A method and terminal for site selection and capacity determination of distributed power supply of power distribution network

Abstract

The application discloses a kind of distributed power site selection and capacity determination method and terminal of distribution network, determine multiple candidate distributed power access schemes based on preset investment cost constraint, under the condition that distribution network is in normal operating state and is in fault state, respectively based on multiple candidate distributed power access schemes according to corresponding objective function and constraint condition are operated, to simulate the operating condition of each scheme under different scenarios, finally, according to the operating result, the resilience index of distribution network is calculated, according to it, the optimal distributed power access scheme is selected, when distribution network is in normal operating state, economy is mainly targeted, when being in fault state, join load shedding cost, so as to guarantee the economy of distribution network, effectively improve the resilience of distribution network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed power supply, and particularly relates to a distributed power supply site selection and capacity determination method for a power distribution network and a terminal. BACKGROUND

[0002] Under the influence of typhoon and other extreme natural disasters, how to maintain the continuous power supply of key loads of the power distribution network and improve the recovery ability after the occurrence of a small probability high risk event is of great significance and is one of the challenges faced by the power distribution network in recent years. Microgrid provides an effective solution for the power distribution system to cope with extreme disasters. As a key flexible resource in the power distribution network, the distributed power supply is the basis for realizing the power supply of the microgrid of the power distribution system. Before the occurrence of an extreme disaster, the access position of the distributed power supply is considered, and the power distribution network is pre-reconstructed, so that the power distribution network forms an active island, which can effectively reduce the influence of the extreme disaster on the power distribution network. After the occurrence of an extreme disaster, the fault should be isolated first, and then the load is quickly recovered. When the fault is not isolated, the nodes directly or indirectly connected with the fault in the power distribution network will be affected by the fault and cause power service interruption. In the recovery process, the microgrid based on the distributed power supply can ensure the normal power supply of the non-fault nodes that lose contact with the main power supply. Through the above process and measures of the power distribution system under extreme disasters, it can be found that the distributed power supply is of great significance to the continuous power supply and rapid recovery of key loads of the power distribution network under extreme disasters. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a distributed power supply site selection and capacity determination method for a power distribution network and a terminal, which can effectively improve the resilience of the power distribution network while ensuring the economy of the power distribution network.

[0004] In order to solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0005] A distributed power supply site selection and capacity determination method for a power distribution network, comprising the following steps:

[0006] A first objective function for minimizing the system operation cost and a corresponding first constraint condition are established, and a second objective function for minimizing the system operation cost and load shedding cost and a corresponding second constraint condition are established;

[0007] A plurality of candidate distributed power supply access schemes are determined based on a preset investment cost constraint;

[0008] When the power distribution network is in a normal operating state, the first objective function and the first constraint condition are used to operate based on the plurality of candidate distributed power supply access schemes, and a plurality of first operating results are obtained;

[0009] In the case that the power distribution network is in a fault state, the second target function and the second constraint condition are used to run the multiple candidate distributed power source access schemes, and multiple second running results are obtained;

[0010] The resilience indexes of the power distribution network are calculated according to the multiple first running results and the multiple second running results, and the optimal distributed power source access scheme is selected from the multiple candidate distributed power source access schemes according to the resilience indexes.

[0011] To solve the above technical problems, another technical solution adopted by the present application is:

[0012] A distributed power source site selection and capacity determination terminal for a power distribution network comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0013] A first target function for minimizing system running cost and corresponding first constraint conditions are established, and a second target function for minimizing system running cost and load shedding cost and corresponding second constraint conditions are established;

[0014] Multiple candidate distributed power source access schemes are determined based on a preset investment cost constraint;

[0015] In the case that the power distribution network is in a normal running state, the first target function and the first constraint condition are used to run the multiple candidate distributed power source access schemes, and multiple first running results are obtained;

[0016] In the case that the power distribution network is in a fault state, the second target function and the second constraint condition are used to run the multiple candidate distributed power source access schemes, and multiple second running results are obtained;

[0017] The resilience indexes of the power distribution network are calculated according to the multiple first running results and the multiple second running results, and the optimal distributed power source access scheme is selected from the multiple candidate distributed power source access schemes according to the resilience indexes.

[0018] The present application has the following beneficial effects: multiple candidate distributed power source access schemes are determined based on a preset investment cost constraint, in the case that the power distribution network is in a normal running state and in a fault state, the corresponding target function and constraint condition are used to run the multiple candidate distributed power source access schemes respectively to simulate the running conditions of each scheme in different scenarios, finally, the resilience indexes of the power distribution network are calculated according to the running results, and the optimal distributed power source access scheme is selected according to the resilience indexes, in the case that the power distribution network is in a normal running state, the economic efficiency is taken as the main target, in the case that the power distribution network is in a fault state, the load shedding cost is added, so that the economic efficiency of the power distribution network is ensured, and the resilience of the power distribution network is effectively improved. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the steps of a distributed power source location and capacity determination method for a distribution network according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the structure of a distributed power source addressing and capacity determination terminal for a distribution network according to an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of the IEEE 33-node example network in the distributed power source location and capacity determination method for distribution networks according to an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram illustrating the load shedding penalty cost for each candidate distributed power source access scheme in the distributed power source location and capacity determination method of the distribution network according to an embodiment of the present invention. Detailed Implementation

[0023] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0024] Please refer to Figure 1 A method for location and capacity determination of distributed generation sources in a distribution network, comprising the following steps:

[0025] Establish a first objective function that minimizes the system operating cost and its corresponding first constraint, and establish a second objective function that minimizes the system operating cost and the load shedding cost and its corresponding second constraint;

[0026] Multiple candidate distributed power supply access schemes were determined based on preset investment cost constraints;

[0027] When the distribution network is in normal operation, the multiple candidate distributed power source access schemes are run according to the first objective function and the first constraint conditions to obtain multiple first running results.

[0028] When the distribution network is in a fault state, the multiple candidate distributed power source access schemes are run according to the second objective function and the second constraint conditions to obtain multiple second running results;

[0029] The resilience index of the distribution network is calculated based on the multiple first operating results and the multiple second operating results, and the optimal distributed power source access scheme is selected from the multiple candidate distributed power source access schemes based on the resilience index.

[0030] From the above description, the beneficial effects of the present application are that: based on the preset investment cost constraint, a plurality of candidate distributed power access schemes are determined, in the normal operation state and the fault state of the power distribution network, the operation is carried out based on the plurality of candidate distributed power access schemes according to the corresponding target function and constraint condition respectively, so as to simulate the operation of each scheme in different scenarios, and finally the resilience index of the power distribution network is calculated according to the operation result, and the optimal distributed power access scheme is selected according to the resilience index, the economic efficiency is taken as the main target when the power distribution network is in the normal operation state, and the load shedding cost is added when the power distribution network is in the fault state, so that the economic efficiency of the power distribution network is ensured, and the resilience of the power distribution network is effectively improved.

[0031] Further, the preset investment cost constraint is:

[0032]

[0033] In the formula, cost DG represents the total construction cost of the distributed power, k represents the number of constructed micro gas turbines, C MT represents the variable cost of the micro gas turbine construction, P i N,MT represents the rated power of the micro gas turbine, C inst,MT represents the fixed cost of the micro gas turbine construction, m represents the number of constructed photovoltaic power generation, C PV represents the variable cost of the photovoltaic power generation construction, P i N,PV represents the rated power of the photovoltaic power generation, C inst,PV represents the fixed cost of the photovoltaic power generation construction, C DG,max represents the maximum cost of the distributed power construction.

[0034] From the above description, when the distributed power access position and capacity are preliminarily planned, if only the effect of improving the system performance is considered, the scale of construction may be relatively large, which may not be feasible in actual operation, therefore, the investment cost constraint is determined, and a plurality of candidate distributed power access schemes are determined under the constraint, so as to ensure the economic efficiency of the power distribution network.

[0035] Further, the first target function of minimizing the system operation cost and the corresponding first constraint condition include:

[0036] The first target function of minimizing the system operation cost is established;

[0037] The network flow constraint, the radial topology constraint and the safe operation constraint corresponding to the first target function are established, and the first constraint condition is constituted according to the network flow constraint, the radial topology constraint and the safe operation constraint.

[0038] From the above description, according to the network flow constraint, the radial topology constraint and the safe operation constraint, the first constraint condition is formed, which ensures that the distribution network is operated in a radial manner, and meanwhile, the distribution network can be safely and reliably operated while taking into account the economy.

[0039] Further, the first objective function of minimizing the system operation cost is:

[0040] Obj1:

[0041]

[0042]

[0043]

[0044]

[0045] In the formula, Obj1 represents the first objective function, N s1 represents the number of scenarios in the normal operation state, p s represents the probability of the occurrence of the scenario s, T m represents the observation period, cost SUB represents the substation power purchase cost, cost PV represents the power generation cost of all photovoltaic power generations, cost MT represents the power generation cost of all micro gas turbine power generations, cost LOSS represents the network loss cost, Ω sub represents a set belonging to the substation node, represents the cost of purchasing power from the substation at time t, represents the active power output of the i-node substation at scenario s and time t, and Δt represents the observation step, Ω PV represents a set of nodes equipped with photovoltaic power generation, C PV,OP represents the photovoltaic power generation cost, represents the active power output of the i-node photovoltaic power generation at scenario s and time t, Ω MT represents a set of nodes equipped with micro gas turbines, C fuel represents the micro gas turbine fuel cost, represents the active power output of the i-node micro gas turbine at scenario s and time t, Ω L represents a set of lines, C LOSS represents the line network loss cost, I ij,t,s represents the current flowing through the line ij at scenario s and time t, R ij represents the resistance of the line ij.

[0046] From the above description, it is known that the first objective function is to minimize the system operation cost, because extreme weather belongs to a small probability and high risk event, and the power system runs in a normal state most of the time. In the normal operation condition, by minimizing the system operation cost, the economic efficiency of the power distribution network operation is ensured.

[0047] Further, the network flow constraint in the first constraint condition is:

[0048]

[0049]

[0050]

[0051]

[0052] wherein P i,t,s represents the active power injected by the node i, β(i) represents a set of child nodes of the node i, P ij,t,s represents the active power flowing through the line ij close to the node i, α(i) represents a set of parent nodes of the node i, P ki,t,s represents the active power flowing through the line ki close to the node i, l ki,t,s represents the current flowing through the linearized line ki, R ki represents the resistance of the line ki, Q i,t,s represents the reactive power injected by the node i, Q ij,t,s represents the reactive power flowing through the line ij close to the node i, Q ki,t,s represents the reactive power flowing through the line ki close to the node i, X ki represents the reactance of the line ki, M represents a constant, z ij,s represents a 0 / 1 variable of the open state of the line ij, v i,t,s represents the voltage of the linearized node i, v j,t,s represents the voltage of the linearized node j, R ij represents the resistance of the line ij, X ij represents the reactance of the line ij, l ij,t,s represents the current flowing through the linearized line ij;

[0053] The radial topology constraint in the first constraint condition is:

[0054]

[0055]

[0056] -Mz ij,s ≤F ij,s ≤Mzij,s ;

[0057] where n denotes the number of nodes, is a variable indicating whether node i is a source node, a(j) denotes the set of parent nodes of node j, F ij,s denotes the commodity flow passing through line ij, β(j) denotes the set of child nodes of node j, F jk,s denotes the commodity flow passing through line jk;

[0058] The safety operation constraint in the first constraint condition is:

[0059] 0≤l ij,t,s ≤(I max ) 2 z ij,s ;

[0060] (V min ) 2 ≤v i,t,s ≤(V max ) 2 ;

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067] where I max denotes the maximum line current, V min denotes the minimum node voltage, V max denotes the maximum node voltage, P i MT,max denotes the maximum active power output of the micro gas turbine at node i, denotes the reactive power output of the micro gas turbine at node i at scene s and time t, denotes the maximum reactive power output of the micro gas turbine at node i, P i SUB,min denotes the minimum active power output of the substation at node i, P i SUB,max denotes the maximum active power output of the substation at node i, denotes the minimum reactive power output of the substation at node i, represents the reactive power output of the substation at node i at scenario s and time t, represents the maximum reactive power output of the substation at node i, r i MT,down represents the maximum ramp-down rate of the micro gas turbine, represents the active power output of the micro gas turbine at node i at scenario s and time t-1, r i MT,up represents the maximum ramp-up rate of the micro gas turbine, r i sub,down represents the maximum ramp-down rate of the substation, represents the active power output of the substation at node i at scenario s and time t-1, r sub,up represents the maximum ramp-up rate of the substation.

[0068] As can be seen from the above description, the variables in the network flow constraint are linear variables, and the constraint form is a standard second-order cone form, which effectively improves the subsequent solving efficiency.

[0069] Further, the second objective function for minimizing the system operation cost and load shedding cost is:

[0070] Obj2:

[0071]

[0072] In the formula, Obj2 represents the second objective function, N s2 represents the number of scenarios in the fault state, cost CUT represents the load shedding penalty cost, and n represents the total number of nodes in the system, represents the penalty cost of the load at node i, ΔP i,t,s represents the amount of load shedding at node i at scenario s and time t.

[0073] As can be seen from the above description, in the emergency operation stage after the extreme weather disaster, the system may lose part of the power supply due to line faults, and part of the load needs to be cut off to ensure that the frequency and voltage of the system do not collapse. Therefore, unlike the economic target in normal operation, the penalty cost of cutting off the load is added to the objective function in this stage, and considering that in actual situations, the operation target in the fault stage is generally to prioritize the maximum power supply, so the penalty cost of cutting off the load is much larger than the operation cost when setting parameters. Therefore, the minimum cost problem is converted into the problem of minimizing the amount of load shedding, which effectively improves the resilience of the distribution network.

[0074] Further, the resilience index of the distribution network is calculated according to the plurality of first operation results and the plurality of second operation results, respectively, including:

[0075] The total power supply of the loads corresponding to the first operation results and the second operation results is calculated respectively, and the total power supply of the loads is taken as the system performance index;

[0076] The resilience index of the power distribution network is calculated according to the system performance index corresponding to the first operation results and the system performance index corresponding to the second operation results.

[0077] As can be seen from the above description, the resilience index of the power distribution network is calculated according to the system performance index corresponding to the first operation results and the system performance index corresponding to the second operation results, and the optimal distributed power access scheme can be accurately and effectively judged through the resilience index, so that the optimal distributed power site selection and capacity determination is realized.

[0078] Further, the calculation of the total power supply of the loads corresponding to the first operation results and the second operation results respectively includes:

[0079]

[0080]

[0081] In the formula, F(t) represents the system performance index corresponding to the first operation result, n represents the total number of loads in the system, ω i represents the weight of the importance of each load, P i L (t) represents the load of each node corresponding to the first operation result, F0 represents the system performance index corresponding to the second operation result, represents the load of each node corresponding to the second operation result.

[0082] As can be seen from the above description, the total power supply of the loads can effectively reflect the system performance, so the total power supply of the loads is taken as the system performance index to ensure that the finally selected distributed power access scheme can make the power distribution network effectively and reliably operate.

[0083] Further, the calculation of the resilience index of the power distribution network according to the system performance index corresponding to the first operation results and the system performance index corresponding to the second operation results includes:

[0084]

[0085] In the formula, EV represents the resilience index of the power distribution network, t1 represents the time when the disaster starts to cause the fault to occur, and t4 represents the time when the system returns to the normal operation state.

[0086] From the above description, it can be seen that in measuring the resilience of the power distribution network, the activeness is the ability of various resources to ensure the power supply of key system loads, and the rapidity refers to the ability of the system to restore power supply to the normal operation state at a faster speed, and therefore, in selecting the resilience index, the ratio of the integral of the system performance index in the post-fault period to the integral of the system performance index in the fault-free operation period is selected to evaluate the resilience, which is more reasonable and accurate.

[0087] Please refer to Figure 2 Another embodiment of the present application provides a distributed power source site selection and capacity determination terminal of a power distribution network, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes each step in the above-mentioned distributed power source site selection and capacity determination method of the power distribution network when executing the computer program.

[0088] The above-mentioned distributed power source site selection and capacity determination method and terminal of the power distribution network can be applied to the power distribution network which needs to improve the resilience level, and the following will be described through a specific embodiment:

[0089] Please refer to Figure 1 、 Figure 3 and Figure 4 Embodiment one is:

[0090] A distributed power source site selection and capacity determination method of a power distribution network, comprising the steps of:

[0091] S1, a first objective function for minimizing system operation cost and corresponding first constraint condition are established, and a second objective function for minimizing system operation cost and load shedding cost and corresponding second constraint condition are established, specifically comprising S11-S14:

[0092] S11, the first objective function for minimizing system operation cost is established, specifically:

[0093] Obj1:

[0094]

[0095]

[0096]

[0097]

[0098] In the formula, Obj1 represents the first objective function, N s1 represents the number of scenarios in the normal operation state, p s represents the probability of the scenario s, T m represents the observation period, which is 24 periods, cost SUBdenotes the cost of electricity purchase by the substation, cost PV denotes the cost of all photovoltaic generation, cost MT denotes the cost of all microturbine generation, cost LOSS denotes the cost of network losses, Ω sub denotes the set of substation nodes, C denotes the cost of electricity purchase by the substation at time t, cost denotes the active power output of the i-th substation at scenario s and time t, Δt denotes the observation step, Ω PV denotes the set of nodes equipped with photovoltaic generation, C PV ,OP denotes the cost of photovoltaic generation, cost denotes the active power output of the i-th photovoltaic generation at scenario s and time t, Ω MT denotes the set of nodes equipped with microturbines, C fuel denotes the cost of microturbine fuel, cost denotes the active power output of the i-th microturbine at scenario s and time t, Ω L denotes the set of lines, C LOSS denotes the cost of line network losses, I ij,t,s denotes the current flowing through line ij at scenario s and time t, R ij denotes the resistance of line ij.

[0099] S12, establish network flow constraints, radial topology constraints and safe operation constraints corresponding to the first objective function, and form a first constraint condition according to the network flow constraints, the radial topology constraints and the safe operation constraints.

[0100] wherein,

[0101]

[0102]

[0103] wherein, denotes the active power of the load at node i, P denotes the reactive power output of the i-th substation at scenario s and time t, Q denotes the reactive power output of the i-th microturbine at scenario s and time t, Q denotes the reactive power output of the i-th photovoltaic generation at scenario s and time t, Q denotes the reactive power of the load at node i. The formula is P i,t,s and Q i,t,s calculation formula.

[0104]

[0105]

[0106] where I ki,t,s represents the current flowing through line ij. This equation represents the power balance at node i and the lines connected to it, i.e. the power injected at the node is equal to the power flowing out of the lines minus the power flowing into the lines.

[0107]

[0108] where V i,t,s represents the voltage at node i, V j,t,s represents the voltage at node j. This equation finds the voltage drop across line ij, and uses the big M method to ensure that the voltage across the line is no longer directly related when the line is disconnected.

[0109]

[0110] This equation finds the current flowing through line ij. Since the above equation contains quadratic terms of current and voltage, the non-linear variable will affect the solution of the model, so the current and voltage are linearized, let and the equation finding the current flowing through line ij is relaxed, which is transformed into a second-order cone: Rewrite it into the standard second-order cone form, so the network flow constraint in the first constraint condition is:

[0111]

[0112]

[0113]

[0114]

[0115] where P i,t,s represents the active power injected at node i, β(i) represents the set of child nodes of node i, P ij,t,s represents the active power flowing through line ij close to node i, α(i) represents the set of parent nodes of node i, P ki,t,s represents the active power flowing through line ki close to node i, l ki,t,s represents the current flowing through line ki after linearization, R ki represents the resistance of line ki, Q i,t,s represents the reactive power injected at node i, Q ij,t,s represents the reactive power flowing through line ij close to node i, Q ki,t,s represents the reactive power flowing through line ki close to node i, Xki Let ki represent the reactance of line ki, M represent a constant, and z represent the reactance of line ki. ij,s A 0 / 1 variable representing the open / closed state of line ij, where 0 indicates the line is open and 1 indicates the line is connected. i,t,s V represents the voltage at node i after linearization. j,t,s R represents the voltage at node j after linearization. ij X represents the resistance of line ij. ij l represents the reactance of line ij. ij,t,s This represents the current flowing through the linearized line ij.

[0116] The radiation topology constraint in the first constraint condition is:

[0117]

[0118]

[0119] -Mz ij,s ≤F ij,s ≤Mz ij,s ;

[0120] In the formula, n represents the number of nodes. Let α(j) be a variable indicating whether node i is a source node. It is a 0 / 1 variable; 0 indicates node i is not a source node, and 1 indicates node i is a source node. Let F be the set of parent nodes of node j. ij,s Let F represent the flow of goods along line ij, β(j) represent the set of child nodes of node j, and F jk,s This represents the flow of goods along line jk. When node i is not a source node, the difference between the flow of goods flowing into node i and the flow of goods flowing out of node i is 1, meaning the non-source node has a load of 1 unit. The Big M method is used to ensure that when a line is disconnected, no goods flow passes through the corresponding line in the virtual network.

[0121] The safe operation constraint in the first constraint condition is:

[0122] 0≤l ij,t,s ≤(I max ) 2 z ij,s ;

[0123] (V min ) 2 ≤v i,t,s ≤(V max ) 2 ;

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130] I max represents the maximum line current, V min represents the minimum node voltage, V max represents the maximum node voltage, P i MT,max represents the maximum active power output of the micro gas turbine at node i, represents the reactive power output of the micro gas turbine at node i at scenario s and time t, represents the maximum reactive power output of the micro gas turbine at node i, P i SUB ,min represents the minimum active power output of the substation at node i, P i SUB,max represents the maximum active power output of the substation at node i, represents the minimum reactive power output of the substation at node i, represents the reactive power output of the substation at node i at scenario s and time t, represents the maximum reactive power output of the substation at node i, r i MT,down represents the maximum ramp-down rate of the micro gas turbine, represents the active power output of the micro gas turbine at node i at scenario s and time t-1, r i MT,up represents the maximum ramp-up rate of the micro gas turbine, r i sub,down represents the maximum ramp-down rate of the substation, represents the active power output of the substation at node i at scenario s and time t-1, r sub,up represents the maximum ramp-up rate of the substation.

[0131] S13, a second objective function of minimizing the system operation cost and the load shedding cost is established, specifically:

[0132] Obj2:

[0133]

[0134] Obj2 represents the second objective function, N s2represents the number of scenarios in the fault state, cost CUT represents the load shedding penalty cost, n represents the total number of nodes in the system, represents the penalty cost of the load of node i, ΔP i,t,s represents the amount of load shedding of node i at scenario s and time t.

[0135] In actual situations, the operation goal in the fault stage is generally to prioritize the maximum power supply, so the penalty cost of load shedding is much larger than the operation cost when setting parameters, and thus the minimum cost problem is converted into the minimum load shedding problem.

[0136] S14, establish network flow constraints, radial topology constraints and safe operation constraints corresponding to the second objective function, and form a second constraint condition according to the network flow constraints, the radial topology constraints and the safe operation constraints.

[0137] Different from the normal operation stage, the emergency operation stage allows load shedding, so the active and reactive power injection of node i is calculated by adding the load shedding variable, which is:

[0138]

[0139]

[0140] In the formula, ΔQ i,t,s represents the amount of load shedding of the reactive power injected by node i at scenario s and time t.

[0141] The network flow constraint in the second constraint condition is the same as the network flow constraint in the first constraint condition.

[0142] The radial topology constraint in the second constraint condition is the same as the radial topology constraint in the first constraint condition.

[0143] The safe operation constraint in the second constraint condition is:

[0144] z ij,s +f ij,s ≤1;

[0145]

[0146]

[0147] 0≤l ij,t,s ≤(I max ) 2 z ij,s ;

[0148] (V min ) 2≤v i,t,s ≤(V max ) 2 ;

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155] In the formula, f ij,s represents a line fault 0 / 1 variable determined by a fault scenario, and is 0 when the line is normal and is 1 when the line is faulty.

[0156] S2, determine a plurality of candidate distributed power access schemes based on a preset investment cost constraint.

[0157] The preset investment cost constraint is:

[0158]

[0159] In the formula, cost DG represents the total construction cost of the distributed power, k represents the number of constructed micro gas turbines, C MT represents the variable cost of micro gas turbine construction, P i N,MT represents the rated power of the micro gas turbine, C inst,MT represents the fixed cost of micro gas turbine construction, m represents the number of constructed photovoltaic power generation, C PV represents the variable cost of photovoltaic power generation construction, P i N,PV represents the rated power of the photovoltaic power generation, C inst,PV represents the fixed cost of photovoltaic power generation construction, C DG,max represents the maximum cost of distributed power construction. From the constraint, it can be known that each DG (distributed power) construction has a fixed cost for construction and the like, and the larger the DG size, the higher the cost, that is, the construction cost of each DG is a first function of its rated capacity.

[0160] S3, when the distribution network is in a normal operating state, running based on the plurality of candidate distributed power access schemes according to the first target function and the first constraint condition, obtaining a plurality of first running results.

[0161] S4, obtaining a plurality of second operation results based on the second objective function and the second constraint condition according to the plurality of candidate distributed power access schemes when the power distribution network is in a fault state.

[0162] S5, calculating a resilience index of the power distribution network according to the plurality of first operation results and the plurality of second operation results respectively, and selecting an optimal distributed power access scheme from the plurality of candidate distributed power access schemes according to the resilience index, specifically comprising S51-S53:

[0163] S51, calculating total power supply amounts of loads corresponding to the plurality of first operation results and the plurality of second operation results respectively, and taking the total power supply amounts of loads as system performance indexes, specifically:

[0164]

[0165]

[0166] In the formula, F(t) represents the system performance index corresponding to the first operation result, n represents the total number of loads in the system, ω i represents the weight of the importance of each load, P i L (t) represents the load of each node corresponding to the first operation result, F0 represents the system performance index corresponding to the second operation result, represents the load of each node corresponding to the second operation result.

[0167] S52, calculating the resilience index of the power distribution network according to the system performance indexes corresponding to the plurality of first operation results and the system performance indexes corresponding to the plurality of second operation results, specifically comprising:

[0168]

[0169] In the formula, EV represents the resilience index of the power distribution network, t1 represents the time when the disaster starts to cause the fault to occur, and t4 represents the time when the system returns to the normal operation state.

[0170] When measuring the resilience of the power distribution network, the activity is the ability of various resources to ensure the power supply of key system loads, and the rapidity refers to the ability to quickly restore the power supply of the system to the normal operation state, and these factors should be considered when selecting the resilience evaluation index. Therefore, the resilience of the power distribution network from the time when the fault occurs to the time when the system returns to the normal operation state is selected to evaluate the ratio of the integral of the system performance function in the post-fault period to the integral of the system performance in the fault-free operation.

[0171] S53. Selecting an optimal distributed power source access scheme from the plurality of candidate distributed power source access schemes according to the resilience index.

[0172] As shown in Figure 3 , an IEEE33 node distribution network standard model is selected as an example to verify the effectiveness of the above method. The selection of some parameters is shown in Table 1, the voltage level of the example is VN=12.66kV, the highest / lowest allowable voltage is 1.1 / 0.9VN, the rated active load is 3.715MW, and the reactive load is 2.3MVar.

[0173] Table 1 Part of the example parameters

[0174]

[0175] In the range of DG planning cost, five candidate DG access schemes are given, as shown in Table 2. Among them, the outside of "()" is the installation position of DG, and the inside of "()" is the installation capacity of DG.

[0176] Table 2 Candidate DG access scheme

[0177]

[0178]

[0179] The post-disaster simulation operation of the distribution network is carried out, and the resilience index of each scheme is calculated, as shown in Table 3, as shown in Figure 4 , as shown in Figure 4 , the corresponding load shedding penalty cost diagram of each scheme.

[0180] Table 3 Resilience index calculation results of each scheme

[0181]

[0182] It can be seen from the solution of the resilience index that, regardless of the DG access scheme, the load shedding cost of the system is reduced compared with the case without DG access, which verifies that the access of DG can improve the resilience of the distribution network. Among the five access schemes, only the scheme one with MT has the minimum load shedding cost within 24 hours after the disaster, and the penalty cost of the scheme four with only PV is the largest. The main reason is that the output of the photovoltaic power is basically 0 after 18 o'clock in the afternoon, but the resident load is the peak period of electricity consumption at night. If the extreme weather occurs at night, the PV cannot play the role of emergency power supply at all, so it is not feasible to rely only on PV to improve the system resilience, but mainly rely on MT which can artificially adjust the output. In addition, there is also a factor that the total capacity of DG in scheme five is less than that in scheme one due to the higher price of photovoltaic than micro gas turbine.

[0183] Please refer to Figure 2 , the second embodiment of the present application is:

[0184] A distributed power supply site selection and capacity determination terminal of a power distribution network comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements each step in the distributed power supply site selection and capacity determination method of the power distribution network in embodiment one when executing the computer program.

[0185] In summary, the present application provides a distributed power supply site selection and capacity determination method and terminal of a power distribution network, determines a plurality of candidate distributed power supply access schemes based on a preset investment cost constraint, simulates the operation of each scheme under different scenarios by performing operation based on the plurality of candidate distributed power supply access schemes according to the corresponding objective function and constraint condition under normal operation state and fault state of the power distribution network, and finally calculates the resilience index of the power distribution network according to the operation results, selects the optimal distributed power supply access scheme, takes economy as the main target when the power distribution network is in normal operation state, and adds load shedding cost when the power distribution network is in fault state, so as to effectively improve the resilience of the power distribution network while ensuring the economy of the power distribution network. In addition, the resilience index of the power distribution network is calculated according to the system performance index corresponding to the plurality of first operation results and the system performance index corresponding to the plurality of second operation results, the optimal distributed power supply access scheme can be accurately and effectively judged through the resilience index, so as to realize the best distributed power supply site selection and capacity determination.

[0186] The above is only an embodiment of the present application, and does not limit the patent range of the present application, and any equivalent transformation or direct or indirect application in the related technical field based on the content of the specification and drawings of the present application is also included in the patent protection range of the present application.

Claims

1. A method for location and capacity determination of distributed generation sources in a distribution network, characterized in that, Including the following steps: Establish a first objective function that minimizes the system operating cost and its corresponding first constraint, and establish a second objective function that minimizes the system operating cost and the load shedding cost and its corresponding second constraint; Multiple candidate distributed power supply access schemes were determined based on preset investment cost constraints; When the distribution network is in normal operation, the multiple candidate distributed power source access schemes are run according to the first objective function and the first constraint conditions to obtain multiple first running results. When the distribution network is in a fault state, the multiple candidate distributed power source access schemes are run according to the second objective function and the second constraint conditions to obtain multiple second running results; The resilience index of the distribution network is calculated based on the multiple first operating results and the multiple second operating results, and the optimal distributed power source access scheme is selected from the multiple candidate distributed power source access schemes based on the resilience index. The calculation of the resilience index of the distribution network based on the plurality of first operating results and the plurality of second operating results includes: Calculate the total load power supply corresponding to the plurality of first operating results and the plurality of second operating results respectively, and use the total load power supply as a system performance index; The resilience index of the distribution network is calculated based on the system performance index corresponding to the plurality of first operating results and the system performance index corresponding to the plurality of second operating results; The calculation of the total load power supply corresponding to the plurality of first operating results and the plurality of second operating results, and the use of the total load power supply as a system performance indicator, includes: ； ； In the formula, This represents the system performance index corresponding to the first running result, where n represents the total number of loads in the system. The weights representing the importance of each load are... F0 represents the load of each node corresponding to the first running result, and F0 represents the system performance index corresponding to the second running result. This indicates the load of each node corresponding to the second running result; The calculation of the resilience index of the distribution network based on the system performance index corresponding to the plurality of first operating results and the system performance index corresponding to the plurality of second operating results includes: ； In the formula, EV represents the resilience index of the distribution network, t1 represents the moment when the disaster begins to cause the fault, and t4 represents the moment when the system returns to normal operation.

2. The method for location and capacity determination of distributed generation in a distribution network according to claim 1, characterized in that, The preset investment cost constraint is: ； In the formula, The value represents the total construction cost of the distributed power source, and k represents the number of micro gas turbines constructed. This represents the variable cost of constructing a micro gas turbine. This indicates the rated power of the micro gas turbine. This represents the fixed cost of constructing a micro gas turbine, and m represents the number of photovoltaic power plants constructed. This represents the variable cost of photovoltaic power generation construction. This indicates the rated power of photovoltaic power generation. This represents the fixed costs of photovoltaic power generation construction. This represents the maximum cost of building distributed power sources.

3. The method for site selection and capacity determination of distributed generation in a distribution network according to claim 1, characterized in that, The establishment of the first objective function that minimizes the system operating cost and its corresponding first constraint conditions include: Establish a primary objective function that minimizes the system operating cost; Establish network power flow constraints, radial topology constraints, and safe operation constraints corresponding to the first objective function, and construct the first constraint condition based on the network power flow constraints, the radial topology constraints, and the safe operation constraints.

4. The method for location and capacity determination of distributed power sources in a distribution network according to claim 3, characterized in that, The first objective function for minimizing the system operating cost is: ； ； ； ； ； In the formula, Obj1 represents the first objective function. This represents the number of scenarios in normal operation. T represents the probability of scenario s occurring. m Indicates the observation period. This indicates the cost of purchasing electricity for the substation. This represents the total cost of electricity generated by all photovoltaic power generation. This represents the total cost of generating electricity using all micro gas turbines. Indicates network loss fees. This represents the set of nodes belonging to the substation. This represents the cost of purchasing electricity from the substation at time t. This represents the active power output of substation i at scenario s and time t. Indicates the observation step size. This represents the set of nodes equipped with photovoltaic power generation. Indicates the cost of photovoltaic power generation. This represents the active power output of the photovoltaic power generation at node i in scenario s and time t. This represents the set of nodes equipped with micro gas turbines. This indicates the fuel cost of a micro gas turbine. This represents the active power output of the micro gas turbine at node i in scenario s and time t. Represents a set of routes. This indicates the cost of network loss. This represents the current flowing through line ij at scene s and time t. This indicates the resistance of line ij.

5. The method for location and capacity determination of distributed power sources in a distribution network according to claim 4, characterized in that, The network power flow constraint in the first constraint condition is: ； ； ； ； In the formula, This represents the active power injected at node i. Let i represent the set of child nodes of node i. This represents the active power flowing along line ij near node i. Let i represent the set of parent nodes of node i. This represents the active power flowing along line ki near node i. This represents the current flowing through the linearized line ki. This represents the resistance of line ki. This represents the reactive power injected at node i. This represents the reactive power flowing along line ij near node i. This represents the reactive power flowing along line ki near node i. This represents the reactance of line ki, and M represents a constant. The 0 / 1 variable represents the on / off state of line ij. Let represent the voltage at node i after linearization. This represents the voltage at node j after linearization. This represents the resistance of line ij. This represents the reactance of line ij. This represents the current flowing through the linearized line ij; The radiation topology constraint in the first constraint condition is: ； ； ； In the formula, n represents the number of nodes. A variable indicating whether node i is the source node. Let j represent the set of parent nodes of node j. This represents the flow of goods along line ij. Let j represent the set of child nodes of node j. This represents the flow of goods along lines j and k; The safe operation constraint in the first constraint condition is: ； ； ； ； ； ； ； ； In the formula, This indicates the maximum value of the line current. This represents the minimum node voltage. Indicates the maximum node voltage. This represents the maximum active power output of the micro gas turbine at node i. This represents the reactive power output of the i-node micro gas turbine in scenario s and time t. This represents the maximum reactive power output of the micro gas turbine at node i. This represents the minimum active power output of the substation at node i. This represents the maximum active power output of the substation at node i. This represents the minimum reactive power output of the substation at node i. This represents the reactive power output of substation i at scenario s and time t. This represents the maximum reactive power output of the substation at node i. This indicates the maximum slip rate of the micro gas turbine. This represents the active power output of the i-node micro gas turbine in scenario s and time t-1. This indicates the maximum ramp rate of the micro gas turbine. This indicates the maximum landslide rate of the substation. This represents the active power output of substation i at scenario s and time t-1. This indicates the maximum ramp rate of the substation.

6. The method for location and capacity determination of distributed generation sources in a distribution network according to claim 4, characterized in that, The second objective function for minimizing system operating costs and load shedding costs is: ； ； In the formula, Obj2 represents the second objective function. This indicates the number of scenarios under fault conditions. This represents the load shedding penalty cost, and n represents the total number of nodes in the system. This represents the penalty cost for the load on node i. This represents the load shedding amount of node i in scene s and time t.

7. A distributed generation addressing and capacity determination terminal for a distribution network, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the distributed power source location and capacity determination method for a power distribution network according to any one of claims 1 to 6.

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