CN115642620A - Double-layer optimization method for energy storage participation low-carbon flexible peak regulation - Google Patents
Double-layer optimization method for energy storage participation low-carbon flexible peak regulation Download PDFInfo
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Abstract
A double-layer optimization method for energy storage participation low-carbon flexible peak regulation is disclosed, and S1, a thermal power generating unit carbon emission measurement model is improved. S2, providing a system risk cost function for energy storage and flexible peak regulation; s3, constructing a double-layer optimization model for energy storage participation low-carbon flexible peak regulation: and the upper layer model determines the energy storage charging and discharging power at each moment by taking the system carbon emission, the load peak-valley difference rate and the energy storage operation cost as the lowest targets. The lower layer model takes the minimum of load shedding risk cost, wind abandoning risk cost and thermal power unit regulation risk cost as a target, and the energy storage charging and discharging power obtained by the upper layer as constraint to determine the energy storage standby capacity at each moment; and S4, optimizing control. The invention considers the mutual restriction of an upper-layer energy storage power optimization model of the carbon emission amount and the load peak-valley difference rate of the system and a lower-layer energy storage spare capacity optimization model of the system operation risk, and provides reference for low-carbon and flexible operation of the system.
Description
Technical Field
The invention belongs to the field of low-carbon flexible peak regulation of a power system, and particularly relates to a double-layer optimization method for energy storage participation in low-carbon flexible peak regulation.
Background
The proportion of renewable energy sources such as wind and light in the power system is continuously increased, and the randomness, the fluctuation and the anti-peak regulation characteristic of the output greatly increase the peak regulation difficulty of the system. The peak regulation of the traditional hydroelectric generating set is limited by the influences of seasons, water inflow, reservoir regulation capacity and the like, and the peak regulation requirement is difficult to meet; if the thermal power generating unit participates in peak shaving, the coal consumption of the unit and the operation and maintenance cost of equipment can be increased when the output of the thermal power generating unit is frequently adjusted or started and stopped, and further the carbon emission is increased. Therefore, in order to achieve low carbon and flexible peak shaving, new system equipment or system operation is required.
The energy storage equipment has bidirectional response capability and is convenient to control, and an effective mode can be provided for low-carbon flexible peak regulation of the power system. The peak-valley difference of the net load and the total operation cost of the system can be effectively reduced by the mode of combining electric smelting magnesium load and energy storage and peak regulation. The optimization scheduling in the day before suitable for renewable energy and adiabatic compressed air energy storage can calibrate the energy storage linear model through a robust online operation scheme. The problem of poor wind power fluctuation stabilizing effect of the wind storage system can be solved by optimizing the operation of the wind storage system. The model associates the energy storage operation with the new energy output or the power load regulation and control to carry out combined scheduling, but the energy storage can actually be used as an independent operation main body to participate in the operation of the power system, such as the energy storage at the side of a power grid.
At present, a plurality of countries and regions build power grid side electricity storage in succession. In countries such as the United states, australia, great Britain and the like, grid side electricity storage is mainly used for participating in the frequency modulation market, and more demonstration projects are provided in China, such as energy storage power station demonstration projects of the Fujian Jinjiang to provide peak-shaving frequency modulation services for local substations; the energy storage demonstration project of the Guangdong power grid is mainly used for solving the problem of power limitation caused by power construction limitation in part of areas; the power and water in Hunan province are rich, the frequency modulation capability is good, but the load peak-valley difference is large, the energy storage power station of the Changsha adopts an application mode of two charging and two discharging every day so as to meet the peak regulation requirements of the 'noon peak' and 'late peak' of the power load in the Changsha area, and the charging and discharging time is fixed. However, the application of the grid-side electric energy storage in these areas is limited to solving the problems existing in the respective regional grids, and the grid-side electric energy storage cannot fully play the role thereof.
The technologies for independently operating the electric energy storage mostly relate to capacity configuration and economic analysis. For example, a capacity configuration two-layer collaborative optimization method for underground energy storage is proposed by analyzing the differences between the overground/underground geographical locations of the configuration energy storage. If the energy storage participates in stabilizing the load fluctuation in the non-peak regulation stage, the operating mode can improve the economical efficiency of energy storage operation and shorten the investment recovery period. By providing the benefit index and the cost index which are combined with the thermal power generating unit for energy storage and deep peak regulation and participate in the system flexibility peak regulation, the comprehensive benefit and the wind abandoning rate of the system are considered. However, few technologies attach importance to the functions of reducing wind power prediction error risks and system carbon emission when independently operating power storage energy to participate in power system peak shaving. The carbon emission measurement of the system needs to be more accurately described, and meanwhile, the contribution measurement of a certain composition to the carbon emission of the system is beneficial to the operation of the subsequent carbon market and carbon trading. When the stored energy is taken as an independent operation main body, the contribution of the stored energy to the carbon emission of the system needs to be measured.
Therefore, the stored energy is regarded as an independent operation main body, the charging and discharging power of the stored energy is counted into the carbon emission measurement of the thermal power generating unit, and the value of the carbon emission intensity of the system with reduced stored energy is related to the carbon emission intensity of the thermal power generating unit. And if certain reserve capacity is set during the operation of energy storage, the system operation risk caused by wind power uncertainty can be reduced, the reserve capacity of the system is increased under the condition of reducing certain energy storage economy, and the operation economy of the power system is improved.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a double-layer optimization method for energy storage participation low-carbon flexible peak regulation, which can effectively reduce the total cost of power system operation, simultaneously play the peak regulation role of the energy storage in the system, and simultaneously reduce the carbon emission of the system and the risk of wind power prediction errors.
The invention provides a double-layer optimization method for energy storage participation low-carbon flexible peak regulation, which is characterized by comprising the following steps of:
s1, improving a thermal power generating unit carbon emission metering model, quantifying the contribution of energy storage in a system to emission reduction, describing the carbon emission generated by the thermal power generating unit output in detail, and providing the carbon emission metering model considering energy storage charging and discharging power;
s2, providing a system risk cost function for energy storage and flexible peak regulation, wherein the system risk cost function comprises a wind power operation risk cost function and a thermal power generating unit regulation risk cost function;
s3, constructing a double-layer optimization model for energy storage participation low-carbon flexible peak regulation:
the upper layer model determines the energy storage charging and discharging power at each moment by taking the system carbon emission, the load peak-valley difference rate and the energy storage operation cost as the lowest targets;
the lower layer model takes load shedding risk cost, wind abandoning risk cost and thermal power unit regulation risk cost minimum as targets, and determines the energy storage reserve capacity at each moment by taking the energy storage charging and discharging power obtained at the upper layer as constraint. Performing day-ahead scheduling plan optimization by taking the wind-solar output prediction time sequence and the load prediction curve as basic data;
s4, optimizing control:
the model reduces the carbon emission of the system while reducing the load peak-valley difference, and measures the contribution of energy storage to emission reduction; by setting the energy storage reserve capacity, the peak shaving flexibility of the system is improved, and the operation risk of the system is reduced.
As a further improvement of the invention, the step S1 of improving the thermal power generating unit carbon emission measurement model includes;
the carbon emission intensity of the coal-fired power supply of the thermal power generating unit and the load of the unit are in a negative correlation relationship, namely the higher the load of the unit is, the lower the carbon emission intensity of the coal-fired power supply is, and the linear function relationship between the output of the thermal power generating unit and the carbon emission intensity of the coal-fired power supply is as follows:
in the formula:
the emission intensity of the carbon for coal-fired power supply of the thermal power generating unit g at the moment t is measured; k is more than or equal to 0, b is more than or equal to 0 and is a linear function parameter;
the output of the thermal power generating unit g at the moment t is obtained; p g The installed capacity of the thermal power generating unit g is obtained;
carbon emission of coal-fired power supply of thermal power generating unit g
Calculating according to the formula;
in the formula: Δ t is the time interval;
adding the system carbon emission with reduced energy storage charging and discharging power, and obtaining an improved thermal power generating unit carbon emission metering model as follows:
in the formula: t is the number of time periods; n is a radical of hydrogen G The number of thermal power generating units; gamma ray t The system carbon emission intensity is defined as the system carbon emission intensity reduced by the unit charging and discharging power of the energy storage, and is related to the carbon emission intensity of the thermal power generating unit at the current moment; n is a radical of B Is the energy storage number;
and
and the charging and discharging power of the energy storage i at the moment t are respectively.
As a further improvement of the invention, the step S1 of improving the carbon emission measurement model of the thermal power generating unit further includes;
the energy storage charge and discharge power model changes the output of the thermal power generating unit according to the stored energy charge and discharge power, so that the carbon emission intensity of the output unit of the thermal power generating unit is changed, and the total carbon emission of the system is changed;
(1)γ t upper limit of value
Because energy storage charging and discharging have certain power loss, the carbon emission deducted in the improved thermal power unit carbon emission metering model is smaller than the carbon emission of energy storage reduction coal-fired power generation, and then the gamma in the improved thermal power unit carbon emission metering model t The value is less than the carbon emission intensity of the coal-fired power supply of the thermal power generating unit, and simultaneously, the stored energy and the consumed power are ensured to be wind power, if the stored energy and the stored thermal power are stored, F is caused c And the first term and the second term of (a) increase simultaneously, and the first term is greater in coefficient than the second term, resulting in F c Increasing;
(2)γ t lower limit of value
Because the active output of other units of the system during energy storage and discharge is smaller than the active output of the unit without energy storage, the carbon emission of the system is not increased during energy storage and discharge, and gamma is t Should be greater than 0, i.e.:
γ t ≥0。
as a further improvement of the present invention, in step S3, the double-layer optimization model first assigns an initial value to the variable of the upper layer model, the lower layer model optimizes its objective function on the basis, the obtained result returns to the upper layer optimization objective, two layers of alternate iteration are performed to finally obtain a result of global interest balance, the upper layer problem is solved by using an immune genetic algorithm, and the lower layer problem is jointly solved by calling a CPLEX solver in MATLAB and a point estimation method.
Compared with the prior art, the invention has the advantages that:
1. the improvement thermal power unit carbon emission measurement that this embodiment provided, at first through the coal-fired power supply carbon emission intensity with thermal power unit with unit load be the negative correlation and count into carbon emission measurement model, promoted thermal power unit carbon emission measurement accuracy. And the energy storage charging and discharging power is counted into the system carbon emission measurement, and the system carbon emission intensity reduced by the energy storage unit charging and discharging power is related to the carbon emission intensity of the thermal power generating unit at the current moment.
2. According to the method, the risk cost of the system with the energy storage participating in the flexible peak regulation is described through the wind power prediction error distribution, and a wind power operation risk cost function with the energy storage standby capacity is given. When the stored energy has certain spare capacity, the system operation risk can be obviously reduced, namely, the load shedding risk and the wind abandoning risk are reduced, the adjustment risk of the thermal power generating unit is reduced, and the system peak regulation is more flexible. .
3. In the double-layer optimization model of the embodiment, the upper layer model focuses on describing the capacity of reducing carbon emission and improving peak shaving of energy storage, the lower layer model focuses on the economy and flexibility of peak shaving of energy storage, and the upper and lower layer decision variables, the charge and discharge power and the reserve capacity of energy storage are mutually restricted, so that an operation mode which considers low carbon and flexible peak shaving of the system simultaneously is provided for energy storage.
Drawings
FIG. 1 is a wind power output prediction error probability distribution diagram according to the embodiment;
FIG. 2 is a flowchart of iterative solution of the model according to the embodiment;
FIG. 3 is a diagram illustrating the optimization result of the energy storage and discharge depth in each time interval of different scenarios in a specific application embodiment;
FIG. 4 shows the optimization results of the energy storage in each time interval of scenario 1 in a specific embodiment;
FIG. 5 shows the optimization results of the energy storage in each time interval of scenario 3 in a specific application embodiment;
FIG. 6 shows the difference γ in the specific application example t The change curve of the energy storage output when the value is obtained;
FIG. 7 shows the difference γ in the specific embodiment t And (4) the output variation curve of the thermal power generating unit in value.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
as shown in fig. 1, the double-layer optimization method for energy storage to participate in low-carbon flexible peak regulation in this embodiment includes the steps of:
s1, improving a thermal power generating unit carbon emission metering model, describing carbon emission generated by the output of the thermal power generating unit in detail, and providing the carbon emission metering model considering energy storage charging and discharging power.
S2, providing a system risk cost function for energy storage and flexible peak regulation, wherein the system risk cost function comprises a wind power operation risk cost function and a thermal power generating unit regulation risk cost function;
s3, constructing a double-layer optimization model for energy storage participation low-carbon flexible peak regulation: and the upper layer model determines the energy storage charging and discharging power at each moment by taking the system carbon emission, the load peak-valley difference rate and the energy storage operation cost as the lowest targets. The lower layer model takes load shedding risk cost, wind abandoning risk cost and thermal power unit regulation risk cost minimum as targets, and determines the energy storage reserve capacity at each moment by taking the energy storage charging and discharging power obtained at the upper layer as constraint. Performing day-ahead scheduling plan optimization by taking the wind-solar output prediction time sequence and the load prediction curve as basic data;
s4, optimizing and controlling: the model can reduce the carbon emission of the system and measure the contribution of energy storage to emission reduction while reducing the load peak-valley difference; by setting the energy storage reserve capacity, the peak shaving flexibility of the system is improved, and the operation risk of the system is reduced. And an upper-layer energy storage power optimization model considering the carbon emission of the system and the load peak-valley difference rate and a lower-layer energy storage spare capacity optimization model considering the operation risk of the system are mutually restricted, so that a reference is provided for the low-carbon and flexible operation of the system.
Probability density function of wind power output prediction error
Obey mean 0 variance σ 2 Normal distribution of [23,24] As shown in fig. 1, in this example,
respectively representing the upper and lower bounds of the wind power prediction error at time t when the confidence level takes beta. If the system can adjust the resources, namely the thermal power generating unit and the energy storage equipmentWind power prediction error range when the up-down adjustable capacity is beta taken as confidence level
And
when the wind power prediction error at the moment t is lower than
In time, the system can adjust the shortage of resources and needs to cut off part of the load; when the wind power prediction error at the moment t is higher than
In time, measures such as wind abandoning and the like are required. The probability weighted value of the shaded part in fig. 1 can be used for representing the load shedding and wind curtailment risk cost when the wind power operates at the moment t of the system.
The double-layer optimization model firstly gives an initial value to the upper-layer model variable, the lower-layer model optimizes the objective function of the upper-layer model variable on the basis of the initial value, the obtained result returns to the upper-layer optimization target, and the two layers of the optimization are alternately iterated to finally obtain a result of global benefit balance. And solving the upper-layer problem by adopting an immune genetic algorithm, and jointly solving the lower-layer problem by calling a CPLEX solver in MATLAB and a point estimation method.
After the conventional power output and the energy storage output are determined, the reserve capacity of the energy storage is optimized by taking the minimum system risk cost as a target, so that a double-layer optimization model is adopted to describe an energy storage optimization decision problem, and the convergence condition is as follows:
in the formula (I), the compound is shown in the specification,
and epsilon is a positive number which is small enough and represents the value of the charging and discharging spare capacity of the energy storage i at the time t in the k iteration. The iterative solution flow is shown in fig. 2.
In step S1 of this embodiment, a carbon emission measurement model of a thermal power generating unit is improved, carbon emission generated by the thermal power generating unit is described in detail, and a carbon emission measurement model considering energy storage charge and discharge power is proposed.
The emission intensity of the carbon for coal-fired power supply of the thermal power generating unit and the load of the unit are in a negative correlation relationship, namely the higher the load of the unit is, the lower the emission intensity of the carbon for coal-fired power supply is. The linear function relationship between the output of the thermal power generating unit and the carbon emission intensity of the coal-fired power supply is as follows:
in the formula:
the carbon emission intensity of the thermal power generating unit g at the time t is provided; k is more than or equal to 0, b is more than or equal to 0 and is a linear function parameter;
the output of the thermal power generating unit g at the moment t is obtained; p g The installed capacity of the thermal power generating unit g.
Carbon emission of coal-fired power supply of thermal power generating unit g
It can be calculated as follows.
In the formula: Δ t is the time interval.
Because the stored energy can produce the electric energy loss at the in-process of charging and discharging, when wind-powered electricity generation and thermal power generating set output can satisfy the load demand, system carbon emission is minimum. However, a large amount of abandoned wind can be caused in certain time periods due to the great reduction of the load and the limitation of the climbing capacity of the thermal power generating unit. The stored energy can reduce the carbon emission of the system by absorbing wind power during charging or reducing the output of a thermal power generating unit during discharging. Meanwhile, the energy storage equipment can improve the output of a low coal consumption unit and reduce the output of a high coal consumption unit through reasonable charging and discharging, and carbon emission is reduced.
Adding the carbon emission of the system with reduced energy storage charging and discharging power, and obtaining an improved thermal power generating unit carbon emission measurement model as follows:
in the formula: t is the number of time periods; n is a radical of G The number of thermal power generating units; gamma ray t The carbon emission intensity of the system reduced by the unit charging and discharging power of the energy storage is defined and is related to the carbon emission intensity of the thermal power generating unit at the current moment; n is a radical of B Is the energy storage number;
and
respectively is the charge and discharge power of the stored energy i at the moment t.
The stored energy charge and discharge power changes the output of the thermal power generating unit, so that the carbon emission intensity of the output unit of the thermal power generating unit changes, and the total carbon emission of the system is changed.
1.γ t Upper limit of value
Because the energy storage charging and discharging have certain power loss, the carbon emission deducted in the above formula is less than the carbon emission of the energy storage reduction coal-fired power generation, and then the gamma in the above formula t The value is less than the carbon emission intensity of the coal-fired power supply of the thermal power generating unit. Meanwhile, wind power can be guaranteed to be consumed by stored energy, and if the stored energy stores thermal power, F is caused c And the first term and the second term of (a) increase simultaneously, and the first term is greater in coefficient than the second term, resulting in F c And is increased.
2.γ t Lower limit of value
Due to other systems during discharge of stored energyThe active output of the unit is smaller than that of the unit without energy storage, the carbon emission of the system is not increased when the energy storage is discharged, and the gamma value is t Should be greater than 0, i.e.:
γ t ≥0
in step S2 in this embodiment, a system risk cost function for energy storage flexible peak shaving is provided, which includes a wind power operation risk cost function and a thermal power generating unit regulation risk cost function:
because the wind power output has randomness and volatility, in order to keep the balance of the generating electric power of the system, the thermal power generating unit and the energy storage equipment need to adjust the power output, and the capacity of the adjustable resources of the system determines the maximum wind power range which can be accepted by the system. When the wind power output exceeds the maximum power range accepted by the system, the system is subjected to operation risk, and economic loss is caused.
Probability density function of wind power output prediction error
Obeying mean 0 and variance σ 2 The normal distribution of (a) is, as shown in figure 1,
respectively representing the upper and lower bounds of the wind power prediction error at time t when the confidence level takes beta. If the system can adjust resources, namely thermal power generating units and energy storage equipment, the upper and lower adjustable capacity is the wind power prediction error range when the confidence level takes beta
And
when the wind power prediction error at the moment t is lower than
In time, the system can adjust the shortage of resources and needs to cut off part of the load; when the wind power prediction error at the moment t is higher than
In time, measures such as wind abandoning and the like are required. The probability weighted value of the shaded part in fig. 1 can be used for representing the load shedding and wind curtailment risk cost when the wind power operates at the moment t of the system.
The wind power operational risk cost of the system can be expressed as:
in the formula: n is a radical of W Representing the number of wind power plants;
and
respectively representing the maximum and minimum power generation output of the wind power field w; mu.s W 、μ D Respectively representing wind curtailment and load shedding punishment cost coefficients;
and outputting wind power for the wind power plant w at the moment t.
Energy storage is added into the system, namely, the capacity of adjustable resources of the system is increased, and the wind power operation risk cost can be obviously reduced. And maintaining the state of charge of the stored energy within a desired state of charge range to cope with the wind power operation risk possibly occurring in the system. The wind power operation risk cost of the system with the energy storage adjustable capacity at the moment t is as follows:
in the formula:
and the standby capacity of the stored energy i at the time t is shown.
2. Adjusting risk cost function of thermal power generating unit
Source load power imbalance caused by uncertainty of wind power output except forThe energy storage can be utilized for balancing, and the thermal power generating unit can be required for adjusting. The fuel costs and the control costs resulting from the uncertainty of the wind power output, i.e. the control risk costs of the thermal power plant, are [22] ：
In the formula:
and
the maximum upward climbing power, the maximum downward climbing power, the upward climbing power and the downward climbing power of the thermal power generating unit g at the moment t are respectively; c g Is a power generation cost function of the thermal power generating unit g at the time t
a g 、b g 、c g The power generation cost coefficient of the thermal power generating unit g is obtained; d is a radical of g And adjusting the cost coefficient for the unit power of the thermal power generating unit g.
In this embodiment, a double-layer optimization model in which energy storage participates in low-carbon flexible peak regulation is constructed in step S3: and the upper layer model determines the energy storage charging and discharging power at each moment by taking the system carbon emission, the load peak-valley difference rate and the energy storage operation cost as the lowest targets. The lower layer model takes load shedding risk cost, wind abandoning risk cost and thermal power unit regulation risk cost minimum as targets, and determines the energy storage reserve capacity at each moment by taking the energy storage charging and discharging power obtained at the upper layer as constraint. Day-ahead scheduling is carried out by taking the wind-solar output prediction time sequence and the load prediction curve as basic data
The upper layer objective function takes the carbon emission of a system, the load peak-valley difference rate and the energy storage operation cost as the lowest targets, and determines the energy storage charging and discharging power at each moment;
as a further improvement of the invention, the energy storage participation low-carbon flexible peak regulation double-layer optimization model is shown as the following formula:
1. upper layer objective function
In the formula: k is a radical of 1 、k 2 、k 3 Is a weight coefficient, and takes the value of 100,1,1; beta, F b 、F c The method comprises the steps of respectively obtaining a load peak-valley difference rate, an energy storage operation cost function and a thermal power generating unit carbon emission metering function.
The stored energy is discharged at the peak of the load and is charged at the valley of the load, and the equivalent load of the system is changed. Recording the power after the load power is superposed with the energy storage power as follows:
in the formula: n is a radical of L The number of nodes;
is the load power of node k at time t.
The load peak-to-valley difference rate of the equivalent load is:
the energy storage operation cost function is:
in the formula: c. C b,i The cost factor is adjusted for the specific power of the stored energy i.
2. Lower layer objective function
The standby capacity of the energy storage equipment of the system at each moment is optimized, and the climbing output of the thermal power generating unit is adjusted, so that the running risk cost of the system is the lowest, including the wind power running risk cost and the thermal power generating unit regulating risk cost.
As a further improvement of the method, the energy storage participation low-carbon flexible peak regulation double-layer optimization model established in the step S3 is also provided with an upper-layer optimization model constraint condition and a lower-layer optimization model constraint condition.
1. Constraint conditions of upper optimization model
1) System power balance constraint:
2) Wind power output constraint:
3) Output restraint of the thermal power generating unit:
in the formula:
and
respectively the minimum output and the maximum output of the thermal power generating unit g.
4) And (3) climbing restraint of the thermal power generating unit:
in the formula: r is u,g 、r d,g The maximum up-down climbing speed of the thermal power generating unit g.
5) And (3) limiting the start and the stop of the thermal power generating unit:
in the formula: u. of g,j The Boolean variable represents the starting and stopping state of the thermal power generating unit g at the moment j, 0 represents shutdown, and 1 represents startup; t is g,on 、T g,off The maximum continuous starting time and the maximum continuous stopping time of the thermal power generating unit g are respectively.
6) Energy storage charge and discharge power constraint:
in the formula: p i c,max And P i d,max The maximum charge and discharge powers of the stored energy i, respectively.
7) Energy storage state of charge constraint:
in the formula: s i,t Representing the SOC value of the stored energy i in the t period; s. the i,max And S i,min Respectively representing the upper limit and the lower limit of the energy storage SOC; sigma i Representing the energy storage self-discharge rate; eta c,i And η d,i Respectively representing the energy storage charge-discharge efficiency.
2. Constraint conditions of lower optimization model
On the premise of meeting the constraint conditions of the upper-layer optimization model, the variables to be solved of the lower-layer model need to meet the following constraints. After the first iterative solution, when the upper model is solved, the energy storage reserve capacity constraint and the state of charge constraint containing the energy storage reserve capacity need to be satisfied, that is, the energy storage reserve capacity of the lower model will limit the upper and lower limits of the state of charge of the upper model.
1) The adjustable power of thermal power unit retrains:
the maximum adjustable power of the thermal power generating unit g at the moment t cannot exceed the maximum up-down climbing speed, and the output cannot exceed the maximum and minimum generating power:
2) Energy storage reserve capacity constraint:
in the formula: v i max Is the capacity to store energy i.
3) State of charge constraints including reserve capacity for energy storage:
in order to ensure that certain backup can be provided for the risk of energy storage at the time t, certain charging and discharging spaces need to be reserved, so that the state of charge constraint containing energy storage backup capacity is proposed as follows:
in step S4, the model can reduce the carbon emission of the system while reducing the load peak-valley difference, and measure the contribution of stored energy to emission reduction; by setting the energy storage reserve capacity, the peak shaving flexibility of the system is improved, and the operation risk of the system is reduced. And the upper-layer energy storage power optimization model considering the carbon emission of the system and the load peak-valley difference rate and the lower-layer energy storage spare capacity optimization model considering the operation risk of the system are mutually restricted, so that reference is provided for low-carbon and flexible operation of the system.
In order to verify the effectiveness of the invention, an IEEE30 node 6 machine system is selected for verification. The system comprises six coal-fired thermal power generating units with the same capacity of 100MW, a wind power plant with the capacity of 200MW and an energy storage system with the capacity of 100MW. The maximum and minimum grid-connected power of the wind power is 200MW and 100MW respectively. The detailed parameters of the thermal power generating unit and the energy storage are shown in tables 1 and 2. According to the carbon emission intensity curve of the coal-fired power supply of a certain coal-fired unit in Shandong under different load rates, a linear function of the output of the thermal power generating unit and the carbon emission intensity of the coal-fired power supply is obtained through linear regression.
TABLE 1 coal-fired thermal power generating unit operating parameters
TABLE 2 energy storage System operating parameters
The remaining simulation parameters are as follows: the wind power prediction error is subject to normal distribution with the mean value of 0 and the variance of 64, and the confidence interval is [ -16](ii) a Wind power operation risk parameter mu W ＝μ D =100; thermal power generating unit start-stop parameter T g,on ＝T g,off =4; energy storage charge-discharge efficiency parameter sigma =0.9, eta c,i ＝η d,i =0.9, initial soc =0.5; Δ t =1.
3 operation scenes are set for comparative analysis, and under the same system parameters, the optimization results are shown in table 3. Scene 1 is a text model; scene 2 is that the energy storage of the lower model does not participate in the optimization of the reserve capacity; and in the scenario 3, the energy storage participates in the spare capacity optimization, but the carbon emission measurement in the upper-layer optimization does not contain the energy storage power.
TABLE 3 comparison of optimization results for different scenarios
As can be seen from table 3, the system carbon emissions of the scenes 1 and 2 are slightly lower than that of the scene 3, and the system carbon emission difference between the scenes 1 and 3 is the carbon emission contribution of the energy storage charging and discharging power to the system, because the carbon emission measurement in the scene 3 does not contain the energy storage power, the energy storage power is added to the model carbon emission measurement, and the real carbon emission condition of the system is described. Different from the scenario 3, the model provided herein, namely the scenario 1, considers the carbon emission of the system to enable the energy storage power to be as large as possible, so that the reserve capacity is reduced, the wind power operation risk cost is increased, and the thermal power unit adjustment is also reduced due to the large energy storage power, namely the thermal power unit adjustment risk cost is reduced.
In table 3, the system carbon emission of scene 2 is lower than that of scene 1, and the reserved energy storage spare capacity can cause the energy storage to be not fully utilized due to the strong uncertainty of wind power output, thereby causing the reserved redundancy. In the scene 2, the energy storage reserve capacity is not reserved, so that the energy storage charging and discharging power is large, the energy storage operation cost is highest, the peak-valley difference rate is minimum, and the system operation risk cost is maximum.
In fig. 3, when the scene 1 and the scene 3 operate in energy storage, the charging and discharging depth is less than that of the scene 2, and the operation is more conservative because a certain spare capacity needs to be reserved for energy storage.
The optimization results of the energy storage periods of the scene 1 and the scene 3 are shown in fig. 4 and 5. The positive and negative standby capacities represent upper and lower standby, and as can be seen from the comparison of the scenes 1 and 3 with the scene 2, the system load shedding and wind abandoning risks of the scene 2 are large, and due to the fact that the standby capacities are not reserved in the energy storage mode, the output of the thermal power unit is limited by climbing or due to economy and the like, sufficient standby cannot be provided. Energy storage in the scene 1 and the scene 3 participates in peak shaving standby, certain standby support is provided, and system operation risk is reduced. For example, in fig. 4 and 5, energy storage provides up-regulation backup during periods 1, 2 and 3 when the load is low and the wind power output is large, and down-regulation backup during periods 12, 13 and 14 when the load is high and the wind power output is small.
Gamma in the carbon emission measurement model presented herein t The range of values is
When gamma is t If =0, this is simulation scenario 3 herein. When in use
Namely simulation scenario 1 herein. If gamma is t Get the
When r is more than 1, the model has no solution. r is 0.1,0.2, 0.9, and different gamma is given in fig. 6 and 7 t And (4) the energy storage output and the thermal power generating unit output when t = 13. At the moment, the load is large, the wind power output is small, and the stored energy is in a discharging state.
As can be seen from fig. 6 and 7, as the r value increases, the energy storage discharge power increases linearly, the output of the thermal power generating unit decreases linearly, and the increase of the energy storage discharge power is equal to the decrease of the output of the thermal power generating unit. This is because the greater r, the greater the intensity of the system carbon emissions for the energy storage discharge power reduction, in order to make the objective function F c And when the minimum value is obtained, the load is fixed at the moment, the energy storage discharge power is increased, and the output of the thermal power generating unit is reduced.
Similarly, when the energy storage is charged, the larger r is, the larger the energy storage charging power is, and the output of the thermal power generating unit is increased. However, the carbon emission intensity of the thermal power generating unit is reduced due to the fact that the output of the thermal power generating unit is increased, and therefore the objective function F c Still, the optimum can be reached and as r increases, F c Will decrease in value. By increasing the r value, i.e. gamma, during regulation of stored energy t The weight of the carbon emission reduction capability of the system can be increased, so that the energy storage scheduling strategy is more aggressive.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.
Claims (4)
1. A double-layer optimization method for energy storage participation low-carbon flexible peak regulation is characterized by comprising the following steps:
s1, improving a thermal power generating unit carbon emission metering model, quantifying the contribution of energy storage in a system to emission reduction, describing the carbon emission generated by the thermal power generating unit output in detail, and providing the carbon emission metering model considering energy storage charging and discharging power;
s2, providing a system risk cost function for energy storage and flexible peak regulation, wherein the system risk cost function comprises a wind power operation risk cost function and a thermal power generating unit regulation risk cost function;
s3, constructing a double-layer optimization model for energy storage participation low-carbon flexible peak regulation:
the upper layer model determines the energy storage charging and discharging power at each moment by taking the system carbon emission, the load peak-valley difference rate and the energy storage operation cost as the lowest targets;
the lower layer model aims at minimizing load shedding risk cost, wind abandoning risk cost and thermal power unit adjustment risk cost, and determines the energy storage reserve capacity at each moment by using the energy storage charging and discharging power obtained at the upper layer as constraint. Performing day-ahead scheduling plan optimization by taking the wind-solar output prediction time sequence and the load prediction curve as basic data;
s4, optimizing control:
the model reduces the carbon emission of the system while reducing the load peak-valley difference, and measures the contribution of energy storage to emission reduction; by setting the energy storage reserve capacity, the peak shaving flexibility of the system is improved, and the operation risk of the system is reduced.
2. The energy storage participation low-carbon flexible peak shaving double-layer optimization method according to claim 1, characterized in that: the step S1 of improving the carbon emission measurement model of the thermal power generating unit comprises the steps of;
the carbon emission intensity of the coal-fired power supply of the thermal power generating unit and the load of the unit are in a negative correlation relationship, namely the higher the load of the unit is, the lower the carbon emission intensity of the coal-fired power supply is, and the linear function relationship between the output of the thermal power generating unit and the carbon emission intensity of the coal-fired power supply is as follows:
in the formula:
the carbon emission intensity of the thermal power generating unit g at the time t is provided; k is more than or equal to 0, b is more than or equal to 0 and is a linear function parameter;
the output of the thermal power generating unit g at the moment t is obtained; p is g The installed capacity of the thermal power generating unit g is obtained;
carbon emission of coal-fired power supply of thermal power generating unit g
Calculating according to the formula;
in the formula: Δ t is the time interval;
adding the system carbon emission with reduced energy storage charging and discharging power, and obtaining an improved thermal power generating unit carbon emission metering model as follows:
in the formula: t is the number of time periods; n is a radical of G The number of thermal power generating units; gamma ray t The system carbon emission intensity is defined as the system carbon emission intensity reduced by the unit charging and discharging power of the energy storage, and is related to the carbon emission intensity of the thermal power generating unit at the current moment; n is a radical of B Is the energy storage number;
and
respectively is the charge and discharge power of the stored energy i at the moment t.
3. The double-layer optimization method for participating in low-carbon flexible peak shaving by energy storage according to claim 2, wherein the step S1 of improving the thermal power generating unit carbon emission measurement model further comprises;
the energy storage charge and discharge power model changes the output of the thermal power generating unit according to the stored energy charge and discharge power, so that the carbon emission intensity of the output unit of the thermal power generating unit is changed, and the total carbon emission of the system is changed;
(1)γ t upper limit of value
Because energy storage charging and discharging have certain power loss, the carbon emission deducted from the improved thermal power unit carbon emission measurement model is smaller than the carbon emission of energy storage reduction coal-fired power generation, and then the gamma in the improved thermal power unit carbon emission measurement model t The value is less than the carbon emission intensity of the coal-fired power supply of the thermal power generating unit, and simultaneously, the stored energy and the consumed power are ensured to be wind power, if the stored energy and the stored thermal power are stored, F is caused c And the first term and the second term of (a) increase simultaneously, and the first term is greater in coefficient than the second term, resulting in F c Increasing;
(2)γ t lower limit of value
Because the active output of other units of the system during energy storage and discharge is smaller than the active output of the unit without energy storage, the carbon emission of the system is not increased during energy storage and discharge, and gamma is t Should be greater than 0, i.e.:
γ t ≥0。
4. the double-layer optimization method for energy storage participation low-carbon flexible peak regulation according to claim 1, wherein the double-layer optimization model in the step S3 firstly gives an initial value to an upper-layer model variable, the lower-layer model optimizes a target function of the upper-layer model on the basis of the initial value, the obtained result is returned to an upper-layer optimization target, two layers of alternate iteration are performed, a global benefit balance result is finally obtained, an immune genetic algorithm is adopted to solve an upper-layer problem, and a CPLEX solver in MATLAB and a point estimation method are called to jointly solve a lower-layer problem.
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