Electric power system double-layer optimization scheduling method based on charge-storage carbon flow model

Abstract

The invention discloses an electric power system double-layer optimization scheduling method based on a load-storage carbon flow model. The technical scheme of the invention is provided. The method comprises the following steps: firstly, introducing concepts of carbon flow rate and carbon flow density according to a carbon emission flow theory, and respectively establishing carbon emission flow models in load and energy storage charge-discharge operation modes; and then, a low-carbon scheduling strategy considering load-storage collaborative optimization is designed, an electricity-carbon coupling price model is established on the load side based on load node carbon potential, an electricity-carbon coupling price type demand response model is obtained through price type demand response (PDR), and meanwhile, an energy storage carbon flow model-based low-carbon scheduling strategy is established on the energy storage side. Load-storage collaborative low-carbon optimization scheduling is realized; and finally, according to the designed load-storage scheduling strategy, establishing a double-layer optimization scheduling model of upper-layer economic scheduling and lower-layer low-carbon scheduling.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a two-layer optimization dispatching method for power systems based on a load-storage carbon flow model. Technical Background

[0002] In order to reduce the output of high-energy-consuming units and increase the wind power absorption capacity, a two-layer economic low-carbon optimization dispatch method for power system based on load-storage carbon discharge model is proposed considering two types of flexible call resources, load and energy storage. Summary of the invention

[0003] The present invention provides a two-layer optimization dispatching method for an electric power system based on a load-storage carbon flow model. This dispatching method reduces wind abandonment and the output of high-energy-consuming units while ensuring economy, thereby effectively reducing carbon emissions of the entire system.

[0004] The present invention adopts a technical solution: a two-layer optimization dispatching method of a power system based on a load-storage carbon flow model, which comprises the following steps:

[0005] (1) Based on the carbon emission flow theory, the concepts of carbon flow rate and carbon flow density are introduced to establish carbon emission flow models under load and energy storage charging and discharging operation modes respectively;

[0006] (2) On the load side, an electricity-carbon coupling price model is established based on the carbon potential of the load node, and an electricity-carbon coupling price-type demand response model is obtained through price-type demand response;

[0007] (3) Establish a low-carbon dispatch strategy based on the energy storage carbon flow model on the energy storage side to achieve load-storage coordinated low-carbon optimized dispatch;

[0008] (4) Considering the economy and low-carbon nature of the system, a two-layer optimization scheduling model is established, which includes upper-level economic scheduling and lower-level low-carbon scheduling.

[0009] The carbon emission flow theory of power system includes two basic variables: carbon flow rate and carbon flow density:

[0010] Carbon flow rate: The carbon emission corresponding to the energy flow through the network node or branch per unit time is defined as carbon flow rate R, in units of t·CO2 / h, and the calculation expression is as follows:

[0011]

[0012] Where: C is the carbon flow of the network node or branch; t is time;

[0013] Carbon flow density: The carbon emissions corresponding to unit electricity in the network are defined as carbon flow density ρ, with units of t·CO2 / (kWh). The calculation expression is as follows:

[0014]

[0015] Where: P is the active power flow in the corresponding network;

[0016] According to P, which represents the active power flow of network nodes and transmission lines, the carbon flow density can be divided into node carbon flow density and branch carbon flow density, where the node carbon flow density includes the generator node carbon flow density and the load node carbon flow density; the generator node carbon flow density is also called the generator carbon emission intensity, which represents the carbon emission corresponding to the unit power production unit, and is expressed by E G The load node carbon flow density is also called the load node carbon potential, which describes the carbon emissions corresponding to the unit electricity consumed by the load, and is expressed as e n express;

[0017] The carbon emission flow model of load and energy storage is established based on the carbon emission flow theory as follows:

[0018] According to the carbon flow theory, a load carbon emission flow model is established:

[0019] The carbon potential of a load node is equivalent to the sum of the carbon flow density of all branches flowing into the node and the weighted average of the carbon emission intensity of the generators connected to the node with respect to the power flow. Therefore, the carbon emission flow model of the load node is:

[0020]

[0021] Where: e j is the carbon potential of load node j; P i-j , i-j are the power and carbon flow density of the branches connected to node i and node j respectively; P Gj 、E Gj are the power generation and carbon emission intensity of the generator connected to node j, respectively; N represents the number of network nodes;

[0022] From equations (1)-(3), it can be seen that the carbon potential of the load node is essentially the weighted average of the carbon emission intensity of all generators that flow power to the load with respect to the output power. Therefore, the higher the carbon emission intensity of the load node, the higher the output proportion of the high carbon emission intensity units to the load, and the greater the carbon emission value of the unit power consumed per unit time of the load.

[0023] Energy storage equipment carbon emission flow model:

[0024] According to the carbon flow tracking model, the charging and discharging process of energy storage equipment is the accumulation and release process of carbon flow. When the energy storage is in charging mode, the carbon flow is accumulated and stored in the battery along with the electric energy. When the energy storage is in discharging mode, the carbon flow is released into the system along with the electric energy. In summary, according to the charging and discharging mode of energy storage, its carbon emission flow model is as follows:

[0025] (1) Charging mode: When the energy storage is charging, it absorbs electric energy from the grid, which can be equivalent to the load. The carbon flow inside it depends on the accumulation of electricity over time. At this time, the carbon potential of the energy storage charging is equivalent to the carbon potential of the node where the component is located. According to the load carbon emission flow model formula of formula (3), the carbon emission flow model of the energy storage charging is cha for:

[0026] e cha =e j (4)

[0027] (2) Discharge mode: When the energy storage is discharged, it releases electric energy to the grid. It can be equivalent to a generator set. The accumulated carbon flow inside it begins to be released along with the amount of electricity. When the energy storage element starts charging from time T0 and discharges at time T, the accumulated carbon flow and electricity inside the element are:

[0028]

[0029]

[0030] Where: Q(T) and Q(T0) are the accumulated remaining electricity in the energy storage at time T and T0 respectively; C(T) and C(T0) are the accumulated remaining carbon flow in the energy storage at time T and T0 respectively; are the carbon flow and electricity accumulated during the charging process respectively;

[0031] According to formula (2), the carbon potential during energy storage discharge is equivalent to the ratio of the accumulated carbon flow and the amount of electricity during the charging process. Considering the energy conversion rate during charging and discharging, when the energy storage device is discharged at time e, its carbon emission flow model is cha for:

[0032]

[0033] Where: η is the energy storage charge and discharge conversion efficiency;

[0034] Discretizing equation (7), we can get

[0035]

[0036] Where: S and Δt are the total number of discrete time periods and the length of the unit time period from T0 to T respectively; P i 、e i are the charging power and carbon potential in the i-th period respectively;

[0037] (3) Idle mode: When the energy storage device is operating in idle mode, the device is in a quasi-off-grid state and does not participate in low-carbon scheduling.

[0038] The higher the carbon potential of each load node in the power system at the same time, the greater the carbon emission value per unit of electricity consumed. Based on this, by optimizing the scheduling of the load side, if the load can be encouraged to use more electricity when the node carbon potential is low and less electricity when the node carbon potential is high, the carbon emission value per unit of electricity consumed in one load cycle will be reduced after scheduling, thus achieving effective energy conservation and emission reduction.

[0039] According to the principles of consumer psychology, price-based demand response guides residential and commercial users to use electricity in a reasonable manner by changing electricity prices. my country has achieved remarkable results in guiding users to participate in peak shaving and valley filling by establishing a demand response model for peak-valley-average time-of-use electricity prices. Referring to the demand response model for peak-valley-average time-of-use electricity prices, this article analyzes from a low-carbon perspective, divides low, medium, and high carbon responsibility intervals according to carbon potential, and establishes a time-of-use electricity-carbon coupling price model within different carbon responsibility intervals. The specific steps are as follows:

[0040] Step 1: According to the carbon potential solution formula of load nodes in formula (3), calculate the carbon potential of each load node at the current time t, and divide it into low, medium and high carbon responsibility intervals. The specific division method is:

[0041]

[0042] Where: e min 、e max is the carbon potential of the minimum and maximum load nodes at time t; e ave is the average carbon potential of all load nodes at time t; e j is the carbon potential of load node j;

[0043] Step 2: Set low, medium and high time-of-use carbon prices based on peak, valley and average time-of-use electricity prices. The specific carbon prices are:

[0044]

[0045] Where: λ is the load carbon price difference between high and low responsibility intervals; q j,c is the carbon value of load j;

[0046] Considering the user electricity price, the electricity-carbon coupling price is established as:

[0047]

[0048] Where: q0 is the basic electricity price, which is 0.56 yuan / (kW·h); q j is the electricity-carbon coupling price of load j;

[0049] Step 3: Based on the electricity-carbon coupling price model and the load power-to-electricity price formula in PDR, calculate the load response:

[0050] The PDR formula is:

[0051]

[0052] Where: Δp and Δq are the changes in load power and electricity price respectively; p and q are the initial load power and electricity value respectively; E is the demand elasticity matrix;

[0053] Q j,c Substituting q0 and q1 into Δq and q2, we can obtain:

[0054]

[0055] Step 4: To describe the change in carbon emission values ​​before and after low-carbon load scheduling, it is assumed that the carbon potential of the load node remains unchanged before and after the response. According to equations (1)-(3) and (9)-(13), the carbon emission value of the load is calculated. By comparing the carbon emission values ​​before and after low-carbon load scheduling, the evaluation and calculation of carbon emission reduction is completed.

[0056] The carbon potential of power system nodes is affected by the carbon emission intensity of generators and the output of clean energy, and the overall carbon potential of the system may be too high or too low. At this time, the regulation ability of the electricity-carbon coupling price model on the load side is limited. Based on this, similar to the "peak shaving and valley filling" scheduling strategy during energy storage peak regulation, the energy storage equipment discharges when the carbon emission intensity of the unit is high, and charges when the overall carbon potential of the unit is low. The carbon potential difference during charging and discharging is used to reduce carbon emissions, thereby achieving low-carbon scheduling in the time dimension, which can effectively alleviate the pressure on the load side.

[0057] According to the carbon emission flow model of energy storage equipment, a low-carbon optimization scheduling strategy for energy storage is established. cha (t) is the carbon potential when the energy storage is discharged at time t, e i (t) is the node carbon potential at node i where the energy storage is located during period t;

[0058] when e cha (t)>e i (t), the energy storage discharge carbon potential is higher than the charging carbon potential, that is, the carbon emissions released during energy storage discharge per unit of electricity are greater than the accumulated carbon emissions during charging. At this time, the energy storage is set to generate a negative carbon potential difference during charging, thereby reducing carbon emissions. Therefore, the energy storage device is in charging mode; when e cha (t)<e i (t), the energy storage discharge carbon potential is lower than the charging carbon potential, that is, the carbon emissions released during energy storage discharge per unit of electricity are less than the accumulated carbon emissions during charging. At this time, the energy storage is set to produce a negative carbon potential difference during discharge, thereby reducing carbon emissions. Therefore, the energy storage device is in discharge mode; when e cha (t) = e i At (t), the carbon emissions accumulated and released by the energy storage charging / discharging state are the same, and no low-carbon benefits will be generated, so the energy storage device will be placed in an off-grid state.

[0059] The upper economic model takes the minimum power generation cost of the power system as the objective function, including the power generation cost of thermal power and wind turbines, the start-up and shutdown cost of thermal power units, and the penalty cost of wind power abandonment, as shown below:

[0060] min F=f g +f w +f k +f q (14)

[0061] Where: f g is the coal consumption cost of thermal power units; f w is the power generation cost of wind turbine; f k is the start-up and shutdown cost of thermal power units; f q is the penalty cost for wind curtailment, and its specific expression is:

[0062]

[0063]

[0064]

[0065]

[0066] Where: a h , b h 、c h are the coal consumption cost coefficients of the units, in units of $ / MW 2 , $ / MW, $; b k , b q,k are wind power generation cost coefficient and wind abandonment cost coefficient, in $ / MW; P G,h,t , P W,k,t , P Wq,k,t are the thermal power output power, wind power output power and wind power abandonment power in period t, in MW; U h,t is the start and stop variable of thermal power unit h in period t, with values ​​of 0 and 1; C U,h is the start-up and shutdown cost of unit h, in $, H and K are the number of thermal power and wind power units respectively;

[0067] Constraints

[0068] 1) Thermal power unit capacity and ramp constraints

[0069] P G,min ≤P G,t ≤P G,max (19)

[0070] Where: P G,min , P G,maxRespectively represent the maximum and minimum active output of thermal power units;

[0071] R down ≤P G,t -P G,t-1 ≤R up (t≥2) (20)

[0072] Where: R up , R down Respectively represent the maximum and minimum values ​​of climbing power;

[0073] 2) Wind turbine capacity constraints

[0074] P W,min ≤P W,t ≤P W,max (twenty one)

[0075] Where: P W,max , P W,min Respectively represent the maximum and minimum active output of the wind turbine;

[0076] 3) Line power flow upper and lower limit constraints

[0077] P Line,min ≤P Line,t ≤P Line,max (twenty two)

[0078] Where: P Line,t Indicates the active power of the line Line; P Line,max , P Line,min Respectively represent the maximum and minimum values ​​of line transmission power;

[0079] 4) Power balance constraints

[0080]

[0081] Where: P L,t Indicates load power;

[0082] The lower-level low-carbon model takes the minimum sum of energy storage operation cost and carbon trading cost as its objective function, as shown below:

[0083]

[0084] Where: f c The operating cost of energy storage; is the carbon trading cost, and its specific expression is:

[0085]

[0086]

[0087] Where: b cha and b dis are the energy storage charging and discharging cost coefficients respectively; P cha,t and P dis,t are the charging and discharging power of energy storage in the tth period respectively; X cha,t , X dis,t It is a 0-1 variable, indicating whether the energy storage is in the charging or discharging state, for example, X cha,t When it is 1, it means it is in charging state, and when it is 0, it means it is not in charging state; i,t 、e cha,t 、e dis,t are the carbon potential of the load node, the carbon potential of energy storage charging and discharging; ΔP L is the power change before and after the load low carbon response;

[0088] Constraints

[0089] 1) Upper and lower limit constraints of energy storage charge state:

[0090] S bat,min ≤S bat,t ≤S bat,max (27)

[0091] Where: S bat,t S is the energy storage charge state; bat,max , S bat,min are the maximum and minimum values ​​of the state of charge, respectively;

[0092] 2) Energy storage charging and discharging state constraints

[0093] X cha,t +X cha,t =1 (28)

[0094] 3) Energy storage charging and discharging power constraints

[0095]

[0096] Where: E bat is the rated capacity of the battery;

[0097] The specific steps for solving the two-layer economic low-carbon dispatch model are as follows:

[0098] 1) Input the basic parameter data of the example, solve the upper-level economic dispatch model, and output the dispatch results, including unit output, etc.;

[0099] 2) Solve the system power flow based on the dispatch results, and use the carbon emission flow model to solve the carbon potential of the load node and the carbon potential of energy storage charging and discharging;

[0100] 3) Establish a low-carbon dispatch strategy for load and energy storage based on the load-storage carbon emission flow model, substitute it into the lower-level low-carbon dispatch model for solution, and output the dispatch results, including load, energy storage power, etc.;

[0101] 4) Substitute the adjusted load and energy storage data back into the upper model and iterate repeatedly until the dispatching result is optimal;

[0102] 5) Output scheduling results, including the system’s total carbon emission cost and total carbon emissions, load and energy storage output, etc.

[0103] The technical solution provided by the present invention has the following beneficial effects: considering the two types of flexible call resources, load and energy storage, a two-layer economic low-carbon optimization scheduling method for the power system based on the load-storage carbon discharge model is proposed. First, based on the carbon emission flow theory of the power system, the carbon emission flow models of the load and energy storage equipment are established respectively; then, a low-carbon scheduling strategy considering load-storage coordinated optimization is designed, and an electricity-carbon coupling price demand response model based on the carbon potential of the load node is established on the load side. At the same time, in view of the limitations of the load-side carbon reduction regulation, a low-carbon scheduling strategy based on the carbon flow model is established on the energy storage side to realize the load-storage coordinated low-carbon scheduling strategy; finally, considering the economy and low carbon, a two-layer optimization scheduling model including the upper economic scheduling and the lower low-carbon scheduling is established. Effectively reduce the carbon emissions of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] The present invention will be further described below in conjunction with the accompanying drawings:

[0105] Figure 1 It is the flow chart of system control strategy;

[0106] Figure 2 It is the flow chart of the two-tier economic low-carbon dispatch model;

[0107] Figure 3 This is the economic dispatch result of scenario 1;

[0108] Figure 4 This is the economic dispatch result of scenario 2;

[0109] Figure 5 This is the economic dispatch result of scenario three; Specific implementation plan

[0110] In order to better understand the purpose, technical scheme and technical effect of the present invention, the present invention is further explained below in conjunction with the accompanying drawings.

[0111] The present invention proposes a two-stage optimization scheduling method for a virtual power plant considering impact loads, and its implementation process includes the following detailed steps.

[0112] Step 1: Based on the carbon emission flow theory, the concepts of carbon flow rate and carbon flow density are introduced to establish carbon emission flow models under load and energy storage charging and discharging operation modes respectively;

[0113] Load carbon emission flow model:

[0114] The carbon potential of a load node is equivalent to the sum of the carbon flow density of all branches flowing into the node and the weighted average of the carbon emission intensity of the generators connected to the node with respect to the power flow. Therefore, the carbon emission flow model of the load node is:

[0115]

[0116] Where: e j is the carbon potential of load node j; P i-j , i-j are the power and carbon flow density of the branches connected to node i and node j respectively; P Gj 、E Gj are the power generation and carbon emission intensity of the generator connected to node j, respectively; N represents the number of network nodes;

[0117] From equations (1)-(3), it can be seen that the carbon potential of the load node is essentially the weighted average of the carbon emission intensity of all generators that flow power to the load with respect to the output power. Therefore, the higher the carbon emission intensity of the load node, the higher the output proportion of the high carbon emission intensity units to the load, and the greater the carbon emission value of the unit power consumed per unit time of the load.

[0118] Energy storage equipment carbon emission flow model:

[0119] According to the carbon flow tracking model, the charging and discharging process of energy storage equipment is the accumulation and release process of carbon flow. When the energy storage is in charging mode, the carbon flow is accumulated and stored in the battery along with the electric energy. When the energy storage is in discharging mode, the carbon flow is released into the system along with the electric energy. In summary, according to the charging and discharging mode of energy storage, its carbon emission flow model is as follows:

[0120] (1) Charging mode: When the energy storage is charging, it absorbs electric energy from the grid, which can be equivalent to the load. The carbon flow inside it depends on the accumulation of electricity over time. At this time, the carbon potential of the energy storage charging is equivalent to the carbon potential of the node where the component is located. According to the load carbon emission flow model formula of formula (3), the carbon emission flow model of the energy storage charging is cha for:

[0121] e cha =e j (31)

[0122] (2) Discharge mode: When the energy storage is discharged, it releases electric energy to the grid. It can be equivalent to a generator set. The accumulated carbon flow inside it begins to be released along with the amount of electricity. When the energy storage element starts charging from time T0 and discharges at time T, the accumulated carbon flow and electricity inside the element are:

[0123]

[0124]

[0125] Where: Q(T) and Q(T0) are the accumulated remaining electricity in the energy storage at time T and T0 respectively; C(T) and C(T0) are the accumulated remaining carbon flow in the energy storage at time T and T0 respectively; are the carbon flow and electricity accumulated during the charging process respectively;

[0126] According to formula (2), the carbon potential during energy storage discharge is equivalent to the ratio of the accumulated carbon flow and the amount of electricity during the charging process. Considering the energy conversion rate during charging and discharging, when the energy storage device is discharged at time e, its carbon emission flow model is cha for:

[0127]

[0128] Where: η is the energy storage charge and discharge conversion efficiency;

[0129] Discretizing equation (7), we can get

[0130]

[0131] Where: S and Δt are the total number of discrete time periods and the length of the unit time period from T0 to T respectively; P i 、e i are the charging power and carbon potential in the i-th period respectively;

[0132] (3) Idle mode: When the energy storage device is operating in idle mode, the device is in a quasi-off-grid state and does not participate in low-carbon scheduling.

[0133] Step 2: Establish an electricity-carbon coupling price model based on the carbon potential of the load node on the load side, and obtain an electricity-carbon coupling price-type demand response model through price-type demand response;

[0134] According to the principles of consumer psychology, price-based demand response guides residential users and industrial and commercial users to use electricity reasonably by changing electricity prices. At present, my country has achieved remarkable results in guiding users to participate in peak shaving and valley filling by establishing a demand response model of peak-valley time-sharing electricity prices. Referring to the demand response model of peak-valley time-sharing electricity prices, this paper analyzes from a low-carbon perspective, divides low, medium and high carbon responsibility intervals according to carbon potential, and establishes a time-sharing electricity-carbon coupling price model within different carbon responsibility intervals. The specific steps are as follows:

[0135] Step 1: According to the carbon potential solution formula of load nodes in formula (3), calculate the carbon potential of each load node at the current time t, and divide it into low, medium and high carbon responsibility intervals. The specific division method is:

[0136]

[0137] Where: e min 、e max is the carbon potential of the minimum and maximum load nodes at time t; e ave is the average carbon potential of all load nodes at time t; e j is the carbon potential of load node j;

[0138] Step 2: Set low, medium and high time-of-use carbon prices based on peak, valley and average time-of-use electricity prices. The specific carbon prices are:

[0139]

[0140] Where: λ is the load carbon price difference between high and low responsibility intervals; q j,c is the carbon value of load j;

[0141] Considering the user electricity price, the electricity-carbon coupling price is established as:

[0142]

[0143] Where: q0 is the basic electricity price, which is 0.56 yuan / (kW·h); q j is the electricity-carbon coupling price of load j;

[0144] Step 3: Based on the electricity-carbon coupling price model and the load power-to-electricity price formula in PDR, calculate the load response:

[0145] The PDR formula is:

[0146]

[0147] Where: Δp and Δq are the changes in load power and electricity price respectively; p and q are the initial load power and electricity value respectively; E is the demand elasticity matrix;

[0148] Q j,c Substituting q0 and q1 into Δq and q2, we can obtain:

[0149]

[0150] Step 4: To describe the change in carbon emission values ​​before and after low-carbon load scheduling, it is assumed that the carbon potential of the load node remains unchanged before and after the response. According to equations (1)-(3) and (9)-(13), the carbon emission value of the load is calculated. By comparing the carbon emission values ​​before and after low-carbon load scheduling, the evaluation and calculation of carbon emission reduction is completed.

[0151] Step 3: Establish a low-carbon dispatch strategy based on the energy storage carbon flow model on the energy storage side to achieve load-storage coordinated low-carbon optimized dispatch;

[0152] According to the carbon emission flow model of energy storage equipment, a low-carbon optimization scheduling strategy for energy storage is established. cha (t) is the carbon potential when the energy storage is discharged at time t, e i (t) is the node carbon potential at node i where the energy storage is located during period t;

[0153] when e cha (t)>e i (t), the energy storage discharge carbon potential is higher than the charging carbon potential, that is, the carbon emissions released during energy storage discharge per unit of electricity are greater than the accumulated carbon emissions during charging. At this time, the energy storage is set to generate a negative carbon potential difference during charging, thereby reducing carbon emissions. Therefore, the energy storage device is in charging mode; when e cha (t)<e i (t), the energy storage discharge carbon potential is lower than the charging carbon potential, that is, the carbon emissions released during energy storage discharge per unit of electricity are less than the accumulated carbon emissions during charging. At this time, the energy storage is set to produce a negative carbon potential difference during discharge, thereby reducing carbon emissions. Therefore, the energy storage device is in discharge mode; when e cha (t) = e i At (t), the carbon emissions accumulated and released by the energy storage charging / discharging state are the same, and no low-carbon benefits will be generated, so the energy storage device will be placed in an off-grid state.

[0154] Step 4 considers economy and low carbon, and establishes a two-layer optimization scheduling model including upper-layer economic scheduling and lower-layer low-carbon scheduling;

[0155] The upper-level economic dispatch objective function is:

[0156] min F=f g +f w +f k +f q (41)

[0157] Where: f g is the coal consumption cost of thermal power units; f w is the power generation cost of wind turbine; f k is the start-up and shutdown cost of thermal power units; f q is the penalty cost for wind curtailment, and its specific expression is:

[0158]

[0159]

[0160]

[0161]

[0162] Where: a h , b h 、c h are the coal consumption cost coefficients of the units, in units of $ / MW 2 , $ / MW, $; b k , b q,k are wind power generation cost coefficient and wind abandonment cost coefficient, in $ / MW; P G,h,t , P W,k,t , P Wq,k,t are the thermal power output power, wind power output power and wind power abandonment power in period t, in MW; U h,t is the start and stop variable of thermal power unit h in period t, with values ​​of 0 and 1; C U,h is the start-up and shutdown cost of unit h, in $, H and K are the number of thermal power and wind power units respectively;

[0163] Constraints

[0164] 1) Thermal power unit capacity and ramp constraints

[0165] P G,min ≤P G,t ≤P G,max (46)

[0166] Where: P G,min , P G,max Respectively represent the maximum and minimum active output of thermal power units;

[0167] R down ≤P G,t -P G,t-1 ≤R up (t≥2) (47)

[0168] Where: R up , R down Respectively represent the maximum and minimum values ​​of climbing power;

[0169] 2) Wind turbine capacity constraints

[0170] P W,min ≤P W,t ≤P W,max (48)

[0171] Where: P W,max, P W,min Respectively represent the maximum and minimum active output of the wind turbine;

[0172] 3) Line power flow upper and lower limit constraints

[0173] P Line,min ≤P Line,t ≤P Line,max (49)

[0174] Where: P Line,t Indicates the active power of the line Line; P Line,max , P Line,min Respectively represent the maximum and minimum values ​​of line transmission power;

[0175] 4) Power balance constraints

[0176]

[0177] Where: P L,t Indicates load power;

[0178] The lower-level low-carbon scheduling objective function is:

[0179]

[0180] Where: f c The operating cost of energy storage; is the carbon trading cost, and its specific expression is:

[0181]

[0182]

[0183] Where: b cha and b dis are the energy storage charging and discharging cost coefficients respectively; P cha,t and P dis,t are the charging and discharging power of energy storage in the tth period respectively; X cha,t , X dis,t It is a 0-1 variable, indicating whether the energy storage is in the charging or discharging state, for example, X cha,t When it is 1, it means it is in charging state, and when it is 0, it means it is not in charging state; i,t 、e cha,t 、e dis,t are the carbon potential of the load node, the carbon potential of energy storage charging and discharging; ΔP L is the power change before and after the load low carbon response;

[0184] Constraints

[0185] 1) Upper and lower limit constraints of energy storage charge state:

[0186] S bat,min ≤S bat,t ≤S bat,max (54)

[0187] Where: S bat,t S is the energy storage charge state; bat,max , S bat,min are the maximum and minimum values ​​of the state of charge, respectively;

[0188] 2) Energy storage charging and discharging state constraints

[0189] X cha,t +X cha,t =1 (55)

[0190] 3) Energy storage charging and discharging power constraints

[0191]

[0192] Where: E bat is the rated capacity of the battery;

[0193] The flow chart of the two-tier economic low-carbon dispatch model is as follows: Figure 2

[0194] As shown, the specific steps for solving are as follows:

[0195] 1) Input the basic parameter data of the example, solve the upper-level economic dispatch model, and output the dispatch results, including unit output, etc.;

[0196] 2) Solve the system power flow based on the dispatch results, and use the carbon emission flow model to solve the carbon potential of the load node and the carbon potential of energy storage charging and discharging;

[0197] 3) Establish a low-carbon dispatch strategy for load and energy storage based on the load-storage carbon emission flow model, substitute it into the lower-level low-carbon dispatch model for solution, and output the dispatch results, including load, energy storage power, etc.;

[0198] 4) Substitute the adjusted load and energy storage data back into the upper model and iterate repeatedly until the dispatching result is optimal;

[0199] 5) Output scheduling results, including the system’s total carbon emission cost and total carbon emissions, load and energy storage output, etc.

[0200] In order to further understand the present invention, an IEEE-14 node system is taken as an example.

[0201] This paper uses the improved IEEE-14 node system for example analysis, in which 4 thermal power units are connected to 1, 2, 3, and 6 nodes respectively, 1 wind turbine unit is connected to 8 nodes, energy storage equipment is connected to 12 nodes, and loads are connected to {2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14}, a total of 11 nodes. A cycle is set to 24h and a step length is 1h. The CPLEX simulation platform is used for solution. In order to verify the effectiveness of the proposed optimization scheduling model, this paper sets 3 scenarios for comparison:

[0202] 1) Economic dispatch of demand response and energy storage is not considered.

[0203] 2) Low-carbon economic dispatch that takes demand response into account but does not take energy storage into account.

[0204] 3) Low-carbon economic dispatch considering demand response and energy storage.

[0205] The scheduling results of the three scenarios are analyzed, as shown in Table 1.

[0206] Table 1 Scheduling costs for each scenario

[0207]

[0208] As shown in Table 1, compared with scenario 1, after considering demand response, scenario 2 reduces carbon emissions by 8.41%, generating carbon emission benefits of $3,984, and ultimately reducing total costs by 8.03%, thus verifying the positive role of demand response in low-carbon economic scheduling; scenario 3 considers the role of energy storage in "peak shaving and valley filling" on the system's carbon potential on the basis of scenario 2, further reducing system carbon emissions by 4.98%, and increasing carbon emission benefits by 43.47%, thus verifying that low-carbon economic scheduling can be further achieved after the cooperation of energy storage and demand response.

[0209] Figures 3 to 5 These are the unit scheduling situations for scenarios one to three.

[0210] Depend on Figure 3 It can be seen that the objective function of the scenario below is the lowest economic cost. At this time, the amount of wind abandonment is large and the output of thermal power units is high, which is not conducive to low-carbon scheduling.

[0211] Compared with scenario 1, scenario 2 embodies low-carbon benefits in many aspects. First, after the introduction of demand response, the load actively consumes zero-carbon emission wind power, which greatly reduces the wind curtailment rate; second, as can be seen from Appendix B, the carbon emission coefficients of the four thermal power units in the system are different, among which the carbon emission coefficient of unit 4 is the lowest. Considering that the smaller the carbon emission coefficient of the unit, the lower the carbon potential flowing to the load node, and the lower the electricity price at the load node, the greater the load response, which increases the unit output. Figure 4 It can be seen that compared with Scenario 1, the output of Unit 4 in Scenario 2 has increased significantly.

[0212] Scenario 3 adds energy storage based on Scenario 2. Considering that the energy storage charging and discharging process is the process of storing / releasing electrical energy to the system, Figure 5 It can be seen that after adding energy storage, the unit and load output are no longer consistent. And when the output of unit 4 is high, that is, during the periods of 8:00, 16:00-17:00, and 22:00-24:00, the unit output is greater than the load power, that is, the energy storage is in a charging state, which is consistent with the scheduling strategy of discharging energy storage at high carbon potential and charging at low carbon potential. In addition, compared with scenario 2, it can be seen that energy storage can also further absorb wind power and reduce wind curtailment.

Claims

1. A two-layer optimization dispatching method for power system based on load-storage carbon flow model, characterized in that: The following steps are involved: (1) Based on the carbon emission flow theory, the concepts of carbon flow rate and carbon flow density are introduced to establish carbon emission flow models under load and energy storage charging and discharging operation modes respectively; (2) On the load side, an electricity-carbon coupling price model is established based on the carbon potential of the load node, and an electricity-carbon coupling price-type demand response model is obtained through price-type demand response; (3) Establish a low-carbon dispatch strategy based on the energy storage carbon flow model on the energy storage side to achieve load-storage coordinated low-carbon optimized dispatch; (4) Considering both economy and low carbon, a two-layer optimization scheduling model is established, which includes upper-layer economic scheduling and lower-layer low-carbon scheduling.

2. According to claim 1, a two-layer optimization scheduling method for power system based on load-storage carbon flow model is characterized in that: The step (1) establishes a carbon emission flow model for load and energy storage charging and discharging: According to the carbon emission flow theory, a load carbon emission flow model is established: The carbon potential of a load node is equivalent to the sum of the carbon flow density of all branches flowing into the node and the weighted average of the carbon emission intensity of the generators connected to the node with respect to the power flow. Therefore, the carbon emission flow model of the load node is: Where: e j is the carbon potential of load node j; P i-j , i-j are the power and carbon flow density of the branches connected to node i and node j respectively; P Gj 、E Gj are the power generation and carbon emission intensity of the generator connected to node j, respectively; N represents the number of network nodes; According to the energy storage charging and discharging mode, the carbon emission flow model is as follows: (1) Charging mode: When the energy storage is charging, it absorbs electric energy from the grid, which can be equivalent to the load. The carbon flow inside it depends on the accumulation of electricity over time. At this time, the carbon potential of the energy storage charging is equivalent to the carbon potential of the node where the component is located. The carbon emission flow model when the energy storage is charging is cha for: And cha =and j (2) Where: e j is the carbon potential of node j; (2) Discharge mode: When the energy storage is discharged, it releases electric energy to the grid. It can be equivalent to a generator set. The accumulated carbon flow inside it begins to be released along with the electricity. When the energy storage element starts charging from time T0 and discharges at time T, its carbon emission flow model is cha for: Where: η is the energy storage charge-discharge conversion efficiency; Q(T) and Q(T0) are the accumulated remaining electricity in the energy storage at time T and T0, respectively; C(T) and C(T0) are the accumulated remaining carbon flow in the energy storage at time T and T0, respectively; are the carbon flow and electricity accumulated during the charging process respectively; Discretizing equation (3), we can get Where: S and Δt are the total number of discrete time periods and the length of the unit time period from T0 to T respectively; P i 、e i are the charging power and carbon potential in the i-th period, respectively; Q(T0) and C(T0) are the accumulated remaining power and carbon flow in the energy storage at time T0, respectively; (3) Idle mode: When the energy storage device is operating in idle mode, the device is in a quasi-off-grid state and does not participate in low-carbon scheduling.

3. The method for double-layer optimization scheduling of power system based on load-storage carbon flow model according to claim 1 is characterized in that: The step (2) establishes an electricity-carbon coupling price model based on the carbon potential of the load node on the load side, and obtains an electricity-carbon coupling price-type demand response model through price-type demand response: Step 1: According to the carbon potential solution formula of load nodes in formula (1), calculate the carbon potential of each load node at the current time t, and divide it into low, medium and high carbon responsibility intervals. The specific division method is: Where: e min 、e max is the carbon potential of the minimum and maximum load nodes at time t; e ave is the average carbon potential of all load nodes at time t; e j is the carbon potential of load node j; Step 2: Set low, medium and high time-of-use carbon prices based on peak, valley and average time-of-use electricity prices, consider user electricity prices, and establish an electricity-carbon coupling price, which is: Where: q0 is the basic electricity price, which is 0.56 yuan / (kW·h); λ is the load carbon price difference between the high and low responsibility intervals; q j is the electricity-carbon coupling price of load j; e j is the carbon potential of load node j; Step 3: Based on the electricity-carbon coupling price model and the load power-to-electricity price formula in price-based demand response (PDR), calculate the load response: The PDR formula is: Where: Δp and Δq are the changes in load power and electricity price respectively; p and q are the initial load power and electricity value respectively; E is the demand elasticity matrix; Q j,c Substituting q0 and q1 into Δq and q2, we can obtain: Where: Δp and Δq are the changes in load power and electricity price respectively; p and q are the initial load power and electricity value respectively; E is the demand elasticity matrix; Step 4: To describe the change in carbon emission values ​​before and after low-carbon load scheduling, it is assumed that the carbon potential of the load node remains unchanged before and after the response. According to equations (1) and (5)-(8), the carbon emission value of the load is calculated. By comparing the carbon emission values ​​before and after low-carbon load scheduling, the evaluation and calculation of carbon emission reduction is completed.

4. The method for double-layer optimization scheduling of power system based on load-storage carbon flow model according to claim 1 is characterized in that: The step (3) establishes a low-carbon dispatching strategy based on the energy storage carbon flow model on the energy storage side to achieve load-storage coordinated low-carbon optimized dispatching: According to the carbon emission flow model of energy storage equipment, a low-carbon optimization scheduling strategy for energy storage is established. cha (t) is the carbon potential when the energy storage is discharged at time t, e i (t) is the node carbon potential at node i where the energy storage is located during period t; the specific scheduling strategy is as follows: When e cha (t)>e i At (t), the energy storage discharge carbon potential is higher than the charging carbon potential. At this time, the energy storage is set to generate a negative carbon potential difference when charging, and the energy storage device is in charging mode; When e cha (t)<e i At (t), the energy storage discharge carbon potential is lower than the charging carbon potential. At this time, the energy storage is set to produce a negative carbon potential difference when discharging, and the energy storage device is in the discharge mode; When e cha (t) = e i At (t), the carbon emissions accumulated and released by the energy storage charging / discharging state are the same, and the energy storage device will be placed in an off-grid state.

5. The method for double-layer optimization dispatch of power system based on load-storage carbon flow model according to claim 1 is characterized in that: The step (4) considers economy and low carbon, and establishes a two-layer optimization scheduling model including upper-layer economic scheduling and lower-layer low carbon scheduling; The upper-level economic dispatch objective function is: Where: f g is the coal consumption cost of thermal power units; f w is the power generation cost of wind turbine; f k is the start-up and shutdown cost of thermal power units; f q Penalty cost for wind curtailment; a h 、b h 、c h are the coal consumption cost coefficients of the units, in units of $ / MW 2 , $ / MW, $; b k 、b q,k are wind power generation cost coefficient and wind abandonment cost coefficient, in $ / MW; P G,h,t , P W,k,t , P Wq,k,t are the thermal power output power, wind power output power and wind power abandonment power in period t, in MW; U h,t is the start and stop variable of thermal power unit h in period t, with values ​​of 0 and 1; C U,h is the start-up and shutdown cost of unit h, in $, H and K are the number of thermal power and wind power units respectively; The lower-level low-carbon scheduling objective function is: Where: f c is the energy storage operation cost; f co2 is the carbon trading cost; b cha and b dis are the energy storage charging and discharging cost coefficients respectively; P cha,t and P dis,t are the charging and discharging power of energy storage in the tth period respectively; X cha,t , X dis,t It is a 0-1 variable, indicating whether the energy storage is in the charging or discharging state, for example, X cha,t When it is 1, it means it is in charging state, and when it is 0, it means it is not in charging state; i,t 、e cha,t 、e dis,t are the carbon potential of the load node, the carbon potential of energy storage charging and discharging; ΔP L is the power change before and after the load low carbon response; Energy storage element constraints: Where: S bat,t S is the energy storage charge state; bat,max , S bat,min are the maximum and minimum values ​​of the state of charge respectively; X cha,t It is a 0-1 variable, indicating whether the energy storage is in a charging state; E bat is the rated capacity of the battery; X cha,t , X dis,t It is a 0-1 variable, indicating whether the energy storage is in the charging or discharging state; The specific steps for solving the two-layer economic low-carbon dispatch model are as follows: 1) Input the basic parameter data of the example, solve the upper-level economic dispatch model, and output the dispatch results, including unit output, etc.; 2) Solve the system power flow based on the dispatch results, and use the carbon emission flow model to solve the carbon potential of the load node and the carbon potential of energy storage charging and discharging; 3) Establish a low-carbon dispatch strategy for load and energy storage based on the load-storage carbon emission flow model, substitute it into the lower-level low-carbon dispatch model for solution, and output the dispatch results, including load, energy storage power, etc.; 4) Substitute the adjusted load and energy storage data back into the upper model and iterate repeatedly until the dispatching result is optimal; 5) Output scheduling results, including the system’s total carbon emission cost and total carbon emissions, load and energy storage output, etc.

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