Multi-target source-load-storage collaborative planning method based on power grid carbon emission reduction constraint

Abstract

The invention relates to a multi-target source-load-storage collaborative planning method based on a power grid carbon emission reduction constraint, and the method comprises the steps: constructing an ideal carbon emission intensity, real-time carbon potential and energy storage carbon potential model at the level of a power distribution network according to a regional carbon emission target total amount and carbon emission flow theory; combining the ideal carbon emission intensity, the real-time node carbon potential and the net load curve to construct a multi-dimensional index model to determine the low-carbon priority of the optimized region; an energy storage charging and discharging model is formulated, renewable energy sources are charged and consumed in a load trough and a low-carbon-potential period, and a traditional high-carbon power supply is replaced by discharging in a load peak and a high-carbon-potential period; and establishing a mixed integer programming model which aims at minimizing the carbon emission cost, the investment cost and the light and wind abandoning penalty cost. According to the method, fine carbon emission management of the regional power distribution network is realized, the low-carbon operation level of the power distribution network can be effectively improved, the utilization rate of renewable energy sources is improved, the total carbon emission of the system is reduced, and the method has good popularization and application values.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system planning, and specifically relates to a multi-objective source-load-storage collaborative planning method based on carbon emission reduction constraints of a power grid. BACKGROUND

[0002] The power grid planning method oriented by carbon emission reduction in the prior art has many deficiencies: a multi-dimensional index model based on carbon potential, ideal carbon emission intensity and characteristics of net load is lacking, and it is difficult to comprehensively depict the characteristics of regional carbon emission; some methods take carbon potential as the only reference index, ignoring the influence of load characteristics on carbon emission, and it is difficult to scientifically measure the relationship between load and carbon emission; at the same time, there is generally a lack of collaborative optimization mechanism between source, load and storage with carbon emission as the core, and the efficient coupling and complementary regulation of energy resources based on system carbon emission dynamics cannot be achieved. SUMMARY

[0003] In order to solve the above problems, the application considers the characteristics of carbon emission flow, determines the low-carbon priority of the region and collaborates with the energy storage "low charging and high discharging" strategy to optimize the low-carbon power grid, and provides key theoretical support for formulating a low-carbon planning scheme and guiding user-side interaction to reduce carbon emission. The application establishes a source-load-storage collaborative low-carbon power system considering the characteristics of carbon emission flow, aims to realize fine carbon emission management of the regional distribution network, and through the method of the application, the low-carbon operation level of the distribution network can be effectively improved, the utilization rate of renewable energy can be improved, and the total amount of system carbon emission can be reduced, so the application has good popularization and application value.

[0004] In order to achieve the above purpose, the application provides a multi-objective source-load-storage collaborative planning method based on carbon emission reduction constraints of a power grid, which comprises the following steps:

[0005] S1: According to the regional carbon emission total amount target and carbon emission flow theory, ideal carbon emission intensity, real-time carbon potential and energy storage carbon potential models are constructed at the distribution network level;

[0006] S2: A multi-dimensional index model (electricity carbon emission, anti-carbon potential load ratio, etc.) is constructed in combination with the real-time carbon potential, ideal carbon emission intensity and net load curve, and the low-carbon priority of the optimized region is determined;

[0007] S3: A storage charging and discharging model is formulated, renewable energy is consumed in the load valley and low-carbon potential period, and traditional high-carbon power is replaced in the load peak and high-carbon potential period, so as to reduce the system carbon emission;

[0008] S4: A mixed integer programming model with the minimum carbon emission cost, investment cost and penalty fee of abandoned light and wind as the target is established, and the DG capacity, energy storage configuration and flexible load adjustment scheme are optimized to verify the carbon reduction effect.

[0009] Further, in the step S1, according to the regional carbon emission total target and the carbon emission flow theory, the ideal carbon emission intensity, real-time carbon potential and energy storage carbon potential model is constructed at the power distribution network level, and the following process is included:

[0010] Firstly, the renewable energy output and load prediction at time t are obtained according to historical meteorological data and load data, and then the ideal carbon emission intensity of the node is calculated according to the regional carbon emission total target:

[0011]

[0012] In the formula: C m,t is the regional carbon emission total target at time t; L total,t is the predicted total load at time t; α is the influence weight of renewable energy penetration rate on carbon potential; P re,t is the wind and light power prediction value at time t.

[0013] The carbon potential of a certain node and the discharging carbon potential of energy storage are calculated:

[0014]

[0015] In the formula: is the branch set injecting active power to node i; is the generator set connected to node i; is the energy storage connected to node i; P l is the active power injected by branch l to node i; G g is the active output of generator g to node i; P h is the discharging power of energy storage; ρ l,t is the carbon flow density of branch l; e g,t is the carbon emission intensity of distributed power supply; c h,t is the carbon potential of energy storage. is the carbon content of energy storage at time t0; is the electric quantity of energy storage at time t0; η essc,h is the charging efficiency of energy storage h respectively; L(t) and P(t) represent the charging carbon flow rate instantaneous value and the energy storage charging power respectively.

[0016] In the step S2, the multi-dimensional index model (electric carbon emission, anti-carbon potential load ratio, etc.) is constructed by combining the real-time carbon potential, ideal carbon emission intensity and net load curve, and the low-carbon priority of the optimized region is determined. The following contents are included:

[0017] Firstly, the net load of a certain node is calculated:

[0018] P i,t = P l,t -P DG,t + P essc,t -Pessdis,t (37)

[0019] P = P + P + P + P l,t P is the real-time load of node i at time period t; P DG,t P is the real-time output of distributed generation (DG) of node i at time period t; P essc,t P is the charging power of energy storage at time period t; P essdis,t P is the discharging power of energy storage at time period t.

[0020] To improve the accuracy, robustness and applicability of the planning model and reduce the interference of load fluctuation and dynamic changes of unit carbon emission on the results, a refined modeling method based on typical load characteristic curve is adopted. Specifically, by extracting the representative steady-state load curve in the long-term operation of each distribution network, reasonable sampling period is divided and feature sampling is carried out. The sampling frequency is differentiated according to the regional scale and load fluctuation characteristics: for regions with wide coverage and gentle load time series fluctuation, lower sampling frequency is adopted to balance the calculation efficiency; for regions with small range and severe load fluctuation, high sampling frequency is adopted to accurately capture the change trend.

[0021] The carbon emission of electricity consumption is calculated as follows:

[0022]

[0023] y = y + y + y + y 1,i y is the carbon emission of electricity consumption of node i; N represents the total number of sampling points in the sampling period; c i,n c is the node carbon potential unit value of node i at the nth sampling point; p l,i,n p is the node net load unit value of node i at the nth sampling point; Δt represents the interval time length value of adjacent sampling points under the current sampling frequency.

[0024] The trend low-carbon degree is calculated as follows:

[0025]

[0026] y = y + y + y + y 2,i y is the trend low-carbon degree of node i; k ci,n k is the slope of the carbon potential curve of node i at the sampling point n; k pi,n k is the slope of the net load curve of node i at the sampling point n; N-1 slopes of sampling points are calculated; N' represents the sampling point set with opposite trend of the node net load curve and the node carbon potential curve; N" represents the sampling point set with the same trend of the node net load curve and the node carbon potential curve.

[0027] The anti-carbon potential load ratio is calculated as follows:

[0028]

[0029] Distance to low carbon:

[0030]

[0031] y 4,i = λ1d 1,i + (1- λ1)d 2,i (46)

[0032] Wherein: d 1,i is the sum of the micro-increments of node i; d 2,i is the product of the micro-increments of node i. λ1 is a weight coefficient, taking a value of 0.5.

[0033] Calculate the distance to low carbon:

[0034]

[0035] Wherein: c′ i,n is the node ideal carbon emission intensity standard value of node i at the nth sampling point.

[0036] The correlation and difference between the indicators are combined to weight the electricity carbon emission, trend low carbon degree, anti-carbon potential load ratio, distance to low carbon, and low carbon target distance:

[0037]

[0038] Wherein: Y c,1 is the load carbon emission characteristics of node i; Y 1,j , Y 2,j , Y 3,j , Y 4,j , Y 5,j are the normalized electricity carbon emission, trend low carbon degree, anti-carbon potential load ratio, distance to low carbon, and low carbon target distance of node i, respectively; is a weight coefficient.

[0039] By constructing a load carbon emission characteristic quantification model, the carbon emission level of the power distribution network under different operating conditions is evaluated, and a basis is provided for different carbon emission reduction strategies of the power distribution network. At the same time, a source-load-storage collaborative planning model is established, combined with the demand response and distributed power supply access mechanism, to realize the reasonable transfer or reduction of electricity load in high-carbon emission periods, thereby significantly improving the carbon emission reduction effect of the system.

[0040] In the step S3, an energy storage charging and discharging model is formulated, renewable energy is consumed in the load valley and low carbon potential period, and traditional high-carbon power is replaced in the load peak and high carbon potential period to reduce system carbon emission. Specifically, the following contents are included:

[0041] The energy storage system model is as follows:

[0042] P ess,i,t essc,i,t -P essdis,i,t (49)

[0043]

[0044] P essc,i,t ·P essdis,i,t

[0045]

[0046] SOC init,i T,i (55)

[0047] P ess,i,t is the access power of the energy storage system accessing the ith node at t; P essc,i,t , P essdis,i,t are the charging power and discharging power of the energy storage system accessing the ith node at t, respectively; are the maximum charging power and discharging power of the energy storage system accessing the ith node at t, respectively; a i,t is a 0-1 variable of the charging and discharging state of the energy storage system accessing the ith node at t, a i,t = 1 indicates that the energy storage system accessing the ith node at t is in the charging state, a i,t = 0 indicates that the energy storage system accessing the ith node at t is in the discharging state, and it is noted that the energy storage system cannot be in the charging state and the discharging state at the same time; SOC i,t is the state of charge of the energy storage system accessing the ith node at t; η essc,i , η essdis,i are the charging energy conversion efficiency and discharging energy conversion efficiency of the energy storage system accessing the ith node, respectively; E ess,i is the capacity of the energy storage system accessing the ith node; are the lower limit and upper limit of the state of charge of the energy storage system accessing the ith node during operation; SOC init,i , SOC T,i ​​​SoC (i, t) and SoC (i, t+T) are the state of charge of the energy storage system connected to the ith node at the initial time and the end of the dispatch period, respectively.

[0048] Further, in the step 4, a mixed integer programming model is established to minimize the carbon emission cost, planning cost and light and wind curtailment penalty cost, to optimize the DG capacity and energy storage configuration scheme, and to verify the carbon reduction effect. Specifically, the following contents are included:

[0049] The objective function is shown below:

[0050]

[0051] F3 = ε w ΔP w,i,t Δt+ε pv ΔP pv,i,t Δt (58)

[0052] min F = F1 + F2 + F3 (59)

[0053] In the formula, N is the set of nodes participating in optimization; T represents the total number of sampling periods within the sampling period corresponding to the current sampling frequency; e i,t is the carbon potential of node i at time t; P i,t is the load of node i at time t; ΔP i,t is the response power of the load of node i at time t; P pv,i,t and P w,i,t are the output of the distributed photovoltaic and wind power of node i at time t, respectively; is the unit carbon emission cost of the power grid; m z is the electricity price of t period purchased from the main grid; m ess is the operation cost coefficient of the energy storage system; m dr is the unit demand response cost; m pv and m w are the unit generation investment costs of the distributed photovoltaic and wind power, respectively; ε w is the wind curtailment penalty price; ΔP w,i,t is the amount of wind power curtailment of the wind turbine at node t period; ε pv is the light curtailment penalty price; ΔP pv,i,t is the amount of light curtailment of the photovoltaic generator at t period.

[0054] Installed capacity constraint:

[0055] E pv,i +E wt,i +E g,i ≤ β i E base,i (60)

[0056] In the formula, E pv,t , Ewt,i respectively, are the installed capacity of distributed photovoltaic and wind power of node i; β i is the maximum penetration rate of DG of node i, E base,i is the reference load of node i.

[0057] Load regulation constraint:

[0058]

[0059] In the formula: and are the up-regulation and down-regulation power of node i at time t; ω i,t represents the proportion of load that can participate in demand response of node i at time t; respectively represent the load value of node i at time t before and after response.

[0060] Branch power flow constraint:

[0061] X l,i,t = B l,i (θ b,l,t -θ e,l,t ) (62)

[0062]

[0063] In the formula: B l,i is the susceptance of branch l connected with node i; θ b,l,t and θ e,l,t are the phase angles of the head and tail nodes of branch l at time t; is the upper limit of transmission power of branch l connected with node i; θ i,t is the phase angle of node i at time t; are the upper and lower limits of voltage phase angle of node i. Equation (29) is the branch power flow equation; equations (30) and (31) are respectively the upper and lower limit constraints of branch power flow and node phase angle.

[0064] Node power balance constraint:

[0065]

[0066] In the formula: Ω PV,i and Ω W,i are respectively the photovoltaic and wind power equipment sets of node i; Ω ess,i is the energy storage equipment set of node i; Ω i- and Ω i+ respectively represent the power flow injected into node i and the power flow branch set flowing out of node i.

[0067] Voltage constraint:

[0068]

[0069] In the formula: U i,t is the voltage amplitude of node i at time t, and respectively represent the upper and lower limits of the voltage amplitude of node i, for ensuring that the voltage does not exceed the limit.

[0070] Compared with the prior art, the application has the beneficial effects that:

[0071] 1. The multi-objective source-load-storage collaborative planning method based on carbon emission reduction constraints of a power grid provided by the application first dynamically decomposes the total regional carbon emission target into ideal carbon emission intensity at a time period (such as an hourly level), to form carbon constraints matched with the load curve and renewable energy output; according to the carbon emission flow theory, a real-time carbon potential and energy storage carbon potential model is constructed at the distribution network level, so that carbon emission control is upgraded from total amount management to fine regulation and control; secondly, a multi-dimensional index model is constructed in combination with the ideal carbon emission intensity, real-time node carbon potential and net load curve, to determine the low-carbon priority of the optimized region; then, a storage charging and discharging model is developed, to charge renewable energy at the load valley and low-carbon potential period, and to discharge to replace traditional high-carbon power sources at the load peak and high-carbon potential period, so as to avoid concentrated over-standard carbon emission at the peak period; finally, a mixed integer programming model with the minimum carbon emission cost, investment cost and light and wind abandonment penalty fee as the target is established, to optimize the DG capacity, energy storage configuration and flexible load adjustment scheme, and to verify the carbon reduction effect.

[0072] 2. The multi-objective source-load-storage collaborative planning method based on carbon emission reduction constraints of a power grid provided by the application dynamically constructs a source-load-storage collaborative response mechanism by introducing an ideal carbon emission intensity modeling method, realizes unified coordinated control of distributed resources, improves the fine level of carbon emission management, effectively alleviates the contradiction between renewable energy consumption and carbon emission reduction targets, reduces the system operation and investment cost, and is suitable for active distribution network planning and operation under the background of the“double carbon”target. BRIEF DESCRIPTION OF DRAWINGS

[0073] Figure 1 is a flowchart of the multi-objective source-load-storage collaborative planning method based on carbon emission reduction constraints of a power grid. DETAILED DESCRIPTION

[0074] The application will be further described below in combination with the drawings and examples.

[0075] It should be pointed out that the following detailed description is exemplary and is intended to provide further description of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0076] Example 1:

[0077] The embodiment provides a multi-objective source-load-storage collaborative planning method based on carbon emission reduction constraints of a power grid, comprising the following steps:

[0078] According to the total regional carbon emission target and the carbon emission flow theory, ideal carbon emission intensity, real-time carbon potential and energy storage carbon potential models are constructed at the distribution network level;

[0079] In combination with the real-time carbon potential, the ideal carbon emission intensity and the net load curve, a multi-dimensional index model (electricity carbon emission, anti-carbon potential load ratio, etc.) is constructed to determine the low-carbon priority of the optimized region;

[0080] A storage charging and discharging model is formulated, renewable energy is consumed in the load valley and low-carbon potential period, and traditional high-carbon power is replaced in the load peak and high-carbon potential period, so as to reduce the system carbon emission.

[0081] A mixed integer programming model is established to minimize the carbon emission cost, investment cost and light and wind abandonment penalty fee, and the DG capacity, energy storage configuration and flexible load adjustment scheme are optimized to verify the carbon reduction effect.

[0082] S1. According to the total regional carbon emission target and the carbon emission flow theory, ideal carbon emission intensity, real-time carbon potential and energy storage carbon potential models are constructed at the distribution network level, and the following processes are included:

[0083] First, the renewable energy output and load prediction at time t are obtained according to historical meteorological data and load data, and then the ideal carbon emission intensity of the node is calculated according to the total regional carbon emission target:

[0084]

[0085] In the formula: C m,t is the total regional carbon emission target at time t; L total,t is the total load predicted at time t; α is the influence weight of renewable energy penetration rate on carbon potential; P re,t is the wind and light power prediction value at time t.

[0086] According to the carbon emission flow theory, a load and energy storage system carbon emission flow model is constructed at the distribution network level. The carbon potential of a node and the energy storage discharging carbon potential are calculated:

[0087]

[0088] In the formula: is the branch set injecting active power to node i; is the generator set connected to node i; is the energy storage set connected to node i; P l is the active power injected by branch l to node i; G gP represents the active power output of generator set g to node i; h The discharge power of the stored energy; ρ l,t The carbon flux density of branch l; e g,t Carbon emission intensity of distributed power sources; c h,t For energy storage carbon potential. The carbon content of the stored energy at time t0; η represents the amount of electricity stored at time t0; essc,h Let be the charging efficiency of energy storage h, respectively; L(t) and P(t) represent the instantaneous value of the charging carbon flow rate and the energy storage charging power, respectively.

[0089] S2. Combining real-time carbon potential, ideal carbon emission intensity, and net load curves, construct a multi-dimensional index model (electricity consumption carbon emissions, reverse carbon potential load ratio, etc.) to determine the low-carbon priority of the optimization area. First, calculate the net load of a certain node:

[0090] P i,t =P l,t -P DG,t +P essc,t -P essdis,t (70)

[0091] In the formula: P i,t P represents the real-time load of node i during time period t. DG,t For the node to provide real-time output of distributed generation (DG) in time period t; P essc,t P is the charging power of energy storage during time period t; essdis,t Let be the discharge power of the energy storage during time period t.

[0092] Calculate carbon emissions from electricity consumption:

[0093]

[0094] In the formula: y 1,i Let N represent the carbon emissions from electricity consumption at node i; N represents the total number of sampling points within the sampling period; c i,n p is the per-unit value of the nodal carbon potential of node i at the nth sampling point; i,n Δt represents the per-unit value of the node net load at sampling point n for node i; Δt represents the time interval between adjacent sampling points at the current sampling frequency.

[0095] Calculating the trend of low carbon content:

[0096]

[0097] In the formula: y 2,i The trend of low carbon intensity for node i; k ci,n k is the slope of the carbon potential curve at node i at sampling point n; pi,nThe slope of the node i net load curve at the sampling point n; the slope of N-1 sampling points is calculated; N' represents the sampling point set of the node net load curve and the node carbon potential curve with opposite change trend; N" represents the sampling point set of the node net load curve and the node carbon potential curve with the same change trend.

[0098] The anti-carbon potential load ratio is calculated:

[0099]

[0100] The distance to low carbon is calculated:

[0101]

[0102] y 4,i = λ1d 1,i + (1- λ1)d 2,i (79)

[0103] In the formula: d 1,i is the sum of the micro-increments of node i; d 2,i is the product of the micro-increments of node i. λ1 is a weight coefficient, and the value is 0.5.

[0104] The low carbon target distance is calculated:

[0105]

[0106] In the formula: c′ i,n is the node ideal carbon emission intensity unit value of node i at the n sampling point.

[0107] The weight distribution of the electricity carbon emission, the trend low carbon degree, the anti-carbon potential load ratio, the distance to low carbon and the low carbon target distance is combined with the correlation and difference between the indexes:

[0108]

[0109] In the formula: Y c,1 is the load carbon emission characteristics of node i; Y 1,j , Y 2,j , Y 3,j , Y 4,j , Y 5,j are the normalized electricity carbon emission, the trend low carbon degree, the anti-carbon potential load ratio, the distance to low carbon and the low carbon target distance of node i respectively; is a weight coefficient.

[0110] By constructing a load carbon emission characteristic quantification model, the carbon emission level of the distribution network under different operating conditions is evaluated, which provides a basis for different carbon emission reduction strategies of the distribution network. At the same time, a source-load-storage collaborative planning model is established, which combines demand response and distributed power access mechanism to realize the reasonable transfer or reduction of electricity load in high-carbon emission period, so as to significantly improve the carbon emission reduction effect of the system.

[0111] S3, a storage charging and discharging model is developed, which charges renewable energy in the load valley and low-carbon potential period, and discharges to replace traditional high-carbon power in the load peak and high-carbon potential period, so as to reduce the carbon emission of the system.

[0112] In the system load valley period and the period with low carbon potential (such as noon photovoltaic large production), the storage system executes the charging strategy to consume excess renewable energy and low-carbon power; in the system load peak period and the period with high carbon potential (such as evening peak coal power dominated), the storage system executes the discharging strategy to provide power support and replace high-carbon energy supply, thereby reducing the overall carbon emission intensity of the system. The storage system model is as follows:

[0113] P ess,i,t =P essc,i,t -P essdis,i,t (82)

[0114]

[0115] P essc,i,t ·P essdis,i,t =0 (85)

[0116]

[0117] SOC init,i =SOC T,i (88)

[0118] In the formula: P ess,i,t is the access power of the storage system accessing the i th node at t time; P essc,i,t , P essdis,i,t are the charging power and discharging power of the storage system accessing the i th node at t time, respectively; are the maximum charging power and discharging power of the storage system accessing the i th node at t time, respectively; a i,t is a 0-1 variable of the charging and discharging state of the storage system accessing the i th node at t time, a i,t = 1 indicates that the storage system accessing the i th node at t time is in the charging state, a i,t = 0 indicates that the storage system accessing the i th node at t time is in the discharging state, and it is noted that the storage system cannot be in the charging state and the discharging state at the same time; SOCi,t State of charge of the energy storage system accessing the ith node at time t; η essc,i , η essdis,i are the charging and discharging energy conversion efficiencies of the energy storage system accessing the ith node, respectively; E ess,i is the capacity of the energy storage system accessing the ith node; are the lower and upper limits of the state of charge of the energy storage system during operation, respectively; SOC init,i , SOC T,i are the state of charge of the energy storage system accessing the ith node at the initial time and at the end of the scheduling period, respectively.

[0119] S4, a mixed integer programming model with the minimum carbon emission cost, planning cost and light and wind curtailment penalty fee as the target is established to optimize the DG capacity and energy storage configuration scheme and verify the carbon reduction effect.

[0120] The objective function is shown below:

[0121]

[0122] F3 = ε w ΔP w,i,t Δt + ε pv ΔP pv,i,t Δt (91)

[0123] min F = F1 + F2 + F3 (92)

[0124] In the formula, N is a set of nodes participating in optimization; T represents the total number of sampling periods within the sampling period corresponding to the current sampling frequency; e i,t is the carbon potential of node i at time t; P i,t is the load of node i at time t; ΔP i,t is the response power of the load of node i at time t; P pv,i,t and P w,i,t are the outputs of the distributed photovoltaic and wind power of node i at time t, respectively; is the unit carbon emission cost of the power grid; m z is the electricity price of the t period to the main grid; m ess is the operation cost coefficient of the energy storage system; m dr is the unit demand response cost; m pv and m w are the unit generation comprehensive investment costs of the distributed photovoltaic and wind power, respectively; ε w is the wind curtailment penalty price; ΔP w,i,t is the amount of wind power curtailment of the wind turbine at node t period; ε pv is the light curtailment penalty price; ΔP pv,i,tThe photovoltaic generator unit abandons light electricity at time period t.

[0125] Installed capacity constraint:

[0126] E pv,i +E wt,i +E g,i ≤β i E base,i (93)

[0127] In the formula: E pv,t , E wt,i are the installed capacities of distributed photovoltaic and wind power of node i respectively; β i is the maximum penetration rate of DG of node i, E base,i is the reference load of node i.

[0128] Load regulation constraint:

[0129]

[0130] In the formula: and are the up-regulation and down-regulation powers of the load of node i at time t respectively; ω i,t represents the proportion of the load of node i that can participate in demand response at time t; respectively represent the load values of node i at time t before and after response.

[0131] Branch power flow constraint:

[0132] X l,i,t =B l,i (θ b,l,t -θ e,l,t ) (95)

[0133]

[0134] In the formula: B l,i is the susceptance of branch l connected with node i; θ b,l,t , θ e,l,t are the phase angles of the head and tail nodes of branch l at time t respectively; is the upper limit of the transmission power of branch l connected with node i; θ i,t is the phase angle of node i at time t; are the upper and lower limits of the voltage phase angle of node i. Formula (29) is the branch power flow equation; formula (30) and formula (31) are respectively the upper and lower limit constraints of the branch power flow and the node phase angle.

[0135] Node power balance constraint:

[0136]

[0137] In the formula: ΩPV,i , Omega W,i are the photovoltaic and wind power set of node i respectively; Omega ess,i is the energy storage set of node i; Omega i- and Omega i+ respectively represent the power injection branch set of node i and the power outflow branch set of node i.

[0138] Voltage constraint:

[0139]

[0140] In the formula: U i,t is the voltage amplitude of node i at time t, and respectively represent the upper and lower limits of the voltage amplitude of node i, which are used to ensure that the voltage does not exceed the limit.

[0141] S5, output result:

[0142] Output planning cost, carbon emission cost and energy storage capacity data.

[0143] S6, take the improved IEEE 30 node system as an example to verify the feasibility of the multi-objective source load storage collaborative planning method.

[0144] The method comprises the following steps: according to the total amount of regional carbon emission target and carbon emission flow theory, ideal carbon emission intensity, real-time carbon potential and energy storage carbon potential model are constructed at the distribution network level; secondly, combined with ideal carbon emission intensity, real-time node carbon potential and net load curve, a multi-dimensional index model (electricity carbon emission, anti-carbon potential load ratio, etc.) is constructed to determine the low-carbon priority of the optimization area; then, the energy storage charging and discharging model is developed, and the renewable energy is consumed at the load low valley and low carbon potential period, and the traditional high-carbon power is replaced at the load peak and high carbon potential period, so as to reduce the system carbon emission; finally, a mixed integer programming model with the minimum carbon emission cost, investment cost and abandoned light and wind penalty fee as the target is established, and the DG capacity, energy storage configuration and flexible load adjustment scheme are optimized, and the carbon reduction effect is verified. The present application establishes a source load storage collaborative low-carbon power system considering the characteristics of carbon emission flow, aiming to realize the fine carbon emission management of regional distribution network, and through the method of the present application, the low-carbon operation level of distribution network can be effectively improved, the renewable energy utilization rate can be improved, and the total system carbon emission can be reduced, which has good popularization and application value.

[0145] The comprehensive investment cost of distributed photovoltaic and wind power is set to 0.25 yuan / (kW·h) and 0.35 yuan / (kW·h) respectively. The investment upper limit is set to 1.2 million yuan. The rated parameters of ESS are 200kW / 800kW, the carbon price is 100 (yuan / t), and the load fluctuation range before and after the response is 20%.

[0146] The embodiment analyzes the low-carbon collaborative planning capability of source-load-storage in different collaborative modes from four planning schemes. Scheme 1 adopts the traditional single-objective planning cost minimization strategy, does not consider the carbon emission constraint and load side response, and the energy storage system performs the basic charging and discharging strategy according to the peak and valley electricity price, serving as a benchmark reference. Scheme 2 considers introducing the node carbon potential as a regulation index, constructs a source-storage planning model under the guidance of the carbon potential, but does not introduce the flexible load response mechanism, and only realizes the source-storage collaborative optimization. Scheme 3 introduces the load side response capability, carries out the source-load-storage low-carbon joint planning based on the simple carbon emission constraint, but does not consider the time and space distribution characteristics of the node carbon potential. Scheme 4 adopts the scheme of the application, and adopts the multi-objective source-load-storage collaborative planning method under the constraint of the carbon emission reduction of the power grid.

[0147] The distributed power penetration rates of schemes 1 to 4 are respectively set to 10%, 20%, 30% and 50%, which shows that introducing the carbon constraint, the node carbon potential and the load response mechanism in the low-carbon planning can significantly improve the consumption capacity of clean energy, thereby promoting the realization of the low-carbon target.

[0148] In order to analyze the influence of the multi-objective source-load-storage collaborative planning method model of the embodiment on the system economy, Table 1 compares the cost results of the four planning schemes.

[0149] Table 1: Cost table of four planning schemes

[0150]

[0151]

[0152] As shown in Table 1, scheme 1 only takes the minimization of the planning cost as the optimization target, does not consider the carbon emission and the constraint of abandoned wind and light, and thus the costs of the two items are high. Although the investment cost is the lowest, due to the low running efficiency, the overall total cost is the highest. Compared with scheme 1, scheme 2 introduces the node carbon potential as a regulation signal, constructs a source-storage collaborative planning model under the guidance of the carbon potential, thereby optimizing the charging and discharging strategy of the energy storage system and reducing the main grid power purchase amount. Although the load response mechanism is not introduced, the carbon emission cost is relatively high, but the overall total cost is reduced by about 2.3%. Scheme 3 further introduces the load side response mechanism, and realizes the joint optimization of source-load-storage under the simple carbon emission constraint. Although the carbon emission cost increases slightly (increases by 1.8%) compared with scheme 2, the system scheduling is more flexible, and the overall running efficiency is improved. Scheme 4 adopts the multi-objective source-load-storage collaborative planning method proposed in the application, and comprehensively introduces the node carbon potential and the flexible load response mechanism under the constraint of the carbon emission reduction of the power grid. Although the investment cost is higher than that of the previous three schemes, it performs best in the aspects of abandoned wind and light and carbon emission, and the total cost is reduced by about 8.2% compared with scheme 1, which shows a significant comprehensive benefit advantage.

[0153] In summary, different coordinated planning strategies have significant influence on carbon emission reduction and economy of the system. The traditional planning method with the objective of minimizing investment cost has lower initial investment, but the overall cost is higher due to the lack of consideration of carbon emission and renewable energy utilization efficiency. The introduction of node carbon potential can effectively optimize the operation strategy of energy storage and reduce part of carbon emission and electricity purchase cost. The combination of load side response mechanism further enhances the adjustment ability and operation efficiency of the system. Finally, the multi-objective source-load-storage coordinated planning method proposed in this paper, by comprehensively considering the node carbon potential distribution and load response, achieves better carbon emission reduction effect while ensuring the economy of the system, providing a feasible path and theoretical support for building a low-carbon and efficient active distribution network.

[0154] Obviously, the above embodiments are only examples for clearly illustrating but not limiting the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments need not and cannot be exhausted. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A multi-objective source-load-storage collaborative planning method based on power grid carbon emission reduction constraints, characterized in that, It includes the following steps: S1: Based on the regional total carbon emission target and carbon emission flow theory, construct ideal carbon emission intensity, real-time carbon potential and energy storage carbon potential models at the distribution network level; S2: Combining real-time carbon potential, ideal carbon emission intensity, and net load curves, construct a multi-dimensional index model to determine the low-carbon priority of the optimization area; S3: Design an energy storage charging and discharging model to charge and consume renewable energy during periods of low load and low carbon potential, and to discharge and replace traditional high-carbon power sources during periods of high load and high carbon potential, so as to reduce system carbon emissions. S4: Establish a mixed integer programming model with the goal of minimizing carbon emission costs, investment costs, and penalties for curtailing solar and wind power, optimize DG capacity, energy storage configuration, and flexible load adjustment schemes, and verify the carbon reduction effect.

2. The multi-objective source-load-storage collaborative planning method based on power grid carbon emission reduction constraints as described in claim 1, characterized in that, For S1, based on the local grid operator's total carbon emission target and carbon emission flow theory, ideal carbon emission intensity, real-time carbon potential, and energy storage carbon potential are constructed at the distribution network level. First, the renewable energy output and load forecast at time t are obtained based on historical meteorological data and load data. Then, the ideal carbon emission intensity of the nodes is calculated based on the regional total carbon emission target. In the formula: C m,t Let L be the regional total carbon emission target at time t; total,t The total load at time t is the predicted value; α is the weight of the impact of renewable energy penetration on carbon potential; P re,t Let be the predicted wind and solar power generation at time t; Calculate the real-time carbon potential and energy storage discharge carbon potential of a given node: In the formula: The set of branches that inject active power into node i; The set of generator sets connected to node i; The energy storage set connected to node i; P l Inject active power into node i into branch l; G g P represents the active power output of generator set g to node i; h The discharge power of the stored energy; ρ l,t The carbon flux density of branch l; e g,t Carbon emission intensity of distributed power sources; c h,t For energy storage carbon potential. The carbon content of the stored energy at time t0; η represents the amount of electricity stored at time t0; essc,h Let be the charging efficiency of energy storage h, respectively; L(t) and P(t) represent the instantaneous value of the charging carbon flow rate and the energy storage charging power, respectively.

3. The multi-objective source-load-storage collaborative planning method based on power grid carbon emission reduction constraints as described in claim 1, characterized in that, For S2, a multi-dimensional index model is constructed by combining real-time carbon potential, ideal carbon emission intensity, and net load curves to determine the low-carbon priority of the optimization region; firstly, the net load of a certain node is calculated: P i,t =P l,t -P DG,t +P essc,t -P essdis,t (4) In the formula: P l,t P represents the real-time load of a node during time period t. DG,t For the node to provide real-time output of distributed generation (DG) in time period t; P essc,t P is the charging power of energy storage during time period t; essdis,t The discharge power of the energy storage during time period t; The carbon emission characteristics of each distribution network within the regional power grid are calculated using a refined modeling method based on typical load characteristic curves. Specifically, representative steady-state load curves from the long-term operation of each distribution network are extracted, and reasonable sampling periods are defined and feature sampling is performed. The sampling frequency is set differently based on the size of the area and the characteristics of load fluctuation: for areas with a wide coverage and gentle load time fluctuations, a lower sampling frequency is used to balance calculation efficiency; for areas with a small coverage and drastic load fluctuations, a higher sampling frequency is used to accurately capture the changing trend. Calculate carbon emissions from electricity consumption: In the formula: y 1,i Let N represent the carbon emissions from electricity consumption at node i; N represents the total number of sampling points within the sampling period; c i,n p is the per-unit value of the nodal carbon potential of node i at the nth sampling point; i,n is the per-unit value of the node net load of node i at the nth sampling point; Δt represents the time interval between adjacent sampling points at the current sampling frequency; Calculating the trend of low carbon content: In the formula: y 2,i The trend of low carbon intensity for node i; k ci,n k is the slope of the real-time carbon potential curve at node i at sampling point n; pi,n Let N' be the slope of the net load curve at node i at sampling point n; calculate the slope of N-1 sampling points in total; N' represents the set of sampling points where the net load curve and the carbon potential curve of node i have opposite trends; N'' represents the set of sampling points where the net load curve and the carbon potential curve of node i have the same trend. Calculate the anti-carbon potential loading ratio: Calculate the distance to low carbon: y 4,i =λ1d 1,i +(1-λ1)d 2,i (13) In the formula: d 1,i The sum of the infinitesimal increments of node i; d 2,i λ1 is the product increment of node i; λ1 is the weight coefficient, with a value of 0.

5. Calculate the distance to the low-carbon target: In the formula: c′ i,n Let be the per-unit value of the ideal carbon emission intensity of node i at the nth sampling point; We assign weights to carbon emissions from electricity consumption, trend toward low carbon emissions, anti-carbon load ratio, distance to low carbon emissions, and distance to low carbon targets based on the correlations and differences among the indicators: In the formula: Y c,1 Y represents the load carbon emission characteristics of node i; 1,j Y 2,j Y 3,j Y 4,j Y 5,j These are the normalized carbon emissions from electricity consumption at node i, the trend of low carbon emissions, the anti-carbon load ratio, the distance to low carbon emissions, and the distance to the low carbon target. These are the weighting coefficients.

4. The multi-objective source-load-storage collaborative planning method based on power grid carbon emission reduction constraints as described in claim 1, characterized in that, The S3 energy storage charging and discharging model is designed to charge and consume renewable energy during periods of low load and low carbon potential, and to discharge and replace traditional high-carbon power sources during periods of high load and high carbon potential, so as to reduce system carbon emissions. Specifically, the energy storage system model includes systems built during periods of low system load and low carbon potential, where the energy storage system executes a charging strategy to absorb excess renewable energy and low-carbon electricity; and during periods of high system load and high carbon potential, the energy storage system executes a discharging strategy to provide power support and replace high-carbon energy supply, thereby reducing the overall carbon emission intensity of the system. P ess,i,t =P essc,i,t -P essdis,i,t (16) P essc,i,t ·P essdis,i,t =0 (19) SOCIETY init,i =SOC T,i (22) In the formula: P ess,i,t P represents the access power of the energy storage system connected to the i-th node at time t. essc,i,t P essdis,i,t These are the charging power and discharging power of the energy storage system connected to the i-th node at time t, respectively. Let a be the maximum charging power and the maximum discharging power of the energy storage system connected to the i-th node at time t, respectively. i,t Let a be a 0-1 variable representing the charging and discharging state of the energy storage system connected to the i-th node at time t. i,t =1 indicates that the energy storage system connected to the i-th node at time t is in a charging state, a i,t =0 indicates that the energy storage system connected to the i-th node at time t is in a discharging state; SOC i,t The state of charge of the energy storage system connected to the i-th node at time t; η essc,i η essdis,i These are the charging energy conversion efficiency and the discharging energy conversion efficiency of the energy storage system connected to the i-th node, respectively. E ess,i The capacity of the energy storage system connected to the i-th node; These are the lower and upper limits of the state of charge (SOC) during the operation of the energy storage system connected to the i-th node, respectively. SOC init,i SOC T,i These represent the initial state of charge (SOC) of the energy storage system connected to the i-th node and the SOC at the end of the scheduling cycle, respectively.

5. The multi-objective source-load-storage collaborative planning method based on power grid carbon emission reduction constraints as described in claim 1, characterized in that, For S4, a mixed integer programming model is established with the goal of minimizing carbon emission costs, planning costs, and penalties for curtailing solar and wind power. This model is used to optimize DG capacity and energy storage configuration schemes and verify the carbon reduction effect. The objective function is shown below: F3=e w ΔP w,i,t Δt+ε pv ΔP pv,i,t Δt (25) min F=F1+F2+F3 (26) In the formula: N is the set of nodes participating in the optimization; T represents the total number of sampling periods within the sampling period corresponding to the current sampling frequency; e i,t P represents the carbon potential at node i at time t; i,t Let ΔP be the load at node i at time t; i,t P represents the response power of node i at time t. pv,i,t and P w,i,t These represent the outputs of distributed photovoltaic and wind power at node i at time t; The unit carbon emission cost of the power grid; m z The price of electricity purchased from the main grid during time period t; m ess This is the operating cost coefficient for the energy storage system. m dr Cost per unit of demand response; m pv and m w These represent the unit power generation comprehensive investment costs for distributed photovoltaic and wind power, respectively; ε w The price for wind curtailment penalties; ΔP w,i,t This refers to the amount of wind power curtailed by the wind turbine generator during node t. ε pv Price for light abandonment penalty; ΔP pv,i,t This refers to the amount of solar power curtailed by the photovoltaic power generation unit during time period t. Installed capacity constraints: AND pv,i +E wt,i +E g,i ≤β i AND base,i (27) In the formula: E pv,t E wt,i These represent the installed capacity of distributed photovoltaic and wind power at node i, respectively; β i Let E be the maximum DG penetration rate at node i. base,i The baseline load for node i; Load regulation constraints: In the formula: and Let ω represent the upward and downward adjustments of the load at node i at time t, respectively; i,t This represents the percentage of load that node i can participate in demand response at time t; These represent the load values ​​of node i at time t before and after the response, respectively; Branch flow constraints: X l,i,t =B l,i (i b,l,t -θ e,l,t ) (29) In the formula: B l,i The susceptance of branch l connected to node i; θ b,l,t θ e,l,t These are the phase angles of the first and last nodes of branch l at time t, respectively. The upper limit of transmission power for branch l connected to node i; θ i,t Let be the phase angle of node i at time t; Equation (29) represents the upper and lower limits of the voltage phase angle at node i, respectively; Equation (30) and Equation (31) represent the branch power flow equations; respectively, Equations (30) and (31) represent the branch power flow and the upper and lower limits of the node phase angle, respectively. Node power balance constraints: Where: Ω PV,i Ω W,i These are the sets of photovoltaic and wind power equipment at node i, respectively; Ω ess,i For node i, it is a collection of energy storage devices; Ω i- and Ω i+ Let i represent the power flow injected into node i and the set of power flow branches flowing out of node i, respectively. Voltage constraint: In the formula: U i,t Let be the voltage magnitude of node i at time t. and These represent the upper and lower limits of the voltage amplitude at node i, respectively, to ensure that the voltage does not exceed the limits.

6. The multi-objective source-load-storage collaborative planning method based on power grid carbon emission reduction constraints as described in claim 1, characterized in that, The multi-dimensional indicator model includes carbon emissions from electricity consumption, the anti-carbon potential load ratio, and the distance to the low-carbon target.

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