A Two-Layer Distribution Network Planning Method Considering System Flexibility Requirements

Abstract

The present invention discloses a two - layer distribution network planning method considering system flexibility requirements, which is applied to the technical field of distribution network planning. The specific steps include: obtaining data and making predictions, and generating a basic scenario according to the prediction results; determining the objective function and constraint conditions of the planning layer, and constructing a distribution network planning layer model; calculating the flexibility deficit of each time node of the distribution system according to the time - series data of the prediction results; using the flexibility deficit of each time node of the distribution system to determine the objective function and constraint conditions of the simulation layer, and constructing a distribution network simulation layer model; solving the distribution network planning layer model and the distribution network simulation layer model by using a simulated annealing particle swarm algorithm to obtain a distribution network planning scheme. The present invention establishes a two - layer distribution network planning model considering flexibility requirements, which can minimize or eliminate the consumption - limiting factors as much as possible, further improve the response potential of demand - side response, improve the utilization efficiency of clean energy, and reduce the carbon emission cost and line loss cost.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and more particularly to a distribution network double-layer planning method taking into account system flexibility requirements. Background Art

[0002] The planning of new power systems is an important technical prerequisite for leading the green and low-carbon development and transformation of power systems. As the main feature of new power systems, large-scale renewable energy grid connection makes the operation of power systems have significant uncertainty: large fluctuations in renewable energy output, serious local reverse transmission, and insufficient energy consumption efficiency bring great challenges to grid planning and design, and increase the difficulty of system planning.

[0003] At the same time, the load exhibits the characteristics of initiative and complexity. The planning, operation, and control of the power system have undergone tremendous changes. How to give full play to the active support of renewable energy and flexible loads and improve reliability is an urgent problem to be solved. Therefore, under the background of the dual carbon goals, taking into account the needs of distribution network flexibility and tapping the flexibility resources of the power grid are of great significance for the flexible and stable operation of the distribution network under the new situation.

[0004] With the vigorous promotion of power system reform and the large-scale promotion of new intelligent power consumption technologies, the proportion of flexible loads represented by electric vehicles and temperature control loads connected to the distribution network continues to increase, and they participate in the operation of the distribution network in the form of demand response (DR). DR plays an important role in reducing node peak loads, reducing the scale of grid line construction, and coping with the volatility of renewable energy output. Therefore, taking DR into account in distribution network planning research can greatly improve the effectiveness and practicality of planning results.

[0005] As a concept at the operational level, DR cannot directly participate in planning decisions due to problems such as inconsistency with the planning time scale and high difficulty in solving the problem. To this end, some scholars have proposed a two-level coordinated planning optimization method. The two-level planning model originated from the Stackelberg game theory. Usually, the upper level formulates the distribution network planning scheme, and the lower level determines the optimal operation mode under each scenario. Most current studies use the two-level planning method to formulate planning schemes, but at the operational level, they usually only consider the network topology and the operating costs of distributed power sources, and rarely consider the impact of DR, especially the transferable load, on operation. In addition, when modeling the operational layer, existing studies lack consideration of the impact of the system's flexibility requirements on distribution network planning.

[0006] Therefore, how to provide a two-tier planning method for distribution networks that can improve system flexibility and reduce the investment costs of distribution network operators is an urgent problem that technicians in this field need to solve. Summary of the invention

[0007] In view of this, the present invention provides a two - layer distribution network planning method considering system flexibility requirements. Considering the flexibility of system operation, a two - layer distribution network planning model considering flexibility requirements is established, which can minimize or eliminate the accommodation limiting factors as much as possible, further improve the response potential of demand - side response, improve the utilization efficiency of clean energy, and reduce carbon emission costs and network loss costs.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] A two - layer distribution network planning method considering system flexibility requirements includes the following steps:

[0010] Obtain illumination data, wind speed data, and load data, and perform predictions. Generate a basic scenario according to the prediction results.

[0011] Construct a distribution network planning layer model, and determine the objective function and constraint conditions of the planning layer.

[0012] According to the time - series data of the prediction results, calculate the flexibility deficit of each time node of the distribution system.

[0013] Construct a distribution network simulation layer model, determine the objective function and constraint conditions of the simulation layer, and use the flexibility deficit of each time node of the distribution system as input.

[0014] Use the simulated annealing particle swarm optimization algorithm to solve the distribution network planning layer model and the distribution network simulation layer model to obtain a distribution network planning scheme.

[0015] In the above - mentioned two - layer distribution network planning method considering system flexibility requirements, the specific steps of step S1 are as follows:

[0016] S1 - 1. Input the illumination data, wind speed data, and load data of a certain area for 8760 hours in a year, and complete the prediction of photovoltaic power output, wind power output, and load in this area.

[0017] S1 - 2. According to the prediction data, select four typical daily scenarios in four different seasons of spring, summer, autumn, and winter, and confirm the number of days in each scenario.

[0018] In the above - mentioned two - layer distribution network planning method considering system flexibility requirements, the specific steps of step S2 are as follows:

[0019] S2 - 1. Determine the objective function of the planning layer. With the minimum present value of the total cost of the distribution network operator as the objective function, determine the location and quantity of distributed power sources, and the newly built lines. The objective function is as follows

[0020]

[0021] In the formula: C INVis the investment cost, which consists of the investment cost of distributed power sources and the investment cost of newly built lines ; C MA is the operation and maintenance cost, which consists of the operation and maintenance cost of distributed power sources and the operation and maintenance cost of lines; C LOSS 、C FAIL are the power loss cost and the fault cost respectively; is the carbon emission cost. When using traditional fossil energy for power generation, the country will impose penalties on carbon emissions; C DR 、C PAY are the management cost of implementing demand-side response and the power purchase cost from the main grid respectively.

[0022] The calculation formulas for each cost are as follows:

[0023] 1) Investment cost C INV

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] In the formula: G = {PVG, WG} is the set of distributed power source types. The article considers photovoltaic and wind power; Ψ G represents the set of alternative nodes where the gth type of distributed power source is connected. NAB is the set of newly built lines; are the unit capacity investment cost and the rated power of the gth type of distributed power source respectively; are the unit length investment cost of the newly built line and the length of line ij respectively. is the binary variable for the investment of the gth type of distributed power source, indicating that the distributed power source is connected to node i, otherwise it indicates that node i is not connected; Distributed power source characterizes the binary variable of the newly built line, x l = 1 indicates that line ij is selected, otherwise it indicates that it is not selected; is the number of units of the gth type of distributed power source connected to node i. RR l RR g are the investment return rates of the line and the distributed power source respectively, also known as the annualized coefficient. I is the discount rate; θ l 、θ g are the life cycles of the line and the distributed power source respectively.

[0030] 2) Operation and maintenance cost C MA

[0031]

[0032]

[0033]

[0034] In the formula: are the operation costs of distributed power sources and lines respectively, Ω s and Ω H are the sets of scenarios and one-day time periods respectively, and L is the set of lines. T s is the number of days in the s-th scenario; Δ t is the time of each time period. are the unit capacity operation and maintenance cost and active power output of the g-th type of distributed power source respectively; l ij are the operation and maintenance cost per unit length of the line and the line length respectively.

[0035] 3) Power loss cost C LOSS

[0036]

[0037] In the formula: is the electricity selling price in the t-th time period of the s-th scenario, P loss is the active power loss of the line in this scenario time period.

[0038] 4) Fault cost C FAIL

[0039]

[0040]

[0041] In the formula: Ω N is the set of line load nodes; is the expected power supply shortage in the t-th time period of the s-th scenario, is the failure rate of the line between ij, is the original load of the i-th node in the t-th time period of the s-th scenario.

[0042] 5) Carbon emission cost

[0043]

[0044] In the formula: is the penalty cost per unit carbon emission; is the load of the i-th node in the t-th time period of the s-th scenario. When generating electricity from traditional fossil energy, 0.344 t of coal is required to generate 1 MW·h, and burning 1 t of coal produces 2,620 kg of carbon dioxide. The fine for carbon emissions imposed by the state is 9.75 yuan / t.

[0045] 6) Management cost of implementing demand-side response CDR

[0046]

[0047] In the formula: C dr is the management cost of implementing DR per unit capacity; are respectively the interruption power of the interruptible load and the power transferred in and out of the shiftable load in the t-th time period of the s-th scenario.

[0048] 7) Cost of purchasing electricity from the main grid C PAY .

[0049]

[0050] In the formula: is the electricity price for purchasing electricity from the superior power grid in the t-th time period of the s-th scenario.

[0051] S2-2. Determine the constraint conditions at the planning layer. It is mainly composed of the number of distributed power sources connected, the penetration rate, the output limit constraint, and the power flow constraint.

[0052]

[0053]

[0054]

[0055]

[0056]

[0057] In the formula: is the maximum and minimum values of the number of the g-th type of distributed power source connected to the i-th node; μ is the penetration rate after the distributed power source is connected to the grid, P all is the total maximum load of the node. P gmax P gmin are the maximum and minimum values of the output of the g-th type of distributed power source. U i，min U i，max are respectively the lower and upper limit values of the voltage amplitude of node i, is the voltage amplitude of the i-th node in the t-th time period of the s-th scenario; is the transmission power of branch ij in the t-th time period of the s-th scenario; P ijmaxis the maximum value of the transmission power of branch ij in the s-th scenario time period t.

[0058] In the above comprehensive evaluation method for the distribution network planning with a high proportion of renewable energy grid connection, the specific steps of step S3 include:

[0059] S3-1. Calculate the net load of the system at time t which is the difference between the load and the renewable energy output at this moment;

[0060]

[0061] S3-2. Calculate the flexibility demand of the system at time t The flexibility demand at time t is the difference between the net loads at adjacent times;

[0062]

[0063] S3-3. Calculate the upward and downward flexibility supplies at time t;

[0064]

[0065]

[0066] In the formula: are respectively the upper limits of the transferable load and the interruptible load power at time t, are respectively the transferable load and the interruptible load power at time t. is the lower limit of the transferable load power at time t. It should be noted that since the interruptible load can only cause a reduction in the load, it does not have the ability to provide downward flexibility supply. Therefore, as shown in formula (24), only the transferable load can provide downward flexibility.

[0067] S3-4. Calculate the upward and downward flexibility supplies of the system at time t;

[0068]

[0069] The upward and downward flexibility supplies of the system can be expressed by formula (25). Ω TL Ω IL are respectively the sets of interruptible loads and transferable loads.

[0070] S3-5. Calculate the flexibility deficit of the system at time t, which is the difference between the system flexibility supply and demand;

[0071]

[0072] In the above two-layer distribution network planning method considering the system flexibility demand, the specific steps of step S4 include:

[0073] S4-1. Determine the objective function of the simulation operation layer. Taking the minimization of the present value of the flexibility cost of the distribution network operator as the objective function, determine the demand-side response electricity quantity. The objective function is as follows

[0074] min(C FS +C PEN -C BEN ) (27)

[0075] In the formula: C FS is the response cost of flexibility resources. When flexibility resources participate in flexibility regulation, the response cost needs to be considered; C PEN is the penalty cost for insufficient flexibility. When the system flexibility is insufficient, the distribution network operator will bear penalties due to insufficient upward and downward flexibility; C BEN is the compensation cost for the invocation of flexibility resources. When flexibility resources are invoked, partial flexibility benefits can be obtained from the government.

[0076] The calculation formulas for each cost are as follows:

[0077] 1) The response cost of flexibility resources C FS

[0078]

[0079] In the formula: Ω F is the set of flexibility resources Ω F ={IL, TL}, including interruptible load and shiftable load; is the response cost per unit of flexibility resource; is the response capacity per unit of flexibility resource.

[0080] 2) The penalty cost for insufficient flexibility C PEN

[0081]

[0082] In the formula: are the penalties for insufficient upward and downward flexibility respectively; are the deficits of flexibility demand respectively.

[0083] 3) The compensation cost for the invocation of flexibility resources C BEN

[0084]

[0085] In the formula: are the compensation costs of flexibility resources respectively.

[0086] S4-2. Determine the constraint conditions of the simulation operation layer. It is mainly composed of the operation constraints of demand-side response.

[0087]

[0088] The constraints at the operation layer are the operation constraints of demand response. Interruptible loads need to meet the upper and lower power quantity constraints for interruption. Transferable loads, in addition to meeting the upper and lower constraints, also need to meet the constraint that the transferred power quantity within a day is 0.

[0089] In the above-mentioned distribution network two-layer planning method considering the system flexibility requirements, the specific steps of step S5 include:

[0090] S5-1. Take the planning scheme as the basic particle of the particle swarm to obtain the initial planning scheme;

[0091] S5-2. Transmit each planning scheme to the operation layer respectively. On the basis of meeting the lower-layer objective function and constraint conditions, calculate the relevant variables in each scenario, transmit them to the planning layer, and calculate their optimal fitness values respectively;

[0092] S5-3. Evaluate the fitness value of each particle and obtain the global optimum;

[0093] S5-4. Update the position and velocity of the particles, repeat steps S5-1 - S5-5, and update the global optimal solution of the population

[0094] S5-5. After the iteration ends, obtain the final planning scheme.

[0095] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a distribution network two-layer planning method considering system flexibility requirements. Considering the flexibility of system operation, a distribution network two-layer planning model considering flexibility requirements is established, which can minimize or eliminate the consumption limiting factors as much as possible, further improve the response potential of demand response, improve the utilization efficiency of clean energy, and reduce the carbon emission cost and network loss cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0097] Figure 1 is the flow chart of the distribution network two-layer planning method considering operation flexibility of the present invention;

[0098] Figure 2 is the solution flow chart of the simulated annealing particle swarm two-layer optimization method of the present invention;

[0099] Figure 3It is the standard example topology diagram used in the present invention. Detailed implementation manners

[0100] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0101] The embodiment of the present invention discloses a two-layer distribution network planning method considering system flexibility requirements. Considering the flexibility of system operation, a two-layer distribution network planning model considering flexibility requirements is established, which can minimize or eliminate accommodation limiting factors as much as possible, further improve the response potential of demand-side response, improve the utilization efficiency of clean energy, and reduce carbon emission costs and network loss costs.

[0102] The specific steps of a two-layer distribution network planning method considering system flexibility requirements of the present invention are as follows:

[0103] S1. Input light intensity, wind speed, and load data, and use the scenario analysis method to generate basic scenarios, which further include the following steps:

[0104] S1-1. Input the light data, wind speed data, and load data of a certain area for 8760 hours in a year, and complete the prediction of the output data of photovoltaic and wind power and the load in this area;

[0105] S1-2. According to the prediction data, select four typical daily scenarios in four different seasons of spring, summer, autumn, and winter, and confirm the number of days in each scenario.

[0106] S2. Establish a distribution network planning layer model considering operation flexibility, which further includes the following steps:

[0107] S2-1. Determine the objective function of the planning layer. Taking the minimum present value of the total cost of the distribution network operator as the objective function, determine the location and quantity of distributed power sources, and the newly built lines. The objective function is as follows

[0108]

[0109] In the formula: C INV is the investment cost, and the investment cost is composed of the investment cost of distributed power sources and the investment cost of newly built lines ; C MA is the operation and maintenance cost, and the operation and maintenance cost is composed of the operation and maintenance cost of distributed power sources and the operation and maintenance cost of lines; C LOSS , C FAIL are the network loss cost and the fault cost respectively; Let \(C\) be the carbon emission cost. When using traditional fossil energy for power generation, the country will impose penalties on carbon emissions; DR and \(C\) PAY are respectively the management cost of implementing demand - side response and the cost of purchasing electricity from the main grid.

[0110] The calculation formulas for each cost are as follows:

[0111] 1) Investment cost \(C\) INV

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] In the formula: \(G=\{PVG, WG\}\) is the set of distributed power generation types. This article considers photovoltaic and wind power; \(\varPsi\) G represents the set of alternative nodes where the \(g\) - th type of distributed power generation is connected. \(NAB\) is the set of newly built lines; are respectively the unit - capacity investment cost and the rated power of the \(g\) - th type of distributed power generation; are respectively the unit - length investment cost of the newly built line and the length of line \(ij\). is a binary variable for the investment in the \(g\) - th type of distributed power generation, indicating that a distributed power generation is connected to node \(i\), otherwise it indicates that node \(i\) is not connected; The distributed power generation is a binary variable characterizing the newly built line. \(x\) l \( = 1\) indicates that line \(ij\) is selected, otherwise it indicates that it is not selected; is the number of units of the \(g\) - th type of distributed power generation connected to node \(i\). \(RR\) l \(RR\) g are respectively the investment return rates of the line and the distributed power generation, also known as the annualized coefficient. \(I\) is the discount rate; \(\theta\) l and \(\theta\) g are respectively the life cycles of the line and the distributed power generation.

[0118] 2) Operation and maintenance cost \(C\) MA

[0119]

[0120]

[0121]

[0122] In the formula: are the operating costs of distributed power sources and lines, respectively, Ω s and Ω H are the sets of scenarios and one-day time periods respectively, and L is the set of lines. T s is the number of days in the s-th scenario; Δ t is the time of each time period. are the unit capacity operation and maintenance cost and active power output of the g-th type of distributed power source respectively; l ij are the operation and maintenance cost per unit length of the line and the line length respectively.

[0123] 3) Network loss cost C LOSS

[0124]

[0125] In the formula: is the electricity selling price at the t-th time period in the s-th scenario, P loss is the active power loss of the line in this scenario time period.

[0126] 4) Fault cost C FAIL

[0127]

[0128]

[0129] In the formula: Ω N is the set of line load nodes; is the expected power supply shortage at the t-th time period in the s-th scenario, is the failure rate of the line between ij, is the original load of the i-th node at the t-th time period in the s-th scenario.

[0130] 5) Carbon emission cost

[0131]

[0132] In the formula: is the penalty cost per unit carbon emission; is the load of the i-th node at the t-th time period in the s-th scenario. When traditional fossil energy is used for power generation, 0.344 t of coal is required to generate 1 MW·h, and burning 1 t of coal produces 2,620 kg of carbon dioxide. The national penalty cost for carbon emissions is 9.75 yuan / t.

[0133] 6) Management cost C for implementing demand-side response DR

[0134]

[0135] Where: C dr is the management cost of implementing the unit capacity DR; are respectively the interruption power of the interruptible load and the power transferred in and out of the shiftable load in the t-th time period of the s-th scenario.

[0136] 7) The power purchase cost C from the main grid PAY .

[0137]

[0138] Where: is the electricity price for purchasing electricity from the superior power grid in the t-th time period of the s-th scenario.

[0139] S2-2. Determine the constraint conditions at the planning layer. It is mainly composed of the number of distributed power sources connected, the penetration rate, the output limit constraint, and the power flow constraint.

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] Where: is the maximum and minimum values of the number of the g-th type of distributed power sources connected to the i-th node; μ is the penetration rate after the distributed power source is connected to the grid, P all is the total maximum load of the node. P gmax P gmin are the maximum and minimum values of the output of the g-th type of distributed power source. U i，min U i，max are respectively the lower and upper limit values of the voltage amplitude of node i, is the voltage amplitude of the i-th node in the t-th time period of the s-th scenario; is the transmission power of the branch ij in the t-th time period of the s-th scenario; P ijmax is the maximum value of the transmission power of the branch ij in the t-th time period of the s-th scenario.

[0146] S3. According to the output and load output time series data, calculate the flexibility deficit of each time node of the distribution system, which includes the following steps:

[0147] S3-1. Calculate the net load of the system at time t is the difference between the current load and the output of renewable energy;

[0148]

[0149] S3-2. Calculate the flexibility demand of the system at time t The flexibility demand at time t is the difference in the net load between adjacent times;

[0150]

[0151] S3-3. Calculate the upward and downward flexibility supplies at time t;

[0152]

[0153]

[0154] In the formula: are the upper limits of the transferable load and the interruptible load power at time t, respectively, are the transferable load and the interruptible load power at time t, respectively. is the lower limit of the transferable load power at time t. It should be noted that since the interruptible load can only cause a reduction in the load, it does not have the ability to provide downward flexibility supply. Therefore, as shown in formula (24), only the transferable load can provide downward flexibility.

[0155] S3-4. Calculate the upward and downward flexibility supplies of the system at time t;

[0156]

[0157] The upward and downward flexibility supplies of the system can be expressed by formula (25). Ω TL Ω IL are the sets of interruptible loads and transferable loads, respectively.

[0158] S3-5. Calculate the flexibility deficit at time t, which is the difference between the system flexibility supply and demand;

[0159]

[0160] S4. Establish a distribution network simulation operation layer model considering operation flexibility, which includes the following steps:

[0161] S4-1. Determine the objective function of the simulation operation layer. Taking the minimum present value of the flexibility cost of the distribution network operator as the objective function, determine the demand-side response power. The objective function is as follows

[0162] min(C FS +C PEN -C BEN ) (27)

[0163] Where: C FS is the response cost of flexibility resources. When flexibility resources participate in flexibility regulation, the response cost needs to be considered; c PEN is the penalty cost for insufficient flexibility. When the system flexibility is insufficient, the distribution network operator will bear penalties due to insufficient upward and downward flexibility; C BEN is the compensation cost for the invocation of flexibility resources. When flexibility resources are invoked, partial flexibility benefits can be obtained from the government.

[0164] The calculation formulas for each cost are as follows:

[0165] 4) The response cost of flexibility resources C FS

[0166]

[0167] Where: Ω F is the set of flexibility resources Ω F ={IL, TL}, including interruptible load and shiftable load; is the response cost per unit of flexibility resource; is the response capacity per unit of flexibility resource.

[0168] 5) The penalty cost for insufficient flexibility C PEN

[0169]

[0170] Where: are the penalty costs for upward and downward insufficient flexibility respectively; are the deficiencies in flexibility demand respectively.

[0171] 6) The compensation cost for the invocation of flexibility resources C BEN

[0172]

[0173] Where: are the compensation costs for flexibility resources respectively.

[0174] S4-2. Determine the constraint conditions of the simulation operation layer, which are mainly composed of the operation constraints of demand response.

[0175]

[0176] The constraints of the operation layer are the operation constraints of demand response. Interruptible load needs to meet the upper and lower interruptible power constraints, and shiftable load, in addition to meeting the upper and lower constraints, also needs to meet the constraint that the transferred power within a day is 0.

[0177] S5. Solve the model using the simulated annealing particle swarm optimization algorithm to obtain the distribution network planning scheme, and combine with Figure 2 , and it also includes the following steps:

[0178] S5-1. Take the planning scheme as the basic particles of the particle swarm to obtain the initial planning scheme;

[0179] S5-2. Transmit each planning scheme to the operation layer respectively. On the basis of meeting the lower-layer objective function and constraint conditions, calculate the relevant variables in each scenario, transmit them to the planning layer, and calculate their optimal fitness values respectively;

[0180] S5-3. Evaluate the fitness value of each particle and obtain the global optimum;

[0181] S5-4. Update the position and velocity of the particles, repeat steps S5-1 - S5-5, and update the global optimal solution of the population

[0182] S5-5. After the iteration ends, obtain the final planning scheme.

[0183] Apply the present invention to plan distributed power sources and lines for the IEEE33-node example as Figure 3 shown.

[0184] Nodes 34 - 37 are new load nodes, and the dotted lines are the candidate new lines to be built. For DG, wind power and photovoltaic power generation are considered, and the connection positions to be connected are as Figure 3 shown. The relevant parameters of DG are shown in Table 1.

[0185] Set the flexibility and economy parameters as follows: the response cost of unit flexibility resource is 0.35 yuan / kW, the penalties for upper and lower flexibility deficiencies are 0.517 yuan / kW and 1.29 yuan / kW respectively, and the flexibility resource compensation is 0.18 yuan / kW.

[0186] Set the algorithm parameters as follows: the number of population particles in the planning layer is 30, the maximum number of iterations is 100, and the annealing constant is selected as 0.5; the number of population particles in the operation layer is 20, the maximum number of iterations is 50, and the annealing constant is selected as 0.5.

[0187] Table 1 Relevant parameters of DG

[0188]

[0189] In this embodiment, it is default that all nodes participate in the demand-side response, and the time-of-use electricity price data and the division of peak, valley, and normal periods are shown in Table 2.

[0190] Take the 25 nodes with the largest load in the system as the interruptible load nodes. The interruption time is in summer every year (June, July, and August), 7 days per month, and the interruptible time per day is from 10:00 to 22:00. The subsidy that users can obtain is 0.4 yuan / (kW·h)

[0191] Table 2 Electricity price data

[0192]

[0193] To verify the effectiveness of the established model, the following three different planning scenarios are designed in this embodiment. Scenario 1: DR is not considered at the operation layer, and the objective function is to minimize the operating cost of a typical day; Scenario 2: DR is considered at the operation layer, and the objective function is to minimize the operating cost of a typical day; Scenario 3: DR is considered at the operation layer, and the objective function is to minimize the flexible operating cost of a typical day;

[0194] The planning results obtained under the three scenarios are shown in Table 4:

[0195] The distribution network planning schemes under the three scenarios are given in the table, and the number of renewable energy sources connected to each node and the newly built lines are described in detail.

[0196] Table 3 Planning results

[0197]

[0198] The investment situations under each scenario are shown in Table 4.

[0199] Table 4 Planning costs

[0200]

[0201]

[0202] The following conclusions can be drawn from the above two tables:

[0203] 1) Compared with Scenario 1, although the DR management cost is increased in Scenario 2, the total cost is still lower. The reasons are as follows: the system network loss is significantly reduced. After implementing DR, the distribution network power flow distribution is improved, which improves the economic efficiency of system operation; the line cost is reduced. Implementing DR can reduce the peak value of the line power flow distribution, reduce the line capacity expansion demand, and slow down the line investment; after implementing DR, the electricity purchase of users during the peak period can be reduced, which is beneficial to reducing the electricity purchase cost from the main grid.

[0204] 2) Compared with Scenario 2, the network loss cost, the main grid electricity purchase cost, and the line investment cost are all further reduced in Scenario 3. The reason is that when calling the demand-side response resources, the flexibility is taken into account at the same time, which can minimize or eliminate the accommodation limit factors as much as possible, further improve the response potential of the demand-side response, and improve the utilization efficiency of clean energy. Although the demand-side management cost is increased, the present value of the minimum total cost is still reduced.

[0205] 3) Considering the above three scenarios comprehensively, the carbon emission cost gradually decreases. This shows that considering flexibility while implementing demand-side response can reflect the environmental friendliness of active distribution networks and contribute to the achievement of the dual-carbon goal.

[0206] 4) Considering the above three scenarios comprehensively, the investment, operation and maintenance costs of distributed power sources and the flexibility costs gradually decrease. This shows that implementing demand-side response can effectively improve the flexibility of the system; when the flexibility index is taken into account, the volatility and peak-valley difference of the net load can be significantly reduced, the flexibility of the system can be greatly improved, the power demand at peak times can be reduced, and the investment in distributed power sources can be slowed down.

[0207] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple. For the relevant parts, reference can be made to the descriptions in the method section.

[0208] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A two - layer distribution network planning method considering system flexibility requirements, characterized in that, the specific steps include: Obtain illumination data, wind speed data, and load data, and perform predictions. Generate a basic scenario according to the prediction results; Construct a distribution network planning layer model, determine the objective function and constraint conditions of the planning layer. The specific steps are as follows: Determine the objective function of the planning layer. With the minimum present value of the total cost of the distribution network operator as the objective function, determine the location and quantity of distributed power sources, and new lines. The objective function is as follows ； Wherein: is the investment cost, which consists of the investment cost of distributed power sources and the investment cost of newly built lines ; is the operation and maintenance cost, which consists of the operation and maintenance cost of distributed power sources and the operation and maintenance cost of lines; 、 are the network loss cost and the fault cost respectively; is the carbon emission cost. When using traditional fossil energy for power generation, the state will impose penalties on carbon emissions; 、 are the management cost for implementing demand-side response and the power purchase cost from the main grid respectively; Determine the constraint conditions of the planning layer, which are composed of the access number, penetration rate, and output limit constraints of distributed power sources and power flow constraints; According to the time - series data of the prediction results, calculate the flexibility deficit of each time node of the distribution system; Construct a distribution network simulation layer model, determine the objective function and constraint conditions of the simulation layer, and use the flexibility deficit of each time node of the distribution system as the input. The specific steps are as follows: Determine the objective function of the simulation operation layer. With the minimum present value of the flexibility cost of the distribution network operator as the objective function, determine the demand - side response power. The objective function is as follows ； In the formula: is the response cost of flexibility resources. When flexibility resources participate in flexibility regulation, the response cost needs to be considered; is the penalty cost for insufficient flexibility. When the system has insufficient flexibility, the distribution network operator will bear penalties due to insufficient upward and downward flexibility; is the compensation cost for the invocation of flexibility resources. When flexibility resources are invoked, partial flexibility benefits are obtained from the government; Determine the constraint conditions of the simulation operation layer by demand - side response operation constraints; Use the simulated annealing particle swarm optimization algorithm to solve the distribution network planning layer model and the distribution network simulation layer model to obtain a distribution network planning scheme.

2. A two - layer distribution network planning method considering system flexibility requirements according to claim 1, characterized in that, the specific steps of generating the basic scenario are as follows: Input the illumination data, wind speed data, and load data of one year, and complete the prediction of the photovoltaic output, wind power output, and load output in the target area; According to the prediction data, select four typical daily scenarios in four different seasons of spring, summer, autumn, and winter, and confirm the number of days in each scenario.

3. A two - layer distribution network planning method considering system flexibility requirements according to claim 1, characterized in that, the specific steps of calculating the flexibility deficit of each time node of the distribution system are as follows: Calculate the net load of the system at time t. The net load is the difference between the load at time t and the renewable energy output; Calculate the flexibility requirement of the system at time t according to the net load of the system. The flexibility requirement at time t is the difference between the net loads of adjacent times; Calculate the upward and downward flexibility supplies of each node at time t. The flexibility supply includes interruptible loads and shiftable loads; Calculate the upward and downward flexibility supplies of the system at time t through the upward and downward flexibility supplies of each node; Calculate the flexibility deficit of the system at time t according to the difference between the system flexibility supply and demand.

4. A two - layer distribution network planning method considering system flexibility requirements according to claim 1, characterized in that, using the simulated annealing particle swarm optimization algorithm to solve the two - layer planning model, specifically including: S5 - 1. Use the planning scheme as the basic particle of the particle swarm to obtain the initial planning scheme; S5 - 2. Transmit each planning scheme to the operation layer respectively. On the basis of meeting the objective function and constraint conditions of the lower layer, calculate the relevant variables in each scenario, transmit them to the planning layer, and calculate their optimal fitness values respectively; S5 - 3. Evaluate the fitness value of each particle and obtain the global optimum; S5-4. Update the positions and velocities of the particles, repeat steps S5-1 - S5-5, and update the global optimal solution of the population; S5-5. After the iteration ends, obtain the final planning scheme.

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