CN110601179A - Receiving-end power grid consumption optimization method for wind power participating in frequency modulation - Google Patents

Receiving-end power grid consumption optimization method for wind power participating in frequency modulation Download PDF

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Publication number
CN110601179A
CN110601179A CN201910760804.4A CN201910760804A CN110601179A CN 110601179 A CN110601179 A CN 110601179A CN 201910760804 A CN201910760804 A CN 201910760804A CN 110601179 A CN110601179 A CN 110601179A
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power
particle
wind
receiving
frequency modulation
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夏凡吴双
卜京
孙莹
郑铭洲
张飞云
卞婉春
殷明慧
谢云云
邹云
刘建坤
周前
汪成根
张宁宇
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Nanjing Tech University
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Nanjing Tech University
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Eletrric Generators (AREA)

Abstract

The invention discloses a receiving-end power grid consumption optimization method for wind power participating in frequency modulation, which comprises the following steps: firstly, constructing a receiving-end power grid absorption calculation model, setting system information and initial parameters, and randomly generating a control variable initial value and a particle initial speed; calculating a frequency value of the wind turbine generator participating in frequency modulation under certain disturbance and after the lag time according to the dynamic power flow; then, calculating the system load flow of the particles under each wind speed sample, and judging whether the constraint is met; adding the constraint into a fitness function, calculating the fitness value of each particle, acquiring an individual optimal value and a global optimal value, and updating the speed and the position of each particle; and then judging whether the maximum iteration times are met, if not, returning to recalculate the system load flow of the particles under each wind speed sample, and if so, outputting an optimal decision variable and a receiving end power grid absorption capacity limit value. The method can effectively calculate the consumption capability of the receiving-end power grid under the condition that the wind power participates in frequency modulation.

Description

Receiving-end power grid consumption optimization method for wind power participating in frequency modulation
Technical Field
The invention belongs to the field of electric power systems and automation thereof, and particularly relates to a receiving-end power grid absorption optimization method for wind power participating in frequency modulation.
Background
At present, wind power generation in China is in a rapid development stage, a plurality of established or under-established ten-million kilowatt-level wind power bases are mostly far away from a central network and are often positioned at the edge or the tail end of a power grid, corresponding grid structures are weak, power structures are simple, large-scale access of wind power brings great influence on safe and stable operation of a regional power grid, output power of a wind power plant has volatility due to the volatility of wind, the proportion of wind power installations in the power grid is higher and higher along with the expansion of the scale of the wind power plant, and the influence range of the wind power installations on the power grid is gradually enlarged from part to part. Therefore, the construction and planning of the power grid are further enhanced, and the research on the absorption capacity of the receiving-end power grid has important significance for the development and utilization of wind power in China.
After the frequency of a power grid of a conventional water-gas power generating unit is changed, the output of the unit can be adjusted through a speed regulator. The wind turbine generator cannot respond to the change of the power grid frequency, and the primary frequency modulation capability of the power grid is inevitably reduced after a part of conventional generators are replaced. It follows that as the proportion of wind power in the grid increases, the above-mentioned problems which jeopardize the safe operation of the grid inevitably increase. Wind power is participated in power grid frequency modulation, and the method is an important method for ensuring better grid-connected operation of wind power and improving the wind power consumption level.
However, in the conventional absorption problem, wind power is used as a disturbance source to participate in the power flow calculation, and after the wind power participates in the frequency modulation, power flow analysis under different discontinuities needs to be performed. The existing absorption optimization carries out dynamic simulation experiments on different wind power access restriction factors such as frequency, voltage, peak regulation capacity, static safety, transient stability and the like for multiple times by using assumed wind power injection power, and continuously corrects the assumed value until the restriction conditions are met, thereby determining the limit of the wind power penetration power. The disadvantages of this approach are: 1) the calculated amount is large, the wind power injection power needs to be manually adjusted, no adjustment principle can be followed at present, the adjustment is only carried out by experience, the subjectivity is strong, the operation is not facilitated, and the engineering practicability is poor; 2) generally, only one specific restriction factor is aimed at, and a plurality of restriction factors influencing wind power access cannot be comprehensively considered; 3) various operation modes of the system and wind speed conditions of the wind farm cannot be comprehensively considered.
Disclosure of Invention
The invention aims to provide a receiving-end power grid absorption optimization method for wind power participating in frequency modulation.
The technical solution for realizing the purpose of the invention is as follows: a receiving-end power grid consumption optimization method for wind power participating in frequency modulation comprises the following steps:
step 1, constructing a receiving-end power grid absorption calculation model considering wind power participation frequency modulation, and setting system information and particle swarm algorithm initial parameters;
step 2, randomly generating a control variable initial value and a particle initial speed;
step 3, calculating a frequency value of the wind turbine generator participating in frequency modulation under certain disturbance and after the lag time according to the dynamic power flow;
step 4, calculating the system load flow of the particles under each wind speed sample, and judging whether the constraint is met;
step 5, adding the constraint into a fitness function, calculating the fitness value of each particle, acquiring an individual optimal value and a global optimal value, and updating the speed and the position of each particle;
step 6, judging whether the maximum iteration times are met, and if not, returning to the step 4; if yes, performing step 7;
and 7, outputting the optimal decision variables and the receiving end power grid absorption capacity limit value.
Compared with the prior art, the invention has the remarkable advantages that: (1) the method can effectively calculate the absorption capacity limit value of the receiving-end power grid under the condition that the wind power participates in frequency modulation, and has guiding significance on the planning and design of the wind power plant; (2) aiming at the characteristic that wind power participates in frequency modulation, the traditional optimal power flow cannot be directly applied to wind power access capacity calculation, and the method comprehensively considers a plurality of restriction factors influencing wind power access, comprehensively considers dynamic change of power grid frequency in the frequency modulation process, and effectively calculates the absorption capacity limit value of the receiving-end power grid under the condition that the wind power participates in the frequency modulation.
Drawings
FIG. 1 is a flow chart of a receiving-end power grid absorption optimization method for wind power participating in frequency modulation.
FIG. 2 is a frequency variation diagram of wind power participating in frequency modulation in the method of the present invention.
Detailed Description
The receiving-end power grid consumption optimization method for wind power participation frequency modulation considers the characteristic of wind power participation frequency modulation, and solves the problem that the traditional optimal power flow cannot be directly applied to wind power access capacity calculation. Firstly, a receiving-end power grid absorption calculation model considering wind power participation frequency modulation is constructed, a constraint planning model is established, finally, an improved particle swarm algorithm is adopted to solve an overall model, and an optimal decision variable and an absorption capacity limit value are output.
With reference to fig. 1, the receiving-end power grid absorption optimization method with wind power participating in frequency modulation provided by the invention specifically comprises the following steps:
step 1, constructing a receiving-end power grid absorption calculation model considering wind power participation frequency modulation, and setting system information and particle swarm algorithm initial parameters, wherein the method specifically comprises the following steps:
the receiving-end power grid absorption optimization model for the wind power participating in frequency modulation takes the sum of installed capacities of all wind power plants receivable by a system as a target, and selects the allocation number of fans of each wind power plant and the active power of a conventional unit as decision variables to carry out optimization adjustment on the premise of meeting system flow equality constraints and inequality constraints of reliable and safe operation of a series of systems. Essentially, the problem is a multivariable, multi-constraint, nonlinear mixed integer programming problem with random variables.
The model takes the maximization of the sum of the installed capacities of all wind power plants accepted by the system as a target, and the target function is as follows:
wherein m is the number of wind power plants, niIs the number of fans in the ith wind farm, PNWiThe rated power of the fan in the ith wind power plant.
The equality constraint is the power flow equation of the system:
in the formula, PGi、QGiActive and reactive power, P, respectively, of a conventional generator set at node iWi、QWiRespectively the active and reactive power, P, of the wind farm at node iLi、QLiAre respectively a sectionActive and reactive loads at point i, Ui、Uj、θijVoltage amplitude and phase angle difference, G, of node i and node j, respectivelyij、BijRespectively the real and imaginary parts, C, of the system admittance matrixPQ、CPVSets of PQ, PV nodes, respectively;
the inequality constraints comprise constraints of decision variables and state variables, wherein the decision variables are the number of fans and the power of a conventional unit, and the constraints are as follows:
in the formula (I), the compound is shown in the specification,the maximum number of the fans in the ith wind power plant,minimum and maximum active power, C, of the ith conventional generator set, respectivelyGIs a collection of conventional generator sets.
The state variables are dependent variables of the decision variables, including system frequency, node voltage amplitude, reactive power of the conventional generator set, line power flow, up-down rotation standby of the system and climbing capacity constraint of the conventional generator set. The state variable constraints are:
in the formula (f)min、fmaxRespectively, the minimum and maximum frequencies of the system;minimum and maximum voltage amplitudes for node i, respectively;respectively the minimum and maximum reactive power of the ith conventional generator set; pLiOn the ith lineIn the flow of (2) to (2),the maximum limit value of the power flow on the ith line is set; pGiThe active power of the ith conventional generator set,the minimum active power of the ith conventional generator set;the upper and lower rotation of the system are respectively reserved, and generally 5% of the total load of the system can be taken; r isGiThe maximum climbing capacity of the conventional unit i after the upper and lower power limits are restrained is considered,is a sequence of power samples for wind farm j.
Setting system node parameters, power flow parameters and initial parameters of a particle swarm algorithm;
step 2, randomly generating a control variable initial value and a particle initial speed;
n D-dimensional particles are randomly generated in the control variable range, the initial speed of each particle is generated, and the initial value of each particle needs to be rounded for an integer control variable, namely the maximum configuration number of fans in the wind power plant.
Step 3, calculating frequency values of the wind turbine generator participating in frequency modulation under certain disturbance (load increase or load decrease is 10% or 20%) and lag time (set as 5s) according to the dynamic power flow, and specifically as follows:
establishing a primary frequency modulation characteristic of a variable speed wind turbine generator by adopting an overspeed and variable pitch coordinated frequency modulation control strategy, solving a system frequency differential equation by adopting an unbalanced power sectional distribution method and an Euler method, calculating a frequency value of the system after the lag time under certain disturbance, simulating a frequency curve to descend before the set lag time is 5s as shown in figure 2, correcting the power after 5s, and starting to ascend the frequency.
Step 4, calculating the system load flow of the particles under each wind speed sample, and judging whether equality constraint and inequality constraint in the step one are met, wherein the method specifically comprises the following steps:
after a sample matrix of the wind speed is obtained, the relation between the active power and the wind speed of the wind turbine generator can be simplified and expressed by adopting the following piecewise function:
in the formula, vin、vout、vNRespectively cut-in wind speed, cut-out wind speed and rated wind speed, p of the wind turbine generatorNThe actual output power and the rated output power of the wind turbine generator are respectively.
And 5, adding the constraint into a fitness function, calculating the fitness value of each particle, acquiring an individual optimal value and a global optimal value, and updating the speed and the position of each particle, wherein the method specifically comprises the following steps:
calculating whether the wind speed sample of each initial particle meets the constraint condition, and calculating a fitness function as follows:
in the formula, when x is 1, it means that the particle satisfies the constraint condition, and when x is 0, the other way around, so that the fitness function reaches the maximum value only when the particle satisfies the constraint condition.
Assuming that the particle group consists of N particles, each defined as a D-dimensional space, the velocity and position of particle i can be updated according to the following equation:
wherein, i is 1,2, and M is the number of particles; d is the dimension of the particle, i.e. the dimension of the solution of the problem to be optimized; c. C1、c2Is a learning factor; r is1、r2Random numbers uniformly distributed on (0, 1); omega is the inertial weight;respectively the speed and position of the particle i at the kth iteration;the individual historical optimum value of the particle i and the global historical optimum value of all the particles are respectively.
The inertia weight omega adopts a nonlinear decreasing strategy, and decreases by a concave function:
ω＝(ωstart-ωend)(t/tmax)2+(ωend-ωstart)(2t/tmax)+ωstart (8)
in the formula, ωstart、ωendAre respectively an initial inertial weight and a termination inertial weight; t, tmaxRespectively the current iteration number and the maximum iteration number.
For integer decision variables niIn order to ensure that the speed and position in the (k + 1) th iteration are also integers, the formula for updating the speed is as follows:
in the formula, int represents an integer function;random numbers uniformly distributed over the interval are represented whenWhen the temperature of the water is higher than the set temperature,taking intervalsThe random numbers are uniformly distributed on the random number,taking intervalsRandom numbers uniformly distributed thereon; when in useWhen the temperature of the water is higher than the set temperature,taking intervalsThe random numbers are uniformly distributed on the random number,taking intervalsRandom numbers uniformly distributed thereon;
after each speed updating, judging whether the speed exceeds the limit, and if so, correcting the speed according to the following formula:
for search space constraints of [ X ]min,Xmax]Of particles of (2) having a maximum velocity vmaxComprises the following steps:
vmax＝λ(Xmax-Xmin)/2,0.1≤λ≤1 (11)
step 6, judging whether the maximum iteration times are met, and if not, returning to the step 4; if yes, performing step 7;
and 7, outputting the number of the optimal decision variables, the power of the conventional unit and the receiving end power grid absorption capacity limit value H.
The receiving end power grid absorption capacity is defined as the percentage of the maximum wind farm installed capacity accepted by the system to the system load. And calculating the absorption capacity limit value of the receiving-end power grid according to the output decision variable.
The receiving-end power grid absorption optimization method with wind power participating in frequency modulation provided by the invention combines constraint planning and particle swarm optimization, and has a good application prospect in power system simulation; after wind power is added to participate in frequency modulation, the defect that the model cannot be applied to reality can be effectively overcome by means of special control characteristics of the wind power; the method can effectively calculate the wind power penetration power limit value of each access point under the condition that wind power participates in frequency modulation, and has certain guiding significance on the planning and design of the wind power plant.

Claims (4)

1. A receiving-end power grid absorption optimization method for wind power participating in frequency modulation is characterized by comprising the following steps:
step 1, constructing a receiving-end power grid absorption calculation model considering wind power participation frequency modulation, and setting system information and particle swarm algorithm initial parameters;
step 2, randomly generating a control variable initial value and a particle initial speed;
step 3, calculating a frequency value of the wind turbine generator participating in frequency modulation under certain disturbance and after the lag time according to the dynamic power flow;
step 4, calculating the system load flow of the particles under each wind speed sample, and judging whether the constraint is met;
step 5, adding the constraint into a fitness function, calculating the fitness value of each particle, acquiring an individual optimal value and a global optimal value, and updating the speed and the position of each particle;
step 6, judging whether the maximum iteration times are met, and if not, returning to the step 4; if yes, performing step 7;
and 7, outputting the optimal decision variables and the receiving end power grid absorption capacity limit value.
2. The receiving-end power grid absorption optimization method for wind power participation frequency modulation according to claim 1, wherein the receiving-end power grid absorption calculation model in the step 1 takes maximization of sum of installed capacities of all wind power plants which can be received by a system as a target, and the target function is as follows:
wherein m is the number of wind power plants, niIs the number of fans in the ith wind farm, PNWiRated power of a fan in the ith wind power plant;
the equality constraint is the power flow equation of the system:
in the formula, PGi、QGiActive and reactive power, P, respectively, of a conventional generator set at node iWi、QWiRespectively the active and reactive power, P, of the wind farm at node iLi、QLiRespectively active and reactive loads, U, at node ii、Uj、θijVoltage amplitude and phase angle difference, G, of node i and node j, respectivelyij、BijRespectively the real and imaginary parts, C, of the system admittance matrixPQ、CPVSets of PQ, PV nodes, respectively;
the inequality constraints comprise constraints of decision variables and state variables, wherein the decision variables are the number of fans and the power of a conventional unit, and the constraints are as follows:
in the formula (I), the compound is shown in the specification,the maximum number of the fans in the ith wind power plant,minimum and maximum active power, C, of the ith conventional generator set, respectivelyGIs a set of conventional generator sets;
the state variables are dependent variables of the decision variables, and include system frequency, node voltage amplitude, reactive power of a conventional generator set, line power flow, up-down rotation standby of the system and climbing capacity constraint of the conventional generator set, and the state variable constraint is as follows:
in the formula (f)min、fmaxRespectively, the minimum and maximum frequencies of the system;minimum and maximum voltage amplitudes for node i, respectively;respectively the minimum and maximum reactive power of the ith conventional generator set; pLiFor the power flow on the ith line,the maximum limit value of the power flow on the ith line is set; pGiThe active power of the ith conventional generator set,the minimum active power of the ith conventional generator set;respectively rotating the upper part and the lower part of the system for standby; r isGiThe maximum climbing capacity of the conventional unit i after the upper and lower power limits are restrained is considered,is a sequence of power samples for wind farm j.
3. The receiving-end power grid digestion optimization method for wind power participation frequency modulation according to claim 1, wherein the step 2 specifically comprises: n D-dimensional particles are randomly generated in the control variable range, the initial speed of each particle is generated, and the initial value of each particle needs to be rounded for an integer control variable, namely the maximum configuration number of fans in the wind power plant.
4. The receiving-end power grid digestion optimization method for wind power participation frequency modulation according to claim 1, wherein the step 5 specifically comprises:
calculating whether the wind speed sample of each initial particle meets the constraint condition, and calculating a fitness function as follows:
in the formula, when x is 1, the particle satisfies the constraint condition, and when x is 0, the opposite is true, so that the fitness function can reach the maximum value only when the particle satisfies the constraint condition;
assuming that the particle group consists of N particles, each defined as a D-dimensional space, the velocity and position of particle i can be updated according to the following equation:
wherein, i is 1,2, and M is the number of particles; d is the dimension of the particle, i.e. the dimension of the solution of the problem to be optimized; c. C1、c2Is a learning factor; r is1、r2Random numbers uniformly distributed on (0, 1); omega is the inertial weight;respectively the speed and position of the particle i at the kth iteration;respectively obtaining an individual historical optimal value of the particle i and a global historical optimal value of all the particles;
the inertia weight omega adopts a nonlinear decreasing strategy, and decreases by a concave function:
ω＝(ωstart-ωend)(t/tmax)2+(ωend-ωstart)(2t/tmax)+ωstart (7)
in the formula, ωstart、ωendAre respectively an initial inertial weight and a termination inertial weight; t, tmaxRespectively the current iteration times and the maximum iteration times;
for integer decision variables niIn order to ensure that the speed and position in the (k + 1) th iteration are also integers, the formula for updating the speed is as follows:
in the formula, int represents an integer function;random numbers uniformly distributed over the interval are represented whenWhen the temperature of the water is higher than the set temperature,taking intervalsThe random numbers are uniformly distributed on the random number,taking intervalsRandom numbers uniformly distributed thereon; when in useWhen the temperature of the water is higher than the set temperature,taking intervalsThe random numbers are uniformly distributed on the random number,taking intervalsRandom numbers uniformly distributed thereon;
after each speed updating, judging whether the speed exceeds the limit, and if so, correcting the speed according to the following formula:
for search space constraints of [ X ]min,Xmax]Of particles of (2) having a maximum velocity vmaxComprises the following steps:
vmax＝λ(Xmax-Xmin)/2,0.1≤λ≤1 (10)。
CN201910760804.4A 2019-08-16 2019-08-16 Receiving-end power grid consumption optimization method for wind power participating in frequency modulation Pending CN110601179A (en)

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Application publication date: 20191220