A proactive power distribution network planning method and system

Abstract

The present disclosure belongs to the technical field of power system distribution network planning, and particularly relates to an active distribution network planning method and system, comprising the following steps: obtaining basic data of an active distribution network; constructing an active distribution network planning model with a minimum annual comprehensive cost as an objective function, considering renewable energy reserve uncertainty and electric vehicle reserve delay, according to the obtained data; linearizing the nonlinear active distribution network planning model, and solving the linearized active distribution network planning model by using a mixed integer linear programming algorithm to obtain a planning scheme of the active distribution network.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of power system distribution network planning, and particularly relates to an active distribution network planning method and system. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] With the continuous increase in distributed generation capacity in modern distribution networks and the rapid adoption of electric vehicles, existing distribution network architectures and traditional planning and scheduling methods are no longer able to meet the new user requirements for power supply reliability and power quality. The concept of an Active Distribution Network (ADN) has emerged in this context.

[0004] Distributed power generation (DG) refers to small generators with power ratings ranging from several kilowatts to several megawatts, located close to users to meet specific user needs and support the economic operation of distribution networks. Renewable energy sources, such as wind and photovoltaic power, are particularly popular in modern distribution networks. Compared with traditional centralized energy sources, DG offers advantages such as lower investment costs, flexible generation methods, low losses, and energy conservation and environmental protection. DG has become a key research topic in the planning and dispatching of modern power systems. The output of wind and photovoltaic power in active distribution networks is significantly affected by natural factors such as wind speed and sunlight conditions. This results in a certain degree of volatility, randomness, and intermittency in the power supply within the system, creating pressure and challenges for the configuration of system backup. Active distribution networks offer greater flexibility and adjustability. Renewable energy and electric vehicles, as flexible power sources, can also provide backup. These controllable resources are the source of power flexibility in distribution networks. Coordinating these resources allows distribution networks to actively participate in the operation of the larger power grid and allows distribution operators to earn additional revenue from providing backup. Summary of the Invention

[0005] To address these issues, this paper proposes a proactive distribution network planning method and system that fully considers the uncertainty of renewable energy backup and the latency of electric vehicle backup, establishing a proactive distribution network planning model. This enables the distribution network to proactively participate in the operation of the larger power grid, enabling more efficient and flexible transmission network operation while also improving the economic efficiency of distribution operators by providing flexibility.

[0006] According to some embodiments, a first solution of the present disclosure provides a method for proactive distribution network planning, which adopts the following technical solutions:

[0007] A method for active distribution network planning includes the following steps:

[0008] Obtain basic data of active distribution network;

[0009] Based on the acquired data, an active distribution network planning model is constructed with the minimum annual comprehensive cost as the objective function, taking into account the uncertainty of renewable energy backup and the delay of electric vehicle backup.

[0010] The nonlinear active distribution network planning model is linearized, and the mixed integer linear programming algorithm is used to solve the linearized active distribution network planning model to obtain the active distribution network planning scheme.

[0011] As a further technical limitation, in the process of obtaining the basic data of the active distribution network, the load forecast data of the planning year and the statistical data of regional environmental factors are obtained, and 4 typical daily scenes representing each quarter of the annual distribution network are generated for each year within the planning year. In each typical daily scene, it is divided into 24 planning periods, and each period is one hour apart. Assuming that the load forecast data and the statistical data of regional environmental factors remain unchanged in each period, the load forecast data of each typical daily scene is obtained according to the distribution network load forecast technology, and the statistical data of regional environmental factors are obtained by statistical data of regional historical environmental factors.

[0012] As a further technical limitation, the objective function includes investment cost, operation cost, standby cost and load loss cost.

[0013] Furthermore, the load loss cost is the product of the load loss unit price, the load loss expectation and the planning time; the load loss expectation takes into account the uncertainty of renewable energy backup and the delay of electric vehicle backup, including the backup provided by renewable energy and electric vehicles.

[0014] As a further technical limitation, the constraints of the objective function include total power balance constraints, line flow constraints, line capacity constraints, node voltage constraints, distributed power output control constraints, backup constraints provided by distributed power supplies, and load loss expectation constraints.

[0015] Furthermore, the backup constraint provided by the distributed power source is obtained by actively controlling the renewable energy unit, and the backup load provided by the renewable energy is equal to the difference between the output prediction value of the renewable energy and the load reduction operation value.

[0016] Furthermore, the load loss expectation constraint includes reducing the load loss by taking into account the backup load provided by the electric vehicle.

[0017] According to some embodiments, a second solution of the present disclosure provides an active distribution network planning system, which adopts the following technical solutions:

[0018] An active distribution network planning system, comprising:

[0019] an acquisition module configured to acquire basic data of the active power distribution network;

[0020] A modeling module is configured to construct, based on the acquired data, an active distribution network planning model with the minimum annual comprehensive cost as the objective function, taking into account the uncertainty of renewable energy backup and the delay of electric vehicle backup;

[0021] The planning module is configured to linearize the nonlinear active distribution network planning model, use a mixed integer linear programming algorithm to solve the linearized active distribution network planning model, and obtain a planning scheme for the active distribution network.

[0022] According to some embodiments, a third solution of the present disclosure provides a computer-readable storage medium, which adopts the following technical solution:

[0023] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps in the active distribution network planning method as described in the first aspect of the present disclosure.

[0024] According to some embodiments, a fourth solution of the present disclosure provides an electronic device, which adopts the following technical solution:

[0025] An electronic device comprises a memory, a processor and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the active distribution network planning method as described in the first aspect of the present disclosure are implemented.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] (1) The present disclosure takes into account that renewable energy and electric vehicles, as flexible and controllable resources in the distribution network, can provide backup for the system, thereby expanding the sources of backup.

[0028] (2) This disclosure takes into account the uncertainty of renewable energy providing backup and the delay of electric vehicles providing backup, making the active distribution network planning results more reliable.

[0029] (3) This disclosure adopts the Mixed Integer Linear Programming (MILP) algorithm, which greatly improves the efficiency of model solving by converting complex problems into relatively simple linear problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0031] Figure 1is a flow chart of the active distribution network planning method in the first embodiment of the present disclosure;

[0032] Figure 2 is a schematic diagram of the backup load provided by renewable energy in the first embodiment of the present disclosure;

[0033] Figure 3 is the expected value of load loss caused by the backup provided by the electric vehicle in the first embodiment of the present disclosure;

[0034] Figure 4 is a discretized distribution diagram of the probability density function of renewable energy output in the first embodiment of the present disclosure;

[0035] Figure 5 This is a structural block diagram of the active distribution network planning system in the second embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0038] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0039] In the absence of conflict, the embodiments of the present disclosure and the features thereof may be combined with each other.

[0040] Example 1

[0041] Embodiment 1 of the present disclosure introduces a method for active distribution network planning.

[0042] like Figure 1 The active distribution network planning method shown includes the following steps:

[0043] Step S01: Obtain basic data of the active power distribution network;

[0044] Step S02: Based on the acquired data, construct an active distribution network planning model with the minimum annual comprehensive cost as the objective function, taking into account the uncertainty of renewable energy backup and the delay of electric vehicle backup;

[0045] Step S03: linearize the nonlinear active distribution network planning model, and use a mixed integer linear programming algorithm to solve the linearized active distribution network planning model to obtain a planning scheme for the active distribution network.

[0046] As one or more implementation methods, in step S01, in the process of obtaining the basic data of the active distribution network, the load forecast data for the planning year and the statistical data of regional environmental factors (for example, wind speed, light, etc.) are obtained, and 4 typical day scenes representing each quarter of the annual distribution network are generated for each year within the planning year. In each typical day scene, it is divided into 24 planning time periods, and each time period is one hour apart. Assuming that the load forecast data and the statistical data of regional environmental factors remain unchanged in each time period, the load forecast data of each typical day scene is obtained according to the distribution network load forecast technology, and the statistical data of regional environmental factors are obtained by statistically analyzing the statistical data of regional historical environmental factors.

[0047] As one or more implementation methods, in step S02, the objective function is to minimize the annual comprehensive cost, which can be expressed as:

[0048] min(C inv +C ope +C r +C eens ) (1)

[0049] Among them, C inv , C ope , C r , C eens They represent investment cost, operating cost, standby cost and load loss cost respectively.

[0050] The investment cost data includes the present value conversion coefficient, discount rate, economic service life and unit capacity installation cost of distributed power sources; the operating cost data includes the cost data of purchasing electricity from substations and the operating cost data of distributed power sources; the backup cost data includes the price of backup provided by renewable energy and electric vehicles, and the load loss cost data includes the load loss penalty price and planning time.

[0051] The objective function can be further expressed in detail as follows:

[0052]

[0053]

[0054]

[0055]

[0056] C eens =365·VOLL·EENS (6)

[0057] Where N G and They represent the existing generator sets and the generator sets to be expanded, including wind turbines and photovoltaic panels; is the construction cost coefficient of the unit capacity unit; is the residual value of the equipment, taking the initial construction cost 5%; is the equipment operation and maintenance cost, taking the initial construction cost 3% of cap g is the optimized construction capacity of unit g; R g Indicates the coefficient of converting the present value of power investment into equivalent annual value; d and y g are the discount rate and economic useful life respectively; c f , c EV They represent the power purchase cost of the upper power grid, the active power operation cost coefficient of generator g, the backup cost provided by renewable energy units, and the backup cost provided by electric vehicles; P g,t ,r g,t , are the active power flowing into the distribution network from the upper power grid, the active output value of distributed generation in time period t, the provided reserve capacity and the reserve provided by electric vehicles in time period t; VOLL is the penalty cost of load loss, and EENS is the expected load loss.

[0058] The constraints of the objective function include:

[0059] 1) Total power balance constraint:

[0060]

[0061] Where i represents the node, P t EV Indicates the active capacity that electric vehicles can provide. denote the active and reactive load demands of node i respectively.

[0062] 2) Line flow constraints

[0063]

[0064] Where ref represents the assumed reference node; and is the active and reactive power flow of the line based on the load forecast value; and are the power generation load transfer factors corresponding to active and reactive power in the distribution network respectively;

[0065] 3) Line capacity constraints

[0066]

[0067] Where, is the upper limit of the line apparent power, and Equation (9) limits the power flow in line ij to not exceed the upper limit of the line capacity.

[0068] 4) Node voltage constraints:

[0069]

[0070] Where, Represents the node voltage value based on the load forecast value; V base is the voltage value at the reference node; V i min With V i max are the upper and lower limits of the node voltage.

[0071] 5) Distributed power output control constraints:

[0072]

[0073] Where, is the output value of the distributed power supply, P g,t is the actual output value of the distributed power generation, that is, reducing the output of the distributed power generation through active control measures can reduce uncertainty on the one hand and provide backup on the other hand. In this paper, the distributed power generation refers to renewable energy sources such as wind power and photovoltaic power.

[0074] 6) Backup constraints provided by distributed power sources:

[0075] Renewable energy units can provide backup through active control, and the backup they provide should meet the following constraints:

[0076]

[0077] Where r g,t Represents the reserve provided by renewable energy. However, unlike the reserve provided by traditional units, the output of renewable energy is uncertain, so the actual available renewable energy reserve is affected by the random output of renewable energy P. rndg,t When the actual output value of renewable energy is P rndg,t Lower than the wind farm load reduction operation value P g,t When the renewable energy can provide a reserve of zero, the actual output value of renewable energy P rndg,t Higher than the load-reducing operation value P of renewable energy g,t But it is lower than the forecast output of renewable energy When the renewable energy can provide a reserve of P rndg,t and Pg,t When the actual output value of renewable energy P rndg,t Higher than the forecast output of renewable energy At this time, the backup power that renewable energy can provide is the predicted value of renewable energy output and the load reduction value P g,t difference.

[0078] Therefore, renewable energy can provide backup g,t It can be expressed as follows:

[0079]

[0080] Expression (13) can be represented graphically as Figure 2 , Figure 2 P in maxg,t That is cap g :

[0081] 7) EENS Constraints:

[0082] EENS can be calculated based on the system disturbance amount, the probability of disturbance occurrence, and the system backup capacity. When considering the backup provided by electric vehicles, the EENS formula should be remodeled to take into account the reduced load loss caused by the backup provided by electric vehicles. Since electric vehicles need a certain response time to take action when participating in the backup, the backup provided by electric vehicles has a delay. Figure 3 As shown in Figure 1, τ1 represents the response time required for electric vehicles to provide backup, and τ2 represents the time it takes for electric vehicles to provide backup. From the figure, it can be seen intuitively that providing backup by electric vehicles can reduce the expected load loss.

[0083] Therefore, considering the delay of electric vehicle backup, the calculation of EENS should be divided into two parts, namely:

[0084] EENS=EENS 1 +EENS 2 (14)

[0085]

[0086]

[0087]

[0088] Where N S is the set of all disturbance scenarios, p s,t is the probability of disturbance scenario s occurring in time period t, Pr(P rndg,t ) is the probability density function of renewable energy output, b s,g,tis a 0-1 variable that represents whether the disturbance scenario s occurs in time period t, ΔC s,t is the power outage capacity caused by disturbance scenario s in time period t, ΔP g,t is the disturbance caused by the uncertainty of renewable energy.

[0089] As one or more implementation methods, in step S03, the nonlinear part of the model is processed according to a linearization processing method, converted into a mixed integer linear programming model, and then the model is solved by a CPLEX solver.

[0090] CPLEX is an optimization engine developed by IBM. It is used to solve four basic problems: linear programming (LP), quadratic programming (QP), constrained quadratic programming (QCQP), and second-order cone programming (SOCP), as well as the corresponding mixed integer programming (MIP) problems. CPLEX has the following advantages: (1) it can solve some very difficult industry problems; (2) it solves them very quickly; and (3) it sometimes offers superlinear acceleration.

[0091] There is an integral term in the expression of EENS, which requires further linearization.

[0092] EENS linearization steps:

[0093] Due to EENS 1 and EENS 2 The linearization steps are similar to those of 1 Calculation as an example.

[0094] 1) Integration interval segmentation:

[0095] EENS 1 The integral term includes ΔP g,t and Both with P rndg,t The interval in which it is located is related, so according to the interval additivity principle of integration, EENS 1 The original integral interval [P ming,t ,P maxg,t ] is divided into three parts, namely [P ming,t ,P g,t ], and ΔP g,t and Substituting the values ​​in each interval, EENS can be rewritten 1 as follows:

[0096]

[0097] 2) Simplification and merging of integral intervals:

[0098] In the ΔP g,t and After substituting the values ​​of each interval, we observe formula (18) and find that the interval [P ming,t ,P g,t ]and The expressions of the inner integrals are the same, so the two intervals can be combined, namely:

[0099]

[0100] The interval The inner integral part and P rndg,t It is irrelevant and can be directly extracted outside the integral symbol, so the interval The integral inside can be rearranged as:

[0101]

[0102] Finally, EENS 1 It can be recombined as:

[0103]

[0104] 3) Go to points:

[0105] Observing formula (21), we find that only the interval There is an integral term. To facilitate calculation, the interval is discretized. rndg,t ) in the interval The part is divided into N WL segments, each segment corresponds to a probability value p g,wl,t and an actual output value of renewable energy P g,wl,t , renewable energy probability distribution Pr(P rndg,t ) in the interval is shown in the following diagram Figure 4 shown.

[0106] EENS at this time 1 It can be restated as follows:

[0107]

[0108] Similarly, EENS 2 The final expression is

[0109]

[0110] This embodiment fully considers the uncertainty of renewable energy backup and the delay of electric vehicle backup, establishing a proactive distribution network planning model. This enables the distribution network to actively participate in the operation of the larger power grid, making the transmission network more efficient and flexible, while also improving the economic efficiency of distribution operators by providing flexibility.

[0111] Example 2

[0112] A second embodiment of the present disclosure introduces an active distribution network planning system.

[0113] like Figure 5 An active distribution network planning system is shown, comprising:

[0114] an acquisition module configured to acquire basic data of the active power distribution network;

[0115] A modeling module is configured to construct, based on the acquired data, an active distribution network planning model with the minimum annual comprehensive cost as the objective function, taking into account the uncertainty of renewable energy backup and the delay of electric vehicle backup;

[0116] The planning module is configured to linearize the nonlinear active distribution network planning model, use a mixed integer linear programming algorithm to solve the linearized active distribution network planning model, and obtain a planning scheme for the active distribution network.

[0117] The detailed steps are the same as those of the active distribution network planning method provided in Example 1 and will not be repeated here.

[0118] Example 3

[0119] A third embodiment of the present disclosure provides a computer-readable storage medium.

[0120] A computer-readable storage medium stores a program thereon, which, when executed by a processor, implements the steps of the active distribution network planning method as described in the first embodiment of the present disclosure.

[0121] The detailed steps are the same as those of the active distribution network planning method provided in Example 1 and will not be repeated here.

[0122] Example 4

[0123] A fourth embodiment of the present disclosure provides an electronic device.

[0124] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the active distribution network planning method as described in the first embodiment of the present disclosure are implemented.

[0125] The detailed steps are the same as those of the active distribution network planning method provided in Example 1 and will not be repeated here.

[0126] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for active distribution network planning, characterized in that: The following steps are involved: Obtain basic data of active distribution network; Based on the acquired data, an active distribution network planning model is constructed with the objective function of minimizing the annual comprehensive cost, taking into account the uncertainty of renewable energy backup and the delay of electric vehicle backup. The objective function includes investment cost, operating cost, backup cost, and load loss cost. The load loss cost is the product of the load loss unit price, the load loss expectation, and the planning time; the load loss expectation takes into account the uncertainty of renewable energy backup and the delay of electric vehicle backup, including the backup provided by renewable energy and electric vehicles; The constraints of the objective function include total power balance constraint, line flow constraint, line capacity constraint, node voltage constraint, distributed power output control constraint, distributed power supply provided backup constraint and load loss expectation constraint. EENS ; The nonlinear active distribution network planning model is linearized, and the mixed integer linear programming algorithm is used to solve the linearized active distribution network planning model to obtain the active distribution network planning scheme; Reserve constraints provided by distributed generation: Renewable energy units provide backup through active control. The backup that renewable energy can provide is g,t The expression is as follows: in, is the actual output value of renewable energy, is the load reduction operation value of the wind farm, It is the forecast value of renewable energy output; EENS Constraints: Calculated based on the system disturbance amount, the probability of disturbance and the system's backup capacity EENS , when considering the backup provided by electric vehicles, EENS The formula is remodeled to take into account the reduced load loss caused by the backup provided by electric vehicles; considering the delay of electric vehicle backup, the calculation of EENS should be divided into two parts, namely: Where, N S is the set of all disturbance scenarios, p s,t is the probability of disturbance scenario s occurring in time period t, Pr(Prnd g,t) is the probability density function of renewable energy output, b s,g,t is a 0-1 variable that represents whether the disturbance scenario s occurs in time period t, Δ C s,t is the power outage capacity caused by disturbance scenario s in time period t, Δ P g,t is the disturbance caused by the uncertainty of renewable energy, τ1 Indicates the response time required for electric vehicles to provide backup, τ2 Indicates that electric vehicles can provide backup time, and They represent the existing generator sets and the generator sets to be expanded, including wind turbines and photovoltaic panels; The backup power provided for electric vehicles during period t.

2. The active distribution network planning method as claimed in claim 1, characterized in that: In the process of obtaining the basic data of the active distribution network, the load forecast data of the planning period and the statistical data of regional environmental factors are obtained, and four typical daily scenarios representing each quarter of the annual distribution network are generated for each year within the planning period. In each typical daily scenario, it is divided into 24 planning periods, and each period is one hour apart. Assuming that the load forecast data and the statistical data of regional environmental factors remain unchanged in each period, the load forecast data of each typical daily scenario is obtained according to the distribution network load forecast technology, and the statistical data of regional environmental factors are obtained by statistical data of regional historical environmental factors.

3. An active distribution network planning system, characterized in that: include: an acquisition module configured to acquire basic data of the active power distribution network; a modeling module configured to construct, based on the acquired data, an active distribution network planning model with a minimum annual comprehensive cost as an objective function, taking into account the uncertainty of renewable energy backup and the delay of electric vehicle backup; the objective function includes investment cost, operating cost, backup cost, and load loss cost; The load loss cost is the product of the load loss unit price, the load loss expectation, and the planning time; the load loss expectation takes into account the uncertainty of renewable energy backup and the delay of electric vehicle backup, including the backup provided by renewable energy and electric vehicles; The constraints of the objective function include total power balance constraint, line flow constraint, line capacity constraint, node voltage constraint, distributed power output control constraint, distributed power supply provided backup constraint and load loss expectation constraint. EENS ; A planning module is configured to linearize a nonlinear active distribution network planning model, solve the linearized active distribution network planning model using a mixed integer linear programming algorithm, and obtain a planning scheme for the active distribution network; Reserve constraints provided by distributed generation: Renewable energy units provide backup through active control. The backup that renewable energy can provide is g,t The expression is as follows: in, is the actual output value of renewable energy, is the load reduction operation value of the wind farm, It is the forecast value of renewable energy output; EENS Constraints: Calculated based on the system disturbance amount, the probability of disturbance and the system's backup capacity EENS , when considering the backup provided by electric vehicles, EENS The formula is remodeled to take into account the reduced load loss caused by the backup provided by electric vehicles; considering the delay of electric vehicle backup, the calculation of EENS should be divided into two parts, namely: Where, N S is the set of all disturbance scenarios, p s,t is the probability of disturbance scenario s occurring in time period t, Pr(Prnd g,t) is the probability density function of renewable energy output, b s,g,t is a 0-1 variable that represents whether the disturbance scenario s occurs in time period t, Δ C s,t is the power outage capacity caused by disturbance scenario s in time period t, Δ P g,t is the disturbance caused by the uncertainty of renewable energy, τ1 Indicates the response time required for electric vehicles to provide backup, τ2 Indicates that electric vehicles can provide backup time, and They represent the existing generator sets and the generator sets to be expanded, including wind turbines and photovoltaic panels; The backup power provided for electric vehicles during period t.

4. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the active distribution network planning method according to any one of claims 1 to 2 are implemented.

5. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the active power distribution network planning method according to any one of claims 1 to 2 are implemented.

Images