CN113300361B - Wind power receiving capacity evaluation method of electric heating combined system based on improved multi-target method - Google Patents

Wind power receiving capacity evaluation method of electric heating combined system based on improved multi-target method Download PDF

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CN113300361B
CN113300361B CN202110686790.3A CN202110686790A CN113300361B CN 113300361 B CN113300361 B CN 113300361B CN 202110686790 A CN202110686790 A CN 202110686790A CN 113300361 B CN113300361 B CN 113300361B
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张新松
朱建锋
顾菊平
姜珂珂
徐扬杨
陆胜男
华亮
李智
郭云翔
卢成
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Abstract

The invention relates to the field of wind power acceptance assessment, in particular to an improved multi-target method-based wind power acceptance assessment method for an electric heating combined system. The method comprises the following steps: step 1, establishing a wind power maximum theoretical output scene probability model based on a day-ahead wind power prediction result and probability characteristics of a wind power prediction error; step 2, establishing an electric heating combined system wind power receiving capacity evaluation model containing a battery energy storage system, and the method comprises the following steps: the method comprises the following steps of optimizing an objective with two different dimensions, namely the lowest system operation cost and the lowest 'wind curtailment' electric quantity, balancing constraint of an electric power system, balancing constraint of a thermodynamic system, technical constraint of a conventional unit, technical constraint of a cogeneration unit, output constraint of a wind power plant and technical constraint of a battery energy storage system; and 3, based on an improved main target method, providing a pareto solution set of the wind power acceptance capability evaluation model by using a DICOPT solver in GAMS, and providing the wind power acceptance capacity and the corresponding acceptance cost of the electric heating combined system to realize the evaluation of the wind power acceptance capability of the electric heating combined system.

Description

Wind power receiving capacity evaluation method of electric heating combined system based on improved multi-target method
Technical Field
The invention relates to the field of wind power acceptance assessment, in particular to an improved multi-target method-based wind power acceptance assessment method for an electric heating combined system.
Background
Currently, with the continuous increase of development strength, wind power has become one of the main power sources of electric power systems in China. By the end of 2019, the accumulated wind power installed capacity in China reaches 2.1 hundred million kilowatts and accounts for 10.4 percent of the total installed capacity of the power generation. In 2019, the wind power generation amount of 4057 hundred million kilowatt hours, which breaks through 4000 million kilowatt hours, accounts for 5.5% of the total power generation amount. Different from a conventional energy unit, wind power output has inherent random fluctuation and uncertainty, so that the operation difficulty of a power grid is increased when large-scale wind power is connected to the grid, the operation efficiency is reduced, and even wind abandon is caused. In 2019, the 'wind abandon' electricity quantity in China is up to 169 hundred million kilowatt hours, the average 'wind abandon' rate is 4%, but the 'wind abandon' rate of partial provinces is still over 10%.
Currently, areas with severe electricity limiting situations of 'wind abandonment' are mainly concentrated in northeast, northwest and north China. In the heating period in winter, the combined heat and power generation units in the power grid of the areas have high occupation ratio and simultaneously undertake the functions of power generation and heat supply, and at the moment, the power and heat supply systems in the areas are typical combined heat and power systems. In a traditional electric heating combined system, the operation mode of 'deciding electricity by heat' is limited, and the peak regulation capability of a cogeneration unit is difficult to exert, so that large-scale 'wind abandoning' and electricity limiting are caused. Therefore, the wind power receiving capacity of the electric heating combined system needs to be evaluated, and reference is provided for system scheduling personnel.
In the literature, "determination of optimal wind power admission level based on approximation and newton interpolation method" (protection and control of power system, 2015, volume 43, period 18, pages 12 to 17) is used for establishing a scheduling model comprehensively considering operation cost of conventional units and 'wind abandonment' electric quantity for determining the optimal wind power admission level of the system. The model introduces a wind power acceptance level coefficient, and provides a particle swarm algorithm-based wind power acceptance level optimization method from the economical point of conventional units and wind power plant operation. The document II, a power grid multi-target day-ahead wind power admission capacity evaluation model based on fuzzy multi-target optimization (power grid technology, 2015, volume 39, no. 2, pages 426 to 431) constructs a power grid multi-target day-ahead wind power admission capacity evaluation model aiming at maximizing wind admission and economic power generation. The model gives consideration to maximization of wind power receiving capacity and economical efficiency of system power generation, and the calculation result can provide reference for scheduling personnel to make a day-ahead plan, so that the wind power utilization rate is improved, and the power generation cost is reduced. The first document and the second document focus on the wind power acceptance assessment capability in the power system, and the influence of a battery energy storage system on the wind power acceptance capability is not considered.
In the third document, "modeling and optimization scheduling of battery energy storage technology power station considering loss cost" (power grid technology, 2017, volume 41, stage 5, pages 1541 to 1548), a wind power admission evaluation model considering loss cost of a battery energy storage system is established, and the model has two scheduling targets: wind power acceptance is the largest and grid operating costs are the lowest. Simulation analysis shows that: the wind power receiving capacity can be effectively improved by accessing the battery energy storage system into a power grid, however, uncertainty of wind power output is not considered in the document, a pareto optimal solution is not given, and the cost and benefit of the battery energy storage system for wind power receiving are not comprehensively analyzed.
Disclosure of Invention
The invention discloses an improved multi-target method based wind power receiving capacity evaluation method for an electric heating combined system, which is used for evaluating the wind power receiving capacity and the corresponding receiving cost of the electric heating combined system after a battery energy storage system is connected and is suitable for the field of wind power receiving capacity evaluation of the electric heating combined system. Firstly, establishing a wind power scene based on the probability characteristic of a wind power prediction error, and considering the uncertainty of the wind power; and then, establishing a wind power receiving capacity evaluation model of the electric heating combined system comprising the battery energy storage system. The model has two different dimensionality optimization targets of lowest system operation cost and lowest 'wind curtailment' electric quantity, and conflicts can exist between the target optimization. In order to solve the problem, the problem is converted into a plurality of single-target optimization problems based on an improved multi-target method, and a pareto solution set of the wind power acceptance evaluation model is given by a DICOPT solver in GAMS. Based on the pareto solution set, the cost and benefit of utilizing a battery energy storage system to promote wind power consumption in the electric heating combination are analyzed in detail.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: the method comprises the following steps:
step 1, establishing a wind power maximum theoretical output scene probability model based on a day-ahead wind power prediction result and probability characteristics of a wind power prediction error;
step 2, establishing an electric heating combined system wind power receiving capacity evaluation model containing a battery energy storage system, and the method comprises the following steps: the method comprises the following steps of optimizing an objective with two different dimensions, namely the lowest system operation cost and the lowest 'wind curtailment' electric quantity, balancing constraint of an electric power system, balancing constraint of a thermodynamic system, technical constraint of a conventional unit, technical constraint of a cogeneration unit, output constraint of a wind power plant and technical constraint of a battery energy storage system;
and 3, based on an improved main target method, providing a pareto solution set of the wind power acceptance capability evaluation model by using a DICOPT solver in GAMS, and providing the wind power acceptance capacity and the corresponding acceptance cost of the electric heating combined system to realize the evaluation of the wind power acceptance capability of the electric heating combined system.
As a preferred technical scheme of the invention: the specific steps of the step 1 are as follows:
when the wind power acceptance performance of the electric heating combination system comprising the battery energy storage system is evaluated from the day-ahead time perspective, the wind power output can be predicted and used as an evaluation reference. The predicted value of the wind power output is the maximum theoretical output of the wind power output, and the maximum theoretical output of the wind power will randomly fluctuate near the predicted value due to the objective existence of the prediction error. Generally speaking, the random fluctuation of the maximum theoretical output of wind power around the predicted value is subject to normal distribution. Due to the fact that a wind power prediction error objectively exists, when the wind power receiving capacity of the electric heating combined system is evaluated, the maximum theoretical output of wind power is located in a feasible domain with a wind power prediction value as the center;
step 1.1, if
Figure GDA0004064992540000021
For a wind power prediction value of the scheduling period t, <' >>
Figure GDA0004064992540000022
The maximum prediction error of the wind power in the scheduling period t is that the upper envelope line and the lower envelope line of the feasible region of the maximum theoretical output of the wind power are->
Figure GDA0004064992540000023
Determining the maximum wind power prediction error of a scheduling time interval t according to the following formula
Figure GDA0004064992540000024
Figure GDA0004064992540000031
In the formula, σ f,t If the maximum wind power prediction error is given according to the formula (1) as the standard deviation of the wind power prediction error in the scheduling time t according to the 3 sigma criterion of normal distribution, the probability that the maximum theoretical wind power output is positioned in the maximum theoretical wind power output feasible region is 99.74 percent;
step 1.2, constructing a structural group consisting of N s The scene probability model formed by the maximum theoretical output curve of the strip wind power is used for approximating the given output band, and the scene probability model consists of N s The maximum theoretical output of wind power under the condition of each scene composition and scene s
Figure GDA0004064992540000032
Comprises the following steps:
Figure GDA0004064992540000033
step 1.3, probability p corresponding to scene s s Comprises the following steps:
Figure GDA0004064992540000034
as a preferred technical scheme of the invention: two optimization targets in the electric heating combined system wind power receiving capacity evaluation model comprising the battery energy storage system in the step 2 are specifically as follows:
optimization objective 1: the electric heating combination system schedules the minimum electric quantity of the 'wind abandon' in the day;
Figure GDA0004064992540000035
in the formula, F cur The expected electric quantity of the 'wind abandon' in the dispatching day; t is the number of scheduling time segments; Δ T is the scheduling period length; k is wind farm index, N wp The number of wind power plants;
Figure GDA0004064992540000036
the actual online wind power of a wind farm k in a scheduling time period t under a scene s is shown;
Figure GDA0004064992540000037
the maximum theoretical output of the wind farm k in the scheduling time period t under the scene s is obtained;
optimization objective 2: the total operation cost of the electric heating combined system in a dispatching day is lowest;
Figure GDA0004064992540000038
in the formula, F oc For the total operation cost in the scheduling day, the total operation cost in the scheduling day comprises: total fuel cost of thermal power generating unit in scheduling day under scene s
Figure GDA0004064992540000039
Boot cost f gen And the fuel cost of the cogeneration unit is->
Figure GDA00040649925400000310
Loss cost F of battery energy storage system bess ；
Total fuel cost of thermal power generating unit in scheduling day under scene s
Figure GDA0004064992540000041
The following:
Figure GDA0004064992540000042
in the formula (6), the first and second groups,
Figure GDA0004064992540000043
scheduling the total fuel cost of the thermal power generating unit in the day under the scene s; i is an index of the thermal power generating unit; n is a radical of gen The number of the thermal power generating units is; x is the number of i,t In order to represent a binary variable of the working state of the thermal power generating unit i in the scheduling period t, 1 represents starting, and 0 represents shutting down; b coal Is the fuel price;
Figure GDA0004064992540000044
The method comprises the steps of obtaining coal consumption of a thermal power generating unit i in a scheduling time period t under a scene s;
boot cost f gen The following:
Figure GDA0004064992540000045
in the formula (7), i is an index of the thermal power generating unit; n is a radical of gen The number of the thermal power generating units is; x is the number of i,t In order to represent a binary variable of the working state of the thermal power generating unit i in the scheduling period t, 1 represents starting, and 0 represents shutting down; f. of gen Scheduling the start-stop cost of the thermal power generating unit within a day;
Figure GDA0004064992540000046
the starting cost of the thermal power generating unit i is calculated;
the described
Figure GDA0004064992540000047
The coal consumption of the thermal power generating unit i in the scheduling time t under the scene s is specifically represented as follows:
Figure GDA0004064992540000048
in the formula, parameter a i 、b i 、c i The coal consumption coefficient of the thermal power generating unit i is obtained;
Figure GDA0004064992540000049
generating power of a thermal power generating unit i in a scheduling time period t under a scene s;
to ensure the quality of heat supply, the cogeneration unit generally does not perform the start-stop operation within a day, and therefore, the daily operating cost of the cogeneration unit only includes the fuel cost of the cogeneration unit
Figure GDA00040649925400000410
The method comprises the following specific steps:
Figure GDA00040649925400000411
in the formula, j is an index of the cogeneration unit; n is a radical of chp The number of the cogeneration units is set;
Figure GDA00040649925400000412
the coal consumption of the cogeneration unit j in the scheduling time t under the scene s is specifically as follows:
Figure GDA00040649925400000413
in the formula, A j 、B j 、C j 、D j 、E j 、F j The coal consumption coefficient of the cogeneration unit j is obtained;
Figure GDA0004064992540000051
respectively the power generation and heat supply power of the cogeneration unit j in the scheduling time t under the scene s;
loss cost F of battery energy storage system bess The estimation can be carried out according to the investment cost and the number of charge-discharge cycles experienced in a scheduling day:
Figure GDA0004064992540000052
in the formula, V bess 、n bess Respectively the investment cost and the cycle life times of the battery energy storage system;
Figure GDA0004064992540000053
for representing the binary variable of the switching condition of the charging and discharging states of the battery energy storage system in the scheduling time t, the system is combined>
Figure GDA0004064992540000054
Taking '1' indicates that the battery energy storage system is switched from the discharging state to the charging state and is in the charging state and/or the charging state>
Figure GDA0004064992540000055
Taking '1' to indicate that the battery energy storage system is switched from a charging state to a discharging state in the scheduling period;
as a preferred technical scheme of the invention: constraint conditions of the wind power receiving capacity evaluation model of the electric heating combined system comprising the battery energy storage system in the step 2 comprise power system balance constraint, thermodynamic system balance constraint, conventional unit technical constraint, combined heat and power unit technical constraint, wind power plant output constraint and battery energy storage system technical constraint;
under any wind power maximum theoretical output scene, the following constraints are required to be met;
power system balance constraint:
Figure GDA0004064992540000056
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000057
under the scene s, the charging and discharging power of the battery energy storage system in the scheduling time t is greater or less than or equal to any scheduling time t>
Figure GDA0004064992540000058
In the method, at most one variable value is larger than zero;
Figure GDA0004064992540000059
An electrical load for a scheduling period t;
thermodynamic system equilibrium constraint:
Figure GDA00040649925400000510
in the formula (I), the compound is shown in the specification,
Figure GDA00040649925400000511
a thermal load for a scheduling period t;
the thermal power generating unit outputs restraint and climbing restraint:
Figure GDA00040649925400000512
Figure GDA0004064992540000061
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000062
is the minimum and maximum technical output of the thermal power unit i>
Figure GDA0004064992540000063
The maximum up-down climbing speed of the thermal power generating unit i is obtained;
minimum start-stop time and start-stop logic constraint of the thermal power generating unit:
Figure GDA0004064992540000064
Figure GDA0004064992540000065
x i,t -x i,t-1 ＝y i,t -z i,t (18)
y i,t +z i,t ≤1 (19)
in the formula, y i,t 、z i,t The method comprises the following steps of obtaining a binary variable which indicates whether a thermal power unit i carries out start-stop operation or not in a scheduling time t, wherein '1' indicates that the thermal power unit i carries out start-stop operation, and '0' indicates that the thermal power unit i is not executed with the start-stop operation; k is a radical of formula i,1 、k i,2 Representing the minimum starting-up and stopping time period number of the thermal power generating unit i; u is an auxiliary index introduced when judging whether the thermal power generating unit i meets the minimum start-stop time constraint;
output constraint of the cogeneration unit:
Figure GDA0004064992540000066
Figure GDA0004064992540000067
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000068
the maximum heating power of the combined heat and power generation unit j is obtained;
Figure GDA0004064992540000069
Respectively the minimum power generation power and the maximum power generation power of the cogeneration unit j under the pure condensation working condition; c j,vm Elastic coefficients representing electric power and thermal power at the time of back pressure operation; c j,v1 、C j,v2 Respectively representing the reduction amount of the power generation power under the maximum power output and the minimum power output when the air input amount is not changed; k j The constant is an auxiliary parameter introduced for judging the boundary of two working conditions of the cogeneration unit j;
and (3) climbing restraint of the cogeneration unit:
Figure GDA0004064992540000071
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000072
the maximum up-down climbing speed of the combined heat and power generation unit j;
wind power plant output constraint:
Figure GDA0004064992540000073
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000074
respectively the maximum theoretical output and the actual online wind power of the wind farm k in the scheduling time t under the scene s;
and (3) constraint of charging and discharging logic states of the battery energy storage system:
Figure GDA0004064992540000075
Figure GDA0004064992540000076
in the formula, O t In order to represent a binary variable of the working state of the battery energy storage system in the scheduling time t, taking '1' to represent that the battery energy storage system is in a charging state in the scheduling time t, and taking '0' to represent that the battery energy storage system is in a discharging state in the scheduling time t;
and (3) restraining the state of charge of the battery energy storage system:
Figure GDA0004064992540000077
Figure GDA0004064992540000078
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000079
representing the state of charge of the battery energy storage system in a scheduling time period t under a scene s; e min And E max Respectively representing the minimum and maximum state of charge allowable values of the battery energy storage system;
Figure GDA00040649925400000710
Respectively representing the charging efficiency and the discharging efficiency of the battery energy storage system; c bess The capacity of the battery energy storage system;
and (3) restricting the charging and discharging rates of the battery energy storage system:
Figure GDA00040649925400000711
Figure GDA0004064992540000081
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000082
and &>
Figure GDA0004064992540000083
The maximum charging and discharging rates of the battery energy storage system are respectively.
As a preferred technical scheme of the invention: the step 3 comprises the following specific steps:
step 3.1, solving a single-target optimization problem with the formula (4) as an optimization target and the formulas (12) to (29) as constraints by adopting a DICOPT solver in GAMS to obtain the minimum 'wind curtailment' electric quantity F in a scheduling day cur,min ；
Step 3.2, solving a single-target optimization problem with the formula (5) as an optimization target and the formulas (12) to (29) as constraints by using a DICOPT solver in GAMS, and enabling the optimal solution to correspond to "Wind abandon electric quantity is used as the maximum wind abandon electric quantity F in a dispatching day cur,max ；
Step 3.3, the electric quantity interval of' wind abandon cur,min ，F cur,max ]Dispersed as a 'wind abandon' electric quantity set omega formed by L discrete points cur Element ε in the set l L =1,2, \8230;, L is given by:
Figure GDA0004064992540000084
step 3.4, based on discretized 'abandoned wind' electricity quantity set omega cur Constructing an optimization problem set consisting of L single-target optimization problems, as shown in formula (31):
Figure GDA0004064992540000085
and 3.5, sequentially solving the L single-target optimization problems given by the formula (31) by adopting a DICOPT solver to obtain a pareto solution set of the wind power acceptance capability evaluation model of the electric heating combination system, wherein the pareto solution set gives the wind power acceptance capacity and the corresponding acceptance cost of the electric heating combination system, and evaluation of the wind power acceptance capability of the electric heating combination system is realized.
Compared with the prior art, the electric heating combined system wind power receiving capacity evaluation method based on the improved multi-target method has the following beneficial effects that:
(1) The influence of wind power prediction errors on wind power acceptance of the electric heating combined system is considered;
(2) The influence of a battery energy storage system on wind power acceptance of an electric heating combined system is considered;
(3) The method comprises the steps of establishing an electric heating combined system wind power receiving capacity evaluation model simultaneously considering two different dimensionality optimization targets of the lowest system operation cost and the minimum 'wind curtailment' electric quantity, and comprehensively displaying the wind power receiving capacity and receiving cost of the electric heating combined system by adopting a pareto solution set of an improved main target method evaluation model.
Drawings
FIG. 1 is a schematic diagram of the process steps of the present invention;
FIG. 2 is a schematic diagram of a feasible region of maximum theoretical output of wind power according to the present invention;
FIG. 3 is a flow chart of solving a wind power receiving capacity evaluation model of the electric heating combined system based on an improved multi-target method.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1 to 2, the invention provides an improved multi-target method-based wind power acceptance assessment method for an electric heating combined system, which specifically comprises the following steps:
step 1, establishing a wind power maximum theoretical output scene probability model based on a day-ahead wind power prediction result and probability characteristics of a wind power prediction error;
step 2, establishing an electric heating combined system wind power receiving capacity evaluation model containing a battery energy storage system, and the method comprises the following steps: the method comprises the following steps of optimizing an objective with two different dimensions, namely the lowest system operation cost and the lowest 'wind curtailment' electric quantity, balancing constraint of an electric power system, balancing constraint of a thermodynamic system, technical constraint of a conventional unit, technical constraint of a cogeneration unit, output constraint of a wind power plant and technical constraint of a battery energy storage system;
and 3, based on an improved main target method, providing a pareto solution set of the wind power acceptance capability evaluation model by using a DICOPT solver in GAMS, and providing the wind power acceptance capacity and the corresponding acceptance cost of the electric heating combined system to realize the evaluation of the wind power acceptance capability of the electric heating combined system.
The specific steps of the step 1 are as follows:
when the wind power acceptance performance of the electric heating combination system comprising the battery energy storage system is evaluated from the day-ahead time, the wind power output can be predicted and used as an evaluation reference. The predicted value of the wind power output is the maximum theoretical output of the wind power output, and the maximum theoretical output of the wind power will randomly fluctuate near the predicted value due to the objective existence of the prediction error. Generally speaking, the random fluctuation of the maximum theoretical output of wind power around the predicted value is subject to normal distribution. Due to the fact that a wind power prediction error objectively exists, when the wind power receiving capacity of the electric heating combined system is evaluated, the maximum theoretical output of wind power is located in a feasible domain with a wind power prediction value as the center;
step 1.1, if
Figure GDA0004064992540000091
For a wind power prediction value of the scheduling period t, <' >>
Figure GDA0004064992540000092
The maximum prediction error of the wind power in the scheduling period t is that the upper envelope line and the lower envelope line of the feasible region of the maximum theoretical output of the wind power are->
Figure GDA0004064992540000093
Determining the maximum wind power prediction error of a scheduling time interval t according to the following formula
Figure GDA0004064992540000094
Figure GDA0004064992540000095
In the formula, σ f,t If the maximum wind power prediction error is given according to the formula (1) as the standard deviation of the wind power prediction error in the scheduling time t according to the 3 sigma criterion of normal distribution, the probability that the maximum theoretical wind power output is positioned in the maximum theoretical wind power output feasible region is 99.74 percent;
step 1.2, constructing a structural group consisting of N s The scene probability model formed by the maximum theoretical output curve of the strip wind power is used for approximating the given output band, and the scene probability model consists of N s The maximum theoretical output of wind power under the condition of each scene composition and scene s
Figure GDA0004064992540000101
Comprises the following steps:
Figure GDA0004064992540000102
step 1.3, probability p corresponding to scene s s Comprises the following steps:
Figure GDA0004064992540000103
two optimization targets in the electric heating combined system wind power receiving capacity evaluation model comprising the battery energy storage system in the step 2 are specifically as follows:
optimization objective 1: the electric heating combined system has the minimum electric quantity of 'wind abandon' in a scheduling day;
Figure GDA0004064992540000104
in the formula, F cur The expected electric quantity of 'wind abandon' in a dispatching day is obtained; t is the number of scheduling time segments; Δ T is the scheduling period length; k is wind farm index, N wp The number of wind power plants;
Figure GDA0004064992540000105
actual online wind power of a wind farm k in a scheduling time period t under a scene s;
Figure GDA0004064992540000106
the maximum theoretical output of the wind farm k in the scheduling time period t under the scene s is obtained;
optimization objective 2: the total operation cost of the electric heating combined system in a dispatching day is lowest;
Figure GDA0004064992540000107
in the formula, F oc For the total operation cost in the scheduling day, the total operation cost in the scheduling day comprises: total fuel cost of thermal power generating unit in scheduling day under scene s
Figure GDA0004064992540000108
Boot cost f gen And the fuel cost of the cogeneration unit is->
Figure GDA0004064992540000109
Loss cost F of battery energy storage system bess ；
Total fuel cost of thermal power generating unit in scheduling day under scene s
Figure GDA00040649925400001010
The following were used:
Figure GDA00040649925400001011
in the formula (6), the first and second groups,
Figure GDA0004064992540000111
scheduling the total fuel cost of the thermal power generating unit in the day under the scene s; i is an index of the thermal power generating unit; n is a radical of gen The number of the thermal power generating units is set; x is the number of i,t In order to represent a binary variable of the working state of the thermal power generating unit i in the scheduling period t, 1 represents starting, and 0 represents shutting down; b is a mixture of coal Is the fuel price;
Figure GDA0004064992540000112
The coal consumption of the thermal power generating unit i in the scheduling time period t under the scene s is calculated;
boot cost f gen The following:
Figure GDA0004064992540000113
in the formula (7), i is an index of the thermal power generating unit; n is a radical of gen The number of the thermal power generating units is set; x is the number of i,t In order to represent a binary variable of the working state of the thermal power generating unit i in the scheduling period t, 1 represents starting, and 0 represents shutting down; f. of gen Scheduling the on-off cost of the thermal power generating unit in the day;
Figure GDA0004064992540000114
the starting cost of the thermal power generating unit i is calculated;
the above-mentioned
Figure GDA0004064992540000115
The coal consumption of the thermal power generating unit i in the scheduling time t under the scene s is specifically represented as follows:
Figure GDA0004064992540000116
in the formula, the parameter a i 、b i 、c i The coal consumption coefficient of the thermal power generating unit i is obtained;
Figure GDA0004064992540000117
generating power of a thermal power generating unit i in a scheduling time period t under a scene s;
to ensure the quality of heat supply, the cogeneration unit is generally not operated to start or stop within a day, so the daily operating cost of the cogeneration unit only includes the fuel cost of the cogeneration unit
Figure GDA0004064992540000118
The method comprises the following specific steps:
Figure GDA0004064992540000119
wherein j is an index of the cogeneration unit; n is a radical of chp The number of the cogeneration units is set;
Figure GDA00040649925400001110
specifically, the coal consumption of the cogeneration unit j in the scheduling time t under the scene s is as follows:
Figure GDA00040649925400001111
in the formula, A j 、B j 、C j 、D j 、E j 、F j The coal consumption coefficient of the cogeneration unit j is obtained;
Figure GDA00040649925400001112
the power generation and heat supply power of the cogeneration unit j in the scheduling time t under the scene s are respectively;
loss cost F of battery energy storage system bess The estimation can be carried out according to the investment cost and the number of charge-discharge cycles experienced in a scheduling day:
Figure GDA0004064992540000121
in the formula, V bess 、n bess Respectively the investment cost and the cycle life times of the battery energy storage system;
Figure GDA0004064992540000122
is a binary variable representing the switching condition of the charging and discharging states of the battery energy storage system in a scheduling time t, and is subjected to judgment>
Figure GDA0004064992540000123
Taking '1' indicates that the battery energy storage system is switched from the discharging state to the charging state and is in the charging state and/or the charging state>
Figure GDA0004064992540000124
Taking '1' to indicate that the battery energy storage system is switched from a charging state to a discharging state in the scheduling period;
the constraint conditions of the wind power receiving capacity evaluation model of the electric heating combined system comprising the battery energy storage system in the step 2 comprise power system balance constraint, thermodynamic system balance constraint, conventional unit technical constraint, cogeneration unit technical constraint, wind power plant output constraint and battery energy storage system technical constraint;
under any wind power maximum theoretical output scene, the following constraints are required to be met;
power system balance constraint:
Figure GDA0004064992540000125
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000126
in a scene s, the charging and discharging power of the battery energy storage system in the scheduling time t is based on the preset scheduling time t>
Figure GDA0004064992540000127
And &>
Figure GDA0004064992540000128
In the method, at most one variable value is larger than zero;
Figure GDA0004064992540000129
An electrical load for a scheduling period t;
thermodynamic system equilibrium constraint:
Figure GDA00040649925400001210
in the formula (I), the compound is shown in the specification,
Figure GDA00040649925400001211
a thermal load for a scheduling period t;
the thermal power generating unit outputs restraint and climbing restraint:
Figure GDA00040649925400001212
Figure GDA00040649925400001213
in the formula (I), the compound is shown in the specification,
Figure GDA00040649925400001214
is the minimum and maximum technical output of the thermal power unit i>
Figure GDA00040649925400001215
The maximum up-down climbing speed of the thermal power generating unit i is obtained;
minimum start-stop time and start-stop logic constraint of the thermal power generating unit:
Figure GDA0004064992540000131
Figure GDA0004064992540000132
x i,t -x i,t-1 ＝y i,t -z i,t (18)
y i,t +z i,t ≤1 (19)
in the formula, y i,t 、z i,t The method comprises the following steps of obtaining a binary variable which indicates whether a thermal power unit i carries out start-stop operation or not in a scheduling time t, wherein '1' indicates that the thermal power unit i carries out start-stop operation, and '0' indicates that the thermal power unit i is not executed with the start-stop operation; k is a radical of i,1 、k i,2 Representing the minimum starting-up and stopping time period number of the thermal power generating unit i; u is an auxiliary index introduced when judging whether the thermal power generating unit i meets the minimum start-stop time constraint;
output constraint of the cogeneration unit:
Figure GDA0004064992540000133
Figure GDA0004064992540000134
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000135
the maximum heating power of the cogeneration unit j;
Figure GDA0004064992540000136
Respectively the minimum power generation power and the maximum power generation power of the cogeneration unit j under the pure condensation working condition; c j,vm Indicating electric work during operation under back pressureElastic coefficients of power and thermal power; c j,v1 、C j,v2 Respectively representing the reduction amount of the power generation power under the maximum and minimum electric output and the multiple extraction unit heat supply heat when the air input amount is not changed; k j The constant is an auxiliary parameter introduced for judging the boundary of two working conditions of the cogeneration unit j;
and (3) climbing restraint of the cogeneration unit:
Figure GDA0004064992540000137
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000138
the maximum up-down climbing rate of the cogeneration unit j;
wind power plant output restraint:
Figure GDA0004064992540000141
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000142
and/or>
Figure GDA0004064992540000143
Respectively representing the maximum theoretical output and the actual online wind power of the wind farm k in the scene s in the scheduling time t;
and (3) constraint of charging and discharging logic states of the battery energy storage system:
Figure GDA0004064992540000144
Figure GDA0004064992540000145
in the formula, O t Taking '1' to represent the binary variable of the working state of the battery energy storage system in the scheduling time period t"represents that the battery energy storage system is in a charging state in the scheduling period, and 0 is taken to represent that the battery energy storage system is in a discharging state in the scheduling period;
and (3) restraining the state of charge of the battery energy storage system:
Figure GDA0004064992540000146
Figure GDA0004064992540000147
in the formula (I), the compound is shown in the specification,
Figure GDA0004064992540000148
representing the state of charge of the battery energy storage system in a scheduling time period t under a scene s; e min And E max Respectively representing the minimum and maximum state of charge allowable values of the battery energy storage system;
Figure GDA0004064992540000149
Respectively representing the charging efficiency and the discharging efficiency of the battery energy storage system; c bess The capacity of the battery energy storage system;
and (3) restricting the charging and discharging rates of the battery energy storage system:
Figure GDA00040649925400001410
Figure GDA00040649925400001411
in the formula (I), the compound is shown in the specification,
Figure GDA00040649925400001412
and &>
Figure GDA00040649925400001413
The maximum charging and discharging rates of the battery energy storage system are respectively.
As shown in fig. 3, the specific steps of step 3 are as follows:
step 3.1, solving a single-target optimization problem with the formula (4) as an optimization target and the formulas (12) to (29) as constraints by adopting a DICOPT solver in GAMS to obtain the minimum 'wind curtailment' electric quantity F in a scheduling day cur,min ；
Step 3.2, solving a single-target optimization problem with the formula (5) as an optimization target and the formulas (12) to (29) as constraints by adopting a DICOPT solver in GAMS, and taking the 'wind curtailment' electric quantity corresponding to the optimal solution as the maximum 'wind curtailment' electric quantity F in a scheduling day cur,max ；
Step 3.3, the electric quantity interval of' wind abandon cur,min ，F cur,max ]Dispersed as a 'wind abandon' electric quantity set omega formed by L discrete points cur Element ε in the set l L =1,2, \8230;, L is given by:
Figure GDA0004064992540000151
step 3.4, based on discretized 'abandoned wind' electricity quantity set omega cur Constructing an optimization problem set consisting of L single-target optimization problems, as shown in formula (31):
Figure GDA0004064992540000152
and 3.5, sequentially solving the L single-target optimization problems given by the formula (31) by adopting a DICOPT solver to obtain a pareto solution set of the electric heating combination system wind power acceptance capability evaluation model, wherein the pareto solution set gives the wind power acceptance capacity and the corresponding acceptance cost of the electric heating combination system, and the evaluation of the electric heating combination system wind power acceptance capability is realized.
The invention discloses an improved multi-target method based wind power receiving capacity evaluation method for an electric heating combined system, which is used for evaluating the wind power receiving capacity and receiving cost of the electric heating combined system after a battery energy storage system is connected from the time of day. Firstly, in order to consider the influence of wind power prediction errors on wind power admission of an electric heating combined system, establishing a wind power maximum theoretical output scene probability model based on the probability characteristics of day-ahead wind power prediction results and wind power prediction errors; then, a wind power receiving capacity evaluation model of the electric-heating combined system comprising the battery energy storage system is established, the model has two different dimensionality optimization targets of the lowest system operation cost and the lowest 'wind abandon' electric quantity, and constraints such as power system balance constraint, thermodynamic system balance constraint, conventional unit technical constraint, cogeneration unit technical constraint, wind power plant output constraint and battery energy storage system technical constraint are considered; and finally, converting the electric heating combined system wind power receiving capacity evaluation model into a series of single-target optimization problems based on an improved main target method, solving the problems by adopting a DICOPT solver in GAMS to obtain a pareto solution set of the wind power receiving capacity evaluation model, and giving the wind power receiving capacity and the corresponding receiving cost of the electric heating combined system to realize the evaluation of the electric heating combined system wind power receiving capacity.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention, and are not intended to limit the scope of the present invention, and any person skilled in the art should understand that equivalent changes and modifications made without departing from the concept and principle of the present invention should fall within the protection scope of the present invention.

Claims (2)

1. The method for evaluating the wind power receiving capacity of the electric heating combined system based on the improved multi-target method is characterized by comprising the following steps of:
step 1, establishing a wind power maximum theoretical output scene probability model based on a day-ahead wind power prediction result and probability characteristics of a wind power prediction error;
step 2, establishing an electric heating combined system wind power receiving capacity evaluation model containing a battery energy storage system, and the method comprises the following steps: the method comprises the following steps of optimizing an objective with two different dimensions, namely the lowest system operation cost and the lowest 'wind curtailment' electric quantity, balancing constraint of an electric power system, balancing constraint of a thermodynamic system, technical constraint of a conventional unit, technical constraint of a cogeneration unit, output constraint of a wind power plant and technical constraint of a battery energy storage system;
step 3, based on an improved main target method, providing a pareto solution set of a wind power acceptance capability evaluation model by using a DICOPT solver in GAMS, providing wind power acceptance capacity and corresponding acceptance cost of the electric heating combined system, and realizing evaluation of the wind power acceptance capability of the electric heating combined system;
two optimization targets in the electric heating combined system wind power receiving capacity evaluation model comprising the battery energy storage system in the step 2 are specifically as follows:
optimization objective 1: the electric heating combination system schedules the minimum electric quantity of the 'wind abandon' in the day;
Figure FDA0004064992530000011
in the formula (4), F cur The expected electric quantity of the 'wind abandon' in the dispatching day; t is the number of scheduling time segments; Δ T is the scheduling period length; k is wind farm index, N wp The number of wind power plants;
Figure FDA0004064992530000012
the actual online wind power of a wind farm k in a scheduling time period t under a scene s is shown;
Figure FDA0004064992530000013
The maximum theoretical output of the wind power plant k in the scheduling time period t under the scene s is obtained;
optimization objective 2: the total operation cost of the electric heating combined system in a dispatching day is lowest;
Figure FDA0004064992530000014
in the formula (5), F oc For the total operation cost in the scheduling day, the total operation cost in the scheduling day comprises: total fuel cost of thermal power generating unit in scheduling day under scene s
Figure FDA0004064992530000015
Boot cost f gen And the fuel cost of the cogeneration unit is greater or less than>
Figure FDA0004064992530000016
Loss cost F of battery energy storage system bess ；
Total fuel cost of thermal power generating unit in scheduling day under scene s
Figure FDA0004064992530000017
The following:
Figure FDA0004064992530000021
in the formula (6), the first and second groups,
Figure FDA0004064992530000022
scheduling the total fuel cost of the thermal power generating unit in the day under the scene s; i is an index of the thermal power generating unit; n is a radical of gen The number of the thermal power generating units is; x is the number of i,t In order to represent a binary variable of the working state of the thermal power generating unit i in the scheduling period t, 1 represents starting, and 0 represents shutting down; b coal Is the fuel price;
Figure FDA0004064992530000023
The method comprises the steps of obtaining coal consumption of a thermal power generating unit i in a scheduling time period t under a scene s;
boot cost f gen The following were used:
Figure FDA0004064992530000024
in the formula (7), i is an index of the thermal power generating unit; n is a radical of gen The number of the thermal power generating units is; x is the number of i,t In order to represent a binary variable of the working state of the thermal power generating unit i in the scheduling time t, 1 represents starting, and 0 represents shutdown; f. of gen Is a fireThe starting and stopping cost of the motor set within a dispatching day;
Figure FDA0004064992530000025
the starting cost of the thermal power generating unit i is calculated;
the above-mentioned
Figure FDA0004064992530000026
The coal consumption of the thermal power generating unit i in the scheduling time t under the scene s is specifically represented as follows:
Figure FDA0004064992530000027
in the formula (8), the parameter a i 、b i 、c i The coal consumption coefficient of the thermal power generating unit i is obtained;
Figure FDA0004064992530000028
generating power of a thermal power generating unit i in a scheduling time period t under a scene s;
the daily operating cost of the cogeneration unit comprises the fuel cost of the cogeneration unit
Figure FDA0004064992530000029
The method comprises the following specific steps:
Figure FDA00040649925300000210
in the formula (9), j is an index of the cogeneration unit; n is a radical of chp The number of the cogeneration units is;
Figure FDA00040649925300000211
specifically, the coal consumption of the cogeneration unit j in the scheduling time t under the scene s is as follows:
Figure FDA00040649925300000212
in the formula (10), A j 、B j 、C j 、D j 、E j 、F j The coal consumption coefficient of the cogeneration unit j is obtained;
Figure FDA00040649925300000213
the power generation and heat supply power of the cogeneration unit j in the scheduling time t under the scene s are respectively;
loss cost F of battery energy storage system bess Estimating according to the investment cost and the number of charge-discharge cycles experienced in a scheduling day:
Figure FDA0004064992530000031
in the formula (11), V bess 、n bess Respectively the investment cost and the cycle life times of the battery energy storage system;
Figure FDA0004064992530000032
is a binary variable representing the switching condition of the charging and discharging states of the battery energy storage system in a scheduling time t, and is subjected to judgment>
Figure FDA0004064992530000033
Taking '1' to indicate that the battery energy storage system is switched from a discharging state to a charging state in the scheduling period, and then based on the scheduling period and the charging state, the battery energy storage system is switched from the discharging state to the charging state, and the battery energy storage system is switched on or off in the charging period>
Figure FDA0004064992530000034
Taking '1' to indicate that the battery energy storage system is switched from a charging state to a discharging state in the scheduling period;
constraint conditions of the wind power receiving capacity evaluation model of the electric heating combined system comprising the battery energy storage system in the step 2 comprise power system balance constraint, thermodynamic system balance constraint, conventional unit technical constraint, combined heat and power unit technical constraint, wind power plant output constraint and battery energy storage system technical constraint;
under any scene of maximum theoretical output of wind power, the following constraints are required to be met;
power system balance constraint:
Figure FDA0004064992530000035
in the formula (12), the first and second groups,
Figure FDA0004064992530000036
under the scene s, the charging and discharging power of the battery energy storage system in the scheduling time t is greater or less than or equal to any scheduling time t>
Figure FDA0004064992530000037
And/or>
Figure FDA0004064992530000038
In the method, at most one variable value is larger than zero;
Figure FDA0004064992530000039
An electrical load for a scheduling period t;
thermodynamic system equilibrium constraint:
Figure FDA00040649925300000310
in the formula (13), the first and second groups,
Figure FDA00040649925300000311
a thermal load for a scheduling period t;
the thermal power generating unit outputs restraint and climbing restraint:
Figure FDA00040649925300000312
Figure FDA00040649925300000313
in the formula (14), the first and second groups,
Figure FDA0004064992530000041
the minimum and maximum technical output of the thermal power generating unit i is obtained;
in the formula (15), the first and second groups of the compound,
Figure FDA0004064992530000042
the maximum up-down climbing speed of the thermal power generating unit i is obtained;
minimum start-stop time and start-stop logic constraint of the thermal power generating unit:
Figure FDA0004064992530000043
Figure FDA0004064992530000044
x i,t -x i,t-1 ＝y i,t -z i,t (18)
y i,t +z i,t ≤1 (19)
in the formulae (16) to (19), y i,t 、z i,t A binary variable indicating whether the thermal power unit i is subjected to start and stop operation in the scheduling time t, wherein '1' indicates that the thermal power unit i is subjected to start and stop operation, and '0' indicates that the thermal power unit i is not subjected to start and stop operation; k is a radical of i,1 、k i,2 Representing the minimum starting-up and stopping time period number of the thermal power generating unit i; u is an auxiliary index introduced when judging whether the thermal power generating unit i meets the minimum start-stop time constraint;
output constraint of the cogeneration unit:
Figure FDA0004064992530000045
Figure FDA0004064992530000046
in the formula (20) and the formula (21),
Figure FDA0004064992530000047
the maximum heating power of the cogeneration unit j;
Figure FDA0004064992530000048
Respectively the minimum power generation power and the maximum power generation power of the cogeneration unit j under the pure condensation working condition; c j,vm Elastic coefficients representing electric power and thermal power at the time of back pressure operation; c j,v1 、C j,v2 Respectively representing the reduction amount of the power generation power under the maximum and minimum electric output and the multiple extraction unit heat supply heat when the air input amount is not changed; k j The constant is an auxiliary parameter introduced for judging the boundary of two working conditions of the cogeneration unit j;
and (3) climbing restraint of the cogeneration unit:
Figure FDA0004064992530000049
in the formula (22), the first and second groups,
Figure FDA0004064992530000051
the maximum up-down climbing rate of the cogeneration unit j;
wind power plant output restraint:
Figure FDA0004064992530000052
in the formula (23), the first and second groups,
Figure FDA0004064992530000053
and &>
Figure FDA0004064992530000054
Respectively representing the maximum theoretical output and the actual online wind power of the wind farm k in the scene s in the scheduling time t;
and (3) constraint of charging and discharging logic states of the battery energy storage system:
Figure FDA0004064992530000055
Figure FDA0004064992530000056
in the formulae (24) and (25), O t Taking '1' to represent the working state binary variable of the battery energy storage system in the scheduling time t, wherein the battery energy storage system is in a charging state in the scheduling time, and taking '0' to represent the battery energy storage system is in a discharging state in the scheduling time;
and (3) restraining the state of charge of the battery energy storage system:
Figure FDA0004064992530000057
Figure FDA0004064992530000058
in the formula (26) and the formula (27),
Figure FDA0004064992530000059
representing the state of charge of the battery energy storage system in a scheduling time period t under a scene s; e min And E max Respectively representing the minimum and maximum state of charge allowable values of the battery energy storage system;
Figure FDA00040649925300000510
Respectively representing the charging efficiency and the discharging efficiency of the battery energy storage system;C bess the capacity of the battery energy storage system;
and (3) restricting the charging and discharging rates of the battery energy storage system:
Figure FDA00040649925300000511
in the formula (28) and the formula (29),
Figure FDA00040649925300000512
and &>
Figure FDA00040649925300000513
Respectively the maximum charging and discharging rates of the battery energy storage system; the specific steps of the step 3 are as follows: />
Step 3.1, solving a single-target optimization problem with the formula (4) as an optimization target and the formulas (12) to (29) as constraints by adopting a DICOPT solver in GAMS to obtain the minimum 'wind curtailment' electric quantity F in a scheduling day cur,min ；
Step 3.2, solving a single-target optimization problem with the formula (5) as an optimization target and the formulas (12) to (29) as constraints by using a DICOPT solver in GAMS, and taking the 'wind curtailment' electric quantity corresponding to the optimal solution as the maximum 'wind curtailment' electric quantity F in a scheduling day cur,max ；
Step 3.3, the electric quantity interval of' wind abandon cur,min ，F cur,max ]Dispersing into a 'wind curtailment' electric quantity set omega formed by L discrete points cur Element ε in the set l L =1,2, \8230;, L is given by:
Figure FDA0004064992530000061
step 3.4, based on discretized 'abandoned wind' electricity quantity set omega cur Constructing an optimization problem set consisting of L single-target optimization problems, as shown in formula (31):
Figure FDA0004064992530000062
and 3.5, sequentially solving the L single-target optimization problems given by the formula (31) by adopting a DICOPT solver to obtain a pareto solution set of the wind power acceptance capability evaluation model of the electric heating combination system, wherein the pareto solution set gives the wind power acceptance capacity and the corresponding acceptance cost of the electric heating combination system, and evaluation of the wind power acceptance capability of the electric heating combination system is realized.
2. The improved multi-target method-based evaluation method for wind power acceptance of electric heating combined system according to claim 1, wherein the specific steps of the step 1 are as follows:
step 1.1, if
Figure FDA0004064992530000063
For a wind power prediction value of the scheduling period t, <' >>
Figure FDA0004064992530000064
The maximum wind power prediction error in the scheduling time period t is that the upper envelope line and the lower envelope line of the feasible region of the maximum theoretical output of the wind power are greater or less than>
Figure FDA0004064992530000065
And/or>
Figure FDA0004064992530000066
Determining the maximum prediction error of the wind power of a scheduling time interval t according to the formula (1)
Figure FDA0004064992530000067
Figure FDA0004064992530000068
In the formula (1), σ f,t Is the standard deviation of the prediction error of the scheduling time interval t wind power;
step 1.2, constructing a structural group consisting of N s The scene probability model formed by the maximum theoretical output curve of the strip wind power is used for approximating the given output band, and the scene probability model consists of N s The maximum theoretical output of wind power under the condition of each scene composition and scene s
Figure FDA0004064992530000069
Comprises the following steps:
Figure FDA00040649925300000610
in the formula (2), the first and second groups of the compound,
Figure FDA0004064992530000071
a wind power predicted value for a scheduling time period t; sigma f,t Is the standard deviation of the prediction error of the t wind power in the scheduling period;
step 1.3, probability p corresponding to scene s s Comprises the following steps:
Figure FDA0004064992530000072
in the formula (3), the first and second groups of the compound,
Figure FDA0004064992530000073
the maximum theoretical output of wind power under the scene s is obtained. />
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