Optimization method and system for distributed energy distribution network multi-time scale reliability system

Abstract

The invention discloses a distributed energy power distribution network multi-time scale reliability system optimization method and system, and relates to the technical field of power systems, and the method comprises the steps: constructing a comprehensive cost model of a power distribution network; setting constraint conditions of operation of the power distribution network; an improved multi-target grey wolf algorithm is adopted for optimization; and outputting a power distribution network reliability operation strategy based on the optimal solution. According to the distributed energy distribution network multi-time scale reliability optimization method provided by the invention, the reliability of the distribution network is optimized through the medium-and-long-term and short-term two-stage time scale system, and the stable operation of the distribution network is set by taking the active power and the load power as targets, so that the operation stability of the distribution network is effectively improved; and defining the reliability of the power distribution network. Algorithm optimization is carried out in an initial population setting stage in the grey wolf algorithm by adopting a Sobol sequence, and an adaptive factor is optimized in a crossover variation stage, so that the calculation efficiency of the grey wolf algorithm is effectively improved, and the optimization efficiency of a power distribution network reliability system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an optimization method and system for a multi-time scale reliability system of a distributed energy distribution network. Background Art

[0002] In recent years, with the increasing penetration of photovoltaic and wind energy in distribution networks, the power system is in an important stage of transformation from traditional distribution systems to smart grids. Although renewable energy has many advantages in the operation of smart grids, its uncertainty and intermittency will cause problems such as voltage fluctuations and complex power flow calculations. The reliability of the distribution system is an important indicator to measure the quality and capacity of power supply. The main factors affecting the reliability of the distribution system include the operating cost of the distribution network. Therefore, it is very important to build a reliability system with the goal of reducing the operating cost of the distribution network.

[0003] At present, some researchers have coordinated and realized the intelligent optimization of the distribution network through day-ahead dispatching and real-time dispatching, and then used wavelet function to solve and verify the rationality of the reliability system. Other researchers have built an intelligent dispatching mechanism based on physical information and proposed a dispatching strategy based on the production enhancement of the distribution network. Through the practice of this dispatching method, the intelligent dispatching of the distribution network is realized, thereby promoting the stable operation of the distribution network. However, the calculation process of the above method does not take into account the uncertainty of the distribution network, which makes the reliability assessment of the distribution network lack stability. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the access of renewable energy (such as photovoltaic and wind energy) brings about the complexity of voltage fluctuations and power flow calculation. Traditional scheduling methods are difficult to deal with these problems, especially they do not fully consider the uncertainty brought by the access of distributed energy.

[0006] Existing methods do not fully consider uncertainty, which makes the stability of distribution network reliability assessment insufficient. Due to the intermittent nature of distributed energy, the assessment results fluctuate greatly.

[0007] The cost optimization of distribution network operation, especially the cost optimization at different time scales, long-term and short-term, is insufficient, and there is no effective way to balance investment, operation and maintenance costs at different time scales.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a multi-time scale reliability optimization method for a distributed energy distribution network, comprising:

[0009] Construct a comprehensive cost model for the distribution network;

[0010] Set constraints on distribution network operation;

[0011] The improved multi-objective grey wolf algorithm is used for optimization;

[0012] Output the distribution network reliability operation strategy based on the optimization solution.

[0013] As a preferred solution of the multi-time scale reliability optimization method of the distributed energy distribution network described in the present invention, the comprehensive cost model of the distribution network is expressed as:

[0014]

[0015] Where m is the number of time periods included in a long analysis period, C OI is the initial investment cost, C DE is the power generation cost of the diesel generator set, C M is the operation and maintenance cost of the distribution network, C L The depreciation loss cost.

[0016] As a preferred solution of the multi-time scale reliability optimization method of the distributed energy distribution network described in the present invention, wherein: the initial investment cost is expressed as,

[0017]

[0018] Among them, C ik is the purchase cost of the i-th equipment, k is the total number of equipment, and β is the ratio coefficient of design and construction costs to the purchase cost;

[0019] The power generation cost of the diesel generator set is expressed as,

[0020]

[0021] Among them, V is the node of the diesel generator set, Fuel costs for diesel generators, The operating and maintenance costs of diesel generators. is the power generation at each node j, eC tn The cyclical nature of electricity generation costs over time;

[0022] The distribution network operation and maintenance cost is expressed as:

[0023]

[0024] in, For maintenance personnel costs, Costs incurred for equipment failure inspection, repair and maintenance;

[0025] The depreciation loss cost is expressed as,

[0026]

[0027]

[0028] in, Average depreciation loss cost for reliability-optimized equipment, ec t,n and ec t,m,n The variable day is the periodic characteristic according to time change. i The day of the week, hour j The hour of the day, min j is the number of minutes in an hour; and are the estimated coefficients for the day, hour, and minute variables, respectively; It is a random interference item.

[0029] As a preferred solution of the multi-time-scale reliability optimization method for distributed energy distribution networks described in the present invention, the constraint conditions include upper and lower limit constraints on diesel generator output and diesel generator climbing constraints.

[0030] As a preferred solution of the multi-time scale reliability optimization method of the distributed energy distribution network described in the present invention, wherein: the upper and lower limit constraints of the diesel generator output are expressed as:

[0031] P DE,min (t)≤P DE,j (t)≤P DE,max (t)

[0032] Among them, P DE,min (t) and P DE,max (t) are the upper and lower limits of the output of the unit at node j;

[0033] The diesel generator climbing constraint is expressed as:

[0034] -H down,j ≤P DE,j (t+1)-P DE,j (t)≤H up,j

[0035] Among them, -H down,j and H up,j They are respectively the maximum downward climbing rate and the maximum upward climbing rate of the generator set output power.

[0036] As a preferred solution of the multi-time-scale reliability optimization method of the distributed energy distribution network described in the present invention, the optimization generates an initial population through a Sobol sequence, and mutates and crosses the population through a differential evolution algorithm to obtain an optimized solution.

[0037] As a preferred solution of the multi-time scale reliability optimization method of the distributed energy distribution network described in the present invention, wherein: the generated initial population is expressed as,

[0038] x i,j =x j,min +(x j,max -x j,min )K n

[0039] x′ i,j =x j,min +x j,max -r4x i,j

[0040] Among them, x j,max and x j,min are the upper and lower limits of the j-th dimension control variable, K n is a random number between [0,1] generated by Sobol sequence, and r4 is a random number between [0,1].

[0041] Another object of the present invention is to provide a multi-time-scale reliability optimization system for a distributed energy distribution network, which can solve the problems of the inability to effectively deal with the uncertainty brought about by distributed energy access, insufficient cost optimization and reliability improvement in existing distribution network scheduling and reliability assessment by constructing a multi-time-scale reliability optimization system for a distributed energy distribution network.

[0042] To solve the above technical problems, the present invention provides the following technical solutions: a distributed energy distribution network multi-time scale reliability optimization system, comprising: a cost analysis module, an operation constraint module, an algorithm optimization module and a strategy output module; the cost analysis module is used to construct a comprehensive cost model of the distribution network; the operation constraint module is used to set the constraint conditions for the operation of the distribution network; the algorithm optimization module is used to optimize using an improved multi-objective grey wolf algorithm; the strategy output module is used to output a distribution network reliability operation strategy based on the optimized solution.

[0043] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the multi-time-scale reliability optimization method for a distributed energy distribution network are implemented as described above.

[0044] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the multi-time-scale reliability optimization method for a distributed energy distribution network as described above.

[0045] Beneficial effects of the present invention: The multi-time scale reliability optimization method for distributed energy distribution networks provided by the present invention optimizes the reliability of the distribution network through a two-stage time scale system of medium- and long-term and short-term, wherein the stable operation of the distribution network is set with active power and load power as targets, effectively improving the stability of the distribution network operation, thereby defining the reliability of the distribution network.

[0046] By using the Sobol sequence to optimize the algorithm in the initial population setting stage of the Grey Wolf Algorithm and optimizing the adaptive factor in the crossover mutation stage, the computational efficiency of the Grey Wolf Algorithm can be effectively improved, thereby improving the optimization efficiency of the distribution network reliability system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0048] Figure 1 An overall flow chart of a multi-time-scale reliability optimization method for a distributed energy distribution network provided by an embodiment of the present invention.

[0049] Figure 2 A flowchart of solving the problem using the Grey Wolf Algorithm in a multi-time-scale reliability optimization method for a distributed energy distribution network provided by an embodiment of the present invention.

[0050] Figure 3 An overall structural diagram of a distributed energy distribution network multi-time scale reliability optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Example 1

[0054] Reference Figure 1-Figure 2 , is an embodiment of the present invention, and provides a distributed energy distribution network multi-time scale reliability optimization method, comprising:

[0055] Collect relevant data involved in the operation process of the distribution network and build a comprehensive cost model of the distribution network;

[0056] Set constraints on distribution network operation;

[0057] The improved multi-objective grey wolf algorithm is used for optimization;

[0058] Output the distribution network reliability operation strategy based on the optimization solution.

[0059] In the context of distributed energy access, the cost-effectiveness of the distribution network at multiple time scales is analyzed to select a feasible optimization plan for the distribution network.

[0060] (1) Constructing a comprehensive cost model for distribution networks:

[0061]

[0062] Where: m is the number of time periods included in a long analysis period, C OI is the initial investment cost, C DE is the power generation cost of the diesel generator set, C M is the operation and maintenance cost of the distribution network, C L The depreciation loss cost.

[0063] (2) The initial investment cost here takes into account the cost of the distribution network generator equipment, including procurement cost, design and construction cost, specifically:

[0064]

[0065] Above C ik is the purchase cost of the i-th equipment, k can be the total number of equipment, and β is the ratio of design and construction costs to the purchase cost. (3)

[0067]

[0068] Where: V is the node of the diesel generator set, Fuel costs for diesel generators, The operating and maintenance costs of diesel generators. is the power generation at each node j, eC tn The cyclical characteristics of power generation costs changing over time. (4)

[0070]

[0071] in: For maintenance personnel costs, Costs incurred for equipment failure inspection and maintenance. (5)

[0073]

[0074] in: Average depreciation loss expense for equipment optimized for reliability. (6)

[0076]

[0077] Where: ec t,n and ec t,m,n They are periodic characteristics that change over time; the variable day i The day of the week, hour j The hour of the day, min j is the number of minutes in an hour; and are the estimated coefficients for the day, hour, and minute variables, respectively; It is a random interference item.

[0078] The above ec t,n and ec t,m,n It can be set according to the intercepted time period.

[0079] 2. Constraint setting:

[0080] (1) Upper and lower limits of diesel generator output:

[0081] P DE,min (t)≤P DE,j (t)≤P DE,max (t)

[0082] Where: P DE,min (t) and P DE,max (t) are the upper and lower limits of the unit output at node j respectively.

[0083] (2) Diesel generator climbing constraints:

[0084] -H down,j ≤P DE,j (t+1)-P DE,j (t)≤H up,j

[0085] Where: -H down,j and H up,jThey are respectively the maximum downward climbing rate and the maximum upward climbing rate of the generator set output power.

[0086] In addition, after reliability optimization, the expected values ​​of load point power outage rate, power outage time, reliability rate and power shortage have been improved.

[0087] It should be noted that the initial investment cost is fully considered in the model with the goal of minimizing the cost, making the calculation of the minimum cost more comprehensive.

[0088] By setting the periodic characteristics of time changes over a long time scale, the impact of different time periods on costs can be reflected according to the intercepted time period, and the optimization accuracy of the reliability system can be more accurately improved through the time dimension.

[0089] Improved multi-objective grey wolf algorithm, for long-term large-scale mixed integer nonlinear programming problems, traditional numerical optimization methods have high requirements on the model, small scope of application, and are difficult to be well applied. Therefore, the present invention uses the grey wolf algorithm that has been well applied in distribution network optimization control to solve the problem, such as Figure 1 The process is as follows:

[0090] 1. For the j-th dimension component x of the i-th initial individual in the gray wolf population i,j Initialize using the Sobol sequence:

[0091] x i,j =x j,min +(x j,max -x j,min )K n

[0092] Where: x j,max and x j,min are the upper and lower limits of the j-th dimension control variable, K n The random number generated by Sobol sequence between [0,1] is calculated and the result of random reverse number is shown in the following formula. The initial population is generated by non-dominated sorting of the initial individuals and random reverse individuals:

[0093] x′ i,j =x j,min +x j,max -r4x i,j

[0094] Where: r4 is a random number between [0,1].

[0095] 2. Convergence factor nonlinear adjustment strategy:

[0096] The convergence factor decreases linearly from 2 to 0 during the execution of the algorithm, which leads to insufficient exploration ability of the gray wolf population in the early stage and easy to fall into the local optimum. Therefore, the convergence factor a is adjusted nonlinearly here:

[0097]

[0098] Where: k is the current iteration number, λ is the linear adjustment range parameter.

[0099] The convergence factor decreases slowly in the early stage of algorithm execution, which enables the algorithm to have a strong global exploration ability. The convergence factor decreases faster in the later stage of algorithm execution, which can effectively improve the local development ability of the algorithm.

[0100] 3. Calculate the objective function value of the population, and select the alpha wolf through each non-dominated solution and fitness function:

[0101] Here, we use the excellent global search capability of the differential algorithm to integrate its mutation, crossover and selection operations into the population evolution mechanism of the optimization algorithm. The specific operations are as follows:

[0102] (1) The mutation operation randomly selects different individuals from the population and performs differential scaling on their position vectors, as shown below:

[0103] v i,k =x r1,k +F(x best,k +x r1,k )+F(x r2,k -x r3,k )

[0104] Where: v i,k is the new individual after the ith mutation, x r1,k 、x r2,k and x r3,k are three random individuals in the parent generation, x best,k is the non-dominated individual selected in the population through the tournament selection strategy, F is the difference scaling factor;

[0105] (2) The crossover operation crosses the parent individuals with the mutant individuals according to a certain rule to generate offspring individuals, as shown below:

[0106]

[0107] Where: u i,j,k is the new individual after crossover, J represents the set of subscripts of the crossover position elements, C r is the crossover rate, r j is a random number between [0,1].

[0108] (3) The selection operation compares the offspring individuals u by non-dominated sorting and crowding distance calculation. i,kWith the parent individual x i,k The dominant individuals generate a new parent population.

[0109] (4) Substitute the differential scaling factor F and the crossover rate C r To make adaptive adjustments:

[0110]

[0111] Among them: F max is the maximum value of the variation factor, F min is the minimum value of the variation factor, k m is the maximum number of iterations, and k is the current number of iterations.

[0112] In the evolution process of the adaptive differential evolution algorithm, as the crossover factor increases with the number of iterations, the crossover rate also changes dynamically. The larger crossover factor in the early stage ensures the global variation, and the smaller crossover rate in the later stage pays more attention to the local convergence. Therefore, the adaptive crossover factor is set as follows:

[0113]

[0114] Among them: CR max is the maximum crossover factor, CR min is the minimum crossover factor.

[0115] 4. Update the position of the gray wolf population based on the above adaptive adjustment of the convergence factor:

[0116]

[0117] Among them: r1 and r2 are two random numbers between [0,1], a is the convergence factor, p is the number of alpha wolves, corresponding to α, β and δ wolves respectively, A and C are the convergence coefficient and swing coefficient respectively, k is the current iteration number, X p is the prey position, D p is the distance between the current gray wolf and the alpha wolf, and X(k is the position of the individual gray wolf. The final position update formula of the gray wolf population guided by the alpha wolf is as follows:

[0118]

[0119] 5. According to the crossover and mutation calculation formula in step 3 above, the population is mutated, crossovered and selected to generate a new population.

[0120] 6. Finally, determine whether the iteration here meets the conditions. If the conditions are met, output the optimal solution, and calculate the optimal strategy for 24h distribution network equipment operation optimization based on the optimal solution.

[0121] Example 2

[0122] Reference Figure 2, is an embodiment of the present invention, and provides a distributed energy distribution network multi-time scale reliability optimization system, comprising:

[0123] Cost analysis module, operation constraint module, algorithm optimization module and strategy output module;

[0124] The cost analysis module is used to build a comprehensive cost model for the distribution network;

[0125] The operation constraint module is used to set the constraints of distribution network operation;

[0126] The algorithm optimization module is used to optimize using the improved multi-objective grey wolf algorithm;

[0127] The strategy output module is used to output the distribution network reliability operation strategy based on the optimization solution.

[0128] Example 3

[0129] An embodiment of the present invention is different from the first two embodiments in that:

[0130] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0131] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0132] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0133] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0134] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-time scale reliability optimization method for distributed energy distribution network, characterized in that: include: Construct a comprehensive cost model for the distribution network; Set constraints on distribution network operation; The improved multi-objective grey wolf algorithm is used for optimization; Output the distribution network reliability operation strategy based on the optimization solution.

2. The multi-time scale reliability optimization method for distributed energy distribution network according to claim 1, characterized in that: The comprehensive cost model of the distribution network is expressed as: Where m is the number of time periods included in a long analysis period, C OI is the initial investment cost, C DE is the power generation cost of the diesel generator set, C M is the operation and maintenance cost of the distribution network, C L The depreciation loss cost.

3. The multi-time scale reliability optimization method for distributed energy distribution network according to claim 2, characterized in that: The initial investment cost is expressed as, Among them, C ik is the purchase cost of the i-th equipment, k is the total number of equipment, and β is the ratio coefficient of design and construction costs to the purchase cost; The power generation cost of the diesel generator set is expressed as, Among them, V is the node of the diesel generator set, Fuel costs for diesel generators, The operating and maintenance costs of diesel generators. is the power generation at each node j, eC tn The cyclical nature of electricity generation costs over time; The distribution network operation and maintenance cost is expressed as: in, For maintenance personnel costs, Costs incurred for equipment failure inspection, repair and maintenance; The depreciation loss cost is expressed as, in, Average depreciation loss cost for reliability-optimized equipment, ec t,n and ec t,m,n The variable day is the periodic characteristic according to time change. i The day of the week, hour j The hour of the day, min j is the number of minutes in an hour; and are the estimated coefficients for the day, hour, and minute variables, respectively; It is a random interference item.

4. The multi-time scale reliability optimization method for distributed energy distribution network according to claim 3, characterized in that: The constraints include upper and lower limit constraints on the output of the diesel generator and climbing constraints on the diesel generator.

5. The multi-time scale reliability optimization method for distributed energy distribution network according to claim 4, characterized in that: The upper and lower limits of the diesel generator output are expressed as: P DE,min (t)≤P DE,j (t)≤P DE,max (t) Among them, P DE,min (t) and P DE,max (t) are the upper and lower limits of the output of the unit at node j; The diesel generator climbing constraint is expressed as: -H down,j ≤P DE,j (t+1)-P DE,j (t)≤H up,j Among them, -H down,j and H up,j They are respectively the maximum downward climbing rate and the maximum upward climbing rate of the generator set output power.

6. The multi-time scale reliability optimization method for distributed energy distribution network according to claim 5, characterized in that: The optimization generates an initial population through a Sobol sequence, and performs mutation and crossover operations on the population through a differential evolution algorithm to obtain an optimized solution.

7. The multi-time scale reliability optimization method for distributed energy distribution network according to claim 6, characterized in that: The generated initial population is expressed as: x i,j =x j,min +(x j,max -x j,min )K n x' i,j =x j,min +x j,max -r4x i,j Among them, x j,max and x j,min are the upper and lower limits of the j-th dimension control variable, K n is a random number between [0,1] generated by Sobol sequence, and r4 is a random number between [0,1].

8. A system using the multi-time scale reliability optimization method for distributed energy distribution network according to any one of claims 1 to 7, characterized in that: include: Cost analysis module, operation constraint module, algorithm optimization module and strategy output module; The cost analysis module is used to construct a comprehensive cost model of the distribution network; The operation constraint module is used to set the constraint conditions for the operation of the distribution network; The algorithm optimization module is used to optimize using an improved multi-objective grey wolf algorithm; The strategy output module is used to output a distribution network reliability operation strategy based on the optimization solution.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distributed energy distribution network multi-time scale reliability optimization method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed energy distribution network multi-time scale reliability optimization method described in any one of claims 1 to 7 are implemented.

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