraint after eliminating the quadratic term is transformed into:

[0101] (26)

[0102] (27)

[0103] (28)

[0104] (29)

[0105] (30)

[0106] Where: Represents the transpose of a matrix;

[0107] Represents the Euclidean norm of a vector;

[0108] 3) Based on the auxiliary variables and , the operation safety constraint equations (20)-(21) of the new energy absorption capacity model of the medium voltage distribution network are transformed into:

[0109] (31)

[0110] (32)

[0111] 4) The constraints (2), (3), (6), and (7) in the SOP constraints of the new energy absorption capacity model of the medium voltage distribution network are transformed into the following rotating cone constraints:

[0112] (33)

[0113] (34)

[0114] (35)

[0115] (36)

[0116] Where: Representation Node With Node The access capacity of SOP between them.

[0117] Preferably, the uncertainty of the new energy output in step 3 is expressed as formula (37), which is used as a constraint of the two-stage robust optimization model:

[0118] (37)

[0119] Where: Indicates the total number of time intervals;

[0120] , , , Respectively Time Node The actual value, predicted value, maximum positive deviation value, and maximum negative deviation value of the renewable energy output;

[0121] , , , Respectively Time Node The actual value, predicted value, maximum positive deviation value, and maximum negative deviation value of the upper load power;

[0122] , , , is a Boolean uncertainty auxiliary variable. and When it is equal to 1, it means that the actual output of new energy and the load demand have reached the upper limit. and When it is equal to 1, it means that the actual output of new energy and the load demand have reached the lower limit, where the new energy includes wind power and photovoltaic power;

[0123] and Represent the robust parameters of new energy and load respectively. If and are all equal to zero, and the new energy output and load demand become a certain value, namely the predicted value.

[0124] Preferably, the two-stage robust optimization model of the medium-voltage distribution network's new energy consumption capacity taking into account SOP access and network reconstruction established in step 3 is expressed as follows:

[0125] (38)

[0126] (39)

[0127] (40)

[0128] (41)

[0129] Where: represents the uncertain set of new energy sources and loads;

[0130] Given and back, The set of possible values;

[0131] represents the decision variable of the first stage, which is as well as After being determined, including SOP operation strategy and network reconstruction strategies , corresponding to the problem of maximizing the capacity of absorbing new energy;

[0132] Represents the coefficient variable. Each element in the variable corresponds to a decision variable in the first stage, reflecting the impact of each decision variable on the consumption of new energy.

[0133] represents the decision variable of the second stage, which is Determined before being determined, including power flow variables , , , and new energy consumption , ;

[0134] represents the coefficient vector, each element in the vector corresponds to a decision variable in the second stage, reflecting the impact of each decision variable on the consumption of new energy;

[0135] Represents uncertainty auxiliary variables, including , , , , is a Boolean variable;

[0136] express Branch within time The state variable, 1 means connected, 0 means disconnected;

[0137] Representation Node exist The active power output by SOP at the moment;

[0138] Representation Node exist The reactive power output by SOP at the moment;

[0139] , Respectively Time Branch Active and reactive power flowing through;

[0140] , Representation Node exist Active and reactive power injected at all times;

[0141] express Time Node Flow to Node The current value;

[0142] Representation Node exist Active power injected from the outside at all times;

[0143] Representation Node exist Reactive power injected from the outside at all times;

[0144] express The voltage amplitude at the moment;

[0145] The second stage corresponds to the minimization-maximization problem of new energy absorption capacity, that is, the min-max problem. The goal is to find the most conservative scenario among the new energy output scenarios to minimize the maximum absorption of new energy.

[0146] Preferably, the two-stage robust optimization model of the medium-voltage distribution network's new energy absorption capacity taking into account SOP access and network reconstruction established in step 3 has the following constraints:

[0147] (42)

[0148] Where: It represents the decision variables of the first stage, which are SOP operation strategy and network reconstruction strategy;

[0149] Represents the decision variables of the second stage, which are the uncertainty auxiliary variables and the amount of new energy consumption;

[0150] , , , , represents the coefficient matrix;

[0151] The meaning of is the first stage decision variable The constraints that must be satisfied correspond to equations (1), (4), (5), and (8)-(11);

[0152] The meaning of the first stage decision variables , second stage decision variables With uncertainty auxiliary variables The constraints correspond to equations (15)-(16), (23), (26)-(29), (31)-(32), (37);

[0153] The meaning of is the norm constraint of the decision variable, corresponding to equations (24), (30), (33)-(36).

[0154] Preferably, the step 4 divides the two-stage robust optimization model into a main problem and a sub-problem for solution;

[0155] Among them, the main problem is the first-stage optimization problem, and its objective function and constraints are as follows:

[0156] (43)

[0157] (44)

[0158] Where: is the optimal value of the main problem;

[0159] The decision variables representing the main problem, i.e., the decision variables in the first stage, include the SOP operation strategy and the network reconstruction strategy;

[0160] and Respectively expressed in The variables of the subproblem after iterations , The solution;

[0161] The sub-problem corresponds to the second-stage problem, and its objective function and constraints are as follows:

[0162] (45)

[0163] (46)

[0164] Where: is the optimal value of the subproblem;

[0165] It represents the operation plan after optimizing the main problem;

[0166] The max problem in the subproblem is dualized. After the dualization, the outer and inner layers are merged into a min problem by introducing Lagrange multipliers. Then, formulas (45)-(46) are transformed into the objective function and Lagrange dual constraints as follows:

[0167] (47)

[0168] (48)

[0169] Where: , , Both represent Lagrange multipliers, and T represents transpose.

[0170] Preferably, the steps of solving the main problem and the sub-problems in step 4 are:

[0171] 1) Initialization parameters: set the number of iterations =1; upper and lower bound parameters , ; The maximum number of iterations is set to ; The convergence accuracy is set to ;

[0172] 2) According to the objective function and constraints of the main problem, solve the SOP operation strategy and network reconstruction strategy and the optimal value of the main problem , update the upper bound parameter value ;

[0173] 3) Known After that, the objective function and constraint conditions of the sub-problem after being processed and transformed by the duality theorem are used to obtain the Uncertain auxiliary variables of new energy under the worst scenario The optimal value of the subproblem , thereby updating the lower bound parameter value , and in the determined and By solving the Lagrange dual constraints, we can find the condition that satisfies the conditions. , , Value, based on , , The value is derived The decision variables of the second stage under the worst scenario ;

[0174] 4) If or , then stop solving and output the optimal solution and , otherwise, let =1+1, return to step 2).

[0175] A second aspect of the present invention provides a distribution network new energy consumption system taking into account SOP access and network reconstruction, using the method described in the first aspect, the distribution network new energy consumption system comprises:

[0176] The first model building module is used to establish a new energy consumption capacity model for the medium-voltage distribution network taking into account SOP access and network reconstruction with the goal of maximizing new energy consumption;

[0177] A model conversion module, used to convert the medium voltage distribution network new energy absorption capacity model into a second-order cone programming model;

[0178] The second model building module is used to consider the uncertainty of renewable energy output based on the second-order cone programming model and establish a two-stage robust optimization model for the renewable energy absorption capacity of the medium-voltage distribution network taking into account SOP access and network reconstruction;

[0179] The model solving module is used to solve the two-stage robust optimization model and obtain the corresponding distribution network new energy consumption plan.

[0180] A third aspect of the present invention provides a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions;

[0181] The processor is used to operate according to the instructions to execute the steps of the method described in the first aspect.

[0182] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0183] Compared with the prior art, the beneficial effects of the present invention include at least:

[0184] The present invention takes the maximum amount of new energy consumption as the goal, fully combines the development trend of flexible interconnection technology, establishes a new energy consumption capacity model of medium-voltage distribution network considering SOP access and network reconstruction, and uses convex relaxation technology to transform the nonlinear model into a second-order cone programming model. On this basis, the uncertainty of new energy output is considered, and a two-stage robust optimization model of new energy consumption capacity of medium-voltage distribution network considering SOP access and network reconstruction is established and solved. The simulation results show that the flexible interconnection technology introduced by the present invention can effectively improve the new energy consumption capacity. While considering SOP access and network reconstruction to improve the consumption capacity, the proposed method combines the current development trend of flexible interconnection technology for system optimization operation, and also introduces robust parameters in the robust optimization model to establish a polyhedron uncertainty set, considering the worst scenario of wind and solar output, and increasing the conservative adjustment to meet the risk preferences of decision makers themselves, so that decision makers can formulate operation plans according to actual conditions, and accelerate the development of my country's energy towards low-carbon and clean directions.

[0185] The uncertainty of the output of renewable energy proposed in the present invention adopts a polyhedral uncertainty set and introduces a robust parameter, which can be used to adjust the conservatism of renewable energy and load forecasting errors to meet the risk preference of decision makers themselves.

[0186] The two-stage robust optimization model of the new energy absorption capacity of the medium-voltage distribution network taking into account SOP access and network reconstruction established in the present invention can improve the new energy absorption capacity of the medium-voltage distribution network and reduce the abandonment of wind and solar power through the synergistic optimization effect of the access of intelligent soft switches and network reconstruction, and take into account the uncertainty of new energy output and load demand to provide superior decision makers with a solution that meets actual needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0187] Figure 1 It is the modified IEEE33 node power distribution network diagram;

[0188] Figure 2 It is the load, wind power and photovoltaic prediction deviation coefficient curve;

[0189] Figure 3 The wind power and photovoltaic power consumption and power curtailment situations under different plans;

[0190] Figure 4 Comparison of active power output of different SOP schemes within the cycle;

[0191] Figure 5 Comparison of reactive power output of SOP in different schemes within the cycle;

[0192] Figure 6 This is a flow chart of the method for accommodating new energy in distribution networks taking into account SOP access and network reconstruction of the present invention. DETAILED DESCRIPTION

[0193] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0194] Embodiment 1 of the present invention provides a method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction, so as to achieve robust optimization and improvement of new energy consumption in a medium-voltage distribution network under the background of flexible interconnection, such as Figure 6 As shown, including:

[0195] Step 1: With the goal of maximizing the amount of new energy consumption, a new energy consumption capacity model for the medium-voltage distribution network is established, taking into account SOP access and network reconstruction;

[0196] Further preferably, first, the principle of SOP and network reconstruction is analyzed, and then a new energy consumption capacity model considering SOP and network reconstruction is established. The specific scheme is as follows:

[0197] 1 SOP Principle and Network Reconstruction

[0198] 1.1 SOP Principle

[0199] The intelligent soft switch is a device based on power electronics. It is designed based on a back-to-back voltage source converter and can be used in a medium voltage distribution network. The distribution network has a radial structure, and the busbars are connected through segmented switches. In addition to these switches, there are also normally open tie-line switches. The SOP is installed at the tie-line position to connect the adjacent feeders at both ends. The present invention takes the back-to-back VSC as an example for research. The nodes at both ends of the SOP can be regarded as PQ nodes, and due to the isolation effect of the intermediate DC link, the voltage and reactive power of the VSCs at both ends are independent of each other. This is because under normal operating conditions, the converter of the SOP The VSC1 can be used to control power transmission, while the VSC2 can realize stable control of DC voltage, that is, the PQ-VdcQ control method is adopted. Its mathematical model includes active power constraint, reactive power constraint and capacity constraint, etc. The details are as follows:

[0200] 1) SOP active power constraint:

[0201] (1)

[0202] (2)

[0203] (3)

[0204] Where: and Represents SOP nodes respectively and nodes In Active power output at all times;

[0205] and Represents SOP nodes respectively and nodes exist The power loss generated at each moment;

[0206] and Represents SOP nodes respectively and nodes exist Reactive power output at all times;

[0207] and Indicates the loss coefficient of the port.

[0208] 2) SOP reactive power constraint:

[0209] (4)

[0210] (5)

[0211] Where: and Represents SOP nodes respectively and nodes exist Reactive power output at all times;

[0212] and Represents SOP nodes respectively and nodes The maximum reactive power that can be output.

[0213] 3) SOP capacity constraints:

[0214] (6)

[0215] (7)

[0216] Where: Indicates the maximum capacity that can be transmitted on the line where the SOP is located.

[0217] 1.2 Network Reconstruction Model

[0218] The distribution network can flexibly change the network topology by controlling the switch state of the line during operation, but the start state cannot be frequently controlled, which will complicate the line flow and require an upper limit on the number of network reconstructions allowed in a typical day. Distribution network reconstruction is a method of changing the network topology by adjusting the on-off state of the branch switch. Through it, a virtual network with node 1 as the source node and the same topology as the medium-voltage distribution network can be established. The virtual load value of other nodes except the source node is 1. Its mathematical model is as follows:

[0219] (8)

[0220] (9)

[0221] (10)

[0222] (11)

[0223] Where: express Time from node Flow to child nodes Virtual power of

[0224] express The moment is determined by the parent node Flow to Node Virtual power of

[0225] Represents a set of distribution network nodes;

[0226] express At time t, the medium voltage distribution network is injected with virtual power from the head node 1 into its child nodes;

[0227] represents any real number;

[0228] Indicates extremely large numbers;

[0229] express Branch within time The state variable, 1 means connected, 0 means disconnected;

[0230] Represents a collection of medium voltage distribution network branches;

[0231] Indicates the number of elements contained in the collection;

[0232] Representation Node The collection of child nodes;

[0233] Representation Node The parent node collection of .

[0234] Formula (8) can ensure the connectivity between nodes in the medium voltage distribution network;

[0235] Formula (9) indicates that the head node 1 injects virtual power into its child nodes;

[0236] Formula (10) represents the branch ij When disconnected, its virtual power is limited to 0;

[0237] Formula (11) indicates that the number of connected branches is the difference between the total number of nodes and the number of source nodes, which ensures the radiation property under the premise that the network topology is connected.

[0238] 2 New energy consumption capacity model considering SOP and network reconstruction

[0239] 2.1 Objective Function

[0240] In order to improve the new energy consumption capacity, the present invention aims to maximize the new energy consumption capacity. The total consumption capacity of the medium voltage distribution network consists of wind power and photovoltaic power. The mathematical model is as follows:

[0241] (12)

[0242] Where: It indicates the total absorption capacity of new energy;

[0243] and They represent the node set containing photovoltaic power and the node set containing wind power respectively;

[0244] and Respectively represent nodes of photovoltaic and wind power in The power consumption at the moment;

[0245] It represents the time interval divided under the research scale, which can convert power into electricity;

[0246] T represents the total number of time intervals, which is 24.

[0247] 2.2 Constraints

[0248] To calculate the maximum absorption capacity of renewable energy, it is necessary to ensure that the DistFlow equation, branch current, voltage deviation, line current carrying capacity, renewable energy output and other constraints meet the conditions to ensure the safe operation of the medium-voltage distribution network.

[0249] 1) Distflow constraints of distribution network

[0250] The Distflow power flow model is a power flow equation established based on branch power. Compared with the traditional power flow algorithm based on node power, it is more suitable for power flow calculation of radial distribution systems.

[0251] (13)

[0252] (14)

[0253] (15)

[0254] (16)

[0255] (17)

[0256] (18)

[0257] (19)

[0258] Where: Representation Node The parent node set of

[0259] Representation Node The collection of child nodes;

[0260] express Time from node Flow to Node Active power of

[0261] and Respectively represent branches resistance and reactance;

[0262] express Time Node Flow to Node The current value;

[0263] express Time from node Flow to Node Reactive power;

[0264] Representation Node exist The voltage amplitude at the moment;

[0265] Representation Node exist Active power injected from the outside at all times;

[0266] express Time from node Flow to Node Active power of

[0267] express Time from node Flow to Node Reactive power;

[0268] express Time Node Flow to Node The current value;

[0269] Representation Node exist Active power injected from the outside at all times;

[0270] Representation Node exist Reactive power injected from the outside at all times;

[0271] Representation Node exist The voltage amplitude at the moment;

[0272] and Respectively represent branches resistance and reactance;

[0273] and express At the node Active power output from photovoltaic and wind power;

[0274] , represent At the node Reactive power output from photovoltaic and wind power;

[0275] , Respectively represent Time SOP to the node Active and reactive power provided;

[0276] and Respectively expressed in Time Node Active and reactive power consumed.

[0277] Formulas (15) and (16) are the power balance formulas with power flow constraints.

[0278] 2) Operational safety constraints

[0279] (20)

[0280] (twenty one)

[0281] Where: express The voltage amplitude at the moment;

[0282] express Time Branch the current flowing;

[0283] and Respectively represent the lower and upper limits of voltage;

[0284] Indicates the upper current limit.

[0285] 3) Line current carrying capacity constraints

[0286] (twenty two)

[0287] Where: express Timeline Active power flowing through;

[0288] express Timeline Reactive power flowing through;

[0289] Indicates branch The maximum apparent power allowed to flow.

[0290] 4) Constraints on new energy output

[0291] (twenty three)

[0292] Where: , Respectively represent nodes exist Wind power and photovoltaic power are producing active power at all times;

[0293] , Respectively represent nodes exist The upper limit of wind power and photovoltaic active output at all times.

[0294] 5) SOP constraints are shown in equations (1) to (7)

[0295] 6) Network reconstruction constraints are shown in equations (8) to (11)

[0296] Step 2: converting the medium voltage distribution network new energy consumption capacity model into a second-order cone programming model;

[0297] Further preferably, by using convex relaxation technology, the original non-convex nonlinear model can be reformulated into a second-order cone programming model that is easy to solve.

[0298] The line current carrying capacity constraint of formula (22) is expressed as follows after model transformation:

[0299] (twenty four)

[0300] Introducing auxiliary variables and After that, the quadratic term of the above constraint model can be eliminated, and the expression is as follows:

[0301] (25)

[0302] After substitution, the corresponding constraints of equations (13), (14), (17), (18), and (19) become:

[0303] (26)

[0304] (27)

[0305] (28)

[0306] (29)

[0307] (30)

[0308] Formula (30) is the second-order cone constraint form transformed from formula (19);

[0309] Where: Represents the transpose of a matrix;

[0310] Represents the Euclidean norm of a vector;

[0311] Formula (20) and formula (21) can be equivalent to:

[0312] (31)

[0313] (32)

[0314] The operation constraints (2), (3), (6), and (7) corresponding to the SOP can be transformed into rotation cone constraints:

[0315] (33)

[0316] (34)

[0317] (35)

[0318] (36)

[0319] It can be understood that the purpose of this step is mainly to convert the model into a second-order cone programming model so that it can be solved using the CPLEX solver; since formulas (1), (4), and (5) in the SOP constraints are already in linear form, no further processing is required here.

[0320] Where: Representation Node With Node The access capacity of SOP between them.

[0321] After the above transformation, the original nonlinear programming model can be transformed into a second-order cone programming model (SOCP), and then CPLEX is called to solve it.

[0322] Step 3: Based on the second-order cone programming model, considering the uncertainty of renewable energy output, a two-stage robust optimization model for the renewable energy absorption capacity of the medium-voltage distribution network is established, taking into account SOP access and network reconstruction;

[0323] Further preferred, robust optimization is another method to solve uncertainty problems besides random optimization. Unlike random optimization, it assumes that the uncertain quantity satisfies a certain uncertain parameter distribution, breaking through many assumptions that rely on prior knowledge and obey probability distribution. Its purpose is to solve all constraints and to find the optimal solution for the objective function value in the worst case for any possible situation. New energy and load power are uncertain. The worst scenario of their uncertainty will minimize the maximum absorption capacity in the medium-voltage distribution network. The uncertainty of robust optimization can be expressed as:

[0324] (37)

[0325] Where: Indicates the total number of time intervals;

[0326] express Time Node The actual value of renewable energy output;

[0327] express Time Node The predicted value of renewable energy output;

[0328] , express Time Node The maximum positive and negative deviation values ​​of renewable energy output;

[0329] , , , Respectively Time Node Actual value, predicted value, maximum positive deviation value, and maximum negative deviation value of upper load power;

[0330] , , , are Boolean variables, representing Time Node The uncertainty auxiliary variable of new energy and load can be used to characterize whether the actual wind power and photovoltaic output reaches the upper and lower limits. and When it is equal to 1, it means that the actual output of new energy and the load demand have reached the upper limit. and When it is equal to 1, it means that the actual output of new energy and load demand have reached the lower limit;

[0331] and Represent the robust parameter size of new energy and load respectively. and If both are equal to zero, there is no moment of uncertainty, and the output of new energy and load demand become a certain value, namely the predicted value (referring to and Becomes the predicted value , that is, in formula (37), becomes , becomes ), the robust parameter can be adjusted according to the demand to adjust the conservative degree of the obtained solution; the size of the robust interval is determined by the number of uncertainties in each node in the distribution system, and is generally rounded. The larger the robust parameter value set in the interval, the more conservative the model is. It can be understood that the size of the robust parameter interval is consistent with the size of the uncertainty time in a day, which is 24. The larger the robust parameter, the larger the uncertainty time. When it is equal to 0, the new energy output and load demand values ​​in formula (37) become predicted values.

[0332] Formula (37) uses a polyhedral uncertainty set and introduces a robust parameter. The advantage of this parameter is that it can adjust the conservatism of the solution together with the new energy and load forecasting errors to meet the decision maker's own risk preference.

[0333] In order to improve the new energy consumption capacity under the worst scenario, the present invention establishes a two-stage robust optimization model with the maximum new energy consumption capacity of the medium-voltage distribution network. Its expression is:

[0334] (38)

[0335] (39)

[0336] (40)

[0337] (41)

[0338] Where:

[0339] represents the uncertain set of new energy sources and loads;

[0340] Indicates the definition of the first stage decision variables The set function of the feasible domain, which means that given the first-stage decision variables and uncertainty variables Then, the second stage decision variables Specifically, it means that in a specific and Under the constraints, all A collection of values.

[0341] Represents the decision variable of the first stage, which is the decision variable And the decision made after the uncertainty auxiliary variables are determined, mainly the SOP operation strategy and network reconstruction strategies , corresponding to the problem of maximizing the capacity of absorbing new energy;

[0342] Represents the coefficient variable, each element in the variable corresponds to a decision variable in the first stage , reflects the impact of each decision variable on the consumption of new energy, through the vector and The inner product operation of can obtain the comprehensive improvement effect of all decision variables in the first stage on the consumption of new energy. The goal is to improve the consumption capacity of the distribution system by using the collaborative control strategy of SOP and network reconstruction;

[0343] Represents the decision variable of the second stage, which is determined before the uncertainty auxiliary variable is determined, mainly the flow variable , , , and new energy consumption , ;

[0344] Represents the coefficient vector, each element in the variable corresponds to a decision variable in the second stage , reflects the impact of each decision variable on the consumption of new energy, through the vector and The inner product operation can calculate the total amount of new energy consumption;

[0345] Represents uncertainty auxiliary variables, including , , , , is a Boolean variable;

[0346] express Branch within time The state variable, 1 means connected, 0 means disconnected;

[0347] Representation Node exist The active power output by SOP at the moment;

[0348] Representation Node exist The reactive power output by SOP at the moment;

[0349] , Respectively Time Branch Active and reactive power flowing through;

[0350] , Representation Node exist Active and reactive power injected at all times;

[0351] It can be understood that formula (12) is the objective function of the entire new energy consumption optimization model of the present invention, and the objective function in the two-stage robust optimization model of formula (38) is also evolved from this formula. Formulas (38)-(41) can improve the new energy consumption of the medium-voltage distribution network and reduce the abandonment of wind and solar power through the coordinated optimization of the access of intelligent soft switches and network reconstruction, and consider the uncertainty of new energy output and load demand, so as to provide a solution that meets actual needs for superior decision makers.

[0352] The second stage corresponds to the minimization-maximization problem of new energy absorption capacity, namely the min-max problem. The goal is to find the most conservative scenario among the new energy output scenarios, so as to minimize the maximum absorption of new energy.

[0353] For the convenience of expression, the constraints are expressed in the following compact form:

[0354] (42)

[0355] Where: u is the optimization variable of the second stage problem, that is, the uncertainty auxiliary variable;

[0356] represents the decision variables in the first stage, corresponding to the SOP control strategy and network reconstruction strategy;

[0357] y represents the decision variable in the second stage, corresponding to the uncertainty auxiliary variable and the amount of new energy consumption;

[0358] , , , , represents the coefficient matrix;

[0359] The meaning of is the first stage decision variable The constraints that must be satisfied correspond to equations (4)-(5), (8)-(11);

[0360] The meaning of the first stage decision variables , second stage decision variables = Uncertain auxiliary variables The constraints therefore correspond to (15)-(16), (23), (26)-(29), (31)-(32), (37);

[0361] The meaning of is the norm constraint of the decision variable, so it corresponds to equations (24), (30), (33)-(36).

[0362] Step 4: Solve the two-stage robust optimization model to obtain the corresponding distribution network new energy consumption plan.

[0363] Further preferably, the two-stage robust optimization model is divided into a main problem and a sub-problem. For the present invention, the main problem is an upper bound value determined by the objective function. It is first assumed that the uncertain scenario is in a discrete form. , and its corresponding decision variable is , since the uncertain scenario does not include all scenarios, the optimization result becomes the upper bound of the objective function. The enumerated uncertain scenarios are irrelevant to the optimization result. This is because the scenarios solved by the sub-problems will be continuously added to the main problem, which will increase the upper bound of the objective function in the main problem. Usually, if the uncertain scenario occupies more scenarios, the convergence speed will be faster. The main problem is the first stage optimization problem, and the objective function and constraints are as follows:

[0364] (43)

[0365] (44)

[0366] Where: is the optimal value of the main problem; it can be understood that in formula (44) The main problem objective function of formula (43) is Correspondingly, it is a new variable added after decomposing the two-stage robust optimization model into the main problem and sub-problems, so it is not included in the constraints corresponding to formula (42);

[0367] Represents the decision variables of the main problem, corresponding to the SOP control strategy and network reconstruction strategy;

[0368] and Respectively expressed in The variable solution of the subproblem after iterations.

[0369] The sub-problem corresponds to the second-stage problem. Based on the solution of the decision variables obtained in the main problem and the constraints of the second-stage decision variables, the values ​​of the second-stage decision variables are determined by taking into account the worst scenario in the uncertainty set and providing the lower bound of the model. At the same time, new constraints are generated and added to the main problem for the next round of iterative solution. Any decision variable value given by the main problem can be passed to the sub-problem to obtain the corresponding optimal solution. The objective function and constraints of the sub-problem are as follows:

[0370] (45)

[0371] (46)

[0372] Where: is the optimal value of the subproblem;

[0373] Represents the operation plan after optimizing the main problem.

[0374] This subproblem belongs to the minimum-maximum problem, which means searching for the maximum absorption capacity under the worst scenario, that is, minimizing the maximum absorption capacity. The outer min problem aims to find the scenario that leads to the minimum absorption of new energy in the uncertain concentration, that is, the "worst" scenario, and the decision variables are auxiliary variables of uncertainty. The inner max problem aims to find the maximum absorption capacity through decision variables under a specific new energy scenario. The decision variables are flow variables and new energy absorption capacity. This min-max problem is a two-layer model, and the solution is relatively complex. To effectively solve the problem, the max problem needs to be dualized. After the dualization, the outer and inner layers can be merged into a min problem by introducing Lagrange multipliers, and then the nonlinear terms in the transformed model are processed again, and the model is converted into a mixed integer linear model. Finally, the CPLEX solver is called to solve it. The objective function and constraints after transformation are as follows:

[0375] (47)

[0376] (48)

[0377] Where: , , Both represent Lagrange multipliers, and the coefficient vector The T of has the same meaning as the T of the other three Lagrange multipliers, all representing the transpose of the matrix.

[0378] (47)-(48) are obtained by transforming (45)-(46) through the dual theory, which is the classic theory for dealing with the two-layer minmax model. Currently, most researchers in this field still use this theory to deal with problems.

[0379] The corresponding algorithm steps are:

[0380] 1) Initialize parameters. Set the number of iterations =1; upper and lower bound parameters , ; The maximum number of iterations is set to ; The convergence accuracy is set to .

[0381] 2) According to the objective function and constraints of the main problem, solve the SOP operation plan and network reconstruction strategy , and the optimal value of the model , update the upper bound value .

[0382] 3) Known After that, the sub-function model processed by the duality theorem can be used to obtain the Uncertain disturbance of new energy under the worst scenario The optimal value of the subproblem Update the lower limit value , and by solving the Lagrange dual condition (i.e. solving formula (48)), we find , , values, based on which the decision variables for the second stage are derived The purpose of this step is to find the worst uncertainty under the current first-stage decision , so that the optimal value corresponding to the sub-problem can be calculated, and the lower bound parameter value can be updated; is determined in step 3) and Next, solve the Lagrangian dual conditions to find the Lagrangian values ​​that satisfy the conditions, and derive from these values.

[0383] 4) If or , then stop calculating and output the optimal solution and Otherwise, let , return to step 2).

[0384] Further, the present invention is as follows Figure 1 The IEEE33 node system shown is used as an example for verification. L36, L37, and L38 represent the tie switches on branches 12-22, 18-33, and 25-29, respectively. L34 and L35 represent branches 8-21 and 9-15 connected to SOP, respectively. The photovoltaic access nodes are 8, 17, 24, and 32, and the access capacity of each node is 250kW. The wind power access nodes are 11, 15, 22, and 29, and the access capacity of each node is 200kW. The maximum number of positive and negative adjustment gears of OLTC is 4, and the adjustment range is [0.95, 1.05]. The loss coefficient of the SOP converter is 0.02, and the capacity is 200kvar. The robust parameters and are set to 5 respectively, and the forecast error of new energy and load is set to 10%. The forecast value and fluctuation range are as follows Figure 2As shown in the figure, the system node voltage safety range is [0.93, 1.07]. The specific analysis is as follows:

[0385] 1) Effectiveness of the proposed model

[0386] In order to verify the effectiveness of the robust optimization model of new energy consumption capacity proposed in this invention, four different schemes are set:

[0387] Solution 1: The model proposed by the present invention;

[0388] Solution 2: A robust optimization model that only considers network reconstruction but not SOP access;

[0389] Solution 3: Robust optimization model without considering network reconstruction and SOP access;

[0390] Solution 4: Considering network reconstruction and SOP access, but not considering robust optimization of the medium-voltage distribution network absorption capacity assessment model.

[0391] After simulation calculation, the new energy consumption and network reconstruction results corresponding to the four schemes are shown in Table 1 and Figure 2 As shown:

[0392] Table 1 New energy consumption and network reconstruction results under different schemes

[0393] ,

[0394] It can be seen from Table 1 that the new energy consumption of Scheme 3 is 14042 kWh, which is less than 14532 kWh of Scheme 2. This shows that considering network reconstruction can promote the improvement of consumption capacity; the new energy consumption of Schemes 2 and 3 is less than 14735 kWh of Scheme 1, which shows that after considering both network reconstruction and SOP access, the consumption capacity can be improved to the greatest extent; however, the new energy consumption of Scheme 1 is less than 14865 kWh of Scheme 4, and the consumption rate increases by 4%. This is because the robust optimization model considers the worst scenario of new energy output and has a certain degree of conservatism. Compared with the traditional scheme that does not consider the worst scenario of new energy output and load demand, this scheme is more practical.

[0395] Depend on Figure 3 It can be seen that wind power is higher at 1-6 and 9-13, and photovoltaic power is higher at noon times such as 9-14, and there is basically a certain degree of machine cutting. At 17:00-24:00 in the evening, the load demand increases, which is the peak period of electricity consumption, and the photovoltaic output is zero. At this time, the wind power output is in full generation and the absorption level is the highest, but Schemes 1, 2, and 3 are still not fully absorbed at this time. This is because the worst scenario of load demand and wind power output is considered, and the results are conservative. Comparing Scheme 1 and Scheme 3, and before and after the SOP and network reconstruction are involved in the optimization, it can be seen that SOP can balance the power flow and promote the absorption of new energy to a certain extent.

[0396] 2) Comparison of different SOP parameter settings

[0397] The present invention focuses on the impact of SOP access on the new energy absorption capacity of the medium-voltage distribution network, so as to reflect the advantages of flexible interconnection technology in the new medium-voltage distribution system. Figure 4 , Figure 5 are the active power output and reactive power output of the SOP at both ends of the tie line during the period. Figure 4 It can be seen that the active output of SOP can be adjusted in real time with the output of new energy and load demand. Taking the interconnection switch of SOP connected to 8-21 as an example, at 0:00-8:00 and 17:00-24:00, since the photovoltaic output is small during this period, SOP is required to absorb more active power from port 8 to balance the power flow distribution of each node and smooth the large fluctuations in power at some nodes due to the access of new energy; at 8:00-16:00 and other times, due to the increase in photovoltaic output, the power absorbed by port 8 to SOP gradually decreases. When the output reaches the peak, SOP begins to transmit active power in the opposite direction. Compared with the non-robust optimization model, the robust optimization model has a greater active output at each moment. This is also because the robust optimization model takes into account the worst scenario and requires SOP to provide more active flow to smooth power fluctuations. By Figure 5 It can be seen that in addition to providing active output, SOP can also provide corresponding reactive output. Unlike the same amplitude of active power at the beginning and end of SOP, its reactive power output at the beginning and end is independent and unrelated. Taking the interconnecting switch of SOP connected to 8-21 as an example, the reactive power output at both ends of SOP is positive, indicating that there is a reactive power shortage in the medium-voltage distribution system at this time, and a certain amount of reactive power compensation is required for voltage support, thereby reducing network losses. Since the worst scenario is taken into account, the overall reactive output of SOP in the robust optimization model is greater than that of the traditional model, which is more in line with the actual situation.

[0398] Figure 4 , Figure 5 This is the active and reactive output situation obtained when the SOP configuration is fixed, but different SOP parameter settings will also have a certain impact on the new energy absorption capacity results. The impact of different SOP access capacities and different access locations and quantities on the new energy absorption capacity of the medium-voltage distribution network is shown in Table 2 and Table 3:

[0399] Table 2 Comparison of different access capacities of SOP in Scheme 1

[0400] SOP capacity (kW) 200 400 600 800 New energy consumption (kWh) 14735 14940 15311 15977

[0401] Table 3 Comparison of different SOP access solutions

[0402] SOP branch L33, L34 L35, L36 L37 L33, L34, L35 New energy consumption (kWh) 14735 14671 14230 16607

[0403] 3) Comparison of robust parameter and prediction error parameter settings

[0404] The present invention considers the value of robust adjustment parameters to quantitatively analyze the risk preference of decision makers. Table 4 shows the absorption capacity of medium-voltage distribution network for new energy under different conservative levels. It can be seen that under the same robust parameters, as the forecast error of new energy and load gradually increases, in order to cope with the worst disturbance that may occur in wind and solar output fluctuations, the corresponding new energy absorption capacity will decrease; and under the same new energy and load forecast error, as the robust adjustment parameters gradually increase, that is, the model robustness gradually increases, considering the operation under the most conservative conditions, it can cope with more uncertainties, but this will reduce the absorption capacity of new energy. When the robust parameter is 0, the absorption capacity of new energy is 15615kWh. At this time, no matter how the size of the uncertainty error is adjusted, the absorption capacity of new energy in the system will not change. This is because the robust optimization problem has been transformed into a deterministic problem.

[0405] Table 4 New energy consumption results with different robust regulation parameters and prediction errors

[0406] ,

[0407] The simulation results show that the flexible interconnection technology introduced in this invention can effectively improve the new energy consumption capacity. The proposed method for improving the new energy consumption capacity of the medium-voltage distribution network not only considers SOP access and network reconstruction to improve the consumption capacity, but also increases the conservative adjustment through the robust optimization method, so that decision makers can formulate operation plans according to actual conditions and accelerate the development of my country's energy towards low-carbon and clean directions.

[0408] Embodiment 2 of the present invention provides a distribution network new energy consumption system taking into account SOP access and network reconstruction, including:

[0409] The first model building module is used to establish a new energy consumption capacity model for the medium-voltage distribution network taking into account SOP access and network reconstruction with the goal of maximizing new energy consumption;

[0410] A model conversion module, used to convert the medium voltage distribution network new energy absorption capacity model into a second-order cone programming model;

[0411] The second model building module is used to consider the uncertainty of renewable energy output based on the second-order cone programming model and establish a two-stage robust optimization model for the renewable energy absorption capacity of the medium-voltage distribution network taking into account SOP access and network reconstruction;

[0412] The model solving module is used to solve the two-stage robust optimization model and obtain the corresponding distribution network new energy consumption plan.

[0413] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0414] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0415] The beneficial effect of the present invention is that, compared with the prior art, the present invention takes the maximum amount of new energy consumption as the goal, fully combines the development trend of flexible interconnection technology, establishes a medium-voltage distribution network new energy consumption capacity model considering SOP access and network reconstruction, and uses convex relaxation technology to convert the nonlinear model into a second-order cone programming model. On this basis, considering the uncertainty of new energy output, a two-stage robust optimization model of the medium-voltage distribution network new energy consumption capacity considering SOP access and network reconstruction is established and solved. The simulation results show that the flexible interconnection technology introduced by the present invention can effectively improve the new energy consumption capacity. The proposed method for improving the new energy consumption capacity of the medium-voltage distribution network not only considers SOP access and network reconstruction to improve the consumption capacity, but also increases the conservative adjustment through the robust optimization method, so that decision makers can formulate operation plans according to actual conditions and accelerate the development of my country's energy towards low-carbon and clean directions.

[0416] The uncertainty of the output of renewable energy proposed in the present invention adopts a polyhedral uncertainty set and introduces a robust parameter, which can adjust the conservatism of the solution together with the renewable energy and load forecasting errors to meet the risk preference of the decision maker himself.

[0417] The two-stage robust optimization model of the new energy absorption capacity of the medium-voltage distribution network taking into account SOP access and network reconstruction established in the present invention can improve the new energy absorption capacity of the medium-voltage distribution network and reduce the abandonment of wind and solar power through the synergistic optimization effect of the access of intelligent soft switches and network reconstruction, and take into account the uncertainty of new energy output and load demand to provide superior decision makers with a solution that meets actual needs.

[0418] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0419] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0420] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0421] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0422] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction, characterized in that: include: Step 1: With the goal of maximizing the amount of new energy consumption, a new energy consumption capacity model for the medium-voltage distribution network is established, taking into account SOP access and network reconstruction. The objective function is: (12) Where: and They represent the node set containing photovoltaic power and the node set containing wind power respectively; and Respectively represent nodes of photovoltaic and wind power in The power consumption at the moment; represents the time interval; T represents the total number of time intervals; Step 2: converting the medium voltage distribution network new energy consumption capacity model into a second-order cone programming model; Step 3: Based on the second-order cone programming model, considering the uncertainty of renewable energy output, a two-stage robust optimization model for the renewable energy absorption capacity of the medium-voltage distribution network is established, which takes into account SOP access and network reconstruction. Its expression is: (38) (39) (40) (41) Where: represents the uncertain set of new energy sources and loads; Indicates a given and back, The set of possible values; represents the decision variable of the first stage. as well as After being determined, including SOP operation strategy and network reconstruction strategies , corresponding to the problem of maximizing the capacity of absorbing new energy; Represents the coefficient variable. Each element in the variable corresponds to a decision variable in the first stage, reflecting the impact of each decision variable on the consumption of new energy. represents the decision variable of the second stage, which is Determined before being determined, including power flow variables , , , and new energy consumption , ; represents the coefficient vector, each element in the vector corresponds to a decision variable in the second stage, reflecting the impact of each decision variable on the consumption of new energy; Represents uncertainty auxiliary variables, including , , , , is a Boolean variable; express Branch road within time The state variable, 1 means connected, 0 means disconnected; Representation Node exist The active power output by SOP at the moment; Representation Node exist The reactive power output by SOP at the moment; , Respectively Time Branch Active and reactive power flowing through; , Representation Node exist Active and reactive power injected at all times; express Time Node Flow to Node The current value; Representation Node exist Active power injected from the outside at all times; Representation Node exist Reactive power injected from the outside at all times; express The voltage amplitude at the moment; The second stage corresponds to the minimization-maximization problem of new energy absorption capacity, that is, the min-max problem. The goal is to find the most conservative scenario among the new energy output scenarios to minimize the maximum absorption of new energy. Step 4: Solve the two-stage robust optimization model to obtain the corresponding distribution network new energy consumption plan.

2. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 1, characterized in that: The new energy absorption capacity model of the medium-voltage distribution network established in step 1 takes into account SOP access and network reconstruction. Its constraints include distribution network Distflow constraints, operation safety constraints, line current carrying capacity constraints, new energy output constraints, SOP constraints and network reconstruction constraints.

3. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 2, characterized in that: The distribution network Distflow flow constraint is: (13) (14) (15) (16) (17) (18) (19) Where: Representation Node The collection of child nodes; Representation Node The parent node set of express Time from node Flow to Node Active power of and Respectively represent branches resistance and reactance; express Time Node Flow to Node The current value; express Time from node Flow to Node Reactive power; Representation Node exist The voltage amplitude at the moment; Representation Node exist Active power injected from the outside at all times; express Time from node Flow to Node Active power of express Time from node Flow to Node Reactive power; Representation Node exist Reactive power injected from the outside at all times; express Time Node Flow to Node The current value; Representation Node exist The voltage amplitude at the moment; Indicates extremely large numbers; express Branch road within time The state variable, 1 means connected, 0 means disconnected; and Respectively represent branches resistance and reactance; and express At the node Active power output from photovoltaic and wind power; , represent At the node Reactive power output from photovoltaic and wind power; , Respectively represent Time SOP to the node Active and reactive power provided; and Respectively expressed in Time Node Active and reactive power consumed.

4. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 3, characterized in that: The operational safety constraints are: (20) (21) Where: express The voltage amplitude at the moment; express Time branch the current flowing; and Respectively represent the lower and upper limits of voltage; Indicates the upper current limit.

5. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 4, characterized in that: The line current carrying capacity constraint is: (22) Where: express Time Branch Active power flowing through; express Time Branch Reactive power flowing through; Indicates branch The maximum apparent power allowed to flow.

6. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 5, characterized in that: The new energy output constraints are: (23) Where: , Respectively represent nodes exist Wind power and photovoltaic power are producing active power at all times; , Respectively represent nodes exist The upper limit of wind power and photovoltaic active output at all times.

7. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 6, characterized in that: The SOP constraints include: 1) SOP active power constraint: (1) (2) (3) Where: and Represents SOP nodes respectively and nodes In Active power output at all times; and Represents SOP nodes respectively and nodes exist The power loss generated at each moment; and Represents SOP nodes respectively and nodes exist Reactive power output at all times; and Representation Node and nodes Loss coefficient of the port; 2) SOP reactive power constraint: (4) (5) Where: and Represents SOP nodes respectively and nodes The maximum reactive power that can be output; 3) SOP capacity constraints: (6) (7) Where: Indicates the maximum capacity that can be transmitted on the line where the SOP is located.

8. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 7, characterized in that: The network reconstruction constraints include: (8) (9) (10) (11) Where: express Time from node Flow to child nodes Virtual power of express The moment is determined by the parent node Flow to Node Virtual power of Represents a set of distribution network nodes; express At time 1, the medium voltage distribution network is injected from the head node 1 into its child node Virtual power of represents any real number; Indicates extremely large numbers; express Branch road within time The state variable, 1 means connected, 0 means disconnected; Represents a collection of medium voltage distribution network branches; Representing a collection The number of elements contained in ; Representation Node The collection of child nodes; Representation Node The parent node collection of .

9. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 8, characterized in that: In step 2, the process of converting the medium voltage distribution network new energy consumption capacity model into a second-order cone programming model is as follows: 1) The line current carrying capacity constraint formula (22) of the medium voltage distribution network new energy absorption capacity model is transformed into: (24) 2) Introduce the auxiliary variable shown in formula (25) and To eliminate the quadratic terms in equations (13), (14), (17), (18), and (19) of the distribution network Distflow constraint: (25) The corresponding constraint after eliminating the quadratic term is transformed into: (26) (27) (28) (29) (30) Where: Represents the transpose of a matrix; Represents the Euclidean norm of a vector; 3) Based on the auxiliary variables and , the operation safety constraint equations (20)-(21) of the new energy absorption capacity model of the medium voltage distribution network are transformed into: (31) (32) 4) The constraints (2), (3), (6), and (7) in the SOP constraints of the new energy absorption capacity model of the medium voltage distribution network are transformed into the following rotating cone constraints: (33) (34) (35) (36) Where: Representation Node With Node The access capacity of SOP between them.

10. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 9, characterized in that: The uncertainty of the renewable energy output described in step 3 is expressed as equation (37), which is used as a constraint in the two-stage robust optimization model: (37) Where: Indicates the total number of time intervals; , , , Respectively Time Node The actual value, predicted value, maximum positive deviation value, and maximum negative deviation value of the renewable energy output; , , , Respectively Time Node The actual value, predicted value, maximum positive deviation value, and maximum negative deviation value of the upper load power; , , , is a Boolean uncertainty auxiliary variable. and When it is equal to 1, it means that the actual output of new energy and the load demand have reached the upper limit. and When it is equal to 1, it means that the actual output of new energy and the load demand have reached the lower limit, where the new energy includes wind power and photovoltaic power; and Represent the robust parameters of new energy and load respectively. If and are all equal to zero, and the new energy output and load demand become a certain value, namely the predicted value.

11. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 1, characterized in that: The two-stage robust optimization model of the medium-voltage distribution network's new energy absorption capacity considering SOP access and network reconstruction established in step 3 has the following constraints: (42) Where: It represents the decision variables of the first stage, which are SOP operation strategy and network reconstruction strategy; Represents the decision variables of the second stage, which are the uncertainty auxiliary variables and the amount of new energy consumption; , , , , represents the coefficient matrix; The meaning of is the first stage decision variable The constraints that must be satisfied correspond to equations (1), (4), (5), and (8)-(11); The meaning of the first stage decision variables , second stage decision variables With uncertainty auxiliary variables The constraints correspond to equations (15)-(16), (23), (26)-(29), (31)-(32), (37); The meaning of is the norm constraint of the decision variable, corresponding to equations (24), (30), (33)-(36).

12. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 11, characterized in that: The step 4 divides the two-stage robust optimization model into a main problem and a sub-problem for solution; Among them, the main problem is the first-stage optimization problem, and its objective function and constraints are as follows: (43) (44) Where: is the optimal value of the main problem; The decision variables representing the main problem, i.e., the decision variables in the first stage, include the SOP operation strategy and the network reconstruction strategy; and Respectively expressed in The variables of the subproblem after iterations , The solution; The sub-problem corresponds to the second-stage problem, and its objective function and constraints are as follows: (45) (46) Where: is the optimal value of the subproblem; It represents the operation plan after optimizing the main problem; The max problem in the subproblem is dualized. After the dualization, the outer and inner layers are merged into a min problem by introducing Lagrange multipliers. Then, formulas (45)-(46) are transformed into the objective function and Lagrange dual constraints as follows: (47) (48) Where: , , Both represent Lagrange multipliers, and T represents transpose.

13. A method for accommodating new energy in a distribution network taking into account SOP access and network reconstruction according to claim 12, characterized in that: Step 4: The steps to solve the main problem and sub-problems are: 1) Initialization parameters: set the number of iterations =1; upper and lower bound parameters , ; The maximum number of iterations is set to ; The convergence accuracy is set to ; 2) According to the objective function and constraints of the main problem, solve the SOP operation strategy and network reconstruction strategy and the optimal value of the main problem , update the upper bound parameter value ; 3) Known After that, the objective function and constraint conditions of the sub-problem after being processed and transformed by the duality theorem are used to obtain the Uncertain auxiliary variables of new energy under the worst scenario The optimal value of the subproblem , thereby updating the lower bound parameter value , and in the determined and By solving the Lagrange dual constraints, we can find the condition that satisfies the conditions. , , Value, based on , , The value is derived The decision variables of the second stage under the worst scenario ; 4) If or , then stop solving and output the optimal solution and , otherwise, let =1+1, return to step 2).

14. A distribution network new energy consumption system taking into account SOP access and network reconstruction, using the method described in any one of claims 1 to 13, characterized in that: The distribution network new energy consumption system comprises: The first model building module is used to establish a new energy consumption capacity model for the medium-voltage distribution network taking into account SOP access and network reconstruction with the goal of maximizing new energy consumption; A model conversion module, used to convert the medium voltage distribution network new energy absorption capacity model into a second-order cone programming model; The second model building module is used to consider the uncertainty of renewable energy output based on the second-order cone programming model and establish a two-stage robust optimization model for the renewable energy absorption capacity of the medium-voltage distribution network taking into account SOP access and network reconstruction; The model solving module is used to solve the two-stage robust optimization model and obtain the corresponding distribution network new energy consumption plan.

15. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

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