CN111125638A

CN111125638A - Method and device for constructing planning model of active power distribution network and computing equipment

Info

Publication number: CN111125638A
Application number: CN201911281787.2A
Authority: CN
Inventors: 吴奎华; 冯亮; 杨波; 郑志杰; 李雪亮; 吴健; 李琨; 贾善杰; 梁荣; 杨慎全; 刘淑莉; 李凯; 张雯; 李昭; 邓少治; 杨扬; 刘钊; 崔灿; 綦陆杰; 王耀雷
Original assignee: State Grid Corp of China SGCC; Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
Priority date: 2019-12-13
Filing date: 2019-12-13
Publication date: 2020-05-08

Abstract

The invention discloses a method for constructing an active power distribution network planning model, which is executed in computing equipment and comprises the following steps: establishing an active power distribution network planning model, wherein the model comprises a target function and a constraint condition; acquiring basic parameters of the active power distribution network, solving a planning model by adopting a predetermined population algorithm with the aim of minimizing the total investment in a planning period, and outputting an optimal planning scheme of the active power distribution network system, wherein the optimal planning scheme comprises planning cost, planning capacity and planning position; wherein the objective function is J ═ C^DG+C^L+C^Cap+C^SW+V^DRWherein J is the total investment of the planning period, C^DGFor investment and operating costs of distributed generator sets during planning periods, C^LInvestment and construction costs for upgrading and rebuilding lines, C^CapFor investment costs of alternative capacitors, C^SWFor installation and maintenance costs of the active power supply, V^DRThe net present value of demand side response charges. The invention also discloses a construction device and a computing device of the corresponding active power distribution network planning model.

Description

Method and device for constructing planning model of active power distribution network and computing equipment

Technical Field

The invention relates to the field of power systems, in particular to a method and a device for constructing an active power distribution network planning model and computing equipment.

Background

Currently, distributed power generation and demand-side response are actively developed, and structural transformation of both power supply and demand sides is promoted to become an important development trend of the power industry. Distributed power supply output has certain randomness and intermittence, along with the increase of the scale of the distributed power supply, the impact of the access of the distributed power supply on the reliability of a power distribution network can influence the safe and stable operation of the whole power distribution network, in the planning process of an active power distribution network, the influence of the access of the distributed power supply on the operation of the power distribution network can be stabilized by introducing demand side response, and the planning of the active power distribution network considering the operation reliability of the power distribution network and the response of the demand side is a hot problem to be further researched urgently.

At present, the following two aspects of researches are mainly carried out on the planning problem after the distributed power generation is connected into the power distribution network: firstly, planning an active power distribution network from the perspective of power grid operation safety, and establishing a planning model considering safety constraints by adopting a random power flow and opportunity constraint technology and taking the safety operation of the power distribution network as a center; the method comprises the steps that an active power distribution network is planned from the perspective of benefit maximization of power grid investors and operators, and a distributed power generation planning optimization model is constructed on the basis of considering investment cost minimization of distributed power generation investors and cost minimization of operators; or from the perspective of annual expenditure cost of the power grid, the output of distributed power generation is simulated by adopting a Monte Carlo method, and comprehensive optimization planning is carried out on the type, position and capacity of a newly-built or upgraded and modified line and a DG to be selected based on an improved genetic algorithm of a recessive coding mode.

However, in the current active power distribution network planning, demand side response is rarely considered, and especially, grid operation reliability and demand side response are not considered, so that the calculation result is not accurate enough, and the real planning requirement cannot be represented.

Disclosure of Invention

To this end, the present invention provides a method, an apparatus and a computing device for constructing an active power distribution network planning model, so as to try to solve or at least alleviate the above problems.

According to an aspect of the present invention, there is provided a method for constructing an active power distribution network planning model, which is suitable for being executed in a computing device, the method including the steps of: establishing an active power distribution network planning model considering power grid operation reliability and demand side response, wherein the planning model comprises a target function and constraint conditions; acquiring basic parameters of the active power distribution network, substituting the basic parameters into a planning model,solving the planning model by adopting a predetermined population algorithm with the aim of minimum total investment in a planning period to obtain an optimal planning scheme of the active power distribution network system, wherein the optimal planning scheme comprises planning cost, planning capacity and planning position; wherein the objective function is J ═ C^DG+C^L+C^Cap+C^SW+V^DRWherein J is the total investment of planning period, C^DGFor investment and operating costs of distributed generator sets during planning periods, C^LInvestment and construction costs for upgrading and rebuilding lines, C^CapFor investment costs of alternative capacitors, C^SWFor installation and maintenance costs of the active power supply, V^DRThe net present value of demand side response charges.

Optionally, in the method according to the invention, the objective function is:

in the formula, I is the total number of nodes; i is a node; d is the discount rate; t is the total years of the planning period; y is the y-th year of the planning period; b is the total number of the load sections; b is the b-th load segment; c^DG,FThe annual unit investment cost of the distributed generator set is saved;

capacity of the distributed generator set at a node i; c^DG,OIs the operating cost of the DG;

the output power of the distributed generator set at the node i load section b is obtained; h is_bThe annual running hours of the distributed generator set in the load section b are counted; j is the jth node; c^LFThe investment cost is fixed for the year of the feeder line; g_i，jA geographical cost factor for the feeder between node i and node j; l is_i，jThe length of the feeder line between the node i and the node j;

cloth for feeder line upgradingAn molar variable; c^LVAnnual change cost for the feeder; s'_i，jIncreased capacity for feeder from node i to node j; c^cThe unit annual investment cost of the parallel capacitors;

the capacitance of the capacitor is connected in parallel at the node i;

is a boolean variable indicating whether or not an active power source is installed at node i; c. C_g、c_a、M_CRespectively the purchase cost, installation cost and annual average maintenance cost for each active power supply.

Optionally, in the method according to the invention, the constraints comprise one or more of power flow constraints, voltage constraints, line capacity constraints, DG capacity constraints, capacitance constraints, supply margin constraints, reliability constraints and power quality constraints.

Optionally, in the method according to the present invention, the power flow constraint comprises:

in the formula, P_i、Q_iAnd U_iRespectively representing active power, reactive power injection and voltage amplitude of a node i under a normal condition; u shape_jRepresents the voltage amplitude of node j under normal conditions; g_ijAnd B_ijRespectively representing the real part and the imaginary part of a system admittance matrix under normal conditions; theta_ijRepresenting the voltage phase angle difference between node i and node j.

Optionally, in the method according to the invention, the voltage constraint comprises:

in the formula, V_i，bRepresenting the magnitude of the voltage at node i in load section b; v^minAnd V^maxRespectively representing the minimum and maximum voltage values allowed by the line.

Optionally, in the method according to the present invention, the line capacity constraint comprises:

wherein the content of the first and second substances,

and

respectively the active and reactive power flows between the load section b and the feeder lines i-j; s_i，jAnd S'_i，j，bRespectively representing the existing feeder capacity and the increased capacity of the feeder between the node i and the node j;

representing the power angle of the current at load segment b, node i to node j; m is a predetermined value.

Optionally, in the method according to the present invention, the DG capacity constraint comprises:

wherein the content of the first and second substances,

the reactive output of the distributed generator set at the node i load section b is realized;

and

respectively allowing the minimum and maximum active power output by the distributed generator set;

and

respectively, the minimum and maximum reactive power output allowed by the distributed generator set.

Optionally, in the method according to the present invention, the capacitance constraint comprises:

in the formula, Q_iReactive capacity, Q, injected for capacitors^CmaxIs the maximum allowable mounting capacity of the capacitor.

Optionally, in the method according to the present invention, the power supply margin constraint comprises:

in the formula I_SIs a power supply margin value, S represents the number of power generation-load states;

and

respectively representing the active power generation amount and the reactive power generation amount on the kth node of the state s;

and

respectively representing the active and reactive power consumption on the kth node of the state s;

and

respectively representing the active and reactive network losses at state s, and η representing the probability of node k being in the power-load state s.

Optionally, in the method according to the invention, the reliability constraint is:

K₁≤K_1，max，K₂≤K_2，max

in the formula, N_LiRepresents the number of users connected by the node i; k₁Is a weighted value of the system average interrupt frequency index, K₂Is a weighted value of the average duration of the power failure of the system, K_1,maxAnd K_2,maxRespectively permitted K₁And K₂Maximum value of (a)_1,iAnd λ_2,iRespectively representing the annual fault rate and the annual outage time of the node i.

Optionally, in the method according to the invention, the power quality constraint is:

VSI_e≤V e＝1,2,...,Nbr

in the formula, VSI_eIs a voltage stability value, V is a fixed value; r_ijAnd X_ijResistance and reactance of branch k, respectively; p_jAnd Q_jIs the active power and reactive power of the receiving endpoint j of branch k; n is a radical of_brIs a system branch assemblyAnd (4) counting.

Optionally, in the method according to the invention, the predetermined population algorithm is a fireworks algorithm.

According to an aspect of the present invention, there is provided an apparatus for constructing an active power distribution network planning model, adapted to reside in a computing device, the apparatus comprising: the model building unit is suitable for building an active power distribution network planning model considering power grid operation reliability and demand side response, and the planning model comprises an objective function and constraint conditions; the model solving unit is suitable for obtaining basic parameters of the active power distribution network, substituting the basic parameters into the planning model, solving the planning model by adopting a predetermined population algorithm with the aim of minimum total investment in a planning period to obtain an optimal planning scheme of the active power distribution network system, wherein the optimal planning scheme comprises planning cost, planning capacity and planning position; wherein the objective function is J ═ C^DG+C^L+C^Cap+C^SW+V^DRWherein J is the total investment of planning period, C^DGFor investment and operating costs of distributed generator sets during planning periods, C^LInvestment and construction costs for upgrading and rebuilding lines, C^CapFor investment costs of alternative capacitors, C^SWFor installation and maintenance costs of the active power supply, V^DRThe net present value of demand side response charges.

According to an aspect of the invention, there is provided a computing device comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions comprising instructions for performing the method of constructing an active power distribution network planning model as described above.

According to an aspect of the present invention, there is provided a readable storage medium storing program instructions, which when read and executed by a computing device, cause the computing device to execute the method for constructing an active power distribution network planning model as described above.

According to the technical scheme, an active power distribution network planning model considering the operation reliability and the demand side response of the power grid is constructed, the demand side response cost is introduced into the objective function of the active power distribution network planning model, and the power supply margin and the reliability constraint of the power grid are strengthened. The optimal active power distribution network planning scheme can be accurately obtained based on the model, and the accuracy and the representativeness of model calculation are improved. The method adopts the firework algorithm to carry out model solution and example analysis on a 32-node system, and verifies the effectiveness and the practicability.

Drawings

To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.

FIG. 1 shows a block diagram of a computing device 100, according to one embodiment of the invention;

fig. 2 shows a flow diagram of a method 200 for constructing an active power distribution network planning model according to an embodiment of the invention;

FIG. 3 shows a flow diagram of a firework algorithm solution model according to one embodiment of the invention;

fig. 4 shows a block diagram of an apparatus 400 for constructing an active power distribution network planning model according to an embodiment of the present invention; and

FIG. 5 illustrates a schematic diagram of system load requirements in a disordered charging scenario, in accordance with one embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

Fig. 1 is a block diagram of an example computing device 100. In a basic configuration 102, computing device 100 typically includes system memory 106 and one or more processors 104. A memory bus 108 may be used for communication between the processor 104 and the system memory 106.

Depending on the desired configuration, the processor 104 may be any type of processing, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 104 may include one or more levels of cache, such as a level one cache 110 and a level two cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations the memory controller 118 may be an internal part of the processor 104.

Depending on the desired configuration, system memory 106 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some embodiments, application 122 may be arranged to operate with program data 124 on an operating system. The program data 124 comprises instructions, and in the computing device 100 according to the invention the program data 124 comprises instructions for performing the method 200 of constructing the active power distribution network planning model.

Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to the basic configuration 102 via the bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communications with one or more other computing devices 162 over a network communication link via one or more communication ports 164.

A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.

Computing device 100 may be implemented as a server, such as a file server, a database server, an application server, a WEB server, etc., or as part of a small-form factor portable (or mobile) electronic device, such as a cellular telephone, a Personal Digital Assistant (PDA), a personal media player device, a wireless WEB-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. Computing device 100 may also be implemented as a personal computer including both desktop and notebook computer configurations. In some embodiments, the computing device 100 is configured to perform a method 200 of constructing an active power distribution network planning model according to the present invention.

Fig. 2 shows a schematic diagram of a method 200 for constructing an active power distribution network planning model, which is suitable for being resident in the computing device 100 and executed according to an embodiment of the present invention.

As shown in fig. 2, the method is adapted to step S210. In step S210, an active power distribution network planning model is established, which includes an objective function and constraint conditions, and takes into account grid operational reliability and demand-side response.

Wherein the objective function is J ═ C^DG+C^L+C^Cap+C^SW+V^DRWherein J is the total investment of the planning period; c^DGInvestment and operating costs for distributed generator sets (DR) during planning; c^LInvestment cost and construction cost required for upgrading and transforming the line; c^CapInvestment cost for alternative capacitors; c^SWThe installation and maintenance costs for the active power supply; v^DRThe net present value of demand side response charges.

Further, the calculation formula of the objective function is:

in the formula, I is the total number of nodes; i is a node; d is the discount rate; t is the total years of the planning period; y is the y-th year of the planning period; b is the total number of the load sections; b is the b-th load segment; c^DG,FThe annual unit investment cost is unit investment cost of the distributed generator set, yuan/MW;

capacity, MW, of the distributed generator set at node i; c^DG,OThe cost of operation of the distributed generator set DR, yuan/MW;

the output power (specifically, active power) of the distributed generator set at the node i load section b, MW; h is_bThe annual running hours of the distributed generator set in the load section b are counted; j is the jth node. C^LFThe investment cost is fixed for the feeder line year; g_i，jIs the geographical cost factor of the feeder between node i and node j, its distance fromThe higher the cost factor, the greater the difficulty of connecting the feeder between two nodes. L is_i，jThe length of a feeder line between a node i and a node j is km;

0/1 Boolean variables that are upgraded for feeder lines, e.g., to 1, not to 0; c^LVThe annual variation cost of the feeder line, yuan/MVA; s'_i，jIncreased capacity, MVA, for the feeder from node i to node j; c^cThe unit annual investment cost of the parallel capacitors, yuan/MVAr;

the capacitance of the capacitor, MVAr, is connected in parallel at node i;

is a boolean variable indicating whether or not an active power source is installed at node i, e.g., 1 is installed and 0 is not installed; c. C_g+c_a+M_C[((1+i)^T-1)/(i(1+i)^T)]Represents the total cost of an active power supply requirement during the planning period, wherein c_g、c_a、M_CRespectively the purchase cost, installation cost and annual average maintenance cost for each active power supply. Here, the active power source may refer to a switch, and it is assumed that the lifetime of the switch is equal to the time of the planning period. It can be considered here that all terms following the fourth plus sign in the right side of the equation together constitute V^DR。

According to one embodiment of the invention, the constraints include one or more of a power flow constraint, a voltage constraint, a line capacity constraint, a DG capacity constraint, a capacitance constraint, a supply margin constraint, a reliability constraint, and a power quality constraint.

1) The formula of the power flow constraint comprises:

2) The voltage constraint ensures that the line voltage level is within its allowable fluctuation range, and the formula comprises:

in the formula, V_i，bRepresents the voltage amplitude, MV, of the load section b at node i; v^minAnd V^maxRespectively, the minimum and maximum voltage values, MV, allowed for the line.

3) Line capacity constraints mean that any flow through a distribution line must follow the capacity limits of the line, and new investments in line upgrades also need to take into account such constraints. The formula for the line capacity constraint includes:

wherein the content of the first and second substances,

and

respectively between the load section b and the feeder line i-jPower and reactive power flow, MW and MVAr; s_i，jAnd S'_i，j，bRespectively representing the existing feeder line capacity and the increased capacity of the feeder line between a node i and a node j, namely MVA;

representing the power angle of the current at load segment b, node i to node j; delta_i，bThe voltage phase angle, rad, of the load segment b at node i is shown. M is a predetermined value, which is a maximum, or a sufficiently large value. This is so that

Of (c), S'_i，j，bIs 0; when in use

The value of M is large enough to ensure that there is enough room to select a new line. Boolean variables

"switching" as a continuous constraint, yields a maximum capacity greater than or equal to that allowed by the line upgrade.

4) The DG capacity constraint means that the active power and the reactive power of a DG unit are limited by the maximum capacity of the DG unit, and the formula comprises the following steps:

wherein the content of the first and second substances,

the reactive output MVAr of the distributed generator set (DR) in the node i load section b is obtained;

and

respectively the minimum and maximum active power, MW, allowed to be output by the distributed generator set;

and

respectively, the minimum and maximum reactive power output, MVAr, allowed by the distributed generator set.

5) The capacitance constraint ensures that the installation capacity of the capacitor does not exceed the maximum allowable installation capacity, and the reactive power injected by the capacitor cannot exceed the installation capacity of the capacitor, and the formula comprises the following components:

in the formula, Q_iReactive capacity, Q, injected for capacitors^CmaxThe maximum allowable mounting capacity of the capacitor, MVAr.

6) The power supply margin constraint is based on the consideration of active power and reactive power, and because a generating element and a load which are changed probabilistically exist in the system, a multi-state generating-load model is selected to calculate the margin index of the system. And defining the power supply margin index of the microgrid g as an accumulated index, and considering the probability of each power generation-load state and the active and reactive network losses of the microgrid in each state. And the power supply margin index of the whole system is the sum of the active and reactive power supply margin indexes of all the micro-grids. It should be noted that, in general, the total output power of the DG is lower than the total power consumption of the users in the distribution grid. This means that in a power distribution system comprising several micro grids, when the amount of generated power is greater than the amount of power used in the micro grids, the other micro grids will have energy storage units to store excess power. In order to enable the electric energy balance of the system to be optimal, the power supply margin index considers two conditions of shortage and surplus of micro-grid power generation. According to one embodiment, the formula of the power supply margin constraint comprises:

and

respectively representing the active power generation amount and the reactive power generation amount, MW and MVAr on the kth node of the state s;

and

respectively representing active and reactive power consumption, MW and MVAr on the kth node of the state s;

and

the active and reactive network losses in state s are represented, respectively, and η represents the probability of node k in the generation-load state s the active and reactive network losses of the microgrid may preferably assume 5% of the active and reactive loads in the current state, although other values are possible and are not limited by the present invention.

7) The reliability constraint mainly considers the reliability of the power distribution system, and the reliability constraint is expressed by combining two typical indexes of system average interruption frequency index (SFI) and system average continuous power failure time (SDI). These two indicators pass through the annual fault rate (λ) in the distribution system₁) Annual power off time (lambda)₂) Expressed, the reliability constraint formula of each node is as follows:

K₁≤K_1，max，K₂≤K_2，max

8) The power quality constraint mainly considers that the access of DGs can have great influence on the node voltage of the power distribution network, and the unreasonable configuration inevitably and seriously influences the stability of the voltage level, so that the capacity of the power distribution network for bearing load increase is limited. Therefore, the Stability of the voltage is very important for the safe and stable operation of the system, the power quality constraint mainly considers the voltage Stability VSI (Voltage Stability index), and the value of all the VSIs is ensured to be smaller than a fixed value V <1, so that the Stability of the system voltage is ensured. The formula for the power quality constraint includes:

VSI_e≤V e＝1,2,...,Nbr

in the formula, VSI_eIs a voltage stability value, V is a fixed value; r_ijAnd X_ijResistance and reactance of branch k, respectively; p_jAnd Q_jIs the active power and reactive power of the receiving endpoint j of branch k; n is a radical of_brIs the total number of system branches. And VSI_eThe branch corresponding to the maximum value in the system is called the weakest branch of the system, and the value corresponding to the weakest branch is certainly less than 1 when the system is stable. And when the system has voltage collapse, f is always from the weakest branch_VSIThe smaller the voltage stability, the better the stability, the larger the stability; when f is_VSINear 1, the system voltage collapses. Therefore, it is necessary to guarantee the values of all the VSIsAre all less than a fixed value V<1, thereby ensuring the stability of the system voltage.

Subsequently, in step S220, the basic parameters of the active power distribution network are obtained and substituted into the planning model, and the planning model is solved by using a predetermined population algorithm with the goal of minimizing the total investment in the planning period, and an optimal planning scheme of the active power distribution network system is output, where the optimal planning scheme includes planning cost, planning location and planning capacity.

According to an embodiment, substituting the basic parameters of the active distribution network into the number of acquisitions required in the model solution may include, for example, parameters to the right of the equation or inequality in the above equations, such as obtaining the total number B of the load segments of the power grid, the operating cost of the total number I, DG of the nodes, the node capacity, the length of the feeder between the nodes, the cost values, the active and reactive power, the grid loss value, the resistive reactance value, the voltage amplitude, the voltage phase angle, the power angle of the current, the number of users, and so on.

According to another embodiment, the optimal planning solution may also include reserve rates and equipment types, such as distributed generator sets, feeders, alternative capacitors, active power sources, and the like. Wherein different reserve rates may result in different load loss target values and incremental capacity values, and may determine whether the plan meets the target margin levels. Generally, when the output planning scheme just meets the target margin level, the output planning scheme can be regarded as the optimal planning scheme. Specifically, the output optimal planning result may include one or more of the number and the position of substations which need to be upgraded or newly built to be selected, the number and the position of distributed generator sets, the number and the position of capacitors, the length and the position of feeders which need to be upgraded or newly built, the number and the position of active power supplies which are turned on, planning cost and planning capacity.

It should be understood that there are many solving methods for the single-target planning model, and the present invention is not limited to the specific implementation manner, and all methods capable of solving the target planning model are within the protection scope of the present invention. According to one embodiment, the predetermined population algorithm may be a particle swarm algorithm or a firework algorithm. The firework algorithm is a group intelligent algorithm, and for an optimization problem, particularly an optimization problem of which the independent variable is a continuous space, a global optimal solution can be effectively and rapidly found according to the firework algorithm in the whole solution space. The fireworks are regarded as a feasible solution in the solution space of the optimization problem, and the process of generating a certain number of sparks by fireworks explosion is the process of searching the neighborhood.

The detailed flow chart of the fireworks algorithm is shown in fig. 3, which can perform model solving according to steps S221-S226:

first, in step S221, N positions x are randomly generated in a feasible solution space_tI.e., n fireworks, each representing a feasible solution in the solution space.

Subsequently, in step S222, the fitness value of each firework is calculated according to the optimization objective function, and the explosion radius and the number of explosion sparks of each firework are calculated, and the quality of the fireworks is evaluated accordingly to generate different numbers of sparks at different explosion radii. In the firework algorithm, the explosion radius and the number of sparks generated by the explosion of each firework are calculated according to the fitness value of each firework relative to other fireworks in the firework population. For fireworks x_iRadius of detonation A_iAnd number of exploding sparks S_iThe calculation formulas of (A) and (B) are respectively as follows:

in the formula, y_min＝min(f(x_t) T is 1, 2, …, N, which is the minimum value of fitness in the current firework population; y is_max＝max(f(x_t) T ═ 1, 2, …, N, is the maximum fitness value in the current population; a is a first constant used for adjusting the size of the explosion radius; m is a second constant for adjusting the magnitude of the number of explosion sparks generated; ε is a machine minimum used to avoid zero operations.

Subsequently, in stepIn S223, an explosion spark and a gaussian variation spark are generated. The fireworks with good fitness value generate more sparks in a smaller adjacent area, and conversely, the fireworks with poor fitness value generate less sparks in a larger adjacent area. The introduction of gaussian sparks enhances the diversity of the population with respect to the explosion sparks. The process of gaussian variant spark generation is as follows: firstly, randomly selecting a firework x in a firework population_tThen, a certain number of dimensions are randomly selected for the fireworks to carry out Gaussian variation operation. For fireworks x_tX 'is obtained by performing Gaussian variation on a selected dimension l'_tl＝x_tlX.w. In the formula, w to N (1, 1), N (1, 1) represents a Gaussian distribution having a mean value of 1 and a variance of 1.

Subsequently, in step S224, an optimal solution of the population is calculated, and it is determined whether or not a termination condition is satisfied. If yes, the search is stopped in step S225, the optimization result is returned, and the optimal planning scheme is output. Otherwise, in step S226, a certain number of individuals are selected from the fireworks, the explosion sparks and the gaussian variation sparks as the fireworks for the next generation of iterative computation, and the process proceeds to step S222 to trigger the next generation of iterative computation.

The specific selection strategy is as follows: assume that the candidate set is K and the firework population size is N. The individual with the least fitness value in the candidate set will be deterministically selected to the next generation as a firework, and the selection of the remaining N-1 fireworks is selected in the candidate set using the roulette method. For candidate x_tThe calculation formula of the selected probability is as follows:

in the formula, R (x)_t) Divide x for current individual into candidate set K_tThe sum of the distances between all individuals. In the set of candidates, the candidate set is,if the individual density is high, i.e., there are many other candidate individuals around the individual, the probability that the individual is selected decreases. The firework algorithm has a local search capability and global search capability self-adjusting mechanism, wherein the explosion radius and the number of explosion sparks of each firework are different, and the explosion radius of the fireworks with poor fitness value is larger, so that the fireworks have larger exploration capability (prospectiveness). Fireworks with good fitness values have a smaller detonation radius, enabling them to have greater excavation capacity (mineability) around the location. And the introduction of Gaussian variation sparks can further increase the diversity of the population and improve the solving precision of the optimal solution of the model.

Fig. 4 shows a block diagram of an apparatus 400 for constructing an active power distribution network planning model according to an embodiment of the present invention, where the apparatus 400 may reside in the computing device 100. As shown in fig. 4, the apparatus 400 includes: a model building unit 410 and a model solving unit 420.

The model construction unit 410 builds an active power distribution network planning model considering the grid operational reliability and demand side response, the planning model including an objective function and constraint conditions. Wherein the objective function is J ═ C^DG+C^L+C^Cap+C^SW+V^DRWherein J is the total investment of planning period, C^DGFor investment and operating costs of distributed generator sets during planning periods, C^LInvestment and construction costs for upgrading and rebuilding lines, C^CapFor investment costs of alternative capacitors, C^SWFor installation and maintenance costs of the active power supply, V^DRThe net present value of demand side response charges. The model construction unit 410 may perform processing corresponding to the processing described above in step S210, and the detailed description thereof will not be repeated.

The model solving unit 420 obtains basic parameters of the active power distribution network, substitutes the basic parameters into the planning model, and solves the planning model by adopting a predetermined population algorithm with the aim of minimizing the total investment in a planning period to obtain an optimal planning scheme of the active power distribution network system, wherein the optimal planning scheme comprises the type, the position and the capacity of the response of a demand side. The model solving unit 420 may perform processing corresponding to the processing described above in step S220, and the detailed description thereof will not be repeated.

The rationality of the scale model constructed by the present invention will be verified using specific cases for an IEEE32 node system, the first system comprising 32 nodes in a radial configuration, two transformers at node 0, one with a transformation capacity of 15MVA and the other 16MVA, with a total peak load requirement of 37 MW. The basic parameters and basic assumptions of the example system are shown in table 1:

TABLE 1 exemplary System basic parameters

The load Loss Objective (LOLE) for the two example system plans was 2.8 hours/year, the outage cost was 6000 RMB/MWh, and the electricity price was in the form of 7 load segments, expressed using a Load Scaling Factor (LSF), as shown in Table 2. The investment costs for the resources required for the two example system planning are shown in table 3. In addition, for the convenience of analysis, it is assumed that the power factors of the connected distributed generator sets are all 0.95, and the annual full load utilization hours is 4000 hours.

TABLE 2 electric power market price

Load class (b)	LSF	Rho (Yuan/MWh)
			1	0.4	63
2	0.5	90
			3	0.6	132
4	0.7	198
			5	0.8	300
6	0.9	427
			7	1	624

TABLE 3 investment cost for System planning

Factors of the fact	Fixed cost	Variable cost (Yuan/MVA)
			Feed line	96 ten thousand yuan/km	6300
Transformer substation	125 ten thousand	32 ten thousand
			Distributed natural gas turbine		522.5 ten thousand
Capacitor with a capacitor element		32 ten thousand

(1) Planning a reference system: and determining the cost required by planning and the prepared gold rate gamma by using the proposed algorithm. Table 4 shows the output results of the iterative process, and finally the optimal gamma value is output. After three iterations, it was found that when γ is 0.21, the resulting LOLE was 2.29 hours/year, which results in meeting the target margin level. At the moment, 6 sections of feeders with the length of 6 kilometers, the voltage transformation capacity of 24.7MVA and 5 distributed generator sets are required to be planned and reconstructed, the planning cost of a required power distribution system is 4123.1 ten thousand yuan, and the reserve rate is 0.21.

TABLE 4 reference System iteration results

γ	Increased capacity (MVA)	LOLE (hour/year)	Target LOLE (hour/year)	Whether or not to satisfy the margin level
					0.15	21.4	3.47	2.80	Whether or not
0.2	24.1	3.17	2.80	Whether or not
					0.21	24.7	2.29	2.80	Is that

(2) Test system (electric vehicle penetration 50%) planning: the influence of electric vehicle charging on the planning of the distributed system is considered in the part, and the influence of electric vehicle charging on the planning is mainly considered in two situations of intelligent charging and uncontrolled charging. Assuming that all loads are in the residential area, by the planned final year, there is one electric vehicle at every two homes. For example, in year T, the electric vehicle has a permeability of 50%. The number of electric vehicles connected to each node was calculated using the average hourly load per household, which was 2.0833kW, and the average daily charging time was 9 hours.

A. Under an intelligent charging scenario (including demand side response), assuming that a power distributor controls the charging schedule of the electric vehicle, the demand side response cost is 6 yuan/kWh, and the total budget cost is 600 ten thousand yuan. The algorithm of the invention is used for determining the cost and the reserve gold rate required by planning, and the target margin constraint is satisfied when gamma is 0.31, but gamma is not 0.21 when no electric vehicle exists; this is because electric vehicle charging increases the load demand of the system. The optimal planning result obtained at this level comprises a transformer substation (31.5MVA), 5 distributed generator sets and 7 feeder line upgrading transformation (7 kilometers), and the planning cost of the power distribution system is 4551.9 ten thousand yuan.

B. Under the disordered charging situation, the charging behavior of the user on the electric automobile is assumed to be random, and the ordinary life behavior rule is met. The load demand of the system is shown in fig. 5. The algorithm of the invention is used for determining the cost and the reserve gold rate required by planning, and the target margin constraint is satisfied when gamma is 0.31, but not gamma is 0.54 when no electric vehicle exists. The optimal planning result obtained at the level comprises a substation (40MVA), 6 distributed generator sets and 9 feeder line upgrading transformation (9 kilometers), and the planning cost of the power distribution system is 5413.6 ten thousand yuan.

In summary, γ is 0.21 in the basic case and is increased to 0.54 in the uncontrolled case of the electric vehicle. This indicates that increased loading due to uncontrolled charging of the electric vehicle will require higher reserve money requirements, while planning costs are much higher than in the smart charging scenario. When the intelligent charging condition of the electric automobile is considered, the value of gamma is 0.31, the influence on system planning is reduced, and the cost is lower.

According to the technical scheme, the comprehensive planning scheme of the power distribution system with the planning period of years is provided for the active power distribution network, and the operation reliability and the power quality of the power grid are considered. The planning aims to determine an optimal upgrading and transformation scheme, the position and the capacity of a distributed generator set, a transformer substation, a capacitor and a feeder line of a power distribution system and consider the influence of the electric automobile under the conditions of no charging control and intelligent charging (including demand side response). In order to determine the optimal planning scheme of the power distribution system, the invention provides two standards, cost-benefit analysis and margin analysis, and comprehensively processes the result. The planning scheme framework can achieve quantitative influence, accurately output upgrading and transforming requirements of the power distribution network, is high in calculation precision, can be applied to any radial distribution system, and has wide application prospects.

A8, the method of any one of A3-A7, wherein the capacitive confinement comprises:

A9, the method of any one of A3-A8, wherein the power supply margin constraints comprise:

and

and

and

A10, the method of any one of A3-A9, wherein the reliability constraint is:

K₁≤K_1，max，K₂≤K_2，max

A11, the method of A4, wherein the power quality constraint is:

VSI_e≤V e＝1,2,...,Nbr

in the formula, VSI_eIs a voltage stability value, V is a fixed value; r_ijAnd X_ijResistance and reactance of branch k, respectively; p_jAnd Q_jIs the active power and reactive power of the receiving endpoint j of branch k; n is a radical of_brIs the total number of system branches.

A12, the method of a1, wherein the predetermined population algorithm is a fireworks algorithm.

The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.

In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to execute the method for constructing the active power distribution network planning model according to the instructions in the program codes stored in the memory.

By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer-readable media includes both computer storage media and communication media. Computer storage media store information such as computer readable instructions, data structures, program modules or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of computer readable media.

In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with examples of this invention. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.

In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.

As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.

Claims

1. A method of constructing an active power distribution network planning model, adapted to be executed in a computing device, the method comprising the steps of:

establishing an active power distribution network planning model considering power grid operation reliability and demand side response, wherein the planning model comprises a target function and constraint conditions;

obtaining basic parameters of the active power distribution network, substituting the basic parameters into the planning model, solving the planning model by adopting a predetermined population algorithm with the aim of minimizing the total investment in a planning period, and outputting an optimal planning scheme of the active power distribution network system, wherein the optimal planning scheme comprises planning cost, planning capacity and planning position;

wherein the objective function is J ═ C^DG+C^L+C^Cap+C^SW+V^DRWherein J is the total investment of the planning period, C^DGFor investment and operating costs of distributed generator sets during planning periods, C^LInvestment and construction costs for upgrading and rebuilding lines, C^CapFor investment costs of alternative capacitors, C^SWFor installation and maintenance costs of the active power supply, V^DRThe net present value of demand side response charges.

2. The method of claim 1, wherein the objective function is:

the output power of the distributed generator set at the node i load section b is obtained; h is_bThe annual running hours of the distributed generator set in the load section b are counted;j is the jth node; c^LFThe investment cost is fixed for the year of the feeder line; g_i，jA geographical cost factor for the feeder between node i and node j; l is_i，jThe length of the feeder line between the node i and the node j;

boolean variables for feeder upgrade; c^LVAnnual change cost for the feeder; s'_i，jIncreased capacity for feeder from node i to node j; c^cThe unit annual investment cost of the parallel capacitors;

the capacitance of the capacitor is connected in parallel at the node i;

3. The method of claim 1 or 2, wherein the constraints include one or more of a power flow constraint, a voltage constraint, a line capacity constraint, a DG capacity constraint, a capacitance constraint, a supply margin constraint, a reliability constraint, and a power quality constraint.

4. The method of claim 3, wherein the power flow constraint comprises:

in the formula, P_i、Q_iAnd U_iRespectively representing the active power of the node i under normal conditionsReactive power injection and voltage amplitude; u shape_jRepresents the voltage amplitude of node j under normal conditions; g_ijAnd B_ijRespectively representing the real part and the imaginary part of a system admittance matrix under normal conditions; theta_ijRepresenting the voltage phase angle difference between node i and node j.

5. The method of claim 3, wherein the voltage constraints comprise:

6. The method of any of claims 3-5, wherein the line capacity constraint comprises:

wherein the content of the first and second substances,

and

7. The method of any of claims 3-6, wherein the DG capacity constraint comprises:

wherein the content of the first and second substances,

and

and

8. An apparatus for constructing an active power distribution network planning model, adapted to reside in a computing device, the apparatus comprising:

the system comprises a model construction unit, a data processing unit and a data processing unit, wherein the model construction unit is suitable for establishing an active power distribution network planning model considering power grid operation reliability and demand side response, and the planning model comprises an objective function and constraint conditions;

the model solving unit is suitable for obtaining basic parameters of the active power distribution network, substituting the basic parameters into the planning model, and solving the planning model by adopting a predetermined population algorithm with the aim of minimum total investment in a planning period to obtain an optimal planning scheme of the active power distribution network system, wherein the optimal planning scheme comprises planning cost, planning capacity and planning position;

wherein the objective function is J ═ C^DG+C^L+C^Cap+C^SW+V^DRWherein J is the total investment of planning period, C^DGFor investment and operating costs of distributed generator sets during planning periods, C^LInvestment and construction costs for upgrading and rebuilding lines, C^CapFor investment costs of alternative capacitors, C^SWFor installation and maintenance costs of the active power supply, V^DRThe net present value of demand side response charges.

9. A computing device, comprising:

at least one processor; and

a memory storing program instructions configured for execution by the at least one processor, the program instructions comprising instructions for performing the method of any of claims 1-7.

10. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method of any of claims 1-7.