A low-carbon planning method for flexible distribution networks based on carbon emission theory

Abstract

A low-carbon planning method for a flexible distribution network based on carbon emission flow theory belongs to the technical field of power system operation and maintenance, and comprises the following steps: first, according to the carbon emission theoretical index of the flexible distribution network, an improved carbon emission flow calculation method for the flexible distribution network is proposed; second, considering the characteristics of distributed power generation and intelligent soft switches, a low-carbon planning model for the flexible distribution network is established based on the improved carbon emission flow theory; third, based on the second-order cone programming solution, the proposed model is obtained to obtain the optimal low-carbon planning scheme of the flexible distribution network, and the carbon flow distribution of the whole network is obtained in combination with the carbon emission flow theory; the present invention can effectively obtain the optimal low-carbon planning scheme of the flexible distribution network and obtain the carbon flow distribution of the whole network in combination with the carbon emission flow theory.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and maintenance, and in particular relates to a low-carbon planning method for a flexible distribution network based on carbon emission flow theory. Background Art

[0002] With the rapid economic development, energy demand continues to increase, and the resulting climate change is one of the global challenges facing society today. Carbon emissions from the power industry account for about 40% of carbon emissions from fossil fuel combustion. In order to achieve my country's strategic goal of "carbon peak and carbon neutrality", the power industry urgently needs to find a low-carbon green approach that meets its own industry characteristics and strive to achieve low-carbon sustainable development. With the widespread application of distributed generation technology, more and more distributed generation (DG) is connected to the distribution network. Common DGs include distributed photovoltaics (PV), distributed wind turbines (WT) and micro turbines (MT). The access of DG makes the distribution network flow distribution more complicated, and the volatility of its output also limits the distribution network's ability to accept DG.

[0003] As a new type of power electronic device, the intelligent soft switch (soft open point, SOP) is connected to the distribution network, which can continuously and in real time control the power in the distribution network, and to a certain extent improve the distribution network's ability to absorb DG, improve the operating state of the distribution network, and make the operation of the distribution network more "flexible". Based on the flexible interconnection technology of SOP, the traditional distribution network has changed from the state of "closed-loop design, open-loop operation" to flexible closed-loop operation, and gradually transformed into a flexible distribution network (flexible distribution network, FDN). Thanks to the closed-loop structure and flexible adjustment ability of the flexible distribution network, the distribution network has the ability to balance the feeder flow distribution and improve the voltage level of the distribution network, thereby improving the ability to absorb the fluctuating output of distributed power sources. The flexible distribution network has the potential to improve the reliability of power supply, reduce system losses and operating costs. The relevant research in this field is still in the initial stage, and the relevant technologies and equipment of the flexible distribution network need a long time to be promoted and applied. With the development of science and technology, the development prospects of FDN are very broad. Flexibility is an important feature of the future distribution network, and low-carbon planning of the flexible distribution network is of great significance to improving the operating efficiency of the distribution network. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a low-carbon planning method for a flexible distribution network based on carbon emission theory, which can effectively obtain the optimal low-carbon planning scheme for the flexible distribution network and obtain the carbon flow distribution of the entire network in combination with the carbon emission flow theory.

[0005] A low-carbon planning method for a flexible distribution network based on carbon emission theory includes the following steps:

[0006] Step 1: Based on the theoretical indicators of carbon emission of flexible distribution network, an improved calculation method of carbon emission flow of flexible distribution network is proposed;

[0007] Step 2: Considering the characteristics of distributed generation and intelligent soft switches, a low-carbon planning model for flexible distribution networks is established based on the improved carbon emission flow theory;

[0008] Step 3: Based on the proposed model solved by second-order cone programming, the optimal low-carbon planning scheme of the flexible distribution network is obtained, and the carbon flow distribution of the entire network is obtained by combining the carbon emission flow theory.

[0009] The carbon emission theoretical index of the flexible distribution network in step 1 is: the carbon emission flow in the power system starts from the power generation side, depends on the active power flow, flows in the power grid, and finally flows to the load side; given the active power flow and carbon flow density of the branch flowing into the node, the carbon flow density of the branch flow flowing out of the node is equal to the node carbon potential of the node,

[0010] I L,l =I N,i l∈Ω i- (1)

[0011] In the formula, Ω i- is the set of branches outflowing from node i; l is the branch number.

[0012] After the soft intelligent switch SOP is connected to the power grid in step 2, the carbon flow rate calculation formula flowing through the soft intelligent switch SOP is:

[0013] P SOP,i +P SOP,j =0 (2)

[0014] |P SOP,i |=|P SOP,j |=|P SOP,ij | (3)

[0015]

[0016] R SOP,ij =I SOP,ij P SOP,ij (5)

[0017] Where P SOP,i , P SOP,j are the active power injected by SOP at nodes i and j respectively; P SOP,ij is the actual active power flowing from node i to node j in SOP; I SOP,ij is the carbon flow density of SOP; R SOP,ijis the carbon flow rate flowing over the SOP; Ω i+ is the set of branches into which the flow flows into node i; P l is the active power flowing through branch l; R l is the carbon flow that passes through branch l with the active power flow per unit time.

[0018] The flexible distribution network low-carbon planning model in step 2 includes the carbon emission flow on the power supply side, the carbon emission flow on the grid side and the carbon emission flow on the load side.

[0019] The optimal low-carbon planning scheme of the flexible distribution network in step three takes the minimization of the annual comprehensive cost C as the objective function, which is converted into annual investment cost C1, annual operation and maintenance cost C2, annual cost of purchasing electricity from the transmission network C3, annual network loss cost C4, and annual carbon emission cost C5.

[0020] The constraints satisfied by the optimal low-carbon planning scheme of the flexible distribution network in step three include node power balance equation constraints, voltage balance equation constraints, line flow constraints, distributed generation DG operation constraints, installed capacity constraints of DG at the selected node i, discreteness constraints of DG installed capacity, SOP operation constraints, installed capacity of SOP at the selected branch ij, discreteness constraints of SOP installed capacity, load node carbon emission equation constraints, branch carbon flow density constraints and node carbon potential equation constraints.

[0021] Through the above-mentioned design scheme, the present invention can bring the following beneficial effects: a low-carbon planning method for a flexible distribution network based on carbon emission theory, and an improved FDN carbon emission flow calculation method. When the SOP is connected to the distribution network, on the basis of the traditional carbon emission flow model, the SOP is regarded as a "branch" that allows active power to pass through and can emit reactive power. In the FDN, the carbon emission flow is attached to the active power flow. Therefore, when the SOP adjusts the active power in the FDN, it can also adjust the distribution of the carbon emission flow. The calculation of the FDN carbon emission flow distribution is successfully realized by this method;

[0022] A FDN low-carbon planning model based on improved carbon emission flow theory was established. On the basis of traditional distribution network planning, the influence of coordinated planning of DG and SOP on carbon emissions of distribution network was explored. The results show that reasonable coordinated planning of DG and SOP can promote the local consumption of new energy, reduce carbon emissions of distribution network, and effectively improve the economy of distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0024] Figure 1 It is a schematic diagram of the entity of the carbon emission flow in the power system of the flexible distribution network low-carbon planning method based on the carbon emission theory of the present invention.

[0025] Figure 2 This is a schematic diagram of a carbon emission flow model considering SOP in a low-carbon planning method for a flexible distribution network based on carbon emission theory of the present invention.

[0026] Figure 3 The present invention is a flow chart of the carbon emission flow calculation of the whole process of a flexible distribution network low-carbon planning method FDN based on carbon emission theory.

[0027] Figure 4 The invention discloses an improved IEEE 33-node distribution network according to a specific implementation method of a flexible distribution network low-carbon planning method based on carbon emission theory.

[0028] Figure 5 This is a specific implementation method of a flexible distribution network low-carbon planning method based on carbon emission theory in the present invention, and a carbon potential diagram of the main network in each time period.

[0029] Figure 6 The present invention discloses a specific implementation method of a flexible distribution network low-carbon planning method based on carbon emission theory, which is a unit cost diagram for each time period when the distribution network purchases electricity from the transmission network.

[0030] Figure 7 This is a carbon potential comparison diagram of nodes in each scenario in a specific implementation method of a flexible distribution network low-carbon planning method based on carbon emission theory of the present invention.

[0031] Figure 8 This is a carbon flow rate distribution diagram of scenario three of a specific implementation method of a flexible distribution network low-carbon planning method based on carbon emission theory of the present invention.

[0032] Fig. 9 This is a flow chart of a low-carbon planning method for a flexible distribution network based on carbon emission theory according to the present invention. DETAILED DESCRIPTION

[0033] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below.

[0034] A low-carbon planning method for flexible distribution networks based on carbon emission flow theory, such as Fig. 9 As shown, the following steps are included:

[0035] 101: First, the existing carbon emission flow theory of power system is summarized, the indicators of carbon emission flow theory are introduced, and the FDN with extensive DG access and the carbon emission characteristics of FDN are specifically analyzed, and an improved FDN carbon emission flow calculation method is proposed;

[0036] 102: Then, in order to solve the problem of DG and SOP site selection and capacity determination in FDN, a FDN low-carbon planning model based on the improved carbon emission flow theory was established. On the basis of traditional distribution network planning, the influence of coordinated planning of DG and SOP on the carbon emission of distribution network was explored. The model takes the minimum annual comprehensive cost of FDN as the objective function.

[0037] 103: Finally, based on step 101 and step 102, the model is solved to obtain the optimal active power flow distribution of FDN, and the active output and carbon emission intensity E of each DG in FDN are counted. G , based on the improved carbon emission flow calculation method, the real-time carbon flow rate distribution map of FDN is obtained.

[0038] In summary, the embodiment of the present invention can quickly and accurately solve the low-carbon planning scheme of the entire FDN and the real-time distribution of carbon emission flow through the above steps 101 to 103.

[0039] The method of the present invention is described in detail below in conjunction with specific calculation formulas and examples, as described below:

[0040] The carbon emission flow in the power system starts from the power generation side, depends on the active power flow, flows in the power grid, and finally flows to the load side. The establishment of the carbon emission flow theory clearly reveals the distribution characteristics of the carbon emission flow in the power grid, such as Figure 1 As shown in Table 1, the theoretical indicators of carbon emission flow and the relationship between carbon emission flow and power flow of the power system are shown.

[0041] Table 1 Theoretical related indicators of carbon emission flow in power system

[0042]

[0043] When the active power flow and carbon flow density flowing into a node branch are given, the carbon flow density of the branch flow flowing out of the node is equal to the node carbon potential of the node, as shown in formula (1).

[0044] I L,l =I N,i l∈Ω i- (1)

[0045] In the formula, Ω i- is the set of branches outflowing from node i; l is the branch number.

[0046] 1) Carbon emission flow model considering intelligent soft switching

[0047] After planning the distribution network and determining the installation location of the SOP, the active power flows from one end node of the SOP to the other end node, and the carbon emission flow flows in the distribution network dependent on the active power. Therefore, the SOP can be regarded as a "branch" that allows active power to pass through and can emit reactive power. When the SOP adjusts the active power in the distribution network, it can also adjust the carbon flow distribution in the distribution network, such as Figure 2 The active power flow in SOP is from node i to node j. According to the relevant characteristics of SOP and the relationship between the node carbon potential and the branch carbon flow density, the carbon flow rate calculation formula flowing through SOP is shown in equations (2)-(5).

[0048] P SOP,i +P SOP,j =0 (2)

[0049] |P SOP,i |=|P SOP,j |=|P SOP,ij | (3)

[0050]

[0051] R SOP,ij =I SOP,ij P SOP,ij (5)

[0052] Where P SOP,i , P SOP,j are the active power injected by SOP at nodes i and j respectively; P SOP,ij is the actual active power flowing from node i to node j in SOP; I SOP,ij is the carbon flow density of SOP; R SOP,ij is the carbon flow rate flowing over the SOP; Ω i+ is the set of branches into which the flow flows into node i; P l is the active power flowing through branch l; R l is the carbon flow that passes through branch l with the active power flow per unit time.

[0053] 2) Calculation of carbon emission flows in flexible distribution networks

[0054] In an FDN with N nodes, there are K nodes connected to DG and M nodes connected to load. Based on the needs of FDN carbon emission flow calculation, the key matrices and vectors are defined as shown in Table 2.

[0055] Table 2 Key matrices and vectors

[0056]

[0057] Statistical generator set carbon emission intensity vector E G , calculate the node carbon potential vector EN , as shown in formula (6).

[0058]

[0059] 1. Calculation of carbon emission flow on the power supply side

[0060] The carbon emission intensity of DG is multiplied by the active power injected by DG into FDN to obtain the carbon emission size on the power supply side.

[0061] 2. Calculation of carbon emission flow on the grid side

[0062] The branch power distribution matrix P B Get the branch carbon flow rate distribution matrix R B , as shown in formula (7).

[0063] R B =diag(E N ) B (7)

[0064] 3. Calculation of carbon emission flow on the load side

[0065] The carbon emission intensity of a load node is equal to the carbon potential of the node, which is represented by the load distribution matrix P L Get the load carbon flow rate vector R L , as shown in formula (8).

[0066] R L =P L E N (8)

[0067] By calculating the carbon emission flows on the FDN power supply side, grid side, and load side, carbon emissions are materialized in the form of network flows in the power system, such as Figure 3 shown.

[0068] Specifically, four typical days in four seasons of the year are selected as the operation scenarios considered by the model, and each scenario lasts for a certain number of days. The model takes the minimum annual comprehensive cost C of FDN as the objective function, as shown in formula (9), which mainly includes five items, namely, the annual investment cost converted to each year C1, the annual operation and maintenance cost C2, the annual cost of purchasing electricity from the transmission network C3, the annual network loss cost C4, and the annual carbon emission cost C5.

[0069] minC=C1+C2+C3+C4+C5 (9)

[0070] 1) Annual investment cost:

[0071]

[0072]

[0073] In the formula, d is the discount rate, which is 0.06; y MT ,y WT ,y PV ,y SOP are the service life of MT, WT, PV and SOP respectively; Ω b N is the branch set of FDN; bus is the number of FDN nodes; cⅠMT, cⅠWT, cⅠPV, c SOP is the investment cost per unit capacity MT, WT, PV and SOP; P MT,i , P WT,i , P PV,i are the installed capacity of the corresponding DG installed at node i; S SOP,ij is the installed capacity of SOP between nodes i and j.

[0074] 2) Annual operation and maintenance costs:

[0075]

[0076] Where N S is the number of running scenarios; d s is the duration of scenario s; cⅡMT, cⅡWT, cⅡPV are the operation and maintenance costs of the corresponding DG per unit of electricity; P MT,i,t,s , P WT,i,t,s , P PV,i,t,s are the active power output of the corresponding DG installed at node i at time t under scenario s; η is the annual operation and maintenance cost coefficient of SOP; T is the number of time periods included in a scenario.

[0077] 3) Annual cost of purchasing electricity from the transmission grid:

[0078]

[0079] In the formula, λ i,t,s P is the unit cost of the i-th FDN node purchasing electricity from the transmission grid at time t under scenario s; TSO,i,t,s is the active power injected into node i in FDN by the transmission grid at time t under scenario s.

[0080] 4) Annual network loss cost:

[0081]

[0082] In the formula, c loss is the unit cost of FDN network loss, which is 0.35 yuan / (kW·h); P loss,l,t,s is the active power loss of branch l at time t in scenario s.

[0083] 5) Annual carbon emission costs:

[0084]

[0085] In the formula, I MT,i,t,s I is the carbon emission coefficient of the MT installed at node i at time t under scenario s; TSO,i,t,s is the carbon potential injected into FDN node i by the transmission grid at time t under scenario s; c is the unit carbon emission price, which is 100 yuan / (tCO2).

[0086] Constraints

[0087] 1) Node power balance equation:

[0088]

[0089] Where Ψ and Φ are the sets of the first and last nodes of the branch respectively; P ki,t,s and Q ki,t,s are the active and reactive power flowing through branch ki at time t in scenario s, and the direction flows from node k to node i; I ki,t,s is the square of the current flowing through branch ki at time t in scenario s; r ki and x ki are the resistance and reactance of branch ki respectively; P ij,t,s and Q ij,t,s are the active and reactive power flowing through branch ij at time t in scenario s, flowing from node i to node j; P D,i,t,s and Q D,i,t,s are the active and reactive loads connected to node i at time t under scenario s; Q MT,i,t,s , Q WT,i,t,s , Q PV,i,t,s are the reactive power output of the corresponding DG installed at node i at time t under scenario s; P SOP,i,t,s and Q SOP,i,t,s are the active and reactive powers injected by SOP at node i at time t under scenario s, respectively.

[0090] 2) Voltage balance equation:

[0091]

[0092] u i,min ≤u i,t,s ≤u i,max (18)

[0093] In the formula, u i,t,s is the square of the voltage of node i at time t in scenario s; u j,t,s is the square of the voltage of node j at time t in scenario s; u i,max and u i,min are the maximum and minimum values ​​of the voltage at node i, respectively; I ij,t,sis the square of the current flowing through branch ij at time t in scenario s; r ij and x ij are the resistance and reactance of branch ij respectively.

[0094] 3) Line flow constraints:

[0095]

[0096] 4) DG operation constraints:

[0097]

[0098] Where P MT,max , P WT,max , P PV,max are the upper limits of the active output of the corresponding DG; P MT,min , P WT,min , P PV,min are the lower limits of the corresponding DG active output respectively; are the power factor angles of the corresponding DGs respectively.

[0099] 5) Installed capacity constraint of DG at the candidate node i:

[0100]

[0101] Where Pmax MT,i, Pmax WT,i, and Pmax PV,i are the upper limits of the corresponding DG installed capacity at node i, and Pmax DG,i is the upper limit of the DG installed capacity at node i.

[0102] 6) Discrete constraints on DG installed capacity:

[0103]

[0104] Where P MT , P WT , P PV are the rated capacities of a single MT, WT, and PV; n MT,i 、n WT,i 、n PV,i are the number of single MT, WT and PV connected to node i respectively.

[0105] 7) SOP operation constraints

[0106] Equations (2)-(5), the capacity constraint is shown in equation (23).

[0107]

[0108] Where P SOP,i,t,s and Q SOP,i,t,sare the active and reactive power injected by SOP at node i at time t under scenario s; P SOP,j,t,s and Q SOP,j,t,s are the active and reactive power injected by SOP at node j at time t under scenario s; S SOP,ij is the SOP capacity installed between nodes i and j.

[0109] 8) Installation capacity constraint of SOP at the selected branch ij:

[0110]

[0111] Where Smax SOP,ij is the upper limit of the installed capacity of SOP at branch ij in the distribution network.

[0112] 9) Discrete constraints on SOP installation capacity:

[0113] S SOP,ij =n SOP,ij S SOP (25)

[0114] In the formula, S SOP is the rated capacity of a single SOP; n SOP,ij The number of single SOPs installed at branch ij.

[0115] 10) Load node carbon emission equation constraint:

[0116] E i,t,s =P D,i,t,s I N,i,t,s (26)

[0117] In the formula, E i,t,s is the carbon emission of load node i at time t under scenario s; I N,i,t,s is the node carbon potential of load node i at time t under scenario s.

[0118] 11) Branch carbon flow density constraint:

[0119] I L,l,t,s =I N,i,t,s (l∈Ω i- ) (27)

[0120] In the formula, I L,l,t,s is the carbon flow density of branch l at time t under scenario s.

[0121] 12) Node carbon potential equality constraint:

[0122]

[0123] Where P l,t,s is the active power flowing through branch l at time t in scenario s; ISOP,ji,t,s is the carbon flow density of SOP at branch ji at time t under scenario s.

[0124] Based on the above models and constraints, the low-carbon planning model is analyzed by taking the improved IEEE33-node distribution system as an example. The wind and solar output and load forecast data for four quarters are input, and the models under different planning schemes are solved based on the second-order cone programming. After traversing all planning schemes, the optimal low-carbon planning scheme of FDN is obtained, and the carbon emission flow distribution of the whole network is obtained by combining the carbon emission flow theory.

[0125] In summary, the embodiment of the present invention can quickly and accurately solve the carbon emission flow distribution of FDN through the above method.

[0126] Example

[0127] The feasibility of the method of the present invention is verified by combining specific experiments, as described below:

[0128] Improved IEEE33 node distribution network Figure 4 As shown in the figure, there are 9 DG installation nodes to be selected; considering installing SOP at the tie switch between feeders, there are 5 installation locations to be selected; the rated capacity of a single PV is 100kW, and the installation nodes to be selected are 18, 22 and 25; the rated capacity of a single WT is 100kW, and the installation nodes to be selected are 6, 11 and 28; the rated capacity of a single MT is 100kW, and the installation nodes to be selected are 3, 15 and 31; the number of DG accesses to a single node is limited to 3, and the relevant parameters of DG are shown in Table 3.

[0129] Table 3 DG related parameters

[0130]

[0131] The rated capacity of a single SOP is 50kVA, and the installation locations to be selected are branches 25-29, 18-33, 9-15, 8-21, and 12-22. The number of SOPs connected to a single branch is limited to 2, and the relevant parameters of the SOP are shown in Table 4.

[0132] Table 4 SOP related parameters

[0133]

[0134] The carbon trend of the main network in each period is as follows Figure 5 As shown in the figure, the unit cost of the distribution network purchasing electricity from the transmission network in each period is as follows: Figure 6 shown.

[0135] Set up different scenarios as follows:

[0136] Scenario 1: Traditional grid planning, where low-carbon factors are not considered in the distribution network planning process;

[0137] Scenario 2: Low-carbon grid planning: low-carbon elements are considered in the distribution network planning process, and carbon emission costs are embedded in the objective function for optimization;

[0138] Scenario 3: Based on Scenario 2, coordinated planning of DG and SOP in the distribution network is carried out.

[0139] The moments when the output of new energy units is large are selected to analyze each scenario, and the location, capacity and various costs of DG and SOP connected to the distribution network under different scenarios are obtained. The planning results under different scenarios are shown in Table 5.

[0140] Table 5 Comparison of planning scheme results

[0141]

[0142]

[0143] Note: 15(3) means that 3 MTs are installed in node 15, and 12-22(2) means that 2 SOPs are installed in branch 12-22.

[0144] As shown in Table 5, when considering low-carbon elements in the distribution network planning process and embedding carbon emission costs into the optimization objective function, the installed capacity of new energy units in scenario 2 is increased compared with scenario 1, which leads to an increase in annual investment costs, but the annual operation and maintenance costs and annual carbon emission costs in scenario 2 are reduced. Compared with scenario 1, scenario 2 reduces the annual comprehensive cost by 4.78% and the annual carbon emission cost by 45.18%, indicating the necessity of considering the low-carbon development of the distribution network on the basis of traditional distribution network planning. The investment cost of SOP is relatively high, so this paper sets the maximum number of SOPs connected to the distribution network to 4. Scenario 3 introduces the planning of SOP on the basis of scenario 2. SOP can realize continuous real-time control of power in the distribution network, adjust and optimize the flow distribution of the entire distribution network, reduce network losses, and improve the economy of distribution network operation. Analysis of different scenarios shows that compared with scenario 1, scenario 3 reduces the annual comprehensive cost by 5.15% and the annual carbon emission cost by 46.96%. The above results show that in the distribution network, the reasonable planning of DG and SOP can promote the local consumption of new energy, effectively improve the utilization rate of new energy, and reduce the carbon emissions of FDN. At the same time, the access of SOP can realize the efficient adjustment of power flow, reduce network losses, and further enhance the flexible adjustment capability of FDN.

[0145] The carbon emission of the distribution network under different scenarios is compared and analyzed by selecting the time when the output of new energy units is larger. Figure 7As shown in Table 5, it can be seen that there are more high-coal-consuming units installed near high-carbon potential nodes in the distribution network or they are close to the transmission network, while there are more new energy units installed near low-carbon potential nodes or they are far away from the transmission network. Compared with scenario 1, scenario 2, MT is no longer installed at node 15, and the load power of each node on all branches from nodes 14 to 18 is provided by the PV installed at node 18. At this time, the electric energy flowing on the branch is zero-carbon electric energy, there is no carbon flow injection at each node, and the carbon potential of the node is 0. The ability of SOP to adjust power is closely related to its installation location and capacity, and is also affected by the operating characteristics of DG. In scenario 3, SOPs are installed at branches 12-22, 18-33, and 25-29. SOPs change the power flow distribution of FDN by adjusting the active and reactive power of the FDN double-ended feeder, effectively balancing the feeder load and reducing network losses. The carbon emission flow depends on the active power. When nodes 12, 33, 29 and their adjacent nodes receive the active power transmitted by SOP, they also receive zero-carbon electricity from the new energy units. The number of nodes with zero carbon potential in FDN increases further, expanding the scope of zero-carbon and low-carbon emission areas of FDN.

[0146] The carbon flow rate distribution of the distribution network has the same distribution characteristics as the active power distribution. The carbon flow rate distribution of FDN under scenario three is analyzed, and the carbon flow rate distribution diagram is obtained as follows: Figure 8 The inflow and outflow of carbon flow rates at each node in the distribution network are balanced. Figure 8 The analysis is carried out by taking the middle node 29 as an example. The SOP is regarded as a "branch" that allows active power to pass through and can generate reactive power. Nodes 25 and 28 respectively inject a carbon flow rate of 0.204tCO2 / h into node 29 through the SOP installed at branch 25-29 and branch 28-29, while the sum of the carbon flow rates from node 29 to node 30 and its own load is 0.203tCO2 / h, which roughly satisfies the equilibrium relationship of the node carbon flow rate, verifying the rationality and effectiveness of the improved FDN carbon emission flow calculation method.

[0147] In summary, the embodiments verify that the present invention can quickly and effectively obtain the optimal low-carbon planning scheme for the flexible distribution network and obtain the carbon emission flow distribution of the entire network in combination with the carbon emission flow theory.

Claims

1. A low-carbon planning method for a flexible distribution network based on carbon emission theory, characterized by: The following steps are included: Step 1: Based on the theoretical indicators of carbon emission of flexible distribution network, an improved calculation method of carbon emission flow of flexible distribution network is proposed; Step 2: Considering the characteristics of distributed generation and intelligent soft switches, a low-carbon planning model for flexible distribution networks is established based on the improved carbon emission flow theory; Step 3: Based on the proposed model solved by second-order cone programming, the optimal low-carbon planning scheme of the flexible distribution network is obtained, and the carbon flow distribution of the whole network is obtained by combining the carbon emission flow theory; The optimal low-carbon planning scheme of the flexible distribution network in step 3 takes the minimum annual comprehensive cost C as the objective function, which is converted into annual investment cost C1, annual operation and maintenance cost C2, annual cost of purchasing electricity from the transmission network C3, annual network loss cost C4, and annual carbon emission cost C5; 1) Annual investment cost: In the formula, d is the discount rate, which is 0.06; y MT ,y WT ,y PV ,y SOP are the service life of MT, WT, PV and SOP respectively; Ω b N is the branch set of FDN; bus is the number of FDN nodes; c Ⅰ MT 、c Ⅰ WT 、c Ⅰ PV 、c SOP is the investment cost per unit capacity MT, WT, PV and SOP; P MT,i , P WT,i , P PV,i are the installed capacity of the corresponding DG installed at node i; S SOP,ij is the installed capacity of SOP between node i and node j; 2) Annual operation and maintenance costs: Where N S is the number of running scenarios; d s is the duration of scene s; c Ⅱ MT 、c Ⅱ WT 、c Ⅱ PV are the operation and maintenance costs of the corresponding DG for each unit of electricity; P MT,i,t,s , P WT,i,t,s , P PV,i,t,s are the active power output of the corresponding DG installed at node i at time t under scenario s; η is the annual operation and maintenance cost coefficient of SOP; T is the number of time periods included in a scenario; 3) Annual cost of purchasing electricity from the transmission grid: In the formula, λ i,t,s P is the unit cost of the i-th FDN node purchasing electricity from the transmission grid at time t under scenario s; TSO,i,t,s is the active power injected into the FDN node i by the transmission grid at time t under scenario s; 4) Annual network loss cost: In the formula, c loss is the unit cost of FDN network loss, which is 0.35 yuan / (kW·h); P loss,l,t,s is the active power loss of branch l at time t in scenario s; 5) Annual carbon emission costs: In the formula, I MT,i,t,s I is the carbon emission coefficient of the MT installed at node i at time t under scenario s; TSO,i,t,s is the carbon potential injected into FDN node i by the transmission grid at time t under scenario s; c is the unit carbon emission price, which is 100 yuan / (tCO2); The constraints satisfied by the optimal low-carbon planning scheme of the flexible distribution network in step 3 include node power balance equation constraints, voltage balance equation constraints, line flow constraints, distributed generation DG operation constraints, DG installed capacity constraints at the selected node i, DG installed capacity discreteness constraints, SOP operation constraints, SOP installed capacity at the selected branch ij, SOP installed capacity discreteness constraints, load node carbon emission equation constraints, branch carbon flow density constraints and node carbon potential equation constraints; 1) Node power balance equation: Where Ψ and Φ are the sets of the first and last nodes of the branch respectively; P ki,t,s and Q ki,t,s are the active and reactive power flowing through branch ki at time t in scenario s, and the direction flows from node k to node i; I ki,t,s is the square of the current flowing through branch ki at time t in scenario s; r ki and x ki are the resistance and reactance of branch ki respectively; P ij,t,s and Q ij,t,s are the active and reactive power flowing through branch ij at time t in scenario s, flowing from node i to node j; P D,i,t,s and Q D,i,t,s are the active and reactive loads connected to node i at time t under scenario s; Q MT,i,t,s , Q WT,i,t,s , Q PV,i,t,s are the reactive power output of the corresponding DG installed at node i at time t under scenario s; P SOP,i,t,s and Q SOP,i,t,s are the active and reactive powers injected by the SOP at node i at time t under scenario s, respectively; 2) Voltage balance equation: in i,min in i,t,s in i,max (18) In the formula, u i,t,s is the square of the voltage of node i at time t in scenario s; u j,t,s is the square of the voltage of node j at time t in scenario s; u i,max and u i,min are the maximum and minimum values ​​of the voltage at node i, respectively; I ij,t,s is the square of the current flowing through branch ij at time t in scenario s; r ij and x ij are the resistance and reactance of branch ij respectively; 3) Line flow constraints: ||[2P ij,t,s 2Q ij,t,s I ij,t,s -u i,t,s ] T ||2≤I ij,t,s +u i,t,s (19) 4) DG operation constraints: Where P MT,max , P WT,max , P PV,max are the upper limits of the active output of the corresponding DG; P MT,min , P WT,min , P PV,min are the lower limits of the corresponding DG active output respectively; are the power factor angles of the corresponding DGs; 5) Installed capacity constraint of DG at the candidate node i: Where Pmax MT,i, Pmax WT,i, and Pmax PV,i are the upper limits of the corresponding DG installed capacity at node i, and Pmax DG,i is the upper limit of the DG installed capacity at node i; 6) Discrete constraints on DG installed capacity: Where P MT , P WT , P PV are the rated capacities of a single MT, WT, and PV; n MT,i 、n WT,i 、n PV,i are the number of single MT, WT, and PV connected to node i, respectively; 7) SOP operation constraints Equations (2)-(5), the capacity constraint is shown in equation (23). Where P SOP,i,t,s and Q SOP,i,t,s are the active and reactive power injected by SOP at node i at time t under scenario s; P SOP,j,t,s and Q SOP,j,t,s are the active and reactive power injected by SOP at node j at time t under scenario s; S SOP,ij is the SOP capacity installed between nodes i and j; 8) Installation capacity constraint of SOP at the selected branch ij: Where Smax SOP,ij is the upper limit of the installed capacity of SOP at branch ij in the distribution network; 9) Discrete constraints on SOP installation capacity: S SOP,ij =n SOP,ij S SOP (25) In the formula, S SOP is the rated capacity of a single SOP; n SOP,ij The number of single SOPs installed at branch ij. 10) Load node carbon emission equation constraint: E i,t,s =P D,i,t,s I N,i,t,s (26) In the formula, E i,t,s is the carbon emission of load node i at time t under scenario s; I N,i,t,s is the node carbon potential of load node i at time t under scenario s; 11) Branch carbon flow density constraint: I L,l,t,s =I N,i,t,s (l∈Ω i- ) (27) In the formula, I L,l,t,s is the carbon flow density of branch l at time t under scenario s; 12) Node carbon potential equality constraint: Where P l,t,s is the active power flowing through branch l at time t in scenario s; I SOP,ji,t,s is the carbon flow density of SOP at branch ji at time t under scenario s.

2. According to a method for low-carbon planning of a flexible distribution network based on carbon emission theory according to claim 1, it is characterized by: The theoretical carbon emission index of the flexible distribution network in step 1 is: the carbon emission flow in the power system starts from the power generation side, depends on the active power flow, flows in the power grid, and finally flows to the load side; Given the active power flow and carbon flow density of the branch flowing into the node, the carbon flow density of the branch flow flowing out of the node is equal to the node carbon potential of the node. I L,l =I N,i l∈Ω i- (1) In the formula, Ω i- is the set of branches outflowing from node i; l is the branch number.

3. According to a method for low-carbon planning of a flexible distribution network based on carbon emission theory according to claim 1, it is characterized by: After the soft intelligent switch SOP is connected to the power grid in step 2, the carbon flow rate calculation formula flowing through the soft intelligent switch SOP is: P SOP,i +P SOP,j =0 (2) |P SOP,i |=|P SOP,j |=|P SOP,ij | (3) R SOP,ij =I SOP,ij P SOP,ij (5) Where P SOP,i , P SOP,j are the active power injected by SOP at nodes i and j respectively; P SOP,ij is the actual active power flowing from node i to node j in SOP; I SOP,ij is the carbon flow density of SOP; R SOP,ij is the carbon flow rate flowing over the SOP; Ω i+ is the set of branches into which the flow flows into node i; P l is the active power flowing through branch l; R l is the carbon flow that passes through branch l with the active power flow per unit time.

4. According to a method for low-carbon planning of a flexible distribution network based on carbon emission theory according to claim 1, it is characterized by: The flexible distribution network low-carbon planning model in step 2 includes the carbon emission flow on the power supply side, the carbon emission flow on the grid side and the carbon emission flow on the load side.

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