Active power distribution network low-carbon scheduling method based on source load uncertainty

Abstract

The invention discloses an active power distribution network low-carbon scheduling method based on source load uncertainty. The method comprises the following steps: processing wind, light and load uncertainty by adopting fuzzy opportunity constraint; a stepped carbon transaction mechanism is introduced; constructing a power distribution network low-carbon optimization scheduling model considering the source load uncertainty; and solving the constructed low-carbon optimal scheduling model of the power distribution network. According to the method, fuzzy opportunity constraint is adopted to perform modeling on uncertainty of the source and load sides of the power distribution network, and a stepped carbon transaction mechanism is introduced to coordinate reactive power output of an energy storage and reactive power regulation device, a micro gas turbine and user side demand response to realize low-carbon economic optimization of the power distribution network. The economical efficiency and the environmental protection property of the system are effectively improved, and uncertain factors are dealt with.

Description

Technical Field

[0001] The present invention relates to the field of power system optimization and dispatching, and in particular to a low-carbon dispatching method for an active distribution network based on source-load uncertainty. Background Art

[0002] The development of social economy, resource and environmental constraints, and scientific and technological progress have driven the distribution network to develop in a more flexible, efficient, and sustainable direction. The penetration rate of distributed generation (DG), represented by wind power and photovoltaic power, in the distribution network has been increasing, but there is a large amount of wind and solar power abandonment. Energy storage systems (ESS) can absorb excess power in the distribution network, thereby improving the DG absorption capacity of the regional distribution network. The high penetration rate of distributed generation and energy storage has brought great challenges to the operation and control of the distribution network.

[0003] For active distribution networks (ADN) that integrate multiple types of distributed energy sources, the power consumption of the load is provided by the upper power grid, distributed power sources and energy storage. Distributed power sources are divided into uncontrollable distributed power sources (including wind power and photovoltaic power) and controllable distributed power sources (micro gas turbines) and belong to the power grid company. They should fully cooperate with the grid dispatch to supply power to users. The energy storage system also belongs to the power grid company. The energy storage system cooperates with the intermittent distributed power sources to smooth the power fluctuations of distributed power sources, improve voltage quality, and optimize network operation. At the same time, many cities have implemented time-of-use electricity prices to guide users to optimize their electricity usage, promote the rationalization of electricity usage behavior, and improve electricity efficiency.

[0004] Considering the carbon trading mechanism can effectively increase the amount of wind power consumption, but as the capacity of wind power and photovoltaic grid-connected increases, their volatility has a more serious impact on the system. Currently, in the research on improving the environmental protection of the system and reducing carbon emissions, there are relatively few economic dispatch models that take into account the uncertain impact of new energy grid connection on the system.

[0005] In the existing distribution network optimization dispatch:

[0006] The Chinese patent "Multi-objective optimization scheduling method and system for distribution network considering distributed power access" (application number: 202310719064.6) normalizes the objective function to obtain a comprehensive objective function; the objective function includes: minimizing network loss objective function, minimizing voltage deviation objective function and minimizing switch operation times objective function; the collected distribution network operation data is input into the comprehensive objective function; and the genetic algorithm is used to optimize and solve the model.

[0007] The Chinese patent "A method for economic optimization and dispatching of distribution networks taking into account the random correlation of wind and solar power" (application number: 202311143195.0) discloses a method for economic optimization and dispatching of distribution networks taking into account the random correlation of wind and solar power, constructs a joint probability distribution model of wind and solar power output prediction errors; transforms the chance constrained programming problem into a mixed integer linear programming optimization solution problem, and calculates the dispatching cost of the system.

[0008] The Chinese patent "A method and system for dynamic economic dispatching of distribution networks" (application number: 202311659212.6) establishes a dynamic economic dispatching model for a new energy distribution network that meets constraints and takes the minimization of the economic cost of distribution network operation as the objective function; the dynamic economic dispatching model is expressed as a Markov decision process, and the state space, action space and reward function are determined; the dynamic economic dispatching model expressed as a Markov decision process is solved by a deep reinforcement learning algorithm to obtain a dynamic dispatching strategy that is adaptive to wind and solar uncertainties.

[0009] The above patents take into account the lack of potential exploration on the user side and how to set price signals and incentives to guide users to use electricity. There is a lack of consideration for the pollution caused by the output of the controllable distributed power source micro gas turbine to the environment, and low-carbon environmental protection is not achieved; the uncertainty of both the source and the load is not considered at the same time, and when there are many discrete variables and nonlinear constraints in the above model, the solution speed of intelligent algorithms and reinforcement learning will be very slow. According to the above analysis, the deficiencies in the prior art are as follows:

[0010] (1) The demand response on the user side is not explored, and the uncertainty of both the source and the load is not considered;

[0011] (2) The addition of micro gas turbines was not considered for their impact on the environment;

[0012] (3) When there are many discrete variables and nonlinear constraints in the model, the solutions of intelligent algorithms and reinforcement learning will be too complicated. Summary of the invention

[0013] In order to solve the above technical problems, the present invention provides an active distribution network low-carbon scheduling method based on source-load uncertainty. The method uses fuzzy opportunity constraints to model the uncertainty on both sides of the source and load of the distribution network, and introduces a step-by-step carbon trading mechanism to coordinate the reactive output of energy storage and reactive regulation devices, micro gas turbines and user-side demand response to achieve low-carbon economic optimization of the distribution network, effectively improve the economy and environmental protection of the system, and deal with uncertain factors.

[0014] The technical solution adopted by the present invention is:

[0015] The active low-carbon dispatching method of distribution network based on source-load uncertainty includes the following steps:

[0016] Step 1: Use fuzzy chance constraints to handle the uncertainty of wind, solar and load;

[0017] Step 2: Introduce a tiered carbon trading mechanism;

[0018] Step 3: Construct a low-carbon optimal dispatch model for distribution networks taking into account source-load uncertainty;

[0019] Step 4: Solve the low-carbon optimization dispatch model of the distribution network constructed in step 3.

[0020] In step 1, fuzzy opportunity constraints are used to address the uncertainties on both sides of the source and the load, and the single-objective opportunity programming model incorporating fuzzy variables is:

[0021]

[0022] Where x represents the decision vector; P F is the fuzzy variable introduced in the model; f(x,P F ) is the objective function after the introduction of fuzzy variables; g(x,P F ) is the constraint condition after the fuzzy variables are introduced.

[0023] Fuzzy chance constraints no longer consider the probability distribution of fuzzy variables, but accept the uncertainty of fuzzy variables and limit the uncertainty to a certain range, that is, to ensure that the constraints are established at a certain confidence level. The scheduling model containing fuzzy variables is expressed as follows based on fuzzy chance constraints:

[0024]

[0025] Where Cr{…} represents the possibility of the occurrence of the event {…}; α represents the confidence level of the constraints involved in the fuzzy parameters.

[0026] In step 1, fuzzy opportunity constraints are used to deal with the uncertainty of wind, solar and load, as follows:

[0027] Since the system power balance constraint contains fuzzy variables, the power balance constraint should be expressed as a fuzzy chance constraint based on the confidence level:

[0028]

[0029] In the formula, is the photovoltaic power received by node j in period t; is the wind power received by node j in period t; is the load power of node j in period t; is the discharge power of energy storage node j at time t; The charging power of node j at time t; is the output power of the gas turbine at node j at time t; is the power purchase from the main grid by the distribution network at time t; P j,t is the active power injected at node j at time t.

[0030] In step 2, the benchmark line method is used for the allocation of carbon emission allowances. It is considered that the system carbon emission quota is proportional to the output of the gas turbine in the distribution network. The calculation formula for the system carbon emission quota is:

[0031]

[0032] In the formula, E L is the total system carbon emission quota; T is the duration of an operation cycle; ε is the unit power emission quota; P t is the total power generation of the gas turbine at time t:

[0033]

[0034] In the formula, M represents the number of gas turbines; represents the power generation of the i-th gas turbine at time t.

[0035] The calculation formula for the system carbon emission is:

[0036]

[0037] In the formula, E P is the total system carbon emission within a scheduling period; δ is the carbon emission intensity of the gas turbine.

[0038] In order to better control the system carbon emission, a stepped carbon emission cost model is adopted, and the carbon emissions are divided into 3 segments. The specific calculation is:

[0039]

[0040] In the formula, F tan is the system carbon emission cost; μ is the carbon trading price; d is the length of the carbon emission interval; k is the increase in the carbon trading price for each 1-step increase in carbon emissions. When Ep < EL, F tan is negative, indicating that the actual carbon emissions of the system are less than the quota of the system, and the excess carbon emission quota can be traded in the carbon trading market at the initial carbon emission price.

[0041] In step 3, in the low-carbon optimal scheduling model of the distribution network considering the uncertainty of power sources and loads:

[0042] The low-carbon scheduling objective function of the active distribution network is as follows:

[0043] minF = F loss +F grid+F PV +F WT +F G +F tan (7);

[0044] Where: F represents the objective function; F loss 、F PV 、F grid They represent network loss cost, abandoned light cost, and cost of purchasing electricity from the main grid; F WT represents the wind curtailment cost, F G represents the gas turbine power generation cost, F tan represents the carbon trading cost of distribution network;

[0045]

[0046] Where: F loss represents the network loss cost; C loss represents the network loss cost coefficient; T represents a dispatching cycle of the distribution network; l ij,t represents the square of the current flowing from node i to node j; R ji represents the resistance of the branch connected to the distribution network node j and node i; n represents the distribution network branch; n l Indicates the total number of distribution network branches.

[0047]

[0048] Where: PV represents the photovoltaic power generation in the distribution network; C PV represents the cost coefficient of abandoned light; represents the predicted power of the photovoltaic node j at time t; Indicates that node j receives photovoltaic power at time t.

[0049]

[0050] Where: F WT represents the wind power curtailment cost; WT represents the wind power collection in the distribution network; C wt represents the unit wind curtailment cost; represents the predicted wind power of node j at time t; It means that node j receives wind power at time t.

[0051]

[0052] Where: C grid P represents the electricity purchase cost coefficient; grid,t It represents the power purchased by the distribution network from the main grid at time t.

[0053]

[0054] Where: FG represents the cost of gas turbine power generation; P G (t) represents the total power generated by the gas turbine at time t; a represents the power cost coefficient of the gas turbine; b represents the cost constant of the gas turbine.

[0055] The objective function contains the following constraints:

[0056] Constraint 1: Power balance constraint:

[0057]

[0058] Where P ij,t , Q ij,t are the active power and reactive power of branch ij at time t respectively; P j,t , Q j,t is the active power and reactive power injected into node j at time t; P jk,t , Q jk,t is the active power and reactive power flowing from node j to node k at time t; U i,t is the voltage amplitude of node i at time t; R ij , X ij are the resistance and reactance between node i and node j respectively.

[0059] φ(j) is the set of nodes connected to node j in the distribution network; B B is the set of all nodes in the distribution network; I ij,t is the current flowing from node j to node i at time t.

[0060] Constraint 2: Branch flow:

[0061]

[0062] Where U j,t is the voltage amplitude of node j at time t.

[0063] The above equations (13), (14), (15), and (16) are nonlinear programming models. Let and And perform the second order cone relaxation (SOCR) transformation on equation (15), and we get:

[0064]

[0065] Where V i,t represents the square of the voltage amplitude of node i at time t; V j,t Represents the square of the voltage amplitude at node j at time t.

[0066] Constraint 3: On-load tap changer OLTC modeling:

[0067] The on-load tap-changing transformer OLTC is used to adjust the voltage on the low-voltage side of the substation. After adding the on-load tap-changing transformer OLTC, the voltage on the low-voltage side of the substation transformer becomes an adjustable variable.

[0068]

[0069] In the formula, It represents the voltage on the high-voltage side of the transformer in the substation, which is a constant value; and are the upper and lower limits of the square of the adjustable OLTC ratio; r j,t is the square of the transformation ratio, which is the ratio of the secondary side to the primary side. j,min Represents the lower voltage limit of distribution network node j; V j,max represents the upper voltage limit of node j in the distribution network; B OLTC Indicates the OLTC node set.

[0070]

[0071] Where: r j,s It represents the square difference of the transformation ratio between OLTC gears s and s-1, that is, the adjacent regulation increment; It is a 0-1 variable.

[0072] Further constraints are placed on the number of adjustments.

[0073]

[0074] In the formula, 0-1 flag variable representing OLTC gear 1; A 0-1 flag variable representing OLTC gear 2; Indicates OLTC gear position SR j 0-1 identification variable; and Represents the gear change flag, which is a 0-1 variable. This means that the OTLC position in period t is larger than that in period t-1. similar; 0-1 flag variable representing OLTC gear position s, 0-1 flag variable representing OLTC gear position s, SR j Indicates the maximum range of OLTC gear changes; The maximum number of OLTC adjustments allowed in one cycle.

[0075] Constraint 4: Reactive equipment:

[0076] Discrete reactive power compensation modeling, taking group switching capacitor bank (CB) as an example.

[0077]

[0078] Where: represents the discrete reactive power compensation power of node j at time t; is the number of capacitor groups in operation, which is a discrete variable; is the upper limit of the number of CB groups connected to node j; is the reactive compensation power of each group of CB; B CB The node set of CB in the distribution network.

[0079]

[0080] Where: A 0-1 auxiliary variable representing adjacent moments of CB; The upper limit of the number of discrete reactive power compensation operations within a cycle;

[0081] Formula (22) is an absolute value constraint, and it is necessary to add an auxiliary variable for the change in CB compensation capacity between adjacent time periods: but:

[0082]

[0083] Continuous reactive compensator modeling,Taking the static reactive compensation device (SVC) as an example, compared with the discrete reactive compensation device, the continuous reactive regulation is relatively simple.

[0084]

[0085] Where: represents the continuous reactive power compensation power of node j at time t; B SVC is a set of nodes including SVC; and They are the lower and upper limits of the SVC compensation power. As a large number of distributed power sources are connected to the distribution network, the power flow of the distribution network system is reversed, and overvoltage problems occur. Therefore, the lower limit of SVC compensation

[0086] Constraint 5: Demand Response Modeling

[0087] The price-based demand response electric load is divided into curtailable load (CL) and shiftable load (SL). The two types of loads are modeled separately below.

[0088] 1) CL characteristic analysis and modeling:

[0089] CL chooses whether to reduce its own load by comparing the changes in electricity prices before and after DR. The price demand elasticity matrix is ​​used to describe the DR characteristics. The element e in the tth row and mth column of the elasticity matrix E(t, m) is t,j , that is, the elasticity coefficient of the load at time t to the electricity price at time m, is defined as:

[0090]

[0091] Where: is the load change at the next moment t; is the initial load at time t; △ρ m is the change in electricity price at time m after demand response; is the initial electricity price at time m; then the load change that can be reduced at time t after DR is

[0092]

[0093] Where: E CL (t,j) represents the CL price demand elasticity matrix; ρ j represents the electricity price at time j.

[0094] 2) SL characteristic analysis and modeling:

[0095] The price demand elasticity matrix is ​​used to describe the DR characteristics. The change in the transferable load at time t after DR is for:

[0096]

[0097] Where: is the initial transferable load at time t; E SL (t,m) is the SL price demand elasticity matrix.

[0098] Constraint 6: Energy storage device modeling:

[0099] The energy storage device takes into account constraints in multiple time periods, including charge and discharge state restrictions, charge and discharge power restrictions, and energy storage capacity restrictions;

[0100] Charge and discharge status restrictions:

[0101]

[0102] Power Limitation:

[0103]

[0104] Capacity Limitation:

[0105]

[0106] Where: B ESS is the node set including ESS; Formula (28) indicates that the energy storage can only be charged or discharged at the same time, but not charged and discharged at the same time; and They are the charging and discharging states of energy storage respectively; represents the discharge power of energy storage node j at time t; represents the charging power of the energy storage node j at time t; They are the upper and lower limits of ESS charging and discharging power, respectively; is the amount of energy stored at the tth moment; Indicates the amount of energy stored at time t-1; Represents the charging power of node j at time t represents the discharge power of energy storage node j at time t; and To consider the upper and lower limits of factors such as energy storage life; is the charging and discharging efficiency coefficient of energy storage, Constraint 7: Microturbine Modeling:

[0107] The controllable distributed power sources in the distribution network include gas turbines, fuel cells, etc., and their power generation costs are expressed by linear functions. Considering the case where the distribution network includes micro gas turbines, its cost function is:

[0108] C G (t) = [aP t +b] (31);

[0109]

[0110] Where: and Indicates the maximum and minimum output power of the gas turbine, which are limited by its rated power and minimum load rate respectively. G (t) represents the power generation cost of the gas turbine, △t represents the time interval, which is 1h; represents the power generation of the i-th gas turbine at time t.

[0111] Hill climbing constraints:

[0112]

[0113] Where: r down and r up are the downward and upward climbing rates of the gas turbine respectively; △T is the time interval.

[0114] Constraint 8: Other constraints:

[0115] Substation power purchase power constraints

[0116]

[0117] Where: The minimum and maximum active power transmitted by the distribution network connecting the main network tie line; and The minimum reactive power and reactive power transmitted by the distribution network connecting the main network tie line; P grid,t It represents the active power input from the main grid to the distribution grid at time t; Q grid,t It represents the reactive power input from the main grid to the distribution network at time t.

[0118] Wind power constraints:

[0119]

[0120] Where: is the wind power received by node j in period t; The predicted power of wind power connected to node j during period t.

[0121] System operation safety constraints:

[0122] V j,min ≤V j,t ≤V j,max (37);

[0123] l ij,t ≤l ij,max (38);

[0124] Where V j,min 、V j,max They represent the upper and lower limits of the square of the voltage amplitude at node j; l ij,max Represents the upper and lower limits of the square of the current amplitude of branch ij.

[0125] System power balance constraints:

[0126] Since this constraint contains fuzzy variables, the power balance constraint should be expressed as a fuzzy chance constraint based on confidence level:

[0127]

[0128] In the formula, is the photovoltaic power received by node j in period t; is the wind power received by node j in period t; is the active power of the load of node j in period t; is the discharge power of energy storage node j at time t; The charging power of node j at time t; is the output power of the gas turbine at node j at time t; P is the power purchased by the distribution network from the main grid at time t; j,t is the active power injected into node j at time t.

[0129]

[0130] In the formula, is the reactive power of the load at node j during period t; is the reactive power input from the main grid to the distribution grid at time t; is the reactive power of the SVC at node j at time t; is the reactive power of CB at node j at time t; Q j,t is the reactive power injected into node j at time t.

[0131] By making a clear equivalence class treatment on (39), we can obtain:

[0132]

[0133] In the formula, ω 1PV,j ,ω 2PV,j ,ω 1WT,j ,ω 2WT,j ,ω 2L,j ,ω 3L,j They are the membership parameters of the predicted value of photovoltaic output, wind turbine output and load power respectively.

[0134] In step 4, the model is subjected to second-order cone relaxation, and the model is solved by the GUROBI solver; the specific steps are:

[0135] Step 1: Input the distribution network node and branch data, daily wind and solar output and daily load data and other parameters.

[0136] Step 2: Construct the optimal power flow model of the distribution network. The constraints include energy storage output constraints, reactive compensation equipment output constraints, on-load tap-changing transformer gear constraints, demand response constraints, and gas turbine power constraints. The objective function includes the economic cost and environmental cost of distribution network operation.

[0137] Step 3: Remove the absolute value of the mathematical model of discrete reactive power compensation constraints and on-load tap-changing transformer constraints and transform them into mixed integer models. Aiming at the uncertainty of source and load, fuzzy chance constraints are introduced, and the deterministic constraints are relaxed into system constraints containing fuzzy variables, which are clarified using trapezoidal fuzzy parameters.

[0138] Step 4: Use the GUROBI solver to solve the second-order cone programming model.

[0139] The flowchart corresponding to the solution of the above model is as follows Figure 7 shown.

[0140] The present invention provides an active distribution network low-carbon dispatching method based on source-load uncertainty, and the technical effects are as follows:

[0141] 1) In step 1 of the present invention, compared with the traditional deterministic constraints, fuzzy chance constraints provide a more flexible way to deal with the uncertainty of sources and loads in the distribution network. It no longer requires strict satisfaction of the constraints, but allows the violation of the constraints at a certain probability level, which provides more flexibility for the dispatching strategy.

[0142] 2) In step 2 of the present invention, the step-by-step carbon trading mechanism uses different carbon prices at different stages. As carbon emissions increase, the carbon price rises in a step-by-step manner to reduce carbon emissions. This is manifested in that when the gas turbine output is large, the carbon price is increased to suppress the gas turbine output, and electricity is purchased from the main grid, thereby achieving the economy and low carbon nature of the distribution network.

[0143] 3) In step 3 of the present invention, by considering the uncertainty of both the source and the load, the model can improve the adaptability of the distribution network to the fluctuation of renewable energy and the change of load, and enhance the resilience of the system. Secondly, the digital model of each important equipment in the active distribution network is linearized and modeled, and the original nonlinear programming model is relaxed and converted into a second-order cone relaxation for rapid solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0144] The present invention will be further described below in conjunction with the accompanying drawings and examples;

[0145] Figure 1 This is the structure diagram of the 33-node distribution network.

[0146] Figure 2 Contribute to new energy.

[0147] Figure 3 It is the load curve diagram.

[0148] Figure 4 It is the dispatching output curve diagram for each unit.

[0149] Figure 5 Load diagram before and after demand response.

[0150] Figure 6 The following is a comparison chart of network loss in various scenarios.

[0151] Figure 7 A flow chart for solving the low-carbon optimal dispatch model of distribution network taking into account source and load uncertainties. DETAILED DESCRIPTION

[0152] Low-carbon dispatching method of active distribution network based on source-load uncertainty. Mathematical modeling is performed for energy storage, reactive compensation equipment, micro gas turbines, user-side demand response, etc., and the influence of uncertainty on both the source and load sides on the low-carbon dispatching of active distribution network is taken into account. The step-type carbon trading cost is introduced into the objective function to reduce the carbon emissions of the system and increase the wind and solar power consumption of the system. In view of the uncertainty factors of the system after wind and solar grid connection, fuzzy opportunity constraints are introduced, and the deterministic constraints are relaxed into system constraints containing fuzzy variables. They are clarified using trapezoidal fuzzy parameters. With the goal of minimizing the daily operating cost of the active distribution network, the model is relaxed by a second-order cone and solved by the GUROBI solver. The example analysis shows that the proposed model can effectively improve the economy and environmental protection of the distribution network.

[0153] (I) Structure of the active distribution network optimization dispatching model considering source-load uncertainty:

[0154] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a low-carbon dispatching method for active distribution networks based on source-load uncertainty, which can effectively improve the economy and environmental protection of the system and cope with uncertain factors.

[0155] A technical solution to achieve the above purpose is: a low-carbon dispatching method for active distribution network based on source-load uncertainty, comprising the following steps:

[0156] 1) Data collection and source load uncertainty modeling:

[0157] The data of each branch of the distribution network, the data of node load, photovoltaic and wind power data. Fuzzy chance constraints are used to deal with the uncertainty of wind, photovoltaic and load.

[0158] 2) Active distribution network objective function and constraints:

[0159] The goal is to minimize the daily operating cost of the active distribution network. The objective function includes network loss cost, wind and solar curtailment cost, main grid power purchase cost, micro gas turbine power purchase cost and carbon trading cost. Constraints include on-load tap-changing transformer and discrete reactive power compensation equipment action constraints, energy storage operation constraints, continuous reactive power compensation equipment output constraints, micro gas turbine ramp output constraints, distribution network flow, power balance constraints, etc.

[0160] 3) Convex processing of active distribution network optimization model:

[0161] The power flow constraints of the distribution network are relaxed by second-order cones, other constraints are linearized, and the model is converted into a mixed integer linear programming model, which is solved using the GUROBI solver.

[0162] (II) Source uncertainty and carbon trading mechanism:

[0163] 2.1: Source load uncertainty analysis:

[0164] The idea of ​​using fuzzy chance constraints to model scheduling problems is to allow the scheduling results to not meet the constraints to some extent, but to require that the probability of the scheduling results being true is no less than the confidence level set in advance by the scheduling decision maker.

[0165] The single-objective opportunity programming model incorporating fuzzy variables is:

[0166]

[0167] Where x represents the decision vector; P F is the fuzzy variable introduced in the model; f(x,P F ) is the objective function after the introduction of fuzzy variables; g(x,P F ) is the constraint condition after the fuzzy variables are introduced.

[0168] After the introduction of fuzzy variables, the constraints related to fuzzy variables in the entire model are no longer certain, and the original constraints cannot be guaranteed in the model, so fuzzy chance constraints are adopted. Fuzzy chance constraints no longer consider the probability distribution of fuzzy variables, but accept the existence of uncertainty in fuzzy variables, but limit this uncertainty to a certain range, that is, to ensure that the constraints are established at a certain confidence level. Then the scheduling model containing fuzzy variables can be expressed as follows based on fuzzy chance constraints:

[0169]

[0170] Where Cr{…} represents the possibility of the occurrence of the event {…}; α represents the confidence level of the constraints involved in the fuzzy parameters.

[0171] Fuzzy chance constraints are used to deal with the uncertainty of wind, solar and load. Since the system power balance constraint contains fuzzy variables, the power balance constraint should be expressed as a fuzzy chance constraint based on the confidence level:

[0172]

[0173] In the formula, is the photovoltaic power received by node j in period t; is the wind power received by node j in period t; is the load power of node j in period t; is the discharge power of energy storage node j at time t; The charging power of node j at time t; is the output power of the gas turbine at node j at time t; P is the power purchased by the distribution network from the main grid at time t; j,tis the active power injected into node j at time t.

[0174] 2.2: Carbon emission cost calculation model:

[0175] The carbon trading mechanism is a free trader of carbon emissions. At present, there are two main methods for allocating carbon trading quotas in China, namely the historical method and the baseline method. This invention adopts the baseline method to allocate carbon emissions, and it is approximately considered that the system carbon emission quota is proportional to the output of the thermal power unit. The system carbon emission quota calculation formula is:

[0176]

[0177] Among them, E L is the total carbon emission quota of the system; T is the duration of an operation cycle; ε is the emission quota per unit of electricity; P t represents the total power generated by all gas turbines; M represents the number of gas turbines; represents the power generated by the i-th gas turbine at time t.

[0178] Wind power and photovoltaic power are pollution-free green energy sources that do not produce carbon emissions. Therefore, the system's carbon emissions mainly come from gas turbines. The system carbon emissions calculation formula is:

[0179]

[0180] Among them, E p is the total carbon emission of the system in a scheduling cycle; δ is the carbon emission intensity of the gas turbine.

[0181] In order to better control the carbon emissions of the system, the present invention adopts a step-by-step carbon emission cost model to divide carbon emissions into three stages. The specific calculation is:

[0182]

[0183] Among them, F tan is the system carbon emission cost; μ is the carbon trading price; d is the length of the carbon emission interval; k is the increase in the carbon trading price for each step increase in carbon emissions. p <E L When F tan A negative value indicates that the actual carbon emissions of the system are less than the system's quota. The excess carbon emission quota can be traded in the carbon trading market at the initial carbon emission price, so that the system can obtain carbon benefits.

[0184] (III) Low-carbon optimization dispatch model of distribution network considering source and load uncertainty:

[0185] 3.1 Objective function:

[0186] The objective function of low-carbon dispatch of active distribution network is as follows:

[0187] minF=F loss +F grid +F PV +F WT +F G +F tan (7);

[0188]

[0189] Where: F represents the objective function; F loss 、F PV 、F WT 、F grid 、F G They represent network loss cost, solar curtailment cost, wind curtailment cost, power purchase cost from the main grid, and gas turbine power generation cost. loss , C PV , C wt , C grid They represent the network loss cost coefficient, unit solar curtailment cost, unit wind curtailment cost, and electricity purchase cost coefficient respectively; T represents a dispatching cycle of the distribution network; l ij,t represents the square of the current flowing from node i to node j; R ji represents the resistance of the branch connected to the distribution network node j and node i; n represents the distribution network branch; n l Indicates the total number of distribution network branches; PV indicates the photovoltaic power collection in the distribution network; WT indicates the wind power collection in the distribution network; represents the predicted power of the photovoltaic node j at time t; Indicates that node j receives photovoltaic power at time t; represents the predicted wind power of node j at time t; P represents the wind power received by node j at time t; t represents the total power generated by the gas turbine at time t; a represents the power cost coefficient of the gas turbine; b represents the gas turbine cost constant.

[0190] 3.2 Constraints:

[0191] Constraint 1: Power balance constraint:

[0192]

[0193] Where P ij,t , Q ij,t are the active power and reactive power of branch ij at time t respectively; P j,t , Q j,t is the active power and reactive power injected into node j at time t; P jk,t , Q jk,tis the active power and reactive power flowing from node j to node k at time t; is the square of the voltage amplitude of node i at time t; is the square of the current flowing from node j to node i at time t. ij , X ij are the resistance and reactance between node i and node j respectively; φ(j) is the set of nodes connected to node j in the distribution network; B B is the set of all nodes in the distribution network; I ij,t is the current flowing from node j to node i at time t.

[0194] Constraint 2: Branch flow:

[0195]

[0196] Where U i,t is the voltage amplitude of node i at time t; U j,t is the voltage amplitude of node j at time t.

[0197] Model conversion:

[0198] The above equations (13), (14), (15), and (16) are nonlinear programming models. Let and And perform the second-order cone relaxation (SOCR) transformation on equation (15), and we get:

[0199]

[0200] Where V i,t represents the square of the voltage amplitude of node i at time t; V j,t Represents the square of the voltage amplitude at node j at time t.

[0201] Constraint 3: On-load tap changer (OLTC) modeling:

[0202] OLTC is mainly used to adjust the low-voltage side voltage of the substation. After adding OLTC, the low-voltage side voltage of the substation transformer becomes an adjustable variable.

[0203]

[0204] In the formula, It represents the voltage on the high-voltage side of the transformer in the substation, which is a constant value; and are the upper and lower limits of the square of the adjustable OLTC transformation ratio. j,t is the square of the transformation ratio, which is the ratio of the secondary side to the primary side. Since it is a discrete variable, it needs to be processed as a relationship containing 0-1 variables; V j,min Represents the lower voltage limit of distribution network node j; Vj,max represents the upper voltage limit of node j in the distribution network; B OLTC Indicates the OLTC node set.

[0205]

[0206] Where: r j,s It represents the square difference of the transformation ratio between OLTC gear positions s and s-1, that is, the adjacent adjustment increment. It is a 0-1 variable. In actual situations, it is also necessary to consider that the number of OLTC adjustments should not be too many to avoid damage to the equipment, so the number of adjustments should be further constrained.

[0207]

[0208] In the formula, 0-1 identification variable representing OLTC gear 1; A 0-1 flag variable representing OLTC gear 2; Indicates OLTC gear position SR j 0-1 identification variable; and Represents the gear change flag, which is a 0-1 variable. This means that the OTLC position in period t is larger than that in period t-1. similar; 0-1 flag variable representing OLTC gear position s, 0-1 flag variable representing OLTC gear position s, SR j Indicates the maximum range of OLTC gear changes; The maximum number of OLTC adjustments allowed in one cycle.

[0209] Constraint 4: Reactive equipment:

[0210] Discrete reactive power compensation modeling, taking group switching capacitor bank (CB) as an example.

[0211]

[0212] Where: represents the discrete reactive power compensation power of node j at time t; is the number of capacitor groups in operation, which is a discrete variable; is the upper limit of the number of CB groups connected to node j; is the reactive compensation power of each group of CB; B CB The node set of CB in the distribution network.

[0213] Taking into account the impact of the economy and life of the reactive power compensation equipment, the number of adjustments of the reactive power compensation equipment should be limited. It is the upper limit of the number of discrete reactive power compensation operations in one cycle.

[0214]

[0215] Where: A 0-1 auxiliary variable representing adjacent moments of CB; It is the upper limit of the number of discrete reactive power compensation operations in one cycle.

[0216] Formula (22) is an absolute value constraint, and it is necessary to add an auxiliary variable for the change in CB compensation capacity between adjacent time periods: but:

[0217]

[0218] Continuous reactive compensator modeling,Taking the static reactive compensation device (SVC) as an example, compared with the discrete reactive compensation device, the continuous reactive regulation is relatively simple.

[0219]

[0220] Where: represents the continuous reactive power compensation power of node j at time t; B SVC is a set of nodes including SVC; and As a large number of distributed power sources are connected to the distribution network, the power flow of the distribution network system is reversed, and overvoltage problems occur. Therefore, the compensation lower limit of the SVC of the present invention is

[0221] Constraint 5: Demand Response Modeling:

[0222] Different types of loads have different sensitivities to the same electricity price signal. Price-based demand response loads are divided into curtailable loads (CL) and shiftable loads (SL). The following models the two types of loads separately.

[0223] 1) CL characteristic analysis and modeling:

[0224] CL chooses whether to reduce its own load by comparing the changes in electricity prices before and after DR. The price demand elasticity matrix is ​​used to describe the DR characteristics. The element e in the tth row and mth column of the elasticity matrix E(t, m) is t,m , that is, the elasticity coefficient of the load at time t to the electricity price at time m, is defined as:

[0225]

[0226] Where: is the load change at the next moment t; is the initial load at time t; △ρ j is the change in electricity price at time m after demand response; is the initial electricity price at time m. Then, the load change that can be reduced at time t after DR is

[0227]

[0228] 2) SL characteristic analysis and modeling:

[0229] Transferable load refers to the load that users can flexibly adjust their working hours by responding to electricity prices according to their own needs. Using the peak, flat and valley time-of-use electricity price as a signal, users can be guided to transfer the load during peak hours to the flat and valley hours. Similarly, the price demand elasticity matrix is ​​used to describe the DR characteristics. The change in transferable load at time t after DR is: for:

[0230]

[0231] Where: is the initial transferable load at time t; E SL (t,m) is the SL price demand elasticity matrix.

[0232] Constraint 6: Energy storage device modeling:

[0233] Generally speaking, energy storage devices need to consider constraints in multiple time periods, mainly including charging and discharging state restrictions, charging and discharging power restrictions, and energy storage capacity restrictions.

[0234] Charge and discharge status limit

[0235]

[0236] Power Limitation

[0237]

[0238] Capacity Limitations

[0239]

[0240] In the above formula: B ESS is the node set including ESS; Formula (28) indicates that the energy storage can only be charged or discharged at the same time, but not charged and discharged at the same time; and They are the charging and discharging states of energy storage respectively; represents the discharge power of energy storage node j at time t; represents the charging power of the energy storage node j at time t; They are the upper and lower limits of ESS charging and discharging power, respectively; is the amount of energy stored at the tth moment; Indicates the amount of energy stored at time t-1; represents the charging power of the energy storage node j at time t; represents the discharge power of energy storage node j at time t; and To consider the upper and lower limits of factors such as energy storage life; is the charging and discharging efficiency coefficient of energy storage,

[0241] Constraint 7: Microturbine Modeling:

[0242] The controllable distributed power sources in the distribution network mainly include gas turbines, fuel cells, etc., and their power generation costs can be expressed by linear functions. In this invention, the case where the distribution network includes micro gas turbines is considered, and its cost function is

[0243] G G (t) = [aP t +b]△t (31);

[0244]

[0245] Where: and Indicates the maximum and minimum output power of the gas turbine, which are limited by its rated power and minimum load rate respectively; represents the power generation of the i-th gas turbine at time t; C G (t) represents the power generation cost of the gas turbine, △t represents the time interval, which is 1h.

[0246] Hill climbing constraints:

[0247] -r down △T≤P i,t -P i,t-1 ≤r up ΔT (33);

[0248] Where: r down and r up are the downward and upward climbing rates of the gas turbine respectively; △T is the time interval.

[0249] Constraint 8: Other constraints

[0250] Substation power purchase power constraints:

[0251]

[0252] Where: are the minimum active power and maximum active power transmitted by the interconnection line connecting the distribution network to the main network, and The minimum reactive power and reactive power transmitted by the distribution network connecting the main network tie line; P grid,t It represents the active power input from the main grid to the distribution grid at time t; Q grid,t It represents the reactive power input from the main grid to the distribution network at time t.

[0253] Wind power constraints:

[0254]

[0255] Where: represents the wind power received by node j in period t, It represents the predicted power of wind power connected to node j in period t.

[0256] System operation safety constraints:

[0257] V j,min ≤V j,t ≤V j,max (37);

[0258] l ij,t ≤l ij ,max (38);

[0259] Where V j,min 、V j,max They represent the upper and lower limits of the square of the voltage amplitude at node j; l ij,max Represents the upper and lower limits of the square of the current amplitude of branch ij.

[0260] System power balance constraints:

[0261] Since this constraint contains fuzzy variables, the power balance constraint should be expressed as a fuzzy chance constraint based on confidence level:

[0262]

[0263] In the formula, is the photovoltaic power received by node j in period t; is the wind power received by node j in period t; is the active power of the load of node j in period t; is the discharge power of energy storage node j at time t; The charging power of node j at time t; is the output power of the gas turbine at node j at time t; P is the power purchased by the distribution network from the main grid at time t; j,t is the active power injected into node j at time t.

[0264]

[0265] In the formula, is the reactive power of the load at node j during period t; is the reactive power input from the main grid to the distribution grid at time t; is the reactive power of the SVC at node j at time t; is the reactive power of CB at node j at time t; Q j,t is the reactive power injected into node j at time t.

[0266] By making a clear equivalence class treatment on (39), we can obtain:

[0267]

[0268] In the formula, ω 1PV,j ,ω 2PV,j ,ω 1WT,j ,ω 2WT,j ,ω 2L,j ,ω 3L,j They are the membership parameters of the predicted value of photovoltaic output, wind turbine output and load power respectively.

[0269] Verification example:

[0270] Verify the feasibility and effectiveness of the proposed method. Figure 1 As shown, the present invention uses the modified IEEE 33-node distribution network for example analysis. B =12.66kW. The transformer has a tapping range of 0.94-1.06, connected between the main grid and node 33, and the maximum number of transformer gear changes in one cycle is set to 5 times. 5×100kW discrete capacitor banks are connected in parallel at nodes 5 and 15, and the upper limit of the number of capacitor bank operations in one cycle is set to 5 times. Micro gas turbines are installed at nodes 8 and 22 respectively; photovoltaic generators are connected at nodes 5 and 32, and wind turbines are connected at node 15. The power is predicted. Figure 2 As shown in the figure, the cost of abandoning wind and solar power is 600 yuan / MW. The cost of distribution network loss is 600 yuan / MW. The carbon trading benchmark price is set to 0.1 yuan / kg, the carbon emission interval length d=150kg, and the carbon trading price increases by 25% of the benchmark price for each step increase. The triangular fuzzy membership parameter values ​​of wind, solar and electric loads are shown in Table 3.

[0271] Table 1 Installation location and parameters of energy storage

[0272]

[0273] Table 2 CB installation location and parameters

[0274]

[0275] Table 3 Triangular fuzzy membership parameters

[0276]

[0277] When the credibility confidence level is 0.9, considering the uncertainty of wind power, photovoltaic power, load forecast and carbon emission cost, the dispatch cycle is 1 day. The comprehensive operation cost of the distribution network is 14,855 yuan, and the network loss is 673.7kW. The carbon emission quota of the distribution network is 5013kg, and the actual carbon emission is 8069kg. The wind and solar power of the distribution network are fully absorbed. At this time, the dispatch output of each unit is Figure 4 .

[0278] Depend on Figure 4 It can be seen that the power generation cost of gas turbines is low during the whole period. Except when the wind power is sufficient and the load is low, the gas turbine has no output, and there is a certain output in other periods. In 6-10h, since the main grid electricity price is lower than the gas turbine electricity purchase price at this time, the distribution network purchases more electricity from the main grid during this period. In the peak period of 11-19h, since the main grid purchase electricity cost is relatively expensive during this period, the distribution network will rely heavily on gas turbines for power supply. Among them, the load reaches the maximum value of the whole day during 16-17h, but the photovoltaic output at this time is not the maximum. The gas turbine output during this period reaches the upper limit but still cannot meet the load demand. At this time, the energy storage is discharged in large quantities, and the rest is purchased from the main grid. In the 20-23h period, the electricity price belongs to the normal period, and the electricity price is still slightly higher than the gas turbine electricity price, but due to the introduction of tiered carbon trading, the power of the gas turbine begins to gradually decline, and the output of the gas turbine is subject to certain restrictions.

[0279] Energy storage equipment is charged during low electricity prices or when clean energy output is high, and the electricity is stored. It is discharged during peak electricity prices, minimizing the purchase of high-priced electricity from the upper power grid and achieving low storage and high generation. It can be seen that the application of energy storage devices in distribution networks can cope with fluctuations in load and wind power and photovoltaic power generation, and to a certain extent make the load curve smoother, reduce system operation pressure, and improve the economy of active distribution network operation.

[0280] In order to reflect the advantages of the present invention, the following scenario is set:

[0281] Scenario 1: Considering only user-side demand response, ignoring gas turbines and energy storage

[0282] Scenario 2: Considering energy storage only, excluding gas turbines and energy storage

[0283] Scenario 3: Considering energy storage and user-side demand response, but not considering gas turbines

[0284] Scenario 4: Considering energy storage and gas turbines and user-side demand response, but not considering the tiered carbon trading mechanism

[0285] Scenario 5: Robust optimization is used for source-load uncertainty. The rest is the same as Scenario 4.

[0286] Scenario 6: Method of the present invention

[0287] Table 4 Comparison of scheduling results in different scenarios

[0288]

[0289] As can be seen from Table 4, the operating cost of scenario 6 proposed by the present invention is the lowest and more economical. Scenario 1 and scenario 2 only consider the addition of energy storage equipment and user-side demand response unilaterally. Compared with scenario 3, which considers both the addition of energy storage equipment and user-side demand response, the network loss is reduced and the wind and solar power are fully absorbed. Since energy storage stores electricity when electricity prices are low and increases the load through demand response, it discharges to the distribution network when electricity prices are high and reduces the load through demand response, thereby reducing the main network's electricity purchase cost.

[0290] Scenario 6 adds a gas turbine unit based on Scenario 3 and introduces a tiered carbon trading. Since the distribution network head end has to travel a long distance to transmit the electricity to the end after purchasing electricity from the main grid, the addition of gas turbines allows users who are far away from the distribution network head end to be provided with electricity through closer gas turbines to reduce the transmission distance of electricity, thereby achieving the effect of reducing network losses. Gas turbines are controllable distributed power sources, and their electricity prices are higher than the main grid's low electricity prices and lower than the main grid's peak electricity prices, and they will not change over time. Therefore, when the electricity price is high, the gas turbine output can be given priority, and when the electricity price is low, the purchase of electricity from the main grid is given priority, thereby improving the economic efficiency of Scenario 6.

[0291] Scenario 4 takes into account the worst conditions of wind, solar and load, so the operating cost of the entire distribution network system is greatly increased compared to Scenario 6. Robust optimization is a control strategy for the worst scenario. For some extremely severe scenarios, in actual situations, the probability of extreme scenarios occurring is low, and they may only exist in theory. Therefore, the results obtained by using robust optimization to deal with uncertainty problems are often too conservative. Scenario 4 is too conservative, and the cost of purchasing electricity increases, such as Figure 5 As shown, the network loss is significantly reduced in 0-8h compared with scenario 6, but it is not economical and practical. On this basis, uncertainty can be added to reduce the conservatism of the low robust optimization.

[0292] Scenario 5 does not consider tiered carbon trading on the basis of Scenario 6. Without the constraints of carbon trading, it can be seen that the power purchase amount of the gas turbine in Scenario 5 increases, thereby increasing carbon emissions, which is less environmentally friendly than Scenario 6.

[0293] In summary, the scheduling model of the present invention that comprehensively considers fuzzy opportunity constraints and step-by-step carbon trading can effectively reduce system operating costs, increase wind power consumption, and reduce carbon emissions, which is in line with the development concept of energy conservation, emission reduction, and low-carbon environmental protection in my country's power industry. Table 5 shows the scheduling results of the method of the present invention under different confidence levels.

[0294] Table 5 Scheduling results under different confidence levels

[0295]

Claims

1. An active low-carbon dispatching method for distribution networks based on source-load uncertainty, characterized by The following steps are involved: Step 1: Use fuzzy chance constraints to handle the uncertainty of wind, solar and load; Step 2: Introduce a tiered carbon trading mechanism; Step 3: Construct a low-carbon optimal dispatch model for distribution networks taking into account source-load uncertainty; Step 4: Solve the low-carbon optimization dispatch model of the distribution network constructed in step 3.

2. The active low-carbon dispatching method for distribution network based on source-load uncertainty according to claim 1 is characterized by: In step 1, fuzzy opportunity constraints are used to address the uncertainties on both sides of the source and the load, and the single-objective opportunity programming model incorporating fuzzy variables is: Where x represents the decision vector; P F is the fuzzy variable introduced in the model; f(x,P F ) is the objective function after the introduction of fuzzy variables; g(x,P F ) is the constraint condition after the fuzzy variables are introduced; Fuzzy chance constraints no longer consider the probability distribution of fuzzy variables, but accept the uncertainty of fuzzy variables and limit the uncertainty to a certain range, that is, to ensure that the constraints are established under a certain confidence level; then the scheduling model containing fuzzy variables is expressed as follows based on fuzzy chance constraints: Where Cr{…} represents the possibility of the occurrence of the event {…}; α represents the confidence level of the constraints involved in the fuzzy parameters.

3. The active low-carbon dispatching method for distribution network based on source-load uncertainty according to claim 2 is characterized in that: In step 1, fuzzy opportunity constraints are used to deal with the uncertainty of wind, solar and load, as follows: Since the system power balance constraint contains fuzzy variables, the power balance constraint should be expressed as a fuzzy chance constraint based on the confidence level: In the formula, is the photovoltaic power received by node j in period t; is the wind power received by node j in period t; is the load power of node j in period t; is the discharge power of energy storage node j at time t; The charging power of node j at time t; is the output power of the gas turbine at node j at time t; P is the power purchased by the distribution network from the main grid at time t; j,t is the active power injected into node j at time t.

4. The active low-carbon dispatching method for distribution network based on source-load uncertainty according to claim 1 is characterized by: In step 2, the baseline method is used to allocate carbon emission quotas. It is considered that the system carbon emission quota is proportional to the output of the gas turbine in the distribution network. The system carbon emission quota calculation formula is: In the formula, E L is the total carbon emission quota of the system; T is the duration of an operation cycle; ε is the emission quota per unit of electricity; P t The total power generated by the gas turbine at time t is: Where, M represents the number of gas turbines; represents the power generated by the i-th gas turbine at time t; The system carbon emissions calculation formula is: In the formula, E P is the total carbon emission of the system in a scheduling cycle; δ is the carbon emission intensity of the gas turbine; In order to better control the carbon emissions of the system, a step-by-step carbon emission cost model is used to divide carbon emissions into three sections; the specific calculation is: where F tan is the system carbon emission cost; μ is the carbon trading price; d is the length of the carbon emission interval; k is the growth rate of the carbon trading price for each 1-step increase in carbon emissions; when Ep < EL, F tan is negative, indicating that the actual carbon emissions of the system are less than the system's quota, and the excess carbon emission quota can be traded in the carbon trading market at the initial carbon emission price.

5. The active low-carbon dispatching method for distribution network based on source-load uncertainty according to claim 1 is characterized by: In step 3, in the low-carbon optimization dispatch model of the distribution network taking into account the uncertainty of source and load: The objective function of low-carbon dispatch of active distribution network is as follows: minF=F loss +F grid +F PV +F WT +F G +F tan (7); Where: F represents the objective function; F loss 、F PV 、F grid They represent network loss cost, abandoned light cost, and cost of purchasing electricity from the main grid; F WT represents the wind curtailment cost, F G represents the gas turbine power generation cost, F tan represents the carbon trading cost of distribution network; Where: F loss represents the network loss cost; C loss represents the network loss cost coefficient; T represents a dispatching cycle of the distribution network; l ij,t represents the square of the current flowing from node i to node j; R ji represents the resistance of the branch connected to the distribution network node j and node i; n represents the distribution network branch; n l Indicates the total number of distribution network branches; Where: PV represents the photovoltaic power generation in the distribution network; C PV represents the cost coefficient of abandoned light; represents the predicted power of photovoltaic node j at time t; Indicates that node j receives photovoltaic power at time t; Where: F WT represents the wind power curtailment cost; WT represents the wind power collection in the distribution network; C wt represents the unit wind curtailment cost; represents the predicted wind power of node j at time t; It means that node j receives wind power at time t; Where: C grid P represents the electricity purchase cost coefficient; grid,t It represents the power purchased by the distribution network from the main grid at time t; Where: F G represents the cost of gas turbine power generation; P G (t) represents the total power generated by the gas turbine at time t; a represents the power cost coefficient of the gas turbine; b represents the cost constant of the gas turbine.

6. The active low-carbon dispatching method for distribution network based on source-load uncertainty according to claim 5 is characterized by: The objective function contains the following constraints: Constraint 1: Power balance constraint: Where P ij,t , Q ij,t are the active power and reactive power of branch ij at time t respectively; P j,t , Q j,t is the active power and reactive power injected into node j at time t; P jk,t , Q jk,t is the active power and reactive power flowing from node j to node k at time t; U i,t is the voltage amplitude of node i at time t; R ij , X ij are the resistance and reactance between node i and node j respectively; φ(j) is the set of nodes connected to node j in the distribution network; B B is the set of all nodes in the distribution network; I ij,t is the current flowing from node j to node i at time t; Constraint 2: Branch flow: Where U j,t is the voltage amplitude of node j at time t; The above equations (13), (14), (15), and (16) are nonlinear programming models. Let and And perform the second order cone relaxation (SOCR) transformation on equation (15), and we get: Where V i,t represents the square of the voltage amplitude of node i at time t; V j,t represents the square of the voltage amplitude at node j at time t; Constraint 3: On-load tap changer OLTC modeling: The on-load tap-changing transformer OLTC is used to adjust the voltage on the low-voltage side of the substation. After adding the on-load tap-changing transformer OLTC, the voltage on the low-voltage side of the substation transformer becomes an adjustable variable; In the formula, It represents the voltage on the high-voltage side of the transformer in the substation, which is a constant value; and are the upper and lower limits of the square of the adjustable OLTC ratio; r j,t is the square of the transformation ratio, which is the ratio of the secondary side to the primary side; V j,min Represents the lower limit of the voltage at node j in the distribution network; V j,max represents the upper voltage limit of node j in the distribution network; B OLTC Represents the OLTC node set; Where: r j,s It represents the square difference of the transformation ratio between OLTC gears s and s-1, that is, the adjacent regulation increment; is a 0-1 variable; further constraints are imposed on the number of adjustments; In the formula, 0-1 flag variable representing OLTC gear 1; 0-1 flag variable representing OLTC gear 2; Indicates OLTC gear position SR j 0-1 identification variable; and Represents the gear change flag, which is a 0-1 variable. This means that the OTLC position in period t is larger than that in period t-1. similar; 0-1 flag variable representing OLTC gear s, 0-1 flag variable representing OLTC gear position s, SR j Indicates the maximum range of OLTC gear changes; The maximum number of OLTC adjustments allowed in one cycle; Constraint 4: Reactive equipment: Discrete reactive power compensation modeling, taking group switching capacitor bank (CB) as an example; Where: represents the discrete reactive power compensation power of node j at time t; is the number of capacitor groups in operation, which is a discrete variable; is the upper limit of the number of CB groups connected to node j; is the reactive compensation power of each group of CB; B CB The node set of CB in the distribution network; Where: A 0-1 auxiliary variable representing adjacent moments of CB; The upper limit of the number of discrete reactive power compensation operations within a cycle; Formula (22) is an absolute value constraint, and it is necessary to add an auxiliary variable for the change in CB compensation capacity between adjacent time periods: but: Modeling of continuous reactive power compensator, taking static reactive power compensation device (SVC) as an example, compared with discrete reactive power compensation device, continuous reactive power regulation is relatively simple; Where: represents the continuous reactive power compensation power of node j at time t; B SVC is a set of nodes including SVC; and are the lower and upper limits of SVC compensation power respectively; As a large number of distributed power sources are connected to the distribution network, the power flow of the distribution network system is reversed, and overvoltage problems occur. Therefore, the compensation lower limit of SVC Constraint 5: Demand Response Modeling: The price-based demand response electric load is divided into curtailable load (CL) and shiftable load (SL). The following two types of loads are modeled separately; 1) CL characteristic analysis and modeling: CL chooses whether to reduce its own load by comparing the changes in electricity prices before and after DR. The price demand elasticity matrix is ​​used to describe the DR characteristics. The element e in the tth row and mth column of the elasticity matrix E(t, m) is t,j , that is, the elasticity coefficient of the load at time t to the electricity price at time m, is defined as: Where: is the load change at the next moment t; is the initial load at time t; △ρ m is the change in electricity price at time m after demand response; is the initial electricity price at time m; then the load change that can be reduced at time t after DR is Where: E CL (t,j) represents the CL price demand elasticity matrix; ρ j represents the electricity price at time j; 2) SL characteristic analysis and modeling: The price demand elasticity matrix is ​​used to describe the DR characteristics. The change in the transferable load at time t after DR is for: Where: is the initial transferable load at time t; E SL (t,m) is the SL price demand elasticity matrix; Constraint 6: Energy storage device modeling: The energy storage device takes into account constraints in multiple time periods, including charge and discharge state restrictions, charge and discharge power restrictions, and energy storage capacity restrictions; Charge and discharge status restrictions: Power Limitation: Capacity Limitation: Where: B ESS is the node set including ESS; Formula (28) indicates that the energy storage can only be charged or discharged at the same time, but not charged and discharged at the same time; and They are the charging and discharging states of energy storage respectively; represents the discharge power of energy storage node j at time t; represents the charging power of energy storage node j at time t; They are the upper and lower limits of ESS charging and discharging power, respectively; is the amount of energy stored at the tth moment; Indicates the amount of energy stored at time t-1; Represents the charging power of node j at time t represents the discharge power of energy storage node j at time t; and To consider the upper and lower limits of factors such as energy storage life; is the charging and discharging efficiency coefficient of energy storage, Constraint 7: Microturbine Modeling: The controllable distributed power sources in the distribution network include gas turbines, fuel cells, etc., and their power generation costs are expressed by linear functions. Considering the case where the distribution network includes micro gas turbines, its cost function is: C G (t)=[aP t +b] (31); Where: and Indicates the maximum and minimum output power of the gas turbine, which are limited by its rated power and minimum load rate respectively; C G (t) represents the power generation cost of the gas turbine, △t represents the time interval, which is 1h; represents the power generation of the i-th gas turbine at time t; Hill climbing constraints: Where: r down and r up are the downward and upward ramp rates of the gas turbine respectively; △T is the time interval; Constraint 8: Other constraints: Substation power purchase power constraints Where: The minimum and maximum active power transmitted by the distribution network connecting the main network tie line; and The minimum reactive power and reactive power transmitted by the distribution network connecting the main network tie line; P grid,t It represents the active power input from the main grid to the distribution grid at time t; Q grid,t It represents the reactive power input from the main grid to the distribution grid at time t; Wind power constraints: Where: is the wind power received by node j in period t; The predicted power of wind power connected to node j in period t; System operation safety constraints: In j,min ≤V j,t ≤V j,max (37); Where V j,min 、V j,max They represent the upper and lower limits of the square of the voltage amplitude at node j; l ij,max Represents the upper and lower limits of the square of the current amplitude of branch ij; System power balance constraints: Since this constraint contains fuzzy variables, the power balance constraint should be expressed as a fuzzy chance constraint based on confidence level: In the formula, is the photovoltaic power received by node j in period t; is the wind power received by node j in period t; is the active power of the load of node j in period t; is the discharge power of energy storage node j at time t; The charging power of node j at time t; is the output power of the gas turbine at node j at time t; P is the power purchased by the distribution network from the main grid at time t; j,t is the active power injected into node j at time t; In the formula, is the reactive power of the load at node j during period t; is the reactive power input from the main grid to the distribution grid at time t; is the reactive power of the SVC at node j at time t; is the reactive power of CB at node j at time t; Q j,t is the reactive power injected into node j at time t; By making a clear equivalence class treatment on (39), we can obtain: In the formula, ω 1PV,j ,ω 2PV,j ,ω 1WT,j ,ω 2WT,j ,ω 2L,j ,ω 3L,j They are the membership parameters of the predicted value of photovoltaic output, the predicted value of wind turbine output and the predicted value of load power respectively.

7. The active low-carbon dispatching method for distribution network based on source-load uncertainty according to claim 1 is characterized by: In step 4, the model is subjected to second-order cone relaxation, and the model is solved by the GUROBI solver; the specific steps are: Step 1: Input the data of distribution network nodes and branches, daily wind and solar output, daily load data and other parameters; Step 2: Construct the optimal power flow model of the distribution network. The constraints include energy storage output constraints, reactive power compensation equipment output constraints, on-load tap-changing transformer gear constraints, demand response constraints, and gas turbine power constraints. The objective function includes the economic cost and environmental cost of the distribution network operation. Step 3: Remove the absolute value of the mathematical model of discrete reactive power compensation constraint and on-load tap-changing transformer constraint and transform it into a mixed integer model. In view of the uncertainty of source and load, introduce fuzzy chance constraint, relax the deterministic constraint into a system constraint containing fuzzy variables, and use trapezoidal fuzzy parameters to clarify it. Step 4: Use the GUROBI solver to solve the established second-order cone programming model.

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