CN109345411B - Quantitative control method applied to improvement of power supply capacity of power distribution network - Google Patents
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Abstract
The invention discloses a quantitative control method applied to improving the power supply capacity of a power distribution network, which comprises the following steps: solving the maximum power supply capacity of the existing power grid, and determining the planned maximum power supply capacity increment of the power distribution network; calculating the power supply capacity of the power distribution network to improve construction investment and operation control cost; judging and updating two target values of the power supply capacity and the source network load control cost of the active power distribution network by using an improved crossover operator and an elite selection strategy, and acquiring a Pareto solution set based on non-dominated sorting; and determining a power supply investment planning scheme based on the set maximum power supply capacity and the obtained Pareto solution set. The invention adapts to the development requirements of power supply reliability, intelligent power distribution and the like in a new situation by the access of the standardized clean energy, the deepening of supply-demand interaction, the release of incremental power distribution business and the implementation of an electric energy replacement strategy.
Description
Technical Field
The invention relates to the field of power system planning, in particular to a quantitative control method applied to improvement of power supply capacity of a power distribution network.
Background
In China, many cities in northern China begin to popularize the policy of changing coal into electricity so as to reduce the coal pollution in winter and improve the air quality. In the context of environmental protection, electric vehicles are receiving more and more attention and are heavily connected to the distribution network. In this context, such a large load access must have an impact on the safe and reliable operation of the distribution network. Meanwhile, under the guidance of policies such as power distribution network construction transformation action plans and new rural power network transformation and upgrading, the investment of the 110 kilovolt and below power networks which account for 58 percent of the total investment of the power networks is increased by 35.6 percent in the same ratio. From the electric power investment trend, the saturation degree of a power grid is not as good as that of a power supply, and continuous investment of the power grid is still urgently needed in the aspects of renewable energy consumption, power supply reliability and the like in China. With the increase and slow of the power consumption of the whole society and the gradual improvement of most power supply and transmission line frameworks, the center of gravity of the power grid construction investment of China is transferred from a backbone network to a distribution network side, and how to carry out operation cost quantitative analysis of the improvement of the power supply capacity of the distribution network becomes a problem which needs to be considered in the planning construction and operation procedures of the distribution network.
The analysis of power supply capacity of a power distribution network and the analysis of investment and operation cost thereof are a typical dynamic comprehensive evaluation problem which relates to a plurality of objects, a plurality of indexes and a plurality of time intervals, the construction target of an evaluation object is different from the structural characteristics, weak links and periodic operation targets of the regional power distribution network, so that great differences exist, and a standard flow and method need to be constructed to improve the practicability. Most of traditional evaluation models singly adopt a subjective scoring method to evaluate a large number of statistical indexes, and the space-time characteristics and the regional differences of the construction and development of the power distribution network are difficult to objectively know. Method for evaluating operation effect and investment efficiency of power distribution network after investment construction from operation angle [1-5] And a plurality of construction schemes and operation strategies are not evaluated in a planning angle, so that accurate investment of the power distribution network is difficult to effectively guide. The operation cost of a system needs to be considered simultaneously in the existing power distribution network planning, and an investment quantitative analysis model mainly has 3 defects, namely, the evaluation method is strong in subjectivity, low in index system comprehensiveness and single in evaluation time scale. The improvement of the power supply capacity of the power distribution network is a dynamic rolling planning process, the accuracy of the model directly influences the efficiency of upgrading and transforming the power distribution network, and the cost quantitative analysis method applied to the improvement of the power supply capacity of the power distribution network is provided, so that the efficiency of planning, constructing and operating the power distribution network in China can be realized.
Disclosure of Invention
The invention provides a quantitative control method applied to the improvement of power supply capacity of a power distribution network, which adapts to the development requirements of power supply reliability, power distribution intellectualization and the like under new situation by the access of standardized clean energy, the deepening of supply-demand interaction, the release of incremental power distribution service and the implementation of electric energy substitution, and is described in detail as follows:
a quantitative control method applied to power distribution network power supply capacity improvement comprises the following steps:
solving the maximum power supply capacity of the existing power grid, and determining the planned maximum power supply capacity increment of the power distribution network;
calculating the power supply capacity of the power distribution network to improve the construction investment and the operation control cost; judging and updating two target values of the power supply capacity and the source network load control cost of the active power distribution network by using an improved crossover operator and an elite selection strategy, and acquiring a Pareto solution set based on non-dominated sorting;
and determining a power supply investment planning scheme based on the set maximum power supply capacity and the obtained Pareto solution set.
Further, the improved crossover operator is specifically:
rank represents the non-dominant ranking level of the individual a of the current generation, a dist represents the congestion distance of the individual a of the current generation, rank represents the non-dominant ranking level of the individual B of the current generation, and B dist represents the congestion distance of the individual B of the current generation.
Wherein the elite selection strategy specifically comprises:
calculating the crowding distance of all non-dominated solutions in the current level individual, and deleting the solution with the minimum crowding distance;
judging the scale of the remaining non-dominated solution in the current level individual, if the number of the individual to be selected is met, executing the next step, otherwise, executing the previous step;
and outputting the remaining non-dominant solutions in the current level individuals.
Wherein the operation control cost includes: the method comprises the steps of distributed power supply output active management cost, switching action cost in network reconstruction and active management cost of demand side load.
Further, the active management cost of the distributed power supply output is specifically as follows:
in the formula, C MT The cost of power generation for the gas turbine; c ng Represents the unit gas cost; p is MT The active output power of the gas turbine; η is the operating efficiency of the gas turbine.
Further, the switching action cost in the network reconfiguration is specifically:
C SW ＝C RCS N RCS
wherein, C RCS The cost generated by one switching action in the network reconstruction process; n is a radical of RCS The number of switching actions in the network reconfiguration process.
Further, the active management cost of the demand side load is specifically:
wherein, c IL,l Contract price, k, representing the interrupt load IL Representing a reactive price coefficient, P IL,l 、Q IL,l Respectively representing the active and reactive power of the interrupted load.
The technical scheme provided by the invention has the beneficial effects that: the invention adapts to the development requirements of power supply reliability, intelligent power distribution and the like in a new situation by the access of the standardized clean energy, the deepening of supply-demand interaction, the release of incremental power distribution service and the implementation of electric energy substitution.
Drawings
Fig. 1 is a flowchart of a quantitative control method applied to power distribution network power supply capacity improvement according to the present invention;
fig. 2 is a flow chart of construction investment cost and operation relationship for improving power supply capacity of the power distribution network provided by the invention;
FIG. 3 is a process of screening Pareto solution sets by the entropy weight base point method provided by the present invention;
FIG. 4 is a NSGA-II solution model flow provided by the present invention;
FIG. 5 is a block diagram of an improved IEEE 33 node power distribution system;
FIG. 6 shows the multi-objective Pareto solution and screening condition of the power supply capacity obtained based on the improved NSGA-II algorithm.
The method comprises the following steps of (a) obtaining a multi-target Pareto optimal solution after optimization of the active power distribution network and a schematic diagram of a selected compromise optimal solution; (b) And selecting a schematic diagram of a final compromise optimal solution according to the ideality values corresponding to the multi-target Pareto optimal solution obtained by the implementation column.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
Example 1
A quantitative control method applied to the increase of power supply capacity of a power distribution network, referring to fig. 1, the method includes:
101: solving the maximum power supply capacity of the existing power grid, and determining the planned maximum power supply capacity increment of the power distribution network;
102: calculating the power supply capacity of the power distribution network to improve construction investment and operation control cost; judging and updating two target values of the power supply capacity of the active power distribution network and the source network load control cost by using an improved crossover operator and an elite selection strategy, and acquiring a Pareto solution set based on non-dominated sorting;
103: and determining a power supply investment planning scheme based on the set maximum power supply capacity and the obtained Pareto solution set.
In summary, the embodiment of the invention adapts to the development requirements of power supply reliability, power distribution intellectualization and the like in new situations by the access of the large-scale clean energy, the deepening of supply-demand interaction, the release of incremental power distribution service and the implementation of electric energy substitution.
Example 2
The scheme in embodiment 1 is further described below with reference to specific calculation formulas, examples, and fig. 1 to 4, and is described in detail below:
1. method for determining maximum power supply capacity of power distribution network
The maximum power supply capacity of the power distribution network can be described as the maximum load of the power distribution network when the N-1 check of all feeder lines and the N-1 check of a main transformer of a transformer substation in the power distribution network are met. And N-1, considering the actual operation constraints of the power distribution network, such as load transfer between the main transformer and the feeder line, the capacities of the main transformer and the feeder line, the communication relationship between the main transformer and the feeder line in the network, and the like.
The maximum power supply capacity of the power distribution network provides a working point with the highest efficiency and economy of the power distribution network in a safe and reliable operation range, and the solving method of the power distribution network can be mainly divided into an analytic method and a linear programming model method. The construction investment cost and operation cost analysis relationship for improving the power supply capacity of the power distribution network provided by the embodiment of the invention is shown in fig. 2.
Wherein, P 1 And P 2 And the construction investment cost is respectively improved for the power supply capacity of the power distribution network under different schemes. S 1 And S 2 The operation cost of the power distribution network in unit time under different schemes is the unit slope of the operation cost of the power distribution network.
On the basis of the total application time T of the power distribution network, the total cost for improving the power supply capacity of the power distribution network is as follows:
C total ＝P i +S i ×T (i∈I) (1)
in the formula, C total The cost for operating the distribution network; i is a feasible power distribution network power supply capacity improvement scheme set; p i And S i The construction investment cost and the power distribution network operation cost in unit time are respectively improved for the power distribution network power supply capacity under different schemes.
The embodiment does not limit the existing method for obtaining the maximum power supply capacity of the power grid.
The maximum power supply capacity increment can be determined by a power system, power load increase (including 'coal-to-electricity' project implementation), power supply reliability improvement and the like.
2. The power supply capacity of the power distribution network improves construction investment and operation control cost calculation.
Power supply capacity of power distribution network improves construction investment cost P i The method comprises the following steps: power grid transformation and new power generation of power distribution networkLand collection of the net, equipment procurement, and construction costs. The solution of the part is consistent with the traditional power distribution network planning cost estimation, and the embodiment does not limit the use of a specific method.
The calculation formula of the construction investment and operation cost for improving the power supply capacity of the power distribution network is as follows:
min Cost＝C DG +C SW +C DSM (2)
in the formula, cost represents the total control Cost of the active power distribution network, C DG Represents the active management cost of the distributed power supply, C SW Represents the cost of the switching operation, C DSM Representing the demand side management cost.
The specific steps of calculating the improvement of the operation control cost of the power supply capacity of the power distribution network are as follows:
(21) The controllable DG type is a Micro Turbine (MTG), and for a gas turbine, the operating efficiency increases with the increase of the output power, and the operating cost and the active output power have the following relation:
in the formula, C MT The cost of power generation for the gas turbine; c ng Represents the unit gas cost; p MT The active output power of the gas turbine is obtained; η is the operating efficiency of the gas turbine.
(22) The network reconstruction cost is in direct proportion to the action times of a switch in the power distribution network, and the calculation method comprises the following steps:
C SW ＝C RCS N RCS (4)
wherein, C RCS The cost generated by one switching action in the network reconstruction process; n is a radical of hydrogen RCS The number of switching actions in the network reconfiguration process.
(23) Cost of demand side management the interruptible load compensation cost is considered herein primarily. Generally, only the influence of two factors, namely, the power shortage and the interruption time, on the compensation cost (because the user load is directly controlled and the user needs to be compensated to ensure the satisfaction degree of the user) of the interruptible load (which is a load control mode commonly used in demand side management) is considered, and the model is represented as follows:
wherein, c IL,l Contract price, k, representing the interrupt load IL Representing a reactive price coefficient, P IL,l 、Q IL,l Respectively representing the active and reactive power of the interrupted load.
3. Optimization solution is carried out on two targets of power distribution network power supply capacity and source network load operation control cost
(31) Performing fast non-dominant sorting on the population based on the two target function values, and calculating corresponding crowding distance;
(32) Sequencing the crowding distance of each individual, and selecting a parent population through a tournament method;
(33) Generating a progeny population through traditional crossover and mutation operations in a genetic algorithm;
the crossover operator is the most important operation in genetic operation, and the gene pattern of excellent individuals can be rapidly propagated and spread in the population in the crossover process, so that other individuals in the population can move towards the direction of the optimal solution. Compared with a simulated binary crossover operator, the arithmetic crossover operator has better global search capability and can better keep the diversity of the population.
The arithmetic crossover operation is as follows: is provided with
And &>
Respectively coding the real numbers of decision variables of two individuals to be crossed in the t generation, wherein the corresponding decision variable values of the two individuals after the crossing are as follows:
wherein, the first and the second end of the pipe are connected with each other,
the decision variable value of the individual A to be crossed is the tth generation; />
The decision variable value of the individual B to be crossed is the tth generation; alpha is a parameter, and when alpha is a constant, the method is called uniform arithmetic crossing; otherwise, it is called non-uniform arithmetic interleaving.
Wherein the crossover operator coefficients are as follows:
where a.rank represents the non-dominated ranking level of the individual a of the current generation, a.dist represents the crowding distance of the individual a of the current generation, b.rank represents the non-dominated ranking level of the individual a of the current generation, and b.dist represents the crowding distance of the individual a of the current generation.
Therefore, in the early stage of the algorithm, the genes of better individuals are better reserved, so that the convergence speed of the algorithm is accelerated; meanwhile, in the later stage of the algorithm, the genes of individuals with better distribution degree are better reserved, so that the diversity of the algorithm is improved.
(34) Constraint judgment and calculation of a response objective function value;
(35) Mixing the parent population and the offspring population to obtain a offspring population;
(36) Rapidly sorting the offspring populations without domination, and calculating corresponding crowding distances;
(37) Progeny elite populations are retained. A step-by-step elimination strategy is introduced in elite selection, and the method specifically comprises the following steps:
step 37a, calculating the crowding distances of all non-dominated solutions in the individuals of the current level, and deleting the solution with the minimum crowding distance;
and step 37b, judging the residual non-dominated solution size in the current level individual, executing step 37c if the requirement of the number of the individual to be selected is met, otherwise, executing step 37a.
Step 37c, outputting the remaining non-dominant solutions in the current level individual, and then executing step (38).
(38) And judging whether the iteration number is reached, if so, outputting a Pareto solution set, and otherwise, executing the step (32).
4. And optimizing the construction scheme of the power distribution network based on the power supply capacity of the power distribution network to be improved.
(41) Establishing an evaluation matrix
Aiming at 2 objective functions and l pareto optimal solutions in the embodiment of the invention, an evaluation matrix P is established:
wherein p is i1 A value representing a 1 st decision index of an ith Pareto solution; p is a radical of i2 The value of the 2 nd decision index representing the ith Pareto solution.
(42) And (5) normalizing the data.
The power supply capacity belongs to a benefit type index, and the control cost belongs to a cost type index. In order to unify dimension and magnitude, all indexes are simultaneously normalized, and the matrix P is subjected to standardization treatment:
wherein q is ij For the ith objective function value corresponding to the normalized jth pareto optimal solution,
and
the maximum and minimum values in the j-th row of P, respectively. Obtaining a normalized value q ij And a standard matrix Q formed therefrom.
(43) And calculating the information entropy value of the j index, wherein the size of the entropy weight is determined by the difference degree of different solutions under the target and represents the size of the information providing quantity of the target. The calculation formula of the entropy weight is as follows:
wherein e is j Information entropy value of j index; w is a j The weight value of the jth index is calculated according to the information entropy; l is the number of pareto optimal solutions.
(44) Establishing a weighted normalized evaluation matrix Y = (Y) ij )。
y ij ＝w j q ij 1≤i≤l,j＝1,2 (11)
(45) A double base point is determined.
Defining a true ideal point
Negative ideal point->
(46) The relative closeness of each pareto optimal solution is calculated.
Wherein the content of the first and second substances,
and &>
Is the ith Pareto optimal solution and point, respectively>
And &>
Euclidean distance of。
And optimizing the construction scheme of the power distribution network based on the power supply capacity of the power distribution network to be improved. The higher the relative closeness value is, the closer the solution is to the ideal point, so the pareto optimal solution with the maximum relative closeness is selected as the compromise optimal solution.
Example 3
In the embodiment of the invention, an improved IEEE 33 node power distribution system is selected as an example of an ADN (adaptive data network) of research, and an improved active power distribution network system diagram is shown in figure 5. 8,14, 24, 30, 32 node load as interruptible load, the interruptible proportion of interruptible load is in the range of 0-10%. Gas turbines are installed at nodes 8, 13, 16 and 25 as controllable DGs. The gas turbine has an installation capacity of 600, 650kW, and a unit gas cost of 0.4 yuan/kWh.
By partitioning the test examples, the power supply capacity and control cost conditions of different regions are calculated, a Pareto frontier graph of multi-objective solutions is made, a Pareto solution set is analyzed, and a source network load control scheme corresponding to a compromise optimal solution is found out to be used as a final decision solution.
Table 1 comparison of power supply capability
TABLE 2 cost distribution
TABLE 3 Power supply capability for different zones
Fig. 3 shows the conflicting relationship between the power supply capacity and the control cost in this example. As can be seen from the Pareto front edge distribution of fig. 3, the solved optimal solution is uniformly distributed on the Pareto optimal solution front edge, and contains richer decision information. The Pareto solution set presents an arc shape, and in the latter half of the graph, along with the increase of the power supply capacity, the control cost is rapidly increased, and the increase of the power supply capacity is not obvious. If the power supply capacity is increased, the cost is increased quickly, and the benefit effect is not obvious. Therefore, solutions of the Pareto solution set need to be screened so as to select an optimal compromise solution with reasonable cost under the condition that the power supply capacity is guaranteed, and the ideality corresponding to each solution on the Pareto front edge is calculated by adopting an entropy weight base point method. And taking the power supply capacity as a decision index 1 and the control cost as a decision index 2, and obtaining a decision matrix P according to a 3.3-section method. From the equations (17) - (19), the entropy weights of the two indices are 0.9028 and 0.0972, respectively. It can be seen that the power supply capability index in the example 1 has more decision information than the control cost index provides, and the compromise optimal solution should be slightly biased to the point of higher power supply capability. And (4) performing ideality ordering on all Pareto solutions, wherein the positions of the selected compromise optimal solutions in the Pareto frontier are shown. Detailed information of power supply capacity comparison and control cost under the optimal solution corresponding scheme is shown in tables 1 and 2.
Through comparison, if no measures are taken in the existing power grid, the allowable load of the area 1 is 0.2017 times that of the existing load, and the main factor influencing the load access is the voltage at the node 18. If the source network load is cooperatively controlled and reasonably arranged, the power supply multiple can be increased to 2.2911. Also, the power supply capacity of each zone can be analyzed as shown in table 3.
Reference to the literature
[1] Xing Haijun, cheng Haozhong, zhang Shenxi, et al. Active power distribution grid planning research overview [ J ]. Grid technology, 2015, 39 (10): 2705-2711.
[2] Wu Zheng, cui Wenting, long Yu, and others.
[3] Li Juan, li Xiaohui, liu Shuyong, etc. power distribution network investment benefit evaluation based on ideal solutions and grey correlation [ J ]. Huadong power, 2012, 40 (1): 13-17.
[4] Zhang Xinjie, ge Shaoyun, liu Hong, etc. intelligent power distribution network comprehensive evaluation system and method [ J ] power grid technology 2014, 38 (1): 40-46.
[5] Li Zhuyun, lei Xia, qiu Shao, et al. Active distribution grid coordination planning considering "source-grid-load" tripartite interests [ J ] grid technologies, 2017, 41 (02): 378-387.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (3)
1. A quantitative control method applied to improving the power supply capacity of a power distribution network is characterized by comprising the following steps:
solving the maximum power supply capacity of the existing power grid, and determining the planned maximum power supply capacity increment of the power distribution network, wherein the maximum power supply capacity increment is determined by a power system, power load increase and power supply reliability improvement; the maximum power supply capacity is described as the maximum load of the power distribution network when N-1 verification of all feeders and N-1 verification of main transformers of a transformer substation in the power distribution network are met, and the operation constraints of load transfer between the main transformers and the feeders, the capacities of the main transformers and the feeders and the contact relation between the main transformers and the feeders in the network are considered during the N-1 verification; the maximum power supply capacity provides a working point with the highest efficiency and economy of the power distribution network in a safe and reliable operation range;
calculating the power supply capacity of the power distribution network to improve construction investment and operation control cost; judging and updating two target values of the power supply capacity and the source network load control cost of the active power distribution network by using an improved crossover operator and an elite selection strategy, and acquiring a Pareto solution set based on non-dominated sorting;
determining a power supply investment planning scheme based on the set maximum power supply capacity and the obtained Pareto solution set, and optimizing a power distribution network construction scheme based on the maximum power supply capacity, namely selecting a Pareto optimal solution with the maximum relative closeness as a compromise optimal solution; taking a source network load control scheme corresponding to the optimal solution as a final decision solution;
the calculation formula for improving the construction investment and the operation control cost of the power supply capacity of the power distribution network is as follows:
minCost＝C DG +C SW +C DSM
in the formula, cost represents the total control Cost of the active power distribution network, C DG Represents the active management cost of the distributed power supply, C SW Represents the cost of the switching operation, C DSM Represents a demand-side management cost;
the specific steps of the calculation for improving the construction investment and the operation control cost of the power supply capacity of the power distribution network are as follows:
the controllable DG type is a micro gas turbine, and the operation cost and the active output power have the following relation:
in the formula, C MT The cost of power generation for the gas turbine; c ng Represents the unit gas cost; p MT The active output power of the gas turbine; η is the operating efficiency of the gas turbine;
the network reconstruction cost is in direct proportion to the action times of a switch in the power distribution network, and the calculation method comprises the following steps:
C SW ＝C RCS N RCS
wherein, C RCS The cost generated by one switching action in the network reconstruction process; n is a radical of RCS The times of switching actions in the network reconstruction process are obtained;
the cost of demand side management considers interruptible load compensation cost, and only two factors of the power shortage and the interruption time are considered to influence the interruptible load compensation cost, and the model is expressed as follows:
wherein, c IL,l Contract price, k, representing the interrupt load IL Representing a reactive price coefficient, P IL,l 、Q IL,l Respectively representing the active and reactive power of the interrupted load.
2. The method according to claim 1, wherein the method comprises the steps of,
the improved crossover operator is specifically:
rank represents the non-dominant ranking level of the individual a of the current generation, a dist represents the congestion distance of the individual a of the current generation, rank represents the non-dominant ranking level of the individual B of the current generation, and B dist represents the congestion distance of the individual B of the current generation.
3. The quantitative control method applied to the improvement of the power supply capacity of the power distribution network according to claim 1,
the elite selection strategy specifically comprises the following steps:
calculating the crowding distance of all non-dominated solutions in the current level individual, and deleting the solution with the minimum crowding distance;
judging the scale of the remaining non-dominated solution in the current level individual, if the number of the individual to be selected is met, executing the next step, otherwise, executing the previous step;
and outputting the remaining non-dominant solutions in the current level individuals.
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| CN106602557A (en) * | 2017-02-24 | 2017-04-26 | 三峡大学 | Multi-period optimization reconstruction method of active power distribution network comprising electric automobiles |
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