CN113765095A - Power distribution network structure planning optimization method based on intelligent algorithm - Google Patents
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Abstract
A power distribution network structure planning and optimizing method based on an intelligent algorithm comprises the following steps: step 1, optimizing an objective function, and establishing the optimized objective function taking the minimum total cost as the target
Including the investment cost of the grid construction project
And the power shortage cost of the system
(ii) a Step 2, setting constraint conditions, including power flow constraint, node voltage constraint, line capacity constraint, radial network constraint and electric energy loss constraint; and 3, solving by adopting an improved quantum particle swarm algorithm. The invention aims at optimizing economy and establishes a power distribution network frame planning model suitable for the Mongolian low-load density power supply environment. By improving and applying the quantum particle swarm algorithm, the optimal power distribution network space frame structure and cost are successfully found by planning and optimizing the power distribution network space frame structure, and the economical efficiency of the power distribution network is fundamentally improved.
Description
Technical Field
The invention relates to the technical field of power distribution network bearing capacity evaluation, in particular to a power distribution network structure planning and optimizing method based on an intelligent algorithm.
Background
The power distribution network frame planning is an important component of power distribution network power supply mode selection, most power supply areas in Mongolian areas belong to low-load density power supply areas, the load distribution is uneven, the transformer substation proportion is insufficient, the line transfer load capacity is insufficient, and the distance between points is long; the power distribution network has a single structure, and under the condition of low power supply reliability, the power distribution network still has large line loss; the distribution network spatial grid structure is comparatively weak. In order to ensure the coverage rate and reliability of power supply of a power grid and achieve optimal power grid economy as much as possible, the original power distribution network frame structure needs to be planned and optimized in consideration of economy and reliability.
High reliability and low investment cost are considered as one spear. The reliability cost is reduced, and the investment cost is inevitably increased by reducing the power failure loss. The coordination of solving the contradiction needs to analyze the reliability cost and the benefit thereof and determine the investment under which the optimal relationship between the economy and the reliability of the power distribution network can be obtained. Therefore, the method for planning and optimizing the grid structure of the power distribution network based on the intelligent algorithm is provided, the economy of the single-stage power distribution network is taken as an optimization objective function, and the electric energy loss constraint is added on the basis of the conventional constraint condition so as to achieve the goal of optimizing and planning the grid structure of the power distribution network in the area.
Disclosure of Invention
In order to solve the problems, the invention selects an optimal grid structure direction as an entry point according to the main problems of the existing power grid in the east Mongolian region, researches a power distribution network grid structure optimization method based on an intelligent algorithm, the intelligent algorithm selects a Particle Swarm Optimization (PSO), high-efficiency optimization search is realized through a memory and feedback mechanism, the method has the advantage of high convergence speed compared with intelligent algorithms such as a Genetic Algorithm (GA) and an Ant Colony Optimization (ACO), and is very effective in solving the complex and nonlinear problems of the power grid, and the technical scheme is as follows:
a power distribution network structure planning and optimizing method based on an intelligent algorithm comprises the following steps:
in the formula, N is the total number of lines; xkFor line coding elements, when the k-th line exists or is newly created, XkIs 1, otherwise is 0;
LCC is the life cycle cost, and the total cost of the electric power equipment of the distribution system from initial construction to retirement in the whole period of validity comprises the initial construction investment cost CnlAnd running maintenance cost ComResidual value CDAnd other costs Coc；
The power shortage cost X comprises the system network loss cost WllC0And system power outage cost WENsCi(ii) a Wherein, WllFor network power consumption, C0Buying electricity prices for grid companies; wENSFor a power shortage of the system, CiThe cost is lost for power outages of different load types.
And 2, setting constraint conditions, including power flow constraint, node voltage constraint, line capacity constraint, radial network constraint and electric energy loss constraint.
step 3.1, starting, setting initial parameters, and initializing particles by using a logistic function;
step 3.2, calculating the fitness of the particles and an attractor, and recording the current optimal solution;
3.3, updating the position of the particle according to the weight, and performing neighborhood searching operation;
step 3.4, calculating the fitness of the particles, if the fitness is better than the current optimal solution, if so, executing step 3.5 after updating the optimal solution, and if not, directly executing step 3.5;
and 3.5, judging whether the maximum iteration times are reached, if so, ending, otherwise, returning to execute the step 3.3.
Step 3.1, the Logistic function has the following expression:
xn+1＝μxn(1-xn) (1.2)
in the formula, when the value of the parameter mu is within the interval, the iteration value xnIs in a chaotic and disordered state.
And 3.3, updating the position of the particle according to the weight, namely giving the weight with different sizes to the particle according to the ratio of the current fitness fi to the current fitness mbest of the particle, wherein the expression is as follows:
Δ＝|mbest-mbest′| (1.3)
in the formula, mbest' is the average fitness value of all particles with fitness superior to mbest, Δ is the basis for judging the prematurity degree of the particle group, and the smaller the value is, the higher the prematurity trend of the particle group is, and the specific strategy is as follows:
fi(> mbest': the current particle fitness is better, smaller weight is given, the local optimization capability of the particle is improved, and the expression is as follows:
mbest＜fi< mbest': the current particle fitness is general, and the expression is as follows:
in the formula, genmax is the maximum iteration number;
fi< mbest: the current particle fitness is poor, passing through k1、k2Two parameters were adjusted, the expression is as follows:
and 3.3, the neighborhood searching operation refers to that after each iteration of the algorithm is completed, a certain probability gamma is provided for performing neighborhood searching operation on the result of the current iteration, another similar line combination is selected for fitness judgment, if the result is better than the current iteration result, the current pbest and the current gbest are updated, otherwise, the combination judged at this time is ignored, and the condition that the algorithm is trapped in local optimization can be effectively reduced.
The operation and maintenance cost C in the step 1omThe expression is as follows:
in the formula: n is a radical ofpIs the equipment life; com[j]The operation and maintenance cost of the equipment in the j year; fDisThe equipment discount rate is obtained; n is a radical ofk[j]Number of repairs of kth element in jth year, Ck[j]Maintenance costs for the kth element in the jth year, Cm[j]The operation cost of the j year comprises the operation expenses of personnel, equipment and the like.
The residual value C in step 1DThe expense incurred at the end of the equipment life cycle, and the expense for cleaning and destroying the equipment, the equipment residual occurs in the last year of the equipment cycle and is thus converted to the present value in the first year of the cycle, the expression of which is as follows:
in the step 2, the power flow constraint has the following expression:
in the formula, PiAnd QiRespectively representing the active and reactive power, U, injected into the i-node by the power supplyiAnd UjRespectively representing the voltages of the ith and jth nodes, Gij、Bij、θijRespectively, phase angle, conductance and susceptance between the nodes.
8. The method for optimizing the power distribution network structure planning based on the intelligent algorithm according to claim 1, wherein in the step 2, the node voltage is constrained according to the following expression:
Ukmin≤Uk≤Ukmax (1.10)
in the formula of UkIs the voltage of the kth node, Ukmax、UkminThe upper and lower voltage limits of the kth node are respectively;
the line capacity constraint is expressed as follows:
0≤Ij≤Ijmax·nj (1.11)
in the formula IjIs the current on the jth branch, Ijmax、njThe maximum current allowed to pass through the jth branch line pattern and the resistivity of the line pattern, respectively.
The radial network constraint in step 2 has the following expression:
in the formula of U1For the overload punishment coefficient, L is the overload part of the network, and the numerical value can be calculated according to the network load flow; u shape2Is a non-radiative net penalty value. In Mongolian areas, the network structure of the power distribution network is mostly radial due to low load density, and the power distribution network mostly adopts closed-loop design and open-loop operation, so that radial network constraint is added, and a non-radial network is removed. Both capacity constraints and structural constraints can be implemented by constructing penalty functions. Due to the particularity of loads in Mongolian region, the applicability of the radiation net is better than that of the non-radiation net, so that U2Can be made large to prioritize the exclusion of unnecessary solutions.
10. The power distribution network architecture planning and optimizing method based on the intelligent algorithm according to claim 1, wherein the electric energy loss constraint in the step 2 is expressed as follows:
in the formula, WyIndicating a certain load point for the whole yearThe load capacity is expressed as follows:
Wy＝Pmax·Δτmax (1.14)。
initial construction investment cost C in step 1nlThe method comprises the steps of designing stage cost, purchasing of equipment and consumables in a construction stage and line installation cost; other one-time investment costs such as miscellaneous costs such as labor costs and the like are expressed as follows:
Cnl＝Crd+Ccs+Copm (1.15)
in the formula, CrdThe annual cost of research and development design stage cost mainly comprises the costs of investigation, preliminary design, construction drawing design and the like; ccsThe equal-year-value cost of the total cost in the construction stage mainly comprises the cost of mechanical lease, consumable purchase and the like; copmThe equal-year-value cost for other costs mainly comprises labor cost, management cost and the like.
Compared with the prior art, its beneficial effect lies in: the invention aims at optimizing economy and establishes a power distribution network frame planning model suitable for the Mongolian low-load density power supply environment. By improving and applying the quantum particle swarm algorithm, the optimal power distribution network space frame structure and cost are successfully found by planning and optimizing the power distribution network space frame structure, and the economical efficiency of the power distribution network is fundamentally improved.
Drawings
FIG. 1 is a flow chart of an improved quantum-behaved particle swarm algorithm;
FIG. 2, a reliability cost-benefit analysis curve;
FIG. 3 is a random function under the chaos principle;
FIG. 4 is an equivalent topology structure diagram of an IEEE-14 node power distribution network system;
FIG. 5, initial investment optimization net rack planning structure;
fig. 6, an LCC optimal net rack planning structure;
FIG. 7, 2018E Wenke autonomous flag (containing Yimin) grid structure;
FIG. 8 and 2025, the network structure of Ewink autonomous flag (containing Yimin).
Detailed Description
Example 1
Selecting an IEEE-14 node power distribution network system for carrying out grid planning, verifying the effectiveness of model and algorithm improvement, wherein the simulation operation environment is an Interi 53210M 2.5GHz processor and an 8G RAM; the programming software is Matlab, and iteration curve comparison is carried out by using a dual interior point method based on Origin software.
The system reference capacity is 100MV & A, and the voltage class is 10 kV. In order to fully verify the planning performance of the algorithm and the model, the IEEE-14 power distribution network system is equivalent to a topological structure model shown in figure 4. The original 1, 2, 3, 4, 7 and 10 branches of the system are established branches, the rest branches are newly-established branches to be planned, and 6 branches to be planned are newly added. In order to compare and verify the effectiveness of the model, the system is subjected to planning simulation of two schemes with the initial investment optimization as a target and the LCC optimization as a target respectively.
The equivalent post-distribution system parameters are shown in table 1. The line type uniformly selects JKLYJ-120, and the cost of the line is 10 multiplied by 104Yuan/km; the voltage constraint is set to ± 6%; setting the electricity price to be 0.3 yuan/kW.h according to the on-line electricity price of Monte grid company; δ is set to 0.2.
k1、k2＝1，ωmax＝0.9，ωmin0.4, 20, and 100 maximum iterations.
Table 114 node distribution network system node parameter
The results of the algorithm run are shown in fig. 5, 6 and table 2.
TABLE 2 planning results
Example 2
As shown in fig. 7, it can be seen from a geographical wiring diagram of a power distribution network of 35kV and above of the ebeck family autonomous flag (including emy), that in the current ebeck family autonomous flag (including emy) area, a 110kV network is a dual power supply network, a 35kV network is mainly supplied with power by single radiation, and according to a simulation result theory, a simulation theory can be introduced to perform auxiliary optimization on planning of a high-voltage power distribution network in a later stage at a power grid planning stage.
And (4) introducing an LCC optimization model, carrying out optimization measurement and calculation on an Ewenke autonomous flag (containing Yimin) high-voltage distribution network, and optimizing the network frame. The specific calculation and optimization drawing results are shown in table 3 and fig. 8.
TABLE 3 planning results
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A power distribution network structure planning optimization method based on an intelligent algorithm is characterized by comprising the following steps:
step 1, optimizing an objective function, and establishing an optimized objective function F with the minimum total cost as a target, wherein the optimized objective function F comprises the investment cost C of a power grid construction project and the power shortage cost X of a system, and the expression is as follows:
in the formula, N is the total number of lines; xkFor line coding elements, when the k-th line exists or is newly created, XkIs 1, otherwise is 0;
LCC is the life cycle cost, and the total cost of the electric power equipment of the distribution system from initial construction to retirement in the whole period of validity comprises the initial construction investment cost CnlAnd running maintenance cost ComResidual value CDAnd other costs Coc；
The power shortage cost X comprises the system network loss cost WllC0And system power outage cost WENSCi(ii) a Wherein, WllFor network power consumption, C0Buying electricity prices for grid companies; wENSFor a power shortage of the system, CiThe cost is lost for power outages of different load types.
And 2, setting constraint conditions, including power flow constraint, node voltage constraint, line capacity constraint, radial network constraint and electric energy loss constraint.
Step 3, solving by adopting an improved quantum particle swarm algorithm, comprising the following steps:
step 3.1, starting, setting initial parameters, and initializing particles by using a logistic function;
step 3.2, calculating the fitness of the particles and an attractor, and recording the current optimal solution;
3.3, updating the position of the particle according to the weight, and performing neighborhood searching operation;
step 3.4, calculating the fitness of the particles, if the fitness is better than the current optimal solution, if so, executing step 3.5 after updating the optimal solution, and if not, directly executing step 3.5;
and 3.5, judging whether the maximum iteration times are reached, if so, ending, otherwise, returning to execute the step 3.3.
2. The method for optimizing the power distribution network structure planning based on the intelligent algorithm according to claim 1, wherein the Logistic function in step 3.1 has the following expression:
xn+1＝μxn(1-xn) (1.2)
in the formula, when the value of the parameter mu is within the interval, the iteration value xnIs in a chaotic and disordered state.
3. The method according to claim 1, wherein the particles in step 3.3 are updated according to their weight based on their current fitness fiThe ratio to mbest, weights are given to the particles of different sizes, and the expression is as follows:
Δ＝|mbest-mbest′| (1.3)
in the formula, mbest' is the average fitness value of all particles with fitness superior to mbest, Δ is the basis for judging the prematurity degree of the particle group, and the smaller the value is, the higher the prematurity trend of the particle group is, and the specific strategy is as follows:
fi>mbest': the current particle fitness is better, smaller weight is given, the local optimization capability of the particle is improved, and the expression is as follows:
mbest<fi<mbest': the current particle fitness is general, and the expression is as follows:
in the formula, genmax is the maximum iteration number;
fi<mbest: the current particle fitness is poor, passing through k1、k2Two parameters were adjusted, the expression is as follows:
4. the method for optimizing the power distribution network structure planning based on the intelligent algorithm as claimed in claim 1, wherein the neighborhood search operation in step 3.3 is that after each iteration of the algorithm is completed, a certain probability γ is provided to perform neighborhood search operation on the result of the current iteration, another similar line combination is selected to perform fitness judgment, if the result is better than the current iteration result, the current pbest and gbest are updated, otherwise, the combination judged this time is ignored, and the situation that the algorithm falls into local optimum can be effectively reduced.
5. The method for optimizing the power distribution network structure planning based on the intelligent algorithm as claimed in claim 1, wherein the operation and maintenance cost C in step 1omThe expression is as follows:
in the formula: n is a radical ofpIs the equipment life; com[j]The operation and maintenance cost of the equipment in the j year; fDisThe equipment discount rate is obtained; n is a radical ofk[j]Number of repairs of kth element in jth year, Ck[j]Maintenance costs for the kth element in the jth year, Cm[j]The operation cost of the j year comprises the operation expenses of personnel, equipment and the like.
6. The method for optimizing the power distribution network architecture planning based on the intelligent algorithm as claimed in claim 1, wherein the residual value C in step 1DThe expense incurred at the end of the equipment life cycle, and the expense for cleaning and destroying the equipment, the equipment residual occurs in the last year of the equipment cycle and is thus converted to the present value in the first year of the cycle, the expression of which is as follows:
7. the method for planning and optimizing the grid structure of the power distribution network based on the intelligent algorithm according to claim 1, wherein in the step 2, the power flow constraint is expressed as follows:
in the formula, PiAnd QiRespectively representing the active and reactive power, U, injected into the i-node by the power supplyiAnd UjRespectively representing the voltages of the ith and jth nodes, Gij、Bij、θijRespectively, phase angle, conductance and susceptance between the nodes.
8. The method for optimizing the power distribution network structure planning based on the intelligent algorithm according to claim 1, wherein in the step 2, the node voltage is constrained according to the following expression:
Ukmin≤Uk≤Ukmax (1.10)
in the formula of UkIs the voltage of the kth node, Ukmax、UkminThe upper and lower voltage limits of the kth node are respectively;
the line capacity constraint is expressed as follows:
0≤Ij≤Ijmax·nj (1.11)
in the formula IjIs the current on the jth branch, Ijmax、njThe maximum current allowed to pass through the jth branch line pattern and the resistivity of the line pattern, respectively.
9. The method for optimizing the power distribution network architecture planning based on the intelligent algorithm according to claim 1, wherein in the step 2, the radial network constraint is expressed as follows:
in the formula of U1For the overload punishment coefficient, L is the overload part of the network, and the numerical value can be calculated according to the network load flow; u shape2Is a non-radiative net penalty value.
10. The power distribution network architecture planning and optimizing method based on the intelligent algorithm according to claim 1, wherein the electric energy loss constraint in the step 2 is expressed as follows:
in the formula, WyThe annual load capacity of a certain load point is represented by the following expression:
Wy＝Pmax·Δτmax (1.14)。
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