CN109657898B - Renewable energy random dynamic economic dispatching method based on convex relaxation - Google Patents

Renewable energy random dynamic economic dispatching method based on convex relaxation Download PDF

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CN109657898B
CN109657898B CN201811223274.1A CN201811223274A CN109657898B CN 109657898 B CN109657898 B CN 109657898B CN 201811223274 A CN201811223274 A CN 201811223274A CN 109657898 B CN109657898 B CN 109657898B
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朱涛
吴文传
高孟平
王彬
段荣华
夏天
许书伟
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Abstract

The invention discloses a renewable energy random dynamic economic dispatching method based on convex relaxation. The method comprises the following steps: establishing a dynamic economic dispatching model considering randomness of renewable energy sources; converting a random dynamic economic dispatching model into a deterministic economic dispatching model through an opportunity constrained convex relaxation algorithm; and solving the deterministic dynamic economic dispatching model, determining the plan of the renewable energy source unit and dispatching. The method is suitable for economic dispatching of the power system including large-scale wind, light and other renewable energy power generation, effectively reduces risks of the system, saves dispatching cost, improves consumption levels of wind, light and other renewable energy, and improves efficiency and flexibility of economic dispatching of the system.

Description

Renewable energy random dynamic economic dispatching method based on convex relaxation
Technical Field
The invention belongs to the technical field of operation of power systems, and particularly relates to a renewable energy random dynamic economic dispatching method based on convex relaxation.
Background
The development and utilization of renewable energy sources and the realization of sustainable development of energy sources are important measures of energy source development strategies in China. With the large-scale access of wind power and photovoltaic to a power grid, the fluctuation and randomness of the power grid make the traditional scheduling method difficult to apply.
In order to reduce the adverse effect of uncertainty of new energy on a power grid, robust economic dispatching is a feasible scheme, however, unnecessary cost is brought to dispatching due to conservation of robust optimization, and therefore random economic dispatching is an effective modeling strategy for reducing the system operation risk and reducing the cost.
The optimization problem of opportunity constraint refers to the optimization problem of the constraint containing random variables, the expectation, the variance and even the probability density function of the random variables are obtained by observing and fitting a large amount of historical data, and the risk constraint jointly determined by decision variables and the random variables needs to be established under a preset confidence level.
The probability-constrained random dynamic economic dispatching model solves the contradiction between the system operation risk and the operation cost, limits the risk of section flow out-of-limit, the system load loss risk, the wind abandoning risk and the light abandoning risk under a certain confidence level, and obtains the dispatching strategy with the lowest cost by minimizing the value of the objective function.
However, the solution of the opportunistic constraint optimization problem is very difficult, and the existing solution method generally has the defect of large calculation amount, so that the high efficiency and flexibility of economic dispatching cannot be realized, and the modeling of dynamic economic dispatching considering the randomness of renewable energy sources and the high-efficiency solution are problems to be solved urgently at present.
Disclosure of Invention
Aiming at the problems, the invention provides a renewable energy random dynamic economic dispatching method based on convex relaxation, which is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: establishing a dynamic economic dispatching model considering the randomness of the renewable energy sources;
step two: converting a random dynamic economic dispatching model into a deterministic economic dispatching model through an opportunity-constrained convex relaxation algorithm;
step three: and solving the deterministic dynamic economic dispatching model, determining the plan of the renewable energy source unit and dispatching.
Further, the dynamic economic dispatching model considering the randomness of the renewable energy sources is composed of an objective function and constraint conditions, and the specific establishment steps are as follows:
the method comprises the following steps: determining an objective function of a dynamic economic dispatch model taking into account randomness of renewable energy sources;
step two: and determining constraint conditions of a dynamic economic dispatching model considering the randomness of the renewable energy sources.
Further, the expression of the objective function is:
Figure BDA0001835319850000021
wherein F is an objective function representing the total cost of accounting for various factors; t, N and J respectively represent the number of scheduling time periods, the number of traditional thermal power units and the number of renewable energy units; t, i and j are respectively the number of a scheduling time interval, the number of a traditional thermal power generating unit and the number of a renewable energy source unit; e2]Representing an expected value of a random variable; pi,tRepresenting the planned output of the ith thermal power generating unit in the t period;
Figure BDA0001835319850000022
representing the actual output of the j renewable energy source unit in the time period t, wherein the actual output is a random variable; CF (compact flash)i,t(Pi,t) Indicating the fuel cost of the ith thermal power generating unit during the period t,
Figure BDA0001835319850000023
the demand cost of positive rotation standby caused by the shortage of the actual output of the jth renewable energy source unit during the t period is shown, namely the punishment of overestimating the output of the renewable energy source;
Figure BDA0001835319850000024
and the demand cost of the negative rotation standby caused by the fact that the actual output of the jth renewable energy unit exceeds the planned value in the t period is represented, namely the penalty cost of underestimating the output of the renewable energy.
Further, the specific expression of the fuel cost of the conventional thermal power generating unit is as follows:
Figure BDA0001835319850000025
in the formula, ai，bi，ciRespectively a secondary term, a primary term coefficient and a constant term of the fuel cost expression;
the expression of the positive spinning reserve demand cost is:
Figure BDA0001835319850000026
in the formula, ej,tRepresenting the planned output of the generation of the jth renewable energy source unit in the t period,
Figure BDA0001835319850000027
the cost of the unit positive rotation standby of the jth renewable energy source unit in the t period;
the expression of the required cost of the negative spinning reserve is:
Figure BDA0001835319850000031
in the formula (I), the compound is shown in the specification,
Figure BDA0001835319850000032
and (4) the cost of unit negative rotation standby for the jth renewable energy source unit in the t period.
Further, the total cost of the positive rotation reserve is obtained according to the requirement cost of the positive rotation reserve, and the expression is as follows:
Figure BDA0001835319850000033
in the formula (I), the compound is shown in the specification,
Figure BDA0001835319850000034
as a random variable
Figure BDA0001835319850000035
A probability density function of;
obtaining the total cost of the negative rotation standby according to the negative rotation standby requirement cost, wherein the expression is as follows:
Figure BDA0001835319850000036
in the formula (I), the compound is shown in the specification,
Figure BDA0001835319850000037
and the maximum value of the renewable energy output of the jth renewable energy source unit in the t period is shown.
Further, the constraints of the dynamic economic dispatch model considering the randomness of the renewable energy sources comprise deterministic constraints and opportunistic constraints;
the deterministic constraint conditions comprise power balance constraint, upper and lower limit constraint of unit output, climbing rate constraint of the unit and rotation standby constraint;
the opportunity constraint conditions comprise line power flow constraint, system load loss risk, wind abandoning risk and light abandoning risk.
Further, the expression of the power balance constraint is:
to pair
Figure BDA0001835319850000038
Figure BDA0001835319850000039
Wherein p isd,tD represents the total number of the loads and the number of the nodes, wherein the load quantity of the D-th node in the t period is the load quantity of the D-th node;
the expression of the upper and lower limit constraints of the unit output is as follows:
for is to
Figure BDA00018353198500000310
pi,min≤pi,t≤pi,max (8)，
Figure BDA0001835319850000041
Wherein p isi,min,pi,maxRespectively representing the upper limit and the lower limit of the output of the ith traditional thermal power generating unit;
the expression of the slope climbing rate constraint of the unit is as follows:
to pair
Figure BDA0001835319850000042
-RDi·Δt≤pi，t+1-pi，t≤RUi·Δt (10)，
Wherein RDiAnd RUiRespectively represents the maximum downward slope rate and the maximum upward slope rate of the ith unit in unit time,
Δ t represents a time interval of each scheduling period;
the expression of the constraint of spinning reserve is:
for is to
Figure BDA0001835319850000044
Figure BDA0001835319850000045
Figure BDA0001835319850000046
Wherein the content of the first and second substances,
Figure BDA0001835319850000047
and
Figure BDA0001835319850000048
respectively representing the number of positive and negative rotation spares provided by the ith thermal power generating unit in the t period,
Figure BDA0001835319850000049
and
Figure BDA00018353198500000410
and respectively representing the maximum positive and negative rotation reserve capacity which can be provided by the ith thermal power generating unit in the time period t.
Further, the expression of the line power flow constraint is as follows:
for is to
Figure BDA00018353198500000411
Figure BDA00018353198500000412
Figure BDA00018353198500000413
Wherein G isi,lTransfer distribution factor G of active power output of ith traditional thermal generator set for the l linej,lTransfer distribution factor G for active output of jth renewable energy source unit for ith lined,lFor the L line to the d node load power transfer distribution factor, LlThe upper limit of the active power flow on the l line is defined, and alpha is the allowable maximum violation level that the active power on the line does not exceed the upper limit;
the expression of the system load loss risk and the wind and light abandoning risk is as follows:
to pair
Figure BDA00018353198500000414
Figure BDA0001835319850000051
Figure BDA0001835319850000052
Further, the opportunity constrained convex relaxation algorithm has a standard form of:
assuming the feasible domain of opportunity constraint determination is:
X＝{x:P[y(x,λ)≥0]≥1-η,x∈A} (17)，
wherein x ∈ RnIs a decision variable, λ is a random variable and satisfies a certain probability distribution, and the sample space is
Figure BDA0001835319850000053
P[B]Represents the probability of occurrence of event B, η ∈ (0,1) represents the likelihood that the constraint is not satisfied,
Figure BDA0001835319850000054
representing a non-empty set defined by other deterministic constraints,
Figure BDA0001835319850000055
representing an opportunity constraint function, and X is a feasible domain determined by opportunity constraint;
when in use
Figure BDA0001835319850000056
Then, the feasible domain after convex relaxation is:
Figure BDA0001835319850000057
in the formula, L is the lower bound of y (x, lambda) in the practical problem and is obtained by substituting the values of x and lambda into a function y (x, lambda) in an extreme scene;
when the prediction error of the renewable energy satisfies the mixed gaussian distribution, the expression is satisfied:
Figure BDA0001835319850000058
wherein the content of the first and second substances,
Figure BDA0001835319850000059
a probability density function representing the actual output predicted value of the renewable energy source unit at the time t,
Figure BDA00018353198500000510
represents the mth Gaussian component, lambdam,j,t，μm,j,t，σm,j,tRespectively representing the coefficient, mean and variance of the component, satisfying
Figure BDA00018353198500000511
Equation (13), equation (14), equation (15), and equation (16) are converted to:
Figure BDA0001835319850000061
Figure BDA0001835319850000062
Figure BDA0001835319850000063
Figure BDA0001835319850000064
in the formula, Lf1,Lf2,Lb1And Lb1The actual lower bounds of the opportunity constraint functions, equation (13), equation (14), equation (15) and equation (16), respectively, are determined by considering the bounds of all the unit active outputs.
The invention has the technical characteristics and beneficial effects that:
1. the invention considers the random dynamic economic dispatching method of wind, light and other renewable energy sources, compared with the traditional economic dispatching method, the risk of the system is effectively reduced, the dispatching cost is saved, and the consumption level of wind, light and other renewable energy sources is improved; by the convex relaxation method, the problem of the opportunity constraint which is difficult to solve is relaxed into the convex optimization problem which is easy to solve, and the efficiency and the flexibility of the economic dispatching of the system are improved.
2. The method can be applied to the economic dispatching of the power system including the large-scale wind, light and other renewable energy sources for power generation.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 shows a schematic flow diagram of an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A renewable energy random dynamic economic dispatching method based on convex relaxation as shown in fig. 1. The scheduling method comprises the following steps: firstly, establishing a dynamic economic dispatching model considering the randomness of renewable energy sources; secondly, converting a random dynamic economic dispatching model into a deterministic economic dispatching model through an opportunity constrained convex relaxation algorithm; and finally, solving the deterministic dynamic economic dispatching model, determining the plan of the renewable energy source unit and dispatching.
The following description will be given taking a renewable energy unit such as wind, light, etc. as an example.
Specifically, the dynamic economic dispatching model considering the randomness of the renewable energy sources consists of an objective function and constraint conditions, and the establishing steps are as follows:
the method comprises the following steps: determining an objective function of a dynamic economic dispatching model considering the randomness of the renewable energy sources; specifically, the objective function of the dynamic economic dispatch model considering the randomness of the renewable energy sources is the minimization of the operation cost, and the expression is as follows:
Figure BDA0001835319850000071
wherein F is an objective function representing the total cost of accounting for various factors; t, N and J respectively represent the number of scheduling time periods, the number of traditional thermal power units and the number of wind, light and other renewable energy units; t, i and j are numbers of a scheduling time interval, a traditional thermal power generating unit and a renewable energy source unit respectively; e2]Representing an expected value of a random variable; p isi,tRepresenting the planned output of the ith thermal power generating unit in the t period;
Figure BDA0001835319850000081
representing the actual output of the j wind, light and other renewable energy source units in the time period t, wherein the actual output is a random variable which accords with certain distribution; CF (compact flash)i,t(Pi,t) Representing the fuel cost of the ith thermal power generating unit in the t period;
Figure BDA0001835319850000082
the method comprises the steps that the demand cost of positive rotation standby caused by the fact that the actual output of the renewable energy source unit such as jth wind, light and the like is insufficient in a t period is shown, namely the punishment of overestimating the output of the renewable energy sources such as wind, light and the like is obtained;
Figure BDA0001835319850000083
and the demand cost of negative rotation standby caused by the fact that the actual output of the j wind, light and other renewable energy source units exceeds the planned value in the t period is represented, namely the penalty cost of underestimating the output of wind, light and other renewable energy sources is obtained.
Specifically, the expression of the fuel cost of the conventional thermal power generating unit is as follows:
Figure BDA0001835319850000084
in the formula, ai，bi，ciRespectively, a quadratic term, a first order coefficient and a constant term of the fuel cost calculation formula.
Specifically, the expression of the positive rotation standby demand cost is as follows:
Figure BDA0001835319850000085
wherein e isj,tRepresenting the planned output of the power generation of the jth renewable energy source unit such as wind, light and the like in the time period t,
Figure BDA0001835319850000086
the cost of the unit positive rotation standby of the jth renewable energy source unit in the t period; specifically, the positive rotation reserve is scheduled to make up for the deficiency of the output only when the output of the renewable energy source cannot reach the planned value.
Further, due to the randomness of the renewable energy output, the total cost of the positive spinning reserve is expressed in the form of a random variable expectation, namely:
Figure BDA0001835319850000087
in the formula (I), the compound is shown in the specification,
Figure BDA0001835319850000088
as random variables
Figure BDA0001835319850000089
A probability density function of (a);
specifically, the expression of the cost of the negative spinning reserve is:
Figure BDA00018353198500000810
in the formula (I), the compound is shown in the specification,
Figure BDA00018353198500000811
cost per unit negative spin standby; specifically, there is a negative spinning reserve cost only if the actual output of the renewable energy source exceeds the projected value.
Further, the total cost of negative spinning reserve is expressed as a desired form of random variables, namely:
Figure BDA0001835319850000091
in the formula (I), the compound is shown in the specification,
Figure BDA0001835319850000092
and the maximum value of the renewable energy output of the jth renewable energy source unit in the t period is represented.
Step two: determining constraint conditions of a dynamic economic dispatching model considering randomness of renewable energy sources; specifically, the constraints of the dynamic economic dispatch model taking renewable energy randomness into account include deterministic constraints and opportunistic constraints.
Further, the deterministic constraint conditions include a power balance constraint, an upper limit constraint and a lower limit constraint of the unit output, a climbing constraint of the unit and a rotation standby constraint.
Specifically, the expression of the power balance constraint is:
for is to
Figure BDA0001835319850000093
Figure BDA0001835319850000094
In the formula, pd,tSpecifically, D represents both the total number of loads and the number of nodes.
Specifically, the expression of the upper and lower limit constraints of the unit output is as follows:
for is to
Figure BDA0001835319850000095
pi,min≤pi,t≤pi,max (8)，
Figure BDA0001835319850000096
In the formula, pi,min,pi,maxAnd respectively representing the upper limit and the lower limit of the output of the ith traditional thermal power generating unit.
Specifically, the expression of the climbing constraint of the unit is as follows:
for is to
Figure BDA0001835319850000097
Figure BDA0001835319850000098
In the formula, RDiAnd RUiRespectively representing the maximum downward slope rate and the maximum upward slope rate of the ith unit in unit time, and delta t represents the time interval of each scheduling period.
Specifically, the expression of the spinning reserve constraint is:
to pair
Figure BDA0001835319850000099
Figure BDA0001835319850000101
Figure BDA0001835319850000102
In the formula (I), the compound is shown in the specification,
Figure BDA0001835319850000103
and
Figure BDA0001835319850000104
respectively representing the number of positive and negative rotation standby provided by the ith thermal power generating unit in the t period,
Figure BDA0001835319850000105
and
Figure BDA0001835319850000106
and respectively representing the maximum positive and negative rotation reserve capacity which can be provided by the ith thermal power generating unit in the time period t. In particular, in order to balance the power fluctuations due to the uncertainty of the renewable energy output, the unit needs to have enough positive and negative spinning reserve capacity, however, the amount of such capacity is limited by other factors, such as the sum of the output of the unit, the maximum reserve capacity, and so on.
Further, opportunity constraint conditions comprise line current constraint, system load loss risk, wind abandon risk and light abandon risk.
Specifically, because the output of the power generation by renewable energy sources such as wind and light is a random variable, the power flow on the line is also a random variable. To compromise the safety and economy of the scheduling, the active power on the line needs to not exceed its upper bound with a certain confidence level 1- α. The specific expression of the line power flow constraint is as follows:
to pair
Figure BDA0001835319850000107
Figure BDA0001835319850000108
Figure BDA0001835319850000109
In the formula, Gi,lTransfer distribution factor G of active power output of ith traditional thermal generator set for the l linej,lTransfer distribution factor G of active power output of jth wind, light and other renewable energy source unit by the ith lined,lDistribution factor, L, for the transfer of load power of the L line to the d nodelAs the upper limit of the active power flow on the l-th line, α is the maximum allowable violation level at which the active power on the line does not exceed its upper bound.
Specifically, the risk of system load loss and the risk of wind and light abandonment are due to the fact that the positive and negative rotating reserve capacity needs to be not less than the fluctuation of the actual output of renewable energy sources such as wind and light with a certain confidence level. When the actual output of the wind, light and other renewable energy sources is smaller than the planned output, the traditional unit is required to provide positive rotation reserve to ensure the safe operation of the system, and if the positive rotation reserve capacity is not enough, the system has the risk of losing load. On the other hand, when the actual output of renewable energy sources such as wind, light and the like is larger than the planned output, a negative rotation standby is required to be provided, otherwise, the power balance of the system must be met through wind abandoning, light abandoning and the like. The above condition can be expressed as satisfying the chance constraint with a confidence level of 1- β:
to pair
Figure BDA0001835319850000111
Figure BDA0001835319850000112
Figure BDA0001835319850000113
Specifically, a random dynamic economic dispatching model is converted into a deterministic economic dispatching model through an opportunity constrained convex relaxation algorithm, and the specific process is as follows;
firstly, the method comprises the following steps: a convex relaxation algorithm that determines chance constraints.
Specifically, the standard form of the chance constrained convex relaxation algorithm is:
assuming the feasible domain of opportunity constraint determination is:
X＝{x:P[y(x,λ)≥0]≥1-η,x∈A} (17)，
wherein x ∈ RnIs a decision variable, lambda is a random variable and satisfies a certain probability distribution, and the sample space is
Figure BDA0001835319850000114
P (B) represents the probability of occurrence of event B, η ∈ (0,1) represents the probability that the constraint is not satisfied,
Figure BDA0001835319850000115
representing a non-empty set defined by other deterministic constraints,
Figure BDA0001835319850000116
representing an opportunity constraint function, and X is the feasible domain determined by the opportunity constraint.
When the temperature is higher than the set temperature
Figure BDA0001835319850000117
Then, the feasible domain after convex relaxation is:
Figure BDA0001835319850000118
wherein, L is the lower bound of y (x, λ) in the practical problem and is obtained by substituting the values of x and λ in the extreme scene into the function y (x, λ).
Secondly, the method comprises the following steps: and converting the opportunity constraint condition into a deterministic convex constraint condition.
Specifically, the prediction error of renewable energy sources such as wind and light meets the mixed gaussian distribution, and the expression is as follows:
Figure BDA0001835319850000121
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0001835319850000122
a probability density function representing the actual output predicted value of the j th wind, light and other renewable energy source units at the time t,
Figure BDA0001835319850000123
represents the mth Gaussian component, λm,j,t，μm,j,t，σm,j,tRespectively representing the coefficient, mean and variance of the component, satisfying
Figure BDA0001835319850000124
Further, the expression after the function transformation of the opportunity constraint condition is as follows:
Figure BDA0001835319850000125
Figure BDA0001835319850000126
Figure BDA0001835319850000127
Figure BDA0001835319850000128
specifically, equations (20), (21), (22), and (23) correspond to opportunistic constraints (13), (14), (15), and (16), L, respectivelyf1,Lf2,Lb1And Lb1The actual lower bounds of the opportunity constraint functions of equations (13), (14), (15), and (16), respectively, may be determined by considering the bounds of all the unit active outputs.
Specifically, solving a deterministic dynamic economic dispatching model, determining a plan of the renewable energy unit and dispatching, namely solving the deterministic dynamic economic dispatching model determined by formulas (1) - (12) and formulas (20) - (23), and solving the obtained Pi,tAnd ej,tAnd respectively serving as the planned output of the ith traditional thermal power generating unit and the jth wind, light and other renewable energy generating units at the moment t for scheduling.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (6)

1. A renewable energy random dynamic economic dispatching method based on convex relaxation is characterized in that: the method comprises the following steps:
establishing a dynamic economic dispatching model considering randomness of renewable energy sources;
the dynamic economic dispatching model considering the randomness of the renewable energy sources consists of an objective function and constraint conditions, and the specific establishment steps are as follows:
the method comprises the following steps: determining an objective function of a dynamic economic dispatch model taking into account randomness of renewable energy sources; the expression of the objective function is:
Figure FDA0003834133140000011
wherein F is an objective function representing the total cost of accounting for various factors; t, N and J respectively represent the number of scheduling time periods, the number of traditional thermal power units and the number of renewable energy units; t, i and j are respectively the number of a scheduling time interval, the number of a traditional thermal power generating unit and the number of a renewable energy source unit; e [ 2 ]]Representing an expected value of a random variable; pi,tRepresenting the planned output of the ith thermal power generating unit in the t period;
Figure FDA0003834133140000012
representing the actual power generation output of the jth renewable energy source unit in the t time period, wherein the actual power generation output is a random variable; CF (compact flash)i,t(Pi,t) Indicating the fuel cost of the ith thermal power generating unit in the period t,
Figure FDA0003834133140000013
the demand cost of positive rotation standby caused by the shortage of the actual output of the jth renewable energy source unit during the t period is shown, namely the punishment of overestimating the output of the renewable energy source;
Figure FDA0003834133140000014
the demand cost of negative rotation standby caused by the fact that the actual output of the jth renewable energy source unit exceeds a planned value in the t period is represented, namely the penalty cost of underestimating the output of the renewable energy sources;
step two: determining constraint conditions of a dynamic economic dispatching model considering randomness of renewable energy sources;
wherein the constraints of the dynamic economic dispatch model taking into account renewable energy randomness comprise deterministic constraints and opportunistic constraints; the deterministic constraint conditions comprise power balance constraint, upper and lower limit constraint of unit output, climbing rate constraint of the unit and rotation standby constraint; the opportunity constraint conditions comprise line power flow constraint, system load loss risk, wind abandoning risk and light abandoning risk;
converting a random dynamic economic dispatching model into a deterministic economic dispatching model through an opportunity constrained convex relaxation algorithm;
and solving the deterministic dynamic economic dispatching model, determining the plan of the renewable energy source unit and dispatching.
2. The scheduling method of claim 1, wherein: the specific expression of the fuel cost of the traditional thermal power generating unit is as follows:
Figure FDA0003834133140000021
in the formula, ai，bi，ciThe second term, the first term coefficient and the constant term of the fuel cost expression are respectively;
the expression of the positive rotation standby demand cost is as follows:
Figure FDA0003834133140000022
in the formula, ej,tRepresenting the planned output of the generation of the jth renewable energy source unit in the t period,
Figure FDA0003834133140000023
the cost of the unit positive rotation standby of the jth renewable energy source unit in the t period;
the expression of the required cost of the negative spinning reserve is:
Figure FDA0003834133140000024
in the formula (I), the compound is shown in the specification,
Figure FDA0003834133140000025
and (4) the cost of unit negative rotation standby for the jth renewable energy unit in the t period.
3. The scheduling method of claim 2, wherein: obtaining the total cost of the positive rotation standby according to the positive rotation standby requirement cost, wherein the expression is as follows:
Figure FDA0003834133140000026
in the formula (I), the compound is shown in the specification,
Figure FDA0003834133140000027
as a random variable
Figure FDA0003834133140000028
A probability density function of;
obtaining the total cost of the negative rotation standby according to the negative rotation standby requirement cost, wherein the expression is as follows:
Figure FDA0003834133140000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003834133140000032
and the maximum value of the renewable energy output of the jth renewable energy source unit in the t period is represented.
4. The scheduling method of claim 1, wherein the power balance constraint is expressed as:
to pair
Figure FDA0003834133140000033
Figure FDA0003834133140000034
Wherein p isd,tD represents the total number of the loads and the number of the nodes;
the expression of the upper and lower limit constraints of the unit output is as follows:
to pair
Figure FDA0003834133140000035
pi,min≤pi,t≤pi,max (8)，
Figure FDA0003834133140000036
Wherein p isi,min,pi,maxRespectively representing the upper limit and the lower limit of the output of the ith traditional thermal power generating unit;
the expression of the slope climbing rate constraint of the unit is as follows:
to pair
Figure FDA0003834133140000037
-RDi·Δt≤pi,t+1-pi,t≤RUi·Δt (10)，
Wherein RDiAnd RUiRespectively representing the maximum downward slope rate and the maximum upward slope rate of the ith unit in unit time, wherein delta t represents the time interval of each scheduling period;
the expression of the constraint of spinning reserve is:
to pair
Figure FDA0003834133140000038
Figure FDA0003834133140000041
Figure FDA0003834133140000042
Wherein the content of the first and second substances,
Figure FDA0003834133140000043
and
Figure FDA0003834133140000044
respectively representing the number of positive and negative rotation spares provided by the ith thermal power generating unit in the t period,
Figure FDA0003834133140000045
and
Figure FDA0003834133140000046
respectively representing the maximum positive and negative rotation reserve capacity which can be provided by the ith thermal power generating unit in the time period t,
Figure FDA0003834133140000047
and
Figure FDA0003834133140000048
and respectively representing the minimum and maximum technical output of the ith thermal power generating unit in the t period.
5. The scheduling method of claim 4, wherein the expression of the line flow constraint is:
to pair
Figure FDA0003834133140000049
Figure FDA00038341331400000410
Figure FDA00038341331400000411
Wherein G isi,lTransfer distribution factor G of active power output of the ith traditional thermal generator set for the l linej,lTransfer distribution factor G for active output of jth renewable energy source unit for ith lined,lFor the L line to the d node load power transfer distribution factor, LlThe active power flow upper limit on the l-th line is defined, alpha is the allowable maximum violation level that the active power on the line does not exceed the upper limit, and p { } represents the probability that the inequality in the brace is established;
the expression of the system load loss risk and the wind and light abandoning risk is as follows:
to pair
Figure FDA00038341331400000412
Figure FDA00038341331400000413
Figure FDA00038341331400000414
P { } represents the probability that the inequality in braces holds; beta represents the risk of system loss of load or wind/light curtailment.
6. The scheduling method of claim 5, wherein: the chance constrained convex relaxation algorithm has the standard form:
assuming the feasible domain of opportunity constraint determination is:
X＝{x:P[y(x,λ)≥0]≥1-η,x∈A} (17)，
wherein x ∈ RnIs a decision variable, lambda is a random variable and satisfies a certain probability distribution, and the sample space is
Figure FDA0003834133140000051
P[B]Represents the probability of occurrence of the event B, η ∈ (0,1) represents the probability that the constraint condition is not satisfied,
Figure FDA0003834133140000052
representing a non-empty set defined by other deterministic constraints,
Figure FDA0003834133140000059
representing an opportunity constraint function, wherein X is a feasible domain determined by opportunity constraint;
when in use
Figure FDA0003834133140000053
Then, the feasible domain after convex relaxation is:
Figure FDA0003834133140000054
wherein L is the lower bound of y (x, lambda) in practical problem and is obtained by substituting the values of x and lambda into function y (x, lambda) in extreme scene, e is natural constant, lambdajDenotes the j-th component, y, of the random variable λ0(x) Partial expression representing a random-free variable in an opportunistic constraint function y (x, λ), yj(x) Denotes the sum λ in the chance constraint function y (x, λ)jA partial expression of the multiplication;
when the prediction error of the renewable energy satisfies the mixed gaussian distribution, the expression is satisfied:
Figure FDA0003834133140000055
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003834133140000056
a probability density function representing the actual output predicted value of the renewable energy source unit at the time t, wherein M represents the number of Gaussian components contained in the probability density function,
Figure FDA0003834133140000057
represents the mth Gaussian component, λm,j,t，μm,j,t，σm,j,tRespectively representing the coefficient, mean and variance of the component, satisfying
Figure FDA0003834133140000058
Equation (13), equation (14), equation (15), and equation (16) are converted to:
Figure FDA0003834133140000061
Figure FDA0003834133140000062
Figure FDA0003834133140000063
Figure FDA0003834133140000064
in the formula, Lf1,Lf2,Lb1And Lb1The actual lower bounds of the opportunity constraint functions, equation (13), equation (14), equation (15) and equation (16), respectively, are determined by considering the bounds of all the unit active outputs.
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