CN112381375A - Power grid economic operation domain rapid generation method based on power flow distribution matrix - Google Patents

Power grid economic operation domain rapid generation method based on power flow distribution matrix Download PDF

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CN112381375A
CN112381375A CN202011239092.0A CN202011239092A CN112381375A CN 112381375 A CN112381375 A CN 112381375A CN 202011239092 A CN202011239092 A CN 202011239092A CN 112381375 A CN112381375 A CN 112381375A
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徐华廷
郭创新
施云辉
徐春雷
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State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses a method for quickly generating an economic operation domain of a power grid based on a power flow distribution matrix. According to the method for quickly generating the economic operation domain of the power grid, the new energy output interval and the load level interval are predicted, cost functions are minimized according to different requirements, constraint conditions are added according to different requirements, finally, a mathematical optimization model based on a power flow distribution matrix is generated, and the economic operation domain of a unit is obtained through solving. Compared with the traditional solving process based on the two-stage robust optimization algorithm, the method has the advantages of simplicity, higher solving speed and the like, and is more suitable for real-time rolling generation of the daily scheduling plan of the power grid.

Description

Power grid economic operation domain rapid generation method based on power flow distribution matrix
Technical Field
The invention belongs to the field of optimized dispatching operation of a large power grid, and particularly relates to a method for quickly generating an economic operation domain of a power grid based on a power flow distribution matrix.
Background
The day-ahead or day-inside scheduling plan of the power grid is usually obtained by performing abstract modeling on power grid elements based on a mathematical method and solving through an optimization algorithm. However, with the large-scale access of the source end to renewable energy sources and the gradual establishment of power market mechanisms such as load end demand response, the traditional optimal scheduling algorithm based on the deterministic model has become more and more difficult to meet the scheduling requirements of the large power grid nowadays. Therefore, the scholars propose an economic operation domain concept for describing the unit optimization scheduling operation interval under the background of simultaneously considering source-load double-side uncertainty. The method comprises the steps of firstly providing a new energy output and load level interval under a certain confidence level based on historical operating data of source load and a future prediction result, and then calculating a unit output interval capable of safely and stably operating the power grid by using a two-stage robust optimization algorithm. However, the two-stage robust optimization algorithm has a calculation process of mutual iteration between the main problem and the sub-problem, so that the solving speed is slow, and the real-time requirement of generating the scheduling plan by rolling in the day is difficult to meet.
Disclosure of Invention
The invention aims to provide a method for quickly generating an economic operation domain of a power grid based on a power flow distribution matrix, aiming at the problem of low generation speed of the traditional economic operation domain. The method for quickly generating the economic operation domain of the power grid is based on a new energy output interval and a load level interval, an 'economic operation domain' optimization model under different requirements of the power grid is established, and the economic operation domain is obtained through solving.
The purpose of the invention is realized by the following technical scheme: a power grid economic operation domain rapid generation method based on a power flow distribution matrix comprises the following steps:
(1) new energy output interval prediction by using dynamic tengcopula model
Figure BDA0002767823890000011
And load level interval
Figure BDA0002767823890000012
Represents a lower limit value of the new energy output prediction,
Figure BDA0002767823890000013
represents an upper limit value of the new energy output prediction,
Figure BDA0002767823890000014
a lower limit value representing a prediction of the load level,
Figure BDA0002767823890000015
an upper limit value representing a load level prediction;
(2) minimizing a cost function according to different requirements, wherein the cost is the unit start-stop cost CssUnit fuel cost CfuelPenalty cost C of wind curtailment or load shedding caused by maintaining system power balanceunbReserve unit spare production cost CresOne or more combinations of (a);
the unit start-stop cost specifically comprises the following steps:
Figure BDA0002767823890000021
wherein G is a schedulable unit set, T is a total time period, cg,ssCost of a unit g for a single start-up or shut-down, yg,tAn indication variable y for the unit g to start at the moment tg,tThe value 1 or 0, 1 represents the starting of the unit, 0 represents the non-starting of the unit, and zg,tFor an indicator variable of the stoppage of the unit g at time t, zg,tThe value of 1 or 0 is taken, 1 represents that the unit is stopped, and 0 represents that the unit is not stopped;
the unit fuel cost specifically is as follows:
Figure BDA0002767823890000022
wherein, ag、bg、cgThe secondary term coefficient, the primary term coefficient and the constant term coefficient of the unit g fuel cost, Pg,tA reference output u of the unit g at the time tg,tThe starting and stopping state of the unit g at the moment t is shown;
the penalty cost of wind curtailment or load shedding caused by maintaining the power balance of the system is specifically as follows:
Figure BDA0002767823890000023
wherein ls ist、wctLoad shedding power and wind abandoning power at the moment t under the scene of wind power uncertainty are respectively not considered,
Figure BDA0002767823890000024
respectively is the load shedding power and the wind abandoning power under the worst wind power output scene cshedCost compensation for unit load shedding, cwcPunishing cost for unit wind abandon, and lambda is a risk weight reflecting preference of a decision maker;
the standby production cost of the reserved unit is specifically as follows:
Figure BDA0002767823890000025
wherein, cres,gThe unit standby cost of the unit g is saved,
Figure BDA0002767823890000026
andP g,trespectively setting an adjustable power upper limit and an adjusted power lower limit of the unit g at the moment t;
(3) adding constraint conditions according to different requirements, wherein the constraint conditions are one or more combinations of upper and lower limit constraints of unit output, climbing and landslide constraints, shortest continuous start-up or stop time constraints, start-up or stop indication variable constraints, line transmission capacity interval constraints and power balance interval constraints;
(4) according to the predicted new energy output interval
Figure BDA0002767823890000027
And load level interval
Figure BDA0002767823890000028
And generating a mathematical optimization model based on the load flow distribution matrix by minimizing the cost function and the constraint condition, and solving to obtain the economic operation domain of the unit.
Further, the unit output upper and lower limit constraints are specifically:
Figure BDA0002767823890000031
wherein,
Figure BDA0002767823890000032
and
Figure BDA0002767823890000033
the lower limit and the upper limit of the technical output of the unit g are respectively.
Further, the climbing and landslide constraints are specifically:
Figure BDA0002767823890000034
Figure BDA0002767823890000035
wherein, RUgAnd RDgThe upper limit of the power of the unit g for climbing and landslide every hour is respectively.
Further, the shortest continuous startup or shutdown time constraint is specifically:
Figure BDA0002767823890000036
Figure BDA0002767823890000037
wherein,
Figure BDA0002767823890000038
and
Figure BDA0002767823890000039
the shortest shutdown time and the shortest startup time of the unit under the accumulated time segment are respectively.
Further, the power-on or power-off indication variable constraints are specifically:
yg,t≥ug,t-ug,(t-1) (10)
zg,t≥-ug,t+ug,(t-1) (11)
further, the line transmission capacity interval constraint specifically includes:
Figure BDA00027678238900000310
Figure BDA00027678238900000311
TWPt W+TGPt G+TDPt D≤Fmax (14)
wherein, TW、TG、TDA power flow distribution matrix P corresponding to the new energy node, the schedulable unit node and the load node respectivelyt DFor the injected power vector of the load node at time t,
Figure BDA00027678238900000312
P t W、Pt Wrespectively the upper limit, the lower limit and the base value of the injected power vector of the new energy node at the time t,
Figure BDA00027678238900000313
P t G、Pt Grespectively injecting upper limit, lower limit, base value, F of power vector into schedulable unit node at time tmaxThe maximum active transmission power vector of the line.
Further, the power balance interval constraint specifically includes:
Figure BDA0002767823890000041
Figure BDA0002767823890000042
Figure BDA0002767823890000043
wherein D istThe total system load at time t.
Compared with the prior art, the invention has the following beneficial effects: the method for quickly generating the economic operation domain of the power grid simultaneously considers the start-stop cost, the fuel cost, the standby cost and the wind abandoning or load shedding cost of the generator set, quickly solves the optimized operation interval of the generator set, namely the economic operation domain, which considers the uncertainty of the power supply and the load on two sides, has the characteristics of high calculation speed and capability of automatically generating the economic operation interval of each generator set, can be applied to the generation of a real-time rolling scheduling plan in the day, and has great engineering application value and popularization prospect.
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FIG. 1 is a comparison graph of a power grid economic operation domain rapid generation method, robust optimization and deterministic optimization calculation speed.
Detailed Description
The invention provides a method for quickly generating an economic operation domain of a power grid based on a power flow distribution matrix, which comprises the following steps of:
(1) the influence of weather factors such as wind speed and the like on the new energy outputThe output prediction of the power grid is high in uncertainty, and the influence of the uncertainty on the operation of the power grid is more and more strong along with the gradual increase of the permeability of new energy; in addition, load prediction also has uncertainty of prediction error. In actual system decision, it is often difficult to obtain an accurate probability density function, but it is relatively easy to obtain a confidence interval of an uncertain variable, and the required information is greatly reduced. Therefore, the new energy output interval is predicted
Figure BDA0002767823890000044
And load level interval
Figure BDA0002767823890000045
Represents a lower limit value of the new energy output prediction,
Figure BDA0002767823890000046
represents an upper limit value of the new energy output prediction,
Figure BDA0002767823890000047
a lower limit value representing a prediction of the load level,
Figure BDA0002767823890000048
representing the upper limit of the load level prediction. Those skilled in the art can adopt a dynamic Tengcopula algorithm to predict the new energy output interval
Figure BDA0002767823890000049
And a load level interval.
(2) Minimizing a cost function according to different requirements, wherein the cost is the unit start-stop cost CssUnit fuel cost CfuelPenalty cost C of wind curtailment or load shedding caused by maintaining system power balanceunbReserve unit spare production cost CresOne or more combinations of (a);
the unit start-stop cost specifically comprises the following steps:
Figure BDA0002767823890000051
wherein G is a schedulable unit set, T is a total time period, cg,ssCost of a unit g for a single start-up or shut-down, yg,tAn indication variable y for the unit g to start at the moment tg,tThe value 1 or 0, 1 represents the starting of the unit, 0 represents the non-starting of the unit, and zg,tFor an indicator variable of the stoppage of the unit g at time t, zg,tAnd the value of 1 or 0 is taken, wherein 1 represents that the unit is stopped, and 0 represents that the unit is not stopped.
The unit fuel cost specifically is as follows:
Figure BDA0002767823890000052
wherein, ag、bg、cgThe coefficient is a quadratic term coefficient, a primary term coefficient and a constant term coefficient of the unit g fuel cost. Pg,tA reference output u of the unit g at the time tg,tAnd the starting and stopping states of the unit g at the moment t are shown.
The penalty cost of wind curtailment or load shedding caused by maintaining the power balance of the system is specifically as follows:
Figure BDA0002767823890000053
wherein ls ist、wctLoad shedding power and wind abandoning power at the moment t under the scene of wind power uncertainty are respectively not considered,
Figure BDA0002767823890000054
respectively is the load shedding power and the wind abandoning power under the worst wind power output scene cshedCost compensation for unit load shedding, cwcPenalty cost for wind abandon per unit, and λ is a risk weight reflecting preference of the decision maker.
The standby production cost of the reserved unit is specifically as follows:
Figure BDA0002767823890000055
wherein, cres,gThe unit standby cost of the unit g is saved,
Figure BDA0002767823890000056
andP g,tthe upper limit of the adjustable power of the unit g at the moment t and the lower limit of the adjusted power are respectively.
(3) Adding constraint conditions according to different requirements, wherein the constraint conditions are one or more combinations of upper and lower limit constraints of unit output, climbing and landslide constraints, shortest continuous start-up or stop time constraints, start-up or stop indication variable constraints, line transmission capacity interval constraints and power balance interval constraints;
the unit output upper and lower limit constraints are specifically as follows:
Figure BDA0002767823890000057
wherein,
Figure BDA0002767823890000058
and
Figure BDA0002767823890000059
the lower limit and the upper limit of the technical output of the unit g are respectively.
The climbing and landslide restraint is specifically as follows:
Figure BDA0002767823890000061
Figure BDA0002767823890000062
wherein, RUgAnd RDgThe upper limit of the power of the unit g for climbing and landslide every hour is respectively.
The shortest continuous startup or shutdown time constraint is specifically as follows:
Figure BDA0002767823890000063
Figure BDA0002767823890000064
wherein,
Figure BDA0002767823890000065
and
Figure BDA0002767823890000066
the shortest shutdown time and the shortest startup time of the unit under the accumulated time segment are respectively.
The starting-up or stopping indication variable constraints are specifically as follows:
yg,t≥ug,t-ug,(t-1) (10)
zg,t≥-ug,t+ug,(t-1) (11)
the line transmission capacity interval constraint specifically includes:
Figure BDA0002767823890000067
Figure BDA0002767823890000068
TWPt W+TGPt G+TDPt D≤Fmax (14)
wherein, TW、TG、TDA power flow distribution matrix P corresponding to the new energy node, the schedulable unit node and the load node respectivelyt DFor the injected power vector of the load node at time t,
Figure BDA0002767823890000069
P t W、Pt Ware each tThe upper limit, the lower limit and the base value of the injected power vector of the new energy node at the moment,
Figure BDA00027678238900000610
P t G、Pt Grespectively injecting upper limit, lower limit, base value, F of power vector into schedulable unit node at time tmaxThe maximum active transmission power vector of the line.
The electric power balance interval constraint specifically comprises:
Figure BDA00027678238900000611
Figure BDA00027678238900000612
Figure BDA0002767823890000071
wherein D istThe total system load at time t.
(4) Predicting the output interval of the new energy based on the step (1)
Figure BDA0002767823890000072
Load level interval
Figure BDA0002767823890000073
The network topology, schedulable units and unit start-stop plans are used as input data. And (3) determining an objective function to be optimized according to the step (2). And (4) generating a mathematical optimization model based on the power flow distribution matrix according to the column writing constraint conditions in the step (3). Finally, the economic operation domain of the unit can be solved based on a commercial solver (such as CPLEX).
The economic domain rapid calculation method is adopted to verify the rapidity of the method in the calculation speed, 25000 bus nodes are tested, the calculation speed is compared with the calculation speed of various algorithms, the result is shown in figure 1, and the method takes the start-stop cost and the fuel cost of a unit as objective functions and considers all constraint conditions for simulation measurement and calculation. According to the measurement and calculation results, the calculation time of the traditional robust optimization is obviously increased along with the increase of the scale of the calculation example. According to the method for rapidly calculating the economic operation domain based on the load flow distribution matrix, the calculation time is close to that based on a deterministic optimization algorithm, and the measurement and calculation time of 25000 node scale calculation examples can be less than 1 hour.

Claims (7)

1. A power grid economic operation domain rapid generation method based on a power flow distribution matrix is characterized by comprising the following steps:
(1) new energy output interval prediction by using dynamic tengcopula model
Figure FDA0002767823880000011
And load level interval
Figure FDA0002767823880000012
Figure FDA0002767823880000013
Represents a lower limit value of the new energy output prediction,
Figure FDA0002767823880000014
represents an upper limit value of the new energy output prediction,
Figure FDA0002767823880000015
a lower limit value representing a prediction of the load level,
Figure FDA0002767823880000016
an upper limit value representing a load level prediction;
(2) minimizing a cost function according to different requirements, wherein the cost is the unit start-stop cost CssUnit fuel cost CfuelPenalty cost C of wind curtailment or load shedding caused by maintaining system power balanceunbReserve unit spare production cost CresOne or more combinations of (a);
the unit start-stop cost specifically comprises the following steps:
Figure FDA0002767823880000017
wherein G is a schedulable unit set, T is a total time period, cg,ssCost of a unit g for a single start-up or shut-down, yg,tAn indication variable y for the unit g to start at the moment tg,tThe value 1 or 0, 1 represents the starting of the unit, 0 represents the non-starting of the unit, and zg,tFor an indicator variable of the stoppage of the unit g at time t, zg,tThe value of 1 or 0 is taken, 1 represents that the unit is stopped, and 0 represents that the unit is not stopped;
the unit fuel cost specifically is as follows:
Figure FDA0002767823880000018
wherein, ag、bg、cgThe secondary term coefficient, the primary term coefficient and the constant term coefficient of the unit g fuel cost, Pg,tA reference output u of the unit g at the time tg,tThe starting and stopping state of the unit g at the moment t is shown;
the penalty cost of wind curtailment or load shedding caused by maintaining the power balance of the system is specifically as follows:
Figure FDA0002767823880000019
wherein ls ist、wctLoad shedding power and wind abandoning power at the moment t under the scene of wind power uncertainty are respectively not considered,
Figure FDA00027678238800000110
respectively is the load shedding power and the wind abandoning power under the worst wind power output scene cshedCost compensation for unit load shedding, cwcPunishing cost for unit wind abandon, and lambda is a risk weight reflecting preference of a decision maker;
the standby production cost of the reserved unit is specifically as follows:
Figure FDA0002767823880000021
wherein, cres,gThe unit standby cost of the unit g is saved,
Figure FDA0002767823880000022
andP g,trespectively setting an adjustable power upper limit and an adjusted power lower limit of the unit g at the moment t;
(3) adding constraint conditions according to different requirements, wherein the constraint conditions are one or more combinations of upper and lower limit constraints of unit output, climbing and landslide constraints, shortest continuous start-up or stop time constraints, start-up or stop indication variable constraints, line transmission capacity interval constraints and power balance interval constraints;
(4) according to the predicted new energy output interval
Figure FDA0002767823880000023
And load level interval
Figure FDA0002767823880000024
And generating a mathematical optimization model based on the load flow distribution matrix by minimizing the cost function and the constraint condition, and solving to obtain the economic operation domain of the unit.
2. The method for rapidly calculating the economic operation domain of the power grid based on the power flow distribution matrix as claimed in claim 1, wherein the constraints of the upper and lower output limits of the unit are specifically:
Figure FDA0002767823880000025
wherein,
Figure FDA0002767823880000026
and
Figure FDA0002767823880000027
the lower limit and the upper limit of the technical output of the unit g are respectively.
3. The method for rapidly calculating the economic operation domain of the power grid based on the power flow distribution matrix as claimed in claim 1, wherein the climbing and landslide constraints are specifically:
Figure FDA0002767823880000028
Figure FDA0002767823880000029
wherein, RUgAnd RDgThe upper limit of the power of the unit g for climbing and landslide every hour is respectively.
4. The method for rapidly calculating the economic operation domain of the power grid based on the power flow distribution matrix as claimed in claim 1, wherein the shortest continuous startup or shutdown time constraint is specifically as follows:
Figure FDA00027678238800000210
Figure FDA00027678238800000211
wherein,
Figure FDA00027678238800000212
and
Figure FDA00027678238800000213
the shortest shutdown time and the shortest startup time of the unit under the accumulated time segment are respectively.
5. The method for rapidly calculating the economic operation domain of the power grid based on the power flow distribution matrix as claimed in claim 1, wherein the power-on or power-off indication variable constraints are specifically:
yg,t≥ug,t-ug,(t-1) (10)
zg,t≥-ug,t+ug,(t-1) (11)
6. the method for rapidly calculating the economic operation domain of the power grid based on the power flow distribution matrix as claimed in claim 1, wherein the constraint of the transmission capacity interval of the line is specifically as follows:
Figure FDA0002767823880000031
Figure FDA0002767823880000032
Figure FDA0002767823880000033
wherein, TW、TG、TDA power flow distribution matrix P corresponding to the new energy node, the schedulable unit node and the load node respectivelyt DFor the injected power vector of the load node at time t,
Figure FDA0002767823880000034
P t W、Pt Wrespectively the upper limit, the lower limit and the base value of the injected power vector of the new energy node at the time t,
Figure FDA0002767823880000035
P t G、Pt Grespectively injecting upper limit, lower limit, base value, F of power vector into schedulable unit node at time tmaxThe maximum active transmission power vector of the line.
7. The method for rapidly calculating the economic operation domain of the power grid based on the power flow distribution matrix as claimed in claim 1, wherein the power balance interval constraint is specifically as follows:
Figure FDA0002767823880000036
Figure FDA0002767823880000037
Figure FDA0002767823880000038
wherein D istThe total system load at time t.
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CN114188980A (en) * 2021-12-08 2022-03-15 杭州鸿晟电力设计咨询有限公司 Transparent micro-grid group economic operation domain generation method considering energy storage device
CN114662798A (en) * 2022-05-17 2022-06-24 浙江大学 Scheduling method and device based on power grid economic operation domain and electronic equipment
CN115660385A (en) * 2022-12-12 2023-01-31 浙江大学 Power grid convex hull economic operation domain decomposition parallel solving method and device

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