CN116191493A - Thermal power unit depth peak shaving and composite energy storage collaborative planning method and device - Google Patents

Thermal power unit depth peak shaving and composite energy storage collaborative planning method and device Download PDF

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CN116191493A
CN116191493A CN202211697089.2A CN202211697089A CN116191493A CN 116191493 A CN116191493 A CN 116191493A CN 202211697089 A CN202211697089 A CN 202211697089A CN 116191493 A CN116191493 A CN 116191493A
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潘学萍
朱健宇
史雯
秦景辉
孙晓荣
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Abstract

The invention provides a depth peak shaving and composite energy storage collaborative planning method of a thermal power generating unit, which comprises the following steps: establishing a multi-objective optimization model of thermal power generating unit depth peak shaving and composite energy storage collaborative planning by using the optimal economy, the optimal carbon emission reduction and the minimum wind and light rejection amount; preprocessing input data based on entropy weight-theory, calculating the comprehensive score of each decision variable, and determining the input sequence of thermal power depth peak shaving, pumping storage and chemical energy storage; and obtaining the deep peak shaving amount of the thermal power unit by adopting an improved NSGA-II method and combining the fuzzy membership degree, and newly building a composite energy storage planning capacity. The invention provides an improved NSGA-II algorithm for preprocessing data based on entropy weight-ideal solution, and an optimal configuration scheme for solving flexible resources by establishing a multi-objective planning model, so that the method has the advantages of high solving speed, strong adaptability, and capabilities of reducing carbon emission and new energy consumption, obviously reduces the wind-solar rejection rate of the system, and is beneficial to low-carbon economic operation of an electric power system.

Description

Thermal power unit depth peak shaving and composite energy storage collaborative planning method and device
Technical Field
The invention relates to the technical field of power system planning, in particular to a thermal power unit depth peak shaving and composite energy storage collaborative planning method and device.
Background
Due to fluctuation, intermittence and uncertainty of the new energy generated power, the power grid supply adequacy is reduced due to large-scale new energy grid connection, and great pressure is brought to peak shaving of a power system. In the future power grid, the thermal power flexibility transformation, the chemical energy storage and the pumped storage are high in the ratio of the three peak shaving resources in the power grid, and the three are operated in a collaborative planning mode, so that the power balance and the safe and stable operation of the power system are facilitated.
At present, when flexible resource optimization planning is carried out, the aim of economic optimization or the maximum new energy consumption is often adopted. However, the deep peak shaving of the thermal power generating unit can cause the problems of low operation efficiency, insufficient boiler combustion and the like, so that new environmental protection pressure is brought, and the contradiction between the flexibility transformation and the carbon emission reduction capability of the thermal power generating unit needs to be coordinated.
In view of this, there is a need to provide a new structure or method in order to solve at least some of the above problems.
Disclosure of Invention
Aiming at one or more problems in the prior art, the invention provides a thermal power generating unit depth peak shaving and composite energy storage collaborative planning method, provides an improved NSGA-II algorithm for data preprocessing based on entropy weight-ideal solution, establishes a multi-objective planning model to obtain an optimal configuration scheme of flexible resources, has higher solving speed and strong adaptability, combines carbon emission reduction and new energy consumption capability, obviously reduces the wind and light rejection rate of a system, and is beneficial to low-carbon economic operation of an electric power system.
The technical solution for realizing the purpose of the invention is as follows:
a thermal power generating unit depth peak shaving and composite energy storage collaborative planning method comprises the following steps:
s1, building an economy model with the maximum annual income as a target, building a carbon emission reduction model with the maximum carbon emission reduction amount as a target, building a wind-light-discarding model with the minimum wind-light-discarding amount as a target, and building a multi-target optimization model for thermal power unit depth peak regulation and composite energy storage collaborative planning based on the economy model, the carbon emission reduction model and the wind-light-discarding model;
s2, preprocessing input data based on entropy weight-theory, wherein the input data are decision variables comprising thermal power depth peak regulation, pumping storage and chemical energy storage, calculating to obtain the comprehensive score of each decision variable in the multi-objective optimization model, and determining the input sequence of the thermal power depth peak regulation, pumping storage and chemical energy storage from large to small according to the comprehensive score of each decision variable;
and S3, carrying out model solving by adopting an improved NSGA-II method, wherein the improved method generates offspring by introducing a normal distribution crossover operator and a self-adaptive adjustment variation mode on the basis of the NSGA-II method to obtain a Pareto optimal solution set, and obtains comprehensive optimal solution by fuzzy membership degree to obtain the deep peak regulation quantity of the thermal power generating unit and the newly-built composite energy storage planning capacity.
Furthermore, according to the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method, in S1, an economic model is established by taking the maximum annual income as a target, and the economic model is as follows:
max F 1 ＝Pr t +Pr n -C H -C s
wherein F is 1 Is annual income; pr (Pr) t And Pr (Pr) n Generating power benefits respectively for thermal power benefits and new energy sources; c (C) H And C s Respectively pumping and storing unit cost and chemical energy storage cost;
wherein, the thermal power income Pr t The method comprises the steps of selling electricity and income of a power grid company to thermal power and compensating fees of the power grid company to participation in the deep peak shaver set, wherein the method comprises the following steps of:
Figure SMS_1
wherein n is the total annual sampling point number; i represents the i-th time; p (P) t For the actual output k of the thermal power generating unit s And k b The price of electricity sold by the power grid and the price of electricity of the marking rod of the net on the thermal power are respectively; p (P) td The power of the thermal power generating unit is lower than the lower limit of normal output, namely the power participating in deep peak shaving; k (k) td The power price compensation coefficient is used for thermal power depth peak regulation;
new energy power generation income Pr n The method is used for selling electricity and deducting the cost brought by wind and light abandoning, and specifically comprises the following steps:
Figure SMS_2
wherein P is n The total output of the grid-connected new energy is obtained; p (P) d To discard wind and light power; k (k) d The wind and light cost coefficient is abandoned;
cost C of pumping and accumulating unit H The construction cost of the newly added pumping and storage unit and the annual operation and maintenance cost of all pumping and storage units are as follows:
C H ＝P H k H +P nH k nH /m H
wherein P is H And k H The total assembly machine of the pumping and storage unit and the annual operation maintenance cost coefficient thereof are respectively; p (P) nH And k nH Respectively increasing the pumping and accumulating power and the construction cost coefficient of the pumping and accumulating unit; m is m H The service life of the pumping and accumulating unit is prolonged;
cost of chemical energy storage C s The method comprises the following steps of:
Figure SMS_3
wherein E is es To increase chemical energy storage capacity; k (k) es Building a cost coefficient for the chemical energy storage; m is m s And n s The number of times of annual charge and discharge of chemical energy storage and the total number of times of chargeable and dischargeable in the whole life cycle are respectively; v is the annual operation and maintenance cost coefficient of chemical energy storage.
Furthermore, according to the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method, in S1, a carbon emission reduction model is established by taking maximum carbon emission reduction as a target, and the method comprises the following steps:
max F 2 ＝T p +T s -T t
Figure SMS_4
wherein F is 2 The method is characterized in that the carbon emission reduction amount is reduced, and particularly the carbon emission increment caused by the depth peak shaving of the thermal power coal-fired unit is subtracted from the carbon emission reduction amount caused by the composite energy storage; t (T) p And T s Carbon emission reduction amounts converted for the pumping and storage unit and the chemical energy storage operation respectively; t (T) t Carbon emission increment generated by deep peak shaving of the thermal power coal-fired unit; k (k) nf The carbon emission coefficient of unit power for normal operation of the thermal power generating unit; p (P) nf The output of the thermal power unit which does not enter the deep peak regulation is output; k (k) df The unit carbon emission increment coefficient under the deep peak regulation of the thermal power unit is obtained; p (P) p And P s Respectively the real-time power of the pumping and accumulating unit and the real-time power of the chemical energy storage; p (P) t The actual output of the thermal power unit; mu (mu) gen Sum mu n d All are 0-1 variables, and when the value is 1, the generation or chemical energy storage discharge of the pumping and storage unit is indicated.
Furthermore, according to the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method, in S1, the wind and light discarding model is built by taking the minimum wind and light discarding amount as a target, and the wind and light discarding model is built by the following steps:
Figure SMS_5
wherein F is 3 To discard wind and light quantity, P d To discard wind and light power;
the constraint conditions of the wind and light abandoning model comprise:
1) System power balance constraint:
Figure SMS_6
wherein i represents the i-th time; p (P) t The actual output of the thermal power unit; p (P) n The total output of the grid-connected new energy is obtained; mu (mu) pum Sum mu gen Are all 0-1 variable and satisfy mu pum μ gen ＝0，μ pum Mu, as a water pumping sign pum When=1, the water pumping is indicated, μ gen Mu as power generation mark gen When=1, power generation is represented;
Figure SMS_7
and->
Figure SMS_8
Are all 0-1 variables and satisfy +.>
Figure SMS_9
μ n c Charge sign for chemical energy storage->
Figure SMS_10
Time indicates charging, ++>
Figure SMS_11
For chemical energy storage discharge sign->
Figure SMS_12
Time indicates discharge; p (P) p And P s Respectively the real-time power of the pumping and accumulating unit and the real-time power of the chemical energy storage; p (P) d To discard wind and light power; p (P) L Is the system load; p (P) tie To save the inter-exchange power, the outflow is positive;
2) Battery energy storage electric quantity balance constraint:
Figure SMS_13
E s,min ≤E s (i)≤E s,max
Wherein E is s (i) The electric quantity value stored by the battery at the moment i is used as the electric quantity value; t (T) s Is a unit time; η (eta) c And eta d Charging and discharging effects for respectively storing energy of batteryA rate; e (E) s,min And E is connected with s,max Respectively storing a lower limit value and an upper limit value of the electric quantity for battery energy storage;
3) Battery energy storage output constraint:
Figure SMS_14
wherein P is sn Storing rated power for the battery;
4) Reservoir capacity constraint of the pumped storage unit:
Figure SMS_15
E H,min ≤E H (i)≤E H,max
wherein E is H (i) The water storage capacity of the pumped storage power station at the moment i is obtained; e (E) H,min And E is H,max Respectively the minimum water storage capacity and the maximum water storage capacity of the pumped storage power station; η (eta) pum And eta gen Pumping and generating efficiency are respectively carried out;
5) Output constraint of the pumped storage unit:
Figure SMS_16
further, the depth peak shaving and composite energy storage collaborative planning method of the thermal power generating unit comprises the following specific steps of:
s2-1, carrying out normalization processing on economic targets, carbon emission reduction targets or wind and light abandon objective function values of all decision variables in the following mode:
Figure SMS_17
wherein x is ij The j-th objective function value, p, being the i-th decision variable ij Is x ij Is a normalized result of (2); m and n respectively represent the number of objective functions and the number of decision variables;
s2-2, calculating decisionsInformation entropy e of objective function value of variable j Utility value d j And entropy weight w j The method specifically comprises the following steps:
Figure SMS_18
s2-3, determining an optimal scheme by adopting an ideal solution, wherein for positive ideal solutions such as an economic target and a carbon emission reduction target, the optimal scheme comprises the following steps:
Figure SMS_19
For negative ideal solutions such as wind-rejection objectives, there are:
Figure SMS_20
wherein x is j Refers to the set of j-th objective function values of all decision variables, i.e. x j ＝{x 1j ,x 2j ,x 3j -a }; the ideal solution is to process x ij The larger and better the index, the negative ideal solution is to deal with x ij Smaller and better indicators are converted into larger and better indicators x' ij ；
S2-4, establishing a normalized matrix:
Figure SMS_21
z ij ＝x′ ij ×w j
wherein x 'is' ij Refers to all ideal solutions, including positive ideal solutions and negative ideal solutions, Z is the weighted index Z by synthesis ij A matrix of formations;
s2-5, calculating an optimal solution, a worst solution, an optimal distance and a worst distance of the ith decision variable, wherein the optimal solution, the worst solution, the optimal distance and the worst distance are specifically as follows:
Figure SMS_22
/>
wherein z is + Z is the optimal solution - For the worst solution to be the worst,
Figure SMS_23
for the optimal distance->
Figure SMS_24
Is the worst distance;
s2-6, calculating a weighted comprehensive score of the ith variable, wherein the weighted comprehensive score is specifically as follows:
Figure SMS_25
wherein C is i The composite score representing the ith decision variable, the value interval is (0, 1), C i The closer to 1, the closer to the optimal level the various indices representing the decision variables, the higher the composite score.
Furthermore, the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method provided by the invention comprises the following steps of:
(1) Initializing an algorithm to generate a parent population with n individuals, namely, newly-built quantity of each peak regulation means, and calculating objective function values according to the input sequence of each peak regulation means determined by data preprocessing;
(2) Introducing a normal distribution crossover operator and a self-adaptive adjustment mutation mode to carry out crossover mutation to generate offspring with n individuals;
(3) Combining parent and offspring populations, and performing non-dominant sorting and crowding degree calculation on objective function values of the combined populations;
(4) Selecting the first n individuals to generate a new parent population;
(5) And (3) checking whether a stopping condition is met, namely all individuals are non-dominant solutions, if not, returning to the step (2), and if so, outputting a Pareto optimal solution set.
Further, the depth peak shaving and composite energy storage collaborative planning method for the thermal power generating unit provided by the invention comprises the following steps of:
the fuzzy membership function when solving the objective function maximization problem is:
Figure SMS_26
the fuzzy membership function when solving the objective function minimization problem is:
Figure SMS_27
wherein f j Representing the j-th objective function value;
Figure SMS_28
and->
Figure SMS_29
Representing the maximum and minimum of the jth objective function; objective function F 1 And F 2 Selecting a maximum membership function for the maximum total income and the maximum carbon emission reduction of the system respectively; objective function F 3 Selecting a minimum membership function for minimum wind and light quantity discarding;
satisfaction of all individuals of the Pareto optimal solution set was:
Figure SMS_30
Where h represents overall satisfaction, and a larger value of h represents higher satisfaction.
A thermal power generating unit depth peak shaving and composite energy storage collaborative planning device comprises:
the data acquisition unit is used for acquiring output data, load and external regional electricity data of wind power, photovoltaic and synchronous generators at the same time point in one year or more in a certain region, and simultaneously acquiring planning data of new energy sources, pumping and storage units and chemical energy storage in the future year;
the modeling unit is used for establishing a multi-objective optimization model of thermal power unit depth peak shaving and composite energy storage collaborative planning based on three aspects of economy, carbon emission reduction and waste wind quantity, and establishing corresponding constraint conditions;
the solving unit is used for solving the multi-objective optimization model by an improved NSGA-II algorithm for preprocessing data based on the entropy weight ideal solution to obtain a Pareto optimal solution set, and obtaining comprehensive optimal solution through fuzzy membership to obtain the deep peak shaving amount of the thermal power unit and the newly built composite energy storage planning capacity.
Compared with the prior art, the technical scheme provided by the invention has the following technical effects:
1. the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method provided by the invention has the advantages of taking carbon emission reduction and new energy consumption into consideration, and can obviously reduce the wind and light rejection rate of the system, thereby being beneficial to low-carbon economic operation of an electric power system.
2. The improved NSGA-II algorithm for preprocessing data based on the entropy weight-ideal solution provided by the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method has the advantages of high solving speed and strong adaptability.
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The accompanying drawings are included to provide a further understanding of the invention, and together with the description serve to explain the embodiments of the invention, and do not constitute a limitation of the invention. In the drawings:
fig. 1 shows a flow chart of the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method.
Fig. 2 shows an iterative process comparison diagram of an improved NSGA-ii algorithm and a conventional NSGA-ii algorithm of the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method of the present invention.
Fig. 3 shows Pareto optimal solution set and comprehensive optimal solution of improved NSGA-II algorithm multi-objective planning of the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method.
Detailed Description
For a further understanding of the present invention, preferred embodiments of the invention are described below in conjunction with the examples, but it should be understood that these descriptions are merely intended to illustrate further features and advantages of the invention, and are not limiting of the claims of the invention.
The description of this section is intended to be illustrative of only exemplary embodiments and is not intended to be limiting of the scope of the embodiments described herein. Combinations of the different embodiments, and alternatives of features from the same or similar prior art means and embodiments are also within the scope of the description and protection of the invention.
According to one aspect of the invention, a thermal power generating unit depth peak shaving and composite energy storage collaborative planning method comprises the following steps:
s1, building an economy model with the maximum annual income as a target, building a carbon emission reduction model with the maximum carbon emission reduction amount as a target, building a wind-light-discarding model with the minimum wind-light-discarding amount as a target, and building a multi-target optimization model for thermal power unit depth peak regulation and composite energy storage collaborative planning based on the economy model, the carbon emission reduction model and the wind-light-discarding model. Wherein:
(1) The economic model is established with the aim of maximum annual income as follows:
max F 1 ＝Pr t +Pr n -C H -C s
wherein F is 1 Is annual income; pr (Pr) t And Pr (Pr) n Generating power benefits respectively for thermal power benefits and new energy sources; c (C) H And C s Respectively pumping and storing unit cost and chemical energy storage cost;
wherein, the thermal power income Pr t The method comprises the steps of selling electricity and income of a power grid company to thermal power and compensating fees of the power grid company to participation in the deep peak shaver set, wherein the method comprises the following steps of:
Figure SMS_31
Wherein n is the total annual sampling point number; i represents the i-th time; p (P) t For the actual output k of the thermal power generating unit s And k b The price of electricity sold by the power grid and the price of electricity of the marking rod of the net on the thermal power are respectively; p (P) td The power of the thermal power generating unit is lower than the lower limit of normal output, namely the power participating in deep peak shaving; k (k) td The power price compensation coefficient is used for thermal power depth peak regulation;
new energy source generationElectric profit Pr n The method is used for selling electricity and deducting the cost brought by wind and light abandoning, and specifically comprises the following steps:
Figure SMS_32
wherein P is n The total output of the grid-connected new energy is obtained; p (P) d To discard wind and light power; k (k) d The wind and light cost coefficient is abandoned;
cost C of pumping and accumulating unit H The construction cost of the newly added pumping and storage unit and the annual operation and maintenance cost of all pumping and storage units are as follows:
C H ＝P H k H +P nH k nH /m H
wherein P is H And k H The total assembly machine of the pumping and storage unit and the annual operation maintenance cost coefficient thereof are respectively; p (P) nH And k nH Respectively increasing the pumping and accumulating power and the construction cost coefficient of the pumping and accumulating unit; m is m H The service life of the pumping and accumulating unit is prolonged;
cost of chemical energy storage C s The method comprises the following steps of:
Figure SMS_33
wherein E is es To increase chemical energy storage capacity; k (k) es Building a cost coefficient for the chemical energy storage; m is m s And n s The number of times of annual charge and discharge of chemical energy storage and the total number of times of chargeable and dischargeable in the whole life cycle are respectively; v is the annual operation and maintenance cost coefficient of chemical energy storage.
(2) The carbon emission reduction model is established by taking the maximum carbon emission reduction as a target, and the carbon emission reduction model is established as follows:
max F 2 ＝T p +T s -T t
Figure SMS_34
wherein F is 2 For reducing carbon emission, in particular for composite energy storageSubtracting carbon emission increment caused by deep peak shaving of the thermal power coal-fired unit from carbon emission reduction amount; t (T) p And T s Carbon emission reduction amounts converted for the pumping and storage unit and the chemical energy storage operation respectively; t (T) t Carbon emission increment generated by deep peak shaving of the thermal power coal-fired unit; k (k) nf The carbon emission coefficient of unit power for normal operation of the thermal power generating unit; p (P) nf The output of the thermal power unit which does not enter the deep peak regulation is output; k (k) df The unit carbon emission increment coefficient under the deep peak regulation of the thermal power unit is obtained; p (P) p And P s Respectively the real-time power of the pumping and accumulating unit and the real-time power of the chemical energy storage; p (P) t The actual output of the thermal power unit; mu (mu) gen And
Figure SMS_35
all are 0-1 variables, and when the value is 1, the generation or chemical energy storage discharge of the pumping and storage unit is indicated.
(3) The method for building the wind-discarding model by taking the minimum wind-discarding amount as the target comprises the following steps:
Figure SMS_36
wherein F is 3 To discard wind and light quantity, P d To discard wind and light power;
the constraint conditions of the wind and light abandoning model comprise:
1) System power balance constraint:
Figure SMS_37
wherein i represents the i-th time; p (P) t The actual output of the thermal power unit; p (P) n The total output of the grid-connected new energy is obtained; mu (mu) pum Sum mu gen Are all 0-1 variable and satisfy mu pum μ gen ＝0，μ pum Mu, as a water pumping sign pum When=1, the water pumping is indicated, μ gen Mu as power generation mark gen When=1, power generation is represented;
Figure SMS_38
sum mu n d Are all 0-1 variables and satisfy +.>
Figure SMS_39
Charge sign for chemical energy storage->
Figure SMS_40
Time indicates charging, ++>
Figure SMS_41
For chemical energy storage discharge sign->
Figure SMS_42
Time indicates discharge; p (P) p And P s Respectively the real-time power of the pumping and accumulating unit and the real-time power of the chemical energy storage; p (P) d To discard wind and light power; p (P) L Is the system load; p (P) tie To save the inter-exchange power, the outflow is positive;
2) Battery energy storage electric quantity balance constraint:
Figure SMS_43
E s,min ≤E s (i)≤E s,max
wherein E is s (i) The electric quantity value stored by the battery at the moment i is used as the electric quantity value; t (T) s Is a unit time; η (eta) c And eta d Charging and discharging efficiencies of the battery energy storage respectively; e (E) s,min And E is connected with s,max Respectively storing a lower limit value and an upper limit value of the electric quantity for battery energy storage;
3) Battery energy storage output constraint:
Figure SMS_44
wherein P is sn Storing rated power for the battery;
4) Reservoir capacity constraint of the pumped storage unit:
Figure SMS_45
E H,min ≤E H (i)≤E H,max
wherein E is H (i) The water storage capacity of the pumped storage power station at the moment i is obtained; e (E) H,min And E is H,max Respectively the minimum water storage capacity and the maximum water storage capacity of the pumped storage power station; η (eta) pum And eta gen Pumping and generating efficiency are respectively carried out;
5) Output constraint of the pumped storage unit:
Figure SMS_46
s2, preprocessing input data based on entropy weight-theory, wherein the input data are decision variables comprising thermal power deep peak regulation, pumping storage and chemical energy storage, calculating to obtain the comprehensive score of each decision variable in the multi-objective optimization model, and determining the input sequence of the thermal power deep peak regulation, pumping storage and chemical energy storage from large to small according to the comprehensive score of each decision variable. The method comprises the following specific steps:
S2-1, carrying out normalization processing on economic targets, carbon emission reduction targets or wind and light abandon objective function values of all decision variables in the following mode:
Figure SMS_47
wherein x is ij The j-th objective function value, p, being the i-th decision variable ij Is x ij Is a normalized result of (2); m and n respectively represent the number of objective functions and the number of decision variables;
s2-2, calculating the information entropy e of the objective function value of each decision variable j Utility value d j And entropy weight w j The method specifically comprises the following steps:
Figure SMS_48
s2-3, determining an optimal scheme by adopting an ideal solution, wherein for positive ideal solutions such as an economic target and a carbon emission reduction target, the optimal scheme comprises the following steps:
Figure SMS_49
for negative ideal solutions such as wind-rejection objectives, there are:
Figure SMS_50
wherein x is j Refers to the set of j-th objective function values of all decision variables, i.e. x j ＝{x 1j ,x 2j ,x 3j -a }; the ideal solution is to process x ij The larger and better the index, the negative ideal solution is to deal with x ij Smaller and better indicators are converted into larger and better indicators x' ij ；
S2-4, establishing a normalized matrix:
Figure SMS_51
z ij ＝x′ ij ×w j
wherein x 'is' ij Refers to all ideal solutions, including positive ideal solutions and negative ideal solutions, Z is the weighted index Z by synthesis ij A matrix of formations;
s2-5, calculating an optimal solution, a worst solution, an optimal distance and a worst distance of the ith decision variable, wherein the optimal solution, the worst solution, the optimal distance and the worst distance are specifically as follows:
Figure SMS_52
/>
Wherein z is + Z is the optimal solution - For the worst solution to be the worst,
Figure SMS_53
for the optimal distance->
Figure SMS_54
Is the worst distance;
s2-6, calculating a weighted comprehensive score of the ith variable, wherein the weighted comprehensive score is specifically as follows:
Figure SMS_55
wherein C is i The composite score representing the ith decision variable, the value interval is (0, 1), C i The closer to 1, the closer to the optimal level the various indices representing the decision variables, the higher the composite score.
And S3, carrying out model solving by adopting an improved NSGA-II method, wherein the improved method generates offspring by introducing a normal distribution crossover operator and a self-adaptive adjustment variation mode on the basis of the NSGA-II method to obtain a Pareto optimal solution set, and obtains comprehensive optimal solution by fuzzy membership degree to obtain the deep peak regulation quantity of the thermal power generating unit and the newly-built composite energy storage planning capacity. The method for improving NSGA-II specifically comprises the following steps:
(1) Initializing an algorithm to generate a parent population with n individuals, namely, newly-built quantity of each peak regulation means, and calculating objective function values according to the input sequence of each peak regulation means determined by data preprocessing;
(2) Introducing a normal distribution crossover operator and a self-adaptive adjustment mutation mode to carry out crossover mutation to generate offspring with n individuals;
(3) Combining parent and offspring populations, and performing non-dominant sorting and crowding degree calculation on objective function values of the combined populations;
(4) Selecting the first n individuals to generate a new parent population;
(5) And (3) checking whether a stopping condition is met, namely all individuals are non-dominant solutions, if not, returning to the step (2), and if so, outputting a Pareto optimal solution set.
The method for obtaining the comprehensive optimal solution through the fuzzy membership degree comprises the following steps:
the fuzzy membership function when solving the objective function maximization problem is:
Figure SMS_56
the fuzzy membership function when solving the objective function minimization problem is:
Figure SMS_57
wherein f j Representing the j-th objective function value;
Figure SMS_58
and->
Figure SMS_59
Representing the maximum and minimum of the jth objective function; objective function F 1 And F 2 Selecting a maximum membership function for the maximum total income and the maximum carbon emission reduction of the system respectively; objective function F 3 Selecting a minimum membership function for minimum wind and light quantity discarding;
satisfaction of all individuals of the Pareto optimal solution set was:
Figure SMS_60
/>
where h represents overall satisfaction, and a larger value of h represents higher satisfaction.
According to another aspect of the invention, a thermal power generating unit depth peak shaving and composite energy storage collaborative planning device comprises:
the data acquisition unit is used for acquiring output data, load and external regional electricity data of wind power, photovoltaic and synchronous generators at the same time point in one year or more in a certain region, and simultaneously acquiring planning data of new energy sources, pumping and storage units and chemical energy storage in the future year;
The modeling unit is used for establishing a multi-objective optimization model of thermal power unit depth peak shaving and composite energy storage collaborative planning based on three aspects of economy, carbon emission reduction and waste wind quantity, and establishing corresponding constraint conditions;
the solving unit is used for solving the multi-objective optimization model by an improved NSGA-II algorithm for preprocessing data based on the entropy weight ideal solution to obtain a Pareto optimal solution set, and obtaining comprehensive optimal solution through fuzzy membership to obtain the deep peak shaving amount of the thermal power unit and the newly built composite energy storage planning capacity.
Example 1
The invention preprocesses the data before the objective function value is calculated by the improved NSGA-II algorithm, puts forward a plurality of common evaluation indexes (the algorithm objective function can also be used as the evaluation index) for all the decision variables, calculates the comprehensive evaluation score of each decision variable based on the entropy weight-ideal method, determines the priority of each decision variable from high to low, and simplifies the calculation of the objective function according to the priority, thereby reducing the iterative times and the calculated amount of the algorithm.
In the embodiment, the deep peak shaving of the thermal power unit and the collaborative planning of the composite energy storage are performed according to the data of the provincial power grid 2025. According to the fourteen-five planning scheme of the province, the capacity of the pumping and storage unit is 3500MW in 2022, and the maximum pumping and storage capacity can be newly built in the province by 2025. The new energy grid-connected installation is about 38000MW, wherein the wind power installation is 9000MW, the photovoltaic installation is 29000MW, and the new energy installation accounts for about 40%. And predicting and obtaining the load of 2025 and the new energy output data according to the load of the power grid 2020-2021 and the new energy actual output data.
The installed capacity of the thermal power generation of the provincial power grid is about 57000MW by 2022, and as new energy grid-connected installation increasingly causes gradual shutdown of thermal power units and partial thermal power unit equipment is old and cannot be flexibly modified, thermal power units with 20000MW at most can be expected to be flexibly modified to different degrees. The upper limit of the chemical energy storage configuration scale of the provincial power grid is 7000MWh up to 2025, the upper limit of the new construction scale of the pumping and storage unit is 2500MW, the thermal power unit of at most 20000MW can participate in deep peak regulation, and the minimum load rate of the deep peak regulation can be 30%.
The power supply, energy storage parameters and time-of-use electricity price data are shown in tables 1 and 2 respectively. The power price (including desulfurization, denitrification and dust removal) of the coal-fired generator set marker post on-line is 0.3844 yuan/kWh. In 2022, for newly approved land wind power projects, newly documented centralized photovoltaic power stations and industrial and commercial distributed photovoltaic projects, a flat-price internet surfing policy is continued, and internet surfing electricity prices are executed according to local coal-fired electricity generation reference prices. The compensation cost during thermal power depth peak regulation is calculated according to the compensation standard of 0.3 yuan/kWh when the load is 40% -50% of the basic load, and the load is calculated according to the compensation standard of 0.7 yuan/kWh when the load is 30% -40% of the basic load. The existing chemical energy storage commonly used comprises 1h energy storage and 2h energy storage, and the invention selects 2h energy storage for optimization, because the photovoltaic duty ratio in the new energy saving source is very high, the time for which the digestion difficulty occurs in the middle of the day is mostly 2-4 hours, and the unit cost of two-hour energy storage is lower.
Table 1 peak shaver parameters
Figure SMS_61
Table 2 time-of-use electricity price meter
Figure SMS_62
The invention provides a deep peak shaving and composite energy storage collaborative planning method for a thermal power generating unit, and an implementation flow is shown in figure 1.
Step 1, establishing a multi-objective optimization model of thermal power generating unit depth peak regulation and composite energy storage collaborative planning with optimal economy, optimal carbon emission reduction and minimum wind and light rejection.
(1) Economic optimal model with maximum annual income as target
max F 1 ＝Pr t +Pr n -C H -C s (1)
In Pr, pr t And Pr (Pr) n Respectively obtaining thermal power benefits and new energy benefits; c (C) H And C s Respectively pumping and storing and chemical energy storing costs; c (C) d The cost of wind and light is reduced. Wherein:
the thermal power benefits comprise the sales power benefits of the power grid company on the thermal power and the compensation fees of the power grid on the participating deep peak shaver set.
Figure SMS_63
Wherein n is the total sampling point number per year15min one point sampling, n=365×96= 35040; i represents the i-th time, and the same applies below; p (P) t For the actual output k of the thermal power generating unit s And k b The price of electricity sold by the power grid and the price of electricity of the marking rod of the net on the thermal power are respectively; p (P) td The power of the thermal power generating unit is lower than the lower limit of normal output (namely, the power participating in deep peak shaving); k (k) td And the thermal power depth peak regulation electricity price compensation coefficient is obtained.
The new energy power generation income is the electricity selling income, and the cost brought by the wind and light abandoning is deducted.
Figure SMS_64
Wherein P is n The total output of the grid-connected new energy is obtained; p (P) d To discard wind and light power; the country promotes new energy to be connected to the internet at a low price, so that the electricity selling income of the new energy is the same as that of the thermal power; k (k) d To discard wind and light cost coefficients.
The cost of the pumping and storage unit comprises the construction cost of the newly added pumping and storage unit and the annual operation and maintenance cost of all pumping and storage units.
C H ＝P H k H +P nH k nH /m H (4)
Wherein P is H And k H The total assembly machine of the pumping and storage unit and the annual operation maintenance cost coefficient thereof are respectively; p (P) nH And k nH Respectively increasing the pumping and accumulating power and the construction cost coefficient of the pumping and accumulating unit; m is m H The service life of the pumping and storage unit is prolonged.
All chemical energy storage is newly built, and the cost comprises the new construction cost and the operation and maintenance cost.
Figure SMS_65
Wherein E is es To increase chemical energy storage capacity; k (k) es Building a cost coefficient for the chemical energy storage; m is m s And n s The number of times of annual charge and discharge of chemical energy storage and the total number of times of chargeable and dischargeable in the whole life cycle are respectively; annual transport with v being chemical energy storageAnd (5) maintaining a cost coefficient.
(2) Maximum target of carbon emission reduction
The carbon emission reduction amount is the carbon emission reduction amount caused by composite energy storage and less carbon emission increment caused by deep peak shaving of the thermal power coal-fired unit. The new energy output reduces the use of the thermal power, and the more new energy consumed by the system, the less the thermal power output is, so that the carbon emission coefficient is calculated according to the carbon emission coefficient when the composite energy storage is calculated during the carbon emission reduction of the composite energy storage.
Objective function F 2 Expressed as:
max F 2 ＝T p +T s -T t (6)
wherein T is p And T s Carbon emission reduction amounts respectively converted for pumping and accumulating facilities and chemical energy storage operation; t (T) t The carbon emission increment generated by the deep peak shaving of the thermal power coal-fired unit is provided.
The components are specifically as follows:
Figure SMS_66
wherein: k (k) nf The carbon emission coefficient of unit power for normal operation of the thermal power generating unit; p (P) nf The output of the thermal power unit which does not enter the deep peak regulation is output; k (k) df The unit carbon emission increment coefficient under the deep peak regulation of the thermal power unit is obtained; p (P) p And P s Respectively the real-time power of the pumping and accumulating unit and the real-time power of the chemical energy storage; mu (mu) gen And
Figure SMS_67
all are 0-1 variables, and the value of 1 represents the power generation or chemical energy storage discharge of the pumping and storage unit.
(3) Wind and light energy discarding minimum target
Figure SMS_68
Constraints include power balance constraints of the system, equipment operation constraints, decision variable constraints, and the like.
<1> System Power balance constraint
Figure SMS_69
Wherein mu is pum Sum mu gen Are all 0-1 variable and satisfy mu pum μ gen ＝0，μ pum Pumping water when the value is 1; mu (mu) gen Generating power when the value is 1, wherein the power generation mark is a power generation mark;
Figure SMS_70
and->
Figure SMS_71
Are all 0-1 variables and satisfy +.>
Figure SMS_72
Charging a chemical energy storage charging sign with a value of 1; />
Figure SMS_73
Is a chemical energy storage discharge sign, and discharges when the value is 1; p (P) L Is the system load; p (P) tie To save the inter-switching power, the outflow is positive.
<2> Battery energy storage and electric quantity balance constraint
Figure SMS_74
E s,min ≤E s (i)≤E s,max (11)
Wherein E is s (i) The electric quantity value stored by the battery at the moment i is used as the electric quantity value; t (T) s Is a unit time; η (eta) c And eta d Charging and discharging efficiencies of the battery energy storage respectively; because the overcharge and the overdischarge can affect the service life of the chemical energy storage, E s,min And E is connected with s,max And respectively storing a lower limit value and an upper limit value of the electric quantity for battery energy storage.
<3> Battery energy storage output constraint
Figure SMS_75
Wherein P is sn For the rated power of the battery energy storage, the absorption and discharge power of the battery energy storage are smaller than the rated power of the battery energy storage, and the constraint of the residual electric quantity in the battery is satisfied.
<4> pumped storage reservoir Capacity constraint
E H,min ≤E H (i)≤E H,max (13)
Figure SMS_76
Wherein E is H (i) The water storage capacity of the pumped storage power station at the moment i is obtained; e (E) H,min And E is H,max Respectively the minimum water storage capacity and the maximum water storage capacity of the pumped storage power station; η (eta) pum And eta gen Pumping and generating efficiency are respectively achieved.
<5> pumped storage Unit output constraint
Figure SMS_77
And 2, preprocessing input data based on entropy weight-theory to obtain comprehensive scores of all decision variables, and determining the input sequence of thermal power deep peak regulation, pumping storage and chemical energy storage according to the comprehensive scores. The method comprises the following steps:
<1> normalization of data
In order to eliminate the influence of different dimensions on the evaluation result, normalization or standardization processing is required for each index. The method comprises the following steps:
Figure SMS_78
wherein x is ij The j-th objective function value, p, being the i-th decision variable ij Is x ij Is a normalized result of (2); m and n each represent the orderNumber of scalar functions and number of decision variables.
<2>Calculating information entropy e of decision variables j Utility value d j And entropy weight w j The method is characterized by comprising the following steps:
Figure SMS_79
<3> determination of optimal solution using ideal solution
When solving the multi-objective decision problem by using the ideal method, a measure needs to be defined in the objective space to measure the degree to which a solution approaches the positive ideal solution and moves away from the negative ideal solution. The central idea is to first select an ideal solution and a negative ideal solution, and then find out the solution closest to the ideal solution and farthest from the negative ideal solution as the optimal solution.
For a positive ideal solution, there are:
Figure SMS_80
for negative ideal solutions, there are:
Figure SMS_81
<4> normalized matrix
z ij ＝x′ ij ×w j (20)
Figure SMS_82
<5> calculating the optimal worst value and the optimal worst distance
Figure SMS_83
Wherein z is + Z is the optimal solution - For the worst solution to be the worst,
Figure SMS_84
for the optimal distance->
Figure SMS_85
Is the worst distance.
<6> calculation of weighted composite score
Figure SMS_86
Wherein C is i The composite score of the i-th variable is represented by (0, 1), and the closer to 1, the closer to the optimal level the evaluation target is, and the higher the composite score is.
The crossover probability of the genetic algorithm is 0.9, the crossover distribution index is 20, the mutation probability is 0.1, the mutation distribution index is 20, the population number is 100, and the iteration algebra is 50. The optimal input sequence of each flexible resource is pumping and accumulating, deep peak regulation of thermal power and chemical energy storage according to the entropy weight method and the ideal solution.
And 3, carrying out model solving by adopting an improved NSGA-II method to obtain a Pareto optimal solution set, and further obtaining a comprehensive optimal solution through fuzzy membership to obtain the deep peak regulation quantity of the thermal power unit and the newly-built composite energy storage planning capacity.
When the NSGA-II method is improved to carry out model solving, the improvement mainly comprises the following steps:
1) A normal distributed crossover operator (NDX) is introduced to enhance the spatial searching capability of the algorithm.
2) An improved self-adaptive variation adjustment mode is provided, and the population optimization speed is improved. The traditional NSGA-II algorithm adopts a polynomial mutation mode, and as the mutation operator contains random parameters and subjective parameters, the randomness is larger, and the convergence speed is slower. The improved self-adaptive adjustment variation mode can obtain better convergence effect through the action mechanism, so that the convergence speed is improved by utilizing the variation action, and the diversity and stability of the population are improved, and the Pareto boundary distribution is better.
The objective function maximization value is solved by the fuzzy membership function of formula (24):
Figure SMS_87
solving the objective function minimization problem is represented by a fuzzy membership function of formula (25):
Figure SMS_88
wherein f j Representing the j-th objective function value;
Figure SMS_89
and->
Figure SMS_90
Representing the maximum and minimum of the jth objective function. For the multi-objective optimization problem herein, objective function F 1 And F 2 Selecting a maximum membership function for the maximum total income and the maximum carbon emission reduction of the system respectively; objective function F 3 And selecting a minimum membership function for minimum wind and light quantity discarding.
Satisfaction of all chromosomes of the Pareto optimal solution set is shown in equation (26).
Figure SMS_91
Where h represents overall satisfaction (normalization). The larger the number, the higher the satisfaction.
The Pareto optimal solution set result of the thermal power depth peak regulation and composite energy storage collaborative planning is shown in the accompanying figure 2 and the table 3.
TABLE 3 comprehensive optimal solution and Single target optimal solution
Figure SMS_92
Figure SMS_93
/>
As can be seen from fig. 3 and table 3: 1) The satisfaction degree corresponding to the comprehensive optimal solution under the multi-objective collaborative optimization is the highest, and the numerical value is 0.802. The collaborative optimization result is: the reconstruction capacity and peak regulation depth of the thermal power generating unit reach the upper limit, which are 20000MW and 30% respectively; and a newly built pumping and accumulating unit 1964MW and a newly built chemical energy storage unit 403MWh, and the wind and solar energy rejection rate of the system is 0.09%. 2) When the economic optimization is targeted, the optimization results are: the reconstruction capacity and peak regulation depth of the thermal power generating unit reach the upper limit, the new pumping and accumulating unit is built with 64MW, the wind and light rejection rate is 0.273%, and the wind and light rejection amount of the system is large under the condition. The method is preferably used because the cost of the deep peak shaving reconstruction of the thermal power generating unit is minimum. The relationship between the new pumping and accumulating scale and the system economy is shown in fig. 3, and the system economy is optimal when the new pumping and accumulating unit is built for 64 MW. 3) If only the optimal carbon emission is used as a target, the optimal result is that the thermal power depth peak regulation is 0, and the newly built chemical energy storage (70000 MWh) and the pumping and storage unit (2000 MW) reach the maximum value. The deep peak regulation of the thermal power can increase the unit power generation coal so as to increase the carbon emission, and the pumping and storage and the chemical energy storage can bring carbon emission reduction benefits. 4) If the optimal wind and light discarding is only used as a target, the optimal result is that all peak regulation means reach the upper limit value, and the wind and light discarding amount of the system is minimum.
The multi-objective optimization iterative process based on the traditional NSGA-II algorithm and the improved NSGA-II algorithm is shown in figure 3. It can be seen that:
the improved NSGA-II algorithm is adopted, the iteration convergence speed is obviously higher than that of the traditional algorithm, the optimal solution is converged after the iteration is performed for 8 times, and the optimal solution can be achieved after the traditional NSGA-II algorithm is performed for 25 times. Because the converged optimal population numbers are consistent, the optimal results of the two algorithms are consistent. Thus, the convergence speed and the calculation efficiency of the algorithm can be improved by adopting the NSGA-II algorithm.
Example 2
The invention provides a thermal power generating unit depth peak shaving and composite energy storage collaborative planning device, which comprises the following steps:
an acquisition unit: the method comprises the steps of acquiring 96-point wind power, photovoltaic power, output data of a synchronous generator, load and out-of-zone electricity data of 365 days of one or more years. And simultaneously acquiring planning data of new energy, pumping and storing and chemical energy storage in the future year.
Modeling unit: and (3) establishing a multi-target planning model with cooperative deep peak shaving and composite energy storage of the thermal power unit and corresponding constraint conditions in the aspects of economy, carbon emission reduction and wind and light energy rejection.
And a solving unit: the improved NSGA-II algorithm for preprocessing the data based on the entropy weight ideal solution solves the multi-objective planning model to obtain a Pareto optimal solution set, and further obtains the comprehensive optimal solution through fuzzy membership to obtain the deep peak shaving amount of the thermal power generating unit and the newly-built composite energy storage planning capacity.
The description and applications of the present invention herein are illustrative and are not intended to limit the scope of the invention to the embodiments described above. The relevant descriptions of effects, advantages and the like in the description may not be presented in practical experimental examples due to uncertainty of specific condition parameters or influence of other factors, and the relevant descriptions of effects, advantages and the like are not used for limiting the scope of the invention. Variations and modifications of the embodiments disclosed herein are possible, and alternatives and equivalents of the various components of the embodiments are known to those of ordinary skill in the art. It will be clear to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, and with other assemblies, materials, and components, without departing from the spirit or essential characteristics thereof. Other variations and modifications of the embodiments disclosed herein may be made without departing from the scope and spirit of the invention.

Claims (8)

1. The deep peak shaving and composite energy storage collaborative planning method for the thermal power generating unit is characterized by comprising the following steps of:
s1, building an economy model with the maximum annual income as a target, building a carbon emission reduction model with the maximum carbon emission reduction amount as a target, building a wind-light-discarding model with the minimum wind-light-discarding amount as a target, and building a multi-target optimization model for thermal power unit depth peak regulation and composite energy storage collaborative planning based on the economy model, the carbon emission reduction model and the wind-light-discarding model;
S2, preprocessing input data based on entropy weight-theory, wherein the input data are decision variables comprising thermal power depth peak regulation, pumping storage and chemical energy storage, calculating to obtain the comprehensive score of each decision variable in the multi-objective optimization model, and determining the input sequence of the thermal power depth peak regulation, pumping storage and chemical energy storage from large to small according to the comprehensive score of each decision variable;
and S3, carrying out model solving by adopting an improved NSGA-II method, wherein the improved method generates offspring by introducing a normal distribution crossover operator and a self-adaptive adjustment variation mode on the basis of the NSGA-II method to obtain a Pareto optimal solution set, and obtains comprehensive optimal solution by fuzzy membership function to obtain the deep peak regulation transformation amount of the thermal power generating unit and the newly-built composite energy storage planning capacity.
2. The thermal power generating unit depth peak shaving and composite energy storage collaborative planning method according to claim 1, wherein in S1, an economic model is established with the maximum annual income as a target, and the economic model is:
max F 1 ＝Pr t +Pr n -C H -C s
wherein F is 1 Is annual income; pr (Pr) t And Pr (Pr) n Generating power benefits respectively for thermal power benefits and new energy sources; c (C) H And C s Respectively pumping and storing unit cost and chemical energy storage cost;
wherein, the thermal power income Pr t The method comprises the steps of selling electricity and income of a power grid company to thermal power and compensating fees of the power grid company to participation in the deep peak shaver set, wherein the method comprises the following steps of:
Figure FDA0004022685750000011
Wherein n is the total annual sampling point number; i represents the i-th time; p (P) t For the actual output k of the thermal power generating unit s And k b The price of electricity sold by the power grid and the price of electricity of the marking rod of the net on the thermal power are respectively; p (P) td The power of the thermal power generating unit is lower than the lower limit of normal output, namely the power participating in deep peak shaving; k (k) td Power price compensation system for thermal power depth peak regulationA number;
new energy power generation income Pr n The method is used for selling electricity and deducting the cost brought by wind and light abandoning, and specifically comprises the following steps:
Figure FDA0004022685750000012
wherein P is n The total output of the grid-connected new energy is obtained; p (P) d To discard wind and light power; k (k) d The wind and light cost coefficient is abandoned;
cost C of pumping and accumulating unit H The construction cost of the newly added pumping and storage unit and the annual operation and maintenance cost of all pumping and storage units are as follows:
C H ＝P H k H +P nH k nH /m H
wherein P is H And k H The total assembly machine of the pumping and storage unit and the annual operation maintenance cost coefficient thereof are respectively; p (P) nH And k nH Respectively increasing the pumping and accumulating power and the construction cost coefficient of the pumping and accumulating unit; m is m H The service life of the pumping and accumulating unit is prolonged;
cost of chemical energy storage C s The method comprises the following steps of:
Figure FDA0004022685750000021
wherein E is es To increase chemical energy storage capacity; k (k) es Building a cost coefficient for the chemical energy storage; m is m s And n s The number of times of annual charge and discharge of chemical energy storage and the total number of times of chargeable and dischargeable in the whole life cycle are respectively; v is the annual operation and maintenance cost coefficient of chemical energy storage.
3. The thermal power generating unit depth peak shaving and composite energy storage collaborative planning method according to claim 1, wherein in S1, a carbon emission reduction model is established with the maximum carbon emission reduction as a target, and the method is characterized in that:
max F 2 ＝T p +T s -T t
Figure FDA0004022685750000022
wherein F is 2 The method is characterized in that the carbon emission reduction amount is reduced, and particularly the carbon emission increment caused by the depth peak shaving of the thermal power coal-fired unit is subtracted from the carbon emission reduction amount caused by the composite energy storage; t (T) p And T s Carbon emission reduction amounts converted for the pumping and storage unit and the chemical energy storage operation respectively; t (T) t Carbon emission increment generated by deep peak shaving of the thermal power coal-fired unit; k (k) nf The carbon emission coefficient of unit power for normal operation of the thermal power generating unit; p (P) nf The output of the thermal power unit which does not enter the deep peak regulation is output; k (k) df The unit carbon emission increment coefficient under the deep peak regulation of the thermal power unit is obtained; p (P) p And P s Respectively the real-time power of the pumping and accumulating unit and the real-time power of the chemical energy storage; p (P) t The actual output of the thermal power unit; mu (mu) gen And
Figure FDA0004022685750000023
all are 0-1 variables, and when the value is 1, the generation and chemical energy storage discharge of the pumping and storage unit are indicated.
4. The thermal power generating unit depth peak shaving and composite energy storage collaborative planning method according to claim 1, wherein in S1, the building of the wind-discarding model with the minimum wind-discarding amount as the target is:
Figure FDA0004022685750000024
wherein F is 3 To discard wind and light quantity, P d To discard wind and light power;
The constraint conditions of the wind and light abandoning model comprise:
1) System power balance constraint:
Figure FDA0004022685750000025
wherein i represents the i-th time; p (P) t The actual output of the thermal power unit; p (P) n The total output of the grid-connected new energy is obtained; mu (mu) pum Sum mu gen Are all 0-1 variable and satisfy mu pum μ gen ＝0，μ pum Mu, as a water pumping sign pum When=1, the water pumping is indicated, μ gen Mu as power generation mark gen When=1, power generation is represented;
Figure FDA0004022685750000031
and->
Figure FDA0004022685750000032
Are all 0-1 variables and satisfy +.>
Figure FDA0004022685750000033
Figure FDA0004022685750000034
Charge sign for chemical energy storage->
Figure FDA0004022685750000035
Time indicates charging, ++>
Figure FDA0004022685750000036
For chemical energy storage discharge sign->
Figure FDA0004022685750000037
Time indicates discharge; p (P) p And P s Respectively the real-time power of the pumping and accumulating unit and the real-time power of the chemical energy storage; p (P) d To discard wind and light power; p (P) L Is the system load; p (P) tie To save the inter-exchange power, the outflow is positive;
2) Battery energy storage electric quantity balance constraint:
Figure FDA0004022685750000038
E s,min ≤E s (i)≤E s,max
wherein E is s (i) The electric quantity value stored by the battery at the moment i is used as the electric quantity value; t (T) s Is a unit time; η (eta) c And eta d Charging and discharging efficiencies of the battery energy storage respectively; e (E) s,min And E is connected with s,max Respectively storing a lower limit value and an upper limit value of the electric quantity for battery energy storage;
3) Battery energy storage output constraint:
Figure FDA0004022685750000039
wherein P is sn For the rated power of the battery energy storage, the absorption and discharge power of the battery energy storage are smaller than the rated power;
4) Reservoir capacity constraint of the pumped storage unit:
Figure FDA00040226857500000310
E H,min ≤E H (i)≤E H,max
wherein E is H (i) The water storage capacity of the pumped storage power station at the moment i is obtained; e (E) H,min And E is H,max Respectively the minimum water storage capacity and the maximum water storage capacity of the pumped storage power station; η (eta) pum And eta gen Pumping and generating efficiency are respectively carried out;
5) Output constraint of the pumped storage unit:
Figure FDA00040226857500000311
5. the thermal power generating unit depth peak shaving and composite energy storage collaborative planning method according to claim 1, wherein the specific step of S2 comprises:
s2-1, carrying out normalization processing on economic targets, carbon emission reduction targets or wind and light abandon objective function values of all decision variables in the following mode:
Figure FDA0004022685750000041
wherein x is ij The j-th objective function value, p, being the i-th decision variable ij Is x ij Is a normalized result of (2); m and n respectively represent the number of objective functions and the number of decision variables;
s2-2, calculating the information entropy e of the objective function value of each decision variable j Utility value d j And entropy weight w j The method specifically comprises the following steps:
Figure FDA0004022685750000042
s2-3, determining an optimal scheme by adopting an ideal solution, wherein for positive ideal solutions such as an economic target and a carbon emission reduction target, the optimal scheme comprises the following steps:
Figure FDA0004022685750000043
for negative ideal solutions such as wind-rejection objectives, there are:
Figure FDA0004022685750000044
wherein x is j Refers to the set of j-th objective function values of all decision variables, i.e. x j ＝{x 1j ,x 2j ,x 3j -a }; the ideal solution is to process x ij The larger and better the index, the negative ideal solution is to deal with x ij Smaller and better index, converting it into larger and better index x i ′ j ；
S2-4, establishing a normalized matrix:
Figure FDA0004022685750000045
z ij ＝x i ′ j ×w j
wherein x is i ′ j Refers to all ideal solutions, including positive ideal solutions and negative ideal solutions, Z is the weighted index Z by synthesis ij A matrix of formations;
s2-5, calculating an optimal solution, a worst solution, an optimal distance and a worst distance of the ith decision variable, wherein the optimal solution, the worst solution, the optimal distance and the worst distance are specifically as follows:
Figure FDA0004022685750000051
wherein z is + Z is the optimal solution - For the worst solution to be the worst,
Figure FDA0004022685750000052
for the optimal distance->
Figure FDA0004022685750000053
Is the worst distance;
s2-6, calculating a weighted comprehensive score of the ith variable, wherein the weighted comprehensive score is specifically as follows:
Figure FDA0004022685750000054
wherein C is i The composite score representing the ith decision variable, the value interval is (0, 1), C i The closer to 1, the closer to the optimal level the various indices representing the decision variables, the higher the composite score.
6. The thermal power generating unit depth peak shaving and composite energy storage collaborative planning method according to claim 1, wherein the improved NSGA-II method in S3 is as follows:
(1) Initializing an algorithm to generate a parent population with n individuals, namely, newly-built quantity of each peak regulation means, and calculating objective function values according to the input sequence of each peak regulation means determined by data preprocessing;
(2) Introducing a normal distribution crossover operator and a self-adaptive adjustment mutation mode to carry out crossover mutation to generate offspring with n individuals;
(3) Combining parent and offspring populations, and performing non-dominant sorting and crowding degree calculation on objective function values of the combined populations;
(4) Selecting the first n individuals to generate a new parent population;
(5) And (3) checking whether a stopping condition is met, namely all individuals are non-dominant solutions, if not, returning to the step (2), and if so, outputting a Pareto optimal solution set.
7. The thermal power generating unit depth peak shaving and composite energy storage collaborative planning method according to claim 1, wherein the step of obtaining the comprehensive optimal solution through fuzzy membership in the step S3 comprises the following steps:
the fuzzy membership function when solving the objective function maximization problem is:
Figure FDA0004022685750000055
the fuzzy membership function when solving the objective function minimization problem is:
Figure FDA0004022685750000061
wherein f j Representing the j-th objective function value;
Figure FDA0004022685750000062
and->
Figure FDA0004022685750000063
Representing the maximum and minimum of the jth objective function; objective function F 1 And F 2 Maximum total system benefit and carbon emission reduction respectivelyThe maximum value membership function is selected, wherein the maximum value membership function is the maximum value; objective function F 3 Selecting a minimum membership function for minimum wind and light quantity discarding;
satisfaction of all individuals of the Pareto optimal solution set was:
Figure FDA0004022685750000064
where h represents overall satisfaction, and a larger value of h represents higher satisfaction.
8. Deep peak shaving and composite energy storage collaborative planning device of thermal power generating unit, which is characterized by comprising:
the data acquisition unit is used for acquiring output data, load and external regional electricity data of wind power, photovoltaic and synchronous generators at the same time point in one year or more in a certain region, and simultaneously acquiring planning data of new energy sources, pumping and storage units and chemical energy storage in the future year;
The modeling unit is used for establishing a multi-objective optimization model of thermal power unit depth peak shaving and composite energy storage collaborative planning based on three aspects of economy, carbon emission reduction and waste wind quantity, and establishing corresponding constraint conditions;
the solving unit is used for solving the multi-objective optimization model by an improved NSGA-II algorithm for preprocessing data based on the entropy weight ideal solution to obtain a Pareto optimal solution set, and obtaining comprehensive optimal solution through fuzzy membership to obtain the deep peak shaving amount of the thermal power unit and the newly built composite energy storage planning capacity.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116992242A (en) * 2023-09-26 2023-11-03 华北电力大学 Thermal power-energy storage joint overhaul optimization method and system and electronic equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116992242A (en) * 2023-09-26 2023-11-03 华北电力大学 Thermal power-energy storage joint overhaul optimization method and system and electronic equipment
CN116992242B (en) * 2023-09-26 2023-12-22 华北电力大学 Thermal power-energy storage joint overhaul optimization method and system and electronic equipment

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