CN112446521B - Multi-objective planning method for wind power plant access system considering economy and safety - Google Patents
Multi-objective planning method for wind power plant access system considering economy and safety Download PDFInfo
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Abstract
The invention discloses a wind power plant access system multi-target planning method considering economy and safety, which comprises the following steps: acquiring system parameters and planning information; constructing a multi-target planning model of the wind power plant access system, wherein the multi-target planning model comprises an economic target, a safety target and planning constraint conditions; solving the multi-target planning model by adopting an NSGA-II algorithm, performing probability load flow calculation on the generated planning scheme and calculating a target function value; and after the algorithm reaches a termination condition, determining the optimal scheme of the wind power plant access system by using an entropy weight method and a TOPSIS strategy. The method simultaneously considers the economy and the safety of the wind power plant access system in the planning process, and compared with the situation of only considering the economy, the obtained planning scheme has stronger adaptability to uncertain factors of the system; when the method is applied to practical problems, more reasonable schemes can be provided from different angles, and reference is provided for planning decision-making personnel.
Description
Technical Field
The invention relates to the field of power system planning, in particular to a wind power plant access system multi-target planning method considering economy and safety.
Background
Due to energy crisis and environmental pollution problems in countries around the world, new environmentally-friendly renewable energy sources are being vigorously developed and utilized, and new energy power generation, including wind power generation, accounts for an increasingly higher proportion of power grids. Due to the characteristics of intermittence, randomness, volatility and the like of wind power, more uncertainty is injected into a system by high-proportion wind power access, and the influence is caused on the safe and stable operation of the system. Therefore, the uncertain factors need to be considered in the initial planning stage of the wind power plant access system, so that the operation and maintenance cost can be reduced, and the basis for ensuring the safe and efficient operation of the system is provided.
Aiming at the characteristic of random fluctuation of output of the wind power plant, the current wind power plant access system planning research is mainly started from investment cost, and an economic optimal planning scheme meeting operation constraint conditions is sought. The common planning method comprises random planning and multi-scenario planning, uncertainty in a power system is expressed in a probability mode, and opportunity constraint planning for representing line overload constraint by using a confidence degree is also applied to wind power planning containing random variables. For the safety problem of the planning scheme, more methods mainly divide the planning model into a main investment problem and a sub-operation problem, and perform static safety verification or reliability analysis on the obtained planning scheme. Still other methods add load shedding or wind curtailment power as a penalty to the economic objective to minimize the overall investment of the project. However, these methods check the planning scheme on the premise of optimal economy, and do not comprehensively evaluate the safety index of the planning scheme. In actual operation, random factors of the power system cannot be predicted, and when the extreme end operation conditions such as power imbalance or large wind power output fluctuation are faced, the adaptability of the planning schemes is insufficient, and the safe operation of the system is possibly threatened.
Disclosure of Invention
The invention aims to provide a wind power plant access system multi-target planning method which reasonably and effectively considers the economy and the safety, so that the obtained planning scheme has stronger adaptability.
The technical solution for realizing the purpose of the invention is as follows: a wind power plant access system multi-target planning method considering economy and safety comprises the following steps:
step 3, solving the multi-objective planning model by adopting an NSGA-II algorithm, carrying out probability direct current power flow calculation on a planning scheme generated randomly and calculating an objective function value;
and 4, after the algorithm reaches a termination condition, determining the optimal scheme of the wind power plant access system by using an entropy weight method and a TOPSIS strategy.
Further, the system parameters in the step 1 comprise the existing grid structure, branch parameters, generator data and load level of the wind power plant grid-connected region; the planning information comprises the grid-connected point, installed capacity and random output data of the wind power plant which is planned to be accessed.
Further, the step 2 is a multi-objective planning model of the wind farm access system, which specifically comprises:
planned economic objective function f 1 The sum of the investment cost, the network loss cost and the load shedding penalty cost of a newly-built line is as follows:
planned safety objective function f 2 The product of the maximum value and the average value of the beta horizontal load rate of the line and the expected value power flow entropy is as follows: min f 2 ＝J 2max ·J 2a ·s。
In the above objective function: i is the index of the line corridor, c i For the construction cost of the line corridor i, n i The number of newly-built lines of the line corridor i is N, the total number of the line corridors to be selected is N, alpha is unit network loss cost, and l is an index of the lines; r is l Is the resistance of the line l, I l Is the current of line l, t is the running time, N l Is the total number of lines; gamma is the unit load shedding penalty cost, E loadcut The total load is cut for the system; j. the design is a square 2max Is the maximum value of the beta horizontal load factor in all lines, J 2a The mean value of beta horizontal load rates of all lines is obtained, and s is the power flow entropy of the system;
the planned constraint conditions comprise a power flow equation constraint, a branch power flow constraint, an opportunity constraint of line overload, an upper limit constraint and a lower limit constraint of variables and a network connectivity constraint, and specifically comprise the following steps:
P＝Bθ (2)
-Bθ+P G +P W +P cut ＝P D
Pr{|P l |≤P l.max }≥β
P G.min ≤P G ≤P G.max
θ min ≤θ≤θ max
0≤P cut ≤P D
0≤n i ≤n i.max
N netbranch ＝1
among the above constraints: p is branch flow vector, B is node admittance matrix, theta is node voltage phase angle vector, P G 、P W 、P cut 、P D Respectively a conventional unit output vector, a wind power plant output vector, a cut load vector and a node load power vector; p l For branch active power flow variables, P l.max An upper limit of line power flow; beta is the probability that the transmission line is not overloaded in the opportunity constraint; p G.min 、P G.max The output limit is the upper and lower limit of the conventional generator set; theta min 、θ max The upper limit and the lower limit of a node voltage phase angle are set; n is i Number of new lines, n, for line corridor i i.max The maximum allowable loop number of the line corridor i; n is a radical of netbranch Is the number of networks included in the system.
Further, the step 3 of solving the multi-objective planning model by using the NSGA-II algorithm, performing probability dc power flow calculation on the generated planning scheme, and calculating an objective function value specifically includes the following steps:
step 3-1, randomly generating an initial population by taking the number of newly-built lines as a control variable and the maximum allowable loop number of a line corridor as an upper limit;
step 3-2, judging whether the network is connected or not for each individual in the population, if so, performing step 3-3, and if not, returning and performing judgment on the next individual;
3-3, performing probability direct current power flow calculation to obtain the probability distribution of the line active power flow, judging whether constraint conditions are met or not according to the power flow result, if so, performing the step 3-4, and if not, returning and performing judgment on the next individual;
step 3-4, respectively calculating the investment cost, the network loss cost and the load shedding punishment cost of the newly-built line according to the load flow calculation result to obtain an economic objective function value f 1 ；
Step 3-5, calculating the expected load rate and the beta horizontal load rate of the line, specifically:
the expected load rate for branch l is: j. the design is a square 1l ＝|E(P l )/P l.max |
The beta horizontal load rate of branch l is: j. the design is a square 2l ＝|P l.β /P l.max |
In the formula: p l Active power flow variable, P, for branch l l.max At the upper limit of its transmission capacity, E (P) l ) Representing expected value of power flow, P, of branch l l.β Representing branch current | P l |≤P l.β The probability of (a) is the confidence level β that the line is not overloaded;
step 3-6, respectively obtaining the maximum value and the average value of the beta horizontal load rate of the line and the expected value power flow entropy according to the results of the step 3-5, and calculating a safety objective function value f 2 The method specifically comprises the following steps:
maximum value of β horizontal load rate: j. the design is a square 2max ＝max J 2l
Average value of β horizontal load rate:
expected value power flow entropy:
wherein: r ═ ln10, a constant; setting an equal difference load rate sequence J ═ J 1 ，j 2 ，…，j n ]The interval of the load rate is taken as [0, 1 ]]Tolerance of 0.05, p 1k Is J 1l Is located at (j) k ，j k+1 ]The ratio of the number of branches to the total number of branches, then f 2 ＝J 2max ·J 2a ·s。
And 3-7, after selecting a parent population, crossing and mutating to obtain a child population, calculating child adaptive values according to the steps 3-2 to 3-6, combining the children and the parent to obtain an intermediate population, performing rapid non-dominant sorting, calculating crowding distances, selecting a new population as the parent, and repeating the steps 3-7 until the maximum iteration number is reached.
Further, the step 4 of determining the optimal scheme of the wind farm access system by using the entropy weight method and the TOPSIS strategy specifically comprises the following steps: and obtaining weights of two target functions by using an entropy weight method, and sequencing Pareto optimal solution sets obtained by using a TOPSIS strategy through an NSGA-II algorithm to obtain an optimal planning scheme.
Compared with the prior art, the invention has the following remarkable advantages: (1) taking the product of the maximum value and the average value of the beta horizontal load rate of the line and the expected value tidal current entropy as a safety objective function, and comprehensively reflecting the safety of the planning scheme in the face of extreme operation conditions; (2) the economy and the safety are considered simultaneously during planning, and compared with the planning only considering the economy, the obtained planning scheme has stronger adaptability.
Drawings
FIG. 1 is a flow chart of a wind farm access system multi-objective planning method considering economy and safety.
Fig. 2 is a diagram of an IEEE18 node system configuration modified in an embodiment of the present invention.
Fig. 3 is a schematic diagram of Pareto optimal leading edge distribution in an embodiment of the present invention.
Detailed Description
With reference to fig. 1, a wind farm access system multi-objective planning method considering economy and safety specifically includes the following steps:
The system parameters comprise the existing grid structure of the wind power plant grid-connected region, branch parameters, generator data and load level; the planning information comprises the grid-connected point, installed capacity and random output data of the wind power plant which is planned to be accessed.
And 2, constructing a multi-target planning model of the wind power plant access system, wherein the multi-target planning model comprises an economic target, a safety target and planning constraint conditions.
The multi-target planning model of the wind power plant access system specifically comprises the following steps:
planned economic objective function f 1 Investment cost, network loss cost and cut for newly-built lineThe sum of the three parts of the load penalty cost:
planned safety objective function f 2 The product of the maximum value and the average value of the beta horizontal load rate of the line and the expected value power flow entropy is as follows: min f 2 ＝J 2max ·J 2a ·s。
In the above objective function: i is an index of the line corridor; c. C i For the construction cost of the line corridor i, n i The number of newly built lines of the line corridor i is N, and the N is the total number of the line corridors to be selected; alpha is unit network loss cost, and l is the index of the line; r is l Is the resistance of the line l, I l Is the current of line l, t is the running time, N l Is the total number of lines; gamma is the unit load shedding penalty cost, E loadcut The total load is cut for the system; j. the design is a square 2max Is the maximum value of the beta horizontal load factor in all lines, J 2a The mean value of beta horizontal load rates of all lines is obtained, and s is the power flow entropy of the system;
the planned constraint conditions comprise power flow equation constraint, branch power flow constraint, opportunity constraint of line overload, upper and lower limit constraint of variables and network connectivity constraint, and specifically comprise the following steps:
P＝Bθ (3)
-Bθ+P G +P W +P cut ＝P D
Pr{|P l |≤P l.max }≥β
P G.min ≤P G ≤P G.max
θ min ≤θ≤θ max
0≤P cut ≤P D
0≤n i ≤n i.max
N netbranch ＝1
among the above constraints: p is branch power flow vector, B is node admittance matrix, theta is node voltage phase angle vector, P G 、P W 、P cut 、P D Respectively is the output vector of the conventional unit, the output vector of the wind power plant and the cut-off loadA load vector and a node load power vector; p l For branch active power flow variable, P l.max An upper limit of line power flow; beta is the probability that the transmission line is not overloaded in the opportunity constraint; p G.min 、P G.max The output limit is the upper and lower limit of the conventional generator set; theta min 、θ max The upper limit and the lower limit of a node voltage phase angle are set; n is i Number of new lines, n, for line corridor i i.max The maximum allowable loop number of the line corridor i; n is a radical of hydrogen netbranch Is the number of networks included in the system.
And 3, solving the multi-objective planning model by adopting an NSGA-II algorithm, performing probability direct current power flow calculation on the randomly generated planning scheme and calculating an objective function value.
The method comprises the following steps of solving a multi-objective planning model by adopting an NSGA-II algorithm, carrying out probability direct current power flow calculation on a generated planning scheme and calculating an objective function value, and specifically comprises the following steps:
step 3-1, randomly generating an initial population by taking the number of newly-built lines as a control variable and the maximum allowable loop number of a line corridor as an upper limit;
step 3-2, judging whether the network is connected or not for each individual in the population, and if so, performing the step 3-3;
3-3, performing probability direct current power flow calculation to obtain probability distribution of line active power flow, judging whether constraint conditions are met according to power flow results, and performing the step 3-4 if the constraint conditions are met;
step 3-4, respectively calculating the investment cost, the network loss cost and the load shedding punishment cost of the newly-built line according to the load flow calculation result to obtain an economic objective function value f 1 ；
Step 3-5, calculating the expected load rate and the beta horizontal load rate of the line, specifically:
the expected load rate for branch l is: j. the design is a square 1l ＝|E(P l )/P l.max |
The beta horizontal load rate of branch l is: j. the design is a square 2l ＝|P l.β /P l.max |
In the formula: p l Active power flow variable, P, for branch l l.max To the upper limit of its transmission capacity, E (P) l ) Representing expected value of power flow, P, of branch l l.β Represents the branch current | P l |≤P l.β The probability of (c) is a confidence that the line is not overloaded.
Step 3-6, respectively obtaining the maximum value and the average value of the beta horizontal load rate of the line and the expected value power flow entropy according to the results of the step 3-5, and calculating a safety objective function value f 2 . The method specifically comprises the following steps:
maximum value of β horizontal load rate: j. the design is a square 2max ＝max J 2l
Average value of β horizontal load rate:
expected value power flow entropy:
wherein: r ═ ln10, a constant; setting an equal difference load rate sequence J ═ J 1 ，j 2 ，…，j n ]The interval of the load rate is taken as [0, 1 ]]Tolerance of 0.05, p 1k Is J 1l Is located at (j) k ，j k+1 ]The ratio of the number of branches to the total number of branches,
f is then 2 ＝J 2max ·J 2a ·s。
And 3-7, after selecting a parent population, crossing and mutating to obtain a child population, calculating child adaptive values according to the steps 3-2 to 3-6, combining the children and the parent to obtain an intermediate population, performing rapid non-dominant sorting, calculating crowding distances, selecting a new population as the parent, and repeating the steps 3-7 until the maximum iteration number is reached.
And 4, after the algorithm reaches a termination condition, determining the optimal scheme of the wind power plant access system by using an entropy weight method and a TOPSIS strategy.
The method for determining the optimal scheme of the wind power plant access system by using the entropy weight method and the TOPSIS strategy specifically comprises the following steps: and (3) obtaining weights of the two target functions by using an entropy weight method, and sequencing the Pareto optimal solution set obtained by the NSGA-II algorithm by using a TOPSIS strategy to obtain an optimal planning scheme.
The invention is described in further detail below with reference to the figures and specific embodiments.
Examples
FIG. 1 is a flow chart of a wind farm access system multi-objective planning method considering economy and safety.
And 2, constructing a multi-target planning model of the wind power plant access system, wherein the multi-target planning model comprises an economic target, a safety target and planning constraint conditions, and setting the confidence coefficient beta to be 0.95.
And 3, solving the multi-objective planning model by adopting an NSGA-II algorithm, performing probability direct current power flow calculation on the randomly generated planning scheme and calculating an objective function value. The population scale of the algorithm is 100, the iteration number is 100, and fig. 3 is a Pareto optimal leading edge schematic diagram obtained by the NSGA-II algorithm.
TABLE 1 planning preferences
The multi-objective planning proposed by the present invention is compared with a single-objective planning considering only economic objectives, and the results are shown in table 2.
TABLE 2 Multi-objective planning vs. Single-objective planning scheme
The planning scheme obtained by single-target planning has poor safety, higher probability of line overload and more unbalanced load rate, and the planning scheme obtained by considering the economy and the safety has higher safety index and is not easy to cause line out-of-limit. By comparison, the rationality and effectiveness of the planning method provided by the invention are verified.
Claims (4)
1. A wind power plant access system multi-objective planning method considering economy and safety is characterized by comprising the following steps:
step 1, acquiring parameters and planning information of an access system of a wind power plant;
step 2, constructing a multi-target planning model of the wind power plant access system, wherein the multi-target planning model comprises an economic target, a safety target and planning constraint conditions; the method specifically comprises the following steps:
planned economic objective function f 1 The sum of the investment cost, the network loss cost and the load shedding penalty cost of a newly-built line is as follows:
planned safety objective function f 2 The product of the maximum value and the average value of the beta horizontal load rate of the line and the expected value power flow entropy is as follows: minf 2 ＝J 2max ·J 2a ·s；
In the above objective function: i is an index of the line corridor; c. C i For the construction cost of line corridor i, n i The number of newly built lines of the line corridor i is N, and the N is the total number of the line corridors to be selected; alpha is unit network loss cost, and l is the index of the line; r is a radical of hydrogen l Is the resistance of the line l, I l Is the current of line l, t is the running time, N l Is the total number of lines; gamma is the unit load shedding penalty cost, E loadcut The total load is cut for the system; j is a unit of 2max Is the maximum value of the beta horizontal load rate in all lines, J 2a The mean value of beta horizontal load rates of all lines is obtained, and s is the power flow entropy of the system;
the planned constraint conditions comprise a power flow equation constraint, a branch power flow constraint, an opportunity constraint of line overload, an upper limit constraint and a lower limit constraint of variables and a network connectivity constraint, and specifically comprise the following steps:
P＝Bθ (1)
-Bθ+P G +P W +P cut ＝P D
Pr{|P l |≤P l.max }≥β
P G.min ≤P G ≤P G.max
θ min ≤θ≤θ max
0≤P cut ≤P D
0≤n i ≤n i.max
N netbranch ＝1
among the above constraints: p is branch power flow vector, B is node admittance matrix, theta is node voltage phase angle vector, P G 、P W 、P cut 、P D Respectively a conventional unit output vector, a wind power plant output vector, a cut load vector and a node load power vector; p l For branch active power flow variable, P l.max An upper limit of line power flow; beta is the probability that the transmission line is not overloaded in the opportunity constraint; p G.min 、P G.max The output limit is the upper and lower limit of the conventional generator set; theta min 、θ max The upper limit and the lower limit of a node voltage phase angle are set; n is a radical of an alkyl radical i Number of new lines, n, for line corridor i i.max The maximum allowable loop number of the line corridor i; n is a radical of netbranch The number of networks included in the system;
step 3, solving the multi-objective planning model by adopting an NSGA-II algorithm, performing probability direct current power flow calculation on the generated planning scheme and calculating an objective function value;
and 4, after the algorithm reaches a termination condition, determining a preferred scheme of the wind power plant access system by using an entropy weight method and a TOPSIS strategy.
2. The multi-objective planning method for the wind power plant access system considering the economy and the safety as claimed in claim 1, wherein the system parameters in the step 1 comprise an existing grid structure, branch parameters, generator data and load level of a wind power plant grid-connected region; the planning information comprises the grid-connected point, installed capacity and random output data of the wind power plant which is planned to be accessed.
3. The wind farm access system multi-objective planning method considering economy and safety as claimed in claim 1, wherein the step 3 of solving the multi-objective planning model by using the NSGA-II algorithm, performing probability DC power flow calculation on the generated planning scheme and calculating objective function values specifically comprises the following steps:
step 3-1, randomly generating an initial population by taking the number of newly-built lines as a control variable and the maximum allowable loop number of a line corridor as an upper limit;
step 3-2, judging whether the network is connected or not for each individual in the population, if so, performing step 3-3, otherwise, returning and performing judgment on the next individual;
3-3, performing probability direct current power flow calculation to obtain the probability distribution of the line active power flow, judging whether constraint conditions are met or not according to the power flow result, if so, performing the step 3-4, and if not, returning and performing judgment on the next individual;
step 3-4, respectively calculating the investment cost, the network loss cost and the load shedding punishment cost of the newly-built line according to the load flow calculation result to obtain an economic objective function value f 1 ；
Step 3-5, calculating the expected load rate and the beta horizontal load rate of the line, specifically:
the expected load rate for branch l is: j. the design is a square 1l ＝|E(P l )/P l.max |
The beta horizontal load rate of branch l is: j. the design is a square 2l ＝|P l.β /P l.max |
In the formula, P l Active power flow variable, P, for branch l l.max For its transmission capacityUpper limit, E (P) l ) Representing expected value of power flow, P, of branch l l.β Representing branch current | P l |≤P l.β The probability of (a) is the confidence level β that the line is not overloaded;
step 3-6, respectively obtaining the maximum value and the average value of the beta horizontal load rate of the line and the expected value power flow entropy according to the results of the step 3-5, and calculating a safety objective function value f 2 The method specifically comprises the following steps:
maximum value of β horizontal load rate: j. the design is a square 2max ＝maxJ 2l
Average value of β horizontal load factor:
expected value power flow entropy:
wherein: r ═ ln10, a constant; setting an equal difference load rate sequence J ═ J 1 ,j 2 ,…,j n ]The interval of the load rate is taken as [0, 1 ]]Tolerance of 0.05, p 1k Is J 1l Is located at (j) k ,j k+1 ]The ratio of the number of branches to the total number of branches,
f is then 2 ＝J 2max ·J 2a ·s；
And 3-7, after selecting a parent population, crossing and mutating to obtain a child population, calculating child adaptive values according to the steps 3-2 to 3-6, combining the children and the parent to obtain an intermediate population, performing rapid non-dominant sorting, calculating crowding distances, selecting a new population as the parent, and repeating the steps 3-7 until the maximum iteration number is reached.
4. The method for multi-objective planning of a wind farm access system with consideration of economy and safety according to claim 1, wherein the step 4 of determining the preferred scheme of the wind farm access system by using an entropy weight method and a TOPSIS strategy specifically comprises the following steps:
and obtaining weights of two target functions by using an entropy weight method, and sequencing Pareto optimal solution sets obtained by using a TOPSIS strategy through an NSGA-II algorithm to obtain an optimal planning scheme.
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