CN107506878B - Power system multi-source scheduling method considering wind and light smoothing effect - Google Patents

Power system multi-source scheduling method considering wind and light smoothing effect Download PDF

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CN107506878B
CN107506878B CN201710938148.3A CN201710938148A CN107506878B CN 107506878 B CN107506878 B CN 107506878B CN 201710938148 A CN201710938148 A CN 201710938148A CN 107506878 B CN107506878 B CN 107506878B
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孙丹丹
苗世洪
李力行
刘子文
李姚旺
韩佶
晁凯云
叶畅
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Huazhong University of Science and Technology
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Abstract

The invention discloses a multi-source scheduling method of an electric power system considering wind and light smoothing effect, which comprises the following steps: classifying the natural annual weather according to the clear sky index of each day and the average wind speed index of each day to obtain a plurality of weather categories, and obtaining the grid-connected capacity coefficient of the wind power plant and the grid-connected capacity coefficient of the photovoltaic power plant under different weather categories according to an outer layer optimized scheduling model, wherein the outer layer optimized scheduling model takes the fluctuation of the combined output of the wind power plant and the photovoltaic power plant as a target function; and obtaining start-stop and output of the conventional unit under different weather types according to the inner layer optimization scheduling model, the wind power plant grid-connected capacity coefficient and the photovoltaic electric field grid-connected capacity coefficient. The invention provides instructive opinions for wind-solar grid connection under different weather types, and can effectively reduce the impact of wind-solar-electric field combined output on a power grid. On the basis of the grid-connected capacity of a wind power plant and a photovoltaic electric field, a conventional unit in the power system is optimized, and the economic operation of a power grid is realized while the impact of the wind power and photovoltaic grid-connection on the power system is reduced.

Description

Power system multi-source scheduling method considering wind and light smoothing effect
Technical Field
The invention belongs to the field of multi-source optimization research of an electric power system, and particularly relates to a multi-source scheduling method of the electric power system considering a wind-light smoothing effect.
Background
In recent years, with the updating of renewable energy power generation equipment and the increasing maturity of renewable energy grid-connected technology, renewable energy represented by wind power and photovoltaic is rapidly developed in the global scope. According to the '2017 Global Renewable Energy Status Report' (GSR) published by the 21st Century Renewable Energy Policy Network (REN 21) in the recent days, the new capacity of the Global Renewable Energy power reaches 161GW in 2016, and the Global cumulative loading rate reaches 2017GW, which is increased by about 9% compared with 2015. The solar photovoltaic accounts for the highest percentage of the newly added power capacity, and reaches 47%, and the wind power accounts for 34%. However, the output of the wind power and the photovoltaic generator set is influenced by the randomness of natural factors (wind speed, light intensity and the like), so that strong intermittency, randomness and fluctuation are presented. Such large-scale wind power and photovoltaic grid connection inevitably brings severe challenges to the safe operation of a power system, especially the safe reliability and economy of a unit combination. How to reduce the impact of renewable energy grid connection on a power system is an urgent problem to be solved. Therefore, the smoothing effect between the wind power field and the photovoltaic electric field is researched, the grid-connected capacity coefficients of different electric fields are optimized, the impact on a large power grid is reduced by a cluster output curve more smoothly, the safe operation of the power grid is ensured, and the method has important significance for the multi-source optimized operation of a power system.
Most of the existing researches concern the optimization configuration of the capacity of the wind power photovoltaic electric field and the smoothing effect of the wind power plant cluster, and application researches on the smoothing effect of the wind power plant and the photovoltaic electric field cluster and the application research of the smoothing effect in the dispatching operation of the power system are lacked. Considering that the output of wind power photovoltaic and the cluster smoothing effect are influenced by weather factors.
Disclosure of Invention
Aiming at the defects or the improvement requirements of the prior art, the invention provides a power system multi-source scheduling method considering a wind-solar smoothing effect, and aims to solve the technical problem that the scheduling of a power system is incomplete because the smoothing effect of a photovoltaic electric field cluster is not considered to the scheduling operation of the power system in the conventional power system multi-source scheduling method.
In order to achieve the above object, the present invention provides a multi-source scheduling method for an electric power system considering wind and light smoothing effect, comprising the following steps:
step 1: classifying the natural weather according to the clear sky index of each day and the average wind speed index of each day to obtain a plurality of weather categories;
step 2: acquiring wind power plant grid-connected capacity coefficients and photovoltaic power plant grid-connected capacity coefficients under different weather categories according to an outer layer optimized scheduling model, wherein the outer layer optimized scheduling model takes wind-solar power plant combined output fluctuation under different weather categories as a target function, and a constraint set of the outer layer optimized scheduling model comprises electric field grid-connected capacity coefficient constraint and renewable energy consumption rate constraint;
and step 3: acquiring the starting and stopping states of the conventional unit and the output of the conventional unit in different weather categories according to the inner layer optimized scheduling model, the grid-connected capacity coefficient of the wind power plant in different weather categories and the grid-connected capacity coefficient of the photovoltaic power plant in different weather categories; the inner-layer optimized scheduling model takes the scheduling cost of the conventional unit as an objective function, and the constraint set of the inner-layer optimized scheduling model comprises power balance constraint of a power system, rotation standby constraint of the power system, output power constraint of the conventional unit, minimum on-off time constraint of the conventional unit and climbing constraint of the conventional unit.
Preferably, the step 1 of classifying the natural weather includes the following steps:
step 11: establishing a clustering target function, wherein the clustering target function is the sum of all clustering target sub-functions, the clustering target sub-functions are the products of the membership degree of the feature vector of a single day to a single category and the Euclidean distance between the mapping function of the feature vector of the single day and the mapping function of the feature vector of the single category, establishing clustering target function constraints, and obtaining a weather clustering model;
step 12: obtaining the membership degree of the feature vector of each weather to a single category and the input spatial clustering center of each category by adopting a Lagrange multiplier method for a weather clustering model;
the feature vector of the single weather comprises a clear sky index and an average wind speed index.
Preferably, step 1 is according to the formula
Figure BDA0001430197520000031
Obtaining a clustering target function;
according to the formula
Figure BDA0001430197520000032
Obtaining clustering objective function constraints;
wherein, | | φ (x)k)-φ(vi)||2＝K(xk,xk)+K(vi,vi)-2K(xk,vi)，uikIs the degree of membership of the eigenvector to the ith class at day K, m is a weighted index, K (x)k,vi)＝exp[-||xk-vi||/(2σ2)]C is the number of categories, n is the total number of days, k is more than or equal to 1 and less than or equal to n, i is more than or equal to 1 and less than or equal to c, phi (·) is a mapping function, and sigma is a Gaussian kernel parameter.
Preferably, the objective function of the external layer optimization scheduling model in the step 2 is a ratio of a mean square difference between the output of the wind power plant cluster and the average output of the wind power plant cluster at different moments to the total installed capacity of the wind-solar power plant;
the electric field grid-connected capacity coefficient is constrained to be within an allowed value range;
the renewable energy consumption rate is restricted to be larger than the minimum renewable energy consumption rate;
renewable energy sources include wind farms and photovoltaic farms, among others.
Preferably according to a formula
Figure BDA0001430197520000033
Obtaining an objective function of an outer layer optimization scheduling model;
according to the formula 0 ≦ κpKappa is less than or equal to 1 and 0qObtaining the electric field grid-connected capacity coefficient constraint with the value less than or equal to 1;
according to the formula
Figure BDA0001430197520000034
Obtaining the consumption rate constraint of the renewable energy source;
wherein T represents a time period, and T is a total scheduling time period; ptFor the actual power output of the wind-solar electric field cluster on the internet at the moment t,
Figure BDA0001430197520000041
Figure BDA0001430197520000042
for the mean value of the wind-solar power field cluster output, PNThe total installed capacity of the wind-solar electric field;
Figure BDA0001430197520000043
is the maximum output, kappa, of the wind farm p in the time period tpA p power grid-connected capacity coefficient of the wind power plant;
Figure BDA0001430197520000044
is the maximum output of the photovoltaic electric field q in the period t, kappaqIs a grid-connected capacity coefficient of the photovoltaic electric field q power, ξminFor minimum renewable energy consumption, p is the wind farm sequence, q is the photovoltaic farm sequence, NwRepresenting number of wind farms, NSP is more than or equal to 1 and less than or equal to N representing the number of photovoltaic electric fieldsW，1≤q≤NS。
Preferably, the objective function of the inner-layer optimized scheduling model in the step 3 includes start-stop cost and running cost of a conventional unit;
the power balance constraint in the inner layer optimization scheduling model is that the output power of the power system is equal to the load power, wherein the output power of the power system comprises the output of a conventional unit, the actual output of a wind power plant and the actual output of a photovoltaic electric field;
the rotation standby constraint of the power system in the inner-layer optimization scheduling model is used for representing that the maximum output of all the units at any moment is larger than the sum of the maximum load and the load up-regulation standby at the moment, and simultaneously, the minimum output of all the units at any moment is constrained to be smaller than the sum of the minimum load and the load down-regulation standby at the moment;
the output power constraint of the conventional unit in the inner-layer optimization scheduling model is used for constraining the output power of the conventional unit to be within an allowable output power range;
the minimum on-off time constraint of the conventional unit in the inner-layer optimized scheduling model is used for constraining the on-off duration and the off-off duration of the conventional unit to be not less than the minimum on-off duration;
and the climbing restraint of the conventional unit in the inner-layer optimized dispatching model is used for restraining that the output of the conventional unit cannot exceed a climbing preset value when climbing upwards and climbing downwards, and is used for restraining that the output of the conventional unit cannot exceed a preset output value when the unit is turned on and turned off.
Preferably according to a formula
Figure BDA0001430197520000045
Obtaining an objective function of the inner-layer optimized scheduling model in the step 3;
according to the formula
Figure BDA0001430197520000051
Obtaining power balance constraint in an inner layer optimization scheduling model;
according to the formula
Figure BDA0001430197520000052
Obtaining a rotation standby constraint in the inner layer optimization scheduling model;
according to the formula
Figure BDA0001430197520000053
Obtaining the output power constraint of a conventional unit in an inner layer optimization scheduling model;
according to the formula
Figure BDA0001430197520000054
Obtaining the minimum on-off time constraint of a conventional unit in the inner-layer optimized scheduling model;
according to the formula
Figure BDA0001430197520000055
Obtaining conventional machine in inner-layer optimization scheduling modelThe group is restricted by the climbing of the slope,
wherein i represents a conventional electric field serial number, and t represents a time period serial number; n is a radical ofGRepresents the total number of conventional electric fields; siRepresents the startup cost, U, of the ith conventional uniti tRepresenting the on-off state of the ith conventional unit at the t moment, Pi tThe output of a conventional unit i at the moment t, fi(Pi t) Is an operation cost function of a conventional unit, N is the total node number of the load of the power system, k is more than or equal to 1 and less than or equal to N,
Figure BDA0001430197520000056
the bus active load quantity R of the j-node power system in the t periodup,iThe maximum upward climbing rate, R, of the conventional unit idown,iThe maximum downward climbing speed of the conventional unit i; pmax,iIs the maximum output, P, of the conventional unit imin,iIs the minimum output of the conventional unit i, rupReserved up-regulation rotation reserve factor r for electric power system loaddownA turndown reserve factor reserved for power system loads,
Figure BDA0001430197520000057
for the duration of the start-up of the conventional unit i in time period t,
Figure BDA0001430197520000058
respectively representing the shutdown duration of the conventional unit i in a time period t;
Figure BDA0001430197520000059
for the minimum run time of the conventional unit i,
Figure BDA0001430197520000061
minimum down time, P, of conventional units ii,onThe power output at the time of starting up the ith unit, Pi,offThe output is the output of the ith unit before shutdown.
Generally, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:
1. aiming at the problem of determining the grid-connected capacity of the wind power plant and the photovoltaic electric field, the invention fully considers the difference of the output characteristics of the wind power plant and the photovoltaic electric field and provides a smooth effect coefficient for measuring the output smoothness of the wind power photovoltaic electric field. Meanwhile, considering that the output of the wind power photovoltaic electric field is influenced by weather factors, the wind power photovoltaic electric field power generation method identifies wind and light fluctuation and weather categories from the viewpoint of weather system classification, and determines corresponding grid-connected capacity coefficients of the wind power field and the photovoltaic electric field according to different weather categories. The wind-solar power grid-connection method provides instructive opinions for wind-solar power grid connection under different weather types, and can effectively reduce impact of wind-solar power field combined output on a power grid. On the basis of the grid-connected capacity of a wind power plant and a photovoltaic electric field, a conventional unit in the power system is optimized, and the economic operation of a power grid is realized while the impact of the wind power and photovoltaic grid-connection on the power system is reduced.
2. By adopting the multisource optimization scheduling strategy of the power system considering the wind-light smoothing effect under different weather types, the grid-connected capacity coefficient of the wind-power photovoltaic electric field is obtained, the starting, stopping and output of the conventional unit are optimized, and the economic operation of the power system with the multisource coexistence is realized.
Drawings
FIG. 1 is a diagram of the variation of multi-electric field combination fluctuation;
FIG. 2 is a flow chart of a multi-source scheduling method of an electric power system considering wind and light smoothing effect provided by the invention;
FIG. 3 is a graph of typical solar radiation characteristics provided by the present invention;
FIG. 4 is a load graph of an embodiment of a multi-source scheduling method of an electrical power system according to the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Fig. 1 is a diagram of fluctuation of a multi-electric-field combination provided by the present invention, and as can be obtained from fig. 1, the general trend is that as the number of wind farms participating in a cluster and the number of photovoltaic electric fields are larger, the cluster output fluctuation performance of the wind farms and the photovoltaic electric fields is smaller.
Fig. 2 is a flowchart of a multi-source scheduling method of an electric power system considering a wind-solar smoothing effect, which is provided by the invention, and is used for determining a grid-connected capacity coefficient of a wind power photovoltaic electric field and an operation mode of a conventional unit under different weather categories, and comprises the following steps:
step 1: and classifying the natural weather according to the clear sky index of each day and the average wind speed index of each day to obtain a plurality of weather categories.
Clear sky index
The clear sky index is used for measuring the attenuation degree of radiation reaching the earth surface under the effects of absorption, scattering and reflection of cloud layers, atmospheric dust and the like, the specific mathematical meaning refers to the ratio of the total solar radiation amount incident on the horizontal plane to the astronomical radiation amount, and the calculation method is shown as the formula (3):
Figure BDA0001430197520000071
in the formula: gDThe solar radiation quantity value of the surface level century in a given day is represented by KJ/m2；G0The value of the astronomical radiation dose of the out-of-day level for a given day in KJ/m2。
Average wind speed index
Introducing an average wind speed index
Figure BDA0001430197520000072
In order to measure the air volume condition in one day, the calculation method is shown as the formula (2):
Figure BDA0001430197520000073
in the formula (I), the compound is shown in the specification,
Figure BDA0001430197520000074
the wind speed value at the moment t is shown.
Classifying the natural weather to obtain a plurality of weather categories by the following sub-steps:
step 11: given dataset X ═ X1,x2,…,xn}∈RdEach sample xi is described by a clear sky index and an average wind speed index.
Sample xk(k ═ 1,2, …, n) is mapped to φ (x)k) And using phi (x)k) And (6) clustering. (clustering is performed by transforming the input space χ into the high-dimensional feature space F through the nonlinear mapping φ: χ → F.) the clustering objective function is expressed as:
Figure BDA0001430197520000081
in the formula: v. ofi(i ═ 1,2, …, c) is the input spatial clustering center; c is the number of categories; u. ofikThe membership degree of the feature vector of the kth day to the ith class; m is a weighting index, and the value of m is generally more than or equal to 1. u. ofikU is more than or equal to 0ikLess than or equal to 1 and
Figure BDA0001430197520000082
the constraint conditions are as follows:
Figure BDA0001430197520000083
step 12: defining a kernel function K (x, y) ═ phi (x)TPhi (y), so that the Euclidean distance of the kernel function is
||φ(xk)-φ(vi)||2＝K(xk,xk)+K(vi,vi)-2K(xk,vi) (5)
Substituting the formula (5) into the formula (3), and optimizing by using a Lagrange multiplier method under the constraint condition (4) to obtain the membership and the input spatial clustering center:
Figure BDA0001430197520000084
Figure BDA0001430197520000085
the kernel function can be selected from a Gaussian kernel function, a polynomial kernel function and a Sigmoid kernel function, and the Gaussian kernel function is selected by the invention: k (x)k,vi)＝exp[-||xk-vi||/(2σ2)]Wherein sigma is a Gaussian kernel parameter and is generally within 0-1.
In the step 1, a kernel function is introduced as a transformation function, and the samples are mapped into a high-dimensional space H by utilizing nonlinear transformation, so that the clustering capability of the algorithm is provided, and the characteristic difference of the samples is highlighted.
Step 2: the wind-solar power field grid-connected capacity coefficient under different weather categories is obtained according to an outer layer optimized scheduling model, the outer layer optimized scheduling model takes wind-solar power field joint output fluctuation as a target function, and the outer layer optimized scheduling model constraint set comprises electric field grid-connected capacity coefficient constraint and renewable energy consumption rate constraint.
And obtaining the grid-connected capacity coefficient of the wind power plant and the grid-connected capacity coefficient of the photovoltaic power plant under different weather categories according to the outer layer optimization scheduling model. The implementation method for determining the grid-connected capacity coefficient of the wind power plant and the grid-connected capacity coefficient of the photovoltaic power plant under a single weather category is as follows:
and (2.1) an objective function, wherein the fluctuation of the wind-light-electric field joint output is considered to be minimum.
Figure BDA0001430197520000091
Figure BDA0001430197520000092
In the formula, T represents a time interval, and T is a total scheduling time interval; ptThe actual net power output of the wind and light electric field cluster at the moment t is shown as a specific expression (10);
Figure BDA0001430197520000093
is a total ofThe mean value of the wind-solar electric field cluster output in the scheduling period; PN is the total installed capacity of the wind-solar electric field;
Figure BDA0001430197520000094
is the maximum output, K, of the wind farm p in the time period tpThe grid-connected capacity coefficient of the wind power plant power;
Figure BDA0001430197520000095
is the maximum output of the photovoltaic electric field q in the period of t, KqThe grid-connected capacity coefficient of the photovoltaic electric field power is obtained; nw and NS respectively represent the number of wind power plants and the number of photovoltaic electric fields.
The constraint set of the outer-layer optimization scheduling model is obtained according to the following sub-steps:
① electric field grid-connected capacity coefficient constraint
The value of the grid-connected capacity coefficient of the wind-solar electric field is between 0 and 1, and the mathematical expression is as follows:
Figure BDA0001430197520000096
in the formula, p is more than or equal to 1 and less than or equal to NW，1≤q≤NS。
② renewable energy consumption rate constraint
Figure BDA0001430197520000101
In the formula (I), the compound is shown in the specification,
Figure BDA0001430197520000102
is the maximum available output, kappa, of the wind farm p during the time period tpThe grid-connected capacity coefficient of the wind power plant power;
Figure BDA0001430197520000103
is the maximum available output of the photovoltaic electric field q in the period t, kappaqPower grid connection capacity factor for the photovoltaic electric field, ξminThe minimum consumption rate of renewable energy is generally determined by local policy, and a superior power grid can provide requirements for a subordinate power grid and require new energy to surf the internetAnd (4) proportion. At present, the absorption rate in most areas is 60% -70%, and the land with high internet access proportion can reach 80%. This constraint is used to require that the renewable energy consumption rate be greater than the minimum renewable energy consumption rate.
And step 3: and obtaining start-stop and output of the conventional unit under different weather types according to the inner layer optimization scheduling model, the wind power plant grid-connected capacity coefficient and the photovoltaic electric field grid-connected capacity coefficient. And further acquiring the grid-connected capacity coefficient of the wind power plant, the grid-connected capacity coefficient of the photovoltaic power plant, the start and stop of the conventional unit and the output of the conventional unit under different weather types. The inner-layer optimized scheduling model takes the scheduling cost of the conventional unit as an objective function, and the constraint set of the inner-layer optimized scheduling model comprises power balance constraint of a power system, rotation standby constraint of the power system, output power constraint of the conventional unit, minimum on-off time constraint of the conventional unit and climbing constraint of the conventional unit.
And the inner-layer optimized dispatching model is arranged based on the outer-layer optimized wind-solar electric field grid-connected capacity coefficient to obtain a wind-solar electric field cluster output curve. On the basis of the curve, the lowest economic cost of the power system is taken as an objective function, and the starting, the stopping and the output of the conventional unit are arranged. The specific implementation mode is as follows:
and (3.1) an objective function, taking the scheduling cost of the conventional unit as the objective function, and considering power balance constraint, rotation standby constraint, output power constraint of the conventional unit, minimum on-off time constraint of the unit and ramp-up constraint of the unit.
Figure BDA0001430197520000104
In the formula, i and t respectively represent an electric field number and a time period; n is a radical ofGRepresents the total number of electric fields; the start-stop schedule of the unit is of the hour level, so Th24; the output of the unit is scheduled to be 10min, Tm＝144；Si t、Ui tRespectively representing the startup cost and the startup and shutdown state, U, of the ith conventional unit at the tth momenti t1 indicates that the ith conventional unit is in a starting state at the tth moment, U i t0 means that the ith conventional unit is at the tth momentThe shutdown state, and when the ith conventional unit is in the startup state at the tth moment, t is an hour level, namely when the ith conventional unit is in the startup state at the tth moment, all minute levels of the conventional unit are in the startup state within the tth hour; pi tThe output of the conventional unit at time t, fi(Pi t) The method is determined for the operation cost function of the conventional unit according to the operation characteristics of the conventional unit. The first term represents the start-stop cost of a conventional unit and the second term is the operating cost.
(3.2) in order to realize the constraint condition of the step (3.1), the specific implementation mode is as follows:
① power balance constraints
Figure BDA0001430197520000111
In the formula, Pi tThe output of a conventional unit i in a time period t; n is the total node number of the load of the power system,
Figure BDA0001430197520000112
and the bus active load of the j-node power system in the t period.
The method is used for indicating that the output power of the power system is equal to the load power, wherein the output power of the power system comprises the output of a conventional unit, the actual output of a wind power plant and the actual output of a photovoltaic electric field.
② rotational back-up constraint
Figure BDA0001430197520000113
In the formula, Rup,i、Rdown,iRespectively the maximum upward climbing speed and the maximum downward climbing speed of the conventional unit i; rup,i、Rdown,iThe value taking modes are the same and are determined according to the running condition of a conventional unit, Pmax,i、Pmin,iRespectively the maximum output, the minimum output and P of the conventional unit imax,i、Pmin,iThe value taking mode is the same, and is determined according to the running condition of a conventional unit, rup、rdownUp-and down-regulated rotational reserve factor, r, reserved for the load of the power system respectivelyup、rdownThe value taking mode is the same, and the power grid is regulated to be not less than 5% of the total load in principle and strictly 10%.
The constraint is a constraint on load reservation. The left part of the first inequality in the inequality (14) respectively represents the output of a conventional unit, the on-grid output of a wind power plant and the output of a photovoltaic plant, the output of the conventional unit is the minimum value of the sum of the normal output and the climbing output of the conventional unit and the maximum value which can be reached by the conventional unit, and the maximum output of all units at any moment is larger than the sum of the maximum load and the load up-regulation standby at the moment. The output of the first conventional motor on the left of the second inequality in the inequality (14) is the maximum value taken from the sum of the normal output and the downhill output of the conventional unit and the minimum output which can be achieved by the conventional unit, and the minimum output of all units at any moment is smaller than the sum of the minimum load and the load standby down regulation at the moment.
③ conventional unit output power constraint
Figure BDA0001430197520000121
Equation (15) is used to indicate that the output of the unit i in the time period t is between the maximum output and the minimum output of the unit i, and the time scale of the time period t is 10 min.
④ conventional unit minimum on-off time constraint
Figure BDA0001430197520000122
In the formula (I), the compound is shown in the specification,
Figure BDA0001430197520000123
respectively representing the starting duration and the stopping duration of the conventional unit i in a time period t;
Figure BDA0001430197520000124
respectively the minimum run time and the minimum down time of the conventional unit i,
Figure BDA0001430197520000125
Figure BDA0001430197520000126
the value taking mode is the same, and the time scale of the time period t is an hour level according to the operation condition of the conventional unit.
The constraint is expressed in the sense that the conventional unit cannot be shut down and started up without limitation. The reformers may be rebooted if the shutdown requires a minimum downtime. The boot operation must also reach a minimum boot time to shut down.
⑤ conventional unit climbing restraint
Figure BDA0001430197520000131
In the formula, Pi t-1The output P of the conventional unit i in the time period t-1i,onThe power output at the time of starting up the ith unit, Pi,offThe power output, P, before shutdown of the ith uniti,on、Pi,offThe value taking mode is the same and is determined according to the operation mode of a conventional unit, and the time scale of the time period t is 10 min.
When the unit output is changed, each time interval is a changed limit value. For example, when climbing up a slope, if the unit is in the on state at the time t-1 and the time t, the second term of the right equation is 0, and at this time, that is, the output increased at the time t compared with the time t-1 is smaller than the maximum upward climbing rate.
But if the computer is in a power-on state, namely the time t-1 is in a power-off state, the computer is powered on at the time t. The output force is only required to be smaller than the limit value of starting, and the value is generally larger than the maximum upward climbing speed and the maximum downward climbing speed of the conventional unit.
Or when the machine is in a shutdown state, namely the machine is in a startup running state at the time t-1 and is shut down at the time t. The output force is only required to be smaller than the limit value of shutdown.
The invention provides a multisource scheduling method of an electric power system, which provides a characteristic vector for weather category identification: clear sky index and average wind speed index. And performing category identification on weather of one natural year according to the clear sky index and the average wind speed index to obtain a plurality of weather categories. And (4) providing a smooth effect measuring index of the output of the wind power photovoltaic electric field cluster based on different weather types obtained in the step (1), and measuring the smooth effect of the output of different wind power field combination clusters. And obtaining the grid-connected capacity coefficient of the wind-solar electric field under different weather types by taking the smooth effect measurement index as an objective function. And (3) obtaining a wind-solar electric field grid-connected capacity coefficient based on the step (2), and arranging the unit combination of the conventional units.
The new energy grid-connected capacity of the multi-source power system and the economic dispatching result of the conventional unit can be obtained through the steps. In the embodiment of the invention, the effectiveness of the multi-source optimization scheduling strategy of the power system considering the wind-solar smoothing effect under different weather types is verified. Applying the proposed strategy to a modified IEEE39 node system, wherein the system comprises 6 wind power plants, 3 photovoltaic power plants and 6 conventional units, the electric field data are shown in table 1, the conventional unit parameters are shown in table 2, and a typical daily solar radiation characteristic diagram is shown in fig. 3; a typical daily load curve is shown in figure 4. And comparing the wind-solar volatility and the conventional unit economic dispatching result under the condition of capacity grid connection without considering weather factors and the like, and compiling a simulation test example by using CPLEX.
TABLE 1 wind, solar and electric field geographic information data
Data points Type (B) Latitude and longitude Capacity (MW)
Electric field 1 Fan blower 45.66°N/122.29°W 30
Electric field 2 Fan blower 45.71°N/122.04°W 30
Electric field 3 Fan blower 43.99°N/121.26°W 30
Electric field 4 Fan blower 41.43°N/119.86°W 30
Electric field 5 Fan blower 45.11°N/100.97°W 30
Electric field 6 Fan blower 45.14°N/121.67°W 30
Electric field 7 Photovoltaic system 45.51°N/122.69°W 50
Electric field 8 Photovoltaic system 44.06°N/121.32°W 30
Electric field 9 Photovoltaic system 45.84°N/119.29°W 20
TABLE 2 conventional Unit parameters
Unit1 Unit2 Unit3 Unit4 Unit5 Unit6
Pmax,i(MW) 55 55 80 150 150 160
Pmin,i(MW) 10 10 10 20 20 25
Ai($/h) 0.00222 0.00413 0.00712 0.00211 0.00211 0.00398
Bi($/MWh) 27.27 25.92 22.26 16.5 16.5 19.7
Ci($/MW2h) 665 660 370 680 680 450
Min up(h) 1 1 3 5 5 5
Min dn(h) 1 1 3 5 5 5
Start cost($) 30 30 320 200 200 1000
Example results: under the same weather category, the startup times of the unit using the scheduling strategy of the invention are totally less than the startup times of the unit under the traditional strategy. Meanwhile, the average value of the output of the renewable energy source is high after optimization, and the fluctuation range is reduced. On the one hand, higher renewable energy output can reduce the overall power generation cost; on the other hand, a lower fluctuation range may reduce system standby requirements, further reducing standby costs. The two have combined action, and the running cost of the system can be reduced. The simulation operation result and the analysis result of the volatility of the renewable energy source are mutually verified, and the multisource optimization scheduling technology considering the wind-light smoothing effect can improve the consumption rate of the renewable energy source, reduce the volatility of the renewable energy source, reduce the power generation cost and the standby cost of a system and improve the economic benefit of the system under the same internet access capacity of the renewable energy source.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (6)

1. A multi-source scheduling method of a power system considering a wind-solar smoothing effect is characterized by comprising the following steps:
step 1: classifying the natural weather according to the clear sky index of each day and the average wind speed index of each day to obtain a plurality of weather categories;
step 2: acquiring wind power plant grid-connected capacity coefficients and photovoltaic power plant grid-connected capacity coefficients under different weather categories according to an outer layer optimized scheduling model, wherein the outer layer optimized scheduling model takes wind-solar power plant combined output fluctuation under different weather categories as a target function, and a constraint set of the outer layer optimized scheduling model comprises electric field grid-connected capacity coefficient constraint and renewable energy consumption rate constraint;
and step 3: acquiring the starting and stopping states of the conventional unit and the output of the conventional unit in different weather categories according to the inner layer optimized scheduling model, the grid-connected capacity coefficient of the wind power plant in different weather categories and the grid-connected capacity coefficient of the photovoltaic power plant in different weather categories; the inner-layer optimized scheduling model takes the scheduling cost of the conventional unit as an objective function, and the constraint set of the inner-layer optimized scheduling model comprises power balance constraint of a power system, rotation standby constraint of the power system, output power constraint of the conventional unit, minimum on-off time constraint of the conventional unit and climbing constraint of the conventional unit;
the objective function of the external layer optimization scheduling model in the step 2 is the ratio of the mean square difference of the output of the wind power plant cluster and the average output of the wind power plant cluster at different moments to the total installed capacity of the wind-solar power plant;
the electric field grid-connected capacity coefficient is constrained to be within an allowed value range;
the renewable energy consumption rate is restricted to be larger than the minimum renewable energy consumption rate;
renewable energy sources include wind farms and photovoltaic farms, among others.
2. The multi-source scheduling method of the power system according to claim 1, wherein the classifying the natural weather in step 1 comprises the following steps:
step 11: establishing a clustering target function, wherein the clustering target function is the sum of all clustering target sub-functions, the clustering target sub-functions are the products of the membership degree of the feature vector of a single day to a single category and the Euclidean distance between the mapping function of the feature vector of the single day and the mapping function of the feature vector of the single category, establishing clustering target function constraints, and obtaining a weather clustering model;
step 12: obtaining the membership degree of the characteristic vector of each weather to a single category and the input spatial clustering center of each category by adopting a Lagrange multiplier method for a weather clustering model;
the feature vector of the single weather comprises a clear sky index and an average wind speed index.
3. The multi-source dispatching method of the power system as claimed in claim 1 or 2, wherein the step 1 is according to a formula
Figure FDA0002338731930000021
Obtaining a clustering target function;
according to the formula
Figure FDA0002338731930000022
Obtaining clustering objective function constraints;
wherein, | | φ (x)k)-φ(vi)||2＝K(xk,xk)+K(vi,vi)-2K(xk,vi)，uikIs the degree of membership of the eigenvector to the ith class at day K, m is a weighted index, K (x)k,vi)＝exp[-||xk-vi||/(2σ2)]C is the number of categories, n is the total number of days, k is more than or equal to 1 and less than or equal to n, i is more than or equal to 1 and less than or equal to c, phi (·) is a mapping function, and sigma is a Gaussian kernel parameter.
4. The multi-source scheduling method of electric power system of claim 1 wherein the method is based on a formula
Figure FDA0002338731930000023
Obtaining an objective function of an outer layer optimization scheduling model;
according to the formula 0 ≦ κpKappa is less than or equal to 1 and 0qObtaining the electric field grid-connected capacity coefficient constraint with the value less than or equal to 1;
according to the formula
Figure FDA0002338731930000024
Obtaining the consumption rate constraint of the renewable energy source;
wherein T represents a time period, and T is a total scheduling time period; ptFor the time t the wind-solar-electric field cluster actually outputs force,
Figure FDA0002338731930000025
Figure FDA0002338731930000026
for the mean value of the wind-solar power field cluster output, PNThe total installed capacity of the wind-solar electric field;
Figure FDA0002338731930000027
is the maximum output, kappa, of the wind farm p in the time period tpA p power grid-connected capacity coefficient of the wind power plant;
Figure FDA0002338731930000028
is the maximum output of the photovoltaic electric field q in the period t, kappaqIs a grid-connected capacity coefficient of the photovoltaic electric field q power, ξminFor minimum renewable energy consumption, p is the wind farm sequence, q is the photovoltaic farm sequence, NwRepresenting number of wind farms, NSP is more than or equal to 1 and less than or equal to N representing the number of photovoltaic electric fieldsW，1≤q≤NS。
5. The multi-source scheduling method of the power system according to claim 1, wherein the objective function of the optimized scheduling model of the inner layer in the step 3 comprises start-stop cost and running cost of a conventional unit;
the power balance constraint in the inner layer optimization scheduling model is that the output power of the power system is equal to the load power, wherein the output power of the power system comprises the output of a conventional unit, the actual output of a wind power plant and the actual output of a photovoltaic electric field;
the rotation standby constraint of the power system in the inner-layer optimization scheduling model is used for representing that the maximum output of all the units at any moment is larger than the sum of the maximum load and the load up-regulation standby at the moment, and simultaneously, the minimum output of all the units at any moment is constrained to be smaller than the sum of the minimum load and the load down-regulation standby at the moment;
the output power constraint of the conventional unit in the inner-layer optimization scheduling model is used for constraining the output power of the conventional unit to be within an allowable output power range;
the minimum on-off time constraint of the conventional unit in the inner-layer optimized scheduling model is used for constraining the on-off duration and the off-off duration of the conventional unit to be not less than the minimum on-off duration;
and the climbing restraint of the conventional unit in the inner-layer optimized dispatching model is used for restraining that the output of the conventional unit cannot exceed a climbing preset value when climbing upwards and climbing downwards, and is used for restraining that the output of the conventional unit cannot exceed a preset output value when the unit is turned on and turned off.
6. The multi-source scheduling method of power system of claim 5 wherein the method is based on a formula
Figure FDA0002338731930000031
Obtaining an objective function of the inner-layer optimized scheduling model in the step 3;
according to the formula
Figure FDA0002338731930000032
Obtaining power balance constraint in an inner layer optimization scheduling model;
according to the formula
Figure FDA0002338731930000041
Obtaining a rotation standby constraint in the inner layer optimization scheduling model;
according to the formula
Figure FDA0002338731930000042
Obtaining the output power constraint of a conventional unit in an inner layer optimization scheduling model;
according to the formula
Figure FDA0002338731930000043
Obtaining the minimum on-off time constraint of a conventional unit in the inner-layer optimized scheduling model;
according to the formula
Figure FDA0002338731930000044
Obtaining the climbing constraint of the conventional unit in the inner-layer optimized dispatching model,
wherein i represents a conventional electric field serial number, and t represents a time period serial number; t ish＝24，Tm＝144；NGRepresents the total number of conventional electric fields; siRepresents the startup cost, U, of the ith conventional uniti tRepresenting the on-off state of the ith conventional unit at the t moment, Pi tThe output of a conventional unit i at the moment t, fi(Pi t) Is an operation cost function of a conventional unit, N is the total node number of the load of the power system, k is more than or equal to 1 and less than or equal to N,
Figure FDA0002338731930000045
the bus active load quantity R of the j-node power system in the t periodup,iThe maximum upward climbing rate, R, of the conventional unit idown,iThe maximum downward climbing speed of the conventional unit i; pmax,iIs the maximum output, P, of the conventional unit imin,iIs the minimum output of the conventional unit i, rupReserved up-regulation rotation reserve factor r for electric power system loaddownA turndown reserve factor reserved for power system loads,
Figure FDA0002338731930000046
for the duration of the start-up of the conventional unit i in time period t,
Figure FDA0002338731930000047
respectively representing the shutdown duration of the conventional unit i in a time period t;
Figure FDA0002338731930000048
for the minimum run time of the conventional unit i,
Figure FDA0002338731930000049
minimum down time, P, of conventional units ii,onThe power output at the time of starting up the ith unit, Pi,offThe output is the output of the ith unit before shutdown.
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