CN115459349A - Wind, light, water and fire storage multi-source economic-low carbon cooperative scheduling method - Google Patents

Wind, light, water and fire storage multi-source economic-low carbon cooperative scheduling method Download PDF

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CN115459349A
CN115459349A CN202211269158.XA CN202211269158A CN115459349A CN 115459349 A CN115459349 A CN 115459349A CN 202211269158 A CN202211269158 A CN 202211269158A CN 115459349 A CN115459349 A CN 115459349A
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邰伟
刘盼盼
钱俊良
周吉
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Liyang Research Institute of Southeast University
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Abstract

The invention provides a wind, light, water and fire storage multi-source economic-low carbon collaborative scheduling method, aiming at a large power grid scheduling scene under the condition that new energy such as wind, light and the like is accessed, under the constraint of multiple targets and multiple time scales, optimal output sequencing is carried out on sensitivity factors of benefit by each unit, the optimal solution of an overall target is realized, a power grid multi-power optimization scheduling model giving consideration to economy and low carbon is realized, optimal solution is carried out on wind, light, water and fire collaborative scheduling under the scene of multiple targets and multiple time scales, the consumption utilization rate of green energy is increased, and clean scheduling and clean substitution of a power grid in the day are realized.

Description

Wind, light, water and fire storage multi-source economic-low carbon cooperative scheduling method
Technical Field
The invention belongs to the technical field of low-carbon scheduling, and particularly relates to a wind, light, water and fire storage multi-source economic-low-carbon cooperative scheduling method.
Background
With the carbon peak reaching and the carbon neutralization target being proposed and gradually falling to the ground, the main action of the traditional thermal power is gradually changed into the auxiliary supporting action for regulating and controlling the new energy, and the construction of a novel power system taking the new energy as the main body is urgent. With the high proportion of various centralized and distributed new energy sources, the fluctuation, intermittency and uncertainty of the power generation of the new energy sources are difficult to provide continuous and stable energy output, and meanwhile, the safe and stable operation of a power system is greatly challenged. Under the background, key technical requirements of a novel power system such as power balance, electric quantity balance, peak and voltage regulation and frequency modulation, situation real-time perception, low-carbon operation constraint and the like are difficult to meet. In order to improve the consumption capability of a power grid on large-scale clean energy and the safety and stability level of a power system, deep research and application of a wind, light, water, fire and storage cooperative scheduling technology are necessary. The optimized combined dispatching method of the comprehensive energy system composed of power generation forms in different proportions is provided according to the switching characteristics of photovoltaic power generation, wind power generation, hydraulic power generation and thermal power generation, the minimum generator tripping, load shedding and the maximum principle of wind-solar power generation output, and the stability, economy and environmental friendliness of the system.
Disclosure of Invention
The invention develops around the research and application of wind, light, water, fire and energy storage cooperative scheduling technology, focuses on researching wind power, photovoltaic, hydraulic, thermal power and energy storage optimal combination scheduling control methods of a power grid under normal and emergency conditions, and simultaneously needs to consider single-target and multi-target hybrid optimal scheduling methods of single energy and multi-energy respectively under the requirements of economy and environmental protection, thereby realizing the optimal scheduling strategy of the power grid under different variable and optimal target environments, realizing the optimal allocation of economic-environmental cooperative power grid resources and supporting double-carbon targets.
In order to solve the problems, the technical scheme adopted by the invention is as follows: a wind, light, water and fire storage multi-source economic-low carbon cooperative scheduling method is realized by establishing a mathematical scheduling model and a solving process thereof under a multi-source power grid, and the specific process comprises the following steps:
step 1: establishing an optimization target;
step 2: establishing a constraint condition;
and 3, step 3: and solving the scheduling model.
Establishing an optimization target in the step 1 comprises operation cost and equivalent carbon emission; the constraint conditions in the step 2 comprise the constraints of unit output, power balance, reservoir system operation conditions and the like; in step 3, the scheduling model is optimized by adopting an iterative solution method based on sensitivity factors.
The method for defining the operation cost comprises the following steps:
considering the smaller marginal cost of the hydropower station, the operation cost of the wind, light, water and fire system mainly comes from the fuel cost of the thermal power plant and the respective operation cost of the wind and light units, and the total cost can be expressed as:
Figure BDA0003894553650000021
wherein F C For the total operating cost, N t 、N w 、N pv The number of thermal power units, wind power units and photovoltaic units respectively, T is a scheduling period, and the scheduling condition of each hour in a single day, namely 24 scheduling points each day, f is considered in the specification sit The economic cost of the operation of the ith thermal power generating unit at the moment t is related to P sit (the output of the ith thermal power generating unit at the moment t); k wk 、K sm The direct cost coefficients, P, of the kth wind turbine and the mth photovoltaic generator, respectively wkt 、P PVmt Respectively the output of the kth wind turbine generator and the mth photovoltaic generator at the moment t,
the fuel cost function of each thermal power generating unit takes the valve point effect into consideration and can be expressed as
Figure BDA0003894553650000022
Wherein, a si 、b si 、c si 、d si And e si Respectively representing the cost coefficients of the ith thermal power generating unit in a constant term, a primary term, a secondary term and a trigonometric function term,
Figure BDA0003894553650000023
and representing the lowest output limit value of the ith thermal power generating unit.
Wherein, the definition method of the equivalent carbon emission is as follows:
E CO2,e ＝w 1 E CO2,e +w 2 E NOX +w 3 E SO2 (3)
the equivalent carbon emission source comprises all gas types capable of causing greenhouse effect, all the gases are equivalent to carbon dioxide equivalent according to the effect of causing the same greenhouse effect to be calculated, in the power generation process of the thermal power generating unit, the greenhouse gases mainly generated by the equivalent carbon emission source comprise carbon dioxide, nitrogen oxides and sulfur dioxide, and when the equivalent carbon emission is merged, the weighting factor w is 1 、w 2 、w 3 1, 298, 44 respectively;
the carbon emission calculation method of each category of greenhouse gas comprises the following steps:
(1) CO2 emission:
the CO2 emission of the thermal power plant is mainly related to the coal quality and the combustion efficiency thereof, and is expressed by using a combination form of a polynomial term and an exponential term
Figure BDA0003894553650000024
Wherein E is CO2 Is the CO2 emission, alpha ci 、β ci The emission coefficient of the ith thermal power generating unit relative to CO2 emission in a constant term and a primary term;
(2) NOx emissions;
thermal power plants differ in the source of NOx emissions, their production being dependent on factors such as boiler temperature, air content, etc., and are described using a combination of polynomial and exponential terms, such as
Figure BDA0003894553650000031
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003894553650000036
alpha for NOx emissions ni 、β ni 、γ ni Emission coefficient, eta, in constant, first and second terms for NOx emission of the ith thermal power generating unit ni 、δ ni And the emission coefficient of the ith thermal power generating unit in the exponential term about the NOx emission.
(3) Discharging sulfur dioxide;
the SO2 emission of the thermal power plant and the combustion amount of the fuel mainly have a quadratic polynomial relationship, which can be expressed as
Figure BDA0003894553650000032
Wherein the content of the first and second substances,
Figure BDA0003894553650000035
is the amount of SO2 discharged, alpha si 、β si 、γ si The emission coefficient of the ith thermal power generating unit relative to SO2 emission in a constant term, a primary term and a secondary term;
among the two types of optimization targets, the carbon trading price of the carbon trading market is used as a link, and the operation cost of the power generation fuel and the carbon emission are unified into the comprehensive power generation cost.
F＝F C +Price CO2,e *E CO2,e (7)。
Wherein, the step 2: establishing constraint conditions which mainly comprise source network load power balance limitation and output constraint of each unit, and the following steps:
(1) A power balance limit;
Figure BDA0003894553650000033
wherein N is h And P hjt The number of the hydroelectric generating sets and the output of the jth hydroelectric generating set at the moment t are respectively, under the condition, the source network load can reach real-time power balance, P bt 、P Dt 、P Lt Respectively storing charging and discharging power, a load magnitude and line loss of the transmission line at the moment t;
(2) Calculating the output of each unit;
for a hydro-power unit, hydroelectric power generation is a function of displacement and reservoir level, which is a function of water storage, and the output of the hydro-power unit is as follows:
Figure BDA0003894553650000034
wherein, V hjt And Q hjt Respectively the storage water volume and the water discharge rate of the reservoir at the moment t, C of the jth hydroelectric generating set 1j ～C 6j Respectively about V for hydroelectric generation hjt And Q hjt The power generation coefficient of (c);
for the wind generating set, under different wind speeds, the output power of the kth wind generating set at the time t is represented as:
Figure BDA0003894553650000041
wherein v is in 、v out 、v r Respectively the standard cut-in wind speed, cut-out wind speed and rated wind speed, P of the wind turbine generator wrk Rated output of the kth wind generating set;
for a photovoltaic cell, the power it outputs may be expressed as
Figure BDA0003894553650000042
Where G is the light irradiance prediction, R c For a particular spokeShot size, P PVrm Rated output for the mth photovoltaic unit, G std The radiation quantity under the standard environment is adopted;
for energy storage batteries, the battery maximum charge-discharge capacity constraint is as follows.
Figure BDA0003894553650000043
Wherein P is bt Discharging when the voltage is positive, and charging when the voltage is negative.
To total transmission loss P of line Lt It can be calculated with the neighboring matrix B coefficient folding algorithm:
Figure BDA0003894553650000044
wherein the total number of generators N T ，P it 、P jt Is the power generation amount of the ith generator set and the jth generator set. B is ij 、B 0i 、B 00 Respectively a unit adjacent coefficient, a unit independent coefficient and a constant coefficient of the line loss;
(3) Unit output constraint
Based on the output calculation conditions, the maximum output value of each unit is calculated, and the output constraints of the hydroelectric power, the thermal power and the wind turbine are as follows:
Figure BDA0003894553650000045
(4) Reservoir system restrictions;
reservoir constraints include water balance equations, reservoir water storage and discharge targets. These constraints include:
a. practical limitation of reservoir volume
Figure BDA0003894553650000051
b. Practical limits on reservoir discharge rates
Figure BDA0003894553650000052
c. Continuity equation of reservoir pipe network
Figure BDA0003894553650000053
Wherein I hjt 、S hjt Showing the inflow and the outflow of the jth unit at the moment t, R uj Representing the number of upstream banks of the jth hydroelectric station.
The mathematical scheduling model comprises the following mathematical solving processes:
(1) Through random value taking, a system solution set is initialized, namely the output results of thermal power, hydropower, wind power and photovoltaic units at the current moment:
Figure BDA0003894553650000054
for convenience of description, the power generation type is omitted, and is uniformly rewritten as S 1 ＝{P 1 ,P 2 ,...,P n }；
(2) Calculating the objective function value F (S) of the current solution set 1 )；
(3) Sequencing the output priority of each unit, sequentially changing the output of each unit by small output variation, observing the influence of the output on a target function value to judge the contribution sensitivity of the output of each unit to the target function, and aiming at the ith unit, the sensitivity calculation mode is that
Figure BDA0003894553650000055
Wherein S 1 ＝{P 1 ,P 2 ,...P i ,...P n }，S 2 ＝{P 1 ,P 2 ,...P i +ε,...P n ε is a sufficiently small value;
(4) Sequencing the units from high sensitivity to low sensitivity, increasing the output of the unit with the highest sensitivity each time, reducing the output of the unit with the lowest sensitivity in the same size, and generating a new solution set state;
(5) And (4) repeating the steps 2-4 until the target function result meets the requirement or the target function difference between the adjacent states is smaller than a threshold value, and the obtained system solution set is the optimal solution set.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the wind, light, water and fire storage multi-source economic-low carbon collaborative scheduling method can combine wind power, photovoltaic, hydroelectric generating set, energy storage device and other devices, and realize an optimal energy utilization strategy of economic-environmental collaboration under the environment of dynamic change of source load output. By constructing a power grid low-carbon economic regulation and control architecture in a novel power system environment, the operation characteristics, the dispatching characteristics and the interaction influence of various wind, light, water and fire generator sets and system boundary constraints such as a power grid and a reservoir are fully excavated, and a multi-objective optimization dispatching model considering the power grid operation cost and carbon emission is constructed. Therefore, wind, light, water, fire and energy storage cooperative optimization scheduling is achieved, the power generation capacity of new energy resources such as wind and light and the like is maximized, the operation cost and equivalent carbon emission of the power grid are minimized on the premise that the safe and stable operation of the power grid is guaranteed, theoretical support is provided for achieving a low-carbon power grid and a clean power grid, and a reasonable decision is made on a scheduling mode of the new energy resources with high permeability consumed by the power grid.
Drawings
Fig. 1 is a schematic diagram of a power network topology and power distribution.
Fig. 2 is a flow chart of the economic-low carbon cooperative scheduling.
Detailed Description
For the purposes of promoting an understanding and appreciation of the invention, the invention will be further described in connection with the following detailed description and the accompanying drawings.
Example 1: referring to fig. 1, a wind, light, water and fire storage multi-source economic-low carbon cooperative scheduling method is established by aiming at multiple sources
The mathematical scheduling model under the source power grid and the solving process thereof are realized, and the specific process comprises the following steps:
step 1: establishing an optimization target;
and 2, step: establishing a constraint condition;
and step 3: and solving the scheduling model.
Wherein, the optimization target established in the step 1 comprises operation cost and equivalent carbon emission; the constraint conditions in the step 2 comprise the constraints of unit output, power balance, reservoir system operation conditions and the like; in step 3, the scheduling model is optimized by adopting an iterative solution method based on sensitivity factors.
The method for defining the operation cost comprises the following steps:
considering the smaller marginal cost of the hydropower station, the operation cost of the wind, light, water and fire system mainly comes from the fuel cost of the thermal power plant and the respective operation cost of the wind and light units, and the total cost can be expressed as:
Figure BDA0003894553650000061
wherein F C For the total operating cost, N t 、N w 、N pv The number of thermal power units, wind power units and photovoltaic units respectively, T is a scheduling period, and scheduling conditions of each hour in a single day are considered in the text, namely 24 scheduling points each day, f sit The economic cost of the operation of the ith thermal power generating unit at the moment t is related to P sit (the output of the ith thermal power generating unit at the moment t); k wk 、K sm The direct cost coefficients, P, of the kth wind turbine and the mth photovoltaic generator, respectively wkt 、P PVmt Respectively the output of the kth wind turbine generator and the mth photovoltaic generator at the moment t,
the fuel cost function of each thermal power generating unit takes the valve point effect into consideration and can be expressed as
Figure BDA0003894553650000071
Wherein, a si 、b si 、c si 、d si And e si Respectively representing the cost coefficients of the ith thermal power generating unit in a constant term, a primary term, a secondary term and a trigonometric function term,
Figure BDA0003894553650000072
and representing the lowest output limit value of the ith thermal power generating unit.
Wherein, the definition method of the equivalent carbon emission is as follows:
E CO2,e ＝w 1 E CO2,e +w 2 E NOX +w 3 E SO2 (3)
the equivalent carbon emission source comprises all gas types capable of causing greenhouse effect, all the gases are equivalent to carbon dioxide equivalent according to the effect of causing the same greenhouse effect to be calculated, in the power generation process of the thermal power generating unit, the greenhouse gases mainly generated by the equivalent carbon emission source comprise carbon dioxide, nitrogen oxides and sulfur dioxide, and when the equivalent carbon emission is merged, the weighting factor w is 1 、w 2 、w 3 1, 298, 44 respectively;
the carbon emission calculation method of each category of greenhouse gas comprises the following steps:
(1) CO2 emission:
the CO2 emission of the thermal power plant is mainly related to the coal quality and the combustion efficiency of the thermal power plant, and is expressed by using a combination form of a polynomial term and an exponential term
Figure BDA0003894553650000073
Wherein, E CO2 Alpha for CO2 emission ci 、β ci The emission coefficient of the ith thermal power generating unit in a constant term and a primary term relative to CO2 emission;
(2) NOx emissions;
NOx emissions from thermal power plants vary in origin, their production being dependent on factors such as boiler temperature, air content, etc., and are expressed as a combination of polynomial and exponential terms
Figure BDA0003894553650000074
Wherein the content of the first and second substances,
Figure BDA0003894553650000076
alpha for NOx emissions ni 、β ni 、γ ni The emission coefficient, eta, of the ith thermal power generating unit with respect to NOx emission in constant terms, primary terms and secondary terms ni 、δ ni And the emission coefficient of the ith thermal power generating unit in the exponential term about the NOx emission.
(3) Discharging sulfur dioxide;
SO2 emission of a thermal power plant and the combustion quantity of fuel mainly have a quadratic polynomial relationship and can be expressed as
Figure BDA0003894553650000075
Wherein the content of the first and second substances,
Figure BDA0003894553650000081
is the amount of SO2 discharged, alpha si 、β si 、γ si And regarding the emission coefficient of SO2 emission of the ith thermal power generating unit in a constant term, a primary term and a secondary term.
Among the two types of optimization targets, the carbon trading price of the carbon trading market is used as a link, and the operation cost of the power generation fuel and the carbon emission are unified into the comprehensive power generation cost.
F＝F C +Price CO2,e *E CO2,e (7)。
Wherein, the step 2: establishing constraint conditions which mainly comprise source network load-power balance limitation and output constraint of each unit as follows:
(1) A power balance limit;
Figure BDA0003894553650000082
wherein, N h And P hjt The number of the hydroelectric generating sets and the jth hydroelectric generating setThe output of the group at the time t, under the condition, the source network load can reach real-time power balance, P bt 、P Dt 、P Lt Respectively storing charging and discharging power, a load magnitude and line loss of the transmission line at the moment t;
(2) Calculating the output of each unit;
for a hydro-power unit, hydroelectric power generation is a function of displacement and reservoir level, which is a function of water storage, and the output of the hydro-power unit is as follows:
Figure BDA0003894553650000083
wherein, V hjt And Q hjt Respectively the storage water volume and the water discharge rate of the reservoir at the moment t, C of the jth hydroelectric generating set 1j ～C 6j Respectively about V for hydroelectric generation hjt And Q hjt The power generation coefficient of (a);
for the wind generating set, under different wind speeds, the output power of the kth wind generating set at the time t is represented as:
Figure BDA0003894553650000084
wherein v is in 、v out 、v r Respectively the standard cut-in wind speed, cut-out wind speed and rated wind speed, P of the wind turbine generator wrk Rated output of the kth wind generating set;
for a photovoltaic cell, the power it outputs may be expressed as
Figure BDA0003894553650000091
Where G is the light irradiance prediction, R c For a certain amount of radiation, P PVrm Rated output for the mth photovoltaic unit, G std The radiation amount under the standard environment;
for energy storage batteries, the battery maximum charge-discharge capacity constraint is as follows.
Figure BDA0003894553650000092
Wherein P is bt Discharging when the voltage is positive, and charging when the voltage is negative.
To total transmission loss P of line Lt It can be calculated with the adjacent matrix B coefficient folding algorithm:
Figure BDA0003894553650000093
wherein the total number of generators N T ，P it 、P jt Is the power generation amount of the ith generator set and the jth generator set. B ij 、B 0i 、B 00 Respectively a unit adjacent coefficient, a unit independent coefficient and a constant coefficient of line loss;
(3) Unit output constraint
Based on the output calculation conditions, the maximum output value of each unit is calculated, and the output constraints of hydropower, thermal power and wind generation units are as follows:
Figure BDA0003894553650000094
(4) Reservoir system restrictions;
reservoir constraints include water balance equations, reservoir water storage and discharge targets. These constraints include:
a. practical limitation of reservoir volume
Figure BDA0003894553650000095
b. Practical limits on reservoir discharge rates
Figure BDA0003894553650000096
c. Continuity equation of reservoir pipe network
Figure BDA0003894553650000101
Wherein I hjt 、S hjt Showing the inflow and the outflow of the jth unit at the moment t, R uj Representing the number of upstream banks of the jth hydroelectric station.
The mathematical solving process of the scheduling mathematical model is as follows:
(1) Through random value taking, a system solution set is initialized, namely, the output results of thermal power, hydropower, wind power and photovoltaic units at the current moment:
Figure BDA0003894553650000102
for convenience of description, the power generation type is omitted, and is uniformly rewritten as S 1 ＝{P 1 ,P 2 ,...,P n }；
(2) Calculating an objective function value F (S) of the current solution set 1 )；
(3) Sequencing the output priority of each unit, sequentially changing the output of each unit by small output variation, observing the influence of the output on a target function value to judge the contribution sensitivity of the output of each unit to the target function, and aiming at the ith unit, the sensitivity calculation mode is that
Figure BDA0003894553650000103
Wherein S 1 ＝{P 1 ,P 2 ,...P i ,...P n }，S 2 ＝{P 1 ,P 2 ,...P i +ε,...P n ε is a sufficiently small value;
(4) Sequencing the units from high sensitivity to low sensitivity, increasing the output of the unit with the highest sensitivity each time, reducing the output of the unit with the lowest sensitivity in the same size, and generating a new solution set state;
(5) And (4) repeating the steps 2-4 until the target function result meets the requirement or the target function difference between the adjacent states is smaller than a threshold value, and the obtained system solution set is the optimal solution set.
Example 2:
in this embodiment, an actual low-carbon scheduling method in a multi-source power network is analyzed according to a five-machine system, as shown in fig. 1, a 5-machine system network shown in fig. 1 is constructed by using Simulink power network simulation software, and includes five generator sets such as wind power, photovoltaic, hydroelectric, thermal power and energy storage power stations, wherein the hydroelectric and thermal power generator sets respectively include 4 and 3 individual generator sets. The specific unit output parameters are shown in table 1, the whole scheduling period is 1 day, and is divided into 24 intervals, and meanwhile, the influences of variable load and transmission loss are considered.
TABLE 1 types of units and ranges of output
Figure BDA0003894553650000104
Figure BDA0003894553650000111
Based on the technical characteristics of the existing fan and photovoltaic unit, the rated output of the wind turbine unit is 150MW, the direct cost coefficient is 3.25, and the cut-in speed, the cut-out speed and the rated speed are respectively 4m/s, 25m/s and 15m/s. The rated power of the photovoltaic unit is 150MW, and the direct cost coefficient is 3.5. The standard environment and the specific radiation point of the solar radiation are respectively 1000W/m2 and 150W/m2. The rated installed capacity of the battery energy storage system is 300MW.
The operation cost and the equivalent carbon emission are taken as two sub-target functions, the carbon transaction cost is considered, and the operation cost and the equivalent carbon emission are normalized to be the whole cost target. The optimal output of each unit is solved according to the optimal scheduling process shown in fig. 2, and table 2 summarizes the optimized comprehensive scheduling results.
In addition to the optimal comprehensive scheduling method proposed herein, the results of considering only single optimization objectives such as operating cost, NOX emission, SO2 emission, etc. are also calculated, and the four scheduling methods are shown in table 2.
Table 2 wind, light, water, fire and storage comprehensive scheduling result considering economic cost and environmental benefit simultaneously
Figure BDA0003894553650000112
Figure BDA0003894553650000121
The results of the four scheduling methods of comprehensive scheduling, pure economic optimal scheduling, pure SO2 optimal scheduling, pure NOX scheduling and the like are respectively compared, and the cost and greenhouse gas emission are shown in Table 3.
TABLE 3 comparison of the benefits of the four scheduling methods
Figure BDA0003894553650000122
As can be seen from table 3, in the single-target scheduling mode, since only the optimized target is considered, other indexes are neglected, and the scheduling mode is easily involved in local optimization. The latter three scheduling methods can only separately optimize and solve the cost, SO2 and NOX, but the other two index benefits are greatly different from the optimal value. The optimal comprehensive scheduling algorithm provided by the invention realizes a global optimal solution comprehensively considering multi-target constraint and contradiction due to the consideration of the mutually dominating characteristics and generalized cost optimization among multiple targets, and can reduce the emission of greenhouse gases and pollution gases as far as possible on the premise of not causing excessive economic pressure.
The examples described herein are merely illustrative of the preferred embodiments of the present invention and do not limit the spirit and scope of the invention, and various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the design concept of the present invention should fall within the protection scope of the present invention.

Claims (7)

1. A multi-source economic-low carbon cooperative scheduling method for wind, light, water and fire storage is characterized in that: the method is realized by establishing a mathematical scheduling model and a solving process thereof under the multi-source power grid, and the specific process comprises the following steps:
step 1: establishing an optimization target;
step 2: establishing a constraint condition;
and step 3: and solving the scheduling model.
2. The multi-source economic-low-carbon cooperative scheduling method for wind, light, water and fire storage according to claim 1, characterized in that: establishing an optimization target in the step 1 comprises operation cost and equivalent carbon emission; the constraint conditions in the step 2 comprise the constraints of unit output, power balance, reservoir system operation conditions and the like; and 3, optimizing the scheduling model by adopting an iterative solution method based on the sensitivity factor.
3. The multi-source economic-low-carbon collaborative scheduling method for wind, light, water and fire storage according to claim 2, characterized in that: the method for defining the running cost in the step 1 is as follows:
considering the smaller marginal cost of the hydropower station, the operation cost of the wind, light, water and fire system mainly comes from the fuel cost of the thermal power plant and the respective operation cost of the wind and light units, and the total cost is expressed as:
Figure FDA0003894553640000011
wherein F C For the total operating cost, N t 、N w 、N pv The number of thermal power units, wind power units and photovoltaic units respectively, T is a scheduling period, and the scheduling condition of each hour in a single day, namely 24 scheduling points each day, f is considered in the specification sit The economic cost of the operation of the ith thermal power generating unit at the moment t is related to P sit (the output of the ith thermal power generating unit at the moment t); k wk 、K sm The direct cost coefficients, P, of the kth wind turbine and the mth photovoltaic generator, respectively wkt 、P PVmt Respectively the output of the kth wind turbine generator and the mth photovoltaic generator at the moment t,
the fuel cost function of each thermal power generating unit takes into account the valve point effect and is expressed as
Figure FDA0003894553640000012
Wherein, a si 、b si 、c si 、d si And e si Respectively representing the cost coefficients of the ith thermal power generating unit in a constant term, a primary term, a secondary term and a trigonometric function term,
Figure FDA0003894553640000013
and representing the lowest output limit value of the ith thermal power generating unit.
4. The multi-source economic-low-carbon cooperative scheduling method for wind, light, water and fire storage according to claim 2, characterized in that: the method for defining the equivalent carbon emission in step 1 is as follows:
E CO2,e ＝w 1 E CO2,e +w 2 E NOX +w 3 E SO2 (3)
the equivalent carbon emission source comprises all gas types capable of causing greenhouse effect, all the gases are equivalent to carbon dioxide equivalent according to the effect of causing the same greenhouse effect to be calculated, in the power generation process of the thermal power generating unit, the greenhouse gases mainly generated by the equivalent carbon emission source comprise carbon dioxide, nitrogen oxides and sulfur dioxide, and when the equivalent carbon emission is merged, the weighting factor w is 1 、w 2 、w 3 1, 298, 44 respectively;
the carbon emission calculation method of each category of greenhouse gas comprises the following steps:
(1) CO2 emission:
the CO2 emission of the thermal power plant is mainly related to the coal quality and the combustion efficiency thereof, and is expressed by using a combination form of a polynomial term and an exponential term
Figure FDA0003894553640000021
Wherein E is CO2 Alpha for CO2 emission ci 、β ci The emission coefficient of the ith thermal power generating unit in a constant term and a primary term relative to CO2 emission;
(2) NOx emissions;
NOx emissions from thermal power plants vary in origin, their production being dependent on factors such as boiler temperature, air content, etc., and are expressed as a combination of polynomial and exponential terms
Figure FDA0003894553640000022
Wherein the content of the first and second substances,
Figure FDA0003894553640000023
alpha for NOx emissions ni 、β ni 、γ ni The emission coefficient, eta, of the ith thermal power generating unit with respect to NOx emission in constant terms, primary terms and secondary terms ni 、δ ni The emission coefficient of the ith thermal power generating unit in the exponential term with respect to NOx emission;
(3) Discharging sulfur dioxide;
SO2 emission of a thermal power plant and the combustion quantity of fuel mainly have a quadratic polynomial relationship expressed as
Figure FDA0003894553640000024
Wherein the content of the first and second substances,
Figure FDA0003894553640000025
is the amount of SO2 discharged, alpha si 、β si 、γ si And regarding the emission coefficient of SO2 emission of the ith thermal power generating unit in a constant term, a primary term and a secondary term.
5. The multi-source economic-low-carbon cooperative scheduling method for wind, light, water and fire storage according to claim 2, characterized in that: in the two types of optimization targets in the step 1, the operation cost and the carbon emission of the power generation fuel are unified into the comprehensive power generation cost by using the carbon trading price of the carbon trading market as a relation;
F＝F C +Price CO2,e *E CO2,e (7)
wherein F is the comprehensive power generation cost, F C For fuel operating costs, E CO2,e Price for equivalent carbon dioxide emissions CO2,e Is the carbon trade price.
6. The multi-source economic-low-carbon collaborative scheduling method for wind, light, water and fire storage according to claim 2, characterized in that: step 2: establishing constraint conditions which mainly comprise source network load-power balance limitation and output constraint of each unit as follows:
(1) A power balance limit;
Figure FDA0003894553640000031
wherein N is h And P hjt The number of the hydroelectric generating sets and the output of the jth hydroelectric generating set at the moment t are respectively, under the condition, the source network load can reach real-time power balance, P bt 、P Dt 、P Lt Respectively storing charging and discharging power, a load magnitude and line loss of the transmission line at the moment t;
(2) Calculating the output of each unit;
for a hydro-power unit, hydroelectric power generation is a function of displacement and reservoir level, which is a function of water storage, and the output of the hydro-power unit is as follows:
Figure FDA0003894553640000032
wherein, V hjt And Q hjt Respectively for the jth hydroelectric generating set at the moment tVolume of reservoir water storage and discharge rate, C 1j ～C 6j Respectively about V for hydroelectric generation hjt And Q hjt The power generation coefficient of (a);
for the wind generating set, under different wind speeds, the output power of the kth wind generating set at the time t is represented as:
Figure FDA0003894553640000033
wherein v is in 、v out 、v r Respectively the standard cut-in wind speed, cut-out wind speed and rated wind speed, P of the wind turbine generator wrk Rated output of the kth wind generating set;
for a photovoltaic cell, the power output is expressed as
Figure FDA0003894553640000034
Where G is the light irradiance prediction, R c For a certain amount of radiation, P PVrm Rated output for the mth photovoltaic unit, G std The radiation quantity under the standard environment is adopted;
for an energy storage battery, the constraint of the maximum charge-discharge capacity of the battery is as follows;
Figure FDA0003894553640000041
wherein P is bt Discharging when the voltage is positive, and charging when the voltage is negative;
to total transmission loss P of line Lt And calculating by using a coefficient folding algorithm of an adjacent matrix B:
Figure FDA0003894553640000042
wherein the total number of generators N T ，P it 、P jt Is the power generation amount of the ith and j generator sets, B ij 、B 0i 、B 00 Respectively a unit adjacent coefficient, a unit independent coefficient and a constant coefficient of the line loss;
(3) Unit output constraint
Based on the output calculation conditions, the maximum output value of each unit is calculated, and the output constraints of the hydroelectric power, the thermal power and the wind turbine are as follows:
Figure FDA0003894553640000043
(4) Reservoir system restrictions;
reservoir constraints include water balance equations, reservoir storage and discharge targets, these constraints include:
a. practical limitation of reservoir volume
Figure FDA0003894553640000044
b. Practical limits on reservoir discharge rates
Figure FDA0003894553640000045
c. Continuity equation of reservoir pipe network
Figure FDA0003894553640000046
Wherein I hjt 、S hjt Showing the inflow and the outflow of the jth unit at the moment t, R uj Representing the number of upstream banks of the jth hydroelectric station.
7. The multi-source economic-low-carbon collaborative scheduling method for wind, light, water and fire storage according to claim 2, characterized in that: in the mathematical scheduling model in the step 3, the mathematical solving process is as follows:
(1) Through random value taking, a system solution set is initialized, namely, the output results of thermal power, hydropower, wind power and photovoltaic units at the current moment:
Figure FDA0003894553640000051
for convenience of description, the power generation type is omitted, and is uniformly rewritten as S 1 ＝{P 1 ,P 2 ,...,P n }；
(2) Calculating an objective function value F (S) of the current solution set 1 )；
(3) The output priority of each unit is sorted, the output of each unit is changed in sequence by smaller output variation, the influence of the output on the objective function value is observed, so that the contribution sensitivity of the output of each unit on the objective function is judged, and for the ith unit, the sensitivity calculation mode is that
Figure FDA0003894553640000052
Wherein S 1 ＝{P 1 ,P 2 ,...P i ,...P n }，S 2 ＝{P 1 ,P 2 ,...P i +ε,...P n ε is a sufficiently small value;
(4) Sequencing the units from high sensitivity to low sensitivity, increasing the output of the unit with the highest sensitivity each time, reducing the output of the unit with the lowest sensitivity in the same size, and generating a new solution set state;
(5) And (4) repeating the steps 2-4 until the target function result meets the requirement or the target function difference between the adjacent states is smaller than a threshold value, and the obtained system solution set is the optimal solution set.
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